New bosonization scheme for spin systems in any dimension

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precisermanual_Schizosaccharomycespombe说明书

This is a free sample of content from Fission Yeast.Click here for more information on how to buy the book.PrefaceThis manual describes technologies and experimental approaches often used in studies with fissionyeast.In this Preface,we make a far-from-fully inclusive mention of some who have significantlycontributed to these studies,excluding the Editors of course!It has been70years sinceØjvind Winge suggested to a young PhD student,Urs Leupold,that Schizosaccharomyces pombe may be a useful organism for genetic studies and more than60yearssince Murdoch Mitchison picked it as an ideal organism in which to ask the deceptively simple ques-tion,“How does a cell grow between one division and the next?”The prescient,elegant,and metic-ulous work of these forefathers laid the foundations for a fission yeast community that has placedthis excellent model system at the forefront of many areas of fundamental biology.The fusion of the Leupold and Mitchison approaches in pioneering genetic approaches to cell cycle control secured,for fission yeast,an enduring spot in the limelight of cell cycle research.This inspired Mitsuhiro Yanagida to place fission yeast at the forefront of mitosis research,whileAnwar Nasim,Paul Russell,and Paco Antequera showed just how defining fission yeast studiescan be in the arenas of DNA replication,repair,and checkpoints.While this cell-cycle-driven roller coaster was setting off,studies by Richard Egel and Amar Klar of the most fundamental aspect of any genetic fungal system,mating-type switching,uncoveredsome fascinating biology surrounding DNA imprinting and silencing of nonexpressed cassettes.Understanding how cassettes were silenced was informed by the transposition of studies of positioneffect variegation from Drosophila to S.pombe by Robin Allshire and Amar Klar;these laid the foun-dations for S.pombe’s current preeminent position as the best single-cell model in which to addressfundamental questions involving heterochromatin and the molecular basis for epigenetic inheri-tance.A similar rise to fame in the field of sexual differentiation was driven by Masayuki Yamamo-to’s desire to explain why Richard Egel’s mutants failed to initiate meiosis.This ultimately led toYoshinori Watanabe’s seminal use of S.pombe to define the biology that sits at the heart of sexualreproduction:the molecular basis for chromosome partition in meiosis.A further phase of fissionyeast sexual differentiation studies was propelled to fame by Yasushi Hiraoka’s elegant and incisivework on the postreplicative recombination period of“horsetail”movement that Julie Cooper’s stud-ies have so elegantly shown is a gateway to understanding telomeres and global nuclear organization.As with all organisms,genomics technologies heralded a new era in fission yeast research.Not only did the S.pombe genome sequence provide a definitive list of all genes to exclude the“what if another homolog exists?”question,but it opened the way for new and inspired approaches,pioneered by the Ju¨rg Ba¨hler and Nevan Krogan laboratories,to study uncloned genes individuallyand at the genome-wide level.Ever since the assembly of the initial drafts,Val Wood has been anno-tating and interrogating the S.pombe genome to develop what she ensures will remain a dynamic,ever-expanding,and invaluable database:.Genomics work also inspired Nick Rhind toopen the door to comparative studies and molecular interrogation of S.pombe’s cousins S.octospo-rus,S.cryophilus,and S.japonicus.The sequence comparisons alone and the pioneering interroga-tion of S.japonicus by Hironori Niki and Snezhana Oliferenko are already revealing somefascinating biology.Such emphatic demonstrations of the utility of fission yeast are not unique.They stand alongside studies of the cytoskeleton,transcription,and cell wall biogenesis as just some of the areas wherethe malleability of this excellent model organism has been ruthlessly exploited to great gain.Aswe enter an era focused on noncoding RNA and renewed interest in metabolism and fungaldiseases,we hope that more biologists will embrace fission yeast’s endless potential for simple,direct,and incisive experiments in both novel and established fields.xiii © 2016 by Cold Spring Harbor Laboratory Press. All rights reserved.This is a free sample of content from Fission Yeast.Click here for more information on how to buy the book.xiv/PrefaceFission yeast is often described as a“simple eukaryotic model system”;however,nowhere is the complexity of biology exposed more extensively than in this“simple”model.Invariably,the abilityto execute utterly conclusive,fully controlled experiments in fission yeast leads to the inevitable con-clusion that“it is a bit more complicated than we thought”or“Oh...it is precisely the oppositeresult to the one we anticipated.”The greater the complexity,the greater the demand for the defi-nition and malleability that systems such as the fission yeasts have to offer.We firmly believe that thedefinitive nature of experiments in these most malleable of model systems means that the list oflandmark discoveries arising from fission yeast research will continue for many years to come.We hope that this manual will facilitate this exploitation of undiscovered riches.The manual can be divided into two parts.The fundamental technologies that underpin core fission yeast research activity are covered in Chapters1–10,whereas Chapters11–18cover technol-ogies in key areas in which fission yeast is widely exploited.Although space limitations made itimpossible to be as comprehensive as we would have liked,our ambition has been to provideboth a useful resource to facilitate moves into new aspects of fission yeast biology for the experiencedfission yeast laboratory and an easy entry point for newcomers to exploit the bounty fission yeastundoubtedly has to offer.We apologize for omissions but believe certain areas will be covered inthe more dynamic technology review literature,which will undoubtedly surpass sections of thismanual in years to come.We would like to thank the fission yeast community for their support in compiling this manual.We are indebted to the authors for their enthusiasm in embracing the unenviable task of condensingaccounts of their complex fields into such constrained Topic Introduction and Protocol formats.Their attention to detail and engagement made our task as editors a simple one.We are also deeplyindebted to members of the community,too numerous to list,who provided extensive and usefulcomments to guide the evolution of each chapter.Special thanks go to Maria Smit,Maryliz Dick-erson,and Richard Sever at Cold Spring Harbor Laboratory Press,whose positive,enthusiastic,andflexible approach made this manual an easy reality.Iain M.HaganAntony M.CarrAgnes GrallertPaul NurseGeneral Safety and Hazardous Material InformationThis manual should be used by laboratory personnel with experience in laboratory and chemicalsafety or students under the supervision of such trained personnel.The procedures,chemicals,and equipment referenced in this manual are hazardous and can cause serious injury unless per-formed,handled,and used with care and in a manner consistent with safe laboratory practices.Students and researchers using the procedures in this manual do so at their own risk.It is essentialfor your safety that you consult the appropriate Material Safety Data Sheets,the manufacturers’manuals accompanying products,and your institution’s Environmental Health and Safety Office,as well as the General Safety and Hazardous Material Information Appendix,for proper handlingof hazardous materials.Cold Spring Harbor Laboratory makes no representations or warrantieswith respect to the material set forth in this manual and has no liability in connection with theuse of these materials.All registered trademarks,trade names,and brand names mentioned in this book are the prop-erty of the respective owners.Readers should please consult individual manufacturers and otherresources for current and specific product information.Appropriate sources for obtaining safety information and general guidelines for laboratory safety are provided in the General Safety and Hazardous Material Information Appendix.© 2016 by Cold Spring Harbor Laboratory Press. All rights reserved.。

2018年4月雅思阅读模拟题目：药物治疗法

2018年4月雅思阅读模拟题目：药物治疗法 news serviceRoxanne KhamsiNew evidence has linked a commonly prescribed sleep medication with bizarre behaviours, including a case in which a woman painted her front door in her sleep.UK and Australian health agencies have released information about 240 cases of odd occurrences, including sleepwalking, amnesia and hallucinations among people taking the drug zolpidem.While doctors say that zolpidem can offer much-needed relief for people with sleep disorders, they caution that these newly reported cases should prompt a closer look at its possible side effects.Zolpidem, sold under the brand names Ambien, Stilnoct and Stilnox, is widely prescribed to treat insomnia and other disorders such as sleep apnea. Various forms of the drug, made by French pharmaceutical giant Sanofi-Aventis, were prescribed 674,500 times in 2005 in the UK.A newly published report from Australia’s Federal Health Department describes 104 cases of hallucinations and 62 cases of amnesia experienced by people taking zolpidem since marketing of the drug began there in 2000. The health department report also mentioned 16 cases of strange sleepwalking by people taking the medication.Midnight snackIn one of these sleepwalking cases a patient woke with a paintbrush in her hand after painting the front door to her house. Another case involved a woman who gained 23 kilograms over seven months while taking zolpidem. “It was only when she was discovered in front of an open refrigerator while asleep that the problem was resolved,” according to the report.The UK’s Medicines and Healthcare products Regulatory Agency, meanwhile, has recorded 68 cases of adverse reactions to zolpidem from 2001 to 2005.The newly reported cases in the UK and Australia add to a growing list of bizarre sleepwalking episodes linked to the drug inother countries, including reports of people sleep-driving while on the medication. In one case, a transatlantic flight had to be diverted after a passenger caused havoc after taking zolpidem.Hypnotic effectsThere is no biological pathway that has been proven to connect zolpidem with these behaviours. The drug is a benzodiazepine-like hypnotic that promotes deep sleep by interacting with brain receptors for a chemical called gamma-aminobutyric acid. While parts of the brain become less active during deep sleep, the body can still move, making sleepwalking a possibility.The product information for prescribers advises that psychiatric adverse effects, including hallucinations, sleepwalking and nightmares, are more likely in the elderly, and treatment should be stopped if they occur.Patient advocacy groups say they would like government health agencies and drug companies to take a closer look at the possible risks associated with sleep medicines. They stress that strange sleepwalking and sleep-driving behaviours can have risky consequences.“When people do something in which they’re not in full control it’s always a danger,” says Vera Sharav of the New York-based Alliance for Human Research Protection, a US network that advocates responsible and ethical medical research practices.Tried and tested“The more reports that come out about the potential side effects of the drug, the more research needs to be done to understand if these are real side effects,” says sleep researcher Kenneth Wright at the University of Colorado in Boulder, US.Millions of people have taken the drug without experiencing any strange side effects, points out Richard Millman at Brown Medical School, director of the Sleep Disorders Center of Lifespan Hospitals in Providence, Rhode Island, US. He says that unlike older types of sleep medications, zolpidem does not carry as great a risk of addiction.And Wright notes that some of the rep orts of “sleep-driving” linked to zolpidem can be easily explained: some patients have wrongly taken the drug right before leaving work in hopes that themedicine will kick in by the time they reach home. Doctors stress that the medication should be taken just before going to bed.The US Food & Drug Administration says it is continuing to "actively investigate" and collect information about cases linking zolpidem to unusual side effects.The Ambien label currently lists strange behaviour as a “special concern” for people taking the drug. “It’s a possible rare adverse event,” says Sanofi-Aventis spokesperson Melissa Feltmann, adding that the strange sleepwalking behaviours “may not necessarily be caused by the drug” but instead result from an underlying d isorder. She says that “the safety profile [of zolpidem] is well established”. The drug received approval in the US in 1993.。

