Multiple Software Packages Research Articles

Trends and insights in animal and biomedical research: A 5-year scientometric analysis of orthodontic peer-reviewed journals

Biomedical research plays a critical role in advancing orthodontic innovations by identifying new targets for therapeutic interventions and developing more effective, personalized orthodontic treatment. This study evaluates the major contributors and trends in animal-related orthodontic research over the past 5 years (2017-2023). All animal-related studies published in the eight orthodontic journals listed in the 2022 Journal Citation Reports between January 2017 and June 2023 were retrieved from the Web of Science Core Collection. After examination, the following bibliometric data were extracted from each article: title, authors, affiliations, geographic origin, year of publication, journal name, and keywords. Multiple bibliometric software packages including Biblioshiny R-package software, Datawrapper, and Datasmith were used to analyze different bibliometric outcomes. A total of 3669 articles were screened from which 266 were selected and included in the analysis. The annual growth rate of production exhibited a decline of 11.18%. Most of the included studies focused on orthodontic tooth movement (32.3%), mainly utilizing rat models (50%). Although the United States accounted for the highest number of publications (n = 236), the trend in funded research showed a decreasing trajectory over time, with notably limited funding from the National Institutes of Health. This study reveals a declining trend in overall animal-related orthodontic research, highlighted by a significant decrease in National Institutes of Health funding over time. To address this gap, academic institutions and professional organizations should support initiatives fostering biomedical orthodontic research.

Surfalize: A Python Library for Surface Topography and Roughness Analysis Designed for Periodic Surface Structures.

Surface roughness measurement is an integral part of the characterization of microtextured surfaces. Multiple established software packages offer the calculation of roughness parameters according to ISO 25178. However, these packages lack a specific set of features, which we hope to address in this work. Firstly, they often lack or have limited capabilities for automated and batch analysis, making it hard to integrate into other applications. Secondly, they are often proprietary and therefore restrict access to some potential users. Lastly, they lack some capabilities when it comes to the analysis of periodic microtextured surfaces. Namely, common parameters such as the peak-to-valley depth, spatial period and homogeneity cannot be calculated automatically. This work aims to address these challenges by introducing a novel Python library, Surfalize, which intends to fill in the gaps regarding this functionality. The functionality is described and the algorithms are validated against established software packages or manual measurements.

On-the-fly training of polynomial machine learning potentials in computing lattice thermal conductivity.

The application of first-principles calculations for predicting lattice thermal conductivity (LTC) in crystalline materials, in conjunction with the linearized phonon Boltzmann equation, has gained increasing popularity. In this calculation, the determination of force constants through first-principles calculations is critical for accurate LTC predictions. For material exploration, performing first-principles LTC calculations in a high-throughput manner is now expected, although it requires significant computational resources. To reduce computational demands, we integrated polynomial machine learning potentials on-the-fly during the first-principles LTC calculations. This paper presents a systematic approach to first-principles LTC calculations. We designed and optimized an efficient workflow that integrates multiple modular software packages. We applied this approach to calculate LTCs for 103 compounds of wurtzite, zinc blende, and rocksalt types to evaluate the performance of the polynomial machine learning potentials in LTC calculations. We demonstrate a significant reduction in the computational resources required for the LTC predictions.

BerryEasy: a GPU enabled python package for diagnosis of nth-order and spin-resolved topology in the presence of fields and effects

Multiple software packages currently exist for the computation of bulk topological invariants in both idealized tight-binding models and realistic Wannier tight-binding models derived from density functional theory. Currently, only one package is capable of computing nested Wilson loops and spin-resolved Wilson loops. These state-of-the-art techniques are vital for accurate analysis of band topology. In this paper we introduce BerryEasy, a python package harnessing the speed of graphical processing units to allow for efficient topological analysis of supercells in the presence of disorder and impurities. Moreover, the BerryEasy package has built-in functionality to accommodate use of realistic many-band tight-binding models derived from first-principles.

JARVIS-Leaderboard: a large scale benchmark of materials design methods

Lack of rigorous reproducibility and validation are significant hurdles for scientific development across many fields. Materials science, in particular, encompasses a variety of experimental and theoretical approaches that require careful benchmarking. Leaderboard efforts have been developed previously to mitigate these issues. However, a comprehensive comparison and benchmarking on an integrated platform with multiple data modalities with perfect and defect materials data is still lacking. This work introduces JARVIS-Leaderboard, an open-source and community-driven platform that facilitates benchmarking and enhances reproducibility. The platform allows users to set up benchmarks with custom tasks and enables contributions in the form of dataset, code, and meta-data submissions. We cover the following materials design categories: Artificial Intelligence (AI), Electronic Structure (ES), Force-fields (FF), Quantum Computation (QC), and Experiments (EXP). For AI, we cover several types of input data, including atomic structures, atomistic images, spectra, and text. For ES, we consider multiple ES approaches, software packages, pseudopotentials, materials, and properties, comparing results to experiment. For FF, we compare multiple approaches for material property predictions. For QC, we benchmark Hamiltonian simulations using various quantum algorithms and circuits. Finally, for experiments, we use the inter-laboratory approach to establish benchmarks. There are 1281 contributions to 274 benchmarks using 152 methods with more than 8 million data points, and the leaderboard is continuously expanding. The JARVIS-Leaderboard is available at the website: https://pages.nist.gov/jarvis_leaderboard/

BetaBuddy: An automated end-to-end computer vision pipeline for analysis of calcium fluorescence dynamics in β-cells.

Insulin secretion from pancreatic β-cells is integral in maintaining the delicate equilibrium of blood glucose levels. Calcium is known to be a key regulator and triggers the release of insulin. This sub-cellular process can be monitored and tracked through live-cell imaging and subsequent cell segmentation, registration, tracking, and analysis of the calcium level in each cell. Current methods of analysis typically require the manual outlining of β-cells, involve multiple software packages, and necessitate multiple researchers-all of which tend to introduce biases. Utilizing deep learning algorithms, we have therefore created a pipeline to automatically segment and track thousands of cells, which greatly reduces the time required to gather and analyze a large number of sub-cellular images and improve accuracy. Tracking cells over a time-series image stack also allows researchers to isolate specific calcium spiking patterns and spatially identify those of interest, creating an efficient and user-friendly analysis tool. Using our automated pipeline, a previous dataset used to evaluate changes in calcium spiking activity in β-cells post-electric field stimulation was reanalyzed. Changes in spiking activity were found to be underestimated previously with manual segmentation. Moreover, the machine learning pipeline provides a powerful and rapid computational approach to examine, for example, how calcium signaling is regulated by intracellular interactions.

Validation and characterization of algorithms and software for photonics inverse design

In this work, we present a reproducible suite of test problems for large-scale optimization (“inverse design” and “topology optimization”) in photonics, where the prevalence of irregular, non-intuitive geometries can otherwise make it challenging to be confident that new algorithms and software are functioning as claimed. We include test problems that exercise a wide array of physical and mathematical features—far-field metalenses, 2d and 3d mode converters, resonant emission and focusing, and dispersion/eigenvalue engineering—and introduce an a posteriori lengthscale metric for comparing designs produced by disparate algorithms. For each problem, we incorporate cross-checks against multiple independent software packages and algorithms, and reproducible designs and their validations scripts are included. We believe that this suite should make it much easier to develop, validate, and gain trust in future inverse-design approaches and software.

HeapMS: An Automatic Peak-Picking Pipeline for Targeted Proteomic Data Powered by 2D Heatmap Transformation and Convolutional Neural Networks.

