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We report the development and testing of new integrated cyberinfrastructure for performing free energy simulations with generalized hybrid quantum mechanical/molecular mechanical (QM/MM) and machine learning potentials (MLPs) in Amber. The Sander molecular dynamics program has been extended to leverage fast, density-functional tight-binding models implemented in the DFTB+ and xTB packages, and an interface to the DeePMD-kit software enables the use of MLPs. The software is integrated through application program interfaces that circumvent the need to perform "system calls" and enable the incorporation of long-range Ewald electrostatics into the external software's self-consistent field procedure. The infrastructure provides access to QM/MM models that may serve as the foundation for QM/MM–ΔMLP potentials, which supplement the semiempirical QM/MM model with a MLP correction trained to reproduce ab initio QM/MM energies and forces. Efficient optimization of minimum free energy pathways is enabled through a new surface-accelerated finite-temperature string method implemented in the FE-ToolKit package. Furthermore, we interfaced Sander with the i-PI software by implementing the socket communication protocol used in the i-PI client–server model. The new interface with i-PI allows for the treatment of nuclear quantum effects with semiempirical QM/MM–ΔMLP models. The modular interoperable software is demonstrated on proton transfer reactions in guanine-thymine mispairs in a B-form deoxyribonucleic acid helix. The current work represents a considerable advance in the development of modular software for performing free energy simulations of chemical reactions that are important in a wide range of applications. [ABSTRACT FROM AUTHOR]

Using the Eastern Academic Scholars' Trust (EAST) shared print program as the focus, the authors, both of whom are members of the EAST Program Team, discuss the changes that EAST has experienced in the last few years. This includes significant growth of the membership of EAST, particularly with the addition of large library consortia; advances in the software tools that support shared print; the broadening and deepening of collaboration across shared print programs; and the opportunities that exist for further research in support of preserving and providing access to the print scholarly and cultural record. [ABSTRACT FROM AUTHOR]

We study heat exchange in temperature-biased metal–molecule–metal molecular junctions by employing the molecular dynamics simulator LAMMPS. Generating the nonequilibrium steady state with Langevin thermostats at the boundaries of the junction, we show that the average heat current across a gold–alkanedithiol–gold nanojunction behaves physically, with the thermal conductance value matching the literature. In contrast, the full probability distribution function for heat exchange, as generated by the simulator, violates the fundamental fluctuation symmetry for entropy production. We trace this failure back to the implementation of the thermostats and the expression used to calculate the heat exchange. To rectify this issue and produce the correct statistics, we introduce single-atom thermostats as an alternative to conventional many-atom thermostats. Once averaging heat exchange over the hot and cold thermostats, this approach successfully generates the correct probability distribution function, which we use to study the behavior of both the average heat current and its noise. We further examine the thermodynamic uncertainty relation in the molecular junction and show that it holds, albeit demonstrating nontrivial trends. Our study points to the need to carefully implement nonequilibrium molecular dynamics solvers in atomistic simulation software tools for future investigations of noise phenomena in thermal transport. [ABSTRACT FROM AUTHOR]

Background: The explosive growth of next-generation sequencing data has resulted in ultra-large-scale datasets and significant computational challenges. As the cost of next-generation sequencing (NGS) has decreased, the amount of genomic data has surged globally. However, the cost and complexity of the computational resources required continue to be substantial barriers to leveraging big data. A promising solution to these computational challenges is cloud computing, which provides researchers with the necessary CPUs, memory, storage, and software tools. Results: Here, we present Closha 2.0, a cloud computing service that offers a user-friendly platform for analyzing massive genomic datasets. Closha 2.0 is designed to provide a cloud-based environment that enables all genomic researchers, including those with limited or no programming experience, to easily analyze their genomic data. The new 2.0 version of Closha has more user-friendly features than the previous 1.0 version. Firstly, the workbench features a script editor that supports Python, R, and shell script programming, enabling users to write scripts and integrate them into their pipelines. This functionality is particularly useful for downstream analysis. Second, Closha 2.0 runs on containers, which execute each tool in an independent environment. This provides a stable environment and prevents dependency issues and version conflicts among tools. Additionally, users can execute each step of a pipeline individually, allowing them to test applications at each stage and adjust parameters to achieve the desired results. We also updated a high-speed data transmission tool called GBox that facilitates the rapid transfer of large datasets. Conclusions: The analysis pipelines on Closha 2.0 are reproducible, with all analysis parameters and inputs being permanently recorded. Closha 2.0 simplifies multi-step analysis with drag-and-drop functionality and provides a user-friendly interface for genomic scientists to obtain accurate results from NGS data. Closha 2.0 is freely available at https://www.kobic.re.kr/closha2. [ABSTRACT FROM AUTHOR]