SPAS 2023.3.31版 Strati

Package‘SPAS’April21,2023Type PackageTitle Stratiﬁed-Petersen Analysis SystemVersion2023.3.31Date2023-03-31Author Carl James SchwarzMaintainer Carl James Schwarz<******************************>LinkingTo TMB,RcppEigenImports MASS,Matrix,msm,numDeriv,plyr,reshape2,TMB(>=1.7.15)Description The Stratiﬁed-Petersen Analysis System(SPAS)is designedto estimate abundance in two-sample capture-recapture experimentswhere the capture and recaptures are stratiﬁed.This is a generalizationof the simple Lincoln-Petersen estimator.Strata may be deﬁned in time or in space or both,and the s strata in which marking takes placemay differ from the t strata in which recoveries take place.When s=t,SPAS reduces to the method described byDarroch(1961)<https:///stable/2332748>.When s<t,SPAS implements the methods described inPlante,Rivest,and Tremblay(1988)<https:///stable/2533994>.Schwarz and Taylor(1998)<https:///doi/10.1139/f97-238>describe the use of SPAS in estimating return of salmon stratiﬁed bytime and geography.A related package,BTSPAS,deals with temporal stratiﬁcation wherea spline is used to model the distribution of the populationover time as it passes the second capture location.This is the R-version of the(now obsolete)standalone Windowsprogram available at<https://home.cs.umanitoba.ca/~popan/spas/spas_home.html>. License GPL(>=2)RoxygenNote7.2.3Suggests knitr,rmarkdownVignetteBuilder knitrEncoding UTF-81NeedsCompilation yesRepository CRANDate/Publication2023-04-2022:12:40UTCR topics documented:SPAS.autopool (2)SPAS.ﬁt.model (4)SPAS.print.model (6)Index7 SPAS.autopool Autopooling a Stratiﬁed-Petersen(SP)data set.This function appliespooling rules to pool a SPAS dataset to meeting minimum sparsityrequirements.DescriptionAutopooling a Stratiﬁed-Petersen(SP)data set.This function applies pooling rules to pool a SPAS dataset to meeting minimum sparsity requirements.UsageSPAS.autopool(rawdata,min.released=100,min.inspected=50,min.recaps=50,min.rows=1,min.cols=1)Argumentsrawdata An(s+1)x(t+1)of the raw data BEFORE pooling.The s x t upper left matrix is the number of animals released in row stratum i and recovered in column stratumj.Row s+1contains the total number of UNMARKED animals recovered incolumn stratum j.Column t+1contains the number of animals marked in eachrow stratum but not recovered in any column stratum.The rawdata[s+1,t+1]isnot used and can be set to0or NA.The sum of the entries in each of theﬁrsts rows is then the number of animals marked in each row stratum.The sum ofthe entries in each of theﬁrst t columns is then the number of animals captured(marked and unmarked)in each column stratum.The row/column names of thematrix may be set to identify the entries in the output.min.released Minimum number of releases in a pooled rowmin.inspected Minimum number of inspections in a pooled columnmin.recaps Minimum number of recaptures before any rows can be pooledmin.rows,min.colsMinimum number or rows and columns after poolingDetailsIn many cases,the stratiﬁed set of releases and recapture is too sparse(many zeroes)or count are very small.Pooling rows and columns may be needed.Data needs to be pooled both row wise and column wise if the data are sparse to avoid singularities in theﬁt.This function automates pooling rows or columns following Schwarz and Taylor(1998).•All rows that have0releases are discarded•All columns that have0recaptures of taggedﬁsh and0ﬁsh inspected are discarded•Starting at theﬁrst row and working forwards in time,and then working from theﬁnal rowand working backwards in time,.rows are pooled until a minimum of min.released arereleased.An alternating pooling(from the top,from the bottom,from the top,etc)is used•Starting at theﬁrst column and working forwards in time,.and then working from theﬁnalcolumn and working backwards in time,columns are pooled until a minimum of min.inspected are inspected.An alternating pooling(from the left,from the right,from the left,etc)is used.•If the sum of the total recaptures from releasedﬁsh is<=min.recaps,then all rows are pooled(which reduces to a Chapman estimator)ValueA list with a suggest pooling.Examplesconne.data.csv<-textConnection("9,21,0,0,0,0,1710,101,22,1,0,0,7630,0,128,49,0,0,9340,0,0,48,12,0,4340,0,0,0,7,0,490,0,0,0,0,0,4351,2736,3847,1818,543,191,0")conne.data<-as.matrix(read.csv(conne.data.csv,header=FALSE))close(conne.data.csv)SPAS.autopool(conne.data)SPAS.fit.model Fit a Stratiﬁed-Petersen(SP)model using TMB.DescriptionThis functionﬁts a Stratiﬁed-Petersen(Plante,1996)to data and specify which rows/columns of the data should be pooled.The number of rows after pooling should be<=number of columns after pooling.UsageSPAS.fit.model(model.id="Stratified Petersen Estimator",rawdata,autopool=FALSE,row.pool.in=NULL,col.pool.in=NULL,row.physical.pool=TRUE,theta.pool=FALSE,CJSpool=FALSE,optMethod=c("nlminb"),optMethod.control=list(maxit=50000),svd.cutoff=1e-04,chisq.cutoff=0.1,min.released=100,min.inspected=50,min.recaps=50,min.rows=1,min.cols=1)Argumentsmodel.id Character string identifying the name of the model including any pooling..rawdata An(s+1)x(t+1)of the raw data BEFORE pooling.The s x t upper left matrix is the number of animals released in row stratum i and recovered in column stratumj.Row s+1contains the total number of UNMARKED animals recovered incolumn stratum j.Column t+1contains the number of animals marked in eachrow stratum but not recovered in any column stratum.The rawdata[s+1,t+1]isnot used and can be set to0or NA.The sum of the entries in each of theﬁrsts rows is then the number of animals marked in each row stratum.The sum ofthe entries in each of theﬁrst t columns is then the number of animals captured(marked and unmarked)in each column stratum.The row/column names of thematrix may be set to identify the entries in the output.autopool Should the automatic pooling algorithms be used.Give more details here on these rule work.row.pool.in,col.pool.inVectors(character/numeric)of length s and t respectively.These identify therows/columns to be pooled before the analysis is done.The vectors consists ofentries where pooling takes place if the entries are the same.For example,if s=4,then row.pool.in=c(1,2,3,4)implies no pooling because all entries are distinct;row.pool.in=c("a","a","b","b")implies that theﬁrst two rows will be pooled andthe last two rows will be pooled.It is not necessary that row/columns be contin-uous to be pooled,but this is seldom sensible.A careful choice of pooling labelshelps to remember what as done,e.g.row.pool.in=c("123","123","123","4")in-dicates that theﬁrst3rows are pooled and the4th row is not pooled.Characterentries ensure that the resulting matrix is sorted properly(e.g.if row.pool.in=c(123,123,123,4),then the same pooling is done,but the matrix rows are sorted rather strangely.row.physical.poolShould physical pooling be done(default)or should logical pooling be done.Forexample,if there are3rows in the data matrix and row.pool.in=c(1,1,3),then inphysical pooling,the entries in rows1and2are physically added together tocreate2rows in the data matrix beforeﬁtting.Because the data has changed,you cannot compare physical pooling using AIC.In logical pooling,the datamatrix is unchanged,but now parameters p1=p2but the movement parametersfor the rest of the matrix are not forced equal.theta.pool,CJSpoolNOT YET IMPLEMENTED.DO NOT CHANGE.optMethod What optimization method is used.Defaults is the nlminb()function..optMethod.controlControl parameters for optimization method.See the documentation on the dif-ferent optimization methods for details.svd.cutoff Whenﬁnding the variance-covariance matrix,a singular value decomposition isused.This identiﬁes the smallest singular value to retain.chisq.cutoff Whenﬁnding a goodness ofﬁt statistic using(obs-exp)^2/exp,all cell whoseExp<gof.cutoff are ignored to try and remove structural zero cells.min.released Minimum number of releases in a pooled rowmin.inspected Minimum number of inspections in a pooled columnmin.recaps Minimum number of recaptures before any rows can be pooledmin.rows,min.colsMinimum number or rows and columns after poolingValueA list with many entries.Refer to the vignettes for more details.Examplesconne.data.csv<-textConnection("9,21,0,0,0,0,1710,101,22,1,0,0,7630,0,128,49,0,0,9340,0,0,48,12,0,4346SPAS.print.model 0,0,0,0,7,0,490,0,0,0,0,0,4351,2736,3847,1818,543,191,0")conne.data<-as.matrix(read.csv(conne.data.csv,header=FALSE))close(conne.data.csv)mod1<-SPAS.fit.model(conne.data,model.id="Pooling rows1/2,5/6;pooling columns5/6",row.pool.in=c("12","12","3","4","56","56"),col.pool.in=c(1,2,3,4,56,56))mod2<-SPAS.fit.model(conne.data,model.id="Auto pool",autopool=TRUE)SPAS.print.model Print the results from aﬁt of a Stratiﬁed-Petersen(SP)model whenusing the TMB optimizerDescriptionThis function makes a report of the results of the modelﬁtting.UsageSPAS.print.model(x)Argumentsx A result from the modelﬁtting.See SPAS.fit.model.ValueA report to the console.Refer to the vignettes.Examplesconne.data.csv<-textConnection("9,21,0,0,0,0,1710,101,22,1,0,0,7630,0,128,49,0,0,9340,0,0,48,12,0,4340,0,0,0,7,0,490,0,0,0,0,0,4351,2736,3847,1818,543,191,0")conne.data<-as.matrix(read.csv(conne.data.csv,header=FALSE))close(conne.data.csv)mod1<-SPAS.fit.model(conne.data,model.id="Pooling rows1/2,5/6;pooling columns5/6",row.pool.in=c("12","12","3","4","56","56"),col.pool.in=c(1,2,3,4,56,56))SPAS.print.model(mod1)IndexSPAS.autopool,2SPAS.fit.model,4SPAS.print.model,67。

The Bosonic Sector of the Electroweak Interactions, Status and Tests at Present and Future

Lectures given at Regensburg University, January 1995 Extended version of a talk given at the Festkolloqium Dieter Schildknecht, Bielefeld, Oct.14th 1994 Abstract
PM/95-01 January 1995
The Bosonic Sector of the Electroweak Interactions, Status and Tests at Present and Future Colliders
F.M. Renard
Hale Waihona Puke Physique Mathematique et Theorique, CNRS-URA 768 Universite Montpellier II, F-34095 Montpellier Cedex 5.
HEP-PH-9501305
1 Introduction, the status of the Standard Model
It is a common leitmotiv to say that the Standard Model(SM) is largely successful. On the one hand it is already remarkable that this model is able to make de nite and unambiguous preditions for all processes involving usual particles. This property is the consequence of the gauge principle which allows to predict the dynamics once a classi cation group has been chosen. The simpler QED case with the U (1)EM has been extended to the nonabelian cases of QCD with SU (3)colour and to the electroweak interactions with SU (2) U (1). However the speci c feature of electroweak interactions is the fact that W , Z bosons are massive. The gauge principle has to be completed with a mass generation mechanism. In the Standard Model it is chosen as the Higgs mechanism of spontaneous symmetry breaking (SSB). It is this last property that makes the SM a renormalizable theory which allows to compute high order e ects and to make the accurate predictions mentioned above. These predictions practically agree with all available experimental results. In spite of this success many questions arise. Let us rst quickly review the status of the SM by clearly separating the caracteristics of its three sectors: a) The fermionic sector contains the constituents of matter i.e., the three families of leptons and quarks. b) The gauge sector consists in the , W , Z and the 8 gluons, as generated by the SU (3) SU (2) U (1) gauge group . At this stage both fermions and bosons are massless states. They are coupled through gauge interactions. Self-boson interactions appear through the non-abelian Yang-Mills kinetic terms of the W ;3 gauge bosons. c)The scalar sector is constructed with a complex doublet of Higgs elds. The gauge couplings of this scalar doublet provide the W and Z masses proportional to the vacuum expectation value v (the Fermi scale). Fermion masses can be described at the expense of introducing by hand a set of Yukawa couplings between fermion and Higgs elds. The Higgs potential generates the Higgs mass and Higgs self-couplings. The empirical status of these three sectors is now the following. The fermionic sector is well described by the SU (2) U (1) classi cation with left-handed doublets and righthanded singlets. The description of their interactions mediated by the gauge bosons, agrees with the high precision tests performed in particular at LEP1, in some cases up to a few permille accuracy. This agreement may be surprizing when one has in mind the broad spectrum of fermions (from the extremely light neutrinos up to the very heavy top quark), its peculiarities (special quantum numbers, chiralities, family replication with a spectacular hierarchy structure, absence of right-handed neutrinos, similarities but differences between leptons and quarks). One could expect to nd some deviations from universality. Maybe the heavy quark sector, not yet tested at the same accuracy as the light fermion sector, will reveal some speci c features. These questions may seem rather aesthetical, nevertheless the proliferation of 90 basic states is a strong motivation for the search of a simpler and more fundamental theory. To understand these various points one may need so-called "New Physics" (NP) structures like the ones which extend or modify 2

