Three parallel algorithms for classical molecular dynamics are presented. The first assigns each processor a fixed subset of atoms; the second assigns each a fixed subset of inter-atomic forces to compute; the third assigns each a fixed spatial region. The algorithms are suitable for molecular dynamics models which can be difficult to parallelize efficiently—those with short-range forces where the neighbors of each atom change rapidly. They can be implemented on any distributed-memory parallel machine which allows for message-passing of data between independently executing processors. The algorithms are tested on a standard Lennard-Jones benchmark problem for system sizes ranging from 500 to 100,000,000 atoms on several parallel supercomputers--the nCUBE 2, Intel iPSC/860 and Paragon, and Cray T3D. Comparing the results to the fastest reported vectorized Cray Y-MP and C90 algorithm shows that the current generation of parallel machines is competitive with conventional vector supercomputers even for small problems. For large problems, the spatial algorithm achieves parallel efficiencies of 90% and a 1840-node Intel Paragon performs up to 165 faster than a single Cray C9O processor. Trade-offs between the three algorithms and guidelines for adapting them to more complex molecular dynamics simulations are also discussed.

Fast parallel algorithms for short-range molecular dynamics

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

XI. STRATEGIES FOR IMPROVING DIABETES CARE D iabetes is a chronic illness that requires continuing medical care and patient self-management education to prevent acute complications and to reduce the risk of long-term complications. Diabetes care is complex and requires that many issues, beyond glycemic control, be addressed. A large body of evidence exists that supports a range of interventions to improve diabetes outcomes. These standards of care are intended to provide clinicians, patients, researchers, payors, and other interested individuals with the components of diabetes care, treatment goals, and tools to evaluate the quality of care. While individual preferences, comorbidities, and other patient factors may require modification of goals, targets that are desirable for most patients with diabetes are provided. These standards are not intended to preclude more extensive evaluation and management of the patient by other specialists as needed. For more detailed information, refer to Bode (Ed.): Medical Management of Type 1 Diabetes (1), Burant (Ed): Medical Management of Type 2 Diabetes (2), and Klingensmith (Ed): Intensive Diabetes Management (3). The recommendations included are diagnostic and therapeutic actions that are known or believed to favorably affect health outcomes of patients with diabetes. A grading system (Table 1), developed by the American Diabetes Association (ADA) and modeled after existing methods, was utilized to clarify and codify the evidence that forms the basis for the recommendations. The level of evidence that supports each recommendation is listed after each recommendation using the letters A, B, C, or E.

Standards of Medical Care in Diabetes

Inference of macromolecular assemblies from crystalline state.

Ultrasonic-Assisted Fabrication of Superhydrophobic Mg-Mn Layered Double Hydroxides Coating on ZK60 Magnesium Alloy and Its Properties

Proteostasis biology is the network of various pathways associated with protein homeostasis and malfunction, which is the major cause of developing various neurodegenerative diseases like Alzheimer’s Disease. Recently, Accelerated‐Medicine Partnership – Alzheimer’s Disease consortia has released a huge amount of OMICS data generated by thousands of post‐mortem brains from various cohorts. Such data provides excellent opportunities to gain insight about proteostasis pathway network crosstalk behind Alzheimer’s Disease. As a part of early discovery initiatives using transcriptomic bulk RNA‐seq data derived from post‐mortem brain, AMP‐AD consortia published data‐driven identification of 30 network modules that are supposed to capture disease biology unbiased to any hypothesis. These modules are thousands of genes co‐expressed in the population in various conditions robust to any computational methods, batch effect of cohort‐specific effects.

https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/alz.062625

Uncovering proteostasis biology causing Alzheimer’s Disease using AMP‐AD transcriptomic data

Identifying spatially variable genes (SVGs) is critical in linking molecular cell functions with tissue phenotypes. Spatially resolved transcriptomics captures cellular-level gene expression with corresponding spatial coordinates in two or three dimensions and can be used to infer SVGs effectively. However, current computational methods may not achieve reliable results and often cannot handle three-dimensional spatial transcriptomic data. Here we introduce BSP (big-small patch), a spatial granularity-guided and non-parametric model to identify SVGs from two or three-dimensional spatial transcriptomics data in a fast and robust manner. This new method has been extensively tested in simulations, demonstrating superior accuracy, robustness, and high efficiency. BSP is further validated by substantiated biological discoveries in cancer, neural science, rheumatoid arthritis, and kidney studies with various types of spatial transcriptomics technologies.

/pdf/dimension-agnostic-and-granularity-based-spatially-variable-7ycr8l10.pdf

Dimension-agnostic and granularity-based spatially variable gene identification

Computational modeling using GENESIS platform has led to advances in fabricating a model to test the influence of molecular/proteomic adaptations on behavior due to reward. The nucleus accumbens is an area of the brain that processes information from other parts of the brain and is an integral element of the 'reward pathway' in the brain. A simplified model of the accumbens using one neuron is developed as part of a larger effort to study reward and chemical dependency with a focus on cocaine addiction. A preliminary model of a biologically realistic neuron was developed with inhibitory and excitatory afferents as well as intrasynapse dynamics. The neuron displayed characteristic behavior of a neuron found in the nucleus accumbens including bistability. The neuron has afferents from other neurons via dendrites which carry the inputs relating to behavioral aspects and to learning. To add behavioral aspects to the model, a methodology is developed to model contexts and their reinforcing effects on behavior, similar to cocaine addiction. Results using both the biological and behavioral modeling are encouraging for this preliminary model.

Dong Xu

Papers

Ultrasonic-Assisted Fabrication of Superhydrophobic Mg-Mn Layered Double Hydroxides Coating on ZK60 Magnesium Alloy and Its Properties

Uncovering proteostasis biology causing Alzheimer’s Disease using AMP‐AD transcriptomic data

Dimension-agnostic and granularity-based spatially variable gene identification

Framework for developing a hierarchical model of reward focusing on the nucleus accumbens

Copy-number variations reveal divergence among brazilian soybean cultivars.