Recent Advances in the Quantitative Characterisation of Mechanical Scale Failure

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著作一：荧光分析法 (第三版)许金钩王尊本主编

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中国肾移植受者巨细胞病毒感染临床诊疗指南（2023_版）

· 指南与共识·中国肾移植受者巨细胞病毒感染临床诊疗指南（2023版）中华医学会器官移植学分会　中国医师协会器官移植医师分会　中国医疗保健国际交流促进会肾脏移植学分会　【摘要】　近几年在实体器官移植（SOT ）受者巨细胞病毒（CMV ）感染诊疗领域，无论是诊断方法还是新型抗CMV 药物都有了一些新的进展，对CMV 感染的诊治产生了积极的影响。

为了进一步规范中国肾移植术后CMV 感染的管理，中华医学会器官移植学分会组织了国内多个学科相关领域专家，参考《中国实体器官移植受者巨细胞病毒感染诊疗指南（2016版）》和国内外已发表的最新文献和指南，制定了《中国肾移植受者巨细胞病毒感染诊疗指南（2023版）》，新版指南更新了CMV 流行病学，CMV 感染的危险因素和普遍性预防的研究进展，新增CMV 感染定义，细化CMV 血症和CMV 病的诊断标准，并对新型抗CMV 药物进行了介绍。

【关键词】　肾移植；实体器官移植；巨细胞病毒；感染；病毒血症；巨细胞病毒病；普遍性预防；抢先治疗【中图分类号】　R617, R373　【文献标志码】 A 【文章编号】 1674-7445（2024）03-0001-20Clinical diagnosis and treatment guidelines for cytomegalovirus infection in kidney transplant recipients in China (2023edition) Branch of Organ Transplantation of Chinese Medical Association, Branch of Organ Transplantation Physician of Chinese Medical Doctor Association, Branch of Kidney Transplantation of China International Exchange and Promotive Association for Medical and Health Care. *The First Affiliated Hospital of Xi 'an Jiaotong University , Xi 'an 710061, China Correspondingauthors:DingXiaoming,Email:***************.cnXueWujun,Email:******************.cn【Abstract 】 In recent years, there have been significant advances in the diagnosis and treatment of cytomegalovirus (CMV) infection in solid organ transplant (SOT) recipients, including diagnostic method and anti-CMV drugs. These advancements have had a positive impact on the management of CMV infection in SOT recipients. To further standardize the management of CMV infection after kidney transplantation in China, Branch of Organ Transplantation of Chinese Medical Association organized a multidisciplinary group of experts in relevant fields. They referred to the ‘Diagnosis and Treatment Guidelines for Cytomegalovirus Infection in Solid Organ Transplant Recipients in China (2016 edition)’ and the latest published literature and guidelines, resulting in the development of the ‘Clinical Diagnosis and Treatment Guidelines for Cytomegalovirus Infection in Kidney Transplant Recipients in China (2023 edition)’. The updated guideline includes CMV epidemiology, research progress on the risk factors and universal prevention of CMV infection, the definition for CMV infection, detailed diagnostic criteria for CMV viremia and CMV disease, as well as an introduction to new anti-CMV drugs.【Key words 】 Kidney transplantation; Solid organ transplantation; Cytomegalovirus; Infection; Viremia;Cytomegalovirus disease; Universal prevention; Preemptive therapyDOI: 10.3969/j.issn.1674-7445.2024096基金项目：国家自然科学基金（82370802、82170766、82270789、81970646）；陕西省卫生健康肾脏移植科研创新平台（2023PT-06）执笔作者单位： 710061　西安，西安交通大学第一附属医院（丁小明）；首都医科大学附属北京友谊医院（林俊）；首都医科大学附属北京朝阳医院（胡小鹏）；复旦大学附属中山医院（戎瑞明）；西安交通大学第一附属医院（郑瑾）通信作者：丁小明，Email ：***************.cn ；薛武军，Email ：******************.cn第 15 卷　第 3 期器官移植Vol. 15　No.3　2024 年 5 月Organ Transplantation May 2024　巨细胞病毒（cytomegalovirus，CMV）是一种全球传播广泛的β-疱疹病毒，原发感染之后在体内会呈潜伏状态，当人体的免疫功能下降时病毒会被再激活。

第十届上海市科学与工程计算方法学术研讨会日程表.

目录第十届上海市科学与工程计算方法学术研讨会通知 (1)第十届上海市科学与工程计算方法学术研讨会日程表 (3)大会邀请报告 (7)历年上海市科学与工程计算方法研讨会一览表 (11)上海金融学院简介 (12)上海金融学院应用数学系简介 (14)上海金融学院相关区域地图 (15)第十届上海市科学与工程计算方法学术研讨会通知为了推进上海地区计算科学理论与应用的发展，加强计算科学研究人员之间的学术交流、合作与联系，由上海市高校计算科学E—研究院和上海金融学院联合举办的“第十届上海市科学与工程计算方法学术研讨会”定于2014年11月22日(周六) 在上海金融学院举行。

会议组织委员会：郭本瑜(上海师范大学) 马和平（上海大学) 王翼飞（上海大学)程晋(复旦大学) 田红炯（上海师范大学) 黄建国(上海交通大学)苏仰峰(复旦大学) 羊丹平(华东师范大学) 徐承龙(同济大学)何志庆(华东理工大学) 顾桂定(上海财经大学) 刘永辉(上海对外经贸大学)时间：2014年11月22日(周六) 8:45－17:00地点：上海市浦东新区上川路995号上海金融学院会议日程安排：上午08: 45—09: 00 开幕式09: 00—10: 20 大会邀请报告(每个报告40分钟)10: 20—10: 40 茶歇10: 40—12: 00 大会邀请报告(每个报告40分钟)中午12: 00—14: 00 午餐下午14: 00—15: 20 分组报告(每个报告20分钟)15: 20—15: 40 茶歇15: 40—17: 00 分组报告(每个报告20分钟) 热烈欢迎从事科学与工程计算的科技工作者、高校教师和研究生踊跃参加研讨会并作学术报告。

联系方式：沈春根：*******************Tel：139****3948刘永辉：**************.cn Tel：136****8109宋心一：***************.cn Tel：139****3496李瑞：************.cn Tel：138****9612第十届上海市科学与工程计算方法学术研讨会日程表(2014年11月22日)大会邀请报告Computational Challenges in Quantitative Finance: HighDimensionality and Non-Smoothness王小群(清华大学)摘要: Problems with a large number of variables are coming to play an ever more important role in financial applications, such as in the pricing of options and the estimation of sensitivities (Greeks). High dimensional problems pose immense challenges for practi cal computations because of the “curse of dimensionality”. In this talk I will touch briefly on recent advances in understanding the success of quasi-Monte Carlo methods for high dimensional problems, and propose methods that deal with the high dimensionality and non-smoothness for problems in quantitative finance.王小群，清华大学数学科学系教授，2009年获国家杰出青年科学基金，2011年被聘为清华大学教育部长江学者特聘教授。