Spin-Boson and Spin-Fermion Topological Model of C

J. Chem. Chem. Eng. 11 (2017) 55-59doi: 10.17265/1934-7375/2017.02.003Spin-Boson and Spin-Fermion Topological Model of ConsciousnessAibassov Yerkin1, Yemelyanova Valentina1, Nakisbekov Narymzhan1, Alzhan Bakhytzhan1 and Savizky Ruben21. Research Institute of New Chemical Technologies and Materials, Kazakh National University Al-Farabi, Almaty 005012, Kazakhstan2. Columbia University, 3000 Broadway, New York, NY, 10027, USAAbstract: The authors propose a new approach to the theory of spin-boson and spin-fermion topological model of consciousness. The authors will offer a common mechanism of spin-boson and spin-fermion topological model of consciousness.Key words: Spin-boson, spin-fermion, topology, model of consciousness, magnetic field.1. IntroductionRecently, much attention is removed study of theory of consciousness [1-5]. All processes in the human brain occur in the form of electromagnetic processes. Therefore, it was interesting to see consciousness in terms of spin-boson and spin-fermion topological model.The aim is to study the spin-boson and spin-fermion topological model of consciousness.The novelty of the work lies in the fact that the authors have proposed a new mechanism of spin-boson and spin-fermion topological model of work of consciousness.2. TheoryNeuronal membrane saturated carrier spin nuclei such as 1H, 13C and 31P [1, 2]. Neuronal membrane are the matrix of the brain electrical activity and play a vital role in the normal functions of the brain and conscious of their basic molecular components are phospholipids, proteins and cholesterol. Each phospholipid contains 1% 31P, 1.8% 13C and over 60% 1H lipid chain. Neuronal membrane proteins such as ion channels and receptors neural transmittersCorresponding author: Aibassov Yerkin, professor, research field: metal organic chemistry of uranium and thorium, As, Sb and Bi. also contain large clusters spin-containing nuclei. Therefore, they are firmly convinced that the nature of the spin quantum used in the construction of the conscious mind. They suggested within neurobiology that perturbation anesthetics oxygen nerve pathwaysin both membrane proteins and may play a general anesthesia. Each O2 comprises two unpaired valence electrons strongly paramagnetic and at the same time as the chemically reactive bi-radical. It is able to produce large pulsed magnetic field along its path of diffusing. Paramagnetic O2 are the only breed can be found in large quantities in the brain to the same enzyme producing nitric oxide (NO). O2 is one of the main components for energy production in the central nervous system.NO is unstable free radical with an unpaired electron and one recently discovered a small neural transmitter, well known in the chemistry of spin-field concentrated on the study of free radical-mediated chemical reactions in which very small magnetic energy conversion can change the non-equilibrium spin process. Thus, O2 and NO can serve as catalysts in a spin-consciousness associated with neuronal biochemical reactions such as the double paths reaction initiated by free radicals.3. Results and DiscussionThey present the following Postulates: (a)All Rights Reserved.Spin-Boson and Spin-Fermion Topological Model of Consciousness 56Consciousness is intrinsically connected to quantum spin; (b) The mind-pixels of the brain are comprised of the nuclear spins distributed in the neural membranes and proteins, the pixel-activating agents are comprised of biologically available paramagnetic species such as O2 and NO, and the neural memories are comprised of all possible entangled quantum states of the mind-pixels; (c) Action potential modulations of nuclear spin interactions input information to the mind pixels and spin chemistry is the output circuit to classical neural activities; and (d) Consciousness emerges from the collapses of those entangled quantum states which are able to survive decoherence, said collapses are contextual, irreversible and non-computable and the unity of consciousness is achieved through quantum entanglement of the mind-pixels.In Postulate (a), the relationships between quantum spin and consciousness are defined based on the fact that spin is the origin of quantum effects in both Bohm and Hestenes quantum formulism and a fundamental quantum process associated with the structure of space-time.In Postulate (b), they specify that the nuclear spins in both neural membranes and neural proteins serve as the mind-pixels and propose that biologically available paramagnetic species such as O2 and NO are the mind-pixel activating agents. The authors also propose that neural memories are comprised of all possible entangled quantum states of mind-pixels.In Postulate (c), they propose the input and output circuits for the mind-pixels. As shown in a separate paper, the strength and anisotropies of nuclear spin interactions through J-couplings and dipolar couplings are modulated by action potentials. Thus, the neural spike trains can directly input information into the mind-pixels made of neural membrane nuclear spins. Further, spin chemistry can serve as the bridge to the classical neural activity since biochemical reactions mediated by free radicals are very sensitive to small changes of magnetic energies.In Postulate (d), they propose how conscious experience emerges. Thus, they adopt a quantum state collapsing scheme from which conscious experience emerges as a set of collapses of the decoherence-resistant entangled quantum states. They further theorize that the unity of consciousness is achieved through quantum entanglements of these mind-pixels.3.1 Spin-Boson and Spin-Fermion Model of ConsciousnessBosons, unlike fermions obey Bose-Einstein, who admits to a single quantum state could be an unlimited number of identical particles. Systems of many bosons described symmetric with respect to permutations of the particle wave functions.Bosons differ from fermions, which obey Fermi-Dirac statistics. Two or more identical fermions cannot occupy the same quantum state (Pauli exclusion principle).Since bosons with the same energy can occupy the same place in space, bosons are often force carrier particles. Fermions are usually associated with matter. Fermions, unlike bosons, obey Fermi-Dirac statistics:in the same quantum state can be no more than one particle (Pauli exclusion principle).3.2 Topological Model of ConsciousnessIt is known that the topological phase transition Kosterlitz-Thouless-phase transition in a two-dimensional XY-model. This transition is from the bound pairs of vortex-antivortex at low temperatures ina state with vortices and unpaired antivortices at a certain critical temperature.XY-model—a two-dimensional vector spin model which has symmetry U (1). For this system is not expected to have a normal phase transition of the second order. This is because the system is waiting forthe ordered phase that is destroyed by transverse vibrations, i.e. the Goldstone modes (see. Goldstone boson) associated with the breach of the continuousAll Rights Reserved.Spin-Boson and Spin-Fermion Topological Model of Consciousness 57Fig. 1 Symmetric wavefunction for a (bosonic) 2-particle state in an infinite square well potential.All Rights Reserved.Fig. 2 Antisymmetric wavefunction for a (fermionic) 2-particle state in an infinite square well potential.Spin-Boson and Spin-Fermion Topological Model of Consciousness58(a) (b)Fig. 3 Schematic image of a vortex (a) and antivortex (b) in the example of a planar magnetic material (arrows-vectors of the spin magnetic moments).symmetry, which logarithmically diverge with increasing system size. This is a special case of Theorem Mermin- Wagner for spin systems.Fig. 3 shows a schematic image of a vortex (a) and antivortex (b) in the example of a planar magnetic material (arrows - vectors of the spin magnetic moments).Thus, the topology does not depend on the measurement of distances, it is so powerful. The same theorems are applicable to any complex symptom, regardless of its length or belonging to a particular species.4. ConclusionsIn conclusion, the authors have presented an alternative model of consciousness in which the unpaired electron spins are playing a central role as the mind pixels and unity of mind is achieved interweaving these mental pixels.The authors hypothesized that these entangled electron spin states can be formed by the action potential modulated exchange and dipolar interactions, plus O2 and NO drive activations and survive rapid decoherence by quantum Zeno effects or decoherence-free spaces. Further, the authors have assumed that the collective electron spin dynamics associated with these collapses can have effects through the spin on the classic chemistry of neural activity, thereby affecting the neural networks of the brain. Our proposals involve the expansion of the associative neural coding of memories dynamic structures of neuronal membranes and proteins. Therefore, in our electron spin based on the model of the neural substrates of consciousness consists of the following functions: (a) electronic spin networks embedded in neuronal membranes and proteins, which serve as “crazy” screen with unpaired electron spins as pixels, (b) the nerve membrane and the proteins themselves, which serve as templates for the mind and nervous screen memories; and (c) free O2 and NO, which serve as agents pixel activating.Thus, the novelty of our work is that we were the first to propose that the electromagnetic field and free radicals have a great influence on the mind.Thus, the authors have proposed a possible mechanism of the free radical O2 and N2O in the consciousness.References[1]Hu, H. P., and Wu, M. X. 2006. “Nonlocal Effects ofChemical Substances on the Brain Produced ThroughQuantum Entanglement.” Progress in Physics 3: 20-6. [2]Hu, H. P., and Wu, M. X. 2006 “Photon InducedNon-local Effects of General Anaesthetics on the Brain.”Neuro Quantology 4 (1): 17-31. All Rights Reserved.Spin-Boson and Spin-Fermion Topological Model of Consciousness 59[3]Likhtenshtein, G. I. 1974. Spin labeling methods inmolecular biology. John Wiley and Sons; London, New York, Sydney, Toronto.[4]Likhtenshtein, G. I., Yamauchi, J., Nakatsuji, S., Smirnov,A., and Tamura, R. 2008. Nitroxides: Applications inChemistry, Biomedicine, and Materials Science. Cinii: Wiley.[5]Aibassov, Y., Yemelyanova, V., and Savizky, R. 2016.“Magnetic Effects in Brain Chemistry.” Journal of Chemistry and Chemical Engineering 10: 103-8.All Rights Reserved.。

Universities in Evolutionary Systems(系统变革中的大学)