The process of peak picking and quality assessment for multiple reaction monitoring (MRM) data demands significant human effort, especially for signals with low abundance and high interference. Although multiple peak-picking software packages are available, they often fail to detect peaks with low quality and do not report cases with low confidence. Furthermore, visual examination of all chromatograms is still necessary to identify uncertain or erroneous cases. This study introduces HeapMS, a web service that uses artificial intelligence to assist with peak picking and the quality assessment of MRM chromatograms. HeapMS applies a rule-based filter to remove chromatograms with low interference and high-confidence peak boundaries detected by Skyline. Additionally, it transforms two histograms (representing light and heavy peptides) into a single encoded heatmap and performs a two-step evaluation (quality detection and peak picking) using image convolutional neural networks. HeapMS offers three categories of peak picking: uncertain peak picking that requires manual inspection, deletion peak picking that requires removal or manual re-examination, and automatic peak picking. HeapMS acquires the chromatogram and peak-picking boundaries directly from Skyline output. The output results are imported back into Skyline for further manual inspection, facilitating integration with Skyline. HeapMS offers the benefit of detecting chromatograms that should be deleted or require human inspection. Based on defined categories, it can significantly reduce human workload and provide consistent results. Furthermore, by using heatmaps instead of histograms, HeapMS can adapt to future updates in image recognition models. The HeapMS is available at: https://github.com/ccllabe/HeapMS.

A heterozygous pathogenic variant in the ATP6V1A gene triggering epilepsy in a large Chinese pedigree

Epilepsy is one of the most common disorders in children, with an incidence rate of approximately 5%. Although an increasing number of genes have been demonstrated to be pathogenic factors in epilepsy, evidence for a potential pathogenic role of ATP6V1A remains limited. Herein, the clinical and genetic data of a 5-year-old boy who experienced seizures at 9 months of age are collected. Genetic variants are screened using whole-exome sequencing (WES), and the effects of the candidate variants are further validated at both the RNA and protein levels. WES reveals a heterozygous variant [NM_001690.4: c .1132 C>T, p.Leu378Phe] of the ATP6V1A gene. This variant is not reported in the public database, but is predicted to be deleterious by multiple software packages, and classified as a variant of unknown significance following the American College of Medical Genetics and Genomics guidelines. Quantitative PCR and western blotting further confirm its down-regulatory role in both the RNA and protein expression of ATP6V1A. This case report confirms the pathogenicity of ATP6V1A in epilepsy with solid experimental evidence, thereby expanding the phenotype spectrum of ATP6V1A variants. More importantly, we show that seizures triggered by ATP6V1A variants could be controlled by Levetiracetam, crucially rescuing the development of the patient.

MT-SOTA: A Merkle-Tree-Based Approach for Secure Software Updates over the Air in Automotive Systems

The automotive industry has seen a dynamic transformation from traditional hardware-defined to software-defined architecture enabling higher levels of autonomy and connectivity, better safety and security features, as well as new in-vehicle experiences and richer functions through software and ongoing updates of both functional and safety-critical features. Service-oriented architecture plays a pivotal role in realizing software-defined vehicles and fostering new business models for OEMs. Such architecture evolution demands new development paradigms to address the increasing complexity of software. This is crucial to guarantee seamless software development, integration, and deployment—all the way from cloud or backend repositories to the vehicle. Additionally, it calls for enhanced collaboration between car manufacturers and suppliers. Simultaneously, it introduces challenges associated with the necessity for ongoing updates and support ensuring vehicles remain safe and up to date. Current approaches to software updates have primarily been implemented for traditional vehicle architectures, which mostly comprise specialized electronic control units (ECUs) designed for specific functions. These ECUs are programmed with a single comprehensive executable that is then flashed onto the ECU all at once. Different approaches should be considered for new software-based vehicle architectures and specifically for ECUs with multiple independent software packages. These packages should be updated independently and selectively for each ECU. Thus, we propose a new scheme for software updates based on a Merkle tree approach to cope with the complexity of the new software architecture while addressing safety and security requirements of real-time and resource-constrained embedded systems in the vehicle. The Merkle-tree-based software updates over the air (MT-SOTA) proposal enables secure updates for individual software clusters. These clusters are developed and integrated by diverse entities with varying release timelines. Our study demonstrates that the MT-SOTA scheme can enhance the speed of software update execution without significantly increasing the process overhead. Additionally, it offers necessary defense against potential cyberthreats. The results of the performed technical analysis and experiments of the MT-SOTA implementation are presented in this paper.

