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.

/pdf/a-neglected-link-between-the-psychoactive-effects-of-dietary-33r77rd94w.pdf

A Neglected Link Between the Psychoactive Effects of Dietary Ingredients and Consciousness-Altering Drugs.

The scientific process requires a critical attitude towards existing hypotheses and obvious explanations. Teaching this mindset to students is both important and challenging.

/pdf/how-many-scientists-does-it-take-to-change-a-paradigm-new-9t1qbr2yyp.pdf

How many scientists does it take to change a paradigm? New ideas to explain scientific observations are everywhere—we just need to learn how to see them

Null hypothesis statistical testing (NHST) is the primary type of statistical inference taught in schools. The goal is to assess the likelihood of the null hypothesis , which is that an observed difference in means between two groups (generally a control group and an experimental group) can be explained simply by the inherent variability of sampling. Rejecting the null hypothesis implies that the two groups are likely to have different mean values and thus come from different underlying distributions. This is a vital step in deciding if a finding observed in an experiment is worth taking seriously. However, accepting the null hypothesis does not prove that the two groups are identical (since the experiment may not have adequate power). And rejecting the null hypothesis does not establish that a finding is scientifically valid, since your samples might be biased in some manner, your measures might be poor proxies, or your finding might be due to confounding variables. The t-test is by far the most popular test used in NHST. We describe when the t-test is appropriate to use and how to choose the proper parameters (paired vs. unpaired, one tailed vs. two tailed, and equal vs. unequal variance).

Null Hypothesis Statistical Testing and the t-Test

Mining the case report literature identified an intriguing, yet neglected finding: Deoxyribonuclease I (DNase I) as a possible treatment for Alzheimer's disease. This finding is speculative, both because it is based on one patient, and because the underlying mechanism(s) of action remain obscure. However, further literature review revealed that there are several plausible mechanisms by which DNase I might affect the course of Alzheimer's disease. Given that DNase I is an FDA-approved drug, with extensive studies in both animals and man in the context of other diseases, I suggest that investigation of DNAse I in Alzheimer's disease is worthwhile.

Neil R. Smalheiser

Papers

A Neglected Link Between the Psychoactive Effects of Dietary Ingredients and Consciousness-Altering Drugs.

How many scientists does it take to change a paradigm? New ideas to explain scientific observations are everywhere—we just need to learn how to see them

Null Hypothesis Statistical Testing and the t-Test

Mining Clinical Case Reports to Identify New Lines of Investigation in Alzheimer's Disease: The Curious Case of DNase I.

Radiosensitivity and differentiation of ganglion cells within fetal mouse retinal explants in vitro