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

SciPy is an open source scientific computing library for the Python programming language. SciPy 1.0 was released in late 2017, about 16 years after the original version 0.1 release. SciPy has become a de facto standard for leveraging scientific algorithms in the Python programming language, with more than 600 unique code contributors, thousands of dependent packages, over 100,000 dependent repositories, and millions of downloads per year. This includes usage of SciPy in almost half of all machine learning projects on GitHub, and usage by high profile projects including LIGO gravitational wave analysis and creation of the first-ever image of a black hole (M87). The library includes functionality spanning clustering, Fourier transforms, integration, interpolation, file I/O, linear algebra, image processing, orthogonal distance regression, minimization algorithms, signal processing, sparse matrix handling, computational geometry, and statistics. In this work, we provide an overview of the capabilities and development practices of the SciPy library and highlight some recent technical developments.

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SciPy 1.0--Fundamental Algorithms for Scientific Computing in Python

Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

/pdf/self-assembled-monolayers-of-thiolates-on-metals-as-a-form-1neb0hj3k4.pdf

Self-assembled monolayers of thiolates on metals as a form of nanotechnology.

Using molecules as electronic components is a powerful new direction in the science and technology of nanometre-scale systems1. Experiments to date have examined a multitude of molecules conducting in parallel2,3, or, in some cases, transport through single molecules. The latter includes molecules probed in a two-terminal geometry using mechanically controlled break junctions4,5 or scanning probes6,7 as well as three-terminal single-molecule transistors made from carbon nanotubes8, C60 molecules9, and conjugated molecules diluted in a less-conducting molecular layer10. The ultimate limit would be a device where electrons hop on to, and off from, a single atom between two contacts. Here we describe transistors incorporating a transition-metal complex designed so that electron transport occurs through well-defined charge states of a single atom. We examine two related molecules containing a Co ion bonded to polypyridyl ligands, attached to insulating tethers of different lengths. Changing the length of the insulating tether alters the coupling of the ion to the electrodes, enabling the fabrication of devices that exhibit either single-electron phenomena, such as Coulomb blockade, or the Kondo effect.

Coulomb blockade and the Kondo effect in single-atom transistors

We present a theory of the cholesteric blue phase, assuming a first-order cholesteric-isotropic transition. We show, on the basis of the Oseen-Frank elasticity equations, that the planar helix structure, generally associated with the cholesteric phase, becomes unstable at temperatures near the transition point. It transforms into a phase characterized by a network of disclination lines.

Theory of the blue phase of cholesteric liquid crystals.

Quantitative computational models play an increasingly important role in modern biology. Such models typically involve many free parameters, and assigning their values is often a substantial obstacle to model development. Directly measuring in vivo biochemical parameters is difficult, and collectively fitting them to other experimental data often yields large parameter uncertainties. Nevertheless, in earlier work we showed in a growth-factor-signaling model that collective fitting could yield well-constrained predictions, even when it left individual parameters very poorly constrained. We also showed that the model had a ‘‘sloppy’’ spectrum of parameter sensitivities, with eigenvalues roughly evenly distributed over many decades. Here we use a collection of models from the literature to test whether such sloppy spectra are common in systems biology. Strikingly, we find that every model we examine has a sloppy spectrum of sensitivities. We also test several consequences of this sloppiness for building predictive models. In particular, sloppiness suggests that collective fits to even large amounts of ideal time-series data will often leave many parameters poorly constrained. Tests over our model collection are consistent with this suggestion. This difficulty with collective fits may seem to argue for direct parameter measurements, but sloppiness also implies that such measurements must be formidably precise and complete to usefully constrain many model predictions. We confirm this implication in our growth-factor-signaling model. Our results suggest that sloppy sensitivity spectra are universal in systems biology models. The prevalence of sloppiness highlights the power of collective fits and suggests that modelers should focus on predictions rather than on parameters.

https://www.lassp.cornell.edu/sethna/pubPDF/SloppyEverywhere.pdf

Universally sloppy parameter sensitivities in systems biology models.

Crackling noise arises when a system responds to changing external conditions through discrete, impulsive events spanning a broad range of sizes A wide variety of physical systems exhibiting crackling noise have been studied, from earthquakes on faults to paper crumpling Because these systems exhibit regular behavior over many decades of sizes, their behavior is likely independent of microscopic and macroscopic details, and progress can be made by the use of very simple models The fact that simple models and real systems can share the same behavior on a wide range of scales is called universality We illustrate these ideas using results for our model of crackling noise in magnets, explaining the use of the renormalization group and scaling collapses This field is still developing: we describe a number of continuing challenges

Crackling Noise

1. What is Statistical Mechanics? 2. Random walks and emergent properties 3. Temperature and equilibrium 4. Phase-space dynamics and ergodicity 5. Entropy 6. Free Energies 7. Quantum statistical mechanics 8. Calculation and computation 9. Order parameters, broken symmetry, and topology 10. Correlations, response, and dissipation 11. Abrupt phase transitions 12. Continuous phase transitions APPENDIX: FOURIER METHODS

James P. Sethna

Papers

Coulomb blockade and the Kondo effect in single-atom transistors

Theory of the blue phase of cholesteric liquid crystals.

Universally sloppy parameter sensitivities in systems biology models.

Crackling Noise

Statistical Mechanics: Entropy, Order Parameters and Complexity