Cancer Epigenetics From Mechanism to Therapy

Leading EdgeReviewCancer Epigenetics:From Mechanism to TherapyMark A.Dawson1,2and Tony Kouzarides1,*1Gurdon Institute and Department of Pathology,University of Cambridge,Tennis Court Road,Cambridge CB21QN,UK2Department of Haematology,Cambridge Institute for Medical Research and Addenbrooke’s Hospital,University of Cambridge,Hills Road, Cambridge CB20XY,UK*Correspondence:t.kouzarides@/10.1016/j.cell.2012.06.013The epigenetic regulation of DNA-templated processes has been intensely studied over the last15 years.DNA methylation,histone modiﬁcation,nucleosome remodeling,and RNA-mediated target-ing regulate many biological processes that are fundamental to the genesis of cancer.Here,we present the basic principles behind these epigenetic pathways and highlight the evidence suggest-ing that their misregulation can culminate in cancer.This information,along with the promising clin-ical and preclinical results seen with epigenetic drugs against chromatin regulators,signiﬁes that it is time to embrace the central role of epigenetics in cancer.Chromatin is the macromolecular complex of DNA and histone proteins,which provides the scaffold for the packaging of our entire genome.It contains the heritable material of eukaryotic cells.The basic functional unit of chromatin is the nucleosome. It contains147base pairs of DNA,which is wrapped around a histone octamer,with two each of histones H2A,H2B,H3, and H4.In general and simple terms,chromatin can be subdi-vided into two major regions:(1)heterochromatin,which is highly condensed,late to replicate,and primarily contains inac-tive genes;and(2)euchromatin,which is relatively open and contains most of the active genes.Efforts to study the coordi-nated regulation of the nucleosome have demonstrated that all of its components are subject to covalent modiﬁcation,which fundamentally alters the organization and function of these basic tenants of chromatin(Allis et al.,2007).The term‘‘epigenetics’’was originally coined by Conrad Wad-dington to describe heritable changes in a cellular phenotype that were independent of alterations in the DNA sequence. Despite decades of debate and research,a consensus deﬁnition of epigenetics remains both contentious and ambiguous(Berger et al.,2009).Epigenetics is most commonly used to describe chromatin-based events that regulate DNA-templated pro-cesses,and this will be the deﬁnition we use in this review. Modiﬁcations to DNA and histones are dynamically laid down and removed by chromatin-modifying enzymes in a highly regulated manner.There are now at least four different DNA modiﬁcations(Baylin and Jones,2011;Wu and Zhang,2011) and16classes of histone modiﬁcations(Kouzarides,2007;Tan et al.,2011).These are described in Table1.These modiﬁcations can alter chromatin structure by altering noncovalent interac-tions within and between nucleosomes.They also serve as docking sites for specialized proteins with unique domains that speciﬁcally recognize these modiﬁcations.These chromatin readers recruit additional chromatin modiﬁers and remodeling enzymes,which serve as the effectors of the modiﬁcation.The information conveyed by epigenetic modiﬁcations plays a critical role in the regulation of all DNA-based processes, such as transcription,DNA repair,and replication.Conse-quently,abnormal expression patterns or genomic alterations in chromatin regulators can have profound results and can lead to the induction and maintenance of various cancers.In this Review,we highlight recent advances in our understanding of these epigenetic pathways and discuss their role in oncogen-esis.We provide a comprehensive list of all the recurrent cancer mutations described thus far in epigenetic pathways regulating modiﬁcations of DNA(Figure2),histones(Figures3,4,and5), and chromatin remodeling(Figure6).Where relevant,we will also emphasize existing and emerging drug therapies aimed at targeting epigenetic regulators(Figure1).Characterizing the EpigenomeOur appreciation of epigenetic complexity and plasticity has dramatically increased over the last few years following the development of several global proteomic and genomic technol-ogies.The coupling of next-generation sequencing(NGS)plat-forms with established chromatin techniques such as chromatin immunoprecipitation(ChIP-Seq)has presented us with a previ-ously unparalleled view of the epigenome(Park,2009).These technologies have provided comprehensive maps of nucleo-some positioning(Segal and Widom,2009),chromatin confor-mation(de Wit and de Laat,2012),transcription factor binding sites(Farnham,2009),and the localization of histone(Rando and Chang,2009)and DNA(Laird,2010)modiﬁcations.In addi-tion,NGS has revealed surprising facts about the mammalian transcriptome.We now have a greater appreciation of the fact that most of our genome is transcribed and that noncoding RNA may play a fundamental role in epigenetic regulation(Ama-ral et al.,2008).Most of the complexity surrounding the epigenome comes from the modiﬁcation pathways that have been identiﬁed.12Cell150,July6,2012ª2012Elsevier Inc.Recent improvements in the sensitivity and accuracy of mass spectrometry (MS)instruments have driven many of these discoveries (Stunnenberg and Vermeulen,2011).Moreover,although MS is inherently not quantitative,recent advances in labeling methodologies,such as stable isotope labeling by amino acids in cell culture (SILAC),isobaric tags for relative and absolute quantiﬁcation (iTRAQ),and isotope-coded afﬁnity tag (ICAT),have allowed a greater ability to provide quantitative measurements (Stunnenberg and Vermeulen,2011).These quantitative methods have generated ‘‘protein recruit-ment maps’’for histone and DNA modiﬁcations,which contain proteins that recognize chromatin modiﬁcations (Bartke et al.,2010;Vermeulen et al.,2010).Many of these chromatin readers have more than one reading motif,so it is important to under-stand how they recognize several modiﬁcations either simulta-neously or sequentially.The concept of multivalent engagement by chromatin-binding modules has recently been explored by using either modiﬁed histone peptides (Vermeulen et al.,2010)or in-vitro-assembled and -modiﬁed nucleosomes (Bartkeet al.,2010;Ruthenburg et al.,2011).The latter approach in particular has uncovered some of the rules governing the recruit-ment of protein complexes to methylated DNA and modiﬁed histones in a nucleosomal context.The next step in our under-standing will require a high-resolution in vivo genomic approach to detail the dynamic events on any given nucleosome during the course of gene expression.Epigenetics and the Cancer ConnectionThe earliest indications of an epigenetic link to cancer were derived from gene expression and DNA methylation studies.These studies are too numerous to comprehensively detail in this review;however,the reader is referred to an excellent review detailing the history of cancer epigenetics (Feinberg and Tycko,2004).Although many of these initial studies were purely correl-ative,they did highlight a potential connection between epige-netic pathways and cancer.These early observations have been signiﬁcantly strengthened by recent results from the Inter-national Cancer Genome Consortium (ICGC).Whole-genomeTable 1.Chromatin Modiﬁcations,Readers,and Their Function Chromatin Modiﬁcation NomenclatureChromatin-Reader MotifAttributed Functionand Cit,citrulline.Reader domains:MBD,methyl-CpG-binding domain;PHD,plant homeodomain;MBT,malignant brain tumor domain;PWWP,proline-tryptophan-tryptophan-proline domain;BRCT,BRCA1C terminus domain;UIM,ubiquitin interaction motif;IUIM,inverted ubiquitin interaction motif;SIM,sumo interaction motif;and PBZ,poly ADP-ribose binding zinc ﬁnger.aThese are established binding modules for the posttranslational modiﬁcation;however,binding to modiﬁed histones has not been ﬁrmly established.Cell 150,July 6,2012ª2012Elsevier Inc.13sequencing in a vast array of cancers has provided a catalog of recurrent somatic mutations in numerous epigenetic regulators (Forbes et al.,2011;Stratton et al.,2009).A central tenet in analyzing these cancer genomes is the identiﬁcation of ‘‘driver’’mutations (causally implicated in the process of oncogenesis).A key feature of driver mutations is that they are recurrently found in a variety of cancers,and/or they are often present at a high prevalence in a speciﬁc tumor type.We will mostly concentrate our discussions on suspected or proven driver mutations in epigenetic regulators.For instance,malignancies such as follicular lymphoma contain recurrent mutations of the histone methyltransferase MLL2in close to 90%of cases (Morin et al.,2011).Similarly,UTX ,a histone demethylase,is mutated in up to 12histologi-cally distinct cancers (van Haaften et al.,2009).Compilation of the epigenetic regulators mutated in cancer highlights histone acetylation and methylation as the most widely affected epige-netic pathways (Figures 3and 4).These and other pathways that are affected to a lesser extent will be described in the following sections.Deep sequencing technologies aimed at mapping chromatin modiﬁcations have also begun to shed some light on the origins of epigenetic abnormalities in cancer.Cross-referencing of DNA methylation proﬁles in human cancers with ChIP-Seq data for histone modiﬁcations and the binding of chromatinregulators have raised intriguing correlations between cancer-associated DNA hypermethylation and genes marked with ‘‘bivalent’’histone modiﬁcations in multipotent cells (Easwaran et al.,2012;Ohm et al.,2007).These bivalent genes are marked by active (H3K4me3)and repressive (H3K27me3)histone modi-ﬁcations (Bernstein et al.,2006)and appear to identify transcrip-tionally poised genes that are integral to development and lineage commitment.Interestingly,many of these genes are targeted for DNA methylation in cancer.Equally intriguing are recent comparisons between malignant and normal tissues from the same individuals.These data demonstrate broad domains within the malignant cells that contain signiﬁcant alter-ations in DNA methylation.These regions appear to correlate with late-replicating regions of the genome associated with the nuclear lamina (Berman et al.,2012).Although there remains little mechanistic insight into how and why these regions of the genome are vulnerable to epigenetic alterations in cancer,these studies highlight the means by which global sequencing plat-forms have started to uncover avenues for further investigation.Genetic lesions in chromatin modiﬁers and global alterations in the epigenetic landscape not only imply a causative role for these proteins in cancer but also provide potential targets for therapeutic intervention.A number of small-molecule inhibitors have already been developed against chromatin regulators (Figure 1).These are at various stages of development,andthreeFigure 1.Epigenetic Inhibitors as Cancer TherapiesThis schematic depicts the process for epigenetic drug development and the current status of various epigenetic therapies.Candidate small molecules are ﬁrst tested in vitro in malignant cell lines for speciﬁcity and phenotypic response.These may,in the ﬁrst instance,assess the inhibition of proliferation,induction of apoptosis,or cell-cycle arrest.These phenotypic assays are often coupled to genomic and proteomic methods to identify potential molecular mechanisms for the observed response.Inhibitors that demonstrate potential in vitro are then tested in vivo in animal models of cancer to ascertain whether they may provide therapeutic beneﬁt in terms of survival.Animal studies also provide valuable information regarding the toxicity and pharmacokinetic properties of the drug.Based on these preclinical studies,candidate molecules may be taken forward into the clinical setting.When new drugs prove beneﬁcial in well-conducted clinical trials,they are approved for routine clinical use by regulatory authorities such as the FDA.KAT,histone lysine acetyltransferase;KMT,histone lysine methyltransferase;RMT,histone arginine methyltransferase;and PARP,poly ADP ribose polymerase.14Cell 150,July 6,2012ª2012Elsevier Inc.of these(targeting DNMTs,HDACs,and JAK2)have already been granted approval by the US Food and Drug Administra-tion(FDA).This success may suggest that the interest in epige-netic pathways as targets for drug discovery had been high over the past decade.However,the reality is that theﬁeld of drug discovery had been somewhat held back due to concerns over the pleiotropic effects of both the drugs and their targets. Indeed,some of the approved drugs(against HDACs)have little enzyme speciﬁcity,and their mechanism of action remains contentious(Minucci and Pelicci,2006).The belief and investment in epigenetic cancer therapies may now gain momentum and reach a new level of support following the recent preclinical success of inhibitors against BRD4,an acetyl-lysine chromatin-binding protein(Dawson et al.,2011; Delmore et al.,2011;Filippakopoulos et al.,2010;Mertz et al., 2011;Zuber et al.,2011).The molecular mechanisms governing these impressive preclinical results have also been largely uncovered and are discussed below.This process is pivotal for the successful progression of these inhibitors into the clinic. These results,along with the growing list of genetic lesions in epigenetic regulators,highlight the fact that we have now entered an era of epigenetic cancer therapies.Epigenetic Pathways Connected to CancerDNA MethylationThe methylation of the5-carbon on cytosine residues(5mC)in CpG dinucleotides was theﬁrst described covalent modiﬁca-tion of DNA and is perhaps the most extensively characterized modiﬁcation of chromatin.DNA methylation is primarily noted within centromeres,telomeres,inactive X-chromosomes,and repeat sequences(Baylin and Jones,2011;Robertson,2005). Although global hypomethylation is commonly observed in malignant cells,the best-studied epigenetic alterations in cancerare the methylation changes that occur within CpG islands, which are present in 70%of all mammalian promoters.CpG island methylation plays an important role in transcriptional regu-lation,and it is commonly altered during malignant transforma-tion(Baylin and Jones,2011;Robertson,2005).NGS platforms have now provided genome-wide maps of CpG methylation. These have conﬁrmed that between5%–10%of normally unme-thylated CpG promoter islands become abnormally methylated in various cancer genomes.They also demonstrate that CpG hypermethylation of promoters not only affects the expression of protein coding genes but also the expression of various noncoding RNAs,some of which have a role in malignant trans-formation(Baylin and Jones,2011).Importantly,these genome-wide DNA methylome studies have also uncovered intriguing alterations in DNA methylation within gene bodies and at CpG‘‘shores,’’which are conserved sequences upstream and downstream of CpG islands.The functional relevance of these regional alterations in methylation are yet to be fully deciphered, but it is interesting to note that they have challenged the general dogma that DNA methylation invariably equates with transcriptional silencing.In fact,these studies have established that many actively transcribed genes have high levels of DNA methylation within the gene body,suggesting that the context and spatial distribution of DNA methylation is vital in transcrip-tional regulation(Baylin and Jones,2011).Three active DNA methyltransferases(DNMTs)have been identiﬁed in higher eukaryotes.DNMT1is a maintenance methyl-transferase that recognizes hemimethylated DNA generated during DNA replication and then methylates newly synthesized CpG dinucleotides,whose partners on the parental strand are already methylated(Li et al.,1992).Conversely,DNMT3a and DNMT3b,although also capable of methylating hemimethylated DNA,function primarily as de novo methyltransferases to estab-lish DNA methylation during embryogenesis(Okano et al.,1999). DNA methylation provides a platform for several methyl-binding proteins.These include MBD1,MBD2,MBD3,and MeCP2. These in turn function to recruit histone-modifying enzymes to coordinate the chromatin-templated processes(Klose and Bird,2006).Although mutations in DNA methyltransferases and MBD proteins have long been known to contribute to developmental abnormalities(Robertson,2005),we have only recently become aware of somatic mutations of these key genes in human malig-nancies(Figure2).Recent sequencing of cancer genomes has identiﬁed recurrent mutations in DNMT3A in up to25%of patients with acute myeloid leukemia(AML)(Ley et al.,2010). Importantly,these mutations are invariably heterozygous and are predicted to disrupt the catalytic activity of the enzyme. Moreover,their presence appears to impact prognosis(Patel et al.,2012).However,at present,the mechanisms bywhich Figure2.Cancer Mutations Affecting Epigenetic Regulators of DNA MethylationThe5-carbon of cytosine nucleotides are methylated(5mC)by a family of DNMTs.One of these,DNMT3A,is mutated in AML,myeloproliferative diseases(MPD),and myelodysplastic syndromes(MDS).In addition to its catalytic activity,DNMT3A has a chromatin-reader motif,the PWWP domain, which may aid in localizing this enzyme to chromatin.Somatically acquired mutations in cancer may also affect this domain.The TET family of DNA hydroxylases metabolizes5mC into several oxidative intermediates,including 5-hydroxymethylcytosine(5hmC),5-formylcytosine(5fC),and5-carbox-ylcytosine(5caC).These intermediates are likely involved in the process of active DNA demethylation.Two of the three TET family members are mutated in cancers,including AML,MPD,MDS,and CMML.Mutation types are as follows:M,missense;F,frameshift;N,nonsense;S,splice site mutation;and T,translocation.Cell150,July6,2012ª2012Elsevier Inc.15these mutations contribute to the development and/or mainte-nance of AML remains elusive.Understanding the cellular consequences of normal and aber-rant DNA methylation remains a key area of interest,especially because hypomethylating agents are one of the few epigenetic therapies that have gained FDA approval for routine clinical use(Figure1).Although hypomethylating agents such as azaci-tidine and decitabine have shown mixed results in various solid malignancies,they have found a therapeutic niche in the myelo-dysplastic syndromes(MDS).Until recently,this group of disor-ders was largely refractory to therapeutic intervention,and MDS was primarily managed with supportive care.However,several large studies have now shown that treatment with azacitidine, even in poor prognosis patients,improves their quality of life and extends survival time.Indeed,azacitidine is theﬁrst therapy to have demonstrated a survival beneﬁt for patients with MDS (Fenaux et al.,2009).The molecular mechanisms governing the impressive responses seen in MDS are largely unknown. However,recent evidence would suggest that low doses of these agents hold the key to therapeutic beneﬁt(Tsai et al., 2012).It is also emerging that the combinatorial use of DNMT and HDAC inhibitors may offer superior therapeutic outcomes (Gore,2011).DNA Hydroxy-Methylation and Its Oxidation Derivatives Historically,DNA methylation was generally considered to be a relatively stable chromatin modiﬁcation.However,early studies assessing the global distribution of this modiﬁcation during embryogenesis had clearly identiﬁed an active global loss of DNA methylation in the early zygote,especially in the male pronucleus.More recently,high-resolution genome-wide mapping of this modiﬁcation in pluripotent and differentiated cells has also conﬁrmed the dynamic nature of DNA methylation, evidently signifying the existence of an enzymatic activity within mammalian cells that either erases or alters this chromatin modiﬁcation(Baylin and Jones,2011).In2009,two seminal manuscripts describing the presence of5-hydroxymethylcyto-sine(5hmC)offered theﬁrst insights into the metabolism of 5mC(Kriaucionis and Heintz,2009;Tahiliani et al.,2009).The ten-eleven translocation(TET1–3)family of proteins have now been demonstrated to be the mammalian DNA hydroxy-lases responsible for catalytically converting5mC to5hmC. Indeed,iterative oxidation of5hmC by the TET family results in further oxidation derivatives,including5-formylcytosine(5fC) and5-carboxylcytosine(5caC).Although the biological signiﬁ-cance of the5mC oxidation derivatives is yet to be established, several lines of evidence highlight their importance in transcrip-tional regulation:(1)they are likely to be an essential intermediate in the process of both active and passive DNA demethylation,(2) they preclude or enhance the binding of several MBD proteins and,as such,will have local and global effects by altering the recruitment of chromatin regulators,and(3)genome-wide mapping of5hmC has identiﬁed a distinctive distribution of this modiﬁcation at both active and repressed genes,including its presence within gene bodies and at the promoters of bivalently marked,transcriptionally poised genes(Wu and Zhang,2011). Notably,5hmC was also mapped to several intergenic cis-regu-latory elements that are either functional enhancers or insulator elements.Consistent with the notion that5hmC is likely to have a role in both transcriptional activation and silencing, the TET proteins have also been shown to have activating and repressive functions(Wu and Zhang,2011).Genome-wide mapping of TET1has demonstrated it to have a strong prefer-ence for CpG-rich DNA and,consistent with its catalytic function, it also been localized to regions enriched for5mC and5hmC. The TET family of proteins derive their name from the initial description of a recurrent chromosomal translocation, t(10;11)(q22;q23),which juxtaposes the MLL gene with TET1in a subset of patients with AML(Lorsbach et al.,2003).Notably, concurrent to the initial description of the catalytic activity for the TET family of DNA hydroxylases,several reports emerged describing recurrent mutations in TET2in numerous hematolog-ical malignancies(Cimmino et al.,2011;Delhommeau et al., 2009;Langemeijer et al.,2009)(Figure2).Interestingly,TET2-deﬁcient mice develop a chronic myelomonocytic leukemia (CMML)phenotype,which is in keeping with the high prevalence of TET2mutations in patients with this disease(Moran-Crusio et al.,2011;Quivoron et al.,2011).The clinical implications of TET2mutations have largely been inconclusive;however,in some subsets of AML patients,TET2mutations appear to confer a poor prognosis(Patel et al.,2012).Early insights into the process of TET2-mediated oncogenesis have revealed that the patient-associated mutations are largely loss-of-function muta-tions that consequently result in decreased5hmC levels and a reciprocal increase in5mC levels within the malignant cells that harbor them.Moreover,mutations in TET2also appear to confer enhanced self-renewal properties to the malignant clones (Cimmino et al.,2011).Histone ModiﬁcationsIn1964,Vincent Allfrey prophetically surmised that histone modiﬁcations might have a functional inﬂuence on the regulation of transcription(Allfrey et al.,1964).Nearly half a century later, theﬁeld is still grappling with the task of unraveling the mecha-nisms underlying his enlightened statement.In this time,we have learned that these modiﬁcations have a major inﬂuence, not just on transcription,but in all DNA-templated processes (Kouzarides,2007).The major cellular processes attributed to each of these modiﬁcations are summarized in Table1.The great diversity in histone modiﬁcations introduces a remarkable complexity that is slowly beginning to be ing transcription as an example,we have learned that multiple coexisting histone modiﬁcations are associated with activation,and some are associated with repression. However,these modiﬁcation patterns are not static entities but a dynamically changing and complex landscape that evolves in a cell context-dependent fashion.Moreover,active and repres-sive modiﬁcations are not always mutually exclusive,as evi-denced by‘‘bivalent domains.’’The combinatorial inﬂuence that one or more histone modiﬁcations have on the deposition, interpretation,or erasure of other histone modiﬁcations has been broadly termed‘‘histone crosstalk,’’and recent evidence would suggest that crosstalk is widespread and is of great bio-logical signiﬁcance(Lee et al.,2010).It should be noted that the cellular enzymes that modify histones may also have nonhistone targets and,as such,it has been difﬁcult to divorce the cellular consequences of individual histone modiﬁcations from the broader targets of many of these16Cell150,July6,2012ª2012Elsevier Inc.enzymes.In addition to their catalytic function,many chromatin modiﬁers also possess‘‘reader’’domains allowing them to bind to speciﬁc regions of the genome and respond to information conveyed by upstream signaling cascades.This is important, as it provides two avenues for therapeutically targeting these epigenetic regulators.The residues that line the binding pocket of reader domains can dictate a particular preference for speciﬁc modiﬁcation states,whereas residues outside the binding pocket contribute to determining the histone sequence speciﬁcity.This combination allows similar reader domains to dock at different modiﬁed residues or at the same amino acid displaying different modiﬁcation states.For example,some methyl-lysine readers engage most efﬁciently with di/tri-methyl-ated lysine(Kme2/3),whereas others prefer mono-or unmethy-lated lysines.Alternatively,when the same lysines are now acet-ylated,they bind to proteins containing bromodomains(Taverna et al.,2007).The main modiﬁcation binding pockets contained within chromatin-associated proteins is summarized in Table1. Many of the proteins that modify or bind these histone modiﬁ-cations are misregulated in cancer,and in the ensuing sections, we will discuss the most extensively studied histone modiﬁca-tions in relation to oncogenesis and novel therapeutics. Histone Acetylation.The Nε-acetylation of lysine residues is a major histone modiﬁcation involved in transcription,chromatin structure,and DNA repair.Acetylation neutralizes lysine’s posi-tive charge and may consequently weaken the electrostatic interaction between histones and negatively charged DNA.For this reason,histone acetylation is often associated with a more ‘‘open’’chromatin conformation.Consistent with this,ChIP-Seq analyses have conﬁrmed the distribution of histone acetyla-tion at promoters and enhancers and,in some cases,throughout the transcribed region of active genes(Heintzman et al.,2007; Wang et al.,2008).Importantly,lysine acetylation also serves as the nidus for the binding of various proteins with bromodo-mains and tandem plant homeodomain(PHD)ﬁngers,which recognize this modiﬁcation(Taverna et al.,2007).Acetylation is highly dynamic and is regulated by the competing activities of two enzymatic families,the histone lysine acetyltransferases(KATs)and the histone deacetylases (HDACs).There are two major classes of KATs:(1)type-B,which are predominantly cytoplasmic and modify free histones,and(2) type-A,which are primarily nuclear and can be broadly classiﬁedinto the GNAT,MYST,and CBP/p300families.KATs were theﬁrst enzymes shown to modify histones.The importance of theseﬁndings to cancer was immediately apparent,as one of these enzymes,CBP,was identiﬁed by its ability to bind the transforming portion of the viral oncoprotein E1A(Bannister and Kouzarides,1996).It is now clear that many,if not most,of the KATs have been implicated in neoplastic transformation,and a number of viral oncoproteins are known to associate with them.There are numerous examples of recur-rent chromosomal translocations(e.g.,MLL-CBP[Wang et al., 2005]and MOZ-TIF2[Huntly et al.,2004])or coding mutations (e.g.,p300/CBP[Iyer et al.,2004;Pasqualucci et al.,2011]) involving various KATs in a broad range of solid and hematolog-ical malignancies(Figure3).Furthermore,altered expression levels of several of the KATs have also been noted in a range of cancers(Avvakumov and Coˆte´,2007;Iyer et al.,2004).In some cases,such as the leukemia-associated fusion gene MOZ-TIF2,we know a great deal about the cellular conse-quences of this translocation involving a MYST family member. MOZ-TIF2is sufﬁcient to recapitulate an aggressive leukemia in murine models;it can confer stem cell properties and reacti-vate a self-renewal program when introduced into committed hematopoietic progenitors,and much of this oncogenic potential is dependent on its inherent and recruited KAT activity as well as its ability to bind to nucleosomes(Deguchi et al.,2003;Huntly et al.,2004).Despite these insights,the great conundrum with regards to unraveling the molecular mechanisms by which histone acetyl-transferases contribute to malignant transformation has been dissecting the contribution of altered patterns in acetylation on histone and nonhistone proteins.Although it is clear that global histone acetylation patterns are perturbed in cancers(Fraga Figure 3.Cancer Mutations Affecting Epigenetic Regulators Involved in Histone AcetylationThese tables provide somatic cancer-associated mutations identiﬁed in histone acetyltransferases and proteins that contain bromodomains(which recognize and bind acetylated histones).Several histone acetyltransferases possess chromatin-reader motifs and,thus,mutations in the proteins may alter both their catalytic activities as well as the ability of these proteins to scaffold multiprotein complexes to chromatin.Interestingly,sequencing of cancer genomes to date has not identiﬁed any recurrent somatic mutations in histone deacetylase enzymes.Abbreviations for the cancers are as follows: AML,acute myeloid leukemia;ALL,acute lymphoid leukemia;B-NHL,B-cell non-Hodgkin’s lymphoma;DLBCL,diffuse large B-cell lymphoma;and TCC, transitional cell carcinoma of the urinary bladder.Mutation types are as follows:M,missense;F,frameshift;N,nonsense;S,splice site mutation;T, translocation;and D,deletion.Cell150,July6,2012ª2012Elsevier Inc.17。