ABSTRACT Organelle genomes serve as crucial datasets for investigating the genetics and evolution of plants and animals, genome diversity, and species identification. To enhance the collection, analysis, and visualisation of such data, we have developed a novel open‐source software tool named Organelle Genome Utilities (OGU). The software encompasses three modules designed to streamline the handling of organelle genome data. The data collection module is dedicated to retrieving, validating and organising sequence information. The evaluation module assesses sequence variance using a range of methods, including novel metrics termed stem and terminal phylogenetic diversity. The primer module designs universal primers for downstream applications. Finally, a visualisation pipeline has been developed to present comprehensive insights into organelle genomes across different lineages rather than focusing solely on individual species. The performance, compatibility and stability of OGU have been rigorously evaluated through benchmarking with four datasets, including one million mixed GenBank records, plastid genomic data from the Lamiaceae family, mitochondrial data from rodents, and 308 plastid genomes sourced from various angiosperm families. Based on software capabilities, we identified 30 plastid intergenic spacers. These spacers exhibit a moderate evolutionary rate and offer practical utility comparable to coding regions, highlighting the potential applications of intergenic spacers in organelle genomes. We anticipate that OGU will substantially enhance the efficient utilisation of organelle genomic data and broaden the prospects for related research endeavours. [ABSTRACT FROM AUTHOR]

The rapid advancements in sequencing technologies and bioinformatics have enabled metagenomic research of complex microbial systems, but reliable results depend on consistent laboratory and bioinformatics approaches. Current efforts to identify best practices often focus on optimizing specific steps, making it challenging to understand the influence of each stage on microbial population analysis and compare data across studies. This study evaluated DNA extraction, library construction methodologies, sequencing platforms, and computational approaches using a dog stool sample, two synthetic microbial community mixtures, and various sequencing data sources. Our work, the most comprehensive evaluation of metagenomic methods to date. We developed a software tool, termed minitax, which provides consistent results across the range of platforms and methodologies. Our findings showed that the Zymo Research Quick-DNA HMW MagBead Kit, Illumina DNA Prep library preparation method, and the minitax bioinformatics tool were the most effective for high-quality microbial diversity analysis. However, the effectiveness of pipelines or method combinations is sample-specific, making it difficult to identify a universally optimal approach. Therefore, employing multiple approaches is crucial for obtaining reliable outcomes in microbial systems. Evaluation of metagenomic methods for gut microbiome profiling using dog stool and microbial standards, leading to the development of minitax tool. Optimal wetlab and analysis pipelines are recommended for reliable crossplatform microbiome studies. [ABSTRACT FROM AUTHOR]

Background: Clostridium botulinum and Clostridium perfringens, 2 major foodborne pathogenic fusobacteria, have a variety of virulent protein types with nervous and enterotoxic pathogenic potential, respectively. Objective: The relationship between the molecular evolution of the 2 Clostridium genomes and virulence proteins was studied via a bioinformatics prediction method. The genetic stability, main features of gene coding and structural characteristics of virulence proteins were compared and analyzed to reveal the phylogenetic characteristics, diversity, and distribution of virulence factors of foodborne Clostridium strains. Methods: The phylogenetic analysis was performed via composition vector and average nucleotide identity based methods. Evolutionary distances of virulence genes relative to those of housekeeping genes were calculated via multilocus sequence analysis. Bioinformatics software and tools were used to predict and compare the main functional features of genes encoding virulence proteins, and the structures of virulence proteins were predicted and analyzed through homology modeling and a deep learning algorithm. Results: According to the diversity of toxins, genome evolution tended to cluster based on the protein-coding virulence genes. The evolutionary transfer distances of virulence genes relative to those of housekeeping genes in C. botulinum strains were greater than those in C. perfringens strains, and BoNTs and alpha toxin proteins were located extracellularly. The BoNTs have highly similar structures, but BoNT/A/B and BoNT/E/F have significantly different conformations. The beta2 toxin monomer structure is similar to but simpler than the alpha toxin monomer structure, which has 2 mobile loops in the N-terminal domain. The C-terminal domain of the CPE trimer forms a "claudin-binding pocket" shape, which suggests biological relevance, such as in pore formation. Conclusions: According to the genotype of protein-coding virulence genes, the evolution of Clostridium showed a clustering trend. The genetic stability, functional and structural characteristics of foodborne Clostridium virulence proteins reveal the taxonomy and diverse distribution of virulence factors. [ABSTRACT FROM AUTHOR]

Upgrades to advanced scientific user facilities such as next-generation x-ray light sources, nanoscience centers, and neutron facilities are revolutionizing our understanding of materials across the spectrum of the physical sciences, from life sciences to microelectronics. However, these facility and instrument upgrades come with a significant increase in complexity. Driven by more exacting scientific needs, instruments and experiments become more intricate each year. This increased operational complexity makes it ever more challenging for domain scientists to design experiments that effectively leverage the capabilities of and operate on these advanced instruments. Large language models (LLMs) can perform complex information retrieval, assist in knowledge-intensive tasks across applications, and provide guidance on tool usage. Using x-ray light sources, leadership computing, and nanoscience centers as representative examples, we describe preliminary experiments with a Context-Aware Language Model for Science (CALMS) to assist scientists with instrument operations and complex experimentation. With the ability to retrieve relevant information from facility documentation, CALMS can answer simple questions on scientific capabilities and other operational procedures. With the ability to interface with software tools and experimental hardware, CALMS can conversationally operate scientific instruments. By making information more accessible and acting on user needs, LLMs could expand and diversify scientific facilities' users and accelerate scientific output. [ABSTRACT FