To whom all correspondence should be addressed
博士后选择的10个基本原则
1.选择自己感兴趣的方向2.选责适合你工作和生活方式的实验室3.选择能够学到新技能的研究组4.有备份的计划，考虑至少做一主一副两个项目5.选择有明显成果的项目6.开始之前，就跟未来的老板确定第一作者的归属问题7.时间的考虑。
Postdoc是一个过度期，长短随人，随job market的变化而变，去一个有funding保证的组可以令你有更多回旋的余地8.考虑个人发展前景9.努力争取你自己的研究资金。
10.学会发现机遇Ten Simple Rules for Selecting a Postdoctoral Positionwodehongqi 发表于: 2007-1-04 17:00 来源: 考博网Philip E. Bourne*, Iddo FriedbergCitation: Bourne PE, Friedberg I (2006) Ten Simple Rules for Selecting a Postdoctoral Position. PLoS Comput Biol 2(11): e121 DOI:10.1371/journal.pcbi.0020121Published: November 24, 2006Copyright: © 2006 Bourne and Friedberg. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.Philip E. Bourne is a professor in the Department of Pharmacology, University of California San Diego, La Jolla, California, United States of America, and is Editor-in-Chief of PLoS Computational Biology. Iddo Friedberg is a research assistant in the Bioinformatics and Systems Biology program at the Burnham Institute for Medical Research, La Jolla, California, United States of America.* To whom correspondence should be addressed. E-mail: bourne@____________________________________________________________________________________You are a PhD candidate and your thesis defense is already in sight. You have decided you would like to continue with a postdoctoral position rather than moving into industry as the next step in your career (that decision should be the subject of another “Ten Simple Rules”).Further, you already have ideas for the type of research you wish to pursue and perhaps some ideas for specific projects. Here are ten simple rules to help you make the best decisions on a research project and the laboratory in which to carry it out.Rule 1: Select a Position that Excites YouIf you find the position boring, you will not do your best work—believe us, the salary will not be what motivates you, it will be the science.Discuss the position fully with your proposed mentor, review the literature on the proposed project, and discuss it with others to get a balanced view. Try and evaluate what will be published during the process of your research. Being scooped during a postdoc can be a big setback. Just because the mentor is excited about the project does not mean you that will be six months into it.Rule 2: Select a Laboratory That Suits Your Work and LifestyleIf at all possible, visit the laboratory before making a decision. Laboratories vary widely in scope and size. Think about how you like to work—as part of a team, individually, with little supervision, with significant supervision (remembering that this is part of your training where you are supposed to be becoming independent), etc. Talk to other graduate students and postdoctoral fellows in the laboratory and determine the work style of the laboratory. Also, your best work is going to be done when you are happiest with the rest of your life. Does the location of the laboratory and the surrounding environment satisfy your nonwork interests?Rule 3: Select a Laboratory and a Project That Develop New SkillsMaximizing your versatility increases your marketability. Balance this against the need to ultimately be recognized for a particular set of contributions. Avoid strictly continuing the work you did in graduate school. A postdoctoral position is an extension of your graduate training; maximize your gain in knowledge and experience. Think very carefully before extending your graduate work into a postdoc in the same laboratory where you are now—to some professionals this raises a red flag when they look at your resume. Almost never does it maximize your gain of knowledge and experience, but that can be offset by rapid and important publications.Rule 4: Have a Backup PlanDo not be afraid to take risks, although keep in mind that pursuing a risky project does not mean it should be unrealistic: carefully research and plan your project. Even then, the most researched, well-thought-out, and well-planned project may fizzle; research is like that. Then what? Do you have a backup plan? Consider working on at least two projects. One to which you devote most of your time and energy and the second as a fallback. The second project should be more of the “bread and butter” type, guaranteed to generate good (if n ot exciting) results no matter what happens. This contradicts Rule 1, but that is allowed for a backup plan. For as we see in Rule 5, you need tangible outcomes.Rule 5: Choose a Project with Tangible OutcomesThat Match Your Career GoalsFor a future in academia, the most tangible outcomes are publications, followed by more publications. Does the laboratory you are entering have a track record in producing high-quality publications? Is your future mentor well-respected and recognized by the community? Talk to postdocs who have left the laboratory and find out. If the mentor is young, does s/he have the promise of providing those outcomes? Strive to have at least one quality publication per year.Rule 6: Negotiate First Authorship before You StartThe average number of authors on a paper has continued to rise over the years: a sign that science continues to become more collaborative. This is good for science, but how does it impact your career prospects? Think of it this way. If you are not the first author on a paper, your contribution is viewed as 1/n where n is the number of authors. Journals such as this one try to document each author's contributions; this is a relatively new concept, and few people pay any attention to it. Have an understanding with your mentor on your likelihood of first authorship before you start a project. It is best to tackle this problem early during the interview process and to achieve an understanding; this prevents conflicts and disappointments later on. Don't be shy about speaking frankly on this issue. This is particularly important when you are joining an ongoing study.Rule 7: The Time in a Postdoctoral Fellowship Should Be FiniteMentors favor postdocs second only to students. Why? Postdocs are second only to students in providing a talented labor pool for the leastpossible cost. If you are good, your mentor may want you to postdoc for a long period. Three years in any postdoc is probably enough. Three years often corresponds to the length of a grant that pays the postdoctoral fellowship, so the grant may define the duration. Definitely find out about the source and duration of funding before accepting a position. Be very wary about accepting one-year appointments. Be aware that the length of a postdoc will likely be governed by the prevailing job market. When the job market is good, assistant professorships and suitable positions in industry will mean you can transition early to the next stage of your career. Since the job market even a year out is unpredictable, having at least the option of a three-year postdoc fellowship is desirable.Rule 8: Evaluate the Growth PathMany independent researchers continue the research they started during their postdoc well into their first years as assistant professors, and they may continue the same line of work in industry, too. When researching the field you are about to enter, consider how much has been done already, how much you can contribute in your postdoc, and whether you could take it with you after your postdoc. This should be discussed with your mentor as part of an ongoing open dialog, since in the future you may be competing against your mentor. A good mentor will understand, as should you, that your horizon is independence—your own future lab, as a group leader, etc.Rule 9: Strive to Get Your Own MoneyThe ease of getting a postdoc is correlated with the amount of independent research monies available. When grants are hard to get, so are postdocs. Entering a position with your own financing gives you a level of independence and an important extra line on your resume. This requires forward thinking, since most sources of funding come from a joint application with the person who will mentor you as a postdoc. Few graduate students think about applying for postdoctoral fellowships in a timely way. Even if you do not apply for funding early, it remains an attractive option, even after your postdoc has started with a different funding source. Choosing one to two potential mentors and writing a grant at least a year before you will graduate is recommended.Rule 10: Learn to Recognize OpportunitiesNew areas of science emerge and become hot very quickly. Getting involved in an area early on has advantages, since you will be more easily recognized. Consider a laboratory and mentor that have a track record in pioneering new areas or at least the promise to do so.AcknowledgmentsThe authors would like to thank Mickey Kosloff for helpful discussions.申请博士后的过程和申请博士有所不同，一般博士后的申请不需要toefl或gre成绩。
APL-sample 应用物理快报投稿模板
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A numerical reference may be cited within other references; however, it must also be cited at least once in the main body of the paper.See Table III for acceptable reference formats.TABLE III.This table provides instructions on how to prepare references.to books and journal articles, listed at the end of the paper, should appear in one of theseformats:(1) Numerical: By number, in the order of first appearance, giving the names of the authors, the53V. Bargmann, Proc. Natl. Acad. Sci. USA 38, 961 (1952).This paper will be listed as the 53rd in the list of references and cited as 53.(2) Bibliographic: In alphabetical order according to the first author's last name, giving, in addition to the name, volume, year, and first and last page, also the title of the paper cited, as in:Bargmann, V., "On the number of bound states in a central field of force,"' Proc. Natl. Acad. Sci. USA 38, 961–966 (1952).Within the body of the paper, this reference will be cited as "Bargmann (1952)." If there are several articles by the same author(s) and the same year, they should be distinguished by letters, as in (1952a).(3) Numerical Bibliographic: Alphabetically listed references (with full titles and page ranges) may be numbered according to their alphabetical order and cited by their number.1Berger, A., "Instabilities and waves on a columnar vortex in a strongly stratified and rotating fluid,"' Phys. Fluids 25, 961–966 (2013).•Articles “submitted to” or “accepted for publication” (but not yet published) in a journal must include article title: When possible, these references should be updated inthe galley proof.Samples of Numerical References:Books: List authors and editors. Must include publisher, city and year of publication, and the page numbers (unless the entire book is being cited).2R. J. Hunter, Zeta Potential in Colloid Science (Academic, New York, 1981) p.120.AIAA Papers:AIAA Papers: The usual format is: {Author’s names}, {Paper Title}, AIAA Pap. {usual formats are 99-1111 or 2004-2222}, {year -- corresponds to numbers on left side of paper number}..3M.S. Narayan and A. Banaszuk, “Experimental study of a novel active separation control approach,” AIAA Paper No. 2003-0060, 2003.Conference proceedings: Include the list of authors, the title of the proceedings, the city and year of the conference, the name of the publisher (cannot be a laboratory or institution), city and year of publication (or the words “to be published”), and the page numbers. Include the full list of editors, if they are given.4R. K. Ahrenkiel, in Gallium Arsenide and Related Compounds 1993: Proceedings of the20th International Symposium on Gallium Arsenide and Related Compounds, Freiburg, Germany, 29 August–2 September 1993, edited by H. S. Rupprecht and G. Weimann (Institute of Physics, London, 1994), pp. 685–690.Government publications:Format as for a book citation. Each must include the author(s), title of the publication, name of the publisher, city and year of publication, and page numbers (unless the entire publication is being cited).5D. Nunes, The Brillouin Effect (U.S. Department of Energy, Washington, DC, 1992).Journal citations: Include authors (see author rule above), volume number, beginning page number, and publication year:6J. D. Kiely and J. E. Houston, Phys. Rev. B 57, 12588 (1998).Laboratory report: May only be used if first deposited with a national depository such as the National Technical Information Service. (Check with the NTIS librarian at 703-605-6000.) Materials or reports in electronic form—codes, data tables,etc.—may be uploaded as supplementary material files (see Sec. XIII). If the paper is on deposit with NTIS, use the following format:7See National Technical Information Service Document No. DE132450 L. (R. Newchuck, SESAME Tables, LANL Rep. 23453, 1983). Copies may be ordered from the National Technical Information Service, Springfield, VA 22161.MOLPRO:8H.-J. Werner, P. J. Knowles, R. Lindh, F. R. Manby, M. Schütz, et al., Molpro, version 2006.1, a package of ab initio programs, 2006, see Multiple citations are acceptable:8D.-Y. Choi, S. Madden, A. Rode, R. Wang, and B. Luther-Davies, J. Non-Cryst. Solids354, 3179 (2008); J. Appl. Phys. 104, 113305 (2008).(same authors, different journals)or9J.Scaroni and T. Mckee, Solid State Technol. 40, 245 (1997); M. G. Lawrence, Bull. Am. Meteorol. Soc. 86, 225 (2005).(two completely different references)or10Y. de Carlan, A. Alamo, M. H. Mathon, G. Geoffroy, and A. Castaing, J. Nucl. Mater.283–287, 762 (2000); M. H. Mathon, Y. de Carlan, G. Geoffroy, X. Averty, A. Alamo, and C. H. de Novion, ibid.312, 236 (2003).(different authors, same journal)Patents: Titles are allowed.47K. Inoue, U.S. patent 3,508,029 (22 March 1970).48 W. L. Tolin and A. M. Laud, U.S. patent pending (5 October 1996).49 J. R. Smith, U.S. patent application 037/123,456 (18 May 2010).Preprints and electronic postings:Preprints or eprints that have not been submitted to a journal for publication (i.e., are only posted on a preprint server) cannot be used as references.Private communication:May not be one of the authors of the article. Must include the year in which the communication took place.11A. Einstein (private communication, 1954).Software manuals: If published, use the book format; if not published, give the entire address for the software maker.Thesis/dissertation: Include the author, school, and year, but not the title.12S. L. Goldschmidt, Ph.D. thesis, University of California, Los Angeles, 1985.Web sites:References containing URLs are permitted but should have additional explanatory text, as well as the date the website was last accessed.1See /hummingbird for more information about hummingbirds; accessed 28 July 2012.2See / for “Nanometer Pattern Generation Systems for Scanning Electron Microscopes” (last accessed April 15, 2013).。
翻译原文
DOI:10.1093/jxb/erh030Advanced Access publication28November,2003RESEARCH PAPERIdenti®cation of aluminium-regulated genes by cDNA-AFLP in rice(Oryza sativa L.):aluminium-regulated genes for the metabolism of cell wall componentsChuanzao Mao,Keke Yi*,Ling Yang*,Bingsong Zheng*,Yunrong Wu,Feiyan Liu and Ping Wu²The State Key Laboratory of Plant Physiology and Biochemistry,College of Life Sciences,Zhejiang University, Hangzhou310029,PR ChinaReceived20July2003;Accepted17October2003AbstractAluminium(Al)toxicity is the major factor limiting crop productivity in acid soils.To investigate the molecular mechanisms of Al toxicity and Al tolerance of rice,cDNA-ampli®ed fragment length polymorph-ism(cDNA-AFLP)was used for identifying Al-regu-lated genes in roots of an Al-tolerant tropical upland rice,Azucena,and an Al-sensitive lowland rice, IR1552.Nineteen function-known genes were found among34transcript-derived fragments(TDFs)regu-lated by Al stress.The results indicate that Al stress could induce the biosynthesis of lignin and other cell wall components in roots.Temporal expression pat-terns of14genes were identi®ed between the two varieties.In silico mapping was performed for all the 33unique genes.Two genes for a function-unknown protein and for a ubiquitin-like protein,respectively, were mapped on the interval with the common QTL (quantitative trait loci)for Al tolerance in rice on chromosome1.Key words:Aluminium stress,cDNA-AFLP,cell wall components,Oryza sativa L.IntroductionAluminium(Al)is the most abundant metal in the earth's crust and is the major factor limiting crop productivity in acid soils,which comprises up to40%of the world's arable lands(Kochian,1995).The abundance of Al products in the environment and the potential of Al as a plant growth inhibitor make it necessary to understand the mechanisms of Al phytotoxicity and Al tolerance of plant (Kochian,1995).The major symptom of Al toxicity is a rapid inhibition of root growth.Al accumulates rapidly in the apoplast,in the plasma membrane,and eventually enters the cytosol (Lazof et al.,1994).Many different mechanisms of Al toxicity have been hypothesized,including Al interactions with the root cell wall and Al interactions with symplasmic constituents such as the cytoskeleton etc.(Kochian,1995). Alhough the mechanisms of Al toxicity have been studied by many researchers,the speci®c mechanism by which Al inhibits root elongation is still elusive(Matsumoto,2000). It is still not clear whether Al affects the root symplasti-cally or apoplastically,but the increased evidence supports the view that the apoplast plays the major role in Al perception(Horst,1995).Al tolerance has been speculated to be the result of either exclusion of Al from the root apex and/or the tolerance for symplasmic Al.Detoxi®cation of Al in the rhizosphere by releasing organic acids to chelate Al has been reported in wheat and maize,while the detoxi®cation of Al internally was also found by forming complexes with organic acids in plants(Ma et al.,2001).Rice was the most Al tolerance species among small-grain cereal crops(Foy, 1988).However,information on Al tolerance mechanisms in rice is limited.Ma et al.(2002)reported that no organic acid was induced by Al exposure,except citrate in small amounts in rice,and there was no signi®cant effect on Al detoxi®cation in both Al-tolerant and Al-sensitive var-ieties.It means that rice may have a different Al tolerance mechanism other than the release of organic acids.Up to now,a number of Al-regulated genes has been reported from the roots of wheat(Snowden and Gardner,*These authors contributed equally to this study.²To whom correspondence should be addressed.Fax:+8657186971323.E-mail:docpwu@ Journal of Experimental Botany,Vol.55,No.394,ãSociety for Experimental Biology2004;all rightsreserved by guest on August 25, 2011 Downloaded from1993;Richards et al .,1994;Hamel et al .,1998;Sasaki et al .,2002),Arabidopsis (Richards et al .,1998),rye (Milla et al .,2002),and sugarcane (Watt,2003).However,most of these genes were also found to be responsive to other toxic metals,low Ca 2+levels,physical wounding (Snowden et al .,1995),oxidative stress (Watt,2003),and pathogens (Hamel et al .,1998),and were expressed equally well in both Al-tolerant and Al-sensitive genotypes (Hamel et al .,1998).Up to now,there has been no Al-regulated gene reported in rice.cDNA-ampli®ed fragment length polymorphism (cDNA-AFLP)(Bachem et al .,1996)is an extremely ef®cient method for the isolation of differentially ex-pressed genes,which gave reproducible results that were con®rmed using RNA gel blot analysis (Bachem et al .,1996,1998).It is a genome-wide expression analysis tool which does not require prior sequence information and therefore constitutes a useful tool for gene discovery (Ditt et al .,2001).In this study,cDNA-AFLP was used to identify differentially expressed genes from rice subjected to Al stress treatment.Thirty-four Al-regulated transcript derived fragments (TDFs)were isolated.The information from the genes may be helpful for a better understanding of the mechanisms of Al toxicity and the Al tolerance of rice and other plant species.Materials and methodsPlant material and culture experimentAccording to previous work (Wu et al .,2000),an Al-tolerant upland tropical japonica (Oryza sativa L.)rice,Azucena,and an Al-sensitive indica rice,IR1552,were used in this study.Uniform seeds were rinsed with distilled water,and incubated with distilled water in the dark at 30°C for 2d.Germinated seeds were grown in distilled water for another 2d at 27T 2°C.Seedlings were then transferred to plastic trays that were covered by a PVC sheet with a nylon mesh.Half-strength nutrient solution was used (Yoshida et al .,1976).The pH of the solution was adjusted daily to 4.0with 1mol l ±1NaOH or 1mol l ±1HCl.Four-day-old seedlings were used for the Al-stress treatment.Seedlings were exposed to a 0.5mmol l ±1CaCl 2solution (pH 4.0)for 2h and then exposed to a 0.5mmol l ±1CaCl 2solution (pH 4.0)containing 0or 183m mol l ±1AlCl 3,with a free active Al 3+concentration of 100m mol l ±1as determined by the Geochem-PC program (Parker et al .,1995).Roots and shoots of seedlings sampled at 0,0.5,2,12,24,and 48h were cut,quickly frozen in liquid nitrogen,and stored at ±70°C for RNA extraction.The experiment was conducted in a greenhouse under a diurnal photoperiod of 12h light (158m mol m ±2s ±1).The daily maximum and minimum temperatures were 28°C and 22°C,respectively.The relative humidity ranged from 65%to 85%.Root length measurementsThe longest root of each seedling was measured after 60h of growth in the control or Al stress solutions.The relative root elongation (RRE )was calculated asRRE =(T Al ±T initial )/(C control ±C initial )where T and C refer to the measured root lengths under Al-stress and control conditions,respectively.cDNA-AFLP analysisTotal RNA was extracted from the roots,sampled at the ®ve indicated time points,using Trizol reagent (Gibco,Germany)and poly (A)+RNA was puri®ed with the Oligotex mRNA Mini Kit (Qiagen,Germany).Treated RNA and control RNA of Azucena and IR1552were prepared by pooling equal amounts of RNA at the four time points (0.5,2,12,and 24h).Double-stranded cDNA was synthesized using the SMART ÔcDNA Library Construction Kit (Clontech,USA)according to the manufacturer's instructions,puri®ed by QIAquick PCR Puri®cation Kit (Qiagen,Germany)and digested by the Taq I/Ase I enzyme combination.AFLP reactions were performed according to the methods of Bachem et al .(1996).DNA fragments were visualized by silver staining according to the Silver Sequence ÔDNA Sequencing System Technical Manual (Promega,USA).Isolation and sequencing of TDFsThe Al-regulated TDFs were recovered by PCR under the same conditions used for the pre-ampli®cation.Puri®ed PCR products were ligated to the pUCm-T vector.The clones were sequenced using MegaBACE Ô1000(Amersham Pharmacia,USA).Northern blot analysis and densitometryTotal RNA (20m g)was separated by electrophoresis on a 1.2%formaldehyde agarose gel followed by blotting onto nylon mem-brane (Hybond-N +,Amersham Pharmacia,USA).Hybridization was performed as described previously (Sambrook and Russell,2001).The hybridization signals were scanned by Typhoon 8600scanner (Molecular Dynamics,USA)and quanti®ed using the ImageQuant 5.0software (Molecular Dynamics,USA).The signals obtained from the genes were weighted against those obtained from the 18s rRNA to correct for minor differences in RNA loading and normalized to that at 0h of Azucena,which was set at 1for each gene.Gene function analysisDatabase searches were performed using the BLAST Network Service (NCBI,National Center for Biotechnology Information)(/BLAST).The sequence of each TDF was searched against all sequences in the non-redundant databases using the BLASTN,BLASTX and TBLASTX algorithm,and in the EST database using the BLASTN program in turn.Sequences that Returned with no signi®cant homology were compared again against genomic sequence databases using the BLASTN program or Genomic BLAST pages.The retrieved genomic sequences were further annotated at the web site (http://ricegaas.dna.affrc.go.jp/)or analysed using the Genscan program (http://bioweb.pasteur.fr/seqanal/interfaces/genscan.html).The function of function-known genes (BLASTX and TBLASTX,E values less than 1e ±5)(Ditt et al .,2001)was classi®ed according to the putative function.In silico mappingThe sequence of markers near the common QTL for Al tolerance in rice were obtained from the website (/).Rice bacterial arti®cial chromosome (BAC)clones were found,based on the sequence information or accession number of 33TDFs and the markers,and were anchored to the Rice Genetic Map in silico (/tdb/e2k1/osa1/sequencing.shtml).ResultsTolerance performance of the two varietiesAn Al-tolerant upland japonica rice,Azucena,and an Al-sensitive lowland indica rice,IR1552,were used for the Al138Mao et al .by guest on August 25, 2011 Downloaded fromstress treatments using 100m mol l ±1active Al 3+in this work.Root elongation was signi®cantly inhibited by Al stress in the ®rst 12h in both varieties with RRE (relative root elongation)of 76%in Azucena and 34%in IR1552.The RRE was 89%in Azucena and 29%in IR1552after the 48h stress treatment.Identi®cation of Al-regulated genesTo analyse genes responsive to Al stress,cDNA-AFLP analysis was performed on roots of Azucena and IR1552subjected to Al stress by a non-radioactive procedure.The differentially expressed fragments were investigated by selective ampli®cation using 35primer combinations.More than 2100bands were generated and all the bands longer than 100bp in length were compared in all four treatments:Azucena +/±Al and IR1552+/±Al.Fragments up-regulated or down-regulated by Al in Azucena or IR1552,or in both were identi®ed as Al-regulated TDFs.Thirty-four signi®cantly Al-regulated TDFs ranging in length from 100to 600bp were cloned and sequenced,including three up-regulated TDFs in Azucena,one in IR1552,29in both varieties,and one down-regulated TDF in Azucena (Table 1).The clones corresponding to different TDFs were renamed as Os AR (Oryza sativa Al-regulated).OsAR7and OsAR8showed homology with different parts of p -coumarate 3-hydroxylase,suggesting that these two TDFs may be the same gene.Nineteen of the33unique genes showed signi®cant homology with function-known genes by BLAST searches (Table 1).Ten TDFs with no signi®cant homology with function-known protein and four TDFs with no match in the database were classi®ed into a function-unknown gene and an unknown gene,respectively.Of the 19function-known genes,seven genes were possibly involved in the metabolism of cell wall components,including lignin synthesis (OsAR4,OsAR5,OsAR6,and OsAR7),hemicellulose (OsAR9),glycoprotein (OsAR11),and other components (OsAR10).One gene is involved in oxidative stress (OsAR13).Nine genes are related to cellular metabolism (OsAR1,OsAR2and OsAR3),retroelement (OsAR19),transcription (OsAR20),protein metabolism (OsAR14,OsAR15and OsAR16),and the cell cycle (OsAR17).All the 17genes were up-regulated by Al in both Azucena and IR1552(Table 1).One TDF (OsAR18),up-regulated only in Azucena,showed homology to KN1-like protein.Another TDF (OsAR12),up-regulated in IR1552,is for the biosynthesis of taxol (Table 1).Among the ten function-unknown genes and the four unknown genes,two genes (OsAR24and OsAR34)and one gene (OsAR25)were up-regulated and down-regulated in Azucena,respectively,and 11other genes were up-regulated in both Azucena and IR1552(Table 1).Table 1.Function analysis of Al-regulated TDFs and their expression patterns obtained by cDNA-AFLPTDFSize Expression a Homology Function category(bp)A I Os AR1255++Dihydrolipoamide S -acetyltransferase Cellular metabolism Os AR2283++2-oxoglutarate dehydrogenase Os AR3334++Aspartate aminotransferaseOs AR4333++4-coumarate:CoA ligase isoform 2Lignin synthesisOs AR5128++Phenylalanine ammonia-lyaseOs AR6133++Putative cinnamyl-alcohol dehydrogenase Os AR7272++p -coumarate 3-hydroxylase Os AR8232++p -coumarate 3-hydroxylase Os AR9256++Xylose isomerase Cell wall-related Os AR10241++Beta-1,3-glucanaseOs AR11194++UDP-N -acetylglucosamine pyrophosphorylase Os AR12322+O -deacetylbaccatin III-10-O -acetyl transferase Secondary metabolism Os AR13265++Quinone oxidoreductase Oxidative stress Os AR14286++Proteinase inhibitor Protein metabolism Os AR15227++Elongation factor EF-2Os AR16112++SUMO-1Os AR17572++MCT-1protein-like OthersOs AR18253+Rice KN1-like proteinsOs AR19268++Putative retroelement pol polyprotein Os AR20239++Histone H4Os AR21±23++Unknown protein Os AR24+Unknown protein Os AR25±Unknown protein Os AR26±30++Unknown proteinOs AR31±33++No match b Os AR34+No match ba A:Azucena;I:IR1552;(+)means up-regulated;(±)means down-regulated bNo signi®cant sequence homology found in genome,EST and protein database.Identi®cation of Al-regulated genes in rice 139by guest on August 25, 2011 Downloaded fromTemporal expression of some Al-regulated genesTo validate the Al-regulated genes and to analyse the temporal expression patterns,17genes,including seven genes for cell-wall-related functions (OsAR4to OsAR11),one for taxol synthesis (OsAR12),one for cellular metab-olism (OsAR1),two for protein metabolism (OsAR15,OsAR16),two for quinone oxidoreductase (OsAR13)and KN1-like protein (OsAR18),and four for unknown proteins (OsAR24,OsAR25,OsAR28,and OsAR29)with different expression patterns,were used for northern blotting analysis.The results were comparable to the expression patterns revealed by cDNA-AFLP except for OsAR25,which was inhibited by Al in both Azucena and IR1552,but no inhibition was revealed in IR1552by cDNA-AFLP (Table 1;Figs 1,2).The transcripts of three genes (OsAR1,OsAR12and OsAR24)could not be detected in the northern blotting.For genes involved in cell wall metabolism (Fig.1),three genes (OsAR9to OsAR11)for the synthesis of hemicellulose,glycoprotein and other cell wall components,showed the same expression patterns in both Al-tolerant and Al-sensitive varieties with the highest up-regulation at 12h (OsAR9)and 48h (OsAR10and OsAR11).Four genes (OsAR4to OsAR7)for lignin synthesis showed different expression patterns in Al-tolerant and Al-sensitive varieties.OsAR6and OsAR7were up-regulated gradually up to 48h in Azucena,but were up-regulated in IR1552within 2h then decreased.OsAR4and OsAR5showed a biphasic regulation in Azucena,transiently up-regulated within 2h and decreased at 12h and 24h and then up-regulated again;in IR1552,they were transiently up-regulated within 0.5h and then decreased and up-regulated again for OsAR5.For genes involved in other metabolism pathways (Fig.2),four (OsAR13,OsAR16,OsAR28,and OsAR29)showed similar expression patterns in Azucena and IR1552,with OsAR13and OsAR16up-regulated gradually after Al treatment,OsAR25down-regulated,and OsAR29showed a biphasic regulation which was up-regulated within 2h and after 24h,but decreased at 12h.Three genes showed different temporal expression patterns in Azucena and IR1552.The gene for KN1-like protein (OsAR18)was up-regulated in Azucena,but not in IR1552.A function-unkown gene (OsAR28)was more strongly induced within 2h in IR1552than in Azucena and was more strongly induced at 48h in Azucena than in IR1552.By contrast,the gene of the elongation factor EF-2(OsAR15)was induced within 2h in Azucena,but induced up to 48h in IR1552.DiscussioncDNA-AFLP is an ef®cient method for the isolation of differentially expressed genes and this was con®rmed bynorthern blotting analysis of 14genes (42%of the 33Al-regulated genes).Three of the 33Al-regulated genes found are reported to be Al-regulated in wheat (Snowden and Gardner,1993;Snowden et al .,1995;Cruz-Ortega et al .,1997),including phenylalanine ammonia-lyase,proteinase inhibitor and b -1,3-glucanase.The result indicates that different crops may have similar Al-responsive mechan-isms.Al stress-induced genes for cell wall componentsNineteen function-known genes isolated here are involved in different metabolic pathways,which indicates thatAlFig. 1.Temporal expression analysis of Al-regulated cell-wall-related-genes.(A,C)Different expression pro®les during Al treatment of rice varieties Azucena and IR1552over a 48h period.The ®rst lanes (0h)of each variety correspond to unstressed plants.The 18s rRNA shows the RNA integrity and uniform loading.(B,D)Quantitation of mRNA levels.The hybridization signals obtained from the genes were weighted against those obtained from the 18s rRNA to correct for minor differences in RNA loading,normalized to that at 0h of Azucena (which was set at 1for each gene)and plotted against time to compare changes in gene expression.140Mao et al .by guest on August 25, 2011 Downloaded fromtoxicity can affect different physiological and biochemical pathways.Seven of the 19genes may function in modifying cell wall components (Table 1).Morphological and histological studies have revealed that Al stress could increase the amounts of certain cell wall components,such as polysaccharides and lignin (Eleftheriou et al .,1993;Sasaki et al .,1996).Four genes (OsAR4,OsAR5,OsAR6,and OsAR7)encoding the enzymes catalysing different steps of lignin biosynthesis according to the lignin roadmap (Humphreys and Chapple,2002)were up-regulated by Al stress in this work.Northern blotting analysis indicated that transcripts of these genes were accumulated to a much higher extent in roots than in shoots (data not shown).The Al-induced up-regulation of these genes was within 2h in the Al-sensitive variety IR1552,and this was earlier than in the Al-tolerant variety Azucena.The transcripts of phenylala-nine ammonia-lyase (OsAR5),a key enzyme that catalyses the ®rst step of the phenylpropanoid pathway leading to lignin synthesis,was accumulated more in IR1552than inAzucena (Fig.1).Lignin is the principal structural component of plant cell walls.Various stress factors,including ion de®ciency,invasion by fungal pathogens and wounding,can induce the deposition of lignin in cell walls.Research by Sasaki et al .(1996)indicated that the extent of growth inhibition is closely correlated with the extent of lignin deposition in both Al-tolerant and Al-sensitive varieties,and Al-sensitive varieties would accumulate more lignin in the roots than Al-tolerant varieties.This was in accordance with the expression of the genes.Three genes for xylose isomerase (OsAR9),b -1,3-glucanase (OsAR10)and UDP-N -acetylglucosamine (UDP-GlcNAc)pyrophosphorylase (OsAR11),were up-regulated by Al stress in both Al-tolerant and Al-sensitive varieties in this study.Xylose isomerase (OsAR9)catalyses the conversion of D -xylulose to D -xylose (http://www.brenda.uni-koeln.de/),which is a major constituent of hemicellulose.It coincided with the physiological phenomenon that Al can induce hemicellulose deposition in cell walls (Tabuchi and Matsumoto,2001).b -1,3-glucanase plays an important role in plant defence against pathongen attack.It is strongly induced when plants respond to wounding or infection by fungal,bacterial or viral pathogens (Leubner-Metzger and Meins,1999).It was also induced by Al stress in wheat (Cruz-Ortega et al .,1997).It has been hypothesized that b -1,3-glucanase contributes to the hydrolysis of cell wall components during seed germination (Leubner-Metzger and Meins,1999)and suggested that it participates in the modi®cation of cell wall components under Al stress.The UDP-N -acetylglucosamine (UDP-GlcNAc)pyrophosphorylase re-versibly catalyses the synthesis of UDP-GlcNAc from UDP-GlcNAc-1-P and UTP (http://www.brenda.uni-koeln.de/).UDP-GlcNAc acts as a speci®c glycosyl donor for the biosynthesis of N-and O-linked glycopro-teins (Piro et al .,1994),which is one of the plant cell wall components.The transcripts accumulated of the two genes,OsAR10and OsAR11,increased with time of Al treatment,and the transcripts accumulation in the Al-sensitive variety was higher than that in Al-tolerant variety after 48h (Fig.1).It suggested that the up-regulation of the two genes should associate with the extent of Al toxicity.Tabuchi and Matsumoto (2001)suggested that Al modi®es the metabolism of cell wall components and thus makes the cell wall thick and rigid,which is supported by these results.Lignin,hemicellulose,glycoprotein,and other secondary metabolites deposition in the cell wall may thicken the cell wall and prevent Al from entering into the plasma membrane.However,it may also result in the arrest of root growth and cellular elongation (Le Van et al .,1994)and impose oxidative stress on the plant,which was implied by the up-regulation of the gene for quinone oxidoreductase (OsAR13),an enzyme believed to protect an organism against oxidativestress.Fig.2.Temporal expression analysis of some other Al-regulated genes.Experimental conditions and indications are as Fig.1.Identi®cation of Al-regulated genes in rice 141by guest on August 25, 2011 Downloaded fromTemporal expression patterns of the genes result from Al toxicityAmong the 33Al-regulated genes detected in this study,28genes were regulated by Al in both Al-tolerant and Al-sensitive varieties,suggesting that these genes result from Al-toxicity.Temporal expression patterns of 14genes responsive to Al,including seven genes for cell wall metabolism,were revealed by northern blotting analysis (Figs 1,2).Most of the blotted genes (OsAR9to OsAR11,OsAR13,OsAR16,OsAR25,OsAR29)were regulated similarly in both Al-tolerant and Al-sensitive varieties and were induced after 12h of Al stress,except for the gene for xylose isomerase (OsAR9)which was induced before 12h,suggesting that they could be involved in the secondary mechanisms of Al toxicity.Different temporal expression patterns of the Al-induced genes in the Al-tolerant and Al-sensitive varieties were also found.This could be because of the different tolerance levels of the two rice varieties.On the other hand,the two rice varieties with different Al sensitivity were subjected to the same level of Al and this factor may also be contributing to the differences observed in expression patterns.Candidate genes for Al toleranceThirty-three unique genes regulated by Al were isolated in this study,but no gene involved in organic acid synthesis and release was found.This result is consistent with other reports that no organic acid was released other than a small amount of citrate in rice under Al stress (Ma et al .,2002).It is reported that Al-regulated genes may have protective roles (Ezaki et al .,2000,2001),which implies that Al-regulated genes can be tolerance genes.Up to now,severalstudies have reported QTL analysis for Al tolerance in rice.It will be very helpful for selecting candidate Al tolerance genes in rice from the Al-regulated genes.All the Al-regulated genes were used for in silico mapping and 26were mapped (data not shown).Two genes for a function-unknown protein (OsAR28)and for SUMO-1(small ubiquitin-like modi®er-1,OsAR16)were in silico mapped on the common QTL interval for Al tolerance in rice on chromosome 1(Fig.3).OsAR16(112bp)is for a ubiquitin-like protein SUMO-1(a member of the SUMO conjugation system).The SUMO conjugation system appears to be a complex and functionally hetero-geneous pathway for protein modi®cation in plants.It may have important functions in stress protection and/or repair (Kurepa et al .,2003).However,it was expressed equally well in both Al-tolerant and Al-sensitive varieties (Fig.2),and might be the result of Al toxicity.OsAR28(181bp)showed different expression patterns between Azucena and IR1552,its transcript accumulation increased grad-ually up to 48h in Azucena,whereas in IR1552,it was up-regulated early (0.5h)and then decreased gradually almost to the level of CK (0h)(Fig.1).It suggests that OsAR28could be a candidate gene for Al tolerance from its different temporal expression patterns and its location on the QTL interval.To investigate the gene further for possible tolerance to Al toxicity in rice,transgenic work is being carried out.AcknowledgementThis research was funded by The National Science Foundation of China (No.30070070).Fig.3.In silico mapping of two Al-regulated genes.(A)Integrated map of the common QTL interval for Al tolerance in rice on chromosome 1by alignment of RFLP markers with an RGP physical map (http://rgp.dna.affrc.go.jp/).Sequences of the alignment markers were obtained from NCBI (/).Two Al-regulated TDFs were shaded.The vertical black bars denotes the comparable QTL positions identi®ed by Wu et al .(2000)(B),Nguyen et al .(2002)(C),Nguyen et al .(2001)(D),and Ma et al .(2002)(E).142Mao et al .by guest on August 25, 2011 Downloaded fromReferencesBahem CW,Oomen RJF,Visser RG.1998.Transcript imaging with cDNA-AFLP:a step-by-step protocol.Plant Molecular Biology Reporter16,157±173.Bachem CW,van der Hoeven RS,de Bruijn SM,Vreugdenhil D,Zabeau M,Visser RG.1996.Visualization of differential gene expression using a novel method of RNA®ngerprinting based on AFLP:analysis of gene expression during potato tuber development.The Plant Journal9,745±753.Cruz-Ortega R,Cushman JC,Ownby JD.1997.cDNA clones encoding1,3-beta-glucanase and a®mbrin-like cytoskeletal protein are induced by Al toxicity in wheat roots.Plant Physiology114,1453±1460.Ditt RF,Nester EW,Comai L.2001.Plant gene 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Author to whom correspondence should be addressed Informatik Centrum Dortmund (ICD), Dortmund, Germany
1
Abstract
Recently, biochemical systems have been shown to possess interesting computational properties. In a parallel development, the chemical computation metaphor is becoming more and more frequently used as part of the emergent computation paradigm in Computer Science. We review in this contribution the idea behind the chemical computational metaphor and outline its relevance for nanotechnology. We set up a simulated reaction system of mathematical objects and examine its dynamics by computer experiments. Typical problems of computer science, like sorting, parity checking or prime number computation are placed within this context. The implications of this approach for nanotechnology, parallel computers based on molecular devices and DNA-RNA-protein information processing are discussed.