三重四级杆质谱技术在中药研究中的应用进展

第33卷第3期2018年5月Vol.33No.3May2018内蒙古民族大学学报（自然科学版）Journal of Inner Mongolia University for NationalitiesDOI：10.14045/ki.15-1220.2018.03.007三重四级杆质谱技术在中药研究中的应用进展柏慧，张仁慈，王曦烨，刘景林，许良（内蒙古民族大学化学化工学院，天然产物化学及功能分子合成自治区重点实验室，内蒙古通辽028042）〔摘要〕中药活性成分的定性和定量分析一直是解决中药质量标志物及建立中药质量控制方法的主要手段.随着质谱技术的发展，三重四极杆质谱技术在中药成分分析工作中得到了广泛的应用.三重四极杆质谱法具有准确、高效、适宜多种类型药物检测等优点.因此，该技术在中药研究中得到了广泛的应用.本文对三重四级杆质谱技术在中药研究中的应用近期进展进行综述.〔关键词〕三重四级杆质谱；中药研究；应用进展〔中图分类号〕O657.63〔文献标识码〕A〔文章编号〕1671-0185（2018）03-0208-05The Application Progress of Triple Quadrupole MassSpectrometry in Studies of Traditional Chinese MedicineBAI Hui，ZHANG Ren-ci，WANG Xi-ye，LIU Jing-lin，XU Liang（College of Chemistry and Chemical Engineering，Inner Mongolia Key Laboratory of Natural Products Chemis-try and Functional Molecules Synthesis，Inner Mongolia University for Nationalities，Tongliao028042，China）Abstract：The qualitative and quantitative analysis of active components of Chinese herbal medicines is being usedto determine their material basis in traditional Chinese medicine（TCM）and to establish quality control.The analy-sis of triple quadrupole mass spectrometry has been widely applied in the analysis of traditional Chinese medicine.Itis accurate，efficient and suitable for all kinds of drug testing.This paper reviews the recent advances in the applica-tion of triple quadrupole mass spectrometry in traditional Chinese medicine studies.Key words：Triple quadrupole mass spectrometry；Traditional Chinese medicine research；Application progress一直以来，中药难以进入国际药品主流市场的主要原因为尚未全面阐释中药药效物质及作用机制，未能建立与国际接轨的质量标准和安全标准.即，中药中是否含有有害物质以及有害物质的含量是否足以引起不良反应甚至致癌、中药中有效成分含量究竟有多少等问题，都使得西方学者对中药的质量和安全性抱有怀疑态度.因此，通过利用高效准确的质谱分析技术进行中药中各种成分的定性、定量分析研究，对加快中药标准化、现代化和推动中药国际化都有重要意义.众所周知，由于中药种类繁多、成分众多结构复杂，微量乃至痕量成分较多，因此，其化学成分的全面检测成为中药研究的瓶颈问题之一〔1〕.目前，三重四级杆质谱技术已经被广泛地应用到中药研究领域，同时正在和色谱技术及各种计算机技术得到结合，使得该技术在中药研究领域中的应用范围不断拓展〔2〕.三重四极杆质谱技术是一种新型的药物成分分析方法，通过利用分子量的不同，采用多重离子对监测（MRM）技术，有效地排除杂质干扰，增加专属性和灵敏度，并做出快速的分析〔3〕.近年来该技术在分析基金项目：国家自然科学基金资助项目（81260682，81560702）；内蒙古自治区科技创新引导项目及内蒙古民族大学特色交叉学科群建设项目（MDXK003）作者简介：柏慧，内蒙古民族大学化学化工学院硕士研究生.许良为通讯作者.209第3期柏慧等：三重四级杆质谱技术在中药研究中的应用进展化学领域尤其在中药研究领域得到日益广泛的应用.三重四级杆质谱仪是目前质谱领域中最具代表性的定量分析仪器，它具有分析时间短、可分析成分多、定量准确等特点.目前，除了广泛应用于中药多组分体外质量控制研究方面之外，还广泛应用于中药多组分体内药物代谢动力学方面的研究.可见，三重四级杆质谱技术已在中药药物研究方面有了广泛的应用.值得注意的是，三重四级杆质谱仪也有它一定的不足之处，例如在高分辨率和多级串级质谱等定性能力方面比较差一些.不过这完全可以通过配备离子阱仪器或QTOF仪器等来弥补三重四极杆定性能力不足的弱势.总的来说，在三重四级杆质谱技术在对天然药物成分、中药复方药物有效成分定量分析方面体现出特有的优势.1三重四级杆质谱技术在中药研究中的应用近年来，三重四级杆质谱技术在中药残留农药检测、中药定性定量分析、非法掺入化学药物的中药制剂的检测、中成药制剂的质量控制、对中药体内代谢产物的分析、对中药中有害物质含量的测定等方面被广泛应用.1.1对中药残留农药进行检测我国中药生产经常出现农药残留超标等问题，极大的影响了中药走进国际药品市场，也使中药的国际地位受到影响，这极大的延缓了中药现代化和国际化的进程〔4〕.中药中有害残留物分析属于痕量分析范畴，技术难度较大，分析方法验证技术要求不同于常规的常量或微量分析.因此，提高中药农药残留检测准确性就成了一个重要的课题〔5〕.王建国等利用气相色谱-三重四级杆串联质谱（GC-MS/MS）分析中草药中1，2，4-三氯苯等85种农药残留物.其中，茯苓样品检出乐果0.15mg/kg、乙拌磷0.21mg/kg、马拉硫磷0.40mg/kg；白芍样品中检出乐果0.16mg/kg、乙拌磷0.25mg/kg、马拉硫磷0.39mg/kg〔6〕.徐宜宏等曾采用气相色谱-三重四级杆串联质谱法建立了五味子中117种农药残留同时检测的方法〔7〕.李俊等建立了测定鱼腥草中百草枯和乙草胺残留物的三重四级杆气相色谱质谱方法〔8〕.以上三个实验结果显示，应用三重四级杆质谱法对中药农药残留进行检验，具有准确性高、快速高效的特点.1.2对中药进行定性分析用三重四级杆质谱法对中药成分进行定性分析方面已有报道.韩豪等利用超高效液相色谱-三重四级杆串联质谱联用技术（UPLC-MS/MS），对黑豆皮中花青苷类物质进行定性分析.在黑豆皮中检测出共15种花青苷类物质.通过对黑豆皮花青苷类物质的分析，建立了UPLC-MS/MS检测黑豆皮中花青苷类物质方法〔9〕.周丽等曾采用微波辅助法，以80%甲醇作为溶剂提取经冻干后紫山药块根和地上茎叶中的多酚类物质，提取物经超高效液相色谱分离、电喷雾串联三重四级杆质谱正和负离子方式检测分析.分析得到29种酚类化合物〔10〕.1.3对中药成分进行定量分析朱根华等采用三重四级杆液质联用（HPLC-MS-MS）法，建立金樱子中白桦脂酸含量测定方法.质谱采用大气压电喷雾离子源，选择性离子流模式（负离子）.结果显示白桦脂酸的平均样回收率为98.3%（RSD2.6%）〔11〕.李占彬等曾采用高效液相色谱-串联三重四级杆质谱法（LC-MS/MS），针对姜茶中的姜黄素做过定量分析，其结果显示，该方法对于中药成分的定量检测来说简便、快速、溶剂用量少，可消除姜油带来的干扰，结果准确可靠，适合进行批量样品的测定〔12〕.徐新蕊等利用超高效液相色谱-三重四极杆质谱法同时测定乌头汤中8种成分，实验证明该方法快速、简便、重复性好，可用于同时测定乌头汤中的8种成分以及乌头汤的质量控制〔13〕.另外，侯佳等〔14〕曾经采用超高液相色谱-三重四级杆质谱同时测定灯盏细辛药材中的绿原酸、新绿原酸、隐绿原酸、野黄芩苷、灯盏甲素、芹菜素等的含量.1.4对非法掺入化学药物的中药制剂进行检测一些不法分子在中药制剂中违法掺加化学成分以提升短期药效，牟取暴利，但是从长远来看，这些化210内蒙古民族大学学报2018年学成分对患者的伤害非常大.因此，如何准确检测中药制剂中掺入化学药物已成为药物分析工作者面临的挑战.首先以治疗糖尿病的降糖类中药制剂或保健品为例，在降糖类保健食品中，发现非法添加的降糖类化学药品多达十几种，有些保健食品中竟然同时添加2～3种降糖化学药品，尤其以格列苯脲、格列齐特、格列吡嗪和盐酸苯乙双胍这四种临床上治疗糖尿病较为常用的西药被添加到保健品中最为常见.这4种西药降糖效果好，在西医临床上也是被允许使用，但是药物代谢作用强烈，患者若长期服用此类保健品，会导致持续低血糖、损害肝脏、溶血性贫血和再生性贫血等症状，并且对高危人群诱发严重的不良反应，甚至会危及生命〔15〕.因此，应该谨遵医嘱，控制药物用量.而违法商贩为了达到其销售更多保健品的目的，往往添加这些有害成分，患者在不知情的情况下，长期服用会严重影响其健康.因此，急需对市面上的降糖类保健品加强违禁西药药物成分的检测与监控.其次，抗风湿类中药中非法掺入萘普生、吲哚美辛、双氯芬酸钠的情况比较常见.蓝献泉等为建立抗风湿类中成药和保健品中非法添加双氯芬酸钠的快速筛查方法，采用一种化学方法进行快速筛查，同时建立高效液相色谱法测定含量，并用高效液相色谱-二极管阵列检测器和超高效液相色谱-串联三重四级杆质谱联用仪对样品进行确证〔16〕.其三，减肥药也是违法添加化学制剂的重灾区.随着人们生活品质的不断提高，肥胖逐渐成为一种社会性问题.肥胖患者容易患上高血压、糖尿病、冠心病等疾病，因此很多人会寻求减肥药作为辅助减肥手段，但是不法商家为了使他们的减肥保健品短期内出现明显效果，往往会添加一些违禁的化学药品，长期服用，对患者的身体健康影响很大〔17〕.其中，西布曲明就是一种较为常见的减肥保健品中经常被非法添加的一种化学成分.李文杰等为了建立快速定性检测减肥胶囊中添加的西布曲明的DART-MS/MS方法，且检出限达1ppm，效果良好〔18〕.最后，中药保健品种掺入提高产品参数的化学药物或人工合成制剂的情况也是屡见不鲜.刘秀红等曾采用高效液相色谱-串联三重四级杆质谱为分析手段对蜂王浆/蜂王浆冻干粉中掺入的人工合成癸烯酸进行鉴别和测定〔19〕.以上实验报告显示，利用三重四级杆质谱法对中药和中药类保健品进行化学成分的检测，其灵敏度和准确度均良好，可以有效分析鉴别中药制剂中违法添加的西药成分.1.5对中成药制剂进行质量控制我国的中成药质量标准从无到有、从不完善到提高完善，历经了不同阶段的发展.目前，中成药质量标准正在向更高层次迈进.但长期以来中成药质量控制问题一直是中成药制药生产中的一个难点.这主要是由于中成药成分复杂，药效物质基础不明确，原料药材易受品种、产地、加工方法等因素的影响，使得中成药的质量评价成为阻碍中成药走向现代化的障碍〔20〕.但是，随着人们对中药材产品的关注和对药材品质要求的提升，采用高效准确的质谱技术来解决中药整体特性的质量控制已经成为现实.姚慧等利用超高效液相色谱-电喷雾串联三重四级杆质谱法（UPLC-ESI-MS）同时测定了贞芪扶正胶囊中红景天苷、毛蕊异黄酮-7-O-β-D-葡萄糖苷和黄芪甲苷含量〔21〕.另外，张炜等〔22〕利用超高效液相色谱-串联电喷雾三重四级杆质谱（UPLC-MS/MS）法检测妇康宁片中掺加的山麦冬.1.6对中药体内代谢产物的分析欧阳辉等曾利用超高效液相色谱—三重四级杆线性离子肼复合质谱（UPLC-Q-trap-MS）分析白头翁皂苷D体外肠道菌群孵育样品.实验结果显示，采用三重四级杆质谱法对鉴定及分析中药体内代谢产物具有可行性〔23〕.1.7对中药配伍禁忌的机制的研究陈艳琰等曾采用超高效液相色谱-串联四级杆飞行时间质谱（UPLC-Q-TOF/MS）及超高效液相色谱-串联三重四级杆质谱（UPLC-TQ/MS）联用集成技术，研究芫花与甘草合煎液中化学成分相互作用及211第3期柏慧等：三重四级杆质谱技术在中药研究中的应用进展其变化特点，揭示其“反”的可能特征与机制.该实验显示，在中药配伍禁忌的研究工作中，三重四级杆质谱技术在成分的分析鉴定方面有一定优势〔24〕.1.8对中药中有害物质含量的定量分析不可否认的是，很多中药材中确实含有有害物质，古语说是药三分毒就是这个道理.在《黄帝内经》中就有将药分为大毒、常毒、小毒、无毒等四类的记载，其中对用中药治病也有这样的描述“大毒治病，十去其六；常毒治病，十去其七；小毒治病，十去其八；无毒治病，十去其九；谷肉果菜，食养尽之.无使过之，伤其正也.不尽，行复如法.”旨在告诫人们中药也有其副作用.所以，中药的用量应该要适量，以能帮助病人驱邪为宜，扶正固本之事则应由没有毒副作用的食疗完成.但是，现代人盲目崇尚非化学、纯自然的理念.认为中药不是化学的，源自天然，毒副作用一定很小.其实，中药和西药一样，也都是化学元素构成的，也同样存在毒副作用，中药的毒性成分更复杂.因此，强化对中药中有害物质含量的测定就显得尤为重要.有学者研究发现，中国台湾和整个亚洲地区的肝癌组织广泛地可测到提示有马兜铃酸及其衍生物暴露的基因突变，认为肝癌可能与含马兜铃酸中药的使用有关.除此之外，含马兜铃酸中草药可引起肾脏毒性和泌尿系肿瘤，已被广泛证实.这种毒性主要集中于马兜铃科马兜铃属和细辛属的马兜铃酸成分中，曾在中国及东亚、东南亚地区的医药领域应用极为广泛〔25〕.针对马兜铃酸可能引起较重副作用的情况，下一步，会通过进一步规范药物用量、加强含马兜铃酸中药的基础研究、选择合适的产地、炮制方法及提取工艺，同时加强相关中药材、饮片及中成药中马兜铃酸含量的监测等方式来尽量降低用药风险〔26〕.李功辉等采用超高效液相色谱—三重四极杆串联质谱建立复杂中药基质中马兜铃酸Ⅰ的定性定量测定方法.结果显示，此法对测定中药材中的马兜铃酸含量的准确度、精密度均良好.该方法简便、快速、灵敏度高，专属性强，可以用于含有或者可能含有马兜铃酸Ⅰ成分中药材的检测.结果表明，不同种类药材中马兜铃酸Ⅰ的含量差异明显，尤以朱砂莲中马兜铃酸Ⅰ含量最高.同时，该检测结果也可为马兜铃科中药材中马兜铃酸Ⅰ的限量及其合理使用提供实验基础和参考依据〔27〕.2应用展望综上所述，三重四级杆质谱技术已成为中药成分分析中强有力的工具.该技术因具备灵敏度高、抗干扰能力强等特点，因此对样品的净化处理要求低，处理方法将会变得更加简便、快速.在不远的将来，该技术与现代样品预处理技术和化学计量学方法相结合，在中药和蒙药等传统民族药现代研究中微量甚至痕量质量标志物检测方面将发挥更大作用.同时，在环境检测、生物医学药学、毒物分析等众多领域的检测中发挥越来越重要的作用〔28〕.参考文献〔1〕任惠娟，任现志.液相色谱质谱联用技术及其在中药现代化研究中的应用〔J〕.山西中医，2014，4（4）：5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NLRP3和Caspase-1在颞下颌关节骨关节炎滑膜中的表达

NLRP3和Caspase-1在颞下颌关节骨关节炎滑膜中的表达李艳艳，冯亚平，柯金，龙星(武汉大学口腔医院口腔颌面外科，湖北武汉430079#[摘要]目的：探究&LRP3 炎症小体（&ACHT, and PYD dom/ins-cont/ining protein 3 in fl/m m/som e)和腕天蛋白酶-1 (cysteinyl aspartate specific proteinase，Caspase-1)在颗下颌关节骨关节炎（temporomandibular joint osteoarthritis，TM JOA)患者滑膜细胞中的表达。

方法：分离人颞下颌关节滑膜组织并进行滑膜细胞培养，另取3例髁突肥大患者的滑膜组织作为对照组。

实时定量聚合酶链式反应(real-time quantitative polymerase chain reaction，（T-P C()法检测滑膜细胞中&L(P3和C/s p/s e-1的基因表达，W estern blot检测滑膜细胞中&L(P3和C/s p/s e-1的表达。

