FALSE POSITIVES IN FUNCTIONAL NEARINFRARED TOPOGRAPHY

briefings in functional genomics oxford -回复“Functional Genomics in Oxford: Unleashing the Potential of Genome Research”Introduction:Functional genomics is a rapidly evolving field of study that aims to understand the functions and interactions of genes in order to unravel the mysteries of life. The University of Oxford, with its esteemed reputation in scientific research, plays a pivotal role in advancing the frontiers of functional genomics. In this article, we will delve into the exciting world of functional genomics at Oxford, exploring the key focus areas, cutting-edge techniques, and significant contributions made by researchers in thisever-expanding field.1. Understanding Functional Genomics:Functional genomics encompasses the study of how the genome regulates biological processes and influences the phenotype of an organism. At Oxford, researchers employ various approaches, including computational biology, next-generation sequencing, andhigh-throughput screening, to enhance our understanding of gene function.2. Key Focus Areas at Oxford:a. Disease Research: Advances in functional genomics have paved the way for a deeper understanding of the genetic basis of diseases. Oxford researchers employ functional genomics techniques to unravel the complex mechanisms underlying diseases such as cancer, cardiovascular disorders, and neurodegenerative conditions, with the ultimate goal of developing targeted therapeutics.b. Epigenomics: The study of epigenetic modifications, such as DNA methylation and histone modifications, is a vibrant area of research at Oxford's functional genomics laboratories. By elucidating the role of epigenetics in gene expression and disease development, researchers are discovering novel therapeutic targets and potential biomarkers for early diagnosis.c. Gene Regulation: Oxford's functional genomics researchers investigate the intricate web of gene regulation mechanisms, including transcription factors, non-coding RNA, and chromatinstructure. The elucidation of these mechanisms enhances our knowledge of gene expression control, providing insights into normal development and disease progression.d. Functional Annotation of Genomes: Identifying the functions of genes encoded within a genome is a fundamental aim of functional genomics. Oxford researchers apply computational and experimental approaches to annotate gene functions, deciphering the roles of genes in various biological processes and shedding light on the evolutionary significance of gene function divergence.3. Cutting-Edge Techniques at Oxford:a. Next-Generation Sequencing (NGS): NGS technologies have revolutionized functional genomics research at Oxford. These high-throughput sequencing techniques allow for the characterization of entire genomes, transcriptomes, and epigenomes in a cost-effective and time-efficient manner. Researchers use NGS to unravel gene expression profiles, detect genetic variants, and investigate epigenetic alterations associated with diseases.b. CRISPR-Cas9 Genome Editing: Oxford researchers spearhead breakthroughs in CRISPR-Cas9-mediated genome editing, enabling precise manipulation of the genome to study gene function. This technique has expanded the possibilities of functional genomics research, offering unprecedented opportunities to elucidate the role of specific genes in disease mechanisms and therapeutic interventions.c. Functional Screens: High-throughput functional screens allow Oxford researchers to systematically identify genes involved in specific biological processes or diseases. These screens involve large-scale genetic perturbations, such as RNA interference (RNAi) or CRISPR knockout libraries, coupled with phenotypic analyses. By identifying genes essential for specific cellular functions, functional screens contribute to our understanding of gene function and potential therapeutic targets.4. Significant Contributions by Oxford Researchers:a. The Cancer Genome Atlas (TCGA): Oxford researchers were instrumental in the international collaboration that led to the creation of TCGA, a comprehensive catalog of genomic alterationsin various cancer types. TCGA has provided crucial insights into the genetic basis of cancer, paving the way for personalized medicine approaches and targeted therapies.b. ENCODE Project: As part of the ENCODE Project, Oxford researchers contributed to the functional annotation of the human genome. This project aimed to identify all functional elements within the genome, shedding light on gene regulation, non-coding RNA, and the three-dimensional architecture of the genome.c. Single-Cell Genomics: Oxford researchers have made significant contributions to the emerging field of single-cell genomics. By studying individual cells, researchers can unravel cellular heterogeneity, identify rare cell types, and investigate gene expression dynamics at unprecedented resolution. These insights have the potential to revolutionize our understanding of development, diseases, and therapeutic interventions.Conclusion:Functional genomics research at the University of Oxford continues to push the boundaries of our understanding of gene function andits impact on health and disease. Through their focused research areas, cutting-edge techniques, and noteworthy contributions, Oxford researchers play a crucial role in unraveling the mysteries of the genome. As the field of functional genomics continues to evolve, Oxford will undoubtedly remain at the forefront of groundbreaking discoveries with far-reaching implications for human health.。

the number of positive predictionsThe Number of Positive Predictions: Its Importance and ApplicationsIn the realm of statistics and machine learning, the number of positive predictions refers to the count of instances where a model correctly identifies a positive outcome. This metric is crucial in evaluating the performance of a predictive model, especially in scenarios where positive outcomes are of particular interest or have significant implications.The importance of positive predictions cannot be overstated. In medical diagnostics, for instance, a high number of positive predictions ensures that patients with diseases are accurately identified, enabling timely treatment and improved outcomes. In marketing, positive predictions might refer to the successful prediction of customer behavior, such as purchases or responses to advertisements. In such cases, a high number of positive predictions can lead to increased sales and revenue.To achieve a high number of positive predictions, it is essential to have a robust and accurate predictive model. This involves the careful selection of relevant features, the use of appropriate algorithms, and the continuous refinement of models through training and testing. Additionally, it is crucial to have a balanced dataset that represents the real-world distribution of positive and negative outcomes to avoid biases in predictions.The applications of positive predictions are diverse and span across multiple industries. In finance, for instance, positive predictions might involve the accurate identification of profitable investment opportunities. In the field of security, they might refer to the successful detection of malicious activities or threats. In human resources, positive predictions might involve the accurate identification of top candidates for a job position based on their past performance and skills.In conclusion, the number of positive predictions is a critical metric inevaluating the performance of predictive models. It not only provides insights into the model's accuracy but also helps in making informed decisions in various scenarios where positive outcomes are of particular interest. By investing in robust predictive models and continuously improving their accuracy, we can ensure a higher number of positive predictions, leading to better outcomes and increased efficiency across various industries.。

正负相关英语In the realm of language learning, the concepts of positive and negative correlation play a pivotal role in understanding the relationship between language elements and proficiency. A positive correlation indicates that as one variable increases, so does the other, and vice versa. Conversely, a negative correlation implies that an increase in one variable leads to a decrease in the other.Positive Correlation in Language Learning。

When discussing positive correlation in language learning, we often refer to the relationship between exposure and acquisition. The more exposure a learner has to the target language, the greater their proficiency tends to be. This can be seen in immersive environments where learners are constantly surrounded by the language, necessitating its use for daily interactions. For instance, vocabulary acquisition is typically positively correlated with reading; the more a person reads, the broader their vocabulary becomes.Another aspect of positive correlation is the relationship between practice and fluency. Regular speaking practice helps learners become more fluent, as it allows them to use language structures spontaneously and with increasing ease. This is supported by the theory of comprehensible output, which suggests that the act of producing language contributes to language development.Negative Correlation in Language Learning。

T ITLEIdentification of a RAPD marker linked to a male fertility restoration gene in cotton (Gossypium hirsutum L.)Tien-Hung Lan1, Charles G. Cook2 & Andrew H. Paterson1, *1Department of Soil and Crop Sciences, Texas A&M University, College Station, TX 77843-24742USDA-ARS, Weslaco, TX 78596(*authorforcorrespondence,**********************.edu,fax:409-845-0456)K EY WORDSbulked segregant analysis, cytoplasmic male sterility, near-isogenic line, linkage mappingA BSTRACTOne RAPD marker, 6 cM away from a gene which restore male-fertility of a male-sterile cytoplasm was found in upland cotton (Gossypium hirsutum L.). This marker was discovered after screening 400 decamers to identify DNA polymorphism between near-isogenic lines and subsequently verified by bulked segregant analysis in an F2 population of 89 individuals derived from a cross between a cytoplasmic-male-sterile line and a restorer line. The RAPD marker was sub-cloned, sequenced, and mapped to a cotton high density RFLP map. The evaluation and utilization of this RAPD for tagging and ultimately cloning the fertility-restoring gene is discussed.I NTRODUCTIONCytoplasmic male sterility (CMS) is a maternally inherited trait conferring the inability to produce functional pollen because of interaction between cytoplasmic and nuclear genes. Since CMS does not affect female fertility, male sterile plants are able to set seeds as long as viable pollen are provided. The presence of certain nuclear genes, Rf (Restoring fertility), can effectively suppress the male-sterile cytoplasm and restore pollen fertility. The application of CMS/Rf system has proved to be an effective means to produce commercial F1 hybrid seed for many crops (Williams 1992). Cotton is predominantly a self-pollinated crop, however, cotton breeders have long been trying to breed F1 hybrid cotton, to harness F1 heterosis for many desirable traits such as high seedling-vigor, earliness, superior fiber quality and yield (Davis 1978), but also because F1 hybrid seeds could generate huge revenue for the seed industry (Anonymous 1985). Therefore, attempts to produce F1 hybrid cotton on a commercial scale have never stopped (Anonymous 1987). Before the introduction of the CMS/Rf system into Upland cotton, the only way to generate commercial hybrid cotton was through hand emasculation and crossing, which was economical only in China and India where labor cost are low. In US where labor costs are high, hand-crossing made the price of hybrid cotton prohibitive.The first CMS line of commercial cotton was introduced by crossing an upland cotton, G. hirsutum, as male parent, to a wild species, G. harknessii (Meyer 1973). A Rf line was also developed by transferring a nuclear restorer gene from G. harknessii into G. hirsutum simultaneously (Meyer 1975). The F1 hybrid population generated by crossing the CMS line to the Rf line showed a wide range of male fertility expression. Several CMS/Rf lines were then developed through the backcross method (Weaver and Weaver 1977). Later, a second Rf which expresses incomplete dominance, was also identified in G.barbadense (Sheetz and Weaver 1980). Identification of molecular markers closely linked to the nuclear Rf genes could help breeders to distinguish male-sterile and fertile plants prior to pollen shed. Here we report the identification of molecular markers closely-linked to the nuclear Rf gene under male-sterile cytoplasm in commercial cotton.M ATERIAL AND M ETHODA pair of cotton near-isogenic lines, HAF277 and DELCOT277 (kindly provided by R. Bridge, USDA-ARS, Stoneville, MS; Meyer 1975; Weaver and Weaver 1977; Sheetz and Weaver 1980), carrying the Rf gene and CMS respectively were used for initial RAPD-PCR screening. A single primer was used in each PCR reaction, and the PCR products were resolved in 1.6 % agarose gels using established techniques (Williams et al. 1990). An F2 segregating population comprised of 89 individuals from a cross of a CMS line, A2, and a Rf line, B418, were used for bulked segregant analysis (Giovannoni et al. 1991, Michelmore et al. 1991).Linkage analysis was performed using MapMaker (Lander et al. 1987), Macintosh version 2.0 (kindly provided by S. Tingey, DuPont), using the Kosambi centiMorgan function. A threshold of LOD 3.0 was used to test linkage.ResultRAPD screeningAfter screening 400 random decamers obtained from the University of British Columbia, 15 positive RAPDs were identified, which represent 3.75 % of the tested primers . These positive RAPDs were verified twice, on fresh DNA extractions, to avoid false-positives. We applied bulked segregant analysis (Giovannoni et al. 1991; Michelmore et al. 1991) for further exploration of the 15 positive RAPD markers identified in the near-isogenic lines. From the A2 x B418 F2 population, we chose 19 individuals that were clearly fertile, and 19 individuals that were clearly sterile to construct the DNA pools, and to avoid individuals that were of doubtful phenotype. To increase the stringency of our test, two pairs of synthetic DNA pools were constructed for each phenotype (fertile and sterile). One pair of pools contained 9 individuals each in the CMS and Rf pool, the other contained 10 individuals each. The pooled DNA was then used as template for RAPD-PCR analysis. Among the fifteen identified markers, two RAPD markers, R6952 (sequence CGGTTTCGTA ) and R6861 (sequence CGTGACAGGA), putatively distinguished between male-sterile and male-fertile pools.Two RAPD markers were then subjected to linkage analysis using the 38 F2 plants used in constructing the synthetic DNA pools. Four replicas were used, and each amplified band was scored as a dominant locus. Linkage analysis show R6861 was 24.6 cM from the Rf gene, and R6592 was 2.3 cM away.SequencingThe marker R6592 was eluted from the agarose gel, and cloned into the EcoRV site of dTTP-end-filled pBluescript(KS), then sequenced from both ends using the T3/T7 primers on a Applied Biosystem 373 DNA Sequencing System. From the end sequences of R6592, the correct RAPD primer sequence was identified (Fig. 1), and the sequence ofR6952 was deposited into Genbank with accession number AF094829 and AF094830. A blast search of R6592 in Genbank did not found any corresponding sequence at 95% confidence level.Southern AnalysisGenomic DNA of A4 and B418 was digested by BamH1, Cfo1, EcoR1, EcoRV, HindIII and XbaI, gel-separated, and blotted to Hybond N+ membrane as described (Reinisch et al. 1994). Gel-isolated R6592 and R6861 were P32 labeled and applied to the blots. However, a smear pattern was observed and no restriction-enzyme-polymorphism could be identified between A4 and B418, suggesting that both R6592 and R6861 contain repetitive sequence. Thus, R6592 was further digested with a mixture of Acc1, Cfo1, Hinf1, HindIII and EcoR1, and sub-cloned into pBluescript to try to remove the interfering repetitive sequence. One clone, R6592a14, identified a HindIII polymorphism between A4 and B418, though the background on the p32-exposed film was still pretty high. R6592a14 was then used to genotype the 89 individuals of A4 x B418 F2 population for the HindIII polymorphism. Linkage analysis revealed the distance between Rf gene and R6592 to be 6 cM (Fig 2a), not significantly different from the earlier estimate based on 38 individuals. Further, we tried to map R6592a14 in a cotton high-density linkage map based on a cross of Gossypium hirsutum and Gossypium barbadense (Reinisch et al. 1994). R6592a14 did not detect HindIII polymorphism in this population, but one of six genomic restriction fragments did detect EcoRV polymorphism. R6592a14 mapped to a linkage group that was tentatively identified as chromosome 20 (Reinisch et al. 1994), between markers pAR959 and pAR3-41 (Fig. 2b).DiscussionSince the near-isogenic lines we used have been backcrossed for at least 8 generations, the introgressed region should be less than 14.1 cM (Hanson 1959; using the average cotton chromosome length of 200 cM from Reinisch et al. 1994). This is consistent with the distance of R6592a14 to the Rf locus. The larger distance between R6861 and Rfwas unexpected, but R6861 was not extensively verified, so the estimated map distance is based on a small number of individuals.For the purpose of marker-assisted selection, the best scenario was to find tightly linked markers on both side of the target gene to reduce the risk of mis-genotyping due to single recombination events between the marker and the target gene. More markers linked to the Rf gene would be desirable and could be found either by targeted RAPD or AFLP screens, or in the course of further enrichment of the cotton map. The CMS/Rf is a potentially cost-effective way to produce F1 hybrid seeds. Rf genes have been successfully mapped in rice and common bean by RAPD/bulk segregant analysis (He et al. 1995; Zhang et al. 1997), and we provide a marker diagnostic of this important phenotype in cotton. In addition to its utilization in marker-assisted selection, this marker may serve as a starting point for positional cloning of the Rf gene.A CKNOWLEDGMENTWe thank Mark D. Burow for technical assistance. Aspects of the work described here were supported by USDA 91-37300-6570, to AHP, and Texas Higher Education Coordinating Board Award 999902-148 to AHP and Rod A. Wing.Fig. 1Partial DNA sequence of clone R6592. Primer sequences are underlined.Fig. 2a. Linkage of Rf gene and marker R6592 in G. hirsutum A2 x B418 F2 population.b. Marker R6592 mapped to cotton chromosome 20 in G. hirsutum (race palmeri) x G. barbadense “k101” primary mapping population (Reinisch et al., 1994).ReferenceAnonymous, 1985 Seed Money. Forbes 136: 219-210.Anonymous, 1987 Finger-Picking Good: Hybrid Cotton. The Economist 303: 91. Davis, D. D., 1978 Hybrid cotton: specific problems and potentials. Adv Agron 30: 129-157.Giovannoni, J., R. Wing, M. Ganal and S. Tanksley, 1991 Isolation of molecular markers from specific chromosomal intervals using DNA pools from existing mapping populations. Nucl Acids Res 19: 6553-6558.Hanson, W., 1959 Early generation analysis of lengths of heterozygous chromosome segments around a locus held heterozygous with backcrossing or selfing. Genetics 44: 833-837.He, S., Z. H. Yu, C. E. Vallejos and S. A. Mackenzie 1995 Pollen fertility restoration by nuclear gene Fr in CMS common bean: An Fr linkage map and the mode of Fr action. Theor Appl Genet 90: 1056-1062.Lander, E., P. Green, J. Abrahamson, A. Barlow, M. Daly et al., 1987 MAPMAKER: An interactive computer package for constructing primary genetic linkage maps of experimental and natural populations. Genomics 1: 174-181.Meyer, V., 1975 Male sterility from Gossypium harknessii. J Hered 66: 23-27.Meyer, V. G., 1973 Fertility restorer genes for cytoplasmic male-sterility from Gossypium harknessii. Beltwide Cotton Prod. Res. Conf. Proc., p65.Michelmore, R., I. Paran and R. Kesseli, 1991 Identification of markers linked to disease-resistance genes by bulked segregant analysis: a rapid method to detect markers in specific genomic regions by using segregating populations. Proc. Natl. Acad. Sci. USA 88: 9828-9832.Reinisch, A., J.-M. Dong, C. Brubaker, D. Stelly, J. Wendel et al., 1994 A detailed RFLP map of cotton (Gossypium hirsutum x G. barbadense): Chromosome organization and evolution in a disomic polyploid genome. Genetics 138: 829-847.Sheetz, R. H., and J. B. Weaver, 1980 Inheritance of a fertility enhancer factor from Pima cotton when transferred into Upland cotton with Gossypium harknessii Brandegee. Crop Science 20: 272-275.Weaver, D., and J. Weaver, 1977 Inheritance of pollen fertility restoration in cytoplasmic male-sterile upland cotton. Crop Sci 17: 497-499.Williams, J. G. K., A. R. Kabelik, K. J. Livak, J. A. Rafalski and S. V. Tingey, 1990 DNA polymorphisms amplified by arbitrary primers are useful as genetic markers. Nucl. Acids Res. 18: 6531-6535.Williams, M. E. L. C. S., III, 1992 Molecular Biology Of Cytoplasmic Male Sterility, pp. 23-51 in Plant Breeding Reviews, edited by J. Janick. John Wiley and Sons, Inc., New York.Zhang, G., T. S. Bharaj, Y. Lu, S. S. Virmani and N. Huang, 1997 Mapping of the Rf-3 nuclear fertility-restoring gene for WA cytoplasmic male sterility in rice using RAPD and RFLP markers. Theor Appl Genet 94: 27-33.。

Microsoft Advanced Threat Analytics (ATA)provides a simple and fast way to understand whatis happening within your network by identifyingsuspicious user and device activity with built-inintelligence and providing clear and relevant threatinformation on a simple attack timeline.Microsoft Advanced Threat Analytics leverages deep packet inspection technology, as well as information from additional data sources (SIEM and AD) to build an Organizational Security Graph and detect advanced attacks in near real time. The ATA system continuously goes through four steps to ensure protection:How it works Step 3: Detect After building an Organizational Security Graph, ATA can then look for any abnormalities in an entity’s behavior and identify suspicious activities — but not before those abnormal activities have been contextually aggregated and verified. ATA leverages years of world-class security research to detect known attacks and security issues taking place regionally and globally.ATA will also automatically guide you, asking you simple questions to adjustthe detection process according to your input.Step 1: Analyze After installation, by using pre-configured, non-intrusive port mirroring, allActive Directory-related traffic is copied to ATA while remaining invisible to attackers. ATA uses deep packet inspection technology to analyze allActive Directory traffic. It can also collect relevant events from SIEM (security information and event management) and other sources.Step 2: Learn ATA automatically starts learning and profiling behaviors of users, devices, and resources, and then leverages its self-learning technology to build an Organizational Security Graph. The Organizational Security Graph is a map of entity interactions that represent the context and activities of users, devices, and resources.Microsoft Advanced Threat AnalyticsStep 4: Alert While the hope is that this stage is rarely reached, ATA is there to alert you of abnormal and suspicious activities. To further increase accuracy and save you time and resources, ATA doesn’t only compare the entity’s behavior to its own, but also to the behavior of other entities in its interaction path before issuing an alert. This means that the number of false positives are dramatically reduced, freeing you up to focus on the real threats. At this point, it is important for reports to be clear, functional, and actionable in the information presented. The simple attack timeline is similar to a social media feed on a web interface and surfaces events in an easy-to-understand way.© 2015 Microsoft Corporation. All rights reserved. This document is provided "as-is." Information and views expressed in this document, including URL and other Internet Web site references, may change without notice. You bear the risk of using it. This document does not provide you with any legal rights to any intellectual property in any Microsoft product. You may copy and use this document for your internal, reference purposes. You may modify this document for your internal, reference purposes.。

true positive rate 英文解释True positive rate, also known as sensitivity or recall, is a statistical metric used to evaluate the performance of a binary classification model. It measures the proportion of correctly classified positive instances out of all actual positive instances in a dataset.In binary classification, we have two classes: positive and negative. True positive (TP) refers to the cases where the model correctly predicts the positive class, while false negative (FN) refers to the cases where the model incorrectly predicts the negative class. True positive rate is calculated as TP divided by the sum of TP and FN:True Positive Rate = TP / (TP + FN)True positive rate is a crucial metric as it provides insights into the model's ability to correctly identify positive instances. In medicine, for example, it is vital for a model to have a high true positive rate in order to correctly detect diseases or conditions.Let's consider an example to better understand the concept of true positive rate. Suppose we have a dataset of 100 patients, of which 75 have a specific disease. We have a classification model that predicts whether a patient has the disease or not. After applying the model, it correctly identifies 50 of the 75 positive cases (true positives) and misclassifies 5 positive cases as negative (false negatives).To calculate the true positive rate, we divide the number of true positives by the sum of true positives and false negatives:True Positive Rate = 50 / (50 + 5) = 0.909 (or 90.9%)This means that the model has a true positive rate of 90.9%, indicating that it correctly identifies the disease in 90.9% of the positive cases. It is important to note that a high true positive rate indicates a high sensitivity of the model towards positive instances, making it more reliable in detecting the desired condition.The true positive rate is often used alongside other evaluation metrics, such as precision, accuracy, and F1 score, to provide a comprehensive understanding of aclassification model's performance. While true positive rate focuses on the model's ability to correctly classify positive instances, precision measures the proportion of true positive instances out of all predicted positive instances. Accuracy measures the overall correctness of the model's predictions, considering both true positives and true negatives. F1 score is the harmonic average of precision and recall (which is the same as the true positive rate).In conclusion, the true positive rate is a metric that evaluates the ability of a binary classification model to correctly identify positive instances. It is a valuable measure used in various fields, including healthcare, quality control, and information retrieval. Understanding the true positive rate helps to assess the reliability and effectiveness of a classification model in different applications.。

A genome-wide analysis of sumoylation-related biological processesand functions in human nucleusFengfeng Zhou a,1,Yu Xue b,1,Hualei Lu b,Guoliang Chen a,Xuebiao Yao b,c,*a National High-Performance Computing Center at Hefei,University of Science and Technology of China,Hefei230027,Chinab Laboratory of Cell Dynamics,University of Science and Technology of China,Hefei230027,Chinac Department of Physiology,Morehouse School of Medicine,Atlanta,GA30310,USAReceived30March2005;accepted26April2005Available online23May2005Edited by Takashi GojoboriAbstract Protein sumoylation is an important reversible post-translational modification of proteins in the nucleus,and it orchestrates a variety of the cellular processes.Genome-wide analysis of functional abundance and distribution of Small Ubiq-uitin-related MOdifier(SUMO)substrates may shed a light on how sumoylation is involved in nuclear biological processes and functions.Two interesting questions about sumoylation have emerged:(1)how many SUMO substrates exist in mammalian proteomes,such as human and mouse,(2)and what are their functions and how are they involved in a variety of biological pro-cesses?To address these two questions,we present an in silico genome-scale analysis for SUMO substrates in human.Based on the pattern recognition and phylogenetic conservation,we re-trieved a list of2683potential SUMO substrates conserved in both human and mouse.Then,by functional enrichment analysis, we surveyed the over-represented GO terms and functional do-mains of them against the whole human proteome.Besides the consistence between our analyses and in vivo or in vitro work, the in silico predicted candidates also point to several potential roles of sumoylation,e.g.,perception of sound.These potential SUMO substrates in human are of great value for further in vivo or in vitro experimental analysis.Ó2005Federation of European Biochemical Societies.Published by Elsevier B.V.All rights reserved.Keywords:SUMO;Sumoylation;Transcription factor;Signal transduction;Perception of sound1.IntroductionSmall Ubiquitin-related MOdifier(SUMO)proteins are ubiquitously expressed in eukaryotic cells[1–4].They are reversiblylinked to specific lysine residues of numerous sub-strates by sumoylation,and are implicated in various intracel-lular processes,such as nucleocytoplasmic signal transduction [5],transcription[6–8],stress response[9]and mitosis/cell-cycle progression[10,11],etc.SUMO proteins belong to the super-family of ubiquitin-like modifiers(UBLs)[12],and consist of three components in mammalian cells:SUMO-1,SUMO-2, and SUMO-3[13].Only recently was another component SUMO-4discovered in human[14].SUMO proteins are highly conserved from yeast to human.Conventional experimental approaches are employed to identify SUMO substrates with their sites in vivo or in vitro, although labor-intensive and time-consuming.Before millen-nium,there were only12experimentally verified SUMO sub-strates[4].Recently,several genomic/proteomic-wide analyses of SUMO substrates have been deployed by mass spectrometry(MS)approaches in budding yeast[15–20]. Approximately,$500potential SUMO substrates in these large-scale experiments were found.These results are excel-lent candidates for further experimental consideration. Moreover,it is of great interest to identify novel SUMO substrates in mammals,especially in human given the recent completion of human genome project[21–25].Due to the complexity of human proteome,only about two hundred candidates were found so far,and the exact sumoylation sites on most of these substrates remain elusive.In order to provide a more comprehensive view on sumoylation in human and on how they are involved in all kinds of intra-cellular biochemical processes,we developed a program SSP (SUMO substrates prediction)and conducted an in silico genome-wide analysis for nuclear SUMO substrates in hu-man,based on pattern recognition and phylogenetic conser-vation approaches.The majority of the SUMO substrates have a consensus motif with four amino acids.There are several motifs re-ported in the literatures:such as w-K-X-E(w is a hydropho-bic amino acid)[2,4,23]and[VILMAFP]K.E(http:// /elmPages/MOD_SUMO.html)[26],etc.And a nuclear localization signal(NLS)suffices for SUMO conju-gation in vivo[27],with only a few exceptions[28].So we fol-low the w-K-X-E motif with a NLS as the consensus pattern for SUMO substrates prediction.In addition,the potential false positive hits are greatly reduced by phylogenetic conser-vation.For the prediction of sumoylation sites,SSP is nearly as sensitive as the existing tool SUMOplot(http://www. /doc/sumoplot),with significantly improved speci-ficity(see in Table2).We have generated a list of2683potential SUMO substrates conserved between human and mouse.We adopted the functional enrichment analysis to search for*Corresponding author.Fax:+865513607141.E-mail address:yaoxb@(X.Yao)1The authors contributed equally to this work.0014-5793/$30.00Ó2005Federation of European Biochemical Societies.Published by Elsevier B.V.All rights reserved.doi:10.1016/j.febslet.2005.04.076FEBS29615FEBS Letters579(2005)3369–3375Table1The prediction results of known SUMO substratesthe over-represented GO terms and functional domains (Interpro)of the potential SUMO substrates against the whole human proteome.Our analyses of these potential sub-strates support the previous prediction of the functional rele-vance of sumoylation.For example,transcription factors and protein kinases are abundant in SUMO substrates,playing important roles in transcriptional regulation and gene expres-sion [6–8]and signal transduction [1,2,29].However,surpris-ingly,newly identified sumoylation candidates also point to several potential roles of sumoylation, e.g.,perception of sound.Further analyses of these candidates in vivo or in vi-tro will provide insights into the function of sumoylation in mammalians,especially human.2.Materials and methods2.1.Identification of SUMO substrates with their sites in human andmouseWe took the orthology-relationship data of mouse and human with the corresponding sequences from the InParanoid database (Version 2.6,30/03/2004)[30].For the 34499mouse sequences and 36379hu-man sequences in InParanoid,we firstly scanned the sequences for the consensus motif w -K-X-E in mouse and human,respectively.Sequences without such motif were excluded.Then we got 13026sequences in mouse and human,respectively.By PSORT II [31],we predicted the sub-cellular localization of the retained sequences.Only proteins with predicted nuclear localization were retained.After this step,there were 6662sequences in mouse and 7649in human,respectively.In order to eliminate the potential false positive results,we followed a simple rule below:for the pairwise orthologs between the retained mouse and human proteins,there must be at least one consensus SUMO substrate motif at the same position after sequence alignment.Thus,proteins without such orthologs were excluded.After the se-quence alignment,orthologs sharing no consensus motif at the same position were also excluded,resulting a final 2683orthologous proteins in both mouse and human proteomes.2.2.Statistical analysis for SUMO substratesWe downloaded the GO (08/10/2004)and Interpro (23/06/2004)[32]association files from EBI (ftp:///pub/)and searched for the GO and Interpro annotations of human proteins.Among 36379human proteins of InParanoid,there are 24090and 26873annotated with at least one GO and Interpro term,respectively,and there are 1956and 2264proteins of our 2683potential SUMO substrates anno-tated,separately.Following a statistical approach described before [33],we compared the group S (predicted SUMO substrates of human)against the group W (whole human proteome)to find a GO/Interpro term t that occurred more frequently in group S than in group W.Here we define:N total number of proteins in group W annotated by GO/Interpron number of proteins in group W annotated by GO/Interpro term tM total number of proteins in group S annotated by GO/Inter-promnumber of proteins in group S annotated by GO/Interpro term tThen we calculate the enrichment ratio of GO/Interpro term t in group S,and with the equation of the hypergeometric distribution,we can also calculate its P -value:Enrichment ratio ¼mM n ;p -value ¼X n m 0¼mM m 0ÀÁN ÀM n Àm 0ÀÁNnÀÁðEnrichment ratio P 1Þor p -value ¼X m m 0¼0M m 0ÀÁN ÀM n Àm 0ÀÁnÀÁðEnrichment ratio <1Þ.In this work,we only consider the over-representation of GO/Interpro groups with Enrichment_ratio P 1.Table 1(continued)85experiment-verified SUMO substrates are listed.Our method can predict 64of them correctly ($75%).A.Fernandez-Lloris R.et al.(2002)Post-translational Sox6protein modification by SUMO-1.In:28th Meeting of the Federation of European Biochemical Societies,Istanbul,Turkey,pp.20–25.aNo consensus motif (6proteins).bNot ‘‘nuc’’(nuclear)hit by PSORT II prediction (9proteins).cExcluded by orthlogy information (6proteins).3.Results3.1.Accuracy of SSP1.0programIt is reported that evolutionary stable sites can be used to im-prove the prediction specificity for functional sites/motifs[34], based on the hypothesis that functional sites/motifs should be more conserved than random pseudo-sites/motifs.For our phylogenetic conservation analysis,we chose distance near specie mouse for human rather than other too distant species such as budding yeast orfly,to avoid missing too many real sumoylation sites.Too near species such as primates are not used,because these proteomes are too similar with human and cannot reduce the potential false positives much.So we adopted the phylogenetic conservation between human and mouse to reduce the potential false positives.Curated from the published work,we got85experimental verified SUMO substrates(see Table1).The SSP1.0can predict64(75%)of them correctly.For precise sumoylation site prediction,we compare our computational results with the existing tool SUMOplot(see Table2).For the63known sumoylation sites,our method could recover51of them with sensitivity S n$81%,which is similar to the SUMOplot results$81%(motifs with high prob-ability)or$84%(all).Yet the specificity S p of our approach is significantly improved to$60%(51in a total of86),compared to SUMOplot$31%(motifs with high probability)or$15%Table2The comparison of the sumoylation site prediction against SUMOplotProtein name Sumoylation sitesVerified SSP1.0SUMOplotAP-2a K10IKYE a1/1b;(1/4)cAP-2b K10IKYE1/1;(1/5)AP-2c K10IKYE1/1;(1/4)AR(androgen receptor)K386,K520IKLE,VKSE2/3;(2/6) ARNT(aryl hydrocarbon receptor nuclear transporter)K245VKKE0/2;(0/5)C/EBP b-1K173LKAE1/2;(1/4)C/EBP a(CCAAT/enhancer-binding protein alpha)K159LKAE1/1;(1/1)c-Jun K229AKME,LKEE,IKAE1/3;(1/4)c-Myb K503,K527IKQE,IKQE2/5;(2/10)Elk-1K230,K249VKVE2/2;(2/4)FAK(focal adhesion kinase)K152WKYE1/5;(1/17)GR(glucocorticoid receptor)K277,K293VKTE,IKQE,VKRE0/1;(0/5) GRIP1K239,K731,K788VKLE,MKQE2/7;(2/17) HIPK2K1182LKIE,LKPE0/3;(0/7) hnRNP C K237IKKE,VKME0/4;(1/12)HSF1(heat shock transcription factor1)K298VKPE,LKSE,MKHE,VKEE1/6;(1/9)HSF2(heat shock transcription factor2)K82VKQE,IKQE,LKSE1/6;(1/9)I j B a K21LKKE,MKDE1/4;(1/4)LEF1K27,K269FKDE,VKQE2/3;(2/7) NEMO/IKK c K277,K309AKQE,LKEE1/8;(1/13)Nurr1(NR4A2,RNR-1,TINUR,HZF-3)K91,K577IKVE,LKLE2/4;(2/10)p300/CBP K1017,K1029MKTE,VKEE,VKVE,VKEE,FKPE2/11;(2/22)p73a K627IKEE1/3;(1/7)PML(promyelocytic leukaemia protein)K65,K160,K490LKHE,IKME3/6;(3/7)PR(progesterone receptor)K388IKEE1/3;(1/6) SALL1K1086IKTE,IKTE1/7;(1/15) Smad4K113,K159VKDE1/3;(1/7)Sp3K539IKDE,IKEE1/3;(1/5) SREBP-1a K123,K418IKEE,LKQE,VKTE2/7;(2/11)SRF(serum response factor)K147IKME1/1;(1/3) Steroid receptor coactivator SRC-1/NCoA-1K732,K774AKAE,IKLE,VKVE,IKLE,IKSE1/7;(1/13)Tcf-4K297FKDE,VKQE1/6;(1/10) TDG K330VKEE0/0;(0/8)TEL K99IKQE0/2;(0/3)TIF1a K690,K708IKQE,VKQE,IKLE2/5;(2/11) TOPO I K117,K153IKKE,IKTE,IKEE,FKIE,IKGE,MKLE2/12;(2/29) Topors K560LKRE0/4;(1/10) GATA4K366IKTE1/1;(1/2)ZNF67K411VKGE,VKEE1/2;(1/5) PLAG1K244,K263FKCE,VKTE,IKDE,LKGE2/5;(2/11) Steroidogenic factor1K199,K194FKLE,IKSE2/2;(2;3) GATA1K137LKTE1/1;(1/4) NFAT K684,K897IKTE,IKQE2/3;(2/6)Total sites6351/86d51/166;(53/355) 63verified sumoylation sites of43known SUMO substrates are chosen.a SSP1.0hits are in bold character font.b SUMOplot hits(motifs with high probability)/total predicted sites(motifs with high probability).c SUMOplot hits(all)/total predicted sites(all).d SSP1.0hits/total predicted sites.(all).So our method greatly reduces the number of potential false positives while still keeps a satisfying sensitivity.3.2.Functional abundance and distribution of SUMO substrates SUMO substrates are implicated in many intracellular pro-cesses.However,the systems biology of sumoylation remains unclear.Thus,it is of great interest to illustrate in which func-tions the nuclear SUMO substrates of human are significantly abundant.Here we perform a statistical analysis to predict such significance.Our analytical outcomes(see Table3) are consistent with several widely held,but yet to be systemat-ically examined assumptions on the sumoylation involved processes.For example,sumoylation was proposed to play a role in transcriptional regulation and gene expression[6–8],where many SUMO substrates are transcription factors[1,2].Are there any strong correlations between transcriptional regula-tion and sumoylation?From our analysis,we found that the transcription factor and transcriptional regulation are both among the top of the list of significantly enriched functions or processes(Table3).In the human proteome,2255, and1102proteins are annotated with functions of DNA binding(GO:0003677)and transcription factor activity (GO:0003700),respectively.And there are also2174proteins annotated with process of regulation of transcription,DNA-dependent(GO:0006355).In our data set,there are530,304, and510proteins with the above three annotations,respec-tively.So it could be estimated that about1/4–1/3of the tran-scription factors are downregulated by sumoylation. Interestingly,we found that the processes of transcription from Pol II promoter(GO:0006366)and regulation of transcription from Pol II promoter(GO:0006357)are highly correlated with SUMO substrates.This supports the hypothesis that sumoyla-tion may play a role at the promoter by modifying transcrip-tion factors as chromatin-bound complexes,but not by regulating transcription directly[1,2],and the functions of transcription coactivator activity(GO:0003713)(see Table3) and transcription corepressor activity(GO:0003714) (P<10À7)are also significantly represented.Thisfinding is consistent with the recent observations that sumoylation can repress or activate transcription[1,2].Several SUMO substrates were summarized to be essential in signal transduction[1,2,29].In Drosophila brain,the func-tional dynamics of neuronal calcium/calmodulin-dependent protein kinase II was regulated by sumoylation,which is important for the differentiated nervous system[35].In NF-j B signaling pathways,the regulatory subunit of the I j B kinase (IKK)complex NEMO/IKK c will be sumoylated to release NF-j B from its inhibitor I j B a,inducing a survival response against genotoxic stress[5,9].In our data set,the processesTable3The top15most enriched processes and functions in SUMO substratesDescription of GO term Number of proteinsannotated in group S a Number of proteinsannotated in group W bEnrichmentratioP-valueThe top15most enriched processes in SUMO substratesRegulation of transcription,DNA-dependent(GO:0006355)26.1%(510)9.0%(2174) 2.89 6.12EÀ121 Transcription from Pol II promoter(GO:0006366) 3.5%(69)0.8%(204) 4.17 1.00EÀ25 Development(GO:0007275) 5.8%(114) 2.6%(631) 2.23 2.96EÀ16 Signal transduction(GO:0007165)9.1%(178) 5.0%(1207) 1.82 2.06EÀ15 Regulation of transcription from Pol II promoter(GO:0006357) 2.7%(52)0.8%(192) 3.34 4.13EÀ15 Protein amino acid phosphorylation(GO:0006468) 6.7%(131) 3.5%(850) 1.90 5.45EÀ13 Cell growth and/or maintenance(GO:0008151) 3.4%(67) 1.4%(341) 2.429.45EÀ12 Cell cycle(GO:0007049) 2.5%(49) 1.0%(240) 2.51 1.49EÀ09 Intracellular signaling cascade(GO:0007242) 4.6%(90) 2.5%(609) 1.82 2.00EÀ08 Endocytosis(GO:0006897) 1.4%(27)0.4%(108) 3.089.71EÀ08 Mitosis(GO:0007067) 1.3%(26)0.4%(103) 3.11 1.35EÀ07 Perception of sound(GO:0007605) 1.2%(23)0.4%(87) 3.26 2.87EÀ07 Morphogenesis(GO:0009653) 1.2%(23)0.4%(107) 2.65 1.31EÀ05 Frizzled signaling pathway(GO:0007222)0.5%(10)0.1%(26) 4.74 1.92EÀ05 Negative regulation of transcription from Pol II promoter(GO:0000122)0.9%(18)0.3%(74) 3.00 1.93EÀ05The top15most enriched functions in SUMO substratesDNA binding(GO:0003677)27.1%(530)9.4%(2255) 2.89 1.00EÀ126 Transcription factor activity(GO:0003700)15.5%(304) 4.6%(1102) 3.40 3.64EÀ87 Nucleic acid binding(GO:0003676)14.2%(277)7.6%(1823) 1.877.89EÀ26 Zinc ion binding(GO:0008270)14.6%(285)8.2%(1968) 1.78 2.80EÀ23 Protein serine/threonine kinase activity(GO:0004674) 6.1%(119) 2.3%(559) 2.627.18EÀ23 Actin binding(GO:0003779) 3.7%(72) 1.1%(259) 3.42 4.25EÀ21 ATP binding(GO:0005524)13.3%(260)8.0%(1925) 1.66 3.69EÀ17 Protein kinase activity(GO:0004672) 6.5%(128) 3.2%(776) 2.03 6.38EÀ15 RNA polymerase II transcription factor activity(GO:0003702) 2.1%(41)0.6%(138) 3.66 1.12EÀ13 Steroid hormone receptor activity(GO:0003707) 1.5%(29)0.3%(75) 4.76 2.47EÀ13 GTPase activator activity(GO:0005096) 1.8%(35)0.5%(110) 3.927.49EÀ13 Transcription coactivator activity(GO:0003713) 2.2%(43)0.7%(158) 3.358.04EÀ13 Ligand-dependent nuclear receptor activity(GO:0004879) 1.5%(29)0.3%(79) 4.52 1.17EÀ12 Protein binding(GO:0005515)11.9%(233)8.0%(1907) 1.507.58EÀ11 Calmodulin binding(GO:0005516) 1.8%(35)0.5%(132) 3.27 2.31EÀ10 We list the top15of the over-represented functions and processes for further discussion.a Group S,the SUMO substrates.b Group W,whole human proteome.of signal transduction(GO:0007165)and intracellular signal-ing cascade(GO:0007242)are much enriched(P<10À7), which implies that sumoylation may be involved in signal transduction extensively.We alsofind that the GO groups of protein serine/threonine kinase activity(GO:0004674),and protein kinase activity(GO:0004672)are significantly over-represented(P<10À14).In the human proteome,there are 559and776proteins annotated with the two GO terms respec-tively,while there are119and128of them are among our data set.Thus,it could be estimated that$1/5serine/threonine ki-nases could be sumoylated.This result is in accordance with the hypothesis that crosstalk between sumoylation and phos-phorylation may be fundamental and essential in signal trans-duction[8].Another interesting cellular process identified to be highly relevant to sumoylation is the process of perception of sound (GO:0007605)(P<10À6).Thus,we propose that sumoylation may play an important role in the perception of sound path-ways,a novelfinding that was never reported.3.3.Significantly represented protein domains in the data set To provide further insight into the functional enrichment of SUMO substrates,we also perform the statistical analysis to obtain additional evidence of what types of protein domains are more frequently encoded in them.Since sumoylation may be mainly implicated in transcription regulation and signal transduction by sumoylating transcription factors and protein serine/threonine kinases,respectively,it could be anticipated that some specific protein domains,such as DNA binding or kinase,should be abundant in our data set.The analysis on the InterPro annotations[32]satisfyingly confirms with the above results.The top10most enriched pro-tein domains in SUMO substrates are listed(Table4).It is not surprising that the protein domains such as Serine/threonine protein kinase,active site(IPR008271),Serine/threonine pro-tein kinase(IPR002290),Zn-finger,C2H2type(IPR007087), and Zn-finger-like,PHDfinger(IPR001965)are significantly abundant in the data set(P<10À15).Unexpectedly,we notice that Pleckstrin-homology-related(IPR011036)and Pleckstrin-like(IPR001849)are also in our top list.Pleckstrin homology (PH)domains are small modular domains with$100amino-acid residues that occur once,or occasionally several times, in a large variety of proteins involved in intracellular signaling or as constituents of the cytoskeleton[36].This observation may propose that there are some specific protein domains abundant in SUMO substrates to form links between sumoyla-tion and signaling related pathways.Although most of the pro-tein domains are focused on all kinds of DNA-binding domain,Zn-finger,C2H2type(IPR007087)domains can bind both DNA and RNA.And we found that the domain of RNA-binding region RNP-1(RNA recognition motif) (IPR000504)is significant(P<10À7).So our analysis also sup-ports the hypothesis that sumoylation may play a role in RNA metabolism[37].4.DiscussionIn this paper,we provide a genome-scale analysis of sumoy-lation-related biological processes and functions.The results show that sumoylation may be strongly correlated with the transcription regulation and signal transduction,which is con-sistent with the experimental observations.Our analysis also provides several other interesting hints, e.g.,sumoylation may be involved in the perception of sound,offering insights for further experimental manipulation.Taken together,our data set establishes a good resource for potential SUMO sub-strates with high specificity.5.Supplementary materialsSupplementary materials and the software SSP(SUMO Sub-strates Prediction)implemented in Delphi are available from: /sumo/.Acknowledgments:We thank Yi Xing(UCLA)for helpful discussions and Andrew Shaw for critical reading of this manuscript.This work is supported by grants from Chinese Natural Science Foundation (39925018and30121001),Chinese Academy of Science(KSCX2-2-01),Chinese973project(2002CB713700),Beijing Office for Science (H020*********)and American Cancer Society(RPG-99-173-01)to X.Yao.X.Yao is a GCC Distinguished Cancer Research Scholar. References[1]Gill,G.(2004)SUMO and ubiquitin in the nucleus:differentfunctions,similar mechanisms?.Genes Dev.18,2046–2059. [2]Seeler,J.S.and Dejean,A.(2003)Nuclear and unclear functionsof SUMO.Nat.Rev.Mol.Cell.Biol.4,690–699.Table4The top10most over-represented protein domains in SUMO substratesDescription of Interpro term Number of proteinsannotated in group S a Number of proteinsannotated in group W bEnrichmentratioP-valueSerine/threonine protein kinase,active site(IPR008271) 5.1%(115) 1.8%(486) 2.818.69EÀ25 Pleckstrin-homology-related(IPR011036) 5.0%(113) 2.0%(538) 2.49 5.71EÀ20 Pleckstrin-like(IPR001849) 4.2%(94) 1.6%(421) 2.65 1.16EÀ18 Serine/threonine protein kinase(IPR002290) 3.4%(78) 1.2%(328) 2.82 2.34EÀ17 Zn-finger,C2H2type(IPR007087)8.2%(185) 4.4%(1177) 1.87 4.24EÀ17 Zn-finger-like,PHDfinger(IPR001965) 2.0%(46)0.5%(139) 3.93 1.46EÀ16 Homeodomain-like(IPR009057) 3.6%(81) 1.3%(362) 2.66 2.53EÀ16 Protein kinase(IPR000719) 5.8%(132) 3.0%(796) 1.97 2.96EÀ14 Protein kinase-like(IPR011009) 5.8%(131) 2.9%(790) 1.97 3.73EÀ14 Winged helix DNA-binding(IPR009058) 2.6%(59)0.9%(244) 2.878.25EÀ14 We list the top10of the over-represented protein domains for further discussion.a Group S,the SUMO substrates.b Group W,whole human proteome.[3]Melchior, F.,Schergaut,M.and Pichler, A.(2003)SUMO:ligases,isopeptidases and nuclear pores.Trends Biochem.Sci.28, 612–618.[4]Melchior,F.(2000)SUMO–nonclassical ubiquitin.Annu.Rev.Cell Dev.Biol.16,591–626.[5]Hay,R.T.,Vuillard,L.,Desterro,J.M.and Rodriguez,M.S.(1999)Control of NF-kappa B transcriptional activation by signal induced proteolysis of I kappa B alpha.Philos.Trans.R.Soc.Lond.B:Biol.Sci.354,1601–1609.[6]Verger,A.,Perdomo,J.and Crossley,M.(2003)Modificationwith SUMO.A role in transcriptional regulation.EMBO Rep.4, 137–142.[7]Schmidt,D.and Muller,S.(2003)PIAS/SUMO:new partners intranscriptional regulation.Cell.Mol.Life Sci.60,2561–2574. [8]Gill,G.(2003)Post-translational modification by the smallubiquitin-related modifier SUMO has big effects on transcription factor activity.Curr.Opin.Genet.Dev.13,108–113.[9]Huang,T.T.,Wuerzberger-Davis,S.M.,Wu,Z.H.and Miyam-oto,S.(2003)Sequential modification of NEMO/IKKgamma by SUMO-1and ubiquitin mediates NF-kappaB activation by genotoxic stress.Cell115,565–576.[10]Pinsky,B.A.and Biggins,S.(2002)Top-SUMO wrestles centro-meric cohesion.Dev.Cell3,4–6.[11]Muller,S.,Hoege,C.,Pyrowolakis,G.and Jentsch,S.(2001)SUMO,ubiquitinÕs mysterious cousin.Nat.Rev.Mol.Cell.Biol.2,202–210.[12]Schwartz,D.C.and Hochstrasser,M.(2003)A superfamily ofprotein tags:ubiquitin,SUMO and related modifiers.Trends Biochem.Sci.28,321–328.[13]Saitoh,H.and Hinchey,J.(2000)Functional heterogeneity ofsmall ubiquitin-related protein modifiers SUMO-1versus SUMO-2/3.J.Biol.Chem.275,6252–6258.[14]Bohren,K.M.,Nadkarni,V.,Song,J.H.,Gabbay,K.H.andOwerbach,D.(2004)A M55V polymorphism in a novel SUMO gene(SUMO-4)differentially activates heat shock transcription factors and is associated with susceptibility to type I diabetes mellitus.J.Biol.Chem.279,27233–27238.[15]Panse,V.G.,Hardeland,U.,Werner,T.,Kuster,B.and Hurt,E.(2004)A proteome-wide approach identifies sumoylated substrate proteins in yeast.J.Biol.Chem.279,41346–41351.[16]Wykoff,D.D.and OÕShea,E.K.(2005)Identification of sumoy-lated proteins by systematic immunoprecipitation of the budding yeast proteome.Mol.Cell.Proteomics4,73–83.[17]Hannich,J.T.,Lewis,A.,Kroetz,M.B.,Li,S.J.,Heide,H.,Emili,A.and Hochstrasser,M.(2005)Defining the SUMO-modifiedproteome by multiple approaches in Saccharomyces cerevisiae.J.Biol.Chem.280,4102–4110.[18]Denison, C.,Rudner, A.D.,Gerber,S.A.,Bakalarski, C.E.,Moazed, D.and Gygi,S.P.(2005)A proteomic strategy for gaining insights into protein sumoylation in yeast.Mol.Cell.Proteomics4,246–254.[19]Zhou,W.,Ryan,J.J.and Zhou,H.(2004)Global analyses ofsumoylated proteins in Saccharomyces cerevisiae.Induction of protein sumoylation by cellular stresses.J.Biol.Chem.279, 32262–32268.[20]Wohlschlegel,J.A.,Johnson,E.S.,Reed,S.I.and Yates III,J.R.(2004)Global analysis of protein sumoylation in Saccharomyces cerevisiae.J.Biol.Chem.279,45662–45668.[21]Rosas-Acosta,G.,Russell,W.K.,Deyrieux,A.,Russell,D.H.andWilson,V.G.(2005)A universal strategy for proteomic studies ofSUMO and other ubiquitin-like modifiers.Mol.Cell.Proteomics 4,56–72.[22]Gocke,C.B.,Yu,H.and Kang,J.(2005)Systematic identificationand analysis of mammalian small ubiquitin-like modifier sub-strates.J.Biol.Chem.280,5004–5012.[23]Zhao,Y.,Kwon,S.W.,Anselmo,A.,Kaur,K.and White,M.A.(2004)Broad spectrum identification of cellular small ubiquitin-related modifier(SUMO)substrate proteins.J.Biol.Chem.279, 20999–21002.[24]Vertegaal,A.C.,Ogg,S.C.,Jaffray,E.,Rodriguez,M.S.,Hay,R.T.,Andersen,J.S.,Mann,M.and Lamond,A.I.(2004)A proteomic study of SUMO-2target proteins.J.Biol.Chem.279, 33791–33798.[25]Manza,L.L.,Codreanu,S.G.,Stamer,S.L.,Smith,D.L.,Wells,K.S.,Roberts,R.L.and Liebler,D.C.(2004)Global shifts in protein sumoylation in response to electrophile and oxidative stress.Chem.Res.Toxicol.17,1706–1715.[26]Puntervoll,P.,Linding,R.,Gemund,C.,Chabanis-Davidson,S.,Mattingsdal,M.,Cameron,S.,Martin, D.M.,Ausiello,G., Brannetti,B.and Costantini,A.,et al.(2003)ELM server:a new resource for investigating short functional sites in modular eukaryotic proteins.Nucleic Acids Res.31,3625–3630.[27]Rodriguez,M.S.,Dargemont, C.and Hay,R.T.(2001)SUMO-1conjugation in vivo requires both a consensus modification motif and nuclear targeting.J.Biol.Chem.276, 12654–12659.[28]Watts,F.Z.(2004)SUMO modification of proteins other thantranscription factors.Semin.Cell Dev.Biol.15,211–220. [29]Johnson,E.S.(2004)Protein modification by SUMO.Annu.Rev.Biochem.73,355–382.[30]Remm,M.,Storm,C.E.and Sonnhammer,E.L.(2001)Auto-matic clustering of orthologs and in-paralogs from pairwise species comparisons.J.Mol.Biol.314,1041–1052.[31]Nakai,K.and Horton,P.(1999)PSORT:a program for detectingsorting signals in proteins and predicting their subcellular localization.Trends Biochem.Sci.24,34–36.[32]Mulder,N.J.,Apweiler,R.,Attwood,T.K.,Bairoch, A.,Barrell,D.,Bateman,A.,Binns,D.,Biswas,M.,Bradley,P.and Bork,P.,et al.(2003)The InterPro Database,2003brings increased coverage and new features.Nucleic Acids Res.31, 315–318.[33]Xing,Y.,Xu,Q.and Lee,C.(2003)Widespread production ofnovel soluble protein isoforms by alternative splicing removal of transmembrane anchoring domains.FEBS Lett.555,572–578.[34]Blom,N.,Sicheritz-Ponten,T.,Gupta,R.,Gammeltoft,S.andBrunak,S.(2004)Prediction of post-translational glycosylation and phosphorylation of proteins from the amino acid sequence.Proteomics4,1633–1649.[35]Long,X.and Griffith,L.C.(2000)Identification and character-ization of a SUMO-1conjugation system that modifies neuronal calcium/calmodulin-dependent protein kinase II in Drosophila melanogaster.J.Biol.Chem.275,40765–40776.[36]Rebecchi,M.J.and Scarlata,S.(1998)Pleckstrin homologydomains:a common fold with diverse functions.Annu.Rev.Biophys.Biomol.Struct.27,503–528.[37]Li,T.,Evdokimov,E.,Shen,R.F.,Chao,C.C.,Tekle,E.,Wang,T.,Stadtman,E.R.,Yang,D.C.and Chock,P.B.(2004)Sumoy-lation of heterogeneous nuclear ribonucleoproteins,zincfinger proteins,and nuclear pore complex proteins:a proteomic A101,8551–8556.。