2024-2025版英语选择性必修夯实基础进阶训练Unit5课时练习4(带答案)
课时练(四) Reading for Writing & Other Parts of theUnit基础知识夯实进阶训练第一层Ⅰ.单句语法填空1．There is a great demand for volunteers in Africa because many people live in ________ (poor)．2．Children with poor nutrition are supposed to eat food rich in vitamins and ________ (mineral)．3．Some differences between Chinese and foreign social media ________ (root) in culture and language.4．The waste water from the nearby factory has to be ________ (chemical) treated.5．If you are in need of a deadline ________ (extend), simply explain the situation to the professor.6．It is always important to choose enjoyable, ________ (nutrition) foods.7．Agriculture could become the growth engine for hunger reduction and poverty ________ (alleviate) .8．People tend to have a preference for ________ (organic) grown vegetables.9．You'd better not go swimming in this river—it is four metres in ________ (deep) somewhere.10．We must take two ________ (aspect) into consideration, both of which are associated with the safety of the students.Ⅱ.短语运用(一)默写核心短语1．________________ 相应地；转而；依次；轮流2．________________ 例如；比如3．in addition ________________4．in fact ________________5．because of ________________6．cause damage to ________________7．as for ________________8．be rich in ________________9．focus on ________________10. prefer to do sth. ________________(二)选词填空，从上面默写核心短语中选择合适的完成下列句子。
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1
INTRODUCTION
Saccades are rapid, ballistic eye movements which can be triggered by a variety of cues, including visual, auditory and planned cues. A key question is how multiple sources of saccadic commands are integrated. This integration requires learning. For example, auditory cues are initially represented in head-centered coordinates, because the ears are xed in the head, whereas visual cues are initially represented in retinal coordinates, and the eyes move in the head. On the other hand, saccadic eye movements are often controlled by motor error coordinates, which represent the movement required to xate the target (Mays & Sparks, 1980). These several coordinate systems are consistently mapped onto one another through a learning process.
Step Task
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Protein Engineering vol.14no.5pp.329–335,2001Solvent entropy-driven searching for protein modeling examined and tested in simplified modelsRainer Ko¨nig and Thomas Dandekar 1European Molecular Biology Laboratory,Meyerhofstr.1,Postfach 102209,D-69012Heidelberg,Germany1Towhom correspondence should be addressed.E-mail:dandekar@embl-heidelberg.deSolvent entropy is a force to consider in protein folding and protein design but is difficult to model.It is investigated here in the context of the hp model:Two types of residues,hydrophobic and hydrophilic,are modeled on a lattice.Nine chains and two-and three-dimensional simulations are compared.We show that considering solvent entropy alone,efficient folding of lattice chains (identification of the native fold)can be achieved by an entropy-driven simulation on its own.Moreover,in a detailed comparison over a wide range of parameters,entropy-guided searching outperforms an energy-driven search in the model.The combination of energy-and entropy-driven search yields the most efficient searching.It is compared in detail with the above results,indicating also how this solvent shell model may advantageously be implemented in more com-plex protein modeling simulations.Keywords :hp model/lattice simulation/local order/search optimization/search strategyIntroductionSolvent entropy has an impact on protein folding and protein interactions as well as protein design.However,the enumera-tion of the different microstates can become difficult.Imple-mentation of the solvent and especially its entropy is not often found in the literature (Shortle et al.,1992;Warwicker,1997).Exact calculations on entropy are difficult in real systems.Calculating the number of solvent microstates stresses a different perspective of protein folding and protein stability:The global minimum no longer appears as one rare state among a large number of alternative conformations,but as the protein conformation with the highest number of microstate representations of the solvent.A computationally unintensive way is to approximate the impact of the solvent by mean field calculations (Hsiang-Ai and Karplus,1988;Cramer and Truhlar,1992;Fraternali and van-Gunsteren,1996).The mean field calculations neglect the effect of locally ordered structures and can underestimate solvent–protein interactions (Smith and van-Gunsteren,1994).Simulations taking each water molecule of the protein surrounding shell into account (Levitt and Sharon,1988;Braxenthaler et al.,1997;Warwicker,1997;Scheraga and Hao,1999)are computationally intensive and usually are only done for simulation times of a few nanoseconds.We present here a promising implementation of solvent entropy in the context of the hp model.As a search method we took an application of the common Monte Carlo (MC)method (Metropolis et al.,1953,Kirkpatrick et al.,1983;©Oxford University Press329Aarts and Korst,1989),implemented in a standard way for this hp model [see Unger and Moult for further details of this standard implementation (Unger and Moult,1993)].The protein chain is modeled on a lattice and only two types of residues are considered,hydrophobic (h)and hydrophilic (p)(Lau and Dill,1989).We further take the local order of the solvent shell around the protein into account.Two-and three-dimensional models are compared.Long-range interactions in the rest of the solvent (e.g.long-range order brought about by solvating ions or exposed polar groups)are not considered in this simplified model.We show that efficient folding of lattice chains (identification of the native fold)can be achieved by an entropy-driven simulation completely on its own.Furthermore,we show in a detailed comparison in our model that entropy-guided searching can outperform an energy-driven search.A combination of energy-and entropy-driven searching yields the most efficient searching and is compared in detail with the above results.This illustrates,in addition,how our simple solvent shell model may advantageously be imple-mented in more complex simulations.Materials and methodsTwo-dimensional hp modelAll simulations used the hp model (Lau and Dill,1989)and modeled the protein chain on a lattice.Only two types of residues are considered,hydrophobic (H;filled squares)and hydrophilic (P;open squares).The model meets the basic characteristics of real proteins,e.g.similar distributions of secondary structure (Lau and Dill,1990).The protein is represented as a chain on a two-dimensional square lattice (Figure 1).At each point the chain can turn 90°left or right or continue ahead.The following chains with 12,18,24,33and 48residues,respectively,were tested:(1)P H P P H P P H P P H P(2)H H H P H P H P P H P H P P H P P H(3)P H P P H H P P P P H H P P P P H H P P P P H H (4)P H H P P H H P P P P P H H H H H H H P P H H P P P P H H P P H P(5)PP H P P H H P P H H P P P P P H H H H H H H H H HPP P P P P H H P P H H P P H P P H H H H HThese chains yield well-folded compact structures and their global energy minima were known a priori as –4,–9,–8,–14and –23,respectively.Three-dimensional hp modelIn the three-dimensional model at each point the chain can turn left,right,straight,up and down;otherwise the construction of the chain is similar to the two-dimensional model.Four chains with 12,14,22and 28residues and different topologies were tested (Figure 2a):R.Ko¨nig andT.DandekarFig.1.Three chains(18-mer,24-mer,33-mer)for the two-dimensional model,shown in their optimal folded lowest energy state.Their energies (direct hydrophobic contacts)are–9,–8and–14.Fig.2.(a)Chain3of the three-dimensional model,surrounded by ordered (gray spheres)and less ordered(black spheres)water.(b)Two structures of a12-mer on the two-dimensional lattice.L stands for an ensemble of less ordered water,O for a(higher)ordered ensemble.The energy implementation alone(algorithm A)would consider both stuctures the same (E leftϭE right).The objective function of B and C considers the amount of ordered water ensembles(O)and favors the left,globule like,native structure(N l eftϭ3ϽN rightϭ4,so F leftϾF right(for further details,seeMaterials and methods and Results).(1)H H P H P H P H P H P H(2)P H H H H P P P P H H H H P(3)H P P H P P H P P H P P H P P H P P H P P H(4)H P P H P P P P H P P H P P P P H P P H P P P P HP P HTheir energy minima were also known a priori as–5,–6,–4 and–12,respectively.Energy functionThe energy function∆E was kept simplistic.It adds–1for each direct(non-diagonal)lattice contact between two non-consecutive hydrophobic amino acids.‘Clashes’(i.e.two residues in the same place in the lattice)are not allowed. Trials with clashes had to be newly created. Implementation of entropyTo study entropy effects,lattice spaces adjacent to the protein chain were modeled to befilled by solvent in the following (small)water ensembles(Figure2a).Long-range interactions 330in the rest of the solvent(e.g.long-range order brought about by solvating ions or exposed polar groups)were not considered in this simple model.Small water ensembles with two different properties surrounded the model protein:ordered and less ordered.The ordered water ensembles are adjacent to hydro-phobic residues or the solvent(i.e.other water ensembles). The less ordered(with high entropy)exist if no hydrophobic residue is adjacent to them.An O-block(ordered)on the lattice is asigned to the ensemble of ordered water molecules,an L-block to a less ordered ensemble(Figure2b).The number of unordered water ensembles,N i,was counted.It has beenshown experimentally that hydrophobic molecules reduce theentropy of the surrounding(aqueous)solvent(Schulz andSchirmer,1996).The solvent entropy difference,S,betweenone protein chain conformation,N1and a tested next one,N2,during the simulation was set to be proportional to thedifference in the number of unordered adjacent lattice spacescounted(entropy is additive;we assigned1unordered blockϭ1high-entropy unitϭ1/f,with fϽ1;whereas1orderedblockϭ1low-entropy unitϭ1).With the order parameter f(see below)the probability for the new configuration to bechosen is p~e(TS/T)ϭf(N1–N2).This implementation considers the entropy of the solvent according to Boltzmann statistics.The derivation of this can be done by simple algebra(see http://www.embl-heidelberg.de/~dandekar/entropy.html).In contrast,the simple energy function from above does not differentiatebetween ordered and less ordered ensembles(Figure2b).Instead,it is derived(–1for any hydrophobic contact),if thesum(‘hydrogen bonds’in our model)of the connections of(water ensembles–water ensembles)and(water ensembles–hydrophilic residues)is counted and compared between twoconformations.The order parameter f allowed the testing of differententropy weights during simulations,either alone or multi-plying the entropy term,f(N1–N2)),with the energy term,e(–∆E/T),yielding p~e–F/Tϭe(∆E–TS)/T to consider also the energy difference∆E between two chain conformations.The model allowed the examination of three implementations:A, energy effects(∆E;simplified in the context of the model; only hydrophobic interactions are considered or,alternatively, the effect of‘hydrogen bonds’);B,entropy(∆S,difference of ordered small water ensembles);and C,the combination of them(more realistic implementation,FϭE–TS).F denotes the Helmholtz free energy(corresponding also to the Gibbs free energy if changes in volume and pressure are neglected). The new chain conformation was accepted if a random number between zero and1/constant was smaller than e–F/T. Simulation stepsA Monte Carlo algorithm was used with the following steps[implemented as in Unger and Moult(Unger and Moult,1993)]:(1)Start from a random conformation.(2)From a conformation C1with energy E1and entropy S1asingle random change yields conformation C2.If C2is notclash-free a new conformation is created.(3)Otherwise,the algorithms A,B and C decide by differentcriteria on the acceptance of the new conformation C2.The variable‘energy evaluations’is increased by one.This variable is taken as the time-counting variable sincethe energy evaluation is often the most time-demandingfunction in more complex protein folding simulations.TheMC implementation was,as in common classical statisticalSolvent entropy-driven searching for protein modelingmechanics,the probability of the system being in state i was p i ϭexp[(E –TS )/T ],where T is the arti ficial temperature,an optimized parameter (details are given below).The Boltzmann constant k was set to 1for simplicity.T was decreased for algorithms A and C as in the common simulated annealing method (Kirkpatrick et al.,1983).(4)Check if the stop criterion is met.Go back to step 2if thecounter for energy evaluations is Ͻ100000.The value 100000was taken as the limit to keep the runs fast (a few seconds for the smallest chain,30s for the longest)and results were collected from 100runs for each different parameter set.Moreover,this was suf ficient to allow identi fication of the global minimum for most of the chains.Two implementations were tested and compared for each algorithm.Implementation 2models and examines a more detailed partition function as a decision rule to change from conformation C 1to C 2(see Appendix for details),but this may prevent the algorithm from exploring new areas in search space when passing along flat surfaces.A decision constant (see Results)was introduced potentially to balance this effect and compare both implementations for a range of different values of the constant (including constant ϭ1,no constant introduced;the probability to accept C 2could maximally become 1,corresponding to accepting with certainty a change to conformation C 2).Implementation of algorithms A,B and C Algorithm A (energy)Accept C 2ifrnd Ͻconstant ϫe –∆E /T (standard,implementation 1)rnd Ͻconstant ϫ1/(1ϩe–∆E /T )(implementation 2)where rnd is a random number between 0and 1and T is anarti ficial temperature that gradually decreased (cooled)during the run to achieve convergence.T started with T 0ϭ2.00and was decreased after every 100energy evaluations with the cooling rate c :T ϭT i ϭT i –1c(c Ͻ1)c was optimized as a parameter (see Results).Algorithm B (entropy)Accept C 2ifrnd Ͻconstant ϫf (N 1–N 2)(standard,implementation 1)rnd Ͻconstant ϫ1/[1ϩf (N 2–N 1)](implementation 2)f Ͻ1was the order parameter and optimized (see Results).Note that the entropy of the protein conformations need not be considered as the algorithm itself samples over a representative ensemble of equally weighted protein micro-states.Long-range interactions in the rest of the solvent (e.g.long-range order brought about by solvating ions or exposed polar groups)were not considered in this simple model.Furthermore,calculation of the term is easily par-ison with the algorithm A above shows that the calculation is also not more computationally expensive.Algorithm C (energy and entropy)Algorithm C calculated F ϭE –TS (the free enthalpy F ,equal to the energy E minus the product of temperature T and entropy S )before the decision:rnd Ͻconstant ϫf (N 1–N 2)e –∆E /T (standard,implementation 1)rnd Ͻconstant ϫ1/[1ϩf (N 2–N 1)e –∆E /T ](implementation 2)331T and f were taken as in algorithms A and B,respectively.Note that in the comparisons,the optimal folded states (maximally packed,minimum energy but also highest solvent shell entropy)are always the same and known beforehand (cf.Figure 1).Only their energy values are given in the tables for comparison.However,in searching for these states in algorithms B and C their entropy is also considered,comparing alternative conformations.The objective function of the three different algorithms is different and favors different suboptimal folds during searching;the potential surface separating and leading to the optimal folded states is different for the three algorithms.ResultsIn our model the protein is surrounded by water composed of clusters with two different properties,ordered and less ordered,to calculate solvent shell entropy.The ordered water clusters are adjacent to hydrophobic residues and thus engage in hydrogen bonds with hydrophilic residues or the solvent.The less ordered clusters exist if no hydrophobic residue is adjacent to them.For our model no further knowledge about side chain properties is needed.This approach may be compared to an algorithm that favors a reduced hydrophobic surface of the protein.However,the latter would be a mean field approximation of the whole surface,whereas our model takes local structures,the solvent microstates,into account.The search ef ficiency of this entropy implementation in protein folding was compared with that of energy-driven searching.Different chains on both a two-and a three-dimensional lattice were tested to avoid effects depending on the lattice topography chosen.Three search strategies were compared in Monte Carlo (Metropolis et al.,1953;Kirkpatrick et al.,1983;Unger and Moult,1993)simulations:(A)Searching for the energy minimum.The number ofhydrogen bonds gave the energy for each conformation (Table I).(B)Searching for the conformation allowing the highestamount of microstates for the solvent shell (entropy search;Table II):at each step the new and the old conformation were compared.The conformation with the higher entropy (more microstate representations)of the solvent is taken for the next step.