结果：TM JO A患者滑膜细胞中的&L(P3和C/s p/s e-1的表达显著高于对照组(！<0.05)。

结论:&L(P3和C/s p/s e-1在TM JO A滑膜细胞中高表达，说明它们与TM JOA发生发展密切相关，这将有助于进明其分子机制。

[关键词]颞下颌关节骨关节炎'滑膜；&L(P3炎性小体'半胱氨酸蛋白酶-1[中图分类号](782[文献标志码]A DOI：10.3969/j.issn.1005-4979.2021.02.003Expression of NLRP3 and Caspase-1 in synovium of temporomandibular joint osteoarthritisLI Yanyan，FENG Yoping，KE Jin，LONG Xing(Department of Oral and Maxillofacial Surgery，Hospital of Stomatology，Wuhan University，Wuhan430079，Hubei Province，China)[Abstract] Objective: To investigate the expression of NLRP3 (NACHT, LRR and PYD domains-containing protein 3 in- fl/m m/som e) and C/sp/se-1 in the synovium of temporomandibular joint osteoarthritis (TMJOA). Methods：Synovial tissues were collected from TMJ of patients and synovial cells were cultured. Synovial tissues of three patients with condylar hypertrophy were taken as control group. Real-time PCR was used to examine the gene expression of NLRP3 and C/s- p/se-1 in synovial cells. The protein expressions of NLRP3 and C/sp/se-1 in synovial cells were detected by Western blot.Results：The expressions of NLRP3 and Caspase-1 in the synovial cells of TMJOA were significantly increased than the control group (P<0.05). Conclusion：NLRP3 and C/sp/se-1 were increased in the synovial cells of TMJOA and closely related to the occurrence and development of TMJOA, which will help to further clarify its molecular mechanism.[Keywords] temporomandibular joint osteoarthritis; synovium; NLRP3 infl/m m/some; C/sp/se-1颞下颌关节骨关节炎（temporomandibular joint osteoarthritis，TMJOA)是一种以关节软骨破坏、软骨下骨退行性和滑膜炎症为的性，关节区疼痛、口，面：形，患者的的研究表明，滑膜炎与关节软骨软骨下骨的行性收稿日期：2020-05-14 修回日期：2020-06-18基金项目：科学基（81870789)作者简介：（1995—)，，人，硕士研究生.E-m/il: *********************.cn通信作者：，医师.E-m/il:*************.cn;龙星，主任医师.E-m/il: ****************.cn 相关_。

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Advanced Calculus with Applications in Statistics2.A History of Probability and Statistics and Their Applications before 17503.Markov Decision Processes: Discrete Stochastic Dynamic Programming4.Probability and Statistical Inference5.Continuous Univariate Distributions, V ol. 16.Continuous Univariate Distributions, Vol. 27.The Theory of Measures and Integration8.Robust Statistics: Theory and Methods9.Finite Mixture Models10.Generalized, Linear, and Mixed Models11.Statistics of Extremes: Theory and Applications12.Modes of Parametric Statistical Inference13.Univariate Discrete Distributions14.Contemporary Bayesian Econometrics and Statistics15.Approximation Theorems of Mathematical Statistics16.Image Processing and Jump Regression Analysis17.Operational Risk : Modeling Analytics18.Design and Analysis of Experiments, Introduction to Experimental Design19.Introductory Biostatistics for the Health Sciences: Modern ApplicationsIncluding Bootstrap20.Linear Models in Statistics21.Statistics for Research22.Applied Logistic Regression23.Operational Subjective Statistical Methods: A Mathematical, Philosophical,and Historical Introduction24.Probability and Measure, 2nd Edition25.Theory of Preliminary Test and Stein-Type Estimation with Applications26.The EM Algorithm and Extensions27.The Theory of Response-Adaptive Randomization in Clinical Trials28.Models for Probability and Statistical Inference: Theory and Applications29.Applied Life Data Analysis30.Structural Equation Modelling: A Bayesian Approach31.Bootstrap Methods: A Guide for Practitioners and Researchers32.Nonparametric Analysis of Univariate Heavy-Tailed Data: Research andPractice33.Applied Linear Regression, 3rd edition34.Theory of Probability: A Critical Introductory Treatment35.Financial Derivatives in Theory and Practice36.Quantitative Methods in Population Health: Extensions of Ordinary Regression37.Statistical Methods for Survival Data Analysis38.Applied Bayesian Modelling39.Spatial Statistics, 2004-0840.Approximate Dynamic Programming: Solving the Curses of Dimensionality41.Variance Components42.Time Series: Applications to Finance43.Generalized Least Squares44.Statistical Analysis With Missing Data45.Long-Memory Time Series: Theory and Methods46.Statistical Models and Methods for Lifetime Data47.Uncertainty Analysis with High Dimensional Dependence Modelling48.Simulation and the Monte Carlo Method49.A Matrix Handbook for Statisticians50.Meta Analysis: A Guide to Calibrating and Combining Statistical Evidence51.Precedence-Type Tests and Applications52.Statistical Meta-Analysis with Applications53.Management of Data in Clinical Trials54.Periodically Correlated Random Sequences: Spectral Theory and Practice55.Design and Analysis of Experiments, Advanced Experimental Design56.Methods and Applications of Linear Models : Regression and the Analysis ofVariancebinatorial Methods in Discrete Distributions58.Nonparametric Regression Methods for Longitudinal Data Analysis:Mixed-Effects Modeling Approaches59.Response Surfaces, Mixtures, and Ridge Analyses60.Variations on Split Plot and Split Block Experiment Designs61.Recent Advances in Quantitative Methods in Cancerand Human Health Risk Assessment62.The Construction of Optimal Stated Choice Experiments: Theory and Methods63.Nonparametric Density Estimation: The L1 View64.Applied MANOV A and Discriminant Analysis65.Survey Errors and Survey Costs66.Statistical Advances in the Biomedical Sciences: Clinical Trials,Epidemiology, Survival Analysis, and Bioinformaticstent Curve Models: A Structural Equation Perspective68.Regression Diagnostics: Identifying Influential Data and Sources ofCollinearity69.Reliability and Risk: A Bayesian Perspective70.Environmental Statistics71.Bayes Linear Statistics, Theory & Methods72.Introductory Stochastic Analysis for Finance and Insurance73.Bayesian Models for Categorical Data74.Bayesian Statistical Modelling75.Weibull Models76.Analysis of Financial Time Series77.Linear Model Theory: Univariate, Multivariate, and Mixed Models78.An Introduction to Categorical Data Analysis79.Bayesian Statistics and Marketing80.Statistical Shape Analysis81.Nonparametric Statistics with Applications to Science and Engineering82.Longitudinal Data Analysis83.Regression Models for Time Series Analysis84.Introduction to Nonparametric Regression85.Statistical Modeling by Wavelets86.Case Studies in Reliability and Maintenance87.The Geometry of Random Fields88.Biostatistics : A Methodology For the Health Sciences89.Planning, Construction, and Statistical Analysis of Comparative Experiments90.Statistical Size Distributions in Economics and Actuarial Sciences91.Modern Experimental Designparative Statistical Inference93.Methods of Multivariate Analysis94.Robust Statistics95.Order Statistics96.Fourier Analysis of Time Series : An Introduction97.Spatial Statistics 1981-0498.Clinical Trials : A Methodologic Perspective99.Numerical Issues in Statistical Computing for the Social Scientist100.Nonlinear Regression101.Preparing for the Worst : Incorporating Downside Risk in Stock MarketInvestments 102.Nonlinear Regression Analysis and Its Applications103.Mixed Models : Theory and Applications104.Discrete Distributions : Applications in the Health Sciences105.Design and Analysis of Clinical Trials : Concepts and Methodologies106.Applied Bayesian Modeling and Causal Inference from Incomplete-DataPerspectives 107.Flowgraph Models for Multistate Time-?–to-Event Data108.Randomization in Clinical Trials: Theory and Practice109.Regression With Social Data: Modeling Continuous and Limited ResponseVariables 110.Regression Analysis by Example111.Categorical Data Analysis112.Statistical Methods for Reliability Data113.Elements of Stochastic Processes Wit114.Random Graphs for Statistical Pattern Recognition115.Construction and Assessment of Classification Rules116.The Statistical Analysis of Failure Time Data117.Statistical Analysis of Finite Mixture Distributions118.Modern Applied U-Statistics119.Applied Multiway Data Analysis。

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Wiley Series in Probability and Statistics1. A Course in Time Series Analysis2. A Primer on Experiments with Mixtures3. A Probabilistic Analysis of the Sacco and Vanzetti Evidence4. A User's Guide to Principal Components5. A Weak Convergence Approach to the Theory of Large Deviations6.Accelerated Testing: Statistical Models, Test Plans, and Data Analysis7.Advanced Calculus with Applications in Statistics, Second Edition8.Alternative Methods of Regression9.An Elementary Introduction to Statistical Learning Theory10.An Introduction to Categorical Data Analysis, Second Edition11.An Introduction to Probability and Statistics, Second Edition12.An Introduction to Regression Graphics13.Analysis of Financial Time Series14.Analysis of Financial Time Series, Second Edition15.Analysis of Financial Time Series, Third Edition, Third Edition16.Analysis of Ordinal Categorical Data, Second Edition17.Analyzing Microarray Gene Expression Data18.Applications of Statistics to Industrial Experimentation19.Applied Bayesian Modeling and Causal Inference from Incomplete-DataPerspectives: An Essential Journey with Donald Rubin's Statistical Family20.Applied Bayesian Modelling21.Applied Life Data Analysis22.Applied Linear Regression, Third Edition23.Applied MANOVA and Discriminant Analysis, Second Edition24.Applied Multiway Data Analysis25.Applied Regression Including Computing and Graphics26.Applied Spatial Statistics for Public Health Data27.Applied Survival Analysis: Regression Modeling of Time-to-Event Data, SecondEdition28.Approximate Dynamic Programming: Solving the Curses of Dimensionality29.Approximate Dynamic Programming: Solving the Curses of Dimensionality, SecondEdition30.Approximation Theorems of Mathematical Statistics31.Aspects of Multivariate Statistical Theory32.Aspects of Statistical Inference33.Batch Effects and Noise in Microarray Experiments: Sources and Solutions34.Bayes Linear Statistics: Theory and Methods35.Bayesian Analysis for the Social Sciences36.Bayesian Analysis of Stochastic Process Models37.Bayesian Methods and Ethics in a Clinical Trial Design38.Bayesian Models for Categorical Data39.Bayesian Networks: An Introduction40.Bayesian Statistical Modelling, Second Edition41.Bayesian Statistics and Marketing42.Bayesian Theory43.Bias and Causation44.Biostatistical Methods in Epidemiology45.Biostatistical Methods: The Assessment of Relative Risks46.Biostatistical Methods: The Assessment of Relative Risks, Second Edition47.Biostatistics: A Methodology for the Health Sciences, Second Edition48.Bootstrap Methods: A Guide for Practitioners and Researchers, Second Edition49.Business Survey Methods50.Case Studies in Reliability and Maintenance51.Categorical Data Analysis, Second Edition52.Causality: Statistical Perspectives and Applications53.Clinical Trial Design: Bayesian and Frequentist Adaptive Methods54.Clinical Trials: A Methodologic Perspective, Second Edition55.Cluster Analysis, 5th Editionbinatorial Methods in Discrete Distributionsparative Statistical Inference, Third Edition58.Constrained Statistical Inference: Inequality, Order, and Shape Restrictions59.Contemporary Bayesian Econometrics and Statistics60.Continuous Multivariate Distributions: Models and Applications, Volume 1,Second Edition61.Convergence of Probability Measures, Second Edition62.Counting Processes and Survival Analysis63.Decision Theory64.Design and Analysis of Clinical Trials: Concepts and Methodologies, SecondEdition65.Design and Analysis of Experiments: Advanced Experimental Design, Volume 266.Design and Analysis of Experiments: Introduction to Experimental Design, Volume1, Second Edition67.Design and Analysis of Experiments: Special Designs and Applications, Volume 368.Directional Statistics69.Dirichlet and Related Distributions: Theory, Methods and Applications70.Discrete Distributions: Applications in the Health Sciences71.Discriminant Analysis and Statistical Pattern Recognition72.Empirical Model Building73.Empirical Model Building: Data, Models, and Reality, Second Edition74.Environmental Statistics: Methods and Applications75.Exploration and Analysis of DNA Microarray and Protein Array Data76.Exploratory Data Mining and Data Cleaning77.Exploring 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before 175097.Image Processing and Jump Regression Analysis98.Inference and Prediction in Large Dimensions99.Introduction to Nonparametric Regression100.Introduction to Statistical Time Series, Second Edition101.Introductory Stochastic Analysis for Finance and Insurancetent Class and Latent Transition Analysis: With Applications in the Social, Behavioral, and Health Sciencestent Curve Models: A Structural Equation Perspectivetent Variable Models and Factor Analysis: A Unified Approach, 3rd Edition105.Leading Personalities in Statistical Sciences: From the Seventeenth Century to the Present106.Lévy Processes in Finance: Pricing Financial Derivatives107.Linear Models: The Theory and Application of Analysis of Variance108.Linear Statistical Inference and its Applications: Second Editon109.Linear Statistical Models110.LISP-STAT: An Object-Oriented Environment for Statistical Computing and Dynamic Graphics111.Longitudinal Data Analysis112.Long-Memory Time Series: Theory and Methods113.Loss Distributions114.Loss Models: From Data to Decisions, Third Edition115.Management of Data in Clinical Trials, Second Edition116.Markov Decision Processes: Discrete Stochastic Dynamic Programming117.Markov Processes and Applications118.Markov Processes: Characterization and Convergence119.Mathematics of Chance120.Measurement Error Models121.Measurement Errors in Surveys122.Meta Analysis: A Guide to Calibrating and Combining Statistical Evidence 123.Methods and Applications of Linear Models: Regression and the Analysis of Variance124.Methods for Statistical Data Analysis of Multivariate Observations, Second Edition125.Methods of Multivariate Analysis, Second Edition126.Methods of Multivariate Analysis, Third Edition127.Mixed Models: Theory and Applications128.Mixtures: Estimation and Applications129.Modelling Under Risk and Uncertainty: An Introduction to Statistical, Phenomenological and Computational Methods130.Models for Investors in Real World Markets131.Models for Probability and Statistical Inference: Theory and Applications 132.Modern Applied U-Statistics133.Modern Experimental Design134.Modes of Parametric Statistical Inference135.Multilevel Statistical Models, 4th Edition136.Multiple Comparison Procedures137.Multiple Imputation for Nonresponse in Surveys138.Multiple Time Series139.Multistate Systems Reliability Theory with Applications140.Multivariable Model-Building: A pragmatic approach to regression analysis based on fractional polynomials for modelling continuous variables141.Multivariate Density Estimation: Theory, Practice, and Visualization142.Multivariate Observations143.Multivariate Statistics: High-Dimensional and Large-Sample Approximations 144.Nonlinear Regression145.Nonlinear Regression Analysis and Its Applications146.Nonlinear Statistical Models147.Nonparametric Analysis of Univariate Heavy-Tailed Data: Research and Practice148.Nonparametric Regression Methods for Longitudinal Data Analysis: Mixed-Effects Modeling Approaches149.Nonparametric Statistics with Applications to Science and Engineering 150.Numerical Issues in Statistical Computing for the Social Scientist151.Operational Risk: Modeling Analytics152.Optimal Learning153.Order Statistics, Third Edition154.Periodically Correlated Random Sequences: Spectral Theory and Practice 155.Permutation Tests for Complex Data: Theory, Applications and Software 156.Planning and Analysis of Observational Studies157.Planning, Construction, and Statistical Analysis of Comparative Experiments158.Precedence-Type Tests and Applications159.Preparing for the Worst: Incorporating Downside Risk in Stock Market Investments160.Probability and Finance: It's Only a Game!161.Probability: A Survey of the Mathematical Theory, Second Edition162.Quantitative Methods in Population Health: Extensions of Ordinary Regression163.Random Data: Analysis and Measurement Procedures, Fourth Edition164.Random Graphs for Statistical Pattern Recognition165.Randomization in Clinical Trials: Theory and Practice166.Recent Advances in Quantitative Methods in Cancer and Human Health Risk Assessment167.Records168.Regression Analysis by Example, Fourth Edition169.Regression Diagnostics: Identifying Influential Data and Sources of Collinearity170.Regression Graphics: Ideas for Studying Regressions Through Graphics171.Regression Models for Time Series Analysis172.Regression with Social Data: Modeling Continuous and Limited Response Variables173.Reliability and Risk: A Bayesian Perspective174.Reliability: Modeling, Prediction, and Optimization175.Response Surfaces, Mixtures, and Ridge Analyses, Second Edition176.Robust Estimation & Testing177.Robust Methods in Biostatistics178.Robust Regression and Outlier Detection179.Robust Statistics180.Robust Statistics, Second Edition181.Robust Statistics: Theory and Methods182.Runs and Scans with Applications183.Sampling, Third Edition184.Sensitivity Analysis in Linear Regression185.Sequential Estimation186.Shape & Shape Theory187.Simulation and the Monte Carlo Method188.Simulation and the Monte Carlo Method, Second Edition189.Simulation and the Monte Carlo Method: Solutions Manual to Accompany, Second Edition190.Simulation: A Modeler's Approach191.Smoothing and Regression: Approaches, Computation, and Application192.Smoothing of Multivariate Data: Density Estimation and Visualization193.Spatial Statistics194.Spatial Statistics and Spatio-Temporal Data: Covariance Functions and Directional Properties195.Spatial Tessellations: Concepts and Applications of Voronoi Diagrams, Second Edition196.Stage-Wise Adaptive Designs197.Statistical Advances in the Biomedical Sciences: Clinical Trials, Epidemiology, Survival Analysis, and Bioinformatics198.Statistical Analysis of Profile Monitoring199.Statistical Design and Analysis of Experiments: With Applications to Engineering and Science, Second Edition200.Statistical Factor Analysis and Related Methods: Theory and Applications 201.Statistical Inference for Fractional Diffusion Processes202.Statistical Meta-Analysis with Applications203.Statistical Methods for Comparative Studies: Techniques for Bias Reduction 204.Statistical Methods for Forecasting205.Statistical Methods for Quality Improvement, Third Edition206.Statistical Methods for Rates and Proportions, Third Edition207.Statistical Methods for Survival Data Analysis, Third Edition208.Statistical Methods for the Analysis of Biomedical Data, Second Edition 209.Statistical Methods in Diagnostic Medicine210.Statistical Methods in Diagnostic Medicine, Second Edition211.Statistical Methods in Engineering and Quality Assurance212.Statistical Methods in Spatial Epidemiology, Second Edition213.Statistical Modeling by Wavelets214.Statistical Models and Methods for Lifetime Data, Second Edition215.Statistical Rules of Thumb, Second Edition216.Statistical Size Distributions in Economics and Actuarial Sciences217.Statistical Texts for Mixed Linear Models218.Statistical Tolerance Regions: Theory, Applications, and Computation219.Statistics for Imaging, Optics, and Photonics220.Statistics for Research, Third Edition221.Statistics of Extremes: Theory and Applications222.Statistics: A Biomedical Introduction223.Stochastic Dynamic Programming and the Control of Queueing Systems224.Stochastic Processes for Insurance & Finance225.Stochastic Simulation226.Structural Equation Modeling: A Bayesian Approach227.Subjective and Objective Bayesian Statistics: Principles, Models, and Applications, Second Edition228.Survey Errors and Survey Costs229.System Reliability Theory: Models, Statistical Methods, and Applications, Second Edition230.The Analysis of Covariance and Alternatives: Statistical Methods for Experiments, Quasi-Experiments, and Single-Case Studies, Second Edition231.The Construction of Optimal Stated Choice Experiments: Theory and Methods 232.The EM Algorithm and Extensions, Second Edition233.The Statistical Analysis of Failure Time Data, Second Edition234.The Subjectivity of Scientists and the Bayesian Approach235.The Theory of Measures and Integration236.The Theory of Response-Adaptive Randomization in Clinical Trials237.Theory of Preliminary Test and Stein-Type Estimation With Applications 238.Time Series Analysis and Forecasting by Example239.Time Series: Applications to Finance with R and S-Plus, Second Edition 240.Uncertainty Analysis with High Dimensional Dependence Modelling241.Univariate Discrete Distributions, Third Editioning the Weibull Distribution: Reliability, Modeling, and Inference243.Variance Components244.Variations on Split Plot and Split Block Experiment Designs245.Visual Statistics: Seeing Data with Dynamic Interactive Graphics246.Weibull Models。

乙型肝炎病毒核心抗体定量检测研究进展

乙型肝炎病毒核心抗体定量检测研究进展陈翔宇1，2综述张海峰1，3审校1.南通大学附属医院感染科，江苏南通226001；2.南通大学医学院，江苏南通226001；3.新疆克孜勒苏柯尔克孜自治州人民医院感染科，新疆阿图什845350【摘要】近年来多项研究发现乙型肝炎病毒核心抗体检测与抗病毒药物治疗应答、区分慢乙肝不同自然史时期、免疫抑制治疗后乙型肝炎病毒(HBV)再激活等相关，有望成为启动抗病毒治疗、预测疗效、判断预后的重要参考指标，具有巨大临床应用潜力。

本文就乙型肝炎病毒核心抗体(抗HBc)检测的最新研究进展和临床意义进行综述，为早日实现对慢性HBV 感染者的精准化、个体化治疗提供帮助。

【关键词】乙型肝炎病毒核心抗体；慢性HBV 感染自然史；疗效预测；隐匿性乙型肝炎病毒感染；HBV 再激活【中图分类号】R512.6+2【文献标识码】A【文章编号】1003—6350（2023）23—3492—05Clinical research advances in quantitative detection of core antibody of hepatitis B virus.CHEN Xiang-yu 1,2,ZHANG Hai-feng1,3.1.Department of Infectious Diseases,Affiliated Hospital of Nantong University,Nantong 226001,Jiangsu,CHINA;2.Medical College of Nantong University,Nantong 226001,Jiangsu,CHINA;3.Department of Infectious Diseases,Xinjiang Kyzylsu Kirgiz Autonomous Prefecture People's Hospital,Atush 845350,Xinjiang,CHINA【Abstract 】In recent years,many studies have found that the core antibody detection of hepatitis B virus is relat-ed to the response to antiviral drug treatment,the differentiation of different natural history periods of chronic hepatitis B,and Hepatitis B (HBV)reactivation after immunosuppressive treatment.It is expected to become an important refer-ence index for initiating antiviral therapy,predicting efficacy and judging prognosis,and has great clinical application po-tential.This paper summarizes the latest research progress and clinical significance of the detection of hepatitis B virus core antibody (anti-HBc),so as to provide some help for the early realization of accurate and individualized treatment of chronic HBV infected patients.【Key words 】Core antibodies of hepatitis B virus;Natural history of chronic HBV infection;Prediction of effica-cy;Occult hepatitis B virus infection;HBV reactivation ·综述·doi:10.3969/j.issn.1003-6350.2023.23.033基金项目：江苏省南通市科技计划项目(编号：MSZ2022002)；新疆克州科研计划项目(编号：克科字[2022]13号)。