Universities in Evolutionary Systems of InnovationMarianne van der Steen and Jurgen EndersThis paper criticizes the current narrow view on the role of universities in knowledge-based economies.We propose to extend the current policy framework of universities in national innovation systems(NIS)to a more dynamic one,based on evolutionary economic principles. The main reason is that this dynamic viewﬁts better with the practice of innovation processes. We contribute on ontological and methodological levels to the literature and policy discussions on the effectiveness of university-industry knowledge transfer and the third mission of uni-versities.We conclude with a discussion of the policy implications for the main stakeholders.1.IntroductionU niversities have always played a major role in the economic and cultural devel-opment of countries.However,their role and expected contribution has changed sub-stantially over the years.Whereas,since1945, universities in Europe were expected to con-tribute to‘basic’research,which could be freely used by society,in recent decades they are expected to contribute more substantially and directly to the competitiveness ofﬁrms and societies(Jaffe,2008).Examples are the Bayh–Dole Act(1982)in the United States and in Europe the Lisbon Agenda(2000–2010) which marked an era of a changing and more substantial role for universities.However,it seems that this‘new’role of universities is a sort of universal given one(ex post),instead of an ex ante changing one in a dynamic institutional environment.Many uni-versities are expected nowadays to stimulate a limited number of knowledge transfer activi-ties such as university spin-offs and university patenting and licensing to demonstrate that they are actively engaged in knowledge trans-fer.It is questioned in the literature if this one-size-ﬁts-all approach improves the usefulness and the applicability of university knowledge in industry and society as a whole(e.g.,Litan et al.,2007).Moreover,the various national or regional economic systems have idiosyncratic charac-teristics that in principle pose different(chang-ing)demands towards universities.Instead of assuming that there is only one‘optimal’gov-ernance mode for universities,there may bemultiple ways of organizing the role of univer-sities in innovation processes.In addition,we assume that this can change over time.Recently,more attention in the literature hasfocused on diversity across technologies(e.g.,King,2004;Malerba,2005;Dosi et al.,2006;V an der Steen et al.,2008)and diversity offormal and informal knowledge interactionsbetween universities and industry(e.g.,Cohenet al.,1998).So far,there has been less atten-tion paid to the dynamics of the changing roleof universities in economic systems:how dothe roles of universities vary over time andwhy?Therefore,this article focuses on the onto-logical premises of the functioning of univer-sities in innovation systems from a dynamic,evolutionary perspective.In order to do so,we analyse the role of universities from theperspective of an evolutionary system ofinnovation to understand the embeddednessof universities in a dynamic(national)systemof science and innovation.The article is structured as follows.InSection2we describe the changing role ofuniversities from the static perspective of anational innovation system(NIS),whereasSection3analyses the dynamic perspective ofuniversities based on evolutionary principles.Based on this evolutionary perspective,Section4introduces the characteristics of a LearningUniversity in a dynamic innovation system,summarizing an alternative perception to thestatic view of universities in dynamic economicsystems in Section5.Finally,the concludingVolume17Number42008doi:10.1111/j.1467-8691.2008.00496.x©2008The AuthorsJournal compilation©2008Blackwell Publishingsection discusses policy recommendations for more effective policy instruments from our dynamic perspective.2.Static View of Universities in NIS 2.1The Emergence of the Role of Universities in NISFirst we start with a discussion of the literature and policy reports on national innovation system(NIS).The literature on national inno-vation systems(NIS)is a relatively new and rapidly growingﬁeld of research and widely used by policy-makers worldwide(Fagerberg, 2003;Balzat&Hanusch,2004;Sharif,2006). The NIS approach was initiated in the late 1980s by Freeman(1987),Dosi et al.(1988)and Lundvall(1992)and followed by Nelson (1993),Edquist(1997),and many others.Balzat and Hanusch(2004,p.196)describe a NIS as‘a historically grown subsystem of the national economy in which various organizations and institutions interact with and inﬂuence one another in the carrying out of innovative activity’.It is about a systemic approach to innovation,in which the interaction between technology,institutions and organizations is central.With the introduction of the notion of a national innovation system,universities were formally on the agenda of many innovation policymakers worldwide.Clearly,the NIS demonstrated that universities and their interactions with industry matter for innova-tion processes in economic systems.Indeed, since a decade most governments acknowl-edge that interactions between university and industry add to better utilization of scienti-ﬁc knowledge and herewith increase the innovation performance of nations.One of the central notions of the innovation system approach is that universities play an impor-tant role in the development of commercial useful knowledge(Edquist,1997;Sharif, 2006).This contrasts with the linear model innovation that dominated the thinking of science and industry policy makers during the last century.The linear innovation model perceives innovation as an industry activity that‘only’utilizes fundamental scientiﬁc knowledge of universities as an input factor for their innovative activities.The emergence of the non-linear approach led to a renewed vision on the role–and expectations–of universities in society. Some authors have referred to a new social contract between science and society(e.g., Neave,2000).The Triple Helix(e.g.,Etzkowitz &Leydesdorff,1997)and the innovation system approach(e.g.,Lundvall,1988)and more recently,the model of Open Innovation (Chesbrough,2003)demonstrated that innova-tion in a knowledge-based economy is an inter-active process involving many different innovation actors that interact in a system of overlapping organizationalﬁelds(science, technology,government)with many interfaces.2.2Static Policy View of Universities in NIS Since the late1990s,the new role of universi-ties in NIS thinking emerged in a growing number of policy studies(e.g.,OECD,1999, 2002;European Commission,2000).The con-tributions of the NIS literature had a large impact on policy makers’perception of the role of universities in the national innovation performance(e.g.,European Commission, 2006).The NIS approach gradually replaced linear thinking about innovation by a more holistic system perspective on innovations, focusing on the interdependencies among the various agents,organizations and institutions. NIS thinking led to a structurally different view of how governments can stimulate the innovation performance of a country.The OECD report of the national innovation system (OECD,1999)clearly incorporated these new economic principles of innovation system theory.This report emphasized this new role and interfaces of universities in knowledge-based economies.This created a new policy rationale and new awareness for technology transfer policy in many countries.The NIS report(1999)was followed by more attention for the diversity of technology transfer mecha-nisms employed in university-industry rela-tions(OECD,2002)and the(need for new) emerging governance structures for the‘third mission’of universities in society,i.e.,patent-ing,licensing and spin-offs,of public research organizations(OECD,2003).The various policy studies have in common that they try to describe and compare the most important institutions,organizations, activities and interactions of public and private actors that take part in or inﬂuence the innovation performance of a country.Figure1 provides an illustration.Theﬁgure demon-strates the major building blocks of a NIS in a practical policy setting.It includesﬁrms,uni-versities and other public research organiza-tions(PROs)involved in(higher)education and training,science and technology.These organizations embody the science and tech-nology capabilities and knowledge fund of a country.The interaction is represented by the arrows which refer to interactive learn-ing and diffusion of knowledge(Lundvall,Volume17Number42008©2008The AuthorsJournal compilation©2008Blackwell Publishing1992).1The building block ‘Demand’refers to the level and quality of demand that can be a pull factor for ﬁrms to innovate.Finally,insti-tutions are represented in the building blocks ‘Framework conditions’and ‘Infrastructure’,including various laws,policies and regula-tions related to science,technology and entre-preneurship.It includes a very broad array of policy issues from intellectual property rights laws to ﬁscal instruments that stimulate labour mobility between universities and ﬁrms.The ﬁgure demonstrates that,in order to improve the innovation performance of a country,the NIS as a whole should be conducive for innovative activities in acountry.Since the late 1990s,the conceptual framework as represented in Figure 1serves as a dominant design for many comparative studies of national innovation systems (Polt et al.,2001;OECD,2002).The typical policy benchmark exercise is to compare a number of innovation indicators related to the role of university-industry interactions.Effective performance of universities in the NIS is judged on a number of standardized indica-tors such as the number of spin-offs,patents and licensing.Policy has especially focused on ‘getting the incentives right’to create a generic,good innovative enhancing context for ﬁrms.Moreover,policy has also inﬂu-enced the use of speciﬁc ‘formal’transfer mechanisms,such as university patents and university spin-offs,to facilitate this collabo-ration.In this way best practice policies are identiﬁed and policy recommendations are derived:the so-called one-size-ﬁts-all-approach.The focus is on determining the ingredients of an efﬁcient benchmark NIS,downplaying institutional diversity and1These organizations that interact with each other sometimes co-operate and sometimes compete with each other.For instance,ﬁrms sometimes co-operate in certain pre-competitive research projects but can be competitors as well.This is often the case as well withuniversities.Figure 1.The Benchmark NIS Model Source :Bemer et al.(2001).Volume 17Number 42008©2008The AuthorsJournal compilation ©2008Blackwell Publishingvariety in the roles of universities in enhanc-ing innovation performance.The theoretical contributions to the NIS lit-erature have outlined the importance of insti-tutions and institutional change.However,a further theoretical development of the ele-ments of NIS is necessary in order to be useful for policy makers;they need better systemic NIS benchmarks,taking systematically into account the variety of‘national idiosyncrasies’. Edquist(1997)argues that most NIS contribu-tions are more focused onﬁrms and technol-ogy,sometimes reducing the analysis of the (national)institutions to a left-over category (Geels,2005).Following Hodgson(2000), Nelson(2002),Malerba(2005)and Groenewe-gen and V an der Steen(2006),more attention should be paid to the institutional idiosyncra-sies of the various systems and their evolution over time.This creates variety and evolving demands towards universities over time where the functioning of universities and their interactions with the other part of the NIS do evolve as well.We suggest to conceptualize the dynamics of innovation systems from an evolutionary perspective in order to develop a more subtle and dynamic vision on the role of universities in innovation systems.We emphasize our focus on‘evolutionary systems’instead of national innovation systems because for many universities,in particular some science-based disciplinaryﬁelds such as biotechnology and nanotechnology,the national institutional environment is less relevant than the institu-tional and technical characteristics of the technological regimes,which is in fact a‘sub-system’of the national innovation system.3.Evolutionary Systems of Innovation as an Alternative Concept3.1Evolutionary Theory on Economic Change and InnovationCharles Darwin’s The Origin of Species(1859)is the foundation of modern thinking about change and evolution(Luria et al.,1981,pp. 584–7;Gould,1987).Darwin’s theory of natural selection has had the most important consequences for our perception of change. His view of evolution refers to a continuous and gradual adaptation of species to changes in the environment.The idea of‘survival of theﬁttest’means that the most adaptive organisms in a population will survive.This occurs through a process of‘natural selection’in which the most adaptive‘species’(organ-isms)will survive.This is a gradual process taking place in a relatively stable environment, working slowly over long periods of time necessary for the distinctive characteristics of species to show their superiority in the‘sur-vival contest’.Based on Darwin,evolutionary biology identiﬁes three levels of aggregation.These three levels are the unit of variation,unit of selection and unit of evolution.The unit of varia-tion concerns the entity which contains the genetic information and which mutates fol-lowing speciﬁc rules,namely the genes.Genes contain the hereditary information which is preserved in the DNA.This does not alter sig-niﬁcantly throughout the reproductive life-time of an organism.Genes are passed on from an organism to its successors.The gene pool,i.e.,the total stock of genetic structures of a species,only changes in the reproduction process as individuals die and are born.Par-ticular genes contribute to distinctive charac-teristics and behaviour of species which are more or less conducive to survival.The gene pool constitutes the mechanism to transmit the characteristics of surviving organisms from one generation to the next.The unit of selection is the expression of those genes in the entities which live and die as individual specimens,namely(individual) organisms.These organisms,in their turn,are subjected to a process of natural selection in the environment.‘Fit’organisms endowed with a relatively‘successful’gene pool,are more likely to pass them on to their progeny.As genes contain information to form and program the organisms,it can be expected that in a stable environment genes aiding survival will tend to become more prominent in succeeding genera-tions.‘Natural selection’,thus,is a gradual process selecting the‘ﬁttest’organisms. Finally,there is the unit of evolution,or that which changes over time as the gene pool changes,namely populations.Natural selec-tion produces changes at the level of the population by‘trimming’the set of genetic structures in a population.We would like to point out two central principles of Darwinian evolution.First,its profound indeterminacy since the process of development,for instance the development of DNA,is dominated by time at which highly improbable events happen (Boulding,1991,p.12).Secondly,the process of natural selection eliminates poorly adapted variants in a compulsory manner,since indi-viduals who are‘unﬁt’are supposed to have no way of escaping the consequences of selection.22We acknowledge that within evolutionary think-ing,the theory of Jean Baptiste Lamarck,which acknowledges in essence that acquired characteris-tics can be transmitted(instead of hereditaryVolume17Number42008©2008The AuthorsJournal compilation©2008Blackwell PublishingThese three levels of aggregation express the differences between ‘what is changing’(genes),‘what is being selected’(organisms),and ‘what changes over time’(populations)in an evolutionary process (Luria et al.,1981,p.625).According to Nelson (see for instance Nelson,1995):‘Technical change is clearly an evolutionary process;the innovation generator keeps on producing entities superior to those earlier in existence,and adjustment forces work slowly’.Technological change and innovation processes are thus ‘evolutionary’because of its characteristics of non-optimality and of an open-ended and path-dependent process.Nelson and Winter (1982)introduced the idea of technical change as an evolutionary process in capitalist economies.Routines in ﬁrms function as the relatively durable ‘genes’.Economic competition leads to the selection of certain ‘successful’routines and these can be transferred to other ﬁrms by imitation,through buy-outs,training,labour mobility,and so on.Innovation processes involving interactions between universities and industry are central in the NIS approach.Therefore,it seems logical that evolutionary theory would be useful to grasp the role of universities in innovation pro-cesses within the NIS framework.3.2Evolutionary Underpinnings of Innovation SystemsBased on the central evolutionary notions as discussed above,we discuss in this section how the existing NIS approaches have already incor-porated notions in their NIS frameworks.Moreover,we investigate to what extent these notions can be better incorporated in an evolu-tionary innovation system to improve our understanding of universities in dynamic inno-vation processes.We focus on non-optimality,novelty,the anti-reductionist methodology,gradualism and the evolutionary metaphor.Non-optimality (and Bounded Rationality)Based on institutional diversity,the notion of optimality is absent in most NIS approaches.We cannot deﬁne an optimal system of innovation because evolutionary learning pro-cesses are important in such systems and thus are subject to continuous change.The system never achieves an equilibrium since the evolu-tionary processes are open-ended and path dependent.In Nelson’s work (e.g.,1993,1995)he has emphasized the presence of contingent out-comes of innovation processes and thus of NIS:‘At any time,there are feasible entities not present in the prevailing system that have a chance of being introduced’.This continuing existence of feasible alternative developments means that the system never reaches a state of equilibrium or ﬁnality.The process always remains dynamic and never reaches an optimum.Nelson argues further that diversity exists because technical change is an open-ended multi-path process where no best solu-tion to a technical problem can be identiﬁed ex post .As a consequence technical change can be seen as a very wasteful process in capitalist economies with many duplications and dead-ends.Institutional variety is closely linked to non-optimality.In other words,we cannot deﬁne the optimal innovation system because the evolutionary learning processes that take place in a particular system make it subject to continuous change.Therefore,comparisons between an existing system and an ideal system are not possible.Hence,in the absence of any notion of optimality,a method of comparing existing systems is necessary.According to Edquist (1997),comparisons between systems were more explicit and systematic than they had been using the NIS approaches.Novelty:Innovations CentralNovelty is already a central notion in the current NIS approaches.Learning is inter-preted in a broad way.Technological innova-tions are deﬁned as combining existing knowledge in new ways or producing new knowledge (generation),and transforming this into economically signiﬁcant products and processes (absorption).Learning is the most important process behind technological inno-vations.Learning can be formal in the form of education and searching through research and development.However,in many cases,innovations are the consequence of several kinds of learning processes involving many different kinds of economic agents.According to Lundvall (1992,p.9):‘those activities involve learning-by-doing,increasing the efﬁciency of production operations,learning-characteristics as in the theory of Darwin),is acknowledged to ﬁt better with socio-economic processes of technical change and innovation (e.g.,Nelson &Winter,1982;Hodgson,2000).Therefore,our theory is based on Lamarckian evolutionary theory.However,for the purpose of this article,we will not discuss the differences between these theo-ries at greater length and limit our analysis to the fundamental evolutionary building blocks that are present in both theories.Volume 17Number 42008©2008The AuthorsJournal compilation ©2008Blackwell Publishingby-using,increasing the efﬁciency of the use of complex systems,and learning-by-interacting, involving users and producers in an interac-tion resulting in product innovations’.In this sense,learning is part of daily routines and activities in an economy.In his Learning Economy concept,Lundvall makes learning more explicit,emphasizing further that ‘knowledge is assumed as the most funda-mental resource and learning the most impor-tant process’(1992,p.10).Anti-reductionist Approach:Systems and Subsystems of InnovationSo far,NIS approaches are not yet clear and systematic in their analysis of the dynamics and change in innovation systems.Lundvall’s (1992)distinction between subsystem and system level based on the work of Boulding implicitly incorporates both the actor(who can undertake innovative activities)as well as the structure(institutional selection environment) in innovation processes of a nation.Moreover, most NIS approaches acknowledge that within the national system,there are different institu-tional subsystems(e.g.,sectors,regions)that all inﬂuence each other again in processes of change.However,an explicit analysis of the structured environment is still missing (Edquist,1997).In accordance with the basic principles of evolutionary theory as discussed in Section 3.1,institutional evolutionary theory has developed a very explicit systemic methodol-ogy to investigate the continuous interaction of actors and institutional structures in the evolution of economic systems.The so-called ‘methodological interactionism’can be per-ceived as a methodology that combines a structural perspective and an actor approach to understand processes of economic evolu-tion.Whereas the structural perspective emphasizes the existence of independent institutional layers and processes which deter-mine individual actions,the actor approach emphasizes the free will of individuals.The latter has been referred to as methodological individualism,as we have seen in neo-classical approaches.Methodological indi-vidualism will explain phenomena in terms of the rational individual(showingﬁxed prefer-ences and having one rational response to any fully speciﬁed decision problem(Hodgson, 2000)).The interactionist approach recognizes a level of analysis above the individual orﬁrm level.NIS approaches recognize that national differences exist in terms of national institu-tions,socio-economic factors,industries and networks,and so on.So,an explicit methodological interactionist approach,explicitly recognizing various insti-tutional layers in the system and subsystem in interaction with the learning agents,can improve our understanding of the evolution of innovation.Gradualism:Learning Processes andPath-DependencyPath-dependency in biology can be translated in an economic context in the form of(some-times very large)time lags between a technical invention,its transformation into an economic innovation,and the widespread diffusion. Clearly,in many of the empirical case studies of NIS,the historical dimension has been stressed.For instance,in the study of Denmark and Sweden,it has been shown that the natural resource base(for Denmark fertile land,and for Sweden minerals)and economic history,from the period of the Industrial Revolution onwards,has strongly inﬂuenced present specialization patterns(Edquist& Lundvall,1993,pp.269–82).Hence,history matters in processes of inno-vation as the innovation processes are inﬂu-enced by many institutions and economic agents.In addition,they are often path-dependent as small events are reinforced and become crucially important through processes of positive feedback,in line with evolutionary processes as discussed in Section3.1.Evolutionary MetaphorFinally,most NIS approaches do not explicitly use the biological metaphor.Nevertheless, many of the approaches are based on innova-tion theories in which they do use an explicit evolutionary metaphor(e.g.,the work of Nelson).To summarize,the current(policy)NIS approaches have already implicitly incorpo-rated some evolutionary notions such as non-optimality,novelty and gradualism.However, what is missing is a more explicit analysis of the different institutional levels of the economic system and innovation subsystems (their inertia and evolution)and how they change over time in interaction with the various learning activities of economic agents. These economic agents reside at established ﬁrms,start-upﬁrms,universities,govern-ments,undertaking learning and innovation activities or strategic actions.The explicit use of the biological metaphor and an explicit use of the methodological interactionst approach may increase our understanding of the evolu-tion of innovation systems.Volume17Number42008©2008The AuthorsJournal compilation©2008Blackwell Publishing4.Towards a Dynamic View of Universities4.1The Logic of an Endogenous‘Learning’UniversityIf we translate the methodological interaction-ist approach to the changing role of universities in an evolutionary innovation system,it follows that universities not only respond to changes of the institutional environment(government policies,business demands or changes in scientiﬁc paradigms)but universities also inﬂuence the institutions of the selection envi-ronment by their strategic,scientiﬁc and entre-preneurial actions.Moreover,these actions inﬂuence–and are inﬂuenced by–the actions of other economic agents as well.So,instead of a one-way rational response by universities to changes(as in reductionist approach),they are intertwined in those processes of change.So, universities actually function as an endogenous source of change in the evolution of the inno-vation system.This is(on an ontological level) a fundamental different view on the role of universities in innovation systems from the existing policy NIS frameworks.In earlier empirical research,we observed that universities already effectively function endogenously in evolutionary innovation system frameworks;universities as actors (already)develop new knowledge,innovate and have their own internal capacity to change,adapt and inﬂuence the institutional development of the economic system(e.g., V an der Steen et al.,2009).Moreover,univer-sities consist of a network of various actors, i.e.,the scientists,administrators at technology transfer ofﬁces(TTO)as well as the university boards,interacting in various ways with indus-try and governments and embedded in various ways in the regional,national or inter-national environment.So,universities behave in an at least partly endogenous manner because they depend in complex and often unpredictable ways on the decision making of a substantial number of non-collusive agents.Agents at universities react in continuous interaction with the learn-ing activities ofﬁrms and governments and other universities.Furthermore,the endogenous processes of technical and institutional learning of univer-sities are entangled in the co-evolution of institutional and technical change of the evo-lutionary innovation system at large.We propose to treat the learning of universities as an inseparable endogenous variable in the inno-vation processes of the economic system.In order to structure the endogenization in the system of innovation analysis,the concept of the Learning University is introduced.In thenext subsection we discuss the main character-istics of the Learning University and Section5discusses the learning university in a dynamic,evolutionary innovation system.An evolution-ary metaphor may be helpful to make theuniversity factor more transparent in theco-evolution of technical and institutionalchange,as we try to understand how variouseconomic agents interact in learning processes.4.2Characteristics of the LearningUniversityThe evolution of the involvement of universi-ties in innovation processes is a learningprocess,because(we assume that)universitypublic agents have their‘own agenda’.V ariousincentives in the environment of universitiessuch as government regulations and technol-ogy transfer policies as well as the innovativebehaviour of economic agents,compel policymakers at universities to constantly respondby adapting and improving their strategiesand policies,whereas the university scientistsare partly steered by these strategies and partlyinﬂuenced by their own scientiﬁc peers andpartly by their historically grown interactionswith industry.During this process,universityboards try to be forward-looking and tobehave strategically in the knowledge thattheir actions‘inﬂuence the world’(alsoreferred to earlier as‘intentional variety’;see,for instance,Dosi et al.,1988).‘Intentional variety’presupposes that tech-nical and institutional development of univer-sities is a learning process.University agentsundertake purposeful action for change,theylearn from experience and anticipate futurestates of the selective environment.Further-more,university agents take initiatives to im-prove and develop learning paths.An exampleof these learning agents is provided in Box1.We consider technological and institutionaldevelopment of universities as a process thatinvolves many knowledge-seeking activitieswhere public and private agents’perceptionsand actions are translated into practice.3Theinstitutional changes are the result of inter-actions among economic agents deﬁned byLundvall(1992)as interactive learning.Theseinteractions result in an evolutionary pattern3Using a theory developed in one scientiﬁc disci-pline as a metaphor in a different discipline mayresult,in a worst-case scenario,in misleading analo-gies.In the best case,however,it can be a source ofcreativity.As Hodgson(2000)pointed out,the evo-lutionary metaphor is useful for understandingprocesses of technical and institutional change,thatcan help to identify new events,characteristics andphenomena.Volume17Number42008©2008The AuthorsJournal compilation©2008Blackwell Publishing。

