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.

This chapter introduces basic concepts and vocabulary terms related to data distributions, probability, and statistics, which will be used throughout later chapters. Summary measures are used to describe data distributions, including averages (mean, geomean, and median), variability (sum of squares (SS), variance, and standard deviation (SD)), and normalization (stating values in terms of their ranks or their z-scores). Investigators often take a sample from a larger population and use the sample distribution to estimate the mean, variance, and shape of the population. Whether or not the population resembles a bell-shaped curve, or normal distribution, if you take repeated samples, the Central Limit Theorem states that the distribution of the means of the samples will tend to approximate a normal distribution. We call the distribution of sample means the “skinny curve”—its mean is the average across sample means, and its standard deviation is the sample SD divided by the square root of the number of datapoints in each sample (this value is better known as the SEM or standard error of the mean). The construction of the skinny curve is central to hypothesis testing and power estimation. We review several common distributions used in statistics, present discrete (counting) statistics such as those that are involved in coin flips and lottery winnings, and finally, introduce the concept of conditional probabilities.

Chapter 3 – Basics of Data and Data Distributions

This chapter discusses common pitfalls in analyzing data sets and helpful tips to avoid them. For example, when the parameters of an experiment (such as the number of datapoints examined or the number and type of statistical tests) are not fixed in advance, this can greatly increase the risk of obtaining false-positive results. Analyses need to proceed carefully when the population being studied is a mixture of subgroups with different properties, or different outcomes, or when the datapoints are not independent of each other.

Chapter 8 – Topics to Consider When Analyzing Data

Alternative Health Care

Scientists publish their research results for many reasons: For example, publications get personal recognition, establish priority for discoveries, provide an accounting to their funders, set the stage for future studies, compare current results against those reported by others, and warn others about pitfalls and problems that are encountered. The forces encouraging research reproducibility, and those encouraging open science, have combined to create new online journals and to alter traditional journals. This chapter reviews some of the recent innovations in scientific publishing, with an emphasis on open access and new peer-review models, and a peek at proposals to alter the structure of the scientific article itself.

The Revolution in Scientific Publishing

Power estimation is used to calculate the number of samples or subjects needed to ensure that an experiment can give definitive results. Although one might think that power estimation is merely a technical procedure, when we take a closer look at how power is (or is not) handled in practice, we gain insight into why so many experiments are not reproducible, and how the current state of affairs can be improved.

Neil R. Smalheiser

Papers

Chapter 3 – Basics of Data and Data Distributions

Chapter 8 – Topics to Consider When Analyzing Data

Alternative Health Care

The Revolution in Scientific Publishing

Chapter 6 – Power Estimation