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

BackgroundExperimental and clinical data suggest that reducing inflammation without affecting lipid levels may reduce the risk of cardiovascular disease. Yet, the inflammatory hypothesis of atherothrombosis has remained unproved. MethodsWe conducted a randomized, double-blind trial of canakinumab, a therapeutic monoclonal antibody targeting interleukin-1β, involving 10,061 patients with previous myocardial infarction and a high-sensitivity C-reactive protein level of 2 mg or more per liter. The trial compared three doses of canakinumab (50 mg, 150 mg, and 300 mg, administered subcutaneously every 3 months) with placebo. The primary efficacy end point was nonfatal myocardial infarction, nonfatal stroke, or cardiovascular death. ResultsAt 48 months, the median reduction from baseline in the high-sensitivity C-reactive protein level was 26 percentage points greater in the group that received the 50-mg dose of canakinumab, 37 percentage points greater in the 150-mg group, and 41 percentage points greater in t...

https://www.nejm.org/doi/pdf/10.1056/NEJMoa1707914

Antiinflammatory Therapy with Canakinumab for Atherosclerotic Disease

“Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks”の学習報告

One of the core problems of modern statistics is to approximate difficult-to-compute probability densities. This problem is especially important in Bayesian statistics, which frames all inference about unknown quantities as a calculation involving the posterior density. In this article, we review variational inference (VI), a method from machine learning that approximates probability densities through optimization. VI has been used in many applications and tends to be faster than classical methods, such as Markov chain Monte Carlo sampling. The idea behind VI is to first posit a family of densities and then to find a member of that family which is close to the target density. Closeness is measured by Kullback–Leibler divergence. We review the ideas behind mean-field variational inference, discuss the special case of VI applied to exponential family models, present a full example with a Bayesian mixture of Gaussians, and derive a variant that uses stochastic optimization to scale up to massive data...

Variational Inference: A Review for Statisticians

Macrophages, the most plastic cells of the haematopoietic system, are found in all tissues and show great functional diversity. They have roles in development, homeostasis, tissue repair and immunity. Although tissue macrophages are anatomically distinct from one another, and have different transcriptional profiles and functional capabilities, they are all required for the maintenance of homeostasis. However, these reparative and homeostatic functions can be subverted by chronic insults, resulting in a causal association of macrophages with disease states. In this Review, we discuss how macrophages regulate normal physiology and development, and provide several examples of their pathophysiological roles in disease. We define the 'hallmarks' of macrophages according to the states that they adopt during the performance of their various roles, taking into account new insights into the diversity of their lineages, identities and regulation. It is essential to understand this diversity because macrophages have emerged as important therapeutic targets in many human diseases.

/pdf/macrophage-biology-in-development-homeostasis-and-disease-2nq2u94n8n.pdf

Macrophage biology in development, homeostasis and disease

In this work, we propose a composition/decomposition framework for adversarially training generative models on composed data - data where each sample can be thought of as being constructed from a fixed number of components. In our framework, samples are generated by sampling components from component generators and feeding these components to a composition function which combines them into a "composed sample". This compositional training approach improves the modularity, extensibility and interpretability of Generative Adversarial Networks (GANs) - providing a principled way to incrementally construct complex models out of simpler component models, and allowing for explicit "division of responsibility" between these components. Using this framework, we define a family of learning tasks and evaluate their feasibility on two datasets in two different data modalities (image and text). Lastly, we derive sufficient conditions such that these compositional generative models are identifiable. Our work provides a principled approach to building on pre-trained generative models or for exploiting the compositional nature of data distributions to train extensible and interpretable models.

/pdf/composition-and-decomposition-of-gans-256e2cg38g.pdf

Composition and decomposition of gans

Identifying significant causal agents among a large number of candidates is challenging. When experimental resources are limited, exhaustively screening a large number of agents for the desired effect could incur a large cost and take a substantial amount of time. However, in many large scale experiments, such as high-throughput screening (HTS), the ratio of causal to non-causal agents is usually very low. In this paper, we introduce a group-screening strategy to efficiently screen causal agents by grouping them into treatments. Our analysis shows that when a large number of candidates factors are screened and true agent percentage is very low (less than 1%), even in the worst case we could save up to 80% of the experiment runs. In the case where experiments span many rounds, we provide an online version of the group-screening that can determine the best strategy automatically based on the existing results. We applied this method to a real HTS experiment with 50,000 candidates that would require 9 rounds to finish in an exhaustive case. Our analysis showed that by applying the online-group-screening method, in the worst case, we can use 3 rounds and 19.7% (9828/50000) total tests to identify all the agents. Finally, we show that with minor modifications, this framework extends to more complex agent discovery problems.

/pdf/efficient-online-group-screening-designs-for-agent-3s6lh4n6rs.pdf

Efficient online-group-screening designs for agent identification

Transcriptional insights into the CD8[superscript +] T cell response to infection and memory T cell formation

Purpose: In this paper, we describe a method for recovering the tissue properties directly from medical images and study the correlation of tissue (i.e., prostate) elasticity with the aggressiveness of prostate cancer using medical image analysis. Methods: We present a novel method that uses geometric and physical constraints to deduce the relative tissue elasticity parameters. Although elasticity reconstruction, or elastograph, can be used to estimate tissue elasticity, it is less suited for in-vivo measurements or deeply seated organs like prostate. We develop a method to estimate tissue elasticity values based on pairs of images, using a finite-element-based biomechanical model derived from an initial set of images, local displacements, and an optimization-based framework. Results: We demonstrate the feasibility of a statistically based classifier that automatically provides a clinical T-stage and Gleason score based on the elasticity values reconstructed from computed tomography images. Conclusion: We study the relative elasticity parameters by performing cancer grading/staging prediction and achieve up to 85% accuracy for cancer staging prediction and up to 77% accuracy for cancer grading prediction using a feature set, which includes recovered relative elasticity parameters and patient age information.

Vladimir Jojic

Papers

Composition and decomposition of gans

Efficient online-group-screening designs for agent identification

Transcriptional insights into the CD8[superscript +] T cell response to infection and memory T cell formation

Reconstructing Tissue Properties From Medical Images With Application in Cancer Screening