写作业发出噪音的危害英语

When completing homework,making noise can have several detrimental effects on both the individual and their surroundings.Here are some of the potential harms associated with noisy environments while doing homework:1.Distraction:Noise can be a significant distraction,making it difficult to concentrate on the task at hand.This can lead to a decrease in productivity and the quality of work.2.Stress and Anxiety:A noisy environment can increase stress levels,which can negatively impact cognitive functions and the ability to focus.This may result in feelings of anxiety and restlessness.3.Learning Difficulty:For students,especially those with learning disabilities or conditions like ADHD,noise can exacerbate difficulties in learning and retaining information.4.Sleep Disruption:If homework is done late into the night and involves noise,it can disrupt the sleep patterns of the student and others in the household,leading to fatigue and reduced cognitive performance the next day.5.Impaired Memory:Consistently working in a noisy environment can impair shortterm memory and the ability to recall information,which is crucial for academic performance.6.Health Issues:Prolonged exposure to noise can lead to various health issues,including hearing loss,cardiovascular problems,and a weakened immune system.7.Social Conflicts:Noise from homework activities can disturb others in the vicinity, leading to social conflicts and a negative impact on relationships.8.Inefficiency:Trying to work through the noise can lead to inefficiencies,as the student may need to reread or redo work due to errors caused by distractions.9.Miscommunication:If the homework involves group work or online collaboration, background noise can make communication difficult,leading to misunderstandings and misinterpretations.10.Impact on Creativity:A noisy environment can stifle creative thinking,which is essential for problemsolving and innovative approaches to assignments.To mitigate these harms,its important to create a quiet and comfortable studyenvironment,use noisecancelling headphones if necessary,and establish boundaries with others to minimize disruptions.。

tpo35三篇阅读原文译文题目答案译文背景知识

tpo35三篇阅读原文译文题目答案译文背景知识阅读-1 (1)原文 (2)译文 (5)题目 (8)答案 (17)背景知识 (18)阅读-2 (21)原文 (21)译文 (24)题目 (27)答案 (36)背景知识 (36)阅读-3 (39)原文 (39)译文 (43)题目 (46)答案 (54)背景知识 (55)阅读-1原文Earth’ s Age①One of the first recorded observers to surmise a long age for Earth was the Greek historian Herodotus, who lived from approximately 480 B.C. to 425 B.C. He observed that the Nile River Delta was in fact a series of sediment deposits built up in successive floods. By noting that individual floods deposit only thin layers of sediment, he was able to conclude that the Nile Delta had taken many thousands of years to build up. More important than the amount of time Herodotus computed, which turns out to be trivial compared with the age of Earth, was the notion that one could estimate ages of geologic features by determining rates of the processes responsible for such features, and then assuming the rates to be roughly constant over time. Similar applications of this concept were to be used again and again in later centuries to estimate the ages of rock formations and, in particular, of layers of sediment that had compacted and cemented to form sedimentary rocks.②It was not until the seventeenth century that attempts were madeagain to understand clues to Earth's history through the rock record. Nicolaus Steno (1638-1686) was the first to work out principles of the progressive depositing of sediment in Tuscany. However, James Hutton (1726-1797), known as the founder of modern geology, was the first to have the important insight that geologic processes are cyclic in nature. Forces associated with subterranean heat cause land to be uplifted into plateaus and mountain ranges. The effects of wind and water then break down the masses of uplifted rock, producing sediment that is transported by water downward to ultimately form layers in lakes, seashores, or even oceans. Over time, the layers become sedimentary rock. These rocks are then uplifted sometime in the future to form new mountain ranges, which exhibit the sedimentary layers (and the remains of life within those layers) of the earlier episodes of erosion and deposition.③Hutton's concept represented a remarkable insight because it unified many individual phenomena and observations into a conceptual picture of Earth’s history. With the further assumption that these geologic processes were generally no more or less vigorous than they are today, Hutton's examination of sedimentary layers led him to realize that Earth's history must be enormous, that geologic time is anabyss and human history a speck by comparison.④After Hutton, geologists tried to determine rates of sedimentation so as to estimate the age of Earth from the total length of the sedimentary or stratigraphic record. Typical numbers produced at the turn of the twentieth century were 100 million to 400 million years. These underestimated the actual age by factors of 10 to 50 because much of the sedimentary record is missing in various locations and because there is a long rock sequence that is older than half a billion years that is far less well defined in terms of fossils and less well preserved.⑤Various other techniques to estimate Earth's age fell short, and particularly noteworthy in this regard were flawed determinations of the Sun's age. It had been recognized by the German philosopher Immanuel Kant (1724-1804) that chemical reactions could not supply the tremendous amount of energy flowing from the Sun for more than about a millennium. Two physicists during the nineteenth century both came up with ages for the Sun based on the Sun's energy coming from gravitational contraction. Under the force of gravity, the compressionresulting from a collapse of the object must release energy. Ages for Earth were derived that were in the tens of millions of years, much less than the geologic estimates of the lime.⑥It was the discovery of radioactivity at the end of the nineteenth century that opened the door to determining both the Sun’s energy source and the age of Earth. From the initial work came a suite of discoveries leading to radio isotopic dating, which quickly led to the realization that Earth must be billions of years old, and to the discovery of nuclear fusion as an energy source capable of sustaining the Sun's luminosity for that amount of time. By the 1960s, both analysis of meteorites and refinements of solar evolution models converged on an age for the solar system, and hence for Earth, of 4.5 billion years.译文地球的年龄①希腊历史学家希罗多德是最早有记录的推测地球年龄的观察家之一，他生活在大约公元前480年到公元前425年。

