抗原变异可使得多种致病微生物易于逃避宿主免疫应答。表达在感染红细胞表面的恶性疟原虫红细胞表面蛋白1（PfPMP1）与感染红细胞、内皮细胞、树突状细胞以及胎盘的单个或多个受体作用，在黏附及免疫逃避中起关键的作用。每个单倍体基因组var基因家族编码约60种成员，通过启动转录不同的var基因变异体为抗原变异提供了分子基础。

疟原虫var基因转换速率变化导致抗原变异[英]／Paul H, Robert P, Christodoulou Z, et al//Proc Natl Acad Sci U S A

Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

Data Mining Practical Machine Learning Tools and Techniques

MicroRNAs (miRNAs) constitute a large family of noncoding RNAs that function as guide molecules in diverse gene silencing pathways. Current efforts are focused on the regulatory function of miRNAs, while little is known about how these unusual genes themselves are regulated. Here we present the first direct evidence that miRNA genes are transcribed by RNA polymerase II (pol II). The primary miRNA transcripts (pri‐miRNAs) contain cap structures as well as poly(A) tails, which are the unique properties of class II gene transcripts. The treatment of human cells with α‐amanitin decreased the level of pri‐miRNAs at a concentration that selectively inhibits pol II activity. Furthermore, chromatin immunoprecipitation analyses show that pol II is physically associated with a miRNA promoter. We also describe, for the first time, the detailed structure of a miRNA gene by determining the promoter and the terminator of mir‐23a∼27a∼24‐2 . These data indicate that pol II is the main, if not the only, RNA polymerase for miRNA gene transcription. Our study offers a basis for understanding the structure and regulation of miRNA genes.

/pdf/microrna-genes-are-transcribed-by-rna-polymerase-ii-7trd0easzj.pdf

MicroRNA genes are transcribed by RNA polymerase II.

MicroRNAs (miRNAs) are a large family of post-transcriptional regulators of gene expression that are ~21 nucleotides in length and control many developmental and cellular processes in eukaryotic organisms. Research during the past decade has identified major factors participating in miRNA biogenesis and has established basic principles of miRNA function. More recently, it has become apparent that miRNA regulators themselves are subject to sophisticated control. Many reports over the past few years have reported the regulation of miRNA metabolism and function by a range of mechanisms involving numerous protein-protein and protein-RNA interactions. Such regulation has an important role in the context-specific functions of miRNAs.

http://m.docente.unife.it/franco.pagani/lezione-1101a-mirna/articoli-smallrna/2010%20FIlipowic%20The%20widespread%20regulation%20of%20miRNA%20%20copy.pdf

The widespread regulation of microRNA biogenesis, function and decay.

Morphologic plasticity of rapid-onset neurites in NG108-15 cells stimulated by substratum-bound laminin.

We recently designed and deployed a metasearch engine, Metta, that sends queries and retrieves search results from five leading biomedical databases: PubMed, EMBASE, CINAHL, PsycINFO and the Cochrane Central Register of Controlled Trials. Because many articles are indexed in more than one of these databases, it is desirable to deduplicate the retrieved article records. This is not a trivial problem because data fields contain a lot of missing and erroneous entries, and because certain types of information are recorded differently (and inconsistently) in the different databases. The present report describes our rule-based method for deduplicating article records across databases and includes an open-source script module that can be deployed freely. Metta was designed to satisfy the particular needs of people who are writing systematic reviews in evidence-based medicine. These users want the highest possible recall in retrieval, so it is important to err on the side of not deduplicating any records that refer to distinct articles, and it is important to perform deduplication online in real time. Our deduplication module is designed with these constraints in mind. Articles that share the same publication year are compared sequentially on parameters including PubMed ID number, digital object identifier, journal name, article title and author list, using text approximation techniques. In a review of Metta searches carried out by public users, we found that the deduplication module was more effective at identifying duplicates than EndNote without making any erroneous assignments.

http://www.cs.binghamton.edu/~meng/pub.d/deduplication.pdf

Rule-based deduplication of article records from bibliographic databases.

The scientific enterprise reflects the willingness of scientists to collaborate implicitly with people they don't know personally: investigators routinely obtain antibodies, enzymes, sterile supplies, and other items from commercial suppliers without hardly blinking, and often without knowing anything about the companies other than what appears on the sales brochures. This system works because of basic warranties and expectations, as well as competition among companies to maintain high quality standards and easy, quick availability of specialized reagents. It is no exaggeration to say that vendors, as de facto scientific collaborators, are a major driving force in scientific productivity.

/pdf/guidelines-for-negotiating-scientific-collaboration-4jctxsgpc0.pdf

Guidelines for negotiating scientific collaboration.

Formation of functional retinotectal connections in co-cultures of fetal mouse explants

Arrowsmith, a computer-assisted process for literature-based discovery, takes as input two disjoint sets of records (A, C) from the Medline database. It produces a list of title words and phrases, B, that are common to A and C, and displays the title context in which each B-term occurs within A and within C. Subject experts then can try to find A–B and B–C title-pairs that together may suggest novel and plausible indirect A–C relationships (via B-terms) that are of particular interest in the absence of any known direct A–C relationship. The list of B-terms typically is so large that it is difficult to find the relatively few that contribute to scientifically interesting connections. The purpose of the present article is to propose and test several techniques for improving the quality of the B-list. These techniques exploit the Medical Subject Headings (MeSH) that are assigned to each input record. A MesH-based concept of literature cohesiveness is defined and plays a key role. The proposed techniques are tested on a published example of indirect connections between migraine and magnesium deficiency. The tests demonstrate how the earlier results can be replicated with a more efficient and more systematic computer-aided process. © 2006 Wiley Periodicals, Inc.

Neil R. Smalheiser

Papers

Morphologic plasticity of rapid-onset neurites in NG108-15 cells stimulated by substratum-bound laminin.

Rule-based deduplication of article records from bibliographic databases.

Guidelines for negotiating scientific collaboration.

Formation of functional retinotectal connections in co-cultures of fetal mouse explants

Ranking indirect connections in literature-based discovery: The role of medical subject headings: Research Articles