MultiscaleDTM: An open‐source R package for multiscale geomorphometric analysis

AbstractDigital terrain models (DTMs) are datasets containing altitude values above or below a reference level, such as a reference ellipsoid or a tidal datum over geographic space, often in the form of a regularly gridded raster. They can be used to calculate terrain attributes that describe the shape and characteristics of topographic surfaces. Calculating these terrain attributes often requires multiple software packages that can be expensive and specialized. We have created a free, open‐source R package, MultiscaleDTM, that allows for the calculation of members from each of the five major thematic groups of terrain attributes: slope, aspect, curvature, relative position, and roughness, from a regularly gridded DTM. Furthermore, these attributes can be calculated at multiple spatial scales of analysis, a key feature that is missing from many other packages. Here, we demonstrate the functionality of the package and provide a simulation exploring the relationship between slope and roughness. When roughness measures do not account for slope, these attributes exhibit a strong positive correlation. To minimize this correlation, we propose a new roughness measure called adjusted standard deviation. In most scenarios tested, this measure produced the lowest rank correlation with slope out of all the roughness measures tested. Lastly, the simulation shows that some existing roughness measures from the literature that are supposed to be independent of slope can actually exhibit a strong inverse relationship with the slope in some cases.

Involuntary Table Tennis Recognition using Recurrent Neural Network on The Perception-Based Data

Sport performance analysis and sports practise are inextricably linked. It is critical to assist coaches in analysing and improving the performance of their athletes throughout training or game sessions. Because of technological advancements, notational analysis of video footage using multiple software packages is now possible. Unfortunately, the coach must manually recognise the acts before proceeding with additional analysis. The goal of this research is to create an automated system for recognising Table Tennis shots in widely available broadcasted videos using a pre-trained Recurrent Neural Network (RNN) approach. We provide a novel method for gathering video data from table tennis games and performing stroke detection and classification. Using the proposed setup, a diversified dataset encompassing video data of 4 basic strokes taken from 4 professional table tennis players, totalling 2000 films, was collected. With a validation accuracy of 97.02%, the temporal recurrent neural network model is created. Furthermore, the neural network generalises well over the data of a player who was not included in the training and validation datasets, categorising new strokes with an overall best accuracy of 91.12%. Several model architectures were trained for stroke recognition using machine learning and deep learning methodologies, and their results were compared and benchmarked. The model's inferences, such as performance monitoring and stroke comparison, have been examined. As a result, we are contributing to the creation of a computer vision-based sports analytics system for table tennis, namely a player's strokes, and is particularly insightful for performance enhancement.

Topiary: Pruning the manual labor from ancestral sequence reconstruction.

Ancestral sequence reconstruction (ASR) is a powerful tool to study the evolution of proteins and thus gain deep insight into the relationships among protein sequence, structure, and function. A major barrier to its broad use is the complexity of the task: it requires multiple software packages, complex file manipulations, and expert phylogenetic knowledge. Here we introduce topiary, a software pipeline that aims to overcome this barrier. To use topiary, users prepare a spreadsheet with a handful of sequences. Topiary then: (1) Infers the taxonomic scope for the ASR study and finds relevant sequences by BLAST; (2) Does taxonomically informed sequence quality control and redundancy reduction; (3) Constructs a multiple sequence alignment; (4) Generates a maximum-likelihood gene tree; (5) Reconciles the gene tree to the species tree; (6) Reconstructs ancestral amino acid sequences; and (7) Determines branch supports. The pipeline returns annotated evolutionary trees, spreadsheets with sequences, and graphical summaries of ancestor quality. This is achieved by integrating modern phylogenetics software (Muscle5, RAxML-NG, GeneRax, and PastML) with online databases (NCBI and the Open Tree of Life). In this paper, we introduce non-expert readers to the steps required for ASR, describe the specific design choices made in topiary, provide a detailed protocol for users, and then validate the pipeline using datasets from a broad collection of protein families. Topiary is freely available for download: https://github.com/harmslab/topiary.

Protein Structure Predictions, Atomic Model Building, and Validation Using a Cryo-EM Density Map from Hepatitis B Virus Spherical Subviral Particle.

Hepatitis B virus (HBV) infection is a global public health concern. During chronic infection, the HBV small-surface antigen is expressed in large excess as non-infectious spherical subviral particles (SVPs), which possess strong immunogenicity. To date, attempts at understanding the structure of HBV spherical SVP have been restricted to 12-30 Å with contradictory conclusions regarding its architecture. We have used cryo-electron microscopy (cryo-EM) and 3D image reconstruction to solve the HBV spherical SVP to 6.3 Å. Here, we present an extended protocol on combining AlphaFold2 prediction with a moderate-resolution cryo-EM density map to build a reliable 3D model. This protocol utilizes multiple software packages that are routinely used in the cryo-EM community. The workflow includes 3D model prediction, model evaluation, rigid-body fitting, flexible fitting, real-space refinement, model validation, and model adjustment. Finally, the described protocol can also be applied to high-resolution cryo-EM datasets (2-4 Å).

Computational framework for the techno-economic analysis of monoclonal antibody capture chromatography platforms

We developed a computational framework that integrates commercial software components to perform customizable technoeconomic feasibility analyses. The use of multiple software packages overcomes the shortcomings of each to provide a detailed simulation that can be used for sensitivity analyses and optimizations. In this paper, the framework was used to evaluate the performance of monoclonal antibody capture processes. To this end, the simulation framework incorporated dynamic models for the affinity chromatography step that were validated with experimental breakthrough curves. The results were integrated with an Intelligen SuperPro Designer process simulation for the evaluation of key performance indicators of the operations. As proof of concept, the framework was used to perform a sensitivity analysis and optimization for a case study in which we sought to compare membrane and resin chromatography for disposable and reusable batch capture platforms. Two membranes and one resin were selected for the capture media, which yielded six process alternatives to compare. The objective functions were set to be cost of goods, process time, and buffer utilization. The results of the optimization of these process alternatives were a set of operating conditions that display tradeoffs between competing objectives. From this application exercise, we conclude that the framework can handle multiple variables and objectives, and it is adaptable to platforms with different chromatography media and operating modes. Additionally, the framework is capable of providing ad hoc analyses for decision making in a specific production context.

NPFFR2 gene compound heterozygous variants associated with preeclampsia identified by whole-exome sequencing

BackgroundPreeclampsia (PE) is an idiopathic disorder of pregnancy. The exact cause of PE remains unknown. Emerging evidence indicates that the cause of PE is linked to genetic factors. Therefore, the aim of this study was to identify the susceptibility genes for PE. MethodsNine families with severe PE were recruited. The whole-exome sequencing (WES) was performed on each family, and Sanger sequencing was used to identify the potential pathogenic genetic variants. ResultsAfter a rigorous bioinformatics analysis, compound heterozygous variants in the NPFFR2 gene, NM_004885.2: c.601A > G, p.Met201Val and c.995C > T, p.Ala332Val were found in the No.4 pedigree. Bioinformatics analysis showed that these sites were highly conserved among several species and were predicted to be pathogenic variants according to multiple online mutational function prediction software packages. Due to the compound heterozygous variants of NPFFR2, more bonds are generated between mutant amino acids and spatial adjacent amino acids, which may lead to more stable active conformation of protein and not easy to be degraded. ConclusionsWe demonstrated for the first time that compound heterozygous variants of the NPFFR2 gene might be potentially associated with severe PE, the results of this study provide clinicians and researchers with a better understanding of the molecular mechanisms underlying severe PE in pregnant women.

Exploring the underlying mechanism between fear of losing power and knowledge hiding.

Drawing on the assumptions of approach/inhibition theory of power and conservation of resource of theory, this study aims to empirically explore the relationship between fear of losing power and knowledge hiding. To explicate the relationship, this study examines the mediating role of self-serving behavior and moderating role of personal competitiveness. To evaluate the relationships, a moderated-mediation model is devised and tested. Data is collected through a web-based questionnaire from 194 individuals employed in both manufacturing and service sector firms of Pakistan. Multiple statistical software packages are used to analyze the data. After employing several statistical techniques, the findings of the study suggest that self-serving behavior fully mediates the link between fear of losing power and knowledge hiding. Moreover, the result of two-way interaction reveals that personal competitiveness further amplifies the indirect relationship between fear of losing power and knowledge hiding through self-serving behavior. The present study is one of those few types that investigates and uncovers the hidden links between fear of losing power and knowledge hiding. Lastly, theoretical, and practical implications along with future research directions are discussed.

QRAT: an R-based stand-alone application for relative expression analysis of RT-qPCR data

BackgroundReverse transcription quantitative real-time PCR (RT-qPCR) is a well-established method for analysing gene expression. Most RT-qPCR experiments in the field of microbiology aim for the detection of transcriptional changes by relative quantification, which means the comparison of the expression level of a specific gene between different samples by the application of a calibration condition and internal reference genes. Due to the numerous data processing procedures and factors that can influence the final result, relative expression analysis and interpretation of RT-qPCR data are still not trivial and often necessitate the use of multiple separate software packages capable of performing specific functions.ResultsHere we present qRAT, a stand-alone desktop application based on R that automatically processes raw output data from any qPCR machine using well-established and state-of-the-art statistical and graphical techniques. The ability of qRAT to analyse RT-qPCR data was evaluated using two example datasets generated in our laboratory. The tool successfully completed the procedure in both cases, returning the expected results. The current implementation includes functionalities for parsing, filtering, normalizing and visualisation of relative RT-qPCR data, like the determination of the relative quantity and the fold change of differentially expressed genes as well as the correction of inter-plate variation for multiple-plate experiments.ConclusionqRAT provides a comprehensive, straightforward, and easy-to-use solution for the relative quantification of RT-qPCR data that requires no programming knowledge or additional software installation. All application features are available for free and without requiring a login or registration.

Script-based automation of analytical instrument software tasks.

In laboratories with multiple identical analytical instruments and consistent sample workflows, analysts frequently perform repetitive software control steps, yet automation options in vendor-supplied instrument software are generally limited and may not support the desired laboratory workflow. Scripts that automate tasks to monitor systems, streamline worklist creation, or minimize downtime can save valuable personnel time and reduce errors. AutoHotkey is a free, open-source scripting language for Windows that allows users with no programming experience to easily create scripts automating a wide variety of activities. The scripts automate the tasks that a user performs while interacting with the instrument control software, such as mouse clicks and keyboard entries and closing software windows, rather than modify the underlying instrument software, and thus these scripts are compatible with multiple vendor software packages and Windows OS versions. The scripts can be triggered manually from a desktop icon or automatically through Windows Task Scheduler.

CalcUS: An Open-Source Quantum Chemistry Web Platform.

Computational chemistry is an increasingly active field due to the improvement of computing resources and theoretical tools. However, its use remains usually limited to technically inclined users due to the technical challenges of preparing, launching, and analyzing calculations. In this context, we have developed CalcUS, an open-source platform to streamline quantum chemistry studies. Its objective is to democratize access to computational chemistry by providing a user-friendly web interface to simplify running and analyzing quantum mechanical calculations. It is freely available, expandable, and customizable. It promotes connectivity to multiple software packages and algorithms, thus providing state-of-the-art techniques to all practitioners. We propose CalcUS as a standalone tool and infrastructure to support other open-source packages.