Statistical mechanics of complex networks,RevModPhys.74.47

Statistical mechanics of complex networksRe´ka Albert*and Albert-La´szlo´Baraba´siDepartment of Physics,University of Notre Dame,Notre Dame,Indiana46556(Published30January2002)Complex networks describe a wide range of systems in nature and society.Frequently cited examples include the cell,a network of chemicals linked by chemical reactions,and the Internet,a network of routers and computers connected by physical links.While traditionally these systems have been modeled as random graphs,it is increasingly recognized that the topology and evolution of real networks are governed by robust organizing principles.This article reviews the recent advances in the ﬁeld of complex networks,focusing on the statistical mechanics of network topology and dynamics.After reviewing the empirical data that motivated the recent interest in networks,the authors discuss the main models and analytical tools,covering random graphs,small-world and scale-free networks, the emerging theory of evolving networks,and the interplay between topology and the network’s robustness against failures and attacks.CONTENTSI.Introduction48II.The Topology of Real Networks:Empirical Results49A.World Wide Web49B.Internet50C.Movie actor collaboration network52D.Science collaboration graph52E.The web of human sexual contacts52F.Cellular networks52G.Ecological networks53H.Phone call network53I.Citation networks53works in linguistics53 K.Power and neural networks54 L.Protein folding54 III.Random-Graph Theory54A.The Erdo˝s-Re´nyi model54B.Subgraphs55C.Graph evolution56D.Degree distribution57E.Connectedness and diameter58F.Clustering coefﬁcient58G.Graph spectra59 IV.Percolation Theory59A.Quantities of interest in percolation theory60B.General results601.The subcritical phase(pϽp c)602.The supercritical phase(pϾp c)61C.Exact solutions:Percolation on a Cayley tree61D.Scaling in the critical region62E.Cluster structure62F.Inﬁnite-dimensional percolation62G.Parallels between random-graph theory andpercolation63 V.Generalized Random Graphs63A.Thresholds in a scale-free random graph64B.Generating function formalism64ponent sizes and phase transitions652.Average path length65C.Random graphs with power-law degreedistribution66D.Bipartite graphs and the clustering coefﬁcient66 VI.Small-World Networks67A.The Watts-Strogatz model67B.Properties of small-world networks681.Average path length682.Clustering coefﬁcient693.Degree distribution704.Spectral properties70 VII.Scale-Free Networks71A.The Baraba´si-Albert model71B.Theoretical approaches71C.Limiting cases of the Baraba´si-Albert model73D.Properties of the Baraba´si-Albert model741.Average path length742.Node degree correlations753.Clustering coefﬁcient754.Spectral properties75 VIII.The Theory of Evolving Networks76A.Preferential attachment⌸(k)761.Measuring⌸(k)for real networks762.Nonlinear preferential attachment773.Initial attractiveness77B.Growth781.Empirical results782.Analytical results78C.Local events791.Internal edges and rewiring792.Internal edges and edge removal79D.Growth constraints801.Aging and cost802.Gradual aging81petition in evolving networks811.Fitness model812.Edge inheritance82F.Alternative mechanisms for preferentialattachment821.Copying mechanism822.Edge redirection823.Walking on a network834.Attaching to edges83G.Connection to other problems in statisticalmechanics831.The Simon model832.Bose-Einstein condensation85*Present address:School of Mathematics,University of Min-nesota,Minneapolis,Minnesota55455.REVIEWS OF MODERN PHYSICS,VOLUME74,JANUARY20020034-6861/2002/74(1)/47(51)/$35.00©2002The American Physical Society47IX.Error and Attack Tolerance86A.Numerical results861.Random network,random node removal872.Scale-free network,random node removal873.Preferential node removal87B.Error tolerance:analytical results88C.Attack tolerance:Analytical results89D.The robustness of real networks90munication networks902.Cellular networks913.Ecological networks91X.Outlook91A.Dynamical processes on networks91B.Directed networks92C.Weighted networks,optimization,allometricscaling92D.Internet and World Wide Web93E.General questions93F.Conclusions94 Acknowledgments94 References94 I.INTRODUCTIONComplex weblike structures describe a wide variety of systems of high technological and intellectual impor-tance.For example,the cell is best described as a com-plex network of chemicals connected by chemical reac-tions;the Internet is a complex network of routers and computers linked by various physical or wireless links; fads and ideas spread on the social network,whose nodes are human beings and whose edges represent various social relationships;the World Wide Web is an enormous virtual network of Web pages connected by hyperlinks.These systems represent just a few of the many examples that have recently prompted the scien-tiﬁc community to investigate the mechanisms that de-termine the topology of complex networks.The desire to understand such interwoven systems has encountered signiﬁcant challenges as well.Physics,a major beneﬁ-ciary of reductionism,has developed an arsenal of suc-cessful tools for predicting the behavior of a system as a whole from the properties of its constituents.We now understand how magnetism emerges from the collective behavior of millions of spins,or how quantum particles lead to such spectacular phenomena as Bose-Einstein condensation or superﬂuidity.The success of these mod-eling efforts is based on the simplicity of the interactions between the elements:there is no ambiguity as to what interacts with what,and the interaction strength is uniquely determined by the physical distance.We are at a loss,however,to describe systems for which physical distance is irrelevant or for which there is ambiguity as to whether two components interact.While for many complex systems with nontrivial network topology such ambiguity is naturally present,in the past few years we have increasingly recognized that the tools of statistical mechanics offer an ideal framework for describing these interwoven systems as well.These developments have introduced new and challenging problems for statistical physics and unexpected links to major topics in condensed-matter physics,ranging from percolation to Bose-Einstein condensation.Traditionally the study of complex networks has been the territory of graph theory.While graph theory ini-tially focused on regular graphs,since the1950s large-scale networks with no apparent design principles have been described as random graphs,proposed as the sim-plest and most straightforward realization of a complex network.Random graphs wereﬁrst studied by the Hun-garian mathematicians Paul Erdo˝s and Alfre´d Re´nyi. According to the Erdo˝s-Re´nyi model,we start with N nodes and connect every pair of nodes with probability p,creating a graph with approximately pN(NϪ1)/2 edges distributed randomly.This model has guided our thinking about complex networks for decades since its introduction.But the growing interest in complex sys-tems has prompted many scientists to reconsider this modeling paradigm and ask a simple question:are the real networks behind such diverse complex systems as the cell or the Internet fundamentally random?Our in-tuition clearly indicates that complex systems must dis-play some organizing principles,which should be at some level encoded in their topology.But if the topology of these networks indeed deviates from a random graph, we need to develop tools and measurements to capture in quantitative terms the underlying organizing prin-ciples.In the past few years we have witnessed dramatic ad-vances in this direction,prompted by several parallel de-velopments.First,the computerization of data acquisi-tion in allﬁelds led to the emergence of large databases on the topology of various real networks.Second,the increased computing power allowed us to investigate networks containing millions of nodes,exploring ques-tions that could not be addressed before.Third,the slow but noticeable breakdown of boundaries between disci-plines offered researchers access to diverse databases, allowing them to uncover the generic properties of com-plex networks.Finally,there is an increasingly voiced need to move beyond reductionist approaches and try to understand the behavior of the system as a whole.Along this route,understanding the topology of the interac-tions between the components,i.e.,networks,is un-avoidable.Motivated by these converging developments and cir-cumstances,many new concepts and measures have been proposed and investigated in depth in the past few years.However,three concepts occupy a prominent place in contemporary thinking about complex net-works.Here we deﬁne and brieﬂy discuss them,a discus-sion to be expanded in the coming sections.Small worlds:The small-world concept in simple terms describes the fact that despite their often large size,in most networks there is a relatively short path between any two nodes.The distance between two nodes is deﬁned as the number of edges along the short-est path connecting them.The most popular manifesta-tion of small worlds is the‘‘six degrees of separation’’concept,uncovered by the social psychologist Stanley Milgram(1967),who concluded that there was a path of48R.Albert and A.-L.Baraba´si:Statistical mechanics of complex networks Rev.Mod.Phys.,Vol.74,No.1,January2002acquaintances with a typical length of about six betweenmost pairs of people in the United States(Kochen,1989).The small-world property appears to characterizemost complex networks:the actors in Hollywood are onaverage within three co-stars from each other,or thechemicals in a cell are typically separated by three reac-tions.The small-world concept,while intriguing,is notan indication of a particular organizing principle.In-deed,as Erdo˝s and Re´nyi have demonstrated,the typi-cal distance between any two nodes in a random graphscales as the logarithm of the number of nodes.Thusrandom graphs are small worlds as well.Clustering:A common property of social networks isthat cliques form,representing circles of friends or ac-quaintances in which every member knows every othermember.This inherent tendency to cluster is quantiﬁedby the clustering coefﬁcient(Watts and Strogatz,1998),a concept that has its roots in sociology,appearing underthe name‘‘fraction of transitive triples’’(Wassermannand Faust,1994).Let us focusﬁrst on a selected node iin the network,having k i edges which connect it to k iother nodes.If the nearest neighbors of the originalnode were part of a clique,there would be k i(k iϪ1)/2 edges between them.The ratio between the number E iof edges that actually exist between these k i nodes andthe total number k i(k iϪ1)/2gives the value of the clus-tering coefﬁcient of node i,C iϭ2E ik i͑k iϪ1͒.(1)The clustering coefﬁcient of the whole network is the average of all individual C i’s.An alternative deﬁnition of C that is often used in the literature is discussed in Sec.VI.B.2(Barrat and Weigt,2000;Newman,Strogatz, and Watts,2000).In a random graph,since the edges are distributed randomly,the clustering coefﬁcient is Cϭp(Sec.III.F). However,in most,if not all,real networks the clustering coefﬁcient is typically much larger than it is in a compa-rable random network(i.e.,having the same number of nodes and edges as the real network).Degree distribution:Not all nodes in a network have the same number of edges(same node degree).The spread in the node degrees is characterized by a distri-bution function P(k),which gives the probability that a randomly selected node has exactly k edges.Since in a random graph the edges are placed randomly,the major-ity of nodes have approximately the same degree,close to the average degree͗k͘of the network.The degree distribution of a random graph is a Poisson distribution with a peak at P(͗k͘).One of the most interesting de-velopments in our understanding of complex networks was the discovery that for most large networks the de-gree distribution signiﬁcantly deviates from a Poisson distribution.In particular,for a large number of net-works,including the World Wide Web(Albert,Jeong, and Baraba´si,1999),the Internet(Faloutsos et al.,1999), or metabolic networks(Jeong et al.,2000),the degree distribution has a power-law tail,P͑k͒ϳkϪ␥.(2) Such networks are called scale free(Baraba´si and Al-bert,1999).While some networks display an exponential tail,often the functional form of P(k)still deviates sig-niﬁcantly from the Poisson distribution expected for a random graph.These discoveries have initiated a revival of network modeling in the past few years,resulting in the introduc-tion and study of three main classes of modeling para-digms.First,random graphs,which are variants of the Erdo˝s-Re´nyi model,are still widely used in manyﬁelds and serve as a benchmark for many modeling and em-pirical studies.Second,motivated by clustering,a class of models,collectively called small-world models,has been proposed.These models interpolate between the highly clustered regular lattices and random graphs.Fi-nally,the discovery of the power-law degree distribution has led to the construction of various scale-free models that,by focusing on the network dynamics,aim to offer a universal theory of network evolution.The purpose of this article is to review each of these modeling efforts,focusing on the statistical mechanics of complex networks.Our main goal is to present the the-oretical developments in parallel with the empirical data that initiated and support the various models and theo-retical tools.To achieve this,we start with a brief de-scription of the real networks and databases that repre-sent the testing ground for most current modeling efforts.II.THE TOPOLOGY OF REAL NETWORKS:EMPIRICAL RESULTSThe study of most complex networks has been initi-ated by a desire to understand various real systems, ranging from communication networks to ecological webs.Thus the databases available for study span sev-eral disciplines.In this section we review brieﬂy those that have been studied by researchers aiming to uncover the general features of complex networks.Beyond a de-scription of the databases,we shall focus on three robust measures of a network’s topology:average path length, clustering coefﬁcient,and degree distribution.Other quantities,as discussed in the following sections,will again be tested on these databases.The properties of the investigated databases,as well as the obtained expo-nents,are summarized in Tables I and II.A.World Wide WebThe World Wide Web represents the largest network for which topological information is currently available. The nodes of the network are the documents(web pages)and the edges are the hyperlinks(URL’s)that point from one document to another(see Fig.1).The size of this network was close to one billion nodes at the end of1999(Lawrence and Giles,1998,1999).The in-terest in the World Wide Web as a network boomed after it was discovered that the degree distribution of the web pages follows a power law over several orders of magnitude(Albert,Jeong,and Baraba´si,1999;Kumar49R.Albert and A.-L.Baraba´si:Statistical mechanics of complex networks Rev.Mod.Phys.,Vol.74,No.1,January2002et al.,1999).Since the edges of the World Wide Web aredirected,the network is characterized by two degree dis-tributions:the distribution of outgoing edges,P out(k), signiﬁes the probability that a document has k outgoinghyperlinks,and the distribution of incoming edges, P in(k),is the probability that k hyperlinks point to acertain document.Several studies have established that both P out(k)and P in(k)have power-law tails: P out͑k͒ϳkϪ␥out and P in͑k͒ϳkϪ␥in.(3) Albert,Jeong,and Baraba´si(1999)have studied asubset of the World Wide Web containing325729nodes and have found␥outϭ2.45and␥inϭ2.1.Kumar et al. (1999)used a40-million-document crawl by Alexa Inc., obtaining␥outϭ2.38and␥inϭ2.1(see also Kleinberg et al.,1999).A later survey of the World Wide Web to-pology by Broder et al.(2000)used two1999Altavistacrawls containing in total200million documents,obtain-ing␥outϭ2.72and␥inϭ2.1with scaling holding close to ﬁve orders of magnitude(Fig.2).Adamic and Huber-man(2000)used a somewhat different representation of the World Wide Web,with each node representing a separate domain name and two nodes being connected if any of the pages in one domain linked to any page in the other.While this method lumped together pages that were on the same domain,representing a nontrivial ag-gregation of the nodes,the distribution of incoming edges still followed a power law with␥in domϭ1.94.Note that␥in is the same for all measurements at the document level despite the two-years’time delay be-tween theﬁrst and last web crawl,during which the World Wide Web had grown at leastﬁve times larger. However,␥out has a tendency to increase with the sample size or time(see Table II).Despite the large number of nodes,the World Wide Web displays the small-world property.This wasﬁrst re-ported by Albert,Jeong,and Baraba´si(1999),who found that the average path length for a sample of 325729nodes was11.2and predicted,usingﬁnite size scaling,that for the full World Wide Web of800million nodes that would be a path length of around19.Subse-quent measurements by Broder et al.(2000)found that the average path length between nodes in a50-million-node sample of the World Wide Web is16,in agreement with theﬁnite size prediction for a sample of this size. Finally,the domain-level network displays an average path length of3.1(Adamic,1999).The directed nature of the World Wide Web does not allow us to measure the clustering coefﬁcient using Eq.(1).One way to avoid this difﬁculty is to make the net-work undirected,making each edge bidirectional.This was the path followed by Adamic(1999),who studied the World Wide Web at the domain level using a1997 Alexa crawl of50million web pages distributed among 259794sites.Adamic removed the nodes that had have only one edge,focusing on a network of153127sites. While these modiﬁcations are expected to increase the clustering coefﬁcient somewhat,she found Cϭ0.1078, orders of magnitude higher than C randϭ0.00023corre-sponding to a random graph of the same size and aver-age degree.B.InternetThe Internet is a network of physical links between computers and other telecommunication devices(Fig.TABLE I.The general characteristics of several real networks.For each network we have indicated the number of nodes,the average degree͗k͘,the average path length l,and the clustering coefﬁcient C.For a comparison we have included the average path length l rand and clustering coefﬁcient C rand of a random graph of the same size and average degree.The numbers in the last column are keyed to the symbols in Figs.8and9.Network Size͗k͘l l rand C C rand Reference Nr. WWW,site level,undir.15312735.21 3.1 3.350.10780.00023Adamic,19991 Internet,domain level3015–6209 3.52–4.11 3.7–3.76 6.36–6.180.18–0.30.001Yook et al.,2001a,Pastor-Satorras et al.,20012 Movie actors22522661 3.65 2.990.790.00027Watts and Strogatz,19983 LANL co-authorship529099.7 5.9 4.790.43 1.8ϫ10Ϫ4Newman,2001a,2001b,2001c4 MEDLINE co-authorship152025118.1 4.6 4.910.066 1.1ϫ10Ϫ5Newman,2001a,2001b,2001c5 SPIRES co-authorship56627173 4.0 2.120.7260.003Newman,2001a,2001b,2001c6 NCSTRL co-authorship11994 3.599.77.340.4963ϫ10Ϫ4Newman,2001a,2001b,2001c7 Math.co-authorship70975 3.99.58.20.59 5.4ϫ10Ϫ5Baraba´si et al.,20018 Neurosci.co-authorship20929311.56 5.010.76 5.5ϫ10Ϫ5Baraba´si et al.,20019 E.coli,substrate graph2827.35 2.9 3.040.320.026Wagner and Fell,200010 E.coli,reaction graph31528.3 2.62 1.980.590.09Wagner and Fell,200011 Ythan estuary food web1348.7 2.43 2.260.220.06Montoya and Sole´,200012 Silwood Park food web154 4.75 3.40 3.230.150.03Montoya and Sole´,200013 Words,co-occurrence460.90270.13 2.67 3.030.4370.0001Ferrer i Cancho and Sole´,200114 Words,synonyms2231113.48 4.5 3.840.70.0006Yook et al.,2001b15 Power grid4941 2.6718.712.40.080.005Watts and Strogatz,199816C.Elegans28214 2.65 2.250.280.05Watts and Strogatz,199817 50R.Albert and A.-L.Baraba´si:Statistical mechanics of complex networksRev.Mod.Phys.,Vol.74,No.1,January20021).The topology of the Internet is studied at two differ-ent levels.At the router level,the nodes are the routers,and edges are the physical connections between them.At the interdomain (or autonomous system)level,eachwork structure of the World Wide Web and the Internet.Upper panel:the nodes of the World Wide Web are web documents,connected with directed hyperlinks (URL’s).Lower panel:on the Internet the nodes are the routers and computers,and the edges are the wires and cables that physi-cally connect them.Figure courtesy of Istva´n Albert.TABLE II.The scaling exponents characterizing the degree distribution of several scale-free networks,for which P (k )follows apower law (2).We indicate the size of the network,its average degree ͗k ͘,and the cutoff ␬for the power-law scaling.For directed networks we list separately the indegree (␥in )and outdegree (␥out )exponents,while for the undirected networks,marked with an asterisk (*),these values are identical.The columns l real ,l rand ,and l pow compare the average path lengths of real networks with power-law degree distribution and the predictions of random-graph theory (17)and of Newman,Strogatz,and Watts (2001)[also see Eq.(63)above],as discussed in Sec.V .The numbers in the last column are keyed to the symbols in Figs.8and workSize͗k ͘␬␥out ␥in lreallrandlpowReference Nr.WWW 325729 4.51900 2.45 2.111.28.32 4.77Albert,Jeong,and Baraba´si 19991WWW 4ϫ1077 2.38 2.1Kumar et al.,19992WWW 2ϫ1087.54000 2.72 2.1168.857.61Broder et al.,20003WWW,site 260000 1.94Huberman and Adamic,20004Internet,domain *3015–4389 3.42–3.7630–40 2.1–2.2 2.1–2.246.3 5.2Faloutsos,19995Internet,router *3888 2.5730 2.48 2.4812.158.757.67Faloutsos,19996Internet,router *150000 2.6660 2.4 2.41112.87.47Govindan,20007Movie actors *21225028.78900 2.3 2.3 4.54 3.65 4.01Baraba´si and Albert,19998Co-authors,SPIRES *566271731100 1.2 1.24 2.12 1.95Newman,2001b9Co-authors,neuro.*20929311.54400 2.1 2.16 5.01 3.86Baraba´si et al.,200110Co-authors,math.*70975 3.9120 2.5 2.59.58.2 6.53Baraba´si et al.,200111Sexual contacts *2810 3.4 3.4Liljeros et al.,200112Metabolic,E.coli 7787.4110 2.2 2.2 3.2 3.32 2.89Jeong et al.,200013Protein,S.cerev.*1870 2.39 2.4 2.4Jeong,Mason,et al.,200114Ythan estuary *1348.735 1.05 1.052.43 2.26 1.71Montoya and Sole´,200014Silwood Park *154 4.7527 1.13 1.13 3.43.232Montoya and Sole´,200016Citation 7833398.573Redner,199817Phone call 53ϫ1063.16 2.1 2.1Aiello et al.,200018Words,co-occurrence *46090270.13 2.7 2.7Ferrer i Cancho and Sole´,200119Words,synonyms *2231113.48 2.8 2.8Yook et al.,2001b20FIG.2.Degree distribution of the World Wide Web from twodifferent measurements:ᮀ,the 325729-node sample of Albert et al.(1999);᭺,the measurements of over 200million pages by Broder et al.(2000);(a)degree distribution of the outgoing edges;(b)degree distribution of the incoming edges.The data have been binned logarithmically to reduce noise.Courtesy of Altavista and Andrew Tomkins.The authors wish to thank Luis Amaral for correcting a mistake in a previous version of this ﬁgure (see Mossa et al.,2001).51R.Albert and A.-L.Baraba´si:Statistical mechanics of complex networks Rev.Mod.Phys.,Vol.74,No.1,January 2002domain,composed of hundreds of routers and comput-ers,is represented by a single node,and an edge is drawn between two domains if there is at least one route that connects them.Faloutsos et al.(1999)have studied the Internet at both levels,concluding that in each case the degree distribution follows a power law.The inter-domain topology of the Internet,captured at three dif-ferent dates between 1997and the end of 1998,resultedin degree exponents between ␥I as ϭ2.15and ␥I asϭ2.2.The 1995survey of Internet topology at the router level,containing 3888nodes,found ␥I rϭ2.48(Faloutsos et al.,1999).Recently Govindan and Tangmunarunkit (2000)mapped the connectivity of nearly 150000router inter-faces and nearly 200000router adjacencies,conﬁrmingthe power-law scaling with ␥I rӍ2.3[see Fig.3(a)].The Internet as a network does display clustering and small path length as well.Yook et al.(2001a)and Pastor-Satorras et al.(2001),studying the Internet at the do-main level between 1997and 1999,found that its clus-tering coefﬁcient ranged between 0.18and 0.3,to be compared with C rand Ӎ0.001for random networks with similar parameters.The average path length of the In-ternet at the domain level ranged between 3.70and 3.77(Pastor-Satorras et al.,2001;Yook et al.2001a)and at the router level it was around 9(Yook et al.,2001a),indicating its small-world character.C.Movie actor collaboration networkA much-studied database is the movie actor collabo-ration network,based on the Internet Movie Database,which contains all movies and their casts since the 1890s.In this network the nodes are the actors,and two nodes have a common edge if the corresponding actors have acted in a movie together.This is a continuously expand-ing network,with 225226nodes in 1998(Watts and Stro-gatz,1998),which grew to 449913nodes by May 2000(Newman,Strogatz,and Watts,2000).The average path length of the actor network is close to that of a random graph with the same size and average degree,3.65com-pared with 2.9,but its clustering coefﬁcient is more than 100times higher than a random graph (Watts and Stro-gatz,1998).The degree distribution of the movie actor network has a power-law tail for large k [see Fig.3(b)],following P (k )ϳk Ϫ␥actor ,where ␥actor ϭ2.3Ϯ0.1(Bara-ba´si and Albert,1999;Albert and Baraba ´si,2000;Ama-ral et al.,2000).D.Science collaboration graphA collaboration network similar to that of the movie actors can be constructed for scientists,where the nodes are the scientists and two nodes are connected if the two scientists have written an article together.To uncover the topology of this complex graph,Newman (2001a,2001b,2001c)studied four databases spanning physics,biomedical research,high-energy physics,and computer science over a ﬁve-year window (1995–1999).All these networks show a small average path length but a high clustering coefﬁcient,as summarized in Table I.The de-gree distribution of the collaboration network of high-energy physicists is an almost perfect power law with an exponent of 1.2[Fig.3(c)],while the other databases display power laws with a larger exponent in the tail.Baraba´si et al.(2001)investigated the collaboration graph of mathematicians and neuroscientists publishing between 1991and 1998.The average path length of these networks is around l math ϭ9.5and l nsci ϭ6,their clustering coefﬁcient being C math ϭ0.59and C nsci ϭ0.76.The degree distributions of these collaboration networks are consistent with power laws with degree ex-ponents 2.1and 2.5,respectively [see Fig.3(d)].E.The web of human sexual contactsMany sexually transmitted diseases,including AIDS,spread on a network of sexual relationships.Liljeros et al.(2001)have studied the web constructed from the sexual relations of 2810individuals,based on an exten-sive survey conducted in Sweden in 1996.Since the edges in this network are relatively short lived,they ana-lyzed the distribution of partners over a single year,ob-taining for both females and males a power-law degree distribution with an exponent ␥f ϭ3.5Ϯ0.2and ␥m ϭ3.3Ϯ0.2,respectively.F .Cellular networksJeong et al.(2000)studied the metabolism of 43or-ganisms representing all three domains of life,recon-structing them in networks in which the nodes aretheFIG.3.The degree distribution of several real networks:(a)Internet at the router level.Data courtesy of Ramesh Govin-dan;(b)movie actor collaboration network.After Baraba´si and Albert 1999.Note that if TV series are included as well,which aggregate a large number of actors,an exponential cut-off emerges for large k (Amaral et al.,2000);(c)co-authorship network of high-energy physicists.After Newman (2001a,2001b);(d)co-authorship network of neuroscientists.AfterBaraba´si et al.(2001).52R.Albert and A.-L.Baraba´si:Statistical mechanics of complex networks Rev.Mod.Phys.,Vol.74,No.1,January 2002。

