Versions Of Packages Research Articles

Technical leverage analysis in the Python ecosystem

Context:Technical leverage is the ratio between dependencies (other people’s code) and own codes of a software package. It has been shown to be useful to characterize the Java ecosystem and there are also studies on the NPM ecosystem available.Objective:By using this metric we aim to analyze the Python ecosystem, how it evolves, and how secure it is, as a developer would perceive it when deciding to adopt or update (or not) a library.Method:We collect a dataset of the top 600 Python packages (corresponding to 21,205 versions) and used a number of innovative approaches for its analysis including the use of a two-part statistical model to deal with excess zeros, a mathematical closed formulation to estimate vulnerabilities that we confirm with bootstrapping on the actual dataset.Results:Small Python package versions have a median technical leverage of 6.9x their own code, while bigger package versions rely on dependencies code a tenth of their own (median leverage of 0.1). In terms of evolution, Python packages tend to have stable technical leverage through their evolution (once highly leveraged, always leveraged). On security, the chance of getting a safe package version when choosing a package is actually better than previous research has shown based on the ratio of safe package versions in the ecosystem.Coclusions:Python packages ship a lot of other people’s code and tend to keep doing so. However, developers will have a good chance to choose a safe package version.

Toward the Trustworthiness of Industrial Robotics Using Differential Fuzz Testing

Intelligent robots are a current application in Industrial Internet of Things (IIoT), with their trustworthiness being a topic of considerable research interest. Vulnerabilities in robot software may affect the trustworthiness of robotics. To detect these vulnerabilities in robot software, this study proposes a differential fuzz testing method. The main idea is to continuously execute test cases for different versions of software packages to detect inconsistencies among outputs and eventually discover vulnerabilities. First, test cases are generated, combining seed generation and mutation, after which the measured model of the packages in the robot visualization tool (RVIZ) is built and the generated seeds are executed. The differences among inconsistent outputs are calculated and the causes of the differences analyzed. Two evaluation metrics for the inconsistencies and seeds are presented. This method is applied to the crucial package in ROS-MoveIt!. The results show that the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">arm.go()</i> of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">moveit_commander</i> has joint angle overflow.

Microbench: automated metadata management for systems biology benchmarking and reproducibility in Python.

MotivationComputational systems biology analyses typically make use of multiple software and their dependencies, which are often run across heterogeneous compute environments. This can introduce differences in performance and reproducibility. Capturing metadata (e.g. package versions, GPU model) currently requires repetitious code and is difficult to store centrally for analysis. Even where virtual environments and containers are used, updates over time mean that versioning metadata should still be captured within analysis pipelines to guarantee reproducibility.Results Microbench is a simple and extensible Python package to automate metadata capture to a file or Redis database. Captured metadata can include execution time, software package versions, environment variables, hardware information, Python version and more, with plugins. We present three case studies demonstrating Microbench usage to benchmark code execution and examine environment metadata for reproducibility purposes.Availability and implementationInstall from the Python Package Index using pip install microbench. Source code is available from https://github.com/alubbock/microbench.Supplementary information Supplementary data are available at Bioinformatics online.

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SpatialExperiment: infrastructure for spatially-resolvedtranscriptomics data in R using Bioconductor.

Summary SpatialExperiment is a new data infrastructure for storing and accessing spatially-resolved transcriptomics data, implemented within the R/Bioconductor framework, which provides advantages of modularity, interoperability, standardized operations and comprehensive documentation. Here, we demonstrate the structure and user interface with examples from the 10x Genomics Visium and seqFISH platforms, and provide access to example datasets and visualization tools in the STexampleData, TENxVisiumData and ggspavis packages.Availability and implementationThe SpatialExperiment, STexampleData, TENxVisiumData and ggspavis packages are available from Bioconductor. The package versions described in this manuscript are available in Bioconductor version 3.15 onwards.Supplementary information Supplementary data are available at Bioinformatics online.

Pattern Generation for Micropattern Traction Microscopy.

Micropattern traction microscopy allows control of the shape of single cells and cell clusters. Furthermore, the ability to pattern at the micrometer length scale allows the use of these patterned contact zones for the measurement of traction forces, as each micropatterned dot allows for the formation of a single focal adhesion that then deforms the soft, underlying hydrogel. This approach has been used for a wide range of cell types, including endothelial cells, smooth muscle cells, fibroblasts, platelets, and epithelial cells. This review describes the evolution of techniques that allow the printing of extracellular matrix proteins onto polyacrylamide hydrogels in a regular array of dots of prespecified size and spacing. As micrometer-scale patterns are difficult to directly print onto soft substrates, patterns are first generated on rigid glass coverslips that are then used to transfer the pattern to the hydrogel during gelation. First, the original microcontact printing approach to generate arrays of small dots on the coverslip is described. A second step that removes most of the pattern to leave islands of small dots is required to control the shapes of cells and cell clusters on such arrays of patterned dots. Next, an evolution of this approach that allows for the generation of islands of dots using a single subtractive patterning step is described. This approach is greatly simplified for the user but has the disadvantage of a decreased lifetime for the master mold needed to make the patterns. Finally, the computational approaches that have been developed for the analysis of images of displaced dots and subsequent cell-generated traction fields are described, and updated versions of these analysis packages are provided.

Numerical determination of stress intensity factors: J-integral and modified virtual crack closure technique

Together with the development of numerical tools for stress-strain analysis, approaches to deal with fracture mechanics problems have been the object of continuous improvement. For linear-elastic fracture mechanics problems, the determination of Stress Intensity Factors (SIF) is available as a post-processing possibility in most commercial finite element software packages such as Ansys and Abaqus, allowing the users to perform straightforward assessments of cracked mechanical parts or structures. However, available techniques for SIF determination in post-processing of these commercial solutions are almost limited to the so-called displacement extrapolation and J-integral techniques. In the present work, the implementation of the J-integral technique is revised considering a new finite element software, labeled as Omicron. Furthermore, a more recent technique, the modified Virtual Crack Closure Technique (mVCCT) is also implemented and evaluated. For results assessment, finite element models were built in Omicron and SIFs were determined considering the J-integral and mVCCT approaches. From this study, it is concluded that the mVCCT implementation is simpler than the J-integral and it allows to determine accurately SIFs. Nevertheless, this technique is not straightly available in the current versions of the commercial packages for finite element modeling, which require separate post-processing for SIF determination.

BEAGLE 3: Improved Performance, Scaling, and Usability for a High-Performance Computing Library for Statistical Phylogenetics.

BEAGLE is a high-performance likelihood-calculation library for phylogenetic inference. The BEAGLE library defines a simple, but flexible, application programming interface (API), and includes a collection of efficient implementations for calculation under a variety of evolutionary models on different hardware devices. The library has been integrated into recent versions of popular phylogenetics software packages including BEAST and MrBayes and has been widely used across a diverse range of evolutionary studies. Here, we present BEAGLE 3 with new parallel implementations, increased performance for challenging data sets, improved scalability, and better usability. We have added new OpenCL and central processing unit-threaded implementations to the library, allowing the effective utilization of a wider range of modern hardware. Further, we have extended the API and library to support concurrent computation of independent partial likelihood arrays, for increased performance of nucleotide-model analyses with greater flexibility of data partitioning. For better scalability and usability, we have improved how phylogenetic software packages use BEAGLE in multi-GPU (graphics processing unit) and cluster environments, and introduced an automated method to select the fastest device given the data set, evolutionary model, and hardware. For application developers who wish to integrate the library, we also have developed an online tutorial. To evaluate the effect of the improvements, we ran a variety of benchmarks on state-of-the-art hardware. For a partitioned exemplar analysis, we observe run-time performance improvements as high as 5.9-fold over our previous GPU implementation. BEAGLE 3 is free, open-source software licensed under the Lesser GPL and available at https://beagle-dev.github.io.