(C)A combination of energy and entropy (Table III).Boththe number of hydrogen bonds and the number of conformations were taken into account for search selection.All algorithms were run 100times with each parameter set (with different random seeds).The comparison was done with five chains in the two-dimensional model and four chains in the three-dimensional model.Except for the two longest chains in the two-dimensional model (33-mer and 48-mer),the global minimum was found quite reliable by all algorithms.If the global minimum was not found by each run,our comparisons indicate how often the minimum was found per 100runs and the standard deviation of this figure (numbers in parentheses).If the global minimum was found reliably,the number of conformation trials to find it was computed.A difference of Ͼ20%in the number of trials required was considered a signi ficant difference between the search strategies examined.Two different implementations (see Materials and methods)to evaluate the probability,including different values of theR.Ko ¨nig and T.DandekarT a b l e I .I d e n t i fic a t i o n o f t h e g l o b a l m i n i m u m i n 100t r i a l s (‘f o u n d ’)i s c o m p a r e d f o r t w o i m p l e m e n t a t i o n s (i m p 1a n d i m p 2)a n d d i f f e r e n t v a l u e s f o r t h e d e c i s i o n c o n s t a n t c t o a c c e p t a n e w c o n f o r m a t i o n :r e s u l t s c o n s i d e r i n g e n e r g y o n l y (a l g o r i t h m A )C h a i n a[E ]c ϭ1c ϭ2c Ͼ2i m p 1i m p 2i m p 1i m p 2i m p 1i m p 2F o u n dS t e p s aF o u n d S t e p s F o u n d S t e p s F o u n d S t e p s F o u n d S t e p s F o u n d S t e p s (d e v )b (d e v )(d e v )(d e v )(d e v )(d e v )[E ]c [E ][E ][E ][E ][E ]2D ,c h a i n 1[–4]54b(5)b53(5)52(5)50(5)53(5)47(5)2D ,c h a i n 2[–9]4(2)4(2)7(3)6(2)9(3)3(2)2D ,c h a i n 3[–8]11(3)5(2)5(2)5(2)10(3)8(3)2D ,c h a i n 4[–14]0c[–13,6ϫ]c0[–13,2ϫ]0[–13,6ϫ]0[–13,2ϫ]1(1)0[–13,2ϫ]2D ,c h a i n 5[–23]0[–20,1ϫ]0[–20,1ϫ]0[–19,3ϫ]0[–20,1ϫ]0[–20,1ϫ]0[–20,1ϫ]3D ,c h a i n 1[–5]3(2)0(2)4(2)2(1)4(2)5(2)3D ,c h a i n 2[–6]95(2)91(3)93(3)97(2)92(3)94(3)3D ,c h a i n 3[–4]100a28907a1002353698(1)99(1)98(1)99(1)3D ,c h a i n 4[–12]2(1)1(1)3(2)2(1)2(1)3(2)a N i n ec h a i n s w i t h t w o (2D )o r t h r e e (3D )d i me n s i o n s w e r e t e s t e d .T h e e n e r g y of t h e r e s p e c t i v eg l o b a l m i n i m u m i s g i v e n i n s q u a r e b r a c k e t s [E ].I f e v e r y t i m e th e g l o b a l mi n i m u m w a s f o u n d t h e a v e r a g e n u m b e r o f e n e r g y e v a l u a t i o n s (s t e p s )t o fin d i t i s g i v e n .b V a l u e s w i t h s t a n d a r d d e v i a t i o n s (d e v )a r e g i v e n i n p a r e n t h e s e s i f t h e g l o b a l m i n i m u m w a s n o t a l w a y s f o u n d i n 100t r i a l s .c F o r l o n g e r p r o t e i n c h a i n s (2D ,c h a i n s 4a n d 5)t h e 100000s i m u l a t i o n s t e p s u s e d i n t h i s s e a r c h s t r a t e g y c o m p a r i s o n w e r e o n a v e r a g e t o o f e w t o l o c a t e t h e g l o b a l m i n i m u m ;i n s t e a d t h e l o w e s t e n e r g y m i n i m u m f o u n d a n d h o w o f t e n i t w a s f o u n d i n 100t r i a l s i s g i v e n i n s q u a r e b r a c k e t s .332T a b l e I I .I d e n t i fic a t i o n o f t h e g l o b a l m i n i m u m i n 100t r i a l s (‘f o u n d ’)i s c o m p a r e d f o r t w o i m p l e m e n t a t i o n s (i m p 1a n d i m p 2)a n d d i f f e r e n t v a l u e s f o r t h e d e c i s i o n c o n s t a n t c t o a c c e p t a n e w c o n f o r m a t i o n :r e s u l t s c o n s i d e r i n g e n t r o p y o n l y (a l g o r i t h m B )C h a i n a[E ]c ϭ1c ϭ2c Ͼ2i m p 1i m p 2i m p 1i m p 2i m p 1i m p 2F o u n dS t e p s a F o u n d S t e p s F o u n dS t e p s F o u n d S t e p s F o u n d S t e p s F o u n d S t e p s (d e v )b(d e v )(d e v )(d e v )(d e v )(d e v )[E ]c [E ][E ][E ][E ][E ]2D ,c h a i n 1[–4]100a 13978a 1001679010080581008852100374810042282D ,c h a i n 2[–9]14b(4)b12(3)13(3)13(3)33(5)32(5)2D ,c h a i n 3[–8]4(2)10(3)11(3)10(3)25(4)26(4)2D ,c h a i n 4[–14]0c[–12,6ϫ]c0[–13,1ϫ]0[–12,16ϫ]0[–13,1ϫ]1(1)0[–13,3ϫ]2D ,c h a i n 5[–23]0[–20,1ϫ]0[–19,1ϫ]0[–20,1ϫ]0[–20,1ϫ]0[–21,1ϫ]0[–20,1ϫ]3D ,c h a i n 1[–5]2(1)1(1)1(1)2(1)2(1)3(2)3D ,c h a i n 2[–6]1008502100101611001380610019192100925510098793D ,c h a i n 3[–4]1005383100658210058981005872100596110052093D ,c h a i n 4[–12]2(1)1(1)1(1)0(1)4(2)3(2)a –c S e eT a b l e I .Solvent entropy-driven searching for protein modelingT a b l e I I I .I d e n t i fic a t i o n o f t h e g l o b a l m i n i m u m i n 100t r i a l s (‘f o u n d ’)i s c o m p a r e d f o r t w o i m p l e m e n t a t i o n s (i m p 1a n d i m p 2)a n d d i f f e r e n t v a l u e s f o r t h e d e c i s i o n c o n s t a n t c t o a c c e p t a n e w c o n f o r m a t i o n :r e s u l t s c o m b i n i n g e n t r o p y a n d e n e r g y (a l g o r i t h m C )C h a i n a[E ]c ϭ1c ϭ2c Ͼ2i m p 1i m p 2i m p 1i m p 2i m p 1i m p 2F o u n dS t e p s a F o u n dS t e p s F o u n dS t e p s F o u n d S t e p s F o u n dS t e p s F o u n dS t e p s (d e v )b(d e v )(d e v )(d e v )(d e v )(d e v )[E ]c[E ][E ][E ][E ][E ]2D ,c h a i n 1[–4]100a10686a1001186110095531008894100365910036862D ,c h a i n 2[–9]33b(5)b27(4)44(5)40(5)51(5)47(5)2D ,c h a i n 3[–8]22(4)16(4)34(5)24(4)50(5)45(5)2D ,c h a i n 4[–14]0c[–13,4ϫ]c0[–13,7ϫ]1(1)1(1)1(1)1(1)2D ,c h a i n 5[–23]0[–20,3ϫ]0[–20,1ϫ]0[–22,1ϫ]0[–21,1ϫ]0[–22,1ϫ]0[–21,2ϫ]3D ,c h a i n 1[–5]9(3)9(3)6(2)7(3)7(3)4(2)3D ,c h a i n 2[–6]1006428100707410066601005221100740010076423D ,c h a i n 3[–4]1003223100383710040661003070100363810039973D ,c h a i n 4[–12]6(2)5(2)13(3)5(2)4(2)4(2)a –c S e eT a b l e I .333Fig.3.Probability of accepting a new conformation C 2.The (standard)implementation 1and implementation 2are compared.(A )The probability curve of accepting C 2for implementation 2.(B )Probability curve for the (standard)implementation 1.It can be seen that in (A)even for F Ͻ0there is still the possibility of not accepting the step,whereas in (B)every new step with F Ͻ0is accepted.decision constant to accept a new conformation in the next step of the simulation,were compared in each strategy.Additionally,we compared different parameter values for the cooling rate c in A and C and for the entropy parameter f in B and C.We tested a broad range,for f between 0.9and 0.001(0.9,0.7,0.5,0.3,0.1,0.05,0.01,0.001),for c between 0.999999and 0.9(0.999999,0.99999,0.9999,0.999,0.99,0.9)and for the decision constant the values 1,2,4,8,16and 32.f ,c and decision constant were all optimized for the results given.The optimal range for f was 0.1to 0.7;f ϭ0.3produced the best results over all conditions.For c the optimal range was 0.99–0.99999with c ϭ0.99yielding the best results comparing all conditions.A decision constant Ͼ2was advanta-geous and constant ϭ4produced the best results.The search strategy on solvent shell entropy alone is effective in identifying the global minimum.It is more effective in searching than the standard Monte Carlo energy minimization procedure with the simpli fied energy function in more than 60%of the parameter conditions investigated (33of 54cases;including the longer chains).Sixteen cases showed no signi ficant difference,five cases were less ef ficient,including middle sized chain 3in two dimensions,implementation 1,constant ϭ1and chain 4in three dimensions,implementation 2,constant ϭ2.The combination of both search strategies is easily achieved (algorithm C).This yields a further signi ficant improvement for all tested chains including the longer ones in both lattices.It outperformed the energy-driven search (algorithm A)in 45of 54cases.In two of the remaining nine cases it showed a clear positive trend.It was also clearly and signi ficantly superior (but here only in 40of 54cases)than entropy alone (algorithm B);in 10cases there was no signi ficant difference.Implementation 2(see Materials and methods;Appendix for details)was not superior,but slightly less ef ficient than the standard implementation.Exactly 100000steps were compared for each strategy and each chain to allow a fair comparison of all search strategies,comparing batches of 100runs for each condition;100000steps were taken to collect a large amount of data for the algorithm comparison in a convenient time so that hundreds of runs could be compared and average statistics obtained.On average,this value was not suf ficient for the 48-mer on the two-dimensional lattice to find the global minimum.For the 33-mer it was found about once (out of 100trials)in the better strategies.Therefore,the energy of the best local minimum found was compared if the global minimum was not identi fied.It was not our aim to identify the global minimum under each condition but rather to compare the different algorithms underR.Ko¨nig and T.Dandekarsimple(global minimum always found)and demanding condi-tions(only the best strategies succeed sometimes).Note that a gain of one step lower in the energy of a local minimum is a significant search success as the number of local minima increases exponentially with higher energy(Lau and Dill, 1990).Solvent shell-guided searching was also here an improvement compared with the simple energy-guided search strategy and algorithm C also performed best in the comparison for longer chains.It found a local optimum with energy–22 for the48-mer.It also found the global optimum for the 33-mer four times(Tables I–III).DiscussionEntropy is an important concept in protein folding(Scheraga, 1998)including advances from recent work(e.g.Warwicker, 1997;Scheraga and Hao,1999).In general,the implementation of entropy is complex.It includes the correct enumeration of microstates in microcanonical ensembles and considering that protein entropy decreases while solvent shell entropy increases during protein folding.We have shown in a well established, simple model for protein folding(hp model)and for different types of lattices that consideration of the entropy of the adjacent water shell is sufficient to locate the correct fold for these lattice chains.The target function(high entropy,low number of hydrophobic residues on the surface)differs from the energy function(again focusing on hydrophobicity)with better access to the native fold in the overall comparison.As a result,the search speed of the entropy implementation outperformed an energy-driven search.Several factors are potentially involved in this:Considering the entropic forces of the solvent shell helps to enhance globular packing (cf.Schulz and Schirmer,1996).Some of the local minima and traps are more smoothed out as conformations with identical energy are distinguished by their solvent shell entropy differences(cf.Figure2b).Otherwise no further complications are introduced in this simplified model,in particular regarding the energy term.We did not observe trapping in new entropy-driven minima with HP contacts,probably as they can be more easily left than pure energy minima as both entropy forces and energy function may open up potentially favorable changes for such conformations.Considering the microstates focuses on a different perspect-ive of the folding problem.The preferred state of the protein chain is not one rare and single conformation but instead in the context of this model(and in the entropy aspect more realistic than in some other simple protein models)the global minimum of energy displays the highest number of micro-states.Furthermore,breathing(rearrangement of microstate ensembles)can be seen and investigated in such models(Ko¨nig and Dandekar,1999).This allows for the investigation of entropy effects as well as partial unfolding of the protein chain after the global minimum has been reached in these simulations. The solvent shell entropy model presented here achieves efficient folding for a number of simplified protein chains.In the context of the model it was not necessary to include long-range forces to achieve this.This will have to be tested further, in particular further refinement of the structures obtained by inclusion of such forces.The starting information required for any of the search strategies A,B and C was the same,the protein sequence was sufficient.An exhaustive enumeration for some of the smaller protein chains modeled is possible,but here we wanted to compare the speed and efficiency of the different search 334strategies.The hydrophobic effect was compared and consid-ered both by its entropic and energetic consequences.This focus was chosen as hydrophobic interactions are principal forces in protein folding and to compare both strategies with their respective effects on search success.The combined strategy where energy and entropy are both considered is easily achieved and can accommodate also more complex energy functions than the simplistic one chosen for testing.It identifies the native fold best.It yielded a significantly better search effectiveness on both lattices.Additionally,we com-pared the algorithms with a standard(as in common MC methods)and an alternative implementation(partition function motivated)of the decision constant and showed implementa-tion-independent,similar results for both implementations. Furthermore,both lattices tested(two-and three-dimensional) showed similar success as a control against effects from lattice choices.The use of a decision constant further improved search success.The search strategies compared fulfill ergodicity over suffi-cient long observation times and to the extent that the search space is non-reducible and contiguous,any conformation can be reached with some probability from any other conformation and it is aperiodic.Detailed balance is fulfilled for the three search strategies at least so far as reversibility for any conformational change effected is possible during the simulation.Entropy maximization was implemented in the model according to the natural behavior of proteins(Schulz and Schirmer,1996)and,in fact,entropy maximization occurs during time for any molecular interaction(more details on entropy maximization in protein–solvent systems are given on our Web page).Solvent shell optimization can certainly be considered to have an important impact on protein folding.In our simulations this aspect of folding was shown to be sufficient to localize the optimal protein structure of small model proteins.It should be noted that also other well known factors involved in protein folding such as hydrophobic forces,hydrogen bonds and cooperativity(Kolinski et al.,1999)are in part determined by solvent shell entropic forces.Nevertheless,exact experimental quantification of solvent shell effects remains a challenge.Our simplistic model suggests that at present their contribution to protein folding may perhaps be underestimated.Shortle et al.showed that consideration of entropic forces is helpful in predicting and understanding the effects of mutations(Shortle et al.,1992).Also antibody and other protein–protein interaction models are more precise if entropic effects are included.The inclusion of a simplified solvent shell entropy algorithm as given and compared in detail here improves the search success in protein folding simulations and will be taken to improve more complex folding simulations such as more extended,grid free models as a next step. AcknowledgementsWe thank Warren the,III for stylistic corrections.Part of the research was funded by Graduiertenkolleg(University of Heidelberg)and SFB544/B2.ReferencesAarts,E.and Korst,J.(1989)Simulated Annealing and Boltzmann Machines. Wiley,New York.Braxenthaler,M.,Unger,R.,Auerbach,D.,Given,J.A.and Moult,J.(1997) Proteins,29,417–425.Cramer,C.J.and Truhlar,D.G.(1992)Science,256,213–217.Fraternali,F.and van-Gunsteren,W.F.(1996)J.Mol.Biol.,256,939–948.。
反螺旋求解_戴建生
402 • Journal of Robotic Systems—2003
two screws is used.1–5 For any given screw there is a
set of reciprocal screws the evaluation of which caught the notable attention of Sugimoto and Duffy.6
1. INTRODUCTION
Several advances have been made in recent times relating to the theory of freedom to move in a constrained motion in systems of articulated or touching rigid bodies. Contributors to these advances generally recognize that simultaneous command of motion
of homogeneous equations. Hence the process of ob-
taining the reciprocal screws is converted to one of
obtaining the null space of the homogeneous equa-
John Rees Jones Beaumaris Dr. Thingwall, Heswall Wirral CH61 7XP, United Kingdom e-mail: j.reesjones@
Received 26 March 2003; accepted 31 March 2003
Washing Away Your Sins
Washing Away Your Sins:Threatened Morality and Physical CleansingChen-Bo Zhong 1*and Katie Liljenquist 2Physical cleansing has been a focal element in religious ceremonies for thousands of years.The prevalence of this practice suggests a psychological association between bodily purity and moral purity.In three studies,we explored what we call the ‘‘Macbeth effect’’—that is,a threat to one’s moral purity induces the need to cleanse oneself.This effect revealed itself through an increased mental accessibility of cleansing-related concepts,a greater desire for cleansing products,and a greater likelihood of taking antiseptic wipes.Furthermore,we showed that physical cleansing alleviates the upsetting consequences of unethical behavior and reduces threats to one’s moral self-image.Daily hygiene routines such as washing hands,as simple and benign as they might seem,can deliver a powerful antidote to threatened morality,enabling people to truly wash away their sins.When we find ourselves in morally compromising situations,how do we deal with the consequences of un-ethical behavior,given that most if not all of us desire a moral self-image?This paper inves-tigates a basic coping mechanism that has been used by religions for centuries:washing away one _s sins.Physical cleansing,such as bathing or washing hands,is at the core of many religious rituals.Baptism,for instance,is a water puri-fication ritual practiced by Christians,Man-daeanists,and Sikhs.Christians follow the admonition,B Arise and be baptized,and wash away your sins [(1),with faith that through the symbolic cleansing of their bodies they might also achieve a cleansing of conscience.Phys-ical cleansing is also central to Islam;wudu (often translated as B ablution [)is the Muslim act of washing parts of the body in clean water to prepare for worship.Likewise,Hinduism requires great attention to bodily purity (2).Thus,many major religions discipline bodily purity,suggesting that physical cleansing ceremonies can purify the soul.Research on the correspondence between physical and moral purity (3)has speculated that people are predisposed to use categories that are based on bodily experience (such as clean versus dirty)to construct complex so-cial categories (such as moral versus im-moral)(4).For example,in English,words such as B clean [and B pure [describe both physical and moral states (e.g.,he has a clean record).Likewise,the Mandarin phrase B a pair of dirty hands [refers to a person who steals.The association between bodily and moral purity may be based not only in cognition,but in emotion as well.As an example,B disgust [represents an emotion that is experienced in both physical and moral do-mains.Pure disgust was originally a gustatory emotion rooted in evolution to avoid the intake of potentially hazardous food.Over time,it has taken on social and cultural meanings and has expanded to encompass broader categories of aversions including social or moral violations (5,6).Although the experience of pure disgust devoid of mor-al connotations can be subjectively and behaviorally differentiated from the experi-ence of disgust with moral connotations (7),they coincide considerably.Specifically,pre-vious research suggests that pure disgust and moral disgust not only lead to similar facial expressions and physiological activation (6)but also recruit partially overlapping brain regions,mainly in the frontal and temporal lobes (7).Given the psychological,physio-logical,and neurological overlap between physical and moral disgust,physical cleans-ing acts that mitigate physical disgust might also reduce social or moral disgust,thereby alleviating moral condemnation.Thus,Lady Macbeth _s hope that a little bit of water would clear her of the treacherous murder of King Duncan might not have been a product of literary creativity,but of Shakespeare _s acute understanding of thehuman psyche.If physical and moral purity are so psychologically intertwined,Lady Macbeth _s desperate obsession with trying to wash away her bloodied conscience while crying,B Out,damned spot!Out,I say
通讯作者的具体责任
• 通讯作者的责任说起来也是简单的很。 • 给予所有对此工作有贡献的人员恰当的承认。那些对研究工 作有显著贡献的人应该被列入共同作者。在投稿时，通讯作 者要确认所有被列入作者名单的人都同意投稿，承担包括所 有共同作者并且仅仅包括共同作者的责任。通讯作者代表所 有作者签署版权证书。 • 但是做起来也不是很容易的。特别是第一条。 • 我们注意到没有要求通讯作者必需具有怎么样的职务，资历 • 谁应该是通讯作者？
How to Write and Publish Scientific Papers in English （3）
——Authors and Affiliation
ZHAO Jianping Nanjing University of Technology
1
作者地址的标署
• （1）尽可能地给出详细通讯地址，邮政编码 • （2）有二位或多位作者，则每一不同的地址应按之中出现的 先后顺序列出，本以相应上标符号的形式列出与相应作者的 关系。 • （3）如果论文出版时作者调到一个新单位（不同于投稿时作 者完成该研究工作的地址），新地址应以“Present address” （现地址）的形式在脚注中给出，这种做法对地址了解作者 的就职单位十分有用，同时也有利检索系统统计研究机构的 论文产出。
• 通信作者标注：Corresponding author，To whom correspondence should be addressed,
• 或 The person to whom inquiries regarding the paper should be addressed • 如果两个以上的作者在地位上是相同的，可以采取“共同第一 作者”（joint first author)的署名方式，并说明These authors contributed equally to the work(这些作者对研 究工作的贡献是相同的）
磁性应用纳米材料的开发英文原文
NANO-SCALE MATERIALS DEVELOPMENT FOR FUTUREMAGNETIC APPLICATIONSpM.E.McHENRY and UGHLIN {Department of Materials Science and Engineering,Data Storage Systems Center,Carnegie MellonUniversity,Pittsburgh,PA 15213,USA(Received 1June 1999;accepted 15July 1999)Abstract ÐDevelopments in the ®eld of magnetic materials which show promise for future applications are reviewed.In particular recent work in nanocrystalline materials is reviewed,with either soft or hard beha-vior as well as advances in the magnetic materials used for magnetic recording.The role of microstructure on the extrinsic magnetic properties of the materials is stressed and it is emphasized how careful control of the microstructure has played an important role in their improvement.Important microstructural features such as grain size,grain shape and crystallographic texture all are major contributors to the properties of the materials.In addition,the critical role that new instrumentation has played in the better understanding of the nano-phase magnetic materials is demonstrated.#2000Published by Elsevier Science Ltd on behalf of Acta Metallurgica Inc.All rights reserved.Keywords:Soft magnetic materials;Hard magnetic materials;Recording media;Microstructure;Nano-phase1.INTRODUCTIONWhether it can be called a revolution or simply a continuous evolution,it is clear that development of new materials and their understanding on a smaller and smaller length scale is at the root of progress in many areas of materials science [1].This is particularly true in the development of new mag-netic materials for a variety of important appli-cations [2±5].In recent years the focus has moved from the microcrystalline to the nanocrystalline regime.This paper intends to summarize recent developments in the synthesis,structural character-ization,and properties of nanocrystalline and mag-nets for three distinct sets of magnetic applications:1.Soft magnetic materials.2.Hard magnetic materials.3.Magnetic storage media.The underlying physical phenomena that motivate these developments will be described.A unifying theme exists in the understanding of the relation-ships between microstructure and magnetic aniso-tropy (or lack thereof)in materials.The term ``nanocrystalline alloy''is used to describe those alloys that have a majority of grain diameters in the typical range from H 1to 50nm.This term will include alloys made by plasma processing [6±8],rapid solidi®cation,and deposition techniques where the initial material may be in the amorphous state and subsequently crystallized.We discuss processing methods to control chemistry and microstructural morphology on increasingly smaller length scales,and various developing experimental techniques which allow more accurate and quantitative probes of struc-ture on smaller length scales.We review the impact of microstructural control on the develop-ment of state of the art magnetic materials.Finally we o er a view to the future for each of these applications.Over several decades,amorphous and nanocrys-talline materials have been investigated for appli-cations in magnetic devices requiring either magnetically hard or soft materials.In particular,amorphous and nanocrystalline materials have been investigated for various soft magnetic applications including transformers,inductive devices,etc.In these materials it has been determined that an im-portant averaging of the magnetocrystalline aniso-tropy over many grains coupled within an exchange length is the root of the magnetic softness of these materials.The fact that this magnetic exchangeActa mater.48(2000)223±2381359-6454/00/$20.00#2000Published by Elsevier Science Ltd on behalf of Acta Metallurgica Inc.All rights reserved.PII:S 1359-6454(99)00296-7/locate/actamatpThe Millennium Special Issue ÐA Selection of Major Topics in Materials Science and Engineering:Current status and future directions,edited by S.Suresh.{To whom all correspondence should be addressed.length is typically nanometers or tens of nanometers illustrates the underlying importance of this length scale in magnetic systems.In rare earth permanent magnets [9],it has been determined that a microstructure containing two or more phases,where the majority phase is nanocrys-talline (taking advantage of the favorable high coer-civity in particles of optimum size)and one or more of the phases are used to pin magnetic domain walls leads to better hard magnetic properties.Still another exciting recent development has been the suggestion of composite spring exchange magnets [10]that combine the large coercivities in hard mag-nets with large inductions found in softer transition metal magnets.Again chemical and structural vari-ations on a nano-scale are important for determin-ing optimal magnetic properties.In the area of magnetic storage media future pro-gress will also rely on the ability to develop control over microstructure at smaller size scales so as to impact on storage densities.Here the issue of ther-mal stability of the magnetic dipole moment of ®ne particles has become a critical issue,with the so-called superparamagnetic limit on the horizon.The need to store information in smaller and smaller magnetic volumes pushes the need to develop media with larger magnetocrystalline anisotropies.2.DEFINITIONSTechnical magnetic properties [11,12]can be de®ned making use of a typical magnetic hysteresis curve as illustrated in Fig.1.Magnetic hysteresis [Fig.1(a)]is a useful attribute of a permanent mag-net material in which we wish to store a large meta-stable magnetization.Attributes of a good permenent magnet include:(a)large saturation and remnant inductions,B s and B r :a large saturation magnetization,M s ,and induction,B s ,are desirable in applications of both hard (and soft)magnetic materials;(b)large coercivities,H c :coercivity is a measure of the width of a hysteresis loop and a measure of the permanence of the magnetic moment;(c)high Curie temperature,T c :the ability to use soft magnetic materials at elevated tempera-tures is intimately dependent on the Curie tempera-ture or magnetic ordering temperature of the material.A large class of applications requires small hys-teresis losses per cycle.These are called soft mag-netic materials and their attributes include:(a)high permeability:permeability,mB a H 1 w ,is the material's parameter which describes the ¯ux density,B ,produced by a given applied ®eld,H .In high permeability materials we can produce very large changes in magnetic ¯ux density in very small ®elds;(b)low hysteresis loss:hysteresis loss rep-resents the energy consumed in cycling a material between ®elds H and ÀH and back again.The energy consumed in one cycle is W HM d B or the area inside the hysteresis loop.The hysteretic power loss of an a.c.device includes a term equal to the frequency multiplied by the hysteretic loss per cycle;(c)large saturation and remnant magneti-zations;(d)high Curie temperatures.The magnetization curve [Fig.1(a)]illustrates the technical magnetic properties of a ferromagnetic material.Its shape is determined by minimizing the material's magnetic free energy.The magnetic free energy consists of terms associated with the®eldFig.1.(a)Schematic of a hysteresis curve for a magnetic material de®ning some technical magnetic par-ameters and (b)rotation of atomic magnetic dipole moments in a 1808(Bloch)domain wall in a ferro-magnetic material.224McHENRY and LAUGHLIN:NANO-SCALE MATERIALS DEVELOPMENTenergy(Zeeman energy),self-®eld(demagnetization energy),wall energy,and magnetic anisotropy energy.The magnetic Helmholtz free energy[13] can be determined by integrating a magnetic energy density as follows:F M 4A rr MM s!2ÀK1 rMÁnM s!2Àm0MÁH5d r1where A(r)is the local exchange sti ness related to the exchange energy,J and spin dipole moment,S A CJS2a a at0K,with C H1depending on crys-tal structure and a is the interatomic spacing),K1(r) is the(leading term)local magnetic anisotropy energy density,M is the magnetization vector,n is a unit vector parallel to the easy direction of mag-netization,and H is the sum of the applied®eld and demagnetization®eld vectors.The magnetic anisotropy energy describes the angular dependence of the magnetic energy,i.e.its dependence on angles y and f between the magnetization and an easy axis of magnetization.For the case of a uniaxial material the leading term in the anisotropy energy density has a simple K1sin2y form.The anisotropy energy can be further subdivided into magnetocrys-talline,shape and stress anisotropies,etc.For the purposes of the discussions here,however,we will devote most of our attention to the magnetocrystal-line anisotropy.The magnetic anisotropy represents a barrier to switching the magnetization.For soft magnetic ma-terials,a small magnetic anisotropy is desired so as to minimize the hysteretic losses and maximize the permeability.In soft materials,the desire for small magnetocrystalline anisotropy necessitates the choice of cubic crystalline phases of Fe,Co,Ni or alloys such as FeCo,FeNi,etc.(with small values of K1).In crystalline alloys,such as permalloy or FeCo,the alloy chemistry is varied so that the®rst-order magnetocrystalline anisotropy energy density, K1,is minimized.Similarly,stress anisotropy is reduced in alloys with nearly zero magnetostriction. Shape anisotropy results from demagnetization e ects and is minimized by producing materials with magnetic grains with large aspect ratios. Amorphous alloys are a special class of soft ma-terials where(in some notable cases)low magnetic anisotropies result from the lack of crystalline periodicity.For hard magnetic materials a large magnetic anisotropy is desirable.As discussed below,large magnetocrystalline anisotropy results from an ani-sotropic(preferably uniaxial)crystal structure,and large spin orbit rge magnetocrystal-line anisotropy is seen,for example in h.c.p.cobalt, in CoPt where spin±orbit coupling to the relativistic Pt electrons invokes large anisotropies,and impor-tantly in the rare earth permanent magnet ma-terials.In future discussions we will®nd it useful to describe several length scales that are associated with magnetic domains and domain walls[Fig. 1(b)].These are expressed through consideration of domain wall energetics.The energy per unit area in the wall can be expressed as a sum of exchange and anisotropy energy terms:g W g ex g K 2 where the anisotropy energy per unit volume,K,is multiplied by volume contained in a domain wall, A W d W,and divided by cross-sectional area to arrive at an anisotropy energy per unit area:g K KA W d WA WK d W K Na 3where d W Na(a is the lattice constant in the direction of rotation and N is the number of planes over which the rotation takes place)is the thickness of the wall.Thus g W can be expressed asg Wp2J ex S2Na2K1 Na 4where the®rst term considers the cost in exchange energy in rotating magnetic dipole moments in a 1808domain wall as illustrated in Fig.1(b).To determine the optimal wall thickness we di eren-tiate g W with respect to d W yielding:N eqp2J ex S2K1a3sX 5For Fe,N eq H300and the equilibrium thickness, t eq N eq a H50nm X Expressed in terms of the exchange sti ness,A ex,and the domain wall width, d W pA ex a K1pXAnother important length scale is the distance over which the perturbation due to the switching of a single spin decays in a soft material.This length is called the ferromagnetic exchange length,L ex, and can be expressed asL exA ex2ssX 6The ferromagnetic exchange length is H3nm for ferromagnetic iron-or cobalt-based alloys.The ratio of the exchange length to d W/p is a dimension-less parameter,k,called the magnetic hardness par-ameter:kp L exd WK1m0M2ssX 7For hard magnetic materials k is on the order of unity and thus there is little di erence between theMcHENRY and LAUGHLIN:NANO-SCALE MATERIALS DEVELOPMENT225ferromagnetic exchange length and the domain wall width.On the other hand,for good soft magnetic materials,where K 1approaches zero,k can deviate substantially from unity.Structure sensitive magnetic properties may depend on defect concentration (point,line and pla-nar defects),atomic order,impurities,second phases,thermal history,etc.In multi-domain ma-terials,the domain wall energy density ,g 4 AK 1 1a 2g x ,is spatially varying as a result of local variations in properties due to chemical variation,defects,etc.A domain wall will prefer to locate itself in regions where the magnetic order parameter is suppressed,i.e.pinning sites .Since changes in induction in high-permeability materials occur by domain wall motion,it is desirable to limit variation of g (x )(pinning).This is one of the key design issues in developing soft magnetic materials,i.e.that of process control of the microstructure so as to optimize the soft magnetic properties.In hard materials development of two-phase microstructures with pinning phases is desirable.For ®ne particle magnets the possibility of ther-mally activated switching and consequent reduction of the coercivity as a function of temperature must be considered as a consequence of a superparamag-netic response.This is an important limitation in magnetic recording.Superparamagnetism refers to the thermally activated switching of the magnetiza-tion over rotational energy barriers (provided by magnetic anisotropy).Thermally activated switching is described by an Arrhenius law where the acti-vation energy barrier is K u h V i (h V i is the switching volume).The switching frequency becomes larger for smaller particle size,smaller anisotropy energydensity and at higher temperatures.Above a block-ing temperature,T B ,the switching time is less than the experimental time and the magnetic hysteresis loop is observed to collapse,i.e.the coercive force becomes zero.Above T B ,the magnetization scales with ®eld and temperature in the same manner as does a classical paramagnetic material,with the exception that the inferred dipole moment is a par-ticle moment and not an atomic moment.Below the blocking temperature,hysteretic magnetic re-sponse is observed for which the coercivity has the temperature dependence:H c H c 041À TT B 1a 25X 8In the theory of superparamagnetism [14,15],the blocking temperature represents the temperature at which the metastable hysteretic response is lost for a particular experimental timeframe.In other words,below the blocking temperature hysteretic response is observed since thermal activation is not su cient to allow the immediate alignment of par-ticle moments with the applied ®eld.For stability of information over H 10years,the blocking tempera-ture should roughly satisfy the relationship:T B K u h V i a 40k B X The factor of 40[16,17]represents ln o 0a o ,where o is the inverse of the 10year stab-ility time (H 10À4Hz)and o 0an attempt frequency for switching (H 1GHz).3.SOFT MAGNETIC MATERIALSApproaches to improving intrinsic and extrinsic soft ferromagnetic properties involve (a)tailoringFig.2.(a)Herzer diagram [18]illustrating dependence of the coercivity,H c ,with grain size in magnetic alloys and (b)relationship between permeability,m e (at 1kHz)and saturation polarization for soft mag-netic materials [19].226McHENRY and LAUGHLIN:NANO-SCALE MATERIALS DEVELOPMENTchemistry and (b)optimizing the microstructure.Signi®cant in microstructural control has been rec-ognition that a measure of the magnetic hardness (the coercivity,H c )is roughly inversely proportional to the grain size (D g )for grain sizes exceeding H 0.1±1m m [where the D g exceeds the domain (Bloch)wall thickness,d W ].Here grain boundaries act as impediments to domain wall motion,and thus ®ne-grained materials are usually magnetically harder than large grain materials.Signi®cant recent development in the understanding of magnetic coer-civity mechanisms has led to the realization that for very small grain sizes D g `H 100nm ,[18],H c decreases rapidly with decreasing grain size [Fig.2(a)].This can be understood by the fact that the domain wall,whose thickness,d W ,exceeds the grain size,now samples several (or many)grains and ¯uc-tuations in magnetic anisotropy on the grain size length scale which are irrelevant to domain wall pinning.This important concept of random aniso-tropy suggests that nanocrystalline and amorphous alloys have signi®cant potential as soft magnetic materials.Soft magnetic properties require that nanocrystalline grains be exchange coupled and therefore processing routes yielding free standing nanoparticles must include a compaction method in which the magnetic nanoparticles end up exchange coupled.Random anisotropy [20,21]has been realized in a variety of amorphous and nanocrystalline ferro-magnets as illustrated in Fig.2(b)which shows two important ®gures of merit for soft magnetic ma-terials their magnetic permeability and their bined high permeabilities and magnetic inductions are seen for amorphous Fe-and Co-based magnets with more recent improvements in the envelope occurring with the development of nanocrystalline alloys FINEMET,NANOPERM and HITPERM.The last of these combines high permeabilities,large inductions with the potential for high temperature application due to the high Curie temperature of the a '-FeCo nanocrystalline phase.Typical attributes of nanocrystalline ferro-magnetic materials produced by an amorphous pre-cursor route are summarized in Table 1[22].The basis for the random anisotropy model is il-lustrated in Fig.3(a).The concept of a magnetic exchange length and its relationship to the domain wall width and monodomain size is important in the consideration of magnetic anisotropy in nano-crystalline soft magnetic materials.These length scales are de®ned by appealing to a Helmholtz free energy functional described above.These length scales again are:d W p A a K p and L ex A a 4p M 2s p X The extension of the random ani-sotropy model by Herzer [18]to nanocrystalline alloys has been used as the premise for describing e ective anisotropies in nanocrystalline materials.Herzer considers a characteristic volume whose lin-ear dimension is the magnetic exchange length,L ex H A a K 1a 2X The unstated constant of propor-tionality (k )for materials with very small K can beTable 1.Attributes of nanocrystalline ferromagnetic materials produced by an amorphous precursor routeAlloy name Typical composition Nanocrystalline phase B s (T)T c (8C)FINEMET Fe 73.5Si 13.5B 9Nb 3Cu 1a -FeSi,FeSi (DO 3)1.0±1.2<770NANOPERM Fe 88Zr 7B 4Cu a -Fe (b.c.c.)1.5±1.8770HITPERMFe 44Co 44Zr 7B 4Cua -FeCo (b.c.c.),a '-FeCo (B2)1.6±2.1>965Fig.3.(a)Cartoon illustrating N nanocrystalline grains of dimension D ,in a volume L 3ex X (b)TEMmicrographs for an annealed (Fe 70Co 30)88Hf 7B 4Cu HITPERM magnet ribbons [23].McHENRY and LAUGHLIN:NANO-SCALE MATERIALS DEVELOPMENT227quite large.The Herzer argument considers N grains,with random crystallographic easy axes,within a volume of L 3ex ,to be exchange coupled.For random easy axes,a random walk over all N grains yields an e ective anisotropy that is reduced by a factor of 1/(N )1/2from the value K for any one grain,thus K eff K a N 1a 2X The number of grains in this exchange coupled volume is just N L ex a D 3,where D is the average diameter of individual grains.Treating the anisotropy self-con-sistently:K eff H KD 3a 2H K effA !3a 2HK 4D 6A 3!X 9Since the coercivity can be taken as proportional tothe e ective anisotropy,this analysis leads to yield Herzer's prediction that the e ective anisotropy and therefore the coercivity should grow as the sixth power of the grain size:H c H H K H D 6X10Other functional dependences of the coercivity on grain size have been proposed for systems with reduced dimensionality (i.e.thin ®lms)and sup-ported by experimental observations.The D 6power law is observed experimentally in a variety of alloys as illustrated in Fig.2(a).In FINEMET,NANOPERM and HITPERM nanocrystalline alloys,a common synthesis route has been employed resulting in a two-phase nano-crystalline microstructure.This involves rapid soli-di®cation processing of the alloy to produce an amorphous precursor.This is followed by primary (nano)crystallization of the ferromagnetic phase.For synthesis of a nanocrystalline material,the pri-mary crystallization temperature,T x1,is the usefulcrystallization event.In the amorphous precursor route to producing nanocrystalline materials,sec-ondary crystallization is typically of a terminal early transition metal±late transition metal (TL±TE)and/or late transition metal±metalloid (TL±M)phase.This phase is typically deleterious in that it lowers magnetic permeability by domain wall pin-ning.The secondary crystallization temperature,T x2,then represents the upper limit of use for nano-crystalline materials.A typical DTA study of crys-tallization [24,25]is shown in Fig.4(a).Crystallization reactions and kinetics have been examined in some detail for certain of these alloys.For example,Hsiao et al .[26]has examined the crystallization kinetics of a NANOPERM alloy using magnetization as the measure of the volume fraction transformed in the primary crystallization event.Time-dependent magnetization data,at tem-peratures above the crystallization temperature,are illustrated in Fig.4(b).Since the amorphous phase is paramagnetic at the crystallization temperature,the magnetization is a direct measure of the volume fraction of the a -Fe crystalline phase that has trans-formed.M (t )then measures the crystallization kin-etics.Figure 4(b)shows curves reminiscent of Johnson±Mehl±Avrami kinetics for a phase trans-formation.X (t )has been ®t to reveal activation energies of H 3.5eV and JMA kinetic exponents of H 3/2consistent with immediate nucleation and parabolic three-dimensional growth of nanocrystals.Detailed studies of NANOPERM and FINEMET [27,28]alloys have furthered the under-standing of the crystallization events.Ayers et al .[29±31]have proposed a model based on incipient clustering of Cu in FINEMET alloys prior to nucleation of the a -FeSi ferromagnetic nanocrystal-line phase.Hono et al .'s [32±34]atomic probe ®eld ion microscopy (APFIM)studies ofFINEMETFig.4.(a)Di erential thermal analysis (DTA)plot of heat evolved as a function of temperature for a Fe 44Co 44Zr 7B 4Cu 1alloy showing two distinct crystallization events [24,25].(b)Isothermal magnetiza-tion as a function of time (normalized by its value after 1h)for the NANOPERM compositionFe 88Zr 7B 4Cu at 490,500,520and 5508C,respectively [26].228McHENRY and LAUGHLIN:NANO-SCALE MATERIALS DEVELOPMENTalso supported the important role of Cu in the crys-tallization process,though it was thought that Fe±Si nanocrystals grew near but not necessarily on the Cu clusters [Fig.5(b)].Recent three-dimensional APFIM results by Hono et al .elegantly con®rm the original Ayers mechanism.Clear inferences from magnetic measurements,EXAFS,etc.point to the role of partitioning of early transition metals and boron during primary crystallization of NANOPERM and HITPERM alloys [Fig.5(a)].A signi®cant issue in the use of nanocrystalline materials in soft magnetic applications is the strength and especially the temperature dependence of the exchange coupling between the nanocrystal-line grains.The intergranular amorphous phase,left after primary crystallization in FINEMET and NANOPERM,has a lower Curie temperature than the nanocrystalline ferromagnetic phase.This can give rise to exchange decoupling of the nanocrystal-line grains,and resulting magnetic hardening,at relatively low temperatures.HITPERM has been developed with the aim of not only increasing the Curie temperature of the nanocrystals (in this case a '-FeCo)but also in the intragranular amorphous phase.Figure 6(a)shows observations of magnetization as a function of temperature [22,24,25]for two alloys,one of a NANOPERM composition,and the other of a HITPERM composition.The amor-phous precursor to NANOPERM has a T c just above room temperature.The magnetic phase tran-sition is followed by primary crystallization at T x 1H 5008C ;secondary crystallization and ®nally T c of the nanocrystalline a -Fe phase at H 7708C.M (T )for HITPERM,shows a monotonic magnetization decrease up to T c for the amorphous phase.Above 400±5008C structural relaxation and crystallization of the a '-FeCo phase occurs.T x1is well below the Curie temperature of the amorphous phase,so that the magnetization of the amorphous phase is only partially suppressed prior to crystallization.It is this Curie temperature of the amorphous intergra-nular phase that is important to the exchange coup-ling of the nanocrystals in HITPERM.The soft magnetic properties of nanocrystalline magnetic alloys extend to high frequencies due to the fact that the high resistivities of these alloys limit eddy current losses.Figure 7(b)illustrates the frequency dependence of the real and imaginary components of the complex permeability,m 'and m 0,for a HITPERM alloy.m 0re¯ects the power loss due to eddy currents and hysteresis.The losses,m 0(T ),peak at a frequency of H 20kHz.This is re¯ective of the higher resistivity in the nanocrystal-line materials.AC losses re¯ect domain wall in a viscous medium.The largerresistivityFig.5.(a)Schematic representation of the concentration pro®le of Fe and Zr near an a -Fe nanocrystal for during primary crystallization of NANOPERM type alloys [22].(b)Proposed sequence of events inthe nanocrystallization of FINEMET alloys (after Hono et al .[32±34]).Fig.6.(a)M (T )for an alloy with a NANOPERM com-position Fe 88Zr 7B 4Cu and an alloy with a HITPERMcomposition,Fe 44Co 44Zr 7B 4Cu [24,25].McHENRY and LAUGHLIN:NANO-SCALE MATERIALS DEVELOPMENT 229r 50mO cm at 300K)extends the large per-meability to higher frequencies where eddy currents (classical and those due to domain wall motion)dominate the losses.The resistivity of the nanocrys-talline materials is intermediate between the amor-phous precursor and crystalline materials of similar composition and is a signi®cant term in eddy cur-rent related damping of domain wall motion.4.HARD MAGNETIC MATERIALSOver the last few decades the most signi®cant advancements in permanent magnet materials has come in the area of so-called rare earth permanent magnets.These have a magnetic transition metal as the majority species and a rare earth metal as the minority species.The large size di erencebetweenFig.7.AC hysteresis loops for the HITPERM alloy at 0.06,4,10,and 40kHz.The sample was annealed at 6508C for 1h and the measurements were made at room temperature with a ®eld ampli-tude,H m 2X 5Oe [24,25].Fig.8.(a)Cartoon showing cellular structure [48]observed in many 2:17based magnets with cells con-taining the rhombohedral and hexagonal 2:17variants and 1:5intergranular phase;(b)crystal struc-tures of the same and (c)TEM picture (courtesy of J.Dooley)of cellular structure observed in 2:17-based magnet.230McHENRY and LAUGHLIN:NANO-SCALE MATERIALS DEVELOPMENTthe rare earth and transition metal species gives rise to the observation of many anisotropic crystal structures in these systems.In such systems the transition metal(TM)species is responsible for most of the magnetization and TM±TM exchange determines the Curie temperature.On the other hand the rare earth(RE)species determines the magnetocrystalline anisotropy.The anisotropic4f-electron charge densities about the rare earth ion gives rise to large orbital moment and consequently large spin orbit interactions that are at the root of magnetocrystalline anisotropy.The development of large coercivities from materials with large(uniax-ial)magnetic anisotropies involves microstructural development aimed at supplying barriers to the ro-tation of the magnetization and pinning of domain walls.Systems based on Sm±Co[35±38]and Fe±Nd±B[39,40]have been of considerable recent interest.Of the two important classes of rare earth tran-sition metal permanent magnets,i.e.Sm±Co based and Nd2Fe14B alloys[39,40],Sm±Co alloys have much larger Curie temperatures,increasing in com-pounds with larger Co concentrations(e.g.the3:29 phase).The so-called1:5,1:7,and2:17alloys and newly discovered3:29materials[41,42],have received attention,where the ratios refer to the RE:TM concentrations.High Curie temperature, T c,interstitially doped(C,N),2:17magnets have also been studied extensively[43±47].The develop-ment of the Fe±Nd±B magnets has been motivated by the lower cost of Fe as compared with Co and Nd as compared with Sm.These magnets do,how-ever,su er from poorer high temperature magnetic properties due to their lower Curie temperatures. The Sm2Co17phase when compared with SmCo5 o ers larger inductions and Curie temperatures at the expense of some magnetic anisotropy.The2:17 materials have favorable and to date unmatched intrinsic properties:B r 1X2T(258C),intrinsic coer-civity i H c 1X2T(258C)and T c 9208C(e.g.in comparison to7508C for SmCo5).The higher three-dimensional metal content(Co)leads to their high values of T c.The2:17magnets currently in com-mercial production have a composition Sm(CoFeCuM)7.5.Additions of Fe are made to increase the remnant induction;Cu and M Zr, Hf,or Ti)additions are made to in¯uence precipi-tation hardening.Optimum hard magnetic proper-ties,notably coercivities are achieved in magnets in which the primary magnetic phase has a50±100nm grain size(approaching the monodomain size)as described below.Typical2:17Sm±Co magnets with large values of H c are obtained through a low temperature heat treatment used to develop a cellular microstructure (see Fig.8).Small cells of the2:17matrix phase are separated(and usually completely surrounded)by a thin layer of the1:5phase as illustrated in Fig.8. The cell interior contains both a heavily twinned rhombohedral modi®cation of the2:17phase along with coherent platelets of the so-called z-phase[48] is rich in Fe and M and has the hexagonal2:17 structure.Typical microstructures have a50±100nm cellular structure,with5±20nm thick cell walls, and display i H c of1.0±1.5T at room temperature. By1508C H c is diminished by H50%.The loss of H c undoubtedly continues with temperature.In the cellular microstructure shown in Fig.8the magnetic anisotropy of the1:5cell boundary phase is important in determining the coercivity. Coercivity at room temperature in2:17Sm±Co magnets is largely controlled by the magnetocrystal-line anisotropy of Sm3+ions in SmCo5in the cell walls.In a100nm cellular material the room tem-perature coercivity is twice that of conventional 2:17alloys.In Co-rich alloys(2:17,3:29,etc.)devel-opment of su cient magnetic anisotropy for hard applications is intimately related to having a prefer-ential easy c-axis and developing a®ne microstruc-ture.Optimization of the Sm(CoFeCuZr)z magnets dis-cussed above have been the subject of much recent work.In particular,improvement of properties at elevated temperatures for aircraft power generators has been of particular interest[49±52].Ma et al.[49]investigated the e ects of intrinsic coercivity on the thermal stability of2:17magnets up to 4508C.Recently,Liu et al.[52]have investigated the role of Cu content and stoichiometry,z,on the intrinsic coercivity at5008C in Sm(CoFeCuZr)z magnets.For magnets with z 8X5,i.e. Sm(Co bal Fe0.1Cu x Zr0.033)8.5,the optimum coercivity (4.0T at room temperature,1.0T at5008C)occurs for a Cu concentration x 0X088X The role of Cu has been elucidated through microstructural studies as decreasing the cell size while concurrently increasing the density of the lamellar z-phase in these alloys.The development of Sm±Co magnets,especially those with good high temperature magnetic proper-ties has resulted in extensive work on a so-called 1:7phase with a TbCu7structure[53].SmCo7is a metastable phase at room temperature.The struc-tures of SmCo7and Sm2Co17are both derived from the structure of SmCo5.The structure of Sm2Co17 can be viewed as one in which1/3of the Sm atoms in the SmCo5are replaced by dumbbells of Co in an ordered fashion.Kim[54,55]have studied the intrinsic coercivity of SmTM7magnets and attribu-ted higher coercivities at5008C to smaller cell sizes. Recent work[54±57]on SmCo7Àx Zr x magnets has been extended to alloys with composition RCo7Àx Zr x x 0±0X8,R Pr,Y or Er).A small amount of Zr substitution contributes to stabiliz-ation of the TbCu7structure,and improves the magneto-anisotropy®eld,H A.The choice and con-centration of various rare earth species in¯uences the easy axis of magnetization.Most recently there has been considerable interestMcHENRY and LAUGHLIN:NANO-SCALE MATERIALS DEVELOPMENT231。
Using Microreactors In Chemical Synthesis Batch Process versus Continuous
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Pharmaceutical Tecbnoiogy API SYNTHESIS AND FORMULATION 2009
CONTINUOUS PROCESSING
Potential energy
Leads to formation of C and creates losses of B C
*To whom all correspondence should be addressed.
Because ot their small surface-to-volume ratio, microreactors can absorb heat created from a reaction much more efficiently than a batch reactor can. Figure 1 shows the initial heat distribution for a neutralization-model reaction in a simulated 5-m' batch reactor stirred at 5Ü0 rpm. The batch reactor is heated by an exothermic reaction. Cooling only takes place at the surface ofthe reactor. As a result, there is a strong temperature gradient of about 10 °C from the surface ofthe reactor to its center. In a microreactor, the heat created by mixing the two reagents is also detectable, but the temperature gradient is much smaller, only 3 "C (see Figure 2). Additionally, it only takes a few millimeters of path length for the reagent stream to cool down again to the temperature ofthe outside-cooling medium. The formation of hot spots or the accumulation of reaction heat may favor
All correspondence should be addressed to The Secretary
2
where C is a constant independent of ε. Proof. See [2] for example. In what follows we make extensive use of the following decomposition of the solution uε. We write uε = vε + w + wr where the smooth component vε and singular components and w , wr are defined to be the solutions of the problems
1 Introduction.
Consider the singularly perturbed reaction-diffusion equation on the domain Ω = (0, 1)
Lεuε(x) ≡ −εuε(x) + b(x)uε(x) = f (x0) = A uε(1) = B
The Secretary Suite 2B, Olympia House 61/63 Dame St. Dublin 2 Ireland Tel: +353-1-679 7655 Fax: +353-1-679 2469 Email: jmiller@tcd.ie
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A second order parameter-robust overlapping Schwartz methodor reaction-diffusion problems
Preprint No. 1(1997) November 1997
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Ecological Linkages Between Aboveground and Belowground Biota
developed for the Food and Agriculture Organi-zation and the U.S.Department of Agriculture. While still concerned with fertility,soil science increasingly has turned to the ecological function of soils and to the degradation they suffer(12). Nitrogen SynthesisIn von Liebig’s lifetime,population growth and urbanization gradually intensified the problems of nutrient shortage.With improved transporta-tion,however,modern farmers maintained soil fertility with fertilizers from afar,tapping the nutrient banks built up over millennia by sea-bird colonies.Guano from Chile and Peru coun-teracted soil fertility decline on the farms of Western Europe and eastern North America from the1830s,but it was always scarce and expensive.The big breakthrough that made ni-trogenous fertilizer comparatively cheap came with the work of the German chemist Fritz Haber(1868–1934).By1913,Haber found a way to synthesize ammonia from the air,the basis of all subsequent nitrogenous fertilizer. For reasons connected to world wars and the Great Depression,Haber’s work had limited im-pact until the1950s,but ever since,the problem ofnutrient depletion has been treated by variousforms of soil chemotherapy,chiefly nitrogenousfertilizer,at least by farmers who could afford it.Without it,the world’s farms could feed only twoout of three of today’s6.3billion people(6).Soil ecosystems remain firmly,but uncharis-matically,at the foundations of human life.Theintensity and scale of modern soil use and abusesuggest there is much yet to be discovered aboutsoils and their relations with people.Equally,cur-rent behavior implies that there is much that isalready known that is not yet converted into pre-vailing practices.Soil ecosystems are probably theleast understood of nature’s panoply of ecosys-tems and increasingly among the most degraded.Correspondingly,soil history remains the leastunderstood,and least recognized,aspect ofenvironmental history.References and Notes1.M.Ren,X.Zhu,Holocene4,314(1994).2.H.Dregne,in Determinants of Soil Loss Tolerance,American Society of Agronomy Special PublicationNo.45(American Society of Agronomy,Madison,WI,1982),pp.1–14.3.J.R.McNeill,Something New Under the Sun:AnEnvironmental History of the Twentieth-CenturyWorld(Norton,New York,2000).4.T.Beach,S.Luzzader-Beach,N.Dunning,J.Hageman,J.Lohse,Geogr.Rev.92,372(2002).5.J.R.McNeill,The Mountains of the MediterraneanWorld:An Environmental History(Cambridge Univer-sity Press,New York,1992).6.V.Smil,Enriching the Earth(MIT Press,Cambridge,MA,2001).7.Columella II,2,18,cited in(13).8.R.Wasson,in Towards a World Environmental Historyof Soils,J.R.McNeill,V.Winiwarter,Eds.(OregonState Univ.Press,Corvallis,OR,in press).9.F.Bray,Science and Civilisation in China.Vol.6:Biology and Biological Control.Part2:Agriculture(Cambridge University Press,Cambridge,1984).10.Z.Gong,X.Zhang,J.Chen,G.Zhang,Geoderma115,3(2003).11.Varro I,9,2-3,cited in(13).12.D.Yaalon,Nature407,301(2000).13.V.Winiwarter,in Shifting Boundaries of theReal:Making the Invisible Visible,H.Novotny,M.Weiss,Eds.(Hochschulverlag,Zu¨rich,2000),pp.137–156.14.V.W.holds an Austrian Programme for AdvancedResearch and Technology Fellowshipat the AustrianAcademy of Sciences.R E V I E WEcological Linkages Between Abovegroundand Belowground BiotaDavid A.Wardle,1,2*Richard D.Bardgett,3John N.Klironomos,4Heikki Seta¨la¨,5Wim H.van der Putten,6Diana H.Wall7All terrestrial ecosystems consist of aboveground and belowground components thatinteract to influence community-and ecosystem-level processes and properties.Here weshow how these components are closely interlinked at the community level,reinforcedby a greater degree of specificity between plants and soil organisms than has beenpreviously supposed.As such,aboveground and belowground communities can bepowerful mutual drivers,with both positive and negative feedbacks.A combinedaboveground-belowground approach to community and ecosystem ecology is enhancingour understanding of the regulation and functional significance of biodiversity and of theenvironmental impacts of human-induced global change phenomena.The aboveground and belowground compo-nents of ecosystems have traditionally been considered in isolation from one another. There is now increasing recognition of the influence of these components on one other and of the fundamental role played by aboveground-belowground feedbacks in con-trolling ecosystem processes and properties (1–4).Plants(producers)provide both the organic carbon required for the functioning of the decomposer subsystem and the resources for obligate root-associated organisms such as root herbivores,pathogens,and symbiotic mutualists.The decomposer subsystem in turn breaks down dead plant material and indirectly regulates plant growth and commu-nity composition by determining the supply of available soil nutrients.Root-associatedorganisms and their consumers influenceplants more directly,and they also influ-ence the quality,direction,and flow ofenergy and nutrients between plants anddecomposers.Exploration of the interfacebetween population-and ecosystem-levelecology is an area attracting much attention(5,6)and requires explicit consideration of theaboveground and belowground subsystems andtheir interactions.Here we discuss recent advances in ourunderstanding of the links between thesetwo subsystems.We first outline how theaboveground subsystem influences the be-lowground subsystem and vice versa.Wethen discuss biodiversity links between theaboveground and belowground subsystems.Finally,we explain how the study ofaboveground-belowground interactionsmay assist our understanding of theconsequences of human-induced globalchange phenomena.How Aboveground Communities Drivethe Belowground SubsystemIt has long been recognized that soil organismsare responsive to the nature of organic matter1Landcare Research,Post Office Box69,Lincoln,New Zealand.2Department of Forest VegetationEcology,Swedish University of Agricultural Scienc-es,SE90183Umea˚,Sweden.3Department of Bio-logical Sciences,Institute of Environmental andNatural Sciences,Lancaster University,LancasterLA14YQ,UK.4Department of Botany,University ofGuelph,Guelph,Ontario,N1G2W1,Canada.5De-partment of Ecological and Environmental Sciences,University of Helsinki,Niemenkatu73,FIN-15140Lahti,Finland.6Department of Multitrophic Inter-actions,Netherlands Institute of Ecology,Heteren,Netherlands.7Natural Resource Ecology Laborato-ry,Colorado State University,Fort Collins,CO80523,USA.*To whom correspondence should be addressed.E-mail:david.wardle@svek.slu.sety of soil organisms is tremendous;1g.of soil can contain between 5000and 10,000species of microorganisms (50),but plants interact with only a subset of this large spe-cies pool (51).Every individual plant is alsoexposed to potentially hundreds of species of soil fauna,mostly nematodes,microarthro-pods,insects,and earthworms.However,few studies have investigated the extent to which the aboveground subsystem depends upon this diversity of soil organisms.Although the effects of decomposer diver-sity on aboveground plant productivity are poorly understood,these effects are likely to saturate at low levels of diversity.Microcosm studies (52,53)have shown that the presence of five mesofaunal species was sufficient to maximize growth of Betula pendula seed-lings,and one of these (52)found that seed-ling production depended on the presence of the enchytraeid Cognettia sphagnetorum rather than on the number of animal species in the soil.Further,decomposer diversity ef-fects on plant productivity may not necessar-ily be positive,especially at the functional group level.In a microcosm study,addition of protists and nematodes enhanced,whereas earthworms reduced,plant production (54).These positive effects only occurred when earthworms were absent (54),which is con-sistent with other studies that show the inclusion of larger bodied soil organisms to reduce the aboveground effects of small-sized soil organisms (44).Intimate interactions between plants,soil pathogens,root herbivores,and mycorrhizal fungi may be direct drivers of plant commu-nity diversity (55),but consequences of the biodiversity of these soil organisms has been rarely studied.A study of the dune grass Ammophila arenaria showed additive effects of mixtures of soil pathogens (fungal species and a nematode)relative to the effects of pathogen monocultures (56).More diverse mixtures of arbuscular mycorrhizal species were found to promote both the abundance of rare plant species and total plant community biomass and diversity (37),although the mechanistic basis of these results remains unclear.Further,the aboveground effects of mycorrhizal fungi depend on soil fertility (28),and increasing diversity of ectomycor-rhizal fungi has been found to promote tree seedling productivity in low-fertility but not high-fertility substrates (57).Based on the limited evidence available,it appears that the effects of soil biodiversity on aboveground attributes (plant productivity,composition,and diversity)can range from positive to negative depending on context (3,58).In Fig.3,we provide a conceptual frame-work for predicting how diversity of different subsets of the belowground biota may influ-ence plant diversity.This framework sug-gests that the aboveground consequences of soil biodiversity are strongly dependent on context,such as the types of soil organisms considered,the role of plant species in a community (dominant versus rare or subor-dinate species),and site fertility.We alsopredict stronger aboveground effects of the diversity of specialist soil organisms,such as those that are intimately associated with plant roots (e.g.,mycorrhizal fungi and root patho-gens)than of those that show low specificity (e.g.,decomposer biota).Implications for Global ChangeOver the past century,much of the Earth ’s land surface has been transformed by a range of phenomena (59),such as invasions of alien species into new territories,alteration of cli-mate through atmospheric CO 2enrichment,nitrogen deposition,and land use change.Whereas the significance of these phenomena for ecosystem performance is widely recog-nized (60),the mechanisms that drive ecosys-tem responses to them are not well known.Understanding the consequences of these phenomena requires explicit consideration of linkages between aboveground and below-ground biota.This is because,with the ex-ception of some major disturbances that di-rectly affect soil biota (61),global change phenomena indirectly affect soil biota and the processes that they drive through changes that occur aboveground,by changing plant community composition,carbon allocation patterns,or the quantity and quality of plant-derived organic matter,for example.In turn,such belowground responses to global change would create feedbacks that affect aboveground biota (62,63).A growing number of studies point to how atmospheric CO 2enrichment can affect eco-system properties through aboveground-belowground linkages (64,65).Enhanced CO 2can indirectly affect soil organisms through shifts in the quantity and quality of plant litter returned to soil,the rate of root turnover,and the exudation of carbon into the rhizosphere (66–68).Because of the variety of ways in which plants respond to atmo-spheric CO 2enrichment depending on con-text (e.g.,variations in soil fertility),positive,negative,or neutral indirect effects of enrich-ment on belowground organisms and nutrient mineralization can occur (3).Consequently,the direction and magnitude of aboveground feedbacks that result from these belowground changes are also variable,with the possibility of positive (69)and negative (70)responses.Invasion of plants into new territories may greatly affect aboveground-belowground feedbacks,especially when the invading spe-cies has vastly different physiological traits from the native flora.These feedbacks may initially operate through interactions between invasive species and root-associated biota (34,35),but in the longer term they can also involve the effect of the invader on the quantity and quality of resource inputs to soil and on decomposer organisms and the pro-cesses that they drive (71).A classic example is the invasion of the actinorhizal shrubABCSite fertilityP l a n t s p e c i e s d i v e r s i t yFig.3.Relationshipbetween underlying site fertility and plant species diversity,in which diversity is maximized by intermediate fertility (the solid curve in all three panels),as proposed by Al-Mufti et al .(76).(A )Consequences of decomposer activity.When decomposer organ-isms alter nutrient availability,the response curve of plant species diversity to site fertility changes accordingly.If soil decomposers en-hance nutrient availability,then the relation-shipbetween local p lant diversity and site fer-tility shifts from the solid curve to the dotted curve,resulting in plant diversity being maxi-mized in less fertile sites.Conversely,when decomposers reduce nutrient availability,local plant diversity shifts to the dashed line with maximal diversity in more fertile sites.Effects of decomposer diversity may be unpredictable (58),because diversity may enhance or reduce the availability of nutrients to plants and ef-fects depend on initial site fertility.In practice,however,net primary productivity is probably relatively insensitive to decomposer diversity because of the generalist feeding behavior of most consumers in the soil subsystem (77),so that effects of decomposer diversity should be smaller than those for soil organisms that have a more intimate interaction with plant roots.(B )When targeted to subordinate or rare plant species,arbuscular mycorrhizal fungi enhance diversity (dashed line)(37),whereas root pathogens and herbivores reduce diversity (dotted line)(34).At a given site fertility,ar-buscular mycorrhizal fungi should enhance plant phosphorous uptake,so that plant diver-sity peaks under lower site fertility (28).(C )When targeted to dominant plants,arbuscular mycorrhizal fungi (dashed line)reduce diversity (78),whereas root pathogens and herbivores increase plant species diversity (14).As in (B),the enhancement of plant phosphorous uptake by arbuscular mycorrhizal fungi means that maximal plant diversity should occur under lower site fertility (28).Myrica faya into nitrogen-limited stands of Meterosideros polymorpha in Hawaii,which resulted in a more-than-fourfold increase in soil nitrogen input and a consequent increase in ecosystem productivity(6).Less is understood about how invasion of soil organisms influences aboveground biota, although these effects should be strongest when the invading species has functional at-tributes that are not shared by the resident indigenous species.For example,exotic earthworms introduced to North American forests exert a greater effect on surface litter and soil structure than do native soil organ-isms,and they induce pulses of nutrient mo-bilization that result in altered plant growth and community composition,potentially leading to alternate steady-state systems (72).Similarly,predation and reduction of native earthworm populations by invasion of the New Zealand flatworm Arthurdendyus triangulata into the United Kingdom and Ire-land may reduce their beneficial effects on soil conditions such as porosity and drainage, influencing plant community composition and productivity(73).These examples show that effects of glob-al change phenomena on ecosystems consis-tently involve linkages between the above-and belowground subsystems.In nature, ecosystems and communities are generally subjected to several global change phenome-na simultaneously,and different communi-ties are influenced by these phenomena in a variety of ways in the long term.However, ecological responses to global change over very long time scales(74)and to multiple stressors(75)have yet to be thoroughly considered in a combined aboveground-belowground framework.ConclusionsStudies on aboveground-belowground feed-backs are now in the phase of exploring the effects that the two subsystems exert on each other,but to be able to generalize requires a better understanding of the mechanisms be-hind these effects.This understanding will be gained by evaluating how the plant functions as an integrator of these subsystems,because aboveground and belowground consumers are largely spatially separated with the plant as a connector.To date,mechanistic under-standing has focused on the quality and quan-tity of resources that the plant produces both above-and belowground,but many un-knowns remain on the role of plant physio-logical mechanisms,such as plant defense strategies and the proportional contribution of primary and secondary plant compounds(1, 31).An emerging theme is that aboveground consequences of belowground interactions and vice versa are not easily predicted;an organism or group of organisms on one side of the aboveground-belowground interface can often exert positive,neutral,or negativeeffects on the other side of the interface de-pending on context(2,3,16,22).The natureof this context dependency is likely to bedetermined primarily by spatial and temporalscale and by abiotic factors;there is a need todetermine how biotic relationships interactwith abiotic agents to drive community andecosystem properties.New insights fromstudies on aboveground-belowground inter-actions should be used to improve our pre-dictions of the effects of human-inducedenvironmental changes on biodiversity andecosystem properties and to enhance the ef-ficiency of human interventions in restorationand conservation efforts.References and Notes1.J.P.Grime,Plant Strategies,Vegetation Processes andEcosystem Properties(Wiley,Chichester,UK,2001).2.W.H.van der Putten,L.E.M.Vet,J.A.Harvey,F.L.Wackers,Trends Ecol.Evol.16,547(2001).3.D.A.Wardle,Communities and Ecosystems:Linkingthe Aboveground and Belowground Components(Princeton Univ.Press,Princeton,NJ,2002).4.R.D.Bardgett,D.A.Wardle,Ecology84,2258(2003).wton,Oikos71,367(1994).6.P.M.Vitousek,L.R.Walker,Ecol.Monogr.59,247(1989).7.M.J.Swift,O.W.Heal,J.M.Anderson,Decompositionin Terrestrial Ecosystems(Blackwell,Oxford,1979).8.P.C.De 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Author to whom correspondence should be addressed
Keywords: dependency bounds, Dempster-Shafer belief functions, interval probabilities, uncertainty propagation
2
Introduction In this paper we address the general problem of performing convolutions of real-valued continuous random variables under binary operations, when there exists variation and uncertainty in the constituent random variables and uncertainty in the dependency between them. Suppose we wish to add, subtract, multiply or divide two continuous real-valued random variables, A and B, to produce a new random variable. Denoting the binary operation by *, we wish to describe the distribution of A*B from the distributions of A and B. The general formulation of the distribution for A*B, given marginal distributions for A and B and assuming A and B are independent, can be described by the following convolution:
Correspondence should be addressed to
Running Title: Motion Processing by MST
This work was carried out as part of Christopher Pack’s dissertation research. Authors of this article are listed alphabetically. §Supported in part by the Office of Naval Research (ONR N00014-92-J-1309, ONR N00014-951-0409, and ONR N00014-95-1-0657). ‡Supported in part by the Office of Naval Research (ONR N00014-94-1-0597, ONR N00014-951-0409, and ONR N00014-95-1-0657). ¶Supported in part by the Air Force Office of Scientific Research (AFOSR F49620-92-J-0334) the Defense Advanced Research Projects Agency (ONR N00014-92-J-4015), and the Office of Naval Research (ONR N00014-91-J-4100, ONR N00014-92-J-1309, ONR N00014-94-1-0597, ONR N00014-95-1-0409, and ONR N00014-95-1-0657).
2
TTC
Heading
Department of Cognitive and Neural Systems and Center for Adaptive Systems Boston University 677 Beacon St. Boston, MA 02215 January, 1998
protein-coding regions
Abstract
Keywords: Clustering, EM (Expectation Maximization), Protein-Coding Regions, Bacterial Genomes, Pro
As the number of fully sequenced prokaryotic genomes continues to grow rapidly, computational methods for reliably detecting protein-coding regions become even more important. In (Audic & Claverie, 1998), a new method is presented for predicting protein-coding regions in microbial genomic DNA sequences. Unlike other methods (Borodovsky & McIninch, 1993; Borodovsky et al., 1995; Salzberg et al., 1998), that often require an annotated pre-existing training set, this method does not require a training set, or any prior knowledge of the statistical properties of the genome under study.
and Department of Biological Chemistry, College of Medicine, University of California, Irvine. To whom all correspondence should be addressed.