《金融学（第二版）》讲义大纲及课后习题答案详解十二章

《⾦融学（第⼆版）》讲义⼤纲及课后习题答案详解⼗⼆章CHAPTER 12CHOOSING AN INVESTMENT PORTFOLIOObjectivesTo understand the process of personal investing in theory and in practice.To build a quantitative model of the tradeoff between risk and reward.Outline12.1 The Process of Personal Portfolio Selection12.2 The Trade-off between Expected Return and Risk12.3 Efficient Diversification with Many Risky AssetsSummaryThere is no single portfolio selection strategy that is best for all people.Stage in the life cycle is an imp ortant determinant of the optimal composition of a person’s optimal portfolio of assets and liabilities.Time horizons are important in portfolio selection. We distinguish among three time horizons: the planning horizon, the decision horizon, and the trading horizon.In making portfolio selection decisions, people can in general achieve a higher expected rate of return only by exposing themselves to greater risk.One can sometimes reduce risk without lowering expected return by diversifying more completely either withina given asset class or across asset classes.The power of diversification to reduce the riskiness of an investor’s portfolio depends on the correlations among the assets that make up the portfolio. In practice, the vast majority of assets are positively correlated with each other because they are all affected by common economic factors. Consequently, one’s ability to reduce risk through diversification among risky assets without lowering expected return is limited.Although in principle people have thousands of assets to choose from, in practice they make their choices from a menu of a few final products offered by financial intermediaries such as bank accounts, stock and bond mutual funds, and real estate. In designing and producing the menu of assets to offer to their customers theseintermediaries make use of the latest advances in financial technology.Solutions to Problems at End of Chapter1. Suppose that your 58-year-old father works for the Ruffy Stuffed Toy Company and has contributed regularly to his company-matched savings plan for the past 15 years. Ruffy contributes $0.50 for every $1.00 your father puts into the savings plan, up to the first 6% of his salary. Participants in the savings plan can allocate their contributions among four different investment choices: a fixed-income bond fund, a “blend” option that invests in large companies, small companies, and the fixed-income bond fund, a growth-income mutual fund whose investments do not include other toy companies, and a fund whose sole investment is stock in the Ruffy Stuffed Toy Company. Over Thanksgiving vacation, Dad realizes that you have been majoring in finance and decides to reap some early returns on that tuition money he’s been investing in your education. He shows you the most recent quarterly statement for his savings plan, and you see that 98% of its current value is in the fourth investment option, that of the Ruffy Company stock..a.Assume that your Dad is a typical risk-averse person who is considering retirement in five years. Whenyou ask him why he has made the allocation in this way, he responds that the company stock has continually performed quite well, except for a few declines that were caused by problems in a division that the company has long since sold off. Inaddition, he says, many of his friends at work have done the same. What advice would you give your dad about adjustments to his plan allocations? Why?b.If you consider the fact that your dad works for Ruffy in addition to his 98% allocation to the Ruffy stockfund, does this make his situation more risky, less risky, or does it make no difference? Why? SOLUTION:a.Dad has exposed himself to risk by concentrating almost all of his plan money in the Ruffy Stock fund. This is analogous to taking 100% of the money a family has put aside for investment and investing it in a single stock.First, Dad needs to be shown that just because the company stock has continually performed quite well is no guarantee that it will do so indefinitely. The company may have sold off the divisions which produced price declines in the past, but future problems are unpredictable, and so is the movement of the stock price. “Past performance is no guarantee of future results” is the lesson.Second, Dad needs to hear about diversification. He needs to be counseled that he can reduce his risk by allocating his money among several of the options available to him. Indeed, he can reduce his risk considerably merely by moving all of his money into the “blend” fund because it is diversifi ed by design: it has a fixed-income component, a large companies component, and a small companies component. Diversification isachieved not only via the three differing objectives of these components, but also via the numerous stocks that comprise each of the three components.Finally, Dad’s age and his retirement plans need to be considered. People nearing retirement age typically begin to shift the value of their portfolios into safer investments. “Safer” normally connotes less variability, so that the risk of a large decline in the value of a portfolio is reduced. This decline could come at any time, and it would be very unfortunate if it were to happen the day before Dad retires. In this example, the safest option would be the fixed-income bond fund because of its diversified composition and interest-bearing design, but there is still risk exposure to inflation and the level of interest rates. Note that the tax-deferred nature of the savings plan encourages allocation to something that produces interest or dividends. As it stands now, Dad is very exposed to a large decline in the value of his savings plan because it is dependent on the value of one stock.Individual equities over time have proven to produce the most variable of returns, so Dad should definitely move some, probably at least half, of his money out of the Ruffy stock fund. In fact, a good recommendation given his retirement horizon of five years would be to re-align the portfolio so that it has 50% in the fixed- income fund and the remaining 50% split between the Ruffy stock fund (since Dad insists) and the “blend” fund.Or, maybe 40% fixed-income, 25% Ruffy, 15% growth-income fund, and 20% “blend” fund. This latterallocation has the advantage of introducing another income-producing component that can be shielded by the tax-deferred status of the plan.b.The fact that Dad is employed by the Ruffy Company makes his situation more risky. Let’s say that the companyhits a period of slowed business activities. If the stock price declines, so will th e value of Dad’s savings plan. If the company encounters enough trouble, it may consider layoffs. Dad’s job may be in jeopardy. At the same time that his savings plan may be declining in value, Dad may also need to look for a job or go onunemployment. Thus, Dad is exposed on two fronts to the same risk. He has invested both his human capital and his wealth almost exclusively in one company.2. Refer to Table 12.1.a.Perform the calculations to verify that the expected returns of each of the portfolios (F, G, H, J, S) in thetable (column 4) are correct.b.Do the same for the standard deviations in column 5 of the table.c.Assume that you have $1million to invest. Allocate the money as indicated in the table for each of the fiveportfolios and calculate the expected dollar return of each of the portfolios.d.Which of the portfolios would someone who is extremely risk tolerant be most likely to select? SOLUTION:d.An extremely risk tolerant person would select portfolio S, which has the largest standard deviation but also thelargest expected return.3. A mutual fund company offers a safe money market fund whose current rate is4.50% (.045). The same company also offers an equity fund with an aggressive growth objective which historically has exhibited an expected return of 20% (.20) and a standard deviation of .25.a.Derive the equation for the risk-reward trade-off line.b.How much extra expected return would be available to an investor for each unit of extra risk that shebears?c.What allocation should be placed in the money market fund if an investor desires an expected return of15% (.15)?SOLUTION:a.E[r] = .045 + .62b.0.62c.32.3% [.15 = w*(.045) + (1-w)*(.020) ]4. If the risk-reward trade-off line for a riskless asset and a risky asset results in a negative slope, what does that imply about the risky asset vis-a-vis the riskless asset?SOLUTION:A trade-off line wit h a negative slope indicates that the investor is “rewarded” with less expected return for taking on additional risk via allocation to the risky asset.5. Suppose that you have the opportunity to buy stock in AT&T and Microsoft.a.stocks is 0? .5? 1? -1? What do you notice about the change in the allocations between AT&T andMicrosoft as their correlation moves from -1 to 0? to .5? to +1? Why might this be?b.What is the variance of each of the minimum-variance portfolios in part a?c.What is the optimal combination of these two securities in a portfolio for each value of the correlation,assuming the existence of a money market fund that currently pays 4.5% (.045)? Do you notice any relation between these weights and the weights for the minimum variance portfolios?d.What is the variance of each of the optimal portfolios?e.What is the expected return of each of the optimal portfolios?f.Derive the risk-reward trade-off line for the optimal portfolio when the correlation is .5. How much extraexpected return can you anticipate if you take on an extra unit of risk?SOLUTION:a.Minimum risk portfolios if correlation is:-1: 62.5% AT&T, 37.5% Microsoft0: 73.5% AT&T, 26.5% Microsoft.5: 92.1% AT&T, 7.9% Microsoft1: 250% AT&T, short sell 150% MicrosoftAs the correlation moves from -1 to +1, the allocation to AT&T increases. When two stocks have negativec orrelation, standard deviation can be reduced dramatically by mixing them in a portfolio. It is to the investors’benefit to weight more heavily the stock with the higher expected return since this will produce a high portfolio expected return while the standard deviation of the portfolio is decreased. This is why the highest allocation to Microsoft is observed for a correlation of -1, and the allocation to Microsoft decreases as the correlationbecomes positive and moves to +1. With correlation of +1, the returns of the two stocks will move closely together, so you want to weight most heavily the stock with the lower individual standard deviation.b. Variances of each of the minimum variance portfolios:62.5% AT&T, 37.5% Microsoft Var = 073.5% AT&T, 26.5% Microsoft Var = .016592.1% AT&T, 7.9% Microsoft Var = .0222250% AT&T, short 150% Microsoft Var = 0c. Optimal portfolios if correlation is:-1: 62.5% AT&T, 37.5% Microsoft0: 48.1% AT&T, 51.9% Microsoft.5: 11.4% AT&T, 88.6% Microsoft1: 250% AT&T, short 150% Microsoftd. Variances of the optimal portfolios:62.5% AT&T, 37.5% Microsoft Var = 048.1% AT&T, 51.9% Microsoft Var = .022011.4% AT&T, 88.6% Microsoft Var = .0531250% AT&T, short 150% Microsoft Var = 0e. Expected returns of the optimal portfolios:62.5% AT&T, 37.5% Microsoft E[r] = 14.13%48.1% AT&T, 51.9% Microsoft E[r] = 15.71%11.4% AT&T, 88.6% Microsoft E[r] = 19.75%250% AT&T, short 150% Microsoft E[r] = -6.5%f.Risk-reward trade-off line for optimal portfolio with correlation = .5:E[r] = .045 + .66/doc/31dbf23b580216fc700afd59.html ing the optimal portfolio of AT&T and Microsoft stock when the correlation of their price movements is 0.5, along with the results in part f of question 12-5, determine:a.the expected return and standard deviation of a portfolio which invests 100% in a money market fundreturning a current rate of 4.5%. Where is this point on the risk-reward trade-off line?b.the expected return and standard deviation of a portfolio which invests 90% in the money market fundand 10% in the portfolio of AT&T and Microsoft stock.c.the expected return and standard deviation of a portfolio which invests 25% in the money market fundand 75% in the portfolio of AT&T and Microsoft stock.d.the expected return and standard deviation of a portfolio which invests 0% in the money market fundand 100% in the portfolio of AT&T and Microsoft stock. What point is this?SOLUTION:a.E[r] = 4.5%, standard deviation = 0. This point is the intercept of the y (expected return) axis by the risk-rewardtrade-off line.b.E[r] = 6.03%, standard deviation = .0231c.E[r] = 15.9%, standard deviation = .173d.E[r] = 19.75%, standard deviation = .2306. This point is the tangency between the risk-reward line from 12-5part f and the risky asset risk-reward curve (frontier) for AT&T and Microsoft.7. Again using the optimal portfolio of AT&T and Microsoft stock when the correlation of their price movements is 0.5, take $ 10,000 and determine the allocations among the riskless asset, AT&T stock, and Microsoft stock for:a. a portfolio which invests 75% in a money market fund and 25% in the portfolio of AT&T and Microsoftstock. What is this portfolio’s expected return?b. a portfolio which invests 25% in a money market fund and 75% in the portfolio of AT&T and Microsoftstock. What is this portfolio’s expect ed return?c. a portfolio which invests nothing in a money market fund and 100% in the portfolio of AT&T andMicrosoft stock. What is this portfolio’s expected return?SOLUTION:a.$7,500 in the money-market fund, $285 in AT&T (11.4% of $2500), $2215 in Microsoft. E[r] = 8.31%, $831.b.$2,500 in the money-market fund, $855 in AT&T (11.4% of $7500), $6645 in Microsoft. E[r] = 15.94%, $1,594.c.$1140 in AT&T, $8860 in Microsoft. E[r] = 19.75%, $1,975.8. What strategy is implied by moving further out to the right on a risk-reward trade-off line beyond the tangency point between the line and the risky asset risk-reward curve? What type of an investor would be most likely to embark on this strategy? Why?SOLUTION:This strategy calls for borrowing additional funds and investing them in the optimal portfolio of AT&T and Microsoft stock. A risk-tolerant, aggressive investor would embark on this strategy. This person would be assuming the risk of the stock portfolio with no risk-free component; the money at risk is not onl y from this person’s own wealth but also represents a sum that isowed to some creditor (such as a margin account extended by the investor’s broker).9. Determine the correlation between price movements of stock A and B using the forecasts of their rate of return and the assessments of the possible states of the world in the following table. The standard deviations for stock A and stock B are0.065 and 0.1392, respectively. Before doing the calculation, form an expectation of whether that correlation will be closer to1 or -1 by merely inspecting the numbers.SOLUTION:Expectation: correlation will be closer to +1.E[r A] = .05*(-.02) + .15*(-.01) + .60*(.15) + .20*(.15) = .1175, or, 11.75%E[r B] = .05*(-.20) + .15*(-.10) + .60*(.15) + .20*(.30) = .1250, or, 12.50%Covariance = .05*(-.02-.1175)*(-.20-.125) + .15*(-.01-.1175)*(-.10-.125) +.60*(.15-.1175)*(.15-.125) + .20*(.15-.1175)*(.30-.125) =.008163Correlation = .008163/(.065)*(.1392) = .90210.Analyze the “expert’s” answers to the following questions:a.Question:I have approx. 1/3 of my investments in stocks, and the rest in a money market. What do you suggestas a somewhat “safer” place to invest another 1/3? I like to keep 1/3 accessible for emergencies.Expert’s answer:Well, you could try 1 or 2 year Treasury bonds. You’d get a little bit more yie ld with no risk.b.Question:Where would you invest if you were to start today?Expert’s answer:That depends on your age and short-term goals. If you are very young – say under 40 –and don’tneed the money you’re investing for a home or college tuition or such, you would put it in a stockfund. Even if the market tanks, you have time to recoup. And, so far, nothing has beaten stocks overa period of 10 years or more. But if you are going to need money fairly soon, for a home or for yourretirement, you need to play it safer.SOLUTION:a.You are not getting a little bit more yield with no risk. The real value of the bond payoff is subject to inflationrisk. In addition, if you ever need to sell the Treasury bonds before expiration, you are subject to the fluctuation of selling price caused by interest risk.b.The expert is right in pointing out that your investment decision depends on your age and short-term goals. In addition, the investment decision also depends on other characteristics of the investor, such as the special character of the labor income (whether it is highly correlated with the stock market or not), and risk tolerance.Also, the fact that over any period of 10 years or more the stock beats everything else cannot be used to predict the future.。

tpo53三篇托福阅读TOEFL原文译文题目答案译文背景知识

tpo53三篇托福阅读TOEFL原文译文题目答案译文背景知识阅读-1 (2)原文 (2)译文 (5)题目 (8)答案 (16)背景知识 (18)阅读-2 (21)原文 (21)译文 (24)题目 (27)答案 (34)背景知识 (36)阅读-1原文Evidence of the Earliest Writing①Although literacy appeared independently in several parts of the prehistoric world, the earliest evidence of writing is the cuneiform Sumerian script on the clay tablets of ancient Mesopotamia, which, archaeological detective work has revealed, had its origins in the accounting practices of commercial activity. Researchers demonstrated that preliterate people, to keep track of the goods they produced and exchanged, created a system of accounting using clay tokens as symbolic representations of their products. Over many thousands of years, the symbols evolved through several stages of abstraction until they became wedge-shaped (cuneiform) signs on clay tablets, recognizable as writing.②The original tokens (circa 8500 B.C.E.) were three-dimensional solid shapes—tiny spheres, cones, disks, and cylinders. A debt of six units of grain and eight head of livestock, for example might have been represented by six conical and eight cylindrical tokens. To keep batches of tokens together, an innovation was introduced (circa 3250 B. C. E.) whereby they were sealed inside clay envelopes that could be brokenopen and counted when it came time for a debt to be repaid. But because the contents of the envelopes could easily be forgotten, two-dimensional representations of the three-dimensional tokens were impressed into the surface of the envelopes before they were sealed. Eventually, having two sets of equivalent symbols—the internal tokens and external markings—came to seem redundant, so the tokens were eliminated (circa 3250-3100 B.C.E.), and only solid clay tablets with two-dimensional symbols were retained. Over time, the symbols became more numerous, varied, and abstract and came to represent more than trade commodities, evolving eventually into cuneiform writing.③The evolution of the symbolism is reflected in the archaeological record first of all by the increasing complexity of the tokens themselves. The earliest tokens, dating from about 10,000 to 6,000 years ago, were of only the simplest geometric shapes. But about 3500 B.C.E., more complex tokens came into common usage, including many naturalistic forms shaped like miniature tools, furniture, fruit, and humans. The earlier, plain tokens were counters for agricultural products, whereas the complex ones stood for finished products, such as bread, oil, perfume, wool, and rope, and for items produced in workshops, such as metal, bracelets, types of cloth, garments, mats, pieces of furniture, tools, and a variety of stone and pottery vessels. The signs marked onclay tablets likewise evolved from simple wedges, circles, ovals, and triangles based on the plain tokens to pictographs derived from the complex tokens.④Before this evidence came to light, the inventors of writing were assumed by researchers to have been an intellectual elite. Some, for example, hypothesized that writing emerged when members of the priestly caste agreed among themselves on written signs. But the association of the plain tokens with the first farmers and of the complex tokens with the first artisans—and the fact that the token-and-envelope accounting system invariably represented only small-scale transactions—testifies to the relatively modest social status of the creators of writing.⑤And not only of literacy, but numeracy (the representation of quantitative concepts) as well. The evidence of the tokens provides further confirmation that mathematics originated in people’s desire to keep records of flocks and other goods. Another immensely significant step occurred around 3100 B.C.E., when Sumerian accountants extended the token-based signs to include the first real numerals. Previously, units of grain had been represented by direct one-to-one correspondence―by repeating the token or symbol for a unit of grain the required number of times. The accountants, however, devisednumeral signs distinct from commodity signs, so that eighteen units of grain could be indicated by preceding a single grain symbol with a symbol denoting “18.”Their invention of abstract numerals and abstract counting was one of the most revolutionary advances in the history of mathematics.⑥What was the social status of the anonymous accountants who produced this breakthrough? The immense volume of clay tablets unearthed in the ruins of the Sumerian temples where the accounts were kept suggests a social differentiation within the scribal class, with a virtual army of lower-ranking tabulators performing the monotonous job of tallying commodities. We can only speculate as to how high or low the inventors of true numerals were in the scribal hierarchy, but it stands to reason that this laborsaving innovation would have been the brainchild of the lower-ranking types whose drudgery is eased.译文最早文字的证据①虽然读写能力是在史前世界的几个地方分别出现的，但书写的最早证据是古代美索不达米亚泥板上的苏美尔楔形文字，根据考古探查工作揭示，它起源于商业活动的会计实践。

一篇关于化学的英文文献

一篇关于化学的英文文献Title: Recent Advances in Green Chemistry: Innovations and ApplicationsAbstract:Green chemistry, also known as sustainable chemistry, emphasizes the development of environmentally friendly processes and products that minimize toxicity and waste generation. This article provides an overview of recent advances in green chemistry, highlighting innovative approaches and their applications in various fields.Introduction:Traditional chemical processes often result in the release of hazardous substances, leading to pollution and ecological damage. Green chemistry aims to address these issues by utilizing renewable materials, reducing waste and energy consumption, and promoting the development of safer and sustainable alternatives.1. Sustainable Synthesis:Recent innovations in green chemistry have revolutionized the synthesis of chemicals and pharmaceuticals. One example is the use of renewable feedstocks, such as biomass and bio-based materials, as alternatives to fossil fuels. These sustainable sources can be converted into valuable products through efficient and environmentally benign processes, reducing carbon dioxide emissions and dependence on finite resources.2. Catalysis and Reaction Optimization:Catalysis plays a crucial role in green chemistry, enabling moreefficient and selective reactions with lower energy requirements. Researchers have explored the use of catalysts derived from earth-abundant metals, enzymes, and even biomimetic catalysts inspired by natural systems. Additionally, reaction optimization techniques, like flow chemistry and microwave-assisted synthesis, have been developed to improve reaction rates and minimize waste generation.3. Waste Minimization and Reutilization:Green chemistry emphasizes the reduction, reuse, and recycling of waste materials. Advances in this area include solvent replacement with greener alternatives, such as ionic liquids or supercritical fluids, which can be recovered and recycled. Furthermore, innovative approaches, such as bioremediation and enzymatic degradation, have been utilized to transform hazardous waste into harmless substances.4. Energy Efficiency and Renewable Energy Sources:Efficient energy utilization is central to green chemistry. Methods like microwave heating and ultrasonic irradiation have been employed to enhance reaction rates and reduce energy consumption. Moreover, the integration of renewable energy sources, such as solar and wind energy, into chemical processes has gained significant attention, offering sustainable solutions for the industry.5. Designing Safer Chemicals:Green chemistry promotes the development and use of less toxic and bio-degradable chemicals. Molecular design strategies, like quantitative structure-activity relationships (QSAR) and computer-aided molecular design (CAMD), enable the creation of safer and more sustainable compounds, reducing the associated risks to human health and the environment.Conclusion:Recent advancements in green chemistry have paved the way for a more sustainable and environmentally conscious chemical industry. By integrating innovative technologies and strategies, researchers are continuously developing greener synthesis routes, minimizing waste generation, and promoting the use of safer chemicals. These advancements contribute to the overall goal of achieving a more sustainable future.。