star-fusion 原理Title: The Principle of Star-FusionIntroduction:In recent years, the field of genomics has witnessed significant advancements in the identification of gene fusions, which play a crucial role in cancer diagnosis and treatment. One such method is star-fusion, a computational tool that detects fusion genes in transcriptome sequencing data. This article will delve into the principle of star-fusion, highlighting its significance and potential applications.1. Understanding Gene Fusions:Gene fusions occur when two separate genes join together, resulting in a hybrid gene with altered functionality. These fusions can disrupt normal cellular processes and contribute to the development of various diseases, including cancer. Identifying gene fusions is essential for understanding the underlying molecular mechanisms of diseases and developing targeted therapies.2. Transcriptome Sequencing:To detect gene fusions, researchers employ transcriptomesequencing, which involves sequencing RNA molecules present in a sample. This technique provides valuable information about the gene expression patterns and enables the identification of fusion transcripts resulting from gene fusions.3. The Principle of Star-Fusion:Star-fusion is a computational algorithm developed to identify fusion genes in transcriptome sequencing data. It combines multiple computational approaches to accurately detect fusion transcripts. The principle behind star-fusion involves three main steps:Step 1: Read MappingStar-fusion aligns the RNA sequencing reads to the reference genome using a splice-aware aligner. This step ensures that the reads are mapped accurately, considering the presence of exon-exon junctions resulting from gene fusions.Step 2: Fusion Candidate IdentificationAfter read mapping, star-fusion identifies fusion candidate reads that span the fusion breakpoints. It utilizes various criteria, such as read pairs mapping to different genes orspanning a known fusion breakpoint, to filter out false positives.Step 3: Validation and AnnotationIn the final step, star-fusion performs rigorous validation and annotation of the fusion candidates. It checks for specific characteristics, such as the presence of canonical splice signals, in the fusion transcripts to eliminate false positives and enhance the accuracy of fusion gene detection.4. Significance and Applications:The star-fusion algorithm has significant implications in cancer research and clinical practice. By accurately identifying fusion genes, star-fusion aids in cancer diagnosis, prognosis, and the development of targeted therapies. It enables researchers to unravel the complex genomic alterations driving tumorigenesis, leading to novel therapeutic strategies.Moreover, star-fusion can also be utilized in other fields, such as developmental biology and genetic engineering, to identify and study gene fusions involved in various biological processes. Its versatility and accuracy make ita valuable tool for understanding the functional consequences of gene fusions beyond cancer.Conclusion:In conclusion, star-fusion is a powerful computational tool that plays a pivotal role in identifying fusion genes in transcriptome sequencing data. Its principle involves read mapping, fusion candidate identification, and validation to accurately detect fusion transcripts. The significance of star-fusion lies in its ability to unravel the molecular mechanisms underlying diseases, particularly cancer, and aid in the development of targeted therapies. With further advancements, star-fusion holds immense potential for advancing our understanding of gene fusions and their role in various biological processes.。

The Safety of the Intended FunctionalityISO/PAS 21448 and beyondNicolas BECKERSafety Senior Expert for PSAProject leader for ISO 21448 in ISO/TC22/SC32/WG8DQI/DAPFNicolas BeckerCONTENT1.Safety aspects of automated driving2.Motivation –What is the Safety of the IntendedFunctionality (SOTIF)?3.ISO/PAS 21448 status and activities4.Connection with Automated Driving (AD) regulatoryactivities5.Summary2The automated driving systemis safeIts failures are adequately avoided or mitigated Its behaviour is adequate for the intended operationdomainISO26262 : Functional SafetyHazard Analysis and Risk Assessment Design, Verification and Validation (V&V) requirementsSafety management ISO/PAS 21448 : Safety of the Intended FunctionalityScenario identification incl. Reasonably foreseeable misuses Functional improvementsV&V strategyOther safetyrequirements(inclCybersecurity,passive safety, etc)sc Scope of ISO/TC22/SC32/WG8Its behaviour is adequate for the intended operationdomainThe vehicle functionality issafe Its technical implementation issafeThe function expected behaviour is complete and safePotential misuses are identified and mitigated The system performance limitation are identified and acceptable-sensors and environment perception-decision algorithms-actuationsc Scope of ISO 21448SOTIF EXAMPLEAutomatic emergency braking feature :triggering eventscameraunintended braking could be caused by limitations in perception system•weather (rain/sun/fog)•misinterpretation of image •…CAUSES OF HAZARDOUS BEHAVIOURHazardous BehaviourWeather conditionsInfrastructureSensors limitationsAlgorithms limitations Systems weaknessesActuators limitationsPotentials CausesTriggering Eventsdriver behaviour[…] Various causes…Causes : FaultsBounding failureMigrationHarness failure…System FailureFunctional Safety of the Intended FunctionalitySoftware ErrorKEY ASPECTS OF 21448 -SOTIF•ISO/PAS 21448 publication 01/2019•Focuses on driver assistance features with SAE automation levels 1 and 2•Covers potentially hazardous behavior under non-fault conditions•Caused by technological or system limitations•Includes evaluation of reasonably foreseeable misuse•Provides guidance for design, verification and validation measures•Issued as publicly available specification (PAS) (and not as an ISO standard) to enable fast publication•Includes high-level requirements on the objectives to achieve in the SOTIF analyses, and informative guidance on how to achieve them•The work on ISO 21448 started in 11/2018•Extension to higher levels of automation (up to Level 5)•Significant interest in this work•18 countries•80 experts in Plenary featuring worldwide OEMs, Tier 1 and Tier 2 suppliers, and governmental institutes •Publication targeted for 2022Known UnknownSafe Area1Nominalbehavior Area4 System robustnessPotentially hazardous Area 2IdentifiedsystemlimitationsArea 3“Black swans”1234Example of an initial starting point of development1234Known Unknown Safe Area1NominalbehaviorArea4Systemrobustness PotentiallyhazardousArea 2IdentifiedsystemlimitationsArea 3“Black swans”Example of an initial starting point of development1234Triggering EventsWeather conditions InfrastructureSensorslimitationsAlgorithmslimitationsSystemsweaknessesActuatorslimitationsPotentialsCausesdriver behaviour[…] Various causes…Example of an initial starting point of developmentCATEGORIZATION OF REAL-LIFE DRIVING SCENARIOSKnown UnknownSafe Area1Nominalbehavior Area4 System robustnessPotentially hazardous Area 2IdentifiedsystemlimitationsArea 3“Black swans”1234Example of an initial starting point of developmentAREA 31423Example of an initial starting point of developmentCATEGORIZATION OF REAL-LIFE DRIVING SCENARIOS12341234Example of an initial starting point of developmentGoal of the finished developmentIterative processF l o w c h a r t o f S O T I F A c t i v i t i e sCHALLENGES OF THE SOTIF FOR HIGHER AUTOMATION LEVELS FALSE POSITIVES VS FALSE NEGATIVES Potential false positivePotential false negativeFor AEBS : False positives are the main issue (the driver is expected to control the vehicle anyhow)For AD : both false positives and false negatives may lead to an hazardCHALLENGES OF THE SOTIF FOR HIGHER AUTOMATION LEVELS : VALIDATION TARGETThe ISO/PAS 21448 indicates that a quantitative target can be defined as a criteria to claim sufficient validationThis target may be derived from traffic statistics⇒Very stringent target for L3 systems⇒Impossible to achieve only by captured fleetRESIDUAL SCENARIOS EVALUATION –QUANTITATIVE APPROACH•In the ISO/PAS 21448, the quantitative approach is NOT a criteria that would allow to ignore a plausible potentially hazardous scenario : those must beaddressed anyhow•It is ONLY a criteria to claim sufficient validation coverage at the time of the beginning of customer activation of the functionality•For a level 1 or 2 functionality in the scope of the PAS, this leads to a validation strategy that is in the order of what a captured fleet can achieve (~ 104hours)•For a level 3+ functionality in the scope of the future ISO21448, the target derived through this approach are much more stringent, which therefore will require ahigher contribution of simulations for the validation. This is a primary topic for the future ISO21448.•The procedure for the demonstration on how these targets are met is still a topic of discussion.Additions to the ISO PAS 21448 to support AD vehicles safety demonstration1)GSN description of the safety argument2)SOTIF-orientated safety analyses•Derived from conventional analyses such as FMEA, FTA, STPA, tailored for the SOTIF triggering events3)Verification and validation methods for machine learning4)Description of driving policy5)Recommendations for activities after the function is released (fieldmonitoring)6)…Main GOAL : Vehicle SafetyThe automated vehicle is acceptably safe to operate in the specified environmentBefore customer releaseArgument by covering SOTIF constraintsAfter customer releaseArgument byusing monitoring, service updating,prescribing in-use maintenancethrough lifeSpecified Environment = ODDNominal operating environmenthas been defined for the automatedfunction«Acceptably Safe»has been defined Risks ManagementThe risk associated with the function hazards has been reduced to anacceptable levelJAutomated function Functional characteristics and modes for the function are definedBefore customer releaseArgument bycovering SOTIF constraintsSubGOAL 1 Vehicle level function specification is safe SubGOAL 2Technical implementationis safe Specification strategy Argument by treating misusesSpecification strategy Argument by defining drivingpolicyTechnical implementationstrategy Argument by detecting correctly the environment Technical implementationstrategy Argument by managing the right decisionTechnical implementationstrategy Argument by acting safelySubGOAL 2.1 Identifying triggering events & system weaknessesSubGOAL 2.2 Identifying system weaknessesSubGOAL 2.3 Identifying system weaknessesSubGOAL 3V&V activities demonstrate an acceptable residual riskWrap-up of the connection between 21448 and 2626221448 SOTIF26262 Functional SafetyScope Nominal function and systemimplementation insufficiencesItem FailuresSafety management No specific guidance (cf26262)Part 2 / Part 8Addressed Causes Function/ System insufficiencies activated byexternal causes (triggering events)Internal faults(internal or external activation) Risk analysis HARA HARARisk classification N/A –E, C and S are evaluated ASIL considering E, C and SSystem Analyses TBD (STPA, FMEA, FTA derivatives)FMEA, FMEDA, FTARemedies Functional improvements Safety measures, including safety mechanismsMisuses Can be cause of unsafe behaviour orcontribute to unsafe behaviour inconjunction with an external triggering event Can contribute to unsafe behaviour, in conjunction with a failureQualitative approach Safety analyses, Common causes analyses(TBD)Safety aalyses, dependent failures analyses Quantitative approach Criteria to consider sufficient validation, Criteria to consider sufficient design,LIAISONS WITH REGULATION BODIES•The ISO/PAS 21448 has been presented to the GRVA (regulation working group on the AD within UNE/ECE in Geneva)•It has also been presented to the JRC (scientific arm of the European commission) tasked to providing guidance for rules to allow AD in Europe•Both entities intended to continue the exchanges with the ISO experts on this topicCONCLUSION•The Safety of the Intended Functionality is a source of potentially hazardous behavior in addition to functional safety•It is critical to the safety of emerging functions like ADAS and Automated Driving •Methods to design and demonstrate the SOTIF are being standardized in the ISO 21448•It addresses causes complementary to those addressed in ISO 26262, and that can have similar vehicle-level effects•This future standard is being considered as relevant for future AD regulation。

