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

The process of programmed cell death, or apoptosis, is generally characterized by distinct morphological characteristics and energy-dependent biochemical mechanisms. Apoptosis is considered a vital component of various processes including normal cell turnover, proper development and functioning of the immune system, hormone-dependent atrophy, embryonic development and chemical-induced cell death. Inappropriate apoptosis (either too little or too much) is a factor in many human conditions including neurodegenerative diseases, ischemic damage, autoimmune disorders and many types of cancer. The ability to modulate the life or death of a cell is recognized for its immense therapeutic potential. Therefore, research continues to focus on the elucidation and analysis of the cell cycle machinery and signaling pathways that control cell cycle arrest and apoptosis. To that end, the field of apoptosis research has been moving forward at an alarmingly rapid rate. Although many of the key apoptotic proteins have been identified, the molecular mechanisms of action or inaction of these proteins remain to be elucidated. The goal of this review is to provide a general overview of current knowledge on the process of apoptosis including morphology, biochemistry, the role of apoptosis in health and disease, detection methods, as well as a discussion of potential alternative forms of apoptosis.

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Apoptosis: A Review of Programmed Cell Death

At high concentrations, free radicals and radical-derived, nonradical reactive species are hazardous for living organisms and damage all major cellular constituents. At moderate concentrations, how...

/pdf/free-radicals-in-the-physiological-control-of-cell-function-g3w3iyr0ph.pdf

Free Radicals in the Physiological Control of Cell Function

Today's Web-enabled deluge of electronic data calls for automated methods of data analysis. Machine learning provides these, developing methods that can automatically detect patterns in data and then use the uncovered patterns to predict future data. This textbook offers a comprehensive and self-contained introduction to the field of machine learning, based on a unified, probabilistic approach. The coverage combines breadth and depth, offering necessary background material on such topics as probability, optimization, and linear algebra as well as discussion of recent developments in the field, including conditional random fields, L1 regularization, and deep learning. The book is written in an informal, accessible style, complete with pseudo-code for the most important algorithms. All topics are copiously illustrated with color images and worked examples drawn from such application domains as biology, text processing, computer vision, and robotics. Rather than providing a cookbook of different heuristic methods, the book stresses a principled model-based approach, often using the language of graphical models to specify models in a concise and intuitive way. Almost all the models described have been implemented in a MATLAB software package--PMTK (probabilistic modeling toolkit)--that is freely available online. The book is suitable for upper-level undergraduates with an introductory-level college math background and beginning graduate students.

Machine Learning : A Probabilistic Perspective

Comprehensive Integration of Single-Cell Data.

Provided herein is a method and system for analyzing a sample. In some embodiments the method makes use of a plurality of capture agents that are each linked to a different oligonucleotide and a corresponding plurality of labeled nucleic acid probes, wherein each of the labeled nucleic acid probes specifically hybridizes with only one of the oligonucleotides. The sample is labeled with the capture agents en masse, and sub-sets of the capture agents are detected using iterative cycles using corresponding subsets of the labeled nucleic acid probes.

Highly-multiplexed fluorescent imaging

NRASG12V oncogene facilitates self-renewal in a murine model of acute myelogenous leukemia

Advances in the nascent field of gene therapy have created a demand for gene transfer vehicles that are capable of safely and stably delivering genes to both dividing and nondividing cells. Retroviral vectors, by virtue of their ability to permanently integrate genes of interest into the target cell genome, have played a prominent role in much of the gene transfer research to date. However, their inability to transduce quiescent cells has rendered them largely ineffective for clinical applications. In contrast, adenoviral vectors are capable of infecting nondividing cells, but attempts to move these vectors into the clinic have been limited at times by their lack of stable expression and high immunogenicity. Lentiviral vectors offer a unique combination of the advantages of these two systems without many of the pitfalls. Lentiviruses can stably transduce both dividing and nondividing cells, and, unlike adenoviral vectors, are not immunogenic in vivo. The most established and tested lentiviral vectors are those based on the human immunodeficiency virus (HIV-1). As a full-length virus, HIV is naturally optimized for production in and transduction of human cells and, as such, has proved an ideal candidate for development into a human gene transfer vector. Concern over the consequences of using vectors based on a pathogenic virus with such an optimized tropism for human cells has, however, limited the clinical applications of HIV vectors. For these reasons, vector development has begun to shift to other members of the lentivirus family that do not infect primates as part of their normal host range. In addition to the potential biosafety benefits, it is possible that some nonprimate lentiviral vectors will show an increased ability to infect some useful cell types relative to HIV-based vectors. As many of these viruses have not been well characterized biologically, an interesting cross-talk has developed where advances in lentiviral biology impact vector development and vice versa. The recent discovery of the central DNA flap in lentiviral genomes and its role in nuclear import of preintegration complexes, for example, may prove critical in bringing the efficiency of nonprimate lentiviral vectors up to the level of HIV vectors. As it appears that these nonprimate vectors might reach parity with their primate counterparts in terms of efficiency and tropism, it must now be considered whether they are truly safer for use in the clinic. This review will focus on those aspects of nonprimate lentiviral biology which make such vectors potentially useful in clinical settings.

Nonprimate lentiviral vectors.

Algorithmic cellular segmentation is an essential step for the quantitative analysis of highly multiplexed tissue images. Current segmentation pipelines often require manual dataset annotation and additional training, significant parameter tuning, or a sophisticated understanding of programming to adapt the software to the researcher's need. Here, we present CellSeg, an open-source, pre-trained nucleus segmentation and signal quantification software based on the Mask region-convolutional neural network (R-CNN) architecture. CellSeg is accessible to users with a wide range of programming skills.CellSeg performs at the level of top segmentation algorithms in the 2018 Kaggle Data Challenge both qualitatively and quantitatively and generalizes well to a diverse set of multiplexed imaged cancer tissues compared to established state-of-the-art segmentation algorithms. Automated segmentation post-processing steps in the CellSeg pipeline improve the resolution of immune cell populations for downstream single-cell analysis. Finally, an application of CellSeg to a highly multiplexed colorectal cancer dataset acquired on the CO-Detection by indEXing (CODEX) platform demonstrates that CellSeg can be integrated into a multiplexed tissue imaging pipeline and lead to accurate identification of validated cell populations.CellSeg is a robust cell segmentation software for analyzing highly multiplexed tissue images, accessible to biology researchers of any programming skill level. 

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CellSeg: a robust, pre-trained nucleus segmentation and pixel quantification software for highly multiplexed fluorescence images

Cyclic graphical models are unnecessary for accurate representation of joint probability distributions, but are often indispensable when a causal representation of variable relationships is desired. For variables with a cyclic causal dependence structure, DAGs are guaranteed not to recover the correct causal structure, and therefore may yield false predictions about the outcomes of perturbations (and even inference.) In this paper, we introduce an approach to generalize Bayesian Network structure learning to structures with cyclic dependence. We introduce a structure learning algorithm, prove its performance given reasonable assumptions, and use simulated data to compare its results to the results of standard Bayesian network structure learning. We then propose a modified, heuristic algorithm with more modest data requirements, and test its performance on a real-life dataset from molecular biology, containing causal, cyclic dependencies.

Garry P. Nolan

Papers

Highly-multiplexed fluorescent imaging

NRASG12V oncogene facilitates self-renewal in a murine model of acute myelogenous leukemia

Nonprimate lentiviral vectors.

CellSeg: a robust, pre-trained nucleus segmentation and pixel quantification software for highly multiplexed fluorescence images

Structure Learning in Causal Cyclic Networks