猪源性成分检测方法验证分析

猪源性成分检测方法验证分析石　旺，杨　冰，陈帅虎，何雅媛，何　浩*（湖南省产商品质量检验研究院，湖南长沙 410007）摘　要：目的：建立基于基因扩增方法检测食品中动物源性成分的验证方法，提升实验室人员基因扩增的检测能力。

方法：依据SB/T 10923—2012，分析猪源性成分的扩增效率、基质效应参数和实验室间能力比对结果。

结果：猪源性成分扩增效率E＞90%，检出限可以达到0.1 ng，实验室间能力比对样品3116A和3116B获得了满意结果。

结论：实验室条件满足SB/T 10923—2012中猪源性成分扩增要求。

关键词：基因扩增；方法验证；扩增效率；基质效应Validation and Analysis of Detection Methods for Pig DerivedIngredientsSHI Wang, YANG Bing, CHEN Shuaihu, HE Yayuan, HE Hao*(Hunan Provincial Institute of Product and Goods Quality Inspection, Changsha 410007, China) Abstract: Objective: To establish a validation method based on gene amplification for detecting animal derived components in food, and to enhance the detection ability of laboratory personnel in gene amplification. Method: According to SB/T 10923—2012, analyze the amplification eﬃciency, matrix eﬀect parameters, and inter laboratory capability comparison results of pig derived components. Result: The amplification efficiency E of pig derived components was greater than 90%, and the detection limit could reach 0.1 ng. Satisfactory results were obtained in the inter laboratory capability comparison of samples 3116A and 3116B. Conclusion: The laboratory conditions meet the requirements for amplification of pig derived components in SB/T 10923—2012.Keywords: gene amplification; method verification; amplification eﬃciency; matrix eﬀect近年来，因食品假冒伪劣、真伪难辨而产生的食品安全问题越来越多[1-2]。

原子量的英文

原子量的英文The Atomic Weight: A Fundamental Concept in ChemistryAtomic weight, also known as atomic mass, is a fundamental concept in chemistry that describes the average mass of an atom of a particular element. It is a crucial parameter in understanding the properties and behavior of elements, as well as in various chemical calculations and processes. The atomic weight of an element is typically expressed in atomic mass units (u) or grams per mole(g/mol), and it represents the average mass of all the naturally occurring isotopes of that element.The concept of atomic weight has its origins in the early days of chemistry, when scientists were attempting to understand the nature of matter and the composition of elements. In the 19th century, chemists such as John Dalton and Jöns Jacob Berzelius made significant contributions to the development of the atomic theory and the determination of atomic weights.Dalton's atomic theory proposed that all matter is composed of tiny, indivisible particles called atoms, and that each element has a unique type of atom. Berzelius, on the other hand, introduced the concept ofatomic weights, which he determined through careful experimentation and analysis of chemical reactions.The determination of atomic weights is a complex process that involves several factors, including the natural abundance of the element's isotopes and their respective masses. The atomic weight of an element is typically calculated as the weighted average of the masses of its naturally occurring isotopes, taking into account their relative abundances.For example, the atomic weight of carbon is 12.01 u, which is the weighted average of the masses of the three naturally occurring isotopes of carbon: carbon-12 (12.00 u), carbon-13 (13.00 u), and carbon-14 (14.00 u). The relative abundance of these isotopes in nature determines the overall atomic weight of carbon.The importance of atomic weight in chemistry cannot be overstated. It is a fundamental property that is used in a wide range of applications, from stoichiometric calculations in chemical reactions to the determination of molecular masses and the study of isotopic variations in natural and synthetic compounds.One of the primary uses of atomic weight is in the calculation of molar mass, which is the mass of one mole of a substance. The molar mass of a compound is calculated by summing the atomic weights ofthe constituent elements, multiplied by the number of atoms of each element in the compound's formula. This information is crucial for understanding the quantities of reactants and products in chemical reactions, as well as for the quantitative analysis of chemical compositions.Atomic weight also plays a crucial role in the study of isotopes, which are atoms of the same element with different numbers of neutrons in their nuclei. Isotopes can have significantly different physical and chemical properties, and their study has led to important advances in fields such as nuclear chemistry, environmental science, and medical diagnostics.In addition to its scientific applications, atomic weight has also been the subject of ongoing research and debate. The International Union of Pure and Applied Chemistry (IUPAC) is responsible for establishing the standard values of atomic weights, which are periodically updated to reflect the latest scientific findings and technological advancements.The determination of atomic weights has become increasingly complex in recent years, as advances in mass spectrometry and other analytical techniques have allowed for the precise measurement of isotopic abundances. This has led to the recognition of the fact that the atomic weight of an element can vary depending on the sourceand composition of the sample being analyzed.In conclusion, the concept of atomic weight is a fundamental and indispensable aspect of chemistry, with far-reaching implications in various scientific fields. Its accurate determination and understanding are essential for the advancement of our knowledge of the natural world and the development of new technologies and applications. As our understanding of the atomic world continues to evolve, the study of atomic weight will undoubtedly remain a crucial area of research and exploration.。

choice of words

[例句在现代飞机设计中,这个问题的确是例句] 在现代飞机设计中, 例句改进它们的机动飞行性能的一个重要问题. 改进它们的机动飞行性能的一个重要问题. [ 译文 ] The problem is indeed an important one in modern aircraft design to improve their maneuver performance. .
(5) 省略不译的情况 [例句随着飞机速度和性能的不断提高 , 尤其例句] 随着飞机速度和性能的不断提高, 例句是现代跨超声速飞机大量投入使用,气动弹性问是现代跨超声速飞机大量投入使用, 题愈来愈突出. 题愈来愈突出. [ 译文 ] With the increasing speed and performance of an aircraft, especially the , modern transonic and supersonic airplanes burst into use, aeroelasticity has become , more important than before. .
[例句目前有迹象表明,要准确地定量记例句] 目前有迹象表明, 例句录滚转—偏航俯仰(roil-yaw-pitch)组合偏航—俯仰录滚转偏航俯仰组合运动的数据是一项不可能完成的任务, 运动的数据是一项不可能完成的任务,利用简化模型进行定性测量, 解决' 简化模型进行定性测量 , 解决 ' 行 ' 还是不行'这样的问题,看起来还是有前途的. '不行'这样的问题,看起来还是有前途的. [译文 Present indications are that the 译文] 译文 accurate quantitative recording of a combined roil-yaw-pitch motion is a formidable task but qualitative tests using simplified models on a "go" and "no go" basis look promising

国际著名的cabi收录《分子植物育种》英文版

国际著名的cabi收录《分子植物育种》英文版Molecular Plant Breeding is an international, peer-reviewed journal providing rapid publication of articles inall aspects of plant genetics, including genetics and genomics, quantitative and population genetics, functionaland evolutionary genomics, epigenetics, metabolic engineering, physiological and molecular biology, developmental genetics, agroforestry, biotechnology, and applied biochemistry.The journal publishes high-quality original research, reviews, and opinion papers on the most significant advancesin all areas of contemporary plant genetics and theassociated technologies. It serves as an invaluable source of current and emerging concepts for the internationalscientific community.Since its establishment in 1994, Molecular Plant Breeding has maintained a high reputation for its excellence ineditorial selection and sound peer review. It is indexed by Cabi, which includes the Web of Science; Scopus; Biological Abstracts; and AGRICOLA. The journal is also included in the Emerging Sources Citation Index (ESCI) from Clarivate Analytics.Molecular Plant Breeding is published monthly, with the exception of one combined issue in the summer. Each issue contains reviews of recent advances in the field, selected refereed research articles, commissioned book reviews, and welcomes submission of original research articles and review articles relevant to its scope.Molecular Plant Breeding is an essential source of information for plant breeders, geneticists, agronomists, biochemists, bioinformaticians, plant pathologists, and other scientists involved in the study of plant genetics andrelated technologies. The journal seeks to contribute to the advancement of science and the benefit of society. It is becoming increasingly recognized as an important source of research and continues to be a leader in the field.。

血清CEA联合胸腔积液CEA对恶性胸腔积液的诊断意义

血清CEA联合胸腔积液CEA对恶性胸腔积液的诊断意义王敏;江子丰;方浩徽【摘要】目的探讨联合检测血清癌胚抗原(CEA)、胸腔积液CEA对恶性胸腔积液诊断的临床意义.方法收集了2010年10月至2015年1月,安徽省胸科医院和安徽医科大学第一附属医院349例胸腔积液患者为研究对象,其中有155例肺癌伴胸膜转移患者(简称"恶性胸腔积液"),194例胸腔积液为良性病变包括结核性胸膜炎、肺炎旁胸腔积液和漏出性胸腔积液患者(简称"良性胸腔积液").抽取患者外周血及胸腔积液,并采用电化学发光免疫分析法进行CEA定量分析.结果恶性胸腔积液患者血清CEA和胸腔积液CEA均显著高于良性胸腔积液患者,ROC结果提示血清CEA 联合胸腔积液CEA对于恶性胸腔积液的诊断敏感性提高至0.858,且AUC值升至0.946,具有显著性差异.结论血清CEA联合胸腔积液CEA可以显著提高对恶性胸腔积液的诊断意义.%Objective To investigate the clinical significance of combined detection of serum carcinoembry-onic antigen ( CEA) and pleural effusion CEA in diagnosis of malignant pleural effusion. Methods From January 2010 to January 2015, 349 inpatients with pleural effusion in the Anhui Chest Hospital and the First Affiliated Hospi-tal of Anhui Medical University were involved in present study, including 155 cases of lung cancer complicated with pleural metastasis ( referred to as"malignant pleural effusion", the same below) , 194 cases of benign diseases inclu-ding tuberculosis pleurisy, parapneumonic pleural effusion and leakage of pleural effusion ( referred to as "benign pleural effusion", the same below) . The peripheral blood and pleural effusion were extracted, and CEA quantitative analysis was performed by using the method ofelectrochemical luminescence immunoassay. Results The serum CEA and pleural effusion CEA in malignant pleural effusion were significantly higher than those patients with benign pleural effusion. Receiver operating characteristic curve ( ROC) results suggested the combination of serum CEA and pleural effusion CEA could obviously increase the sensitivity to 0. 858 and AUC to 0. 946. Conclusion Serum CEA com-bined with pleural effusion CEA can significantly improve the diagnostic value of malignant pleural effusion.【期刊名称】《临床肺科杂志》【年(卷),期】2017(022)011【总页数】4页(P2068-2071)【关键词】肺癌;恶性胸腔积液;血清癌胚抗原;胸腔积液癌胚抗原【作者】王敏;江子丰;方浩徽【作者单位】230022 安徽合肥,安徽省胸科医院呼吸二科;230022 安徽合肥,安徽医科大学第一附属医院老年呼吸内科;230022 安徽合肥,安徽省胸科医院呼吸二科【正文语种】中文几乎所有的恶性肿瘤都可以转移至胸膜，引起胸腔积液，我们称之为恶性胸腔积液，其中约75%的恶性胸腔积液是由肺癌、乳腺癌或宫颈癌引起[1-2]。

青霉素过敏反应与IL-4Rα Q576R及FcεRIβ E237G基因多态性

青霉素过敏反应与IL-4Rα Q576R及FcεRIβ E237G基因多态性张跃文;乔海灵【摘要】目的探讨青霉素类抗生素过敏反应与IL-4Rα Q576R及FcεRIβ E237G 基因多态性的关系.方法采用ELISA检测了51例过敏患者血清中的IL-4、INF-γ浓度;采用PCR-RFLP检测了51例过敏患者和53例正常对照的IL-4Rα Q576R及FcεRIβ E237G多态性位点的基因型.结果 IL-4RαQ576R位点QQ(AA)基因型患者所占比例在过敏患者中显著高于正常组(P<0.05);位点含G等位基因患者血清中pvo,bpa浓度显著高于EE基因型患者(P<0.05).结论 IL-4Rα Q576R位点可能与青霉素过敏反应有关;FeeRIβ E237G多态性可能与某些青霉素特异性IgE抗体升高有关.【期刊名称】《中国抗生素杂志》【年(卷),期】2010(035)001【总页数】4页(P65-68)【关键词】青霉素;过敏;基因多态性【作者】张跃文;乔海灵【作者单位】郑州大学临床药理研究所,郑州,450052;郑州大学临床药理研究所,郑州,450052【正文语种】中文【中图分类】R978.1~+1青霉素类抗生素临床应用广泛,但其过敏反应发生率居各类药物之首。

IgE高亲和力受体定位于11q13染色体上的免疫球蛋白E高亲合力受体β链(FcεRIβ)基因是变态反应发病的候选基因之一，也是IgE产生的主要调节基因[1]。

在过敏反应中细胞因子如IL-4等细胞因子也起到了重要的作用[2]。

IL-4Rα亚单位在调节IgE产生过程中以及在IL-4和IL-13的信号传导上起到关键的作用。

本次试验主要观察IL-4RαQ576R及FcεRIβ E237G基因多态性位点与青霉素过敏、青霉素特异性抗体以及相关细胞因子之间的相关性。

1 材料与方法1.1 研究对象1.1.1 青霉素过敏组收集青霉素皮试阳性且青霉素特异性抗体阳性患者51例其中男性30例，女性21例。

第八章地下水化学的研究方法

• 地球化学或水文地球化学模型：为地质系统开发的化学模型。
• Speciation: the equilibrium distribution of aqueous species among free ions, ion pairs, and complexes (an integral part of phase distribution, mass transfer, and reactionpath calculation)
• A chemical model, which describes the the behavior of solutes in the water.
这种预测通常基于以下模型： • 一个是描述水流动的水文学模型； • 一个是描述水中溶质行为的化学模型.
Aqueous geochemical modeling began more than 30 years ago as an attempt to apply more quantitative techniques to the interpretation of water-rock interactions.
现场过滤 N2作保护气
4、有机物：棕色玻璃瓶；无机物用硬质塑料瓶；重金属样品分析：加1：1盐酸使pH < 2.0； TOC分析：加H2SO4使pH < 2.0； S同位素分析：加CuCl2灭菌； As形态分析：加0.25M EDTA；微生物分析：冷藏（2-5 0C）；
二、地下水样品的分析
1、IC：主要离子（HCO3-除外）； 2、AFS：As、Hg、Se、Sb等； 3、ICP-AES：金属元素； 4、GC：挥发-半挥发性有机物； 5、LC：不易挥发性有机物； 6、ICP-MS：微量元素；

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294Vol.29No.4 20098Journal of Chinese Society for Corrosion and Protection Aug.2009
Diﬀusion Barrier Coating System and Oxidation
Behavior of Coated Alloys
T.NARITA
(Research Group of Advanced Coatings,Hokkaido University,Kita-13,Nishi-8,Kita-Ku,Sapporo060-8628,Japan)
1Introduction
Research into the formation of Re-based alloys
is in progress in our laboratory to provide a diﬀu-
sion barrier layer between heat-resistant alloys and
Al reservoir layers,which assist in the formation and
maintenance a protective Al2O3scale for long periods.
Coatings with a two-layered structure comprised of in-
ner Re-based alloy layer and outerβ-NiAl layer with
or without Pt addition were successfully formed on
various heat resistant alloys such as Ni-based single-
crystal superalloys,Ni-based heat resistant alloys,Ni-
Mo based alloy,Ni-Cr based alloy,and Fe-based al-
loys.The duplex layer coating proposed is generally
termed a diﬀusion barrier coating system;DBC sys-
tem.
In the present investigation concept of the DBC
system is introduced and oxidation behavior of the
various heat-resistant alloys with the DBC system is
given.Particular attention was paid to the structure
and composition of the coatings before and after the
high temperature oxidation with or without an exter-
nal stress.
2Concept
Figure1shows a schematic cross section of the al-
loys with the DBC system.Diﬀusionﬂux of Al(as one
of examples)across the diﬀusion barrier layer should
be given by a Fick′sﬁrst law,and it is clearly shown
that low chemical diﬀusivity,low solubility,and low
driving force are essential.Particularly,maintaining
of barrier layer thickness is one of key issues for the
DBC system at high temperature and long periods.
4M.Sch¨u tze et al:Recent Advances in the Quantitative Characterisation of (313)
Corresponding author:M.Sch¨u tze,E-mail:gellermann@dechema.de can be described by their own equations,a multilevel
approach was introduced for this diagram where each
level (in Fig.2levels 1to 4are assumed)yields a fur-
ther reﬁnement of the model and a more detailed de-
scription of the quantitative role of each subparame-
ter.
A recent study assessed the quantitative inﬂu-
ence of each of these parameters on the critical strain
to scale failure and revealed that the most critical
parameter is the defect size (pores,ﬁssures,crevices,
microcracks)in the oxide scale [4].The role of the
physical defect size becomes particularly clear if one
looks at the equations in ref.4and the potential
de-
Fig.1The scale failure diagram developed in EPRI-
report FP 686[2]which is based on a critical
oxide scale thickness at which the respective
failure modes occur
31429。