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Funding Information: This research was funded by the European Union Horizon 2020 research and innovation program under Grant Agreement No. 857470 and from the European Regional Development Fund via Foundation for Polish Science International Research Agenda PLUS program Grant No. MAB PLUS/2018/8. | openaire: EC/H2020/857470/EU//NOMATEN Equiatomic NiCoCr solid solutions have been shown by recent experiments and atomistic simulations to display exceptional mechanical properties that have been suggested to be linked to nanostructural short-range order (SRO) features that may arise from thermal treatments, such as annealing or/and aging. Here we use hybrid Monte Carlo-molecular dynamics simulations to gain further insights of thermal effects on the SRO formation as well as the edge dislocation plasticity mechanisms of equiatomic NiCoCr face-centered cubic solid solution. For that purpose, we utilize two well-known NiCoCr interatomic potentials, one of which displays well-documented SRO, believed to be linked to experimental evidence and labeled as the Li-Sheng-Ma potential, while the other (Farkas-Caro) does not. We use these two potentials to discern short-range ordering (from inherent randomness in random solid solutions) and understand how SROs influence dislocation depinning dynamics in various thermal annealing scenarios. In this context, we used robust, scale-dependent metrics to infer a characteristic SRO size in the Li-Sheng-Ma case by probing local concentration fluctuations which otherwise indicate uncorrelated patterns in the Farkas-Caro case in a close agreement with random alloys. Our Voronoi-based analysis shows meaningful variations of local misfit properties owing to the presence of SROs. Using relevant order parameters, we also report on the drastic increase of chemical ordering within the stacking fault region. More importantly, we find that the Li-Sheng-Ma potential leads to excellent edge dislocation depinning strength with low stacking fault width. Our findings indicate an enhanced roughening mechanism due to the SROs-misfit synergy that leads to significant improvements in dislocation glide resistance. We argue that the improvements in alloy strength have their atomistic origins in the interplay between nanoscopic SROs and atomic-level misfit properties.

Xenbase (www.xenbase.org) is an online resource for researchers utilizing Xenopus laevis and Xenopus tropicalis, and for biomedical scientists seeking access to data generated with these model systems. Content is aggregated from a variety of external resources and also generated by in-house curation of scientific literature and bioinformatic analyses. Over the past two years many new types of content have been added along with new tools and functionalities to reflect the impact of high-throughput sequencing. These include new genomes for both supported species (each with chromosome scale assemblies), new genome annotations, genome segmentation, dynamic and interactive visualization for RNA-Seq data, updated ChIP-Seq mapping, GO terms, protein interaction data, ORFeome support, and improved connectivity to other biomedical and bioinformatic resources.

SummaryWe present an unprecedentedly comprehensive characterization of protein dynamics across early development inXenopus laevis, available immediately via a convenient Web portal. This resource allows interrogation of the protein expression data in conjunction with other data modalities such as genome wide mRNA expression. This study provides detailed data for absolute levels of ∼14K uniqueXenopusproteins representing homologues of ∼9K unique human genes – a rich resource for developmental biologists. The purpose of this manuscript is limited to presenting and releasing the data browser.HighlightsRelative protein expression from stage IV oocyte, blastula, gastrula, neurula, and early organogenesisBiological triplicates with confidence intervals on protein expression reflect certainty in dynamic patternsConvenient time-series Web-browser integrated with the multi-media Xenbase portalGene-symbol search and multi-gene protein/mRNA juxtaposition capabilities

At a fundamental level most genes, signaling pathways, biological functions and organ systems are highly conserved between man and all vertebrate species. Leveraging this conservation, researchers are increasingly using the experimental advantages of the amphibian Xenopus to model human disease. The online Xenopus resource, Xenbase, enables human disease modeling by curating the Xenopus literature published in PubMed and integrating these Xenopus data with orthologous human genes, anatomy, and more recently with links to the Online Mendelian Inheritance in Man resource (OMIM) and the Human Disease Ontology (DO). Here we review how Xenbase supports disease modeling and report on a meta-analysis of the published Xenopus research providing an overview of the different types of diseases being modeled in Xenopus and the variety of experimental approaches being used. Text mining of over 50,000 Xenopus research articles imported into Xenbase from PubMed identified approximately 1,000 putative disease- modeling articles. These articles were manually assessed and annotated with disease ontologies, which were then used to classify papers based on disease type. We found that Xenopus is being used to study a diverse array of disease with three main experimental approaches: cell-free egg extracts to study fundamental aspects of cellular and molecular biology, oocytes to study ion transport and channel physiology and embryo experiments focused on congenital diseases. We integrated these data into Xenbase Disease Pages to allow easy navigation to disease information on external databases. Results of this analysis will equip Xenopus researchers with a suite of experimental approaches available to model or dissect a pathological process. Ideally clinicians and basic researchers will use this information to foster collaborations necessary to interrogate the development and treatment of human diseases.