Diagnostic Survey of Pineapple Heart Rot Disease in Lake Victoria Crescent Basin of Uganda

Pineapple (Ananas comosus L. Merril) is a key horticultural crop in tropical and sub-tropical countries valued for its economic and nutritional importance. However, the recent outbreak of pineapple heart rot disease (PHRD) in Uganda is a serious threat to production. Yet, information on the prevalence, incidence and severity is limited and scanty. This study, therefore, assessed the prevalence, incidence and severity of pineapple heart rot disease in the districts of Masaka, Luwero, Kayunga and Mukono in the Lake Victoria crescent of Uganda in 2015. Semi-structured questionnaires were used to solicit information from 216 respondents. Data collected was analyzed using descriptive statistics of SPSS computer package versions 16.0. Varying incidences and severities of pineapple heart rot disease was observed in all fields surveyed. The disease was more prevalent in Kayunga than Masaka districts, respectively. Overall, 48% and 05% pineapple fields were infected with pineapple heart rot disease in Kayunga and Masaka district, respectively. Similarly, the highest and the lowest disease incidence were recorded in Kayunga and Masaka district, respectively. Overall, the incidence and severity varied from field to field. The findings have therefore shown that pineapple heart rot disease is widespread and devastating in central Uganda. Therefore, there is a need for the policymakers to priorities resources to develop an appropriate disease management strategy to combat this devastating epidemic to save the pineapple industry.

A Framework for the Preservation of a Docker Container

Reliably building and maintaining systems across environments is a continuing problem. A project or experiment may run for years. Software and hardware may change as can the operating system. Containerisation is a technology that is used in a variety of companies, such as Google, Amazon and IBM, and scientific projects to rapidly deploy a set of services repeatably. Using Dockerfiles to ensure that a container is built repeatably, to allow conformance and easy updating when changes take place are becoming common within projects. Its seen as part of sustainable software development. Containerisation technology occupies a dual space: it is both a repository of software and software itself. In considering Docker in this fashion, we should verify that the Dockerfile can be reproduced. Using a subset of the Dockerfile specification, a domain specific language is created to ensure that Docker files can be reused at a later stage to recreate the original environment. We provide a simple framework to address the question of the preservation of containers and its environment. We present experiments on an existing Dockerfile and conclude with a discussion of future work. Taking our work, a pipeline was implemented to check that a defined Dockerfile conforms to our desired model, extracts the Docker and operating system details. This will help the reproducibility of results by creating the machine environment and package versions. It also helps development and testing through ensuring that the system is repeatably built and that any changes in the software environment can be equally shared in the Dockerfile. This work supports not only the citation process it also the open scientific one by providing environmental details of the work. As a part of the pipeline to create the container, we capture the processes used and put them into the W3C PROV ontology. This provides the potential for providing it with a persistent identifier and traceability of the processes used to preserve the metadata. Our future work will look at the question of linking this output to a workflow ontology to preserve the complete workflow with the commands and parameters to be given to the containers. We see this provenance within the build process useful to provide a complete overview of the workflow.

A Roadmap to Continuous Integration for ATLAS Software Development

The ATLAS software infrastructure facilitates efforts of more than 1000 developers working on the code base of 2200 packages with 4 million lines of C++ and 1.4 million lines of python code. The ATLAS offline code management system is the powerful, flexible framework for processing new package versions requests, probing code changes in the Nightly Build System, migration to new platforms and compilers, deployment of production releases for worldwide access and supporting physicists with tools and interfaces for efficient software use. It maintains multi-stream, parallel development environment with about 70 multi-platform branches of nightly releases and provides vast opportunities for testing new packages, for verifying patches to existing software and for migrating to new platforms and compilers. The system evolution is currently aimed on the adoption of modern continuous integration (CI) practices focused on building nightly releases early and often, with rigorous unit and integration testing. This paper describes the CI incorporation program for the ATLAS software infrastructure. It brings modern open source tools such as Jenkins and GitLab into the ATLAS Nightly System, rationalizes hardware resource allocation and administrative operations, provides improved feedback and means to fix broken builds promptly for developers. Once adopted, ATLAS CI practices will improve and accelerate innovation cycles and result in increased confidence in new software deployments. The paper reports the status of Jenkins integration with the ATLAS Nightly System as well as short and long term plans for the incorporation of CI practices.

Why a ‘light’ product package should not be light blue: Effects of package colour on perceived healthiness and attractiveness of sugar- and fat-reduced products

Healthier foods (e.g. ‘light’ products with low fat or sugar content) often lead to lower hedonic evaluation and decreased satiating properties, putting these products at a sensory disadvantage compared to their regular counterparts. Nudging consumers towards healthy foods by making healthy foods more attractive may facilitate healthier food choices. Package colour communicates product properties and could be used to make a healthy product more attractive. Healthier alternatives are typically packaged in less vibrantly coloured, watered-down packages compared to their regular counterparts. Does this communicate the intended message?To investigate effects of package colour on perceived healthiness, attractiveness and sensory expectations and perception of food products both explicitly and implicitly.We investigated effects of package hue (green/purple, blue, red), brightness and saturation on expected (experiment 1) and perceived (experiment 2) product properties after tasting, for a low-sugar dairy drink (n=148) and low-fat sausage (n=140). Implicit Association Tests (IATs) were used to measure strength of associations between package colouring cues and perceived attractiveness and healthiness of the products.Effects of package colour were stronger for sensory expectations than for perceptions after tasting. A combination of colour properties (hue, brightness and/or saturation) rendered packaging more attractive and increased sensory evaluation. Implicitly, watered-down coloured ‘healthier’ package versions were strongly associated with healthiness whereas ‘regular’ packages were strongly associated with attractiveness.Packaging healthier alternatives in warmer, saturated, less bright coloured packages (more similar to regular products) explicitly enhances sensory expectations and perceptions, and implicitly improves attractiveness, potentially making them more appealing to consumers.

Enhancing Reproducibility and Collaboration via Management of R Package Cohorts

Science depends on collaboration, result reproduction, and the development of supporting software tools. Each of these requires careful management of software versions. We present a unified model for installing, managing, and publishing software contexts in R. It introduces the package manifest as a central data structure for representing versionspecific, decentralized package cohorts. The manifest points to package sources on arbitrary hosts and in various forms, including tarballs and directories under version control. We provide a high-level interface for creating and switching between side-by-side package libraries derived from manifests. Finally, we extend package installation to support the retrieval of exact package versions as indicated by manifests, and to maintain provenance for installed packages. The provenance information enables the user to publish libraries or sessions as manifests, hence completing the loop between publication and deployment. We have implemented this model across three software packages, switchr, switchrGist and GRANBase, and have released the source code under the Artistic 2.0 license.

A novel pathway analysis approach based on the unexplained disregulation of genes.

Primary dis-regulation analysis is implemented in R and included in ROntoTools Bioconductor package (versions ≥ 2.0.0). https://www.bioconductor.org/packages/release/bioc/html/ROntoTools.html.

Comparison of ten packages that compute ocean carbonate chemistry

Abstract. Marine scientists often use two measured or modeled carbonate system variables to compute others. These carbonate chemistry calculations, based on well-known thermodynamic equilibria, are now available in a dozen public packages. Ten of those were compared using common input data and the set of equilibrium constants recommended for best practices. Current versions of all 10 packages agree within 0.2 μatm for pCO2, 0.0002 units for pH, and 0.1 μmol kg−1 for CO32− in terms of surface zonal-mean values. That represents more than a 10-fold improvement relative to outdated versions of the same packages. Differences between packages grow with depth for some computed variables but remain small. Discrepancies derive largely from differences in equilibrium constants. Analysis of the sensitivity of each computed variable to changes in each constant reveals the general dominance of K1 and K2 but also the comparable sensitivity to KB for the AT–CT input pair. Best-practice formulations for K1 and K2 are implemented consistently among packages. Yet with more recent formulations designed to cover a wider range of salinity, packages disagree by up to 8 μatm in pCO2, 0.006 units in pH, and 1 μmol kg−1 in CO32− under typical surface conditions. They use different proposed sets of coefficients for these formulations, all of which are inconsistent. Users would do well to use up-to-date versions of packages and the constants recommended for best practices.

Empirical Bayesian analysis of paired high-throughput sequencing data with a beta-binomial distribution.