2024-2025学年人教版英语初一上学期复习试题与参考答案一、听力部分（本大题有20小题，每小题1分，共20分）1、Listen to the conversation and choose the best answer to complete the sentence.A. The boy is playing soccer with his friends.B. The girl is reading a book in the park.C. They are having a picnic.Answer: BExplanation: The conversation describes a girl reading a book in a park, which matches option B.2、Listen to the dialogue and answer the question.What is the main topic of the dialogue?A. Planning a vacationB. Discussing school projectsC. Reviewing a movieAnswer: AExplanation: The dialogue focuses on discussing plans for a vacation, making option A the correct answer.3、What are the speakers mainly discussing?A) The weather forecast for the next week.B) The importance of wearing a hat in the sun.C) The benefits of staying hydrated.Answer: CExplanation: The speakers mention that staying hydrated is important, especially when it’s hot outside, which indicates that they are discussing the benefits of staying hydrated.4、Listen to the conversation and answer the question.Who is the woman talking to?A) Her teacher.B) Her friend.C) Her brother.Answer: BExplanation: The woman refers to the person she’s talking to as “my friend,” which clearly indicates that the conversation is between her and her friend.5.You hear a conversation between two students, Alice and Bob. Listen carefully and choose the best answer to the following question: Question: What are Alice and Bob mainly talking about?A. Their weekend plansB. The weatherC. Their school subjectsD. The movie they watched last nightAnswer: AExplanation: In the conversation, Alice asks Bob about his plans for the weekend, which indicates that they are mainly talking about their weekend plans.6.Listen to a short dialogue between a teacher and a student, and answer the following question:Question: What is the student’s main problem according to the teacher?A. He is not good at mathB. He is often late for classC. He can’t remember the vocabularyD. He is not paying attention in classAnswer: CExplanation: The teacher mentions that the student is having trouble with the vocabulary, which implies that the student’s main problem is related to memory issues with the vocabulary.7.W: Hi, John! How was your science project last week?M: It was great! We built a model of a solar system.Q: What did John do last week?A: He built a model of a solar system.解析：这是一道事实细节题。

碱基类似物Nucleotide analogues are biomolecular speciess that resemble nucleotides. They are similar to nucleotides in terms of structure and function, but different from them in terms of the base structure and chemical properties. Nucleotide analogues exist in a variety ofdifferent shapes, sizes and chemical properties, and their versatility makes them useful for a range of applications in biochemical research, drug design and therapy.Nucleotides are in essence the monomers that comprise DNA and RNA. They consist of a sugar molecule, a phosphate group and one of four nitrogenous bases. The four bases are adenine, guanine, cytosine and thymine (A, G, C, T). Nucleotide analogues differ from the true nucleotides in that they contain structural or functional modifications in these components, resulting in differences in their behavior. For instance, some analogues may have modified functional groups, altered bases or modified phosphates.Nucleotide analogues can be found in nature, primarily in plants and other organisms that have adapted to environmental stresses and conditions. Examples of this include modified bases in plants that are resistant to antiviral drugs or other toxins. Nucleotide analogues can also be created artificially in a laboratory. Scientists experiment with different modifications of the DNA chain to create novel molecules that behave differently from the natural nucleotide.These molecules can be used in a variety of ways. In pharmaceutical research, for example, scientists often use nucleotide analogues to create anti-cancer and anti-viral drugs. By creating small-molecule drugs, these analogues can bind to and block specific parts of the virus or cancer cell, leading to its destruction. Nucleotide analogues are also used to improve DNA testing accuracy by reducing the reaction times and avoiding false positives.In addition, nucleotide analogues can also be used to answer questions about evolutionary biology. By studying the different modifications of the DNA sequence in different species and comparing them to the true nucleotides, scientists can gain insights into the evolutionary history of various species. Furthermore, nucleotide analogues can be used to analyze the behavior of proteins, enabling the generation of novel insights that are not possible using traditional DNA analysis.Overall, nucleotide analogues are useful components in many areas of biotechnology research. By creating modifications to the original nucleotide sequence, these molecules can drastically alter the behavior of a cell or virus, as well as provide new insights into how aparticular biological process works. With continuous advancements inthis field, nucleotide analogues are likely to remain an important tool in biomedical research.。

如何辨别互联网信息的真假英语作文英文回答：How to Identify Fake Information on the Internet.With the vast amount of information available online,it can be challenging to determine what is true and what is not. Fake news, misinformation, and disinformation are rampant on the internet, and it is essential to be able to identify them to avoid being misled. Here are some key tips:1. Check the Source: The first step in verifying the authenticity of information is to check the source. Established news organizations, government agencies, and reputable websites are generally more reliable than anonymous or unfamiliar sources. Examine the website's "About Us" page or the author's credentials.2. Examine the Content: Take a critical look at the content itself. Does it contain factual information thatcan be corroborated by other sources? Are there any logical fallacies, such as hasty generalizations or straw man arguments? Check if there are any biases or distortions in the presentation of information.3. Consider the Motives: Who created the informationand why? Is there a financial incentive, political agenda,or other motivation behind the content? Understanding the motives of the source can help you assess the credibilityof the information.4. Investigate Further: Don't rely solely on one source. Verify the information by checking multiple reputable sources. Consult fact-checking websites, such as Snopes or PolitiFact, to see if the information has been debunked.5. Be Skeptical: Approach all information with ahealthy dose of skepticism. If something seems too good to be true or too sensational, it likely is. Be wary of emotionally charged language or unsubstantiated claims.6. Look for Context: Understand the context in whichthe information was created. Consider the date, the purpose, and the intended audience. This can help you determine if the information is still relevant or if it has been taken out of context.7. Check the Images and Videos: Images and videos canbe manipulated or taken out of context. Use reverse image search tools to verify the authenticity of images and videos. Be aware of deepfakes, which are highly realistic fake videos created using artificial intelligence.中文回答：鉴别网络信息真伪。

illumina芯片拷贝数变异分析流程Analyzing copy number variations (CNVs) in Illumina microarray data can be a challenging but incredibly informative process. Illumina芯片是一种广泛用于基因组学研究的高通量技术，其数据可以提供基因组中拷贝数变异的信息。

CNVs refer to structural variations in the DNA that involve gains or losses of sections of the genome, and they have been implicated in various human diseases. Illumina microarrays are commonly used to detect and analyze CNVs due to their high resolution and ability to simultaneously assess thousands of genetic markers.One of the first steps in the analysis of CNVs from Illumina microarray data is the pre-processing of raw intensity signals. This involves normalization of the data to correct for systematic variations in intensities across samples, as well as quality control measures to assess the reliability of the data. The goal is to ensure that the data is of high quality and free from technical artifacts that could impact the accuracy of CNV calling. Pre-processing of the data is crucial to obtaining reliable results in downstream analyses.After pre-processing, the next step is CNV calling, which involves identifying regions of the genome that exhibit differences in copy number compared to a reference sample. There are various algorithms available for CNV calling from Illumina microarray data, each with its own strengths and limitations. Commonly used algorithms include PennCNV, QuantiSNP, and Nexus Copy Number. These algorithms use statistical models to assess the likelihood of a CNV at specific genomic loci and provide a measure of confidence in the call.Once CNVs have been called, the next step is to annotate and interpret the results. This involves mapping the identified CNVs to the human genome and determining their potential functional consequences. CNVs can impact gene expression, disrupt gene structures, or alter regulatory regions, so understanding their effects is crucial for linking them to disease phenotypes. Various bioinformatics tools and databases can assist in the annotation of CNVs and provide insights into their biological significance.In addition to data analysis, it is essential to validate identified CNVs using independent experimental methods. This can includequantitative PCR, droplet digital PCR, or fluorescence in situ hybridization to confirm the presence and precise boundaries of the CNVs. Validation is critical to ensure the reliability of the findings and eliminate false positives that may arise from bioinformatics analyses. By combining computational analysis with experimental validation, researchers can confidently characterize CNVs and their implications in various diseases.Overall, analyzing CNVs from Illumina microarray data is a comprehensive and multi-step process that requires a combination of bioinformatics skills, statistical knowledge, and experimental validation. Despite the challenges, the insights gained from studying CNVs can provide valuable information about the genetic basis of diseases and pave the way for precision medicine approaches. Illumina芯片数据中CNVs的分析是一项既具有挑战性又极具信息价值的过程。

1. A physician suspects that his patient might have gouty arthritis. To confirm his clinical suspicion, the physician orders a microscopic evaluation of the joint fluid for the presence of negatively birefringent, needle-shaped crystals. This is known to be a highly specific test. Relative to the physician’s clinical diagnosis alone, a highly specific test will greatly reduce which of the following?(A) False negatives(B) False positives(C) Prevalence(D) True negatives(E) True positives2. A 26-year-old patient was diagnosed with rheumatoid arthritis (RA) 1 month ago. She returns to her primary care physician for a follow-up visit and is interested in learning how RA might affect her, other than the joint destruction it can cause. The patient is most likely to develop which of the following?(A) Myocarditis(B) Nodules(C) Pericarditis(D) Renal failure(E) Splenomegaly3. Which are the specific physical signs for joint dislocation?(A) Swelling, deformity and dysfunction(B) Tenderness, swelling and ecchymosis(C) Deformity, abnormal movement and empty joint(D) Deformity, abnormal movement and elastic fixation(E) Deformity, elastic fixation and empty joint4. Which one is not right for shoulder dislocation?(A) Trauma history of shoulder(B) Square shoulder deformity(C) Empty glenoid(D) Thomas sign (+)(E) Dugas sign (+)5. Which one is the most common type of hip dislocation?(A) Anterior dislocation(B) Posterior dislocation(C) Central dislocation(D) Dislocation with femoral head fracture(E) Dislocation with acetabulum fracture6. Choose the inappropriate match(A) Effusion of knee joint: Floating patella test(B) Posterior cruciate ligament rupture: Back drawer test and Lancherman test(C) Anterior cruciate ligament rupture: Back drawer test and Lancherman test(D) Meniscal injury: McMurray sign(E) Chondromalacia patella: Patella friction test7. Pathological change of rozen shoulder mainly occur in(A) Glenohumeral joint(B) Acromioclavicular joint(C) Deltoid muscle(D) Supraspinatus(E) Infraspinatus8.A 25-year-old man had friction fremitus during knee flexion and extension, half squat test (+), which diagnosis should be considered?(A) Lateral meniscus injury(B) Medial meniscus injury(C) Chondromalacia patella(D) Joint loose bodies(E) rheumatoid arthritis9. Elbow functional position(A) 0°(B) 30°(C) 60°(D) 90°(E) 120°10.Carrying angle of the elbow(A) 20~25°(B) 16~20°(C) 10~15°(D) 5~10°(E) 1~5°11. Which type of fracture is the most common after total hip arthroplasty?(A) Frature of the femoral trochanter(B) Frature between the femoral trochanter and the distal end of femoral stem(C) Fracture at the distal end of femoral stem(D) Fracture below the distal end of femoral stem(E) Frature of the contralateral femur12. Which of the following examinations help diagnose cruciate ligament tear of the knee?(A) X ray(B) CT(C) B-ultrasound(D) ECT(E) MRI13. The most reliable examination for meniscus tear is(A) Knee hyperextension test(B) Knee hyperflexion test(C) Grinding test(D) McBurney sign(E) Arthroscopy14. A 30-year-old male horse-riding enthusiast complains pain below left knee. Physical examination showed a 6cm*6cm regional tenderness of medial proximal part of left lower leg. The most probable diagnosis is(A) Anserine bursitis(B) Meniscus injury(C) Cruciate ligament injury(D) Mcmedial collateral ligament tear(E) Infrapatellar bursitis15. A 50-year-old female complains of left shoulder pain, radiating to left upper extremity and limited motion of left shoulder for 1 month. The pain aggravated and interfered with sleep for 1 week. Physical examination showed amyotrophy of the left upper extremity. Active and passive motion of left shoulder is limited especially abduction and external rotation. The most probable diagnosis is(A) Osteoarthritis(B) Rheumatic arthritis(C) Myofascitis(D) Frozen shoulder(E) Cervical spondylosisAnswer：1.B, 2.B, 3.E, 4.D, 5.B, 6.B, 7.A, 8.C, 9.D, 10.C, 11.C, 12.E, 13.E, 14.A, 15.D。