微生物降解edta
RESEARCH NOTEMICROBIAL DEGRADATION OF EDTA IN ANINDUSTRIAL WASTEWATER TREATMENT PLANTU.KALUZA,P.KLINGELHO FER and K.TAEGER*BASF Aktiengesellschaft,Speciality Chemicals,67056Ludwigshafen,Germany(First received May1997;accepted in revised form January1998)AbstractÐFor the®rst time it is shown that EDTA is ultimately biodegraded under practical industrial wastewater treatment conditions,involving a Finnish plant dedicated to treat the e uent from a paper mill.Monitoring measurements on the in¯uent and the treated e uent showed an EDTA elimination of about80%.The mean EDTA concentration in the in¯uent was23.8mg lÀ1and in the corresponding e uent5.8mg lÀ1.The biodegradability of EDTA was veri®ed in the laboratory with activated sludge from the treatment ing a combined CO2/DOC method the total mineralization of EDTA was indicated by>80%CO2formation and r99%DOC removal.#1998Elsevier Science Ltd.All rights reservedKey wordsÐEDTA,biodegradability,CO2/DOC-combination test,industrial wastewater treatment plant,chlorine-free bleachingINTRODUCTIONIn the pulp and paper industry totally chlorine-free bleaching(TCF)is increasingly being adopted.This process employs hydrogen peroxide(H2O2)instead of chlorine compounds.The disadvantage is the rapidly catalyzed decomposition of the bleach by the manganese and iron ions contained in wood. Therefore hydrogen peroxide is stabilized by adding metal chelating agents such as EDTA.But it is commonly known that EDTA is poorly degradable in natural compartments despite the fact that there are several investigations showing the biodegrad-ability with mixed cultures under de®ned laboratory conditions(Klu ner et al.,1994;Henneken et al., 1995).The advantages of the TCF process would be even greater if EDTA could also be broken down biologically in wastewater treatment plants.MATERIAL AND METHODSOperating data of the treatment plantHydraulic loading rate:ca.10000m3dÀ1pH of in¯uent:5.3±5.8;pH of e uent:7.6±7.9Sludge retention time:9dTemperature:35±408C over the whole yearMean hydraulic retention time:about1dIn¯uent load:BOD:10t dÀ1;COD:20t dÀ1Urea was added as a source of nitrogen in order to avoid bacterial growth from being curtailed by a lack of nitrogen.700kg of sludge was generated per tonne of BOD.Sampling and analysisFifty separate samples of in¯uent and treated e uent were taken daily over a period of two weeks.The daily samples taken for determining EDTA were un®ltered and preserved with1%formaldehyde.The samples taken for determining the heavy metal content were stabilized with 0.5%HNO3.EDTA was determined in the form of its iron(III) complex by HPLC on RP18phase(Randt et al.,1993) with an UV detector at320nm(detection limit 0.5mg lÀ1).The heavy metal content was determined by means of ICP-AES(inductive coupled plasma-atomic emission spec-trometry).The sample was oxidized with perchloric acid and dissolved in hydrochloric acid and water(detection limit=1mg lÀ1).Biodegradability tests in the laboratory Biodegradability tests were performed with the activated sludge from the above industrial wastewater treatment plant using a combined CO2/DOC method that allows the simultaneously determination of the formation of CO2and of the DOC removal(dissolved organic carbon)(updated ISO9439,1998,Strotmann et al.,1995).The activated sludge was incubated at358C with a concentration of 150mg lÀ1dry solids.The incubation vessels were kept in the dark to exclude any photochemical degradation pro-cesses.Blank controls were proved to show real biological EDTA degradation.Wat.Res.Vol.32,No.9,pp.2843±2845,1998#1998Elsevier Science Ltd.All rights reservedPrinted in Great Britain0043-1354/98$19.00+0.00PII:S0043-1354(98)00048-7*Author to whom all correspondence should be addressed.[Tel:+49-621-6058015;Fax:049-621-6058043].2843RESULTSEDTA removal in an industrial wastewater treatment plantEDTA concentration Ðexpressed as H 4EDTA Ðwas measured in the in¯uent and the e uent of the wastewater treatment plant.The results indicate that about 80%of the EDTA was removed (Table 1).The results in Table 1are reproducible because an earlier investigation indicated a removal degree of 87%at average EDTA in¯uent and e uent con-centrations of 43.5mg l À1and 5.5mg l À1.Biodegradability of EDTA in a laboratory test The evidence of high EDTA removal degrees in the described industrial treatment plant prompted us to con®rm the removal of EDTA was due to a biological mineralization process.Therefore exper-iments were performed according to the combined CO 2/DOC method (Strotmann et al.,1995).This method allows a clear distinction between biodegra-dation and physical removal.According to the elev-ated temperature in the wastewater treatment plant the standard procedure (Strotmann et al.,1995)was performed at 358C.The result shows >80%CO 2-formation and r 99%DOC removal,obviously due to the biodegradation of EDTA (Fig.1).EDTA test concentration:20mg l À1DOC.The biodegradability of EDTA is shown during an incu-bation time of 28d at a temperature of 358C.Concentration and removal of heavy metalsIn wastewater EDTA is usually present in form of its metal complexes.The biodegradability of EDTA depends on the stability of the existing and/or newly formed metal complexes (Henneken et al.,1996).For this reason it is important to determine the metal concentrations in the in¯uent and e uent (Table 2).As a result it could be shown that out of eighteen heavy metals only iron and manganese were present in concentrations above the detection limit and that a stoichiometric excess of the sum of these ions over EDTA was given.All metals were removed below the detection limit during the wastewater treatment.DISCUSSIONThere are several investigations showing the bio-degradability of EDTA with mixed cultures under de®ned laboratory conditions (Lau et al.,1990;Gschwind,1992;Klu ner et al.,1994;Henneken et al.,1995,1996).Now it has been shown for the ®rst time that EDTA is about 80%biodegradable in an industrial wastewater treatment plant under techni-cal operating conditions.Evidence of the biodegra-dation was con®rmed by formation of >80%CO 2and r 99%DOC removal in the combined CO 2/DOC test.Table 1.EDTA removal in an industrial wastewater treatment plant In¯uent concentrationE uent concentrationH 4EDTAmg lÀ1mmol l À1mg l À1mmol l À1Removal degree Mean23.80.0814 5.80.0276%Standard deviation 2.3 2.07Maximum 28.00.0969.00.03168%Minimum18.00.0623.00.0183%Fig.1.Biodegradability of EDTA in the combined CO 2/DOC test.Research Note2844In wastewater EDTA is usually present in form of its metal complexes.The biodegradability of EDTA depends on the stability of the metal com-plexes(Henneken et al.,1996).The total concen-tration of iron and manganese ions in the in¯uent was in stoichiometric excess against EDTA.It can be assumed that EDTA is discharged in the form of its iron and manganese complexes.This obviously has no detrimental e ects on the biodegradation of EDTA nor on the removal of heavy metals,as is shown by the decrease of the Fe and Mn concen-trations in the e uent.In laboratory experiments and by de®ned mi-crobial cultures(Henneken et al.,1996)iron com-plexes and other relatively stable complexes of EDTA are more di cult to break down than mag-nesium or calcium complexes.If Ca and Mg ions exist in a stoichiometric excess,the composition of the metal complexes is slowly displaced in favor of the more degradable Ca and Mg complexes(Xue et al.,1995;Henneken et al.,1996).This process can be accelerated by increasing the pHÐas occurs between in¯uent and e uent of the treatment plant Ðreducing the stability of Fe±EDTA and increas-ing the stability of other metal complexes(BASF, 1988).The phenomenon to improve biodegradation by increasing the pH is shown by(Van Ginkel et al.,1997).However,there is also evidence that the pure iron±EDTA complex is biodegradable(Lau et al.,1990).Furthermore it is known that the iron±EDTA complex is unstable when exposed to sunlight and it is easily broken down directly by photolysis(BUA-Sto bericht,1996).This is an e ective way to elim-inate EDTA,especially in receiving waters that con-tain little suspended solids(Kari et al.,1995).The results presented are a major improvement on those that have been obtained with specially selected cultures under de®ned laboratory con-ditions(Lau et al.,1990;Gschwind,1992; Henneken et al.,1996).They illustrate very clearly that EDTA does not necessarily have to be persist-ent under conditions like wastewater treatment plants.REFERENCESBASF,Brochure Chelating Agents±Trilon11988.BUA-Sto bericht Ethylendiamintetraessigsa ure/Tetrana-triumethylendiamintetraacetat(H4EDTA/Na4EDTA); BUA-Report ethylenediaminetetraacetic acid/tetrasodiu-methylenediaminetetraacetate(H4EDTA/Na4EDTA), 168,S.Hirzel,Wissenschaftliche Verlagsgesellschaft, Stuttgart1996,ISBN3-7776-0699-5.Gschwind N.(1992)Biologischer Abbau von EDTA in einem Modellabwasser:Biological degradation of EDTA in a model waste water.Wasser Abwasser133, 546±549.Henneken L.,No rtemann B.and Hempel D.C.(1995) In¯uence of physiological conditions on EDTA degra-dation.Appl.Microbiol.Biotechnol.44,190±197. Henneken L.,Bru ggenthies A.,No rtemann B.and Hempel D.C.(1996)Teilstrombehandlung EDTA-halti-ger Abwa sser mittels Bio®lm-Wirbelbettreaktoren,treat-ment of partial stream containing EDTA with a moving-bed bio®lm reactor.Chemie-Ing.Tech.68,310±314.ISO9439,Water quality±Evaluation in an aqueous med-ium of the ultimate aerobic biodegradability of organic compounds±Method by analysis of released carbon diox-ide,updated ISO/DIS9439annex D,published1998. Kari F.G.,Hilger S.and Canonica S.(1995) Determination of the reaction quantum yield for the photochemical degradation of Fe(III)±EDTA: Implications for the environmental fate of EDTA in sur-face waters.Environ.Sci.Technol.29,1008±1017.Klu ner T.,Henneken L.,Gehle M.,Bru ggenthies A., No rtemann B.and Hempel D.C.(1994)Katabolismus von Ethylendiamintetraacetat(EDTA),Catabolism of ethylenediaminetetraacetate(EDTA).BIOforum17, 284±288.Lau J.J.,Steele D.B.,Coogan L.A.and Breitfeller J. M.(1990)Degradation of the ferric chelate of EDTA by a pure culture of an Agrobacterium sp..Appl. Environ.Microbiol.56,3346±3353.Randt C.,Wittlinger R.and Merz W.(1993)Analysis of nitrilotriacetic acid(NTA),ethylenediaminetetra-acetic acid(EDTA)and diethylenetriaminepentaacetic acid (DTPA)in water,particularly waste water.Fresenius J. Anal.Chem.346,728±731.Strotmann U.J.,Schwarz H.and Pagga U.(1995)The combined CO2/DOC test±a new method to determine the biodegradability of organic compounds. Chemosphere30,525±538.Van Ginkel C.G.,Vandenbroucke K.L.and Stroo C.A.(1997)Biological removal of EDTA in conventional activated sludge plants operated under alkaline con-ditions.Bioresource Technology59,151±155.Xue H.,Sigg L.and Kari F.G.(1995)Speciation of EDTA in natural waters:Exchange kinetics of Fe±EDTA in river water.Environ.Sci.Technol.29,59±68.Table2.Concentrations of metal ions in the industrial treatment plantIn¯uent concentration E uent concentrationMetal ions mg lÀ1mmol lÀ1mg lÀ1mmol lÀ1 Calcium ca.300ca.7.5ca.300ca.7.5 Magnesium ca.10ca.0.4ca.10ca.0.4 Manganese30.055<1<0.018Iron20.036<1<0.018S manganese and iron0.091<0.036Research Note2845。
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INDUCED BY LINDANEAuthors: S. Rivera, C. Sanfeliu, M. García*, F. Comellas† and E. Rodríguez FarréAddresses:Department of Pharmacology and Toxicology, CSIC, c/Jordi Girona 18-26, 08034 Barcelona*Electronic Engineering Department, Polytechnic University of Catalonia, c/Jordi Girona s/n; 08034 Barcelona†Applied Mathematics and Telematics Department, Polytechnic University of Catalonia, c/Jordi Girona s/n; 08034 BarcelonaPublished at:Current issues in Neurotoxicology, A. Mutti, L. Costa, L. Manzo y J.M. Cranmer. (Eds.), Intox Press, Inc. Publishers, pp. 235-239, (1992). ISBN 0-911369-16-3a n dNeurotoxicology, vol. 13 pp. 235-239, (1992).To whom all correspondence should be addressed:Coral SanfeliuDepartment of Pharmacology and Toxicology, CSIC,c/Jordi Girona 18-26E-08034 BarcelonaSpainTelephone: +34/3/2040600, ext 280Fax: +34/3/2045904INDUCED BY LINDANESummary: The neurotoxic agent lindane was tested for its ability to alter the rate of ultrasonic isolation calls of suckling rats. Doses that did not produce any sign of convulsant activity significantly increased the number of calls and the cumulative time of calling in male pups. At days 10-13 of age after a single dose of 20 mg/kg lindane, animals showed more than twice the control call values. After daily dosing with 10 mg/kg during the first week of age call increases also appeared. It is suggested that lindane has an anxiogenic effect mediated through its action on the benzodiazepine-GABA A receptor-chloride channel complex.Short title: Lindane-induced increase in isolation calls.Key words: Ultrasonic isolation calls, distress calls, suckling rats, lindane, γ-hexachlorocyclohexane, anxiety testing.Figures: 1 A and B (page 4)2 A and B (page 5)INTRODUCTIONMammalian vocalizations are a behavioral measure of emotional expression. One category of vocalization is the isolation call, given by infants when separated from their littermates or parents (Newman, 1988). Young rodents emit isolation calls in the ultrasonic range (Sales and Pye, 1974) which are a potent stimulus for maternal retrieval and care (Smotherman et al., 1974). Calls increase in number and intensity along the 1st week of life to peak during the 2nd week, and abruptly decrease after the eye opening of the rat pup around day 15. This distress response is highly sensitive to environmental stimuli and pharmacological manipulations (Oswalt and Meier, 1975 Winslow and Insel, 1990).Benzodiazepines (BDZ) are highly effective in decreasing vocalizations, probably via their anxiolytic action (Gardner and Budhram, 1987). Indeed, anxiogenic agents such as pentylenetetrazole (PTZ) selectively increase distress call emission (Insel et al., 1986). Therefore, the GABA A receptor complex may play a physiological role in this basic emotional behavior.Lindane (γ-hexachlorocyclohexane) is an hyperstimulant neurotoxic compound that binds to the picrotoxinin site on the chloride channel of the GABA-A receptor (Fishman and Gianutsos, 1988). It has been suggested that this agent induces an anxiogenic response (H ijzen and Slangen, 1989; Llorens et al.,1990). To test whether lindane would mimic anxiogenic agents in their effects on pup distress call behavior the present study was carried out.MATERIAL AND METH ODSAnimals. Wistar rats (IFFA Credo, France) were used. One day after birth (day 1 of age) pups were randomized among the several litters. Each dam and its litter were individually housed under standard conditions.Treatment. Animals were administered vehicle (olive oil) or lindane (99.5% pure, Merck) by gavage (0.03ml/10 g body wt). A Silastic-R silicone elastic tubing was used for animals up to 8 days of age and conventional cannulae for older ones. Non-convulsant doses that were reported to produce some effects on motor behavior and cerebral 2-deoxyglucose uptake were used (Rivera et al., 1990; submitted for publication).a) Single dosing: 30 male rat pups aged 10-13 days (17-25 g body wt) were randomly assigned to the following treatments: vehicle, 10 mg/kg or 20 mg/kg lindane (10 animals/group). Testing was performed 1 hr later. Baseline values obtained in a pretrial performed immediately before administration showed no statistical differences between groups. Animals that failed to emit ultrasounds in this preliminary testing were discarded from the study and replaced.b) Repeated dosing: 12 litters were randomly distributed among4 daily treatments: vehicle or 10 mg/kg lindane, either during the 1st or the 2nd postnatal week. Only 2 males and 2 females from each litter were used in this study to avoid the litter effect (6 male and 6 femalepups/group). Testing was performed on alternate days from the 2nd to the 20th day of age. Body weight was recorded twice a week.Apparatus and procedure. Rat pups were individually tested. Litters were previously separated from their mothers and maintained in a cage with clean bedding warmed by infrared light. Each pup was gently put in a glass beaker inside a sound attenuated chamber. Recording of ultrasonic vocalizations started after 0.5 min of habituation time and lasted 2 min for the animals tested once and 3 min for those daily tested.Vocalizations emitted were detected using a Knowles Electronic (BT1759-146FZ) microphone hanging 10 cm over the animal. Signals were fed through an amplifier (gain 120 dB) with a band-pass filter between 18 KHz (6th order) and 150 KHz (2nd order). A level detector digitalized the signals according as they were upper or lower than 2.2 V. This voltage corresponded to a sound pressure level of 75 dB. Digital signals were sent to a PC computer through a port and recorded each msec during the whole testing time. Records were processed to obtain the number of calls and the cumulative duration of all calling emitted.Analysis of data. One-factor ANOVA followed by Duncan´s range test in the single dosing study and three-factor ANOVA (treatment, day and sex) in the repeated dosing experiments were performed.RESULTSSingle dose experiment. Lindane 20 mg/kg increased (p<0.05) the number of emitted isolation calls and their cumulative time, as compared to the control group (Fig. 1).Repeated dose experiments.Animals dosed with lindane 10 mg/kg from day 1 to 7 of age showed a pattern of ultrasound emission different than the controls in the number of calls (ANOVA F(1,228)=6.16, p=0.014)and in the total call time (F(1,228)=4.75, p=0.030). The effect of sex was significant for the number of calls (F(1,228)=4.10, p=0.044) but did not reach statistical significance for the total call time. If data were further analyzed after spliting them according to sex, ANOVA results for the treatment factor effect in males were F(1,109)=6.47, p=0.012 and F(1,109)=4.14, p=0.044, respectively, for the number of calls and calling time. No significant differences were obtained for females (Fig. 2 ).In the experiment where rat pups were dosed from day 8 to 14 of age, no statistical significance for treatment or sex effects were obtained (not shown).The effect of the day of age at testing was highly significant for both parameters in both experiments (F(9,228)=9.38, p<0.001 and F(9,228)=6.42, p<0.001, for the number of calls and total call time, respectively, in the 1st week administered animals; F(8,205)=5.98, p<0.001 and F(8,205)=5.43, p<0.001, for the same parameters in the 2nd week dosing experiment).Evolution of body weight was similar in control and lindane-treated animals (not shown).DISCUSSIONThe age-related profile of ultrasonic isolation calls obtained in control pups was in general agreement with that described in ontogenic studies (Sales and Pye, 1974).Taken together, the results obtained indicate an increase of ultrasonic distress calls induced by lindane in isolated rat pups. Both parameters measured changed jointly and therefore the mean call duration was not modified by lindane treatment.After a single dose of 20 mg/kg lindane calls were increased but 10 mg/kg had no effect. Insel et al. (1986) have found similar results with pentylenetetrazole (PTZ). Both compounds have been suggested to share common anxiogenic effects in adult rats in the plus-maze test (Llorens et al., 1990). H ijzen and Slangen (1989) have also postulated an anxiogenic action of lindane in adult rats by startle response testing, after obtaining similar results with an inverse benzodiazepine (BDZ) agonist and opposite ones with the anxiolytic midazolam. Therefore, lindane-induced alterations of distress calling may be caused through a BDZ-modulated mechanism. This would be in agreement with the proposed mechanism of lindane action through the GABA system (Fishman and Gianutsos, 1988; Suñol et al., 1989). Indeed, lindane antagonizes the GABA A receptor in the same way as PTZ does. Thus, the present results may provide further evidence for the role of the BDZ-GABA receptor-chloride channel complex in rodent attachment behavior.Alterations of the age-related pattern of vocalizations are present at the dose of 10 mg/kg lindane when administered during the 1st week of age, but not during the 2nd one. Then, it may be postulated a higher sensitivity of the BDZ-binding site to a lindane-mediated effect during this early period. During the 1st postnatal week, for instance, the concentration of BDZ receptors increase from 20% to 60% of adult levels (Mallorga et al., 1980). Distress isolation calls are the expression of an emotional behavior submitted to a multiple modulation which may include sex-related factors as observed in this study.In conclusion, the present work supports an anxiogenic action of the neurotoxic agent lindane as suggested by previous experimental and clinical reports (Llorens et al., 1990; H ijzen and Slangen, 1989; Kbare et al., 1977).ACKNOWLEDGEMENTS. The authors wish to express gratitude to Ms. Carme Cleries for assistance with animal manipulations. This work was supported by grants 83/1648 from FIS and PB0202 from DGICYT.REFERENCESFishman BE, Gianutsos G. CNS biochemical and pharmacological effects of the isomers of hexachlorocyclohexane (lindane) in the mouse. Toxicol. Appl. Pharmacol. 1988; 93:146-153.Gardner CR, Budhram P. Effects of agents which interact with central benzodiazepine binding sites on stress-induced ultrasounds in rat pups. Eur. J. Pharmacol. 1987; 134:275-283.H ijzen TH, Slangen JL. Effects of midazolam, DMCM and lindane on potentiated startle in the rat. Psychopharmacology 1989; 99:362-365.Insel TR, H ill JL, Mayor RB. Rat pup ultrasonic isolation calls: Possible mediation by the benzodiazepine receptor complex. Pharmacol. Biochem. Behav. 1986; 24:1263-1267.Kbare SB, Rizvi AG, Shukta OP, Singh RRP, Perkash O, Misra VD, Gupta JP, Sethi PK. Epidemic outbreak of neuro-ocular manifestations due to chronic BHC poisoning. J. Assoc. Physicians Ind. 1977; 25:215-222.Llorens J, Tusell JM, Suñol C, Rodríguez-Farré E. On the effects of lindane on the plus-maze model of anxiety. Neurotoxicol. Teratol. 1990; 12:643-647.Mallorga P, Hamburg M, Tallman JF, Gallager DW. Ontogenetic changes in GABA modulation of brain benzodiazepine binding. Neuropharmacology 1980; 19:405-408.Newman JD. Investigating the physiological control of mammalian vocalizations. In: The Physiological Control of Mammalian Vocalization, Newman JD, ed., New York, Plenum Press, 1988, pp 1-5.Oswalt GL, Meier GW. Olfactory, thermal, and tactual influences on infantile ultrasonic vocalization in rats. Develop. Psychobiol. 1975; 8:129-135.Rivera S, Sanfeliu C, Rodríguez-Farré E. Behavioral changes induced in developing rats by an early postnatal exposure to lindane. Neurotoxicol. Teratol. 1990; 12:591-595.Sales G, Pye D. Ultrasonic Communication by Animals. London, Chapman and Hall,1974, pp 149-201.Smotherman WP, Bell RW, Starzec J, Elias J. Maternal responses to infant vocalizations and olfactory cues in rats and mice. Behav. Biol. 1974; 12:55-66.Suñol C, Tusell JM, Gelpí E, Rodríguez-Farré E. GABAergic modulation of lindane (γ-hexachlorocyclohexane)-induced seizures. Toxicol. Appl. Pharmacol. 1989; 100:1-8 .Wislow JT, Insel TR. Serotonergic and catecholaminergic reuptake inhibitors have opposite effects on the ultrasonic isolation calls of rat pups. Neuropsychopharmacology 1990; 3:51-59.Age (days)N u m b e r o f c a l l sBAge (days)C u m u l a t i v e t i m e (m s e c )FIGURE CAPTIONSFigure 1. Changes in ultrasonic distress vocalizations after single administration of lindane. Bars represent the mean ± SE of 10 rat pups of 10-13 days of age. A: Total number of calls during 2 min testing; B:Cumulative time of all calling emitted. (*p<0.05).Figure 2. Changes in ultrasonic distress vocalizations after daily administration of 10 mg/kg lindane during the 1st postnatal week. A:Total number of calls during 3 min testing. B: Cumulative time of ), lindane-dosed ), control females (statistical evaluation, see Results.。