《2024年基于肠-脑轴理论探讨甘松对帕金森大鼠运动功能障碍的改善作用及机制》范文

《基于肠-脑轴理论探讨甘松对帕金森大鼠运动功能障碍的改善作用及机制》篇一一、引言帕金森病（Parkinson's Disease，PD）是一种常见的神经退行性疾病，主要表现为运动功能障碍，如震颤、僵硬和行动迟缓等。

目前，虽然针对帕金森病的治疗手段多样，但尚未有根治性疗法。

肠-脑轴（Enteric-Cerebral Axis）是指肠道与中枢神经系统之间的双向通讯和相互影响关系，越来越多的研究表明，肠道微生物与帕金森病之间存在密切联系。

甘松作为一种中药材，具有广泛的药理作用，特别是其在改善神经系统疾病方面的潜力日益受到关注。

本文基于肠-脑轴理论，探讨甘松对帕金森大鼠运动功能障碍的改善作用及机制。

二、材料与方法1. 实验材料本实验采用SD大鼠作为实验对象，建立帕金森大鼠模型。

甘松药材购自正规渠道，经过鉴定后使用。

实验过程中所使用的试剂和仪器均符合实验要求。

2. 实验方法（1）建立帕金森大鼠模型：采用6-羟基多巴胺（6-OHDA）注射法建立PD大鼠模型。

（2）分组与给药：将大鼠随机分为对照组、模型组、甘松治疗组等，每天进行相应药物或生理盐水灌胃治疗。

（3）行为学检测：定期对大鼠进行运动功能评估，包括旋转杆测试、足迹分析等。

（4）肠道微生物检测：收集大鼠粪便样本，进行肠道微生物组成分析。

（5）病理学检查：对大鼠脑组织进行病理学检查，观察甘松对脑内多巴胺能神经元的影响。

（6）机制研究：通过分子生物学技术，探讨甘松改善PD大鼠运动功能的潜在机制。

三、结果1. 甘松对帕金森大鼠运动功能的改善作用通过行为学检测发现，甘松治疗组的大鼠在旋转杆测试和足迹分析中的表现明显优于模型组，说明甘松能够显著改善PD大鼠的运动功能障碍。

2. 肠道微生物变化肠道微生物组成分析显示，甘松治疗组的肠道微生物多样性得到改善，有益菌群比例增加，有害菌群比例降低。

这表明甘松可能通过调节肠道微生物来改善PD大鼠的运动功能。

3. 脑内多巴胺能神经元的变化病理学检查发现，甘松治疗组的脑内多巴胺能神经元损伤程度较模型组减轻，说明甘松对脑内多巴胺能神经元具有保护作用。

神经病理性疼痛专家共识解读

下行纤维：5-HT、NE
《神经病理性疼痛诊疗专家共识》解读
DRG
后角对痛觉的调控—— 闸门控制学说示意图
C
脊髓投射神经元
SG
至丘脑
A
DRG DRG 后根神经节细胞 SG 后角胶质层中间神经元
《神经病理性疼痛诊疗专家共识》解读
脑干下行抑制系统
解剖通路
中脑导水管周围灰质（PAG）延髓上段腹内侧区(RVM) 脊髓后角单胺能通路（5-HT、NE) ―启动神经元（on-cell)‖、“停止神经元(off-cell)‖：存在于RVM中阿片肽：脊髓后角胶质区
《神经病理性疼痛诊疗专家共识》解读
神经病理性疼痛的诊断
病史全身神经科和局部检查实验室检查图象检查、化验血、尿、便、脑脊液、微生物、抗体、可能的毒物电生理检查：肌电图、神经传导速度、异位及自发放电皮肤和神经纤维活检神经影像学检查神经功能评估
《神经病理性疼痛诊疗专家共识》解读
神经痛
《神经病理性疼痛诊疗专家共识》解读
神经病理性疼痛的流行病学
神经病理性疼痛在人群中大约占3% [1] ，欧洲成年人大约5个人中有1个有慢性疼痛[2] 。大约25%的糖尿病患者有神经病理性疼痛，其中1型糖尿病54%，2型糖尿病 45% [3、4] 。大约25-50%的带状疱疹患者遗留疱疹后神经痛。
A C
浅层
脊髓后角
A
深层
C
Woolf CJ and Manion. Lancet 353:1959~64
《神经病理性疼痛诊疗专家共识》解读
神经损伤
功能改变组织学改变
脊髓后角内一级传入神经纤维的重组
中枢致敏

西医神经科术语英文翻译

西医神经科术语英文翻译以下是常见的西医神经科术语英文翻译：1. 神经学：Neurology2. 神经系统：Nervous System3. 大脑：Brain4. 脊髓：Spinal Cord5. 神经元：Neuron6. 神经胶质细胞：Glial Cells7. 突触：Synapse8. 轴突：Axon9. 树突：Dendrites10. 髓鞘：Myelin Sheath11. 神经递质：Neurotransmitters12. 神经传导通路：Nerve Conduction Pathways13. 反射：Reflex14. 痛觉：Pain Sensation15. 感觉运动传导通路：Sensorimotor Pathways16. 自主神经系统：Autonomic Nervous System17. 中枢神经系统：Central Nervous System (CNS)18. 外周神经系统：Peripheral Nervous System (PNS)19. 神经肌肉接头：Neuromuscular Junction20. 癫痫：Epilepsy21. 帕金森病：Parkinson's Disease22. 多发性硬化症：Multiple Sclerosis (MS)23. 脑卒中：Stroke24. 脑外伤：Traumatic Brain Injury (TBI)25. 脑瘤：Brain Tumors26. 脑炎：Brain Infections / Encephalitis27. 神经痛：Neuralgia28. 头痛：Headache29. 失眠：Insomnia30. 肌肉萎缩：Muscle Atrophy31. 肌无力：Muscle Weakness32. 神经根病：Radiculopathy33. 神经丛病变：Plexopathy34. 脊髓病变：Myelopathy35. 脑积水：Hydrocephalus36. 脊髓空洞症：Syringomyelia37. 脑电图（EEG）：Electroencephalogram (EEG)38. 肌电图（EMG）：Electromyogram (EMG)39. 经颅磁刺激（TMS）：Transcranial Magnetic Stimulation (TMS)40. 正电子发射断层扫描（PET）：Positron Emission Tomography (PET)41. 功能磁共振成像（fMRI）：Functional Magnetic Resonance Imaging (fMRI)42. 单光子发射计算机断层扫描（SPECT）：Single Photon Emission Computed Tomography (SPECT)43. 经颅多普勒超声（TCD）：Transcranial Doppler Ultrasound (TCD)44. 认知障碍：Cognitive Dysfunction45. 情绪障碍：Mood Disorders46. 神经退行性疾病：Neurodegenerative Diseases47. 中毒性脑病：Toxic Encephalopathy48. 脑死亡：Brain Death49. 昏迷：Coma50. 意识障碍：Disorders of Consciousness。

帕金森病(英文版)

• Akinesia • Bilateral disease • Poor balance
• Unilateral tremor
Time (years)
• Falls • Dependency • Cognitive decline • Chair/bed bound • Dementia1
Neocortex (secondary & primary)
Prevalence 0.3% general population 1% of population over age 60 50000 new cases annually 130 patients per 100,000 1.5 million cases in USA Men>Women 1.2:1.0 Young onset PD affects 5-10% of patients
FEATURES OF PARKINSONS DISEASE
• DOPAMINERGIC • NONDOPAMINERGIC
• resting tremor
• Loss of sense of smell
• rigidity
• Constipation
• Bradykinesia
• Choking, drooling
inheritance patterns have been described • Major epidemiologic study suggests:
– Genetic factors play larger role in patients with Young Onset PD
– Environmental insults play larger role in patients with PD onset after age 50

脑缺血再灌注的英语

脑缺血再灌注的英语Cerebral Ischemia-Reperfusion InjuryCerebral ischemia-reperfusion injury refers to the damage caused to brain cells when blood flow to the brain is temporarily disrupted and subsequently restored. This condition can occur in various clinical scenarios, such as stroke, cardiac arrest, and neurosurgery. The understanding and study of ischemia-reperfusion injury have led to the identification of potential therapeutic strategies to mitigate brain damage and improve patient outcomes.1. IntroductionCerebral ischemia-reperfusion injury is a complex process involving a cascade of events that occur at both the cellular and molecular levels. Understanding the underlying mechanisms is crucial for the development of effective treatment strategies. This article aims to provide a comprehensive overview of this condition, emphasizing its relevance in the field of medicine.2. PathophysiologyDuring the ischemic phase, reduced blood flow to the brain deprives brain cells of oxygen and essential nutrients, leading to energy depletion and the accumulation of toxic metabolites. Reperfusion, the restoration of blood flow, initiates a series of pathological processes, including inflammation, oxidative stress, and excitotoxicity. These processes exacerbate the initial injury and contribute to tissue damage.3. Inflammatory ResponseIschemia-reperfusion injury triggers an inflammatory response mediated by immune cells and various inflammatory factors. Infiltration of neutrophils and release of pro-inflammatory cytokines lead to the activation of endothelial cells, disruption of the blood-brain barrier, and further brain injury. Targeting these inflammatory processes has shown promise in reducing the extent of ischemia-reperfusion injury.4. Oxidative StressThe reperfusion phase generates reactive oxygen species, which overwhelm the endogenous antioxidant defense mechanisms. The excessive oxidative stress results in damage to cell membranes, proteins, and DNA. Antioxidant therapies have demonstrated potential in alleviating oxidative stress and protecting brain tissue from further harm.5. ExcitotoxicityExcessive release of excitatory neurotransmitters, primarily glutamate, during ischemia-reperfusion injury can trigger a series of events leading to neuronal cell death. The influx of calcium ions and the activation of downstream signaling pathways contribute to mitochondrial dysfunction and apoptosis. Pharmacological modulation of excitatory neurotransmission has emerged as a potential therapeutic strategy.6. Therapeutic ApproachesThe management of cerebral ischemia-reperfusion injury involves a multi-faceted approach. Strategies targeting inflammation, oxidative stress, and excitotoxicity have shown promise in preclinical studies and earlyclinical trials. Pharmacological interventions, hypothermia, and neuroprotective agents are among the potential treatment options being explored.7. ConclusionCerebral ischemia-reperfusion injury is a complex condition associated with significant morbidity and mortality. Understanding the underlying mechanisms and exploring potential therapeutic strategies are crucial for improving patient outcomes. The development of effective treatments to mitigate the damage caused by ischemia-reperfusion injury remains an active area of research and holds great promise for the future.In summary, cerebral ischemia-reperfusion injury is a multifaceted condition that requires a comprehensive understanding of its pathophysiology. Through targeting the inflammatory response, oxidative stress, and excitotoxicity, researchers aim to develop effective therapeutic strategies to minimize brain damage and improve patient prognosis. Continued efforts in this field are essential to advance our knowledge and ultimately provide better clinical outcomes for individuals affected by cerebral ischemia-reperfusion injury.。

NSS Mastering Biology Suggested Answer Book 1B (eng)