Recent experiments (Le Bouil et al., Phys. Rev. Lett., 2014, 112, 246001) have analyzed the statistics of local deformation in a granular solid undergoing plastic deformation. Experiments report strongly anisotropic correlation between events, with a characteristic angle that was interpreted using elasticity theory and the concept of Eshelby transformations with dilation; interestingly, the shear bands that characterize macroscopic failure occur at an angle that is different from the one observed in microscopic correlations. Here, we interpret this behavior using a mesoscale elastoplastic model of solid flow that incorporates a local Mohr-Coulomb failure criterion. This differs from the interpretation of Le Bouil et al., which is based on purely elastic considerations ignoring the potential role of local friction on deformation patterns. We show that the angle observed in the microscopic correlations can be understood by combining the elastic interactions associated with Eshelby transformation with the local failure criterion. At large strains, we also induce permanent shear bands at an angle that is different from the one observed in the correlation pattern. We interpret this angle as the one that leads to the maximal instability of slip lines.

With the advent of whole transcriptome and genome analysis methods, classifying samples containing multiple origins has become a significant task. Nucleotide sequences can be allocated to a genome or transcriptome by aligning sequences to multiple target sequence sets, but this approach requires extensive computational resources and also depends on target sequence sets lacking contaminants, which is often not the case. Here, we demonstrate that raw sequences can be rapidly sorted into groups, in practice corresponding to genera, by exploiting differences in nucleotide GC content. To do so, we introduce GCSpeciesSorter, which uses classification, specifically Support Vector Machines (SVM) and the C4.5 decision tree generator, to differentiate sequences. It also implements a secondary BLAST feature to identify known outliers. In the test case presented, a hermatypic coral holobiont, the cnidarian host includes various endosymbionts. The best characterized and most common of these symbionts are zooxanthellae of the genus Symbiodinium. GCSpeciesSorter separates cnidarian from Symbiodinium sequences with a high degree of accuracy. We show that if the GC contents of the species differ enough, this method can be used to accurately distinguish the sequences of different species when using high-throughput sequencing technologies.

By means of a finite elements technique we solve numerically the dynamics of an amorphous solid under deformation in the quasistatic driving limit. We study the noise statistics of the stress-strain signal in the steady state plastic flow, focusing on systems with low internal dissipation. We analyze the distributions of avalanche sizes and durations and the density of shear transformations when varying the damping strength. In contrast to avalanches in the overdamped case, dominated by the yielding point universal exponents, inertial avalanches are controlled by a non-universal damping dependent feedback mechanism; eventually turning negligible the role of correlations. Still, some general properties of avalanches persist and new scaling relations can be proposed., 10 pages, 9 figures

Xenbase (http://www.xenbase.org), the Xenopus frog model organism database, integrates a wide variety of data from this biomedical model genus. Two closely related species are represented: the allotetraploid Xenopus laevis that is widely used for microinjection and tissue explant-based protocols, and the diploid Xenopus tropicalis which is used for genetics and gene targeting. The two species are extremely similar and protocols, reagents and results from each species are often interchangeable. Xenbase imports, indexes, curates and manages data from both species; all of which are mapped via unique IDs and can be queried in either a species-specific or species agnostic manner. All our services have now migrated to a private cloud to achieve better performance and reliability. We have added new content, including providing full support for morpholino reagents, used to inhibit mRNA translation or splicing and binding to regulatory microRNAs. New genomes assembled by the JGI for both species and are displayed in Gbrowse and are also available for searches using BLAST. Researchers can easily navigate from genome content to gene page reports, literature, experimental reagents and many other features using hyperlinks. Xenbase has also greatly expanded image content for figures published in papers describing Xenopus research via PubMedCentral.