BackgroundPairing of samples arises naturally in many genomic experiments; for example, gene expression in tumour and normal tissue from the same patients. Methods for analysing high-throughput sequencing data from such experiments are required to identify differential expression, both within paired samples and between pairs under different experimental conditions.ResultsWe develop an empirical Bayesian method based on the beta-binomial distribution to model paired data from high-throughput sequencing experiments. We examine the performance of this method on simulated and real data in a variety of scenarios. Our methods are implemented as part of the RbaySeq package (versions 1.11.6 and greater) available from Bioconductor (http://www.bioconductor.org).ConclusionsWe compare our approach to alternatives based on generalised linear modelling approaches and show that our method offers significant gains in performance on simulated data. In testing on real data from oral squamous cell carcinoma patients, we discover greater enrichment of previously identified head and neck squamous cell carcinoma associated gene sets than has previously been achieved through a generalised linear modelling approach, suggesting that similar gains in performance may be found in real data. Our methods thus show real and substantial improvements in analyses of high-throughput sequencing data from paired samples.

Molecular Dynamics Simulations of Clathrate Hydrates on Specialised Hardware Platforms

Classical equilibrium molecular dynamics (MD) simulations have been performed to investigate the computational performance of the Simple Point Charge (SPC) and TIP4P water models applied to simulation of methane hydrates, and also of liquid water, on a variety of specialised hardware platforms, in addition to estimation of various equilibrium properties of clathrate hydrates. The FPGA-based accelerator MD-GRAPE 3 was used to accelerate substantially the computation of non-bonded forces, while GPU-based platforms were also used in conjunction with CUDA-enabled versions of the LAMMPS MD software packages to reduce computational time dramatically. The dependence of molecular system size and scaling with number of processors was also investigated. Considering performance relative to power consumption, it is seen that GPU-based computing is quite attractive.

XDS

The usage and control of recent modifications of the program package XDS for the processing of rotation images are described in the context of previous versions. New features include automatic determination of spot size and reflecting range and recognition and assignment of crystal symmetry. Moreover, the limitations of earlier package versions on the number of correction/scaling factors and the representation of pixel contents have been removed. Large program parts have been restructured for parallel processing so that the quality and completeness of collected data can be assessed soon after measurement.

Thermodynamic Aspects of Tin Segregation during Solidification of Aluminium Alloys

When trying to calculate the approximate constitution of as-cast tin containing aluminium alloys one has to cope with a combination of intricacies: (i) Scheil solidification simulation may reflect strong enrichment of alloying components, especially in multicomponent alloys, thus leaving the safe ground of the underlying thermodynamic database. (ii) Liquid demixing often intensifies by addition of many components to Al-Sn alloys, thus forming monotectic reactions, boosting the segregation and aggravating the first effect. (iii) Scheil simulation in multicomponent Al-x-y-z-Sn alloys not only combines the first two problems, moreover, the current versions of major thermodynamic software packages are not able to perform the Scheil simulation if liquid demixing and monotectic reactions occur. These intricacies are worked out and the development of a dedicated Al-Si-Cu-Mg-Sn thermodynamic database for large composition ranges is presented. Calculations are compared to experimental data of an Al-7.5Si-3.5Cu-0.3Mg-0.1Sn alloy and the need for specific follow-up work is identified.

Simulation of state-dependent high-frequency atmospheric variability associated with ENSO

High-frequency atmospheric variability depends on the phase of El Nino/Southern Oscillation (ENSO). Recently, there is increasing evidence that state-dependent high-frequency atmospheric variability significantly modulates ENSO characteristics. Hence, in this study, we examine the model simulations of high-frequency atmospheric variability and, further, its dependency on the El Nino phase, using atmospheric and coupled GCMs (AGCM and CGCM). We use two versions of physical packages here—with and without convective momentum transport (CMT)—in both models. We found that the CMT simulation gives rise to a large climatological zonal wind difference over the Pacific. Also, both the climate models show a significantly improved performance in simulating the state-dependent noise when the CMT parameterization is implemented. We demonstrate that the better simulation of the state-dependent noise results from a better representation of anomalous, as well as climatological, zonal wind. Our further comparisons between the simulations, demonstrates that low-frequency wind is a crucial factor in determining the state-dependency of high-frequency wind variability. Therefore, it is suggested that the so-called state-dependent noise is directly induced by the low-frequency wind anomaly, which is caused by SST associated with ENSO.