Suggested answers to Exercise and Reading to learn(Note: The overseas examination boards bear no responsibility for the suggested answers contained in this publication. Answers for HKCEE and HKALE questions are not available due to copyright restrictions.)Chapter 7 Gas exchange in humansExerciseMultiple-choice questions (p. 7-26)1 D2 B3 C4 C5 C6 B7 B8 A9 C10 C11 DShort questions (p. 7-28)12The dust particles and bacteria from the air cannot be filtered by cilia or trapped by mucus.1m They can go directly into our lungs. 1m The dust will block the air passage and the bacteria will cause respiratory infection. 1m13 a Air sacs 1mb Nasal cavity 1mTrachea 1mBronchi 1mBronchioles (except the smallest ones) 1mc Intercostal muscles 1mRibs 1mDiaphragm 1m 14 a In sequence:upwards / outwards 1mdownwards / flatten 1mb i On diagram:Oxygen arrow to blood from air and CO2 arrow to air from blood 1mOxygen arrow to red blood cell 1mCO2 arrow from plasma 1m ii Diffusion 1miii Large surface area 1m 15 HKCEE Biology 2005 I Q416Structured questions (p.7-29)17 a B and C 2mMucus traps dust. 1mCilia beat mucus up the trachea,preventing it from entering the lungs. 1mb F, G and H 3mc E, air sac 1m x 2It is the site of gas exchange between air and blood. 1m 18 a General description of pressure changesDecreases to a minimum of –0.29 / –0.3 / –0.31 kPa 1mat 0.8–0.9 s 0.5mThen returns to zero at the end of inspiration 1mat 1.62–1.7 s 0.5mb Changes from –0.29 / –0.3 / –0.31 kPa to 0.29 / 0.3 / 0.31 kPa 1mOverall change of 0.58–0.62 kPa 1mc i Contraction of diaphragm and intercostal muscles 1mIncreased volume in thorax / chest, decreased pressure 1mPressure rises as air moves in 1m ii Relaxation of diaphragm and intercostal muscles 0.5m Reference to elasticity / elastic fibres 0.5mDecreased volume in thorax / chest, increased pressure 1mPressure decreases as air moves out 1m 19HKCEE Biology 2001 I Q4b20HKCEE Human Biology 1999 I Q1b21 a1mb Hydrogencarbonate indicator / lime water 1mc A: Hydrogencarbonate indicator changes to yellow / lime water turns milky 1mB: Hydrogencarbonate indicator remains orange / lime water remains clear 1md i Collect a jar of atmospheric air as inhaled air. 1mCollect a jar of exhaled air by blowing slowly into a gas jar over water. 1mLower a burning candle into the jar of inhaled air and the jar of exhaled air. 1mRecord how long the candle can burn in each jar. 1m ii The candle can burn longer in the jar of inhaled air. 1mIt is because some oxygen of the inhaled air is absorbed in the lungs and theexhaled air contains less oxygen. 1m 22 a i Arrow at peak of curve 1mii Intercostal muscles contract 1m Diaphragm contracts / flattens / moves down 1mRibs move upwards and outwards 1m iii Line goes up 1mb i Bronchiole 1mii Mucus traps dust / microorganisms 1m Cilia sweep mucus away from air sacs 1m iii Any two from: 1m x 2 Stimulates mucus-secreting cells / excess mucus producedInhibits ciliaLeads to cancerEssays (p. 7-31)23CartilageIn trachea / bronchi 0.5mHolds airway open / prevents collapse 0.5mLow resistance to air movement 0.5m Ciliated epithelium / ciliaSweep mucus 0.5mRemove particles from lungs 0.5m Mucus-secreting cellsSecrete mucus 0.5mTrap bacteria / dust / pollen / particles 0.5m Blood vesselsSupply oxygen / nutrients to tissues of lung 0.5mSurround air sacs / good blood supply to air sacs 0.5mDeliver carbon dioxide / pick up oxygen 0.5mReference to wall of capillary being thin 0.5mEase of / rapid gaseous exchange OR short diffusion pathway 0.5m Smooth muscleAdjust size of airways in exercise 0.5m EpitheliumThin wall of air sacs 0.5mEase of / rapid gaseous exchangeOR short diffusion pathway 0.5mReference to larger surface area of numerous air sacs 0.5m Quality of written communication 2m24Any three from: 1m x 3 Inhaled air contains more oxygen than exhaled airInhaled air contains less carbon dioxide than exhaled airInhaled air contains less water vapourRelative amount / percentage of nitrogen also changesExplanation:Respiration results in lower blood oxygen / higher blood carbon dioxide 1m Oxygen enters blood / carbon dioxide leaves blood in air sacs 1m by diffusion 1m Water vapour diffuses from moist surface 1m Breadth of knowledge 2m max Quality of written communication 1m max Reading to learn (p. 7-32)1During inhalation,diaphragm muscles and intercostal muscles contract. 1m Diaphragm flattens and rib cage moves upwards and outwards. 1m Volume of thoracic cavity increases and pressure decreases. 0.5m Air rushes into the lungs. 0.5m During exhalation,diaphragm muscles and intercostal muscles relax. 1m Diaphragm returns to dome shape and rib cage moves downwards and inwards.1m Volume of thoracic cavity decreases and pressure increases.Air is forced out of the lungs. 1m2The iron lung was connected with a pump which changed the pressure inside. 1m When the pressure inside the iron lung is lower than that inside the lungs of the patient, air rushes into the lungs. 1m When the pressure inside the iron lung is higher than that inside the lungs of the patient, air inside the lungs is forced out of it. 1m3Advancement in the making of artificial joint 1m Reduces risk of allergy allows patients to move more flexibly 1m (Accept other reasonable answers)Chapter 8 Transport in humansExerciseMultiple-choice questions (p. 8-31)1 C2 D3 B4 A5 B6 B7 A8 B9 C10 B11 B12 BShort questions (p. 8-33)13 a i Haemoglobin 1mii Carries oxygen / forms oxyhaemoglobin 1m from lungs to tissues 1mb No nucleus / biconcave disc 1m14 HKCEE Biology 2006 I Q115 a Blood flows twice through heart 1mper one full circulation 1mORPulmonary circulation / to lungs 1mSystemic circulation / to the body 1mb Any one from: 1mMore oxygen reaches tissues / cells OR more efficient supply to tissues / cellsHelps sustain high blood pressureLess resistance to flowEasier to return blood to heartMore rapid circulationGreater activity possibleToo high a pressure does not damage lungs16HKCEE Biology 2001 I Q3bStructured questions (p. 8-34)17 HKCEE Biology 2005 I Q8a18 HKCEE Biology 2004 I Q3c19 a As the total cross-sectional area of vessels increases (due to branching of arteries intoarterioles) / large number of capillaries 1mResistance to blood flow increases and blood pressure falls 1mORFormation of tissue fluid at the arterial end of capillary beds 1mDecreases blood volume and therefore decreases blood pressure within the capillarybeds 1mORGreater distance from heart 1mPressure gradually reduces with distance from heart / pressure is maintained by smalllumen of the arteries 1mORVeins have a larger lumen 1mLarger volume equals decreased pressure 1mb Any two from: 1m x 2The arteries have a thick wall (particularly the tunica media) to resist pressureThe arteries contain numerous elastic fibresElastic fibres allow expansion under pressureSmall arterial lumen ensures high pressurec Any two from: 1m x 2The veins have a large lumen to reduce the resistance of blood flowing into themVeins rely on the movement of surrounding muscle tissue to move blood alongThey possess valves to prevent backflowDescription of how valves workd i Tissue fluid forms at the arterial end of capillary networks because of the highblood pressure. 1m ii Reabsorption at the venule end is brought about osmotically because of the lower solute potential provided by the retained proteins. 1m20 a Pulmonary artery 1mb S ➝ D ➝ C ➝ P ➝ X ➝ Q ➝ B ➝ A ➝ R(2m for all correct answers or no marks)c R has a thicker wall than S. 1mR has a smaller lumen than S. 1md Blood in R has more oxygen / less carbon dioxide / more glucose than in S. (any 2)1m x 2e The semilunar valves are closed. 1mThe cardiac muscle of A and C relaxes. 1mThe pressure inside A and C is lower than the pressure in P and R. 1m Essay (p. 8-35)21Any 10 from: 1m x 10 Highest pressure is in the aorta / arteries / closest to heart, where there is rhythmic rise and fall / pulse.Pressure drops progressively from arteries to arterioles.Pressure drops further through capillaries / progressive drop with increased distance from heart.Pressure in veins is low.(Marks of the above points may be awarded on annotated graph)Rise and fall in aorta or arteries corresponds to contraction of ventricles.Friction with walls causes pressure drop.Arterioles have large total cross sectional area. Capillaries give even greater crosssectional area.Few vessels subdividing into many smaller vessels, causing substantial pressure drop from arterial values / narrow lumen increases friction so pressure drops.Effect depends on whether arterioles are dilated or constricted / reference to elastic recoil in artery walls / maintains pressure.Pressure also drops in capillaries because of leakage of fluids into tissues.Pressure in veins / away from heart is non-rhythmic because influence of ventricles has been dissipated.Pressure in veins can be increased by squeezing action of (skeletal) muscles.This works because of the presence of valves in veins.Reading to learn (p. 8-36)1Blood is returned to the heart from different organs through blood vessels, instead of being used up as suggested by Galen. 1m Blood cannot flow from one ventricle to the other through pores in the septum of the heart, because there is no pore in the septum. Blood flows from one ventricle to the other through blood vessels. 1m 2Some of the deoxygenated blood in the right atrium and ventricle will bypass the lungs.1m Blood in the right atrium and ventricle directly goes to the left atrium and ventricle and pumped to different parts of the body. 1m Organs and tissues cannot get enough oxygen supply from the blood. 1m 3Harvey used careful calculations and repeated experiments to show blood was not used up, but flowed in a closed loop. 1m He dissected the septum of the heart to show it contained no pores. 1m 4Yes, scientists should be skeptical of other people’s findings. 1m Though Galen’s idea remained unchallenged for over 1000 years, Harvey was skeptical of the idea and did experiments to prove that it was wrong. Because of his skeptics and hard work, he finally worked out the correct theory of blood flow 1mChapter 9 Nutrition and gas exchange in plants ExerciseMultiple-choice questions (p. 9-23)1 C2 B3 C4 D5 D6 C7 C8 D9 A10 AShort questions (p. 9-25)11 a photosynthesis, autotrophs 0.5m x 2b Minerals, deficiency diseases 0.5m x 2c photosynthesis, respiration 0.5m x 2d Oxygen, carbon dioxide 0.5m x 2e compensation point, respiration 0.5m x 212 HKCEE Biology 2005 I Q8b13 a Any one from: 1mLongThin cell wallLack of waterproof layer / cuticleLarge surface areaPresent in large numbersMembrane proteins / carriers / channelsMany mitochondriab Active transport / diffusion 1mc The water potential of soil water is usually higher than that of the root cells.0.5mWater moves down the water potential gradient into the root cells by osmosis 0.5mthrough the channel proteins / differentially permeable cell membranes and 0.5mthe freely permeable cell walls. 0.5m 14 a D (mesophyll cell), E (air space) and F (guard cell) 0.5m x 3There are many air spaces to allow diffusion of gases on the moist surfacesof mesophyll cells. 1mGuard cells control the opening of stomata, which allow diffusion of gases. 0.5mb A (cuticle) and F (guard cell) 0.5m x 2Cuticle is impermeable to water. 0.5mGuard cells control the opening of stomata, which allow diffusion ofwater vapour. 0.5m 15 a Area of the field of view= 0.1 mm (height) ⨯ (5.7 cm / 3.4 cm ⨯ 0.1 mm) (length)= 0.0168 mm21mStomatal density= 4 stomata / 0.0168 mm2≈ 238 stomata per mm2 of the leaf surface 1mb Sorghum grows in dry conditions. 1mIt loses water through the stomata rapidly. 1mHaving few stomata can reduce water loss and hence conserve more water. 1m16 a To carry out photosynthesis. 1mT he cells locate near the top of the leaf so that they can trap the maximum amount oflight for photosynthesis. 1mThe cells are densely packed and contain many chloroplasts. 1mb BuoyancyStorage of oxygen / carbon dioxide / gasesAllows rapid diffusion of gases(any 2) 1m x 2c To enable exchange of gases. 1mIt would let in water if stomata are in lower epidermis. 1m Structured questions (p. 9-26)17 HKALE Biology 1998 I Q9a18 HKCEE Biology 2004 I Q4c19 HKCEE Biology 2005 I Q9Essay (p. 9-27)20Plants need to obtain oxygen and carbon dioxide from the atmosphere for respiration and photosynthesis respectively.They also need to obtain water and minerals from the soil for the production of different substances they need. 1m Carbon dioxide and oxygen:Plants exchange gases with the environment by diffusion. In terrestrial plants, gasexchange takes place through leaves, stems and roots.In leaves, gases from the environment diffuse into the air space through the stomata. Gases dissolve in the moist surface of the mesophyll cells. They then diffuse to the neighbouring cells. 1m Gases diffuse from the leaves to the environment in the reverse way.In woody stems, gas exchange takes place through the lenticels. 1m In roots, gas exchange takes place all over their surfaces. 1m Water and minerals:The water potential of the soil water is usually higher than that of the cytoplasm of the root hairs, water moves into the root hairs by osmosis. 1m Water passes across the cortex from cell to cell by osmosis or moves along the cell walls.1m Water is drawn up the xylem vessels by transpiration pull. 1m Most minerals are absorbed into the root cells by active transport. They are taken upagainst a concentration gradient using energy from respiration. 1m Some dissolved minerals are absorbed along water. 1m Communication max 3mReading to learn (p. 9-28)1Certain plants can make use of toxic substances as their nutrients. 1m 2It is cost-effective. 1m 3The toxic substances absorbed by the plants may escape from the leaves and pollute the air.The plants containing the toxic substances may affect the environment if they are notproperly disposed of.The clean-up process is slow because the plants take months to grow.(any 2) 1m x 2 4When the plants decay, the toxic substances absorbed by the plants may return to the soil.Animals living in soil may be harmed by the toxic substances. 1mChapter 10 Transpiration, transport and support in plants ExerciseMultiple-choice questions (p. 10-23)1 D2 C3 A4 C5 BShort questions (p. 10-24)6 HKCEE Biology 1997 I Q17 HKCEE Biology 2001 I Q38 HKCEE Biology 2006 I Q99 a Xylem cells have thick cell walls which contain a hard substance called lignin as wellas cellulose. This makes the xylem strong enough to provide support to the plants.2m The cortex cells have thin cell walls only. Support is provided by their turgidity.When the cells are turgid, they become rigid and press against each other. 2mb Diagram: The stem bends greatly and the leaves drooped 1mReason:The non-woody stem contains little xylem tissue. 1mIts support is mainly by the turgidity of cells. The cells become flaccid when there isnot enough water. 1mc The buoyancy of water provides much support to the submerged plant. 1m10 a i Water flow is not restricted. / Transpiration stream is maintained. 1mii Provides support / Waterproof to prevent water loss 1mb i The rate of water flow in xylem decreases as the total area of the stomatalopenings decreases. 1m ii Increasing temperature leads to higher rate of evaporation / transpiration. 1miii Lower plateau (start and finish at same point) 1m 11 HKCEE Biology 2002 I Q3Structured question (p. 10-26)12 a The dye had travelled 9 cm up the stem in two hours. 0.5mRate of water movement = 9/2 0.5m= 4.5 cm per hour 1mb Any two of the following: 1m x 2Increase the light intensity around the plant.Decrease the relative humidity around the plant.Use a fan to increase ventilation around the plant.c Prepare several Coleus plants with different numbers of leaves. 1mPut them under the same condition and start the experiment at the same time. 1mEstimate the total surface area of leaves in each plant by tracing all the leaves ongraph paper and counting the number of squares. 1mThe rate of water movement is expected to increase with the surface areaof leaves. 1mThe relationship may not be a directly proportional one since the surface areas ofstems are not included but transpiration occurs through the cuticle of stems as well.2m Essay (p. 10-27)13Light intensity:The rate of transpiration increases with an increase in light intensity. 1m As the light intensity increases, the stomata open wider. 1m More water vapour in the air space diffuses out through the stomata. 1m In darkness, the stomata close, so that the rate of transpiration decreases.1m Wind:The rate of transpiration increases in windy conditions. 1m In still air, the water vapour that diffuses out of the leaves accumulates around the stomata.1m Wind blows away the water vapour and prevents the decrease in the concentration gradient of water vapour between the air space in the leaves and the surrounding air. 1m Relative humidity:The rate of transpiration decreases with an increase in the relative humidity of thesurrounding air. 1m Since the air space in the leaves is saturated with water vapour, a higher relative humidity of the surrounding air will decrease the concentration gradient of water vapour between the air space and the surrounding air. 1m Therefore, less water vapour from the air space will diffuse out through the stomata. 1m (Or correct answers for other factors, e.g. air temperature, availability of soil water, air pollution, air pressure, etc.)14 HKALE Biology 2005 II Q5aReading to learn (p. 10-28)1Plants lose water rapidly under hot, dry conditions 1m when the stomata open for gas exchange. 1m The availability of water to plants is low. 1m2The needle shape greatly reduces the surface area of leaves. 1m Less water evaporates from the leaf surface. 1m3The needle-like leaves contain few chloroplasts. 0.5m The amount of food produced by photosynthesis in the leaves is small. 0.5m Instead, the epidermal cells of the stems contain many chloroplasts. 0.5m They can carry out photosynthesis to produce sufficient food for the plant.0.5m 4The swollen stems of cacti store a lot of water. 1m The turgidity of cells provides support for the plant. 1m。

脑源性神经营养因子诱发癫痫机制的研究进展

脑源性神经营养因子诱发癫痫机制的研究进展肖秋杰1综述，黄灵2审校1.右江民族医学院，广西百色533000；2.右江民族医学院附属医院神经内科，广西百色533000【摘要】癫痫是常见的神经系统疾病之一，目前关于其发病机制尚未完全明确。

近年来，大量的研究表明脑源性神经营养因子(BDNF)在癫痫的发生和发展过程中发挥了重要作用。

BDNF 通过激活酪氨酸蛋白激酶B (TrkB)及p75神经营养因子受体(p75NTR)从而促进神经元细胞死亡、改变神经元兴奋性/抑制性平衡(E/I balance)、调节MicroRNA 的表达、诱导海马体内苔藓纤维的异常发芽和突触重构等来进一步诱导癫痫发生。

本文通过综述有关对癫痫动物模型及癫痫患者的研究文献,从而揭示BDNF 参与介导癫痫的可能机制,为癫痫治疗新靶点提供参考依据。

【关键词】脑源性神经营养因子；癫痫；机制；研究进展【中图分类号】R742.1【文献标识码】A【文章编号】1003—6350（2023）03—0435—05Research progress on the mechanism of epilepsy induced by brain-derived neurotrophic factor.XIAO Qiu-jie 1，HUANG Ling 2.1.Youjiang Medical University for Nationalities,Baise 533000,Guangxi,CHINA;2.Department of Neurology,Affiliated Hospital of Youjiang Medical University for Nationalities,Baise 533000,Guangxi,CHINA【Abstract 】Epilepsy is one of the common neurological diseases.At present,its pathogenesis is not completely clear.Recent studies have shown that brain-derived neurotrophic factor (BDNF)plays an important role in the occur-rence and development of epilepsy.BDNF can further induce epilepsy by promoting neuronal cell death [activating tyro-sine protein kinase B (TrkB)and P75neurotrophic factor receptor (p75NTR)],changing neuronal excitability/inhibitory balance (E/I balance),regulating the expression of microRNA,inducing abnormal sprouting of mossy fibers in hippo-campus and synaptic remodeling.By summarizing the research literature on animal models of epilepsy and patients with epilepsy,this paper reveals the possible mechanism of BDNF in mediating epilepsy and provides a reference basis for new targets for epilepsy treatment.【Key words 】Brain-derived neurotrophic factor;Epilepsy;Machinism;Research progress　·综述·doi:10.3969/j.issn.1003-6350.2023.03.033基金项目：广西高校中青年教师基础能力提升项目(编号：2018KY0439)。

Biosensors and Bioelectronics

Biosensors and Bioelectronics Biosensors and bioelectronics have become increasingly important in the field of healthcare and biotechnology. These technologies have the potential to revolutionize the way we diagnose and treat diseases, monitor our health, and even detect environmental pollutants. However, there are several challenges and limitations that need to be addressed in order to fully realize the potential of biosensors and bioelectronics. One of the major challenges in the field of biosensors and bioelectronics is the need for improved sensitivity and selectivity. Biosensors need to be able to accurately detect and quantify specific biomolecules or analytes in complex biological samples. This requires the development of new sensing materials and technologies that can provide high sensitivity and selectivity, as well as the ability to operate in real-time and in a variety of conditions. Another challenge is the need for miniaturization and integration of biosensors into portable and wearable devices. The development of point-of-care diagnostic devices and wearable health monitors requires the integration of biosensors with microfluidics, electronics, and data analysis algorithms. This presents significant engineering and manufacturing challenges, as well as the need for new materials and fabrication techniques. Additionally, there are challenges related to the biocompatibility and long-term stability of biosensors. Many biosensors rely on biological molecules or living cells as sensing elements, which can be sensitive to environmental conditions and prone to degradation over time. Improving the stability and biocompatibility of these sensing elements is crucial for the development of reliable and long-lasting biosensors for medical and environmental applications. From a regulatory perspective, there are challenges related to the approval and commercialization of biosensor technologies. The development of new biosensors and bioelectronics often requires extensive testing and validation to ensure safety and efficacy. Navigating the regulatory pathwaysfor new medical devices and diagnostic tests can be time-consuming and costly, which can hinder the translation of innovative biosensor technologies from the lab to the clinic. On the other hand, there are also ethical and socialconsiderations related to the use of biosensors and bioelectronics. The collection and analysis of personal health data raise concerns about privacy and datasecurity. There is also a need to ensure that biosensor technologies are accessible and affordable for all populations, including underserved communities and developing countries. Despite these challenges, there are many exciting opportunities for the future of biosensors and bioelectronics. Advances in nanotechnology, materials science, and biotechnology are driving the development of new sensing platforms with unprecedented capabilities. The integration of biosensors with artificial intelligence and machine learning algorithms has the potential to revolutionize medical diagnostics and personalized medicine. In conclusion, biosensors and bioelectronics hold great promise for the future of healthcare and biotechnology, but there are significant challenges that need to be addressed. Improving sensitivity and selectivity, miniaturization and integration, stability and biocompatibility, regulatory approval, and ethical considerations are all important aspects that require attention. By addressing these challenges, we can unlock the full potential of biosensors and bioelectronics to improve human health and well-being.。

新生物医药英语

新生物医药英语Novel biopharmaceuticalsNovel biopharmaceuticals, also known as new biopharmaceuticals or emerging biopharmaceuticals, refer to a new generation of drugs developed using advanced biotechnology techniques. These drugs often involve the use of recombinant DNA technology, genetic engineering, and protein engineering to produce therapeutic molecules with enhanced efficacy and reduced side effects.One key area of innovation in novel biopharmaceuticals is the development of monoclonal antibodies (mAbs). Monoclonal antibodies are artificially created antibodies that can target specific proteins or cells in the body, allowing for precise and targeted therapy. These drugs have shown great promise in the treatment of various diseases, including cancer, autoimmune disorders, and infectious diseases.Another area of focus in the field of novel biopharmaceuticals is the development of gene therapies. Gene therapy involves the introduction of genetic material into a patient's cells to replace or correct a defective gene. This approach has the potential to treat genetic disorders, such as muscular dystrophy, cystic fibrosis, and hemophilia, by addressing the underlying cause of the disease at the genetic level.In addition to monoclonal antibodies and gene therapy, novel biopharmaceuticals also include other advanced therapies, such as cell-based therapies and RNA-based therapeutics. These treatments involve the use of living cells or RNA molecules to delivertherapeutic effects to patients.The development of novel biopharmaceuticals represents a significant advancement in the field of medicine, offering new treatment options for patients with previously untreatable or difficult-to-treat conditions. However, it is important to note that the development and approval process for these drugs can be complex and time-consuming, requiring rigorous testing and evaluation to ensure safety and efficacy.In conclusion, novel biopharmaceuticals are a rapidly evolving field in medicine, utilizing advanced biotechnology techniques to develop innovative drugs with enhanced therapeutic potential. These drugs, including monoclonal antibodies, gene therapies, and other advanced therapies, offer new treatment options for patients and have the potential to significantly impact healthcare outcomes.。