We numerically study the local stress distribution within athermal, isotropically stressed, mechanically stable, packings of bidisperse frictionless disks above the jamming transition in two dimensions. Considering the Fourier transform of the local stress, we find evidence for algebraically increasing fluctuations in both isotropic and anisotropic components of the stress tensor at small wavenumbers, contrary to recent theoretical predictions. Such increasing fluctuations imply a lack of self-averaging of the stress on large length scales. The crossover to these increasing fluctuations defines a length scale $\ell_0$, however it appears that $\ell_0$ does not vary much with packing fraction $\phi$, nor does $\ell_0$ seem to be diverging as $\phi$ approaches the jamming $\phi_J$. We also find similar large length scale fluctuations of stress in the inherent states of a quenched Lennard-Jones liquid, leading us to speculate that such fluctuations may be a general property of amorphous solids in two dimensions., Comment: 14 pages, 20 figures; updated to published version; new corrected results for shear fluctuations and new tests for lack of protocol dependence

This paper discusses ways to reduce the execution time of a software Global Navigation Satellite System (GNSS) receiver that is meant for offline operation in a cloud environment. Client devices register satellite signals they receive, and send them to the cloud, to be processed by this software. The goal of this project is for each client request to be processed as fast as possible, but also to increase total system throughput by making sure as many requests as possible are processed within a unit of time. The characteristics of the application provided both opportunities and challenges for increasing performance. This paper describes the speedups we obtained by enabling the software to exploit multi-core CPUs and GPGPUs. It mentions which techniques worked and which did not. To increase throughput, it describes how to control the resources allocated for each invocation of the software to process a client request, such that multiple copies of the application can run at the same time. It uses the notion of the effective running time to measure the system's throughput when running multiple instances at the same time, and show how to determine when the system's computing resources have been saturated.

Adiabatic quantum optimization offers a new method for solving hard optimization problems. In this paper we calculate median adiabatic times (in seconds) determined by the minimum gap during the adiabatic quantum optimization for an NP-hard Ising spin glass instance class with up to 128 binary variables. Using parameters obtained from a realistic superconducting adiabatic quantum processor, we extract the minimum gap and matrix elements using high performance Quantum Monte Carlo simulations on a large-scale Internet-based computing platform. We compare the median adiabatic times with the median running times of two classical solvers and find that, for the considered problem sizes, the adiabatic times for the simulated processor architecture are about 4 and 6 orders of magnitude shorter than the two classical solvers' times. This shows that if the adiabatic time scale were to determine the computation time, adiabatic quantum optimization would be significantly superior to those classical solvers for median spin glass problems of at least up to 128 qubits. We also discuss important additional constraints that affect the performance of a realistic system.

Much of the current focus in high-performance computing is on multi-threading, multi-computing, and graphics processing unit (GPU) computing. However, vectorization and non-parallel optimization techniques, which can often be employed additionally, are less frequently discussed. In this paper, we present an analysis of several optimizations done on both central processing unit (CPU) and GPU implementations of a particular computationally intensive Metropolis Monte Carlo algorithm. Explicit vectorization on the CPU and the equivalent, explicit memory coalescing, on the GPU are found to be critical to achieving good performance of this algorithm in both environments. The fully-optimized CPU version achieves a 9x to 12x speedup over the original CPU version, in addition to speedup from multi-threading. This is 2x faster than the fully-optimized GPU version., 17 pages, 17 figures

This paper presents two conceptually simple methods for parallelizing a Parallel Tempering Monte Carlo simulation in a distributed volunteer computing context, where computers belonging to the general public are used. The first method uses conventional multi-threading. The second method uses CUDA, a graphics card computing system. Parallel Tempering is described, and challenges such as parallel random number generation and mapping of Monte Carlo chains to different threads are explained. While conventional multi-threading on CPUs is well-established, GPGPU programming techniques and technologies are still developing and present several challenges, such as the effective use of a relatively large number of threads. Having multiple chains in Parallel Tempering allows parallelization in a manner that is similar to the serial algorithm. Volunteer computing introduces important constraints to high performance computing, and we show that both versions of the application are able to adapt themselves to the varying and unpredictable computing resources of volunteers' computers, while leaving the machines responsive enough to use. We present experiments to show the scalable performance of these two approaches, and indicate that the efficiency of the methods increases with bigger problem sizes., 15 pages, 9 figures, 3 tables. Accepted in the International Journal of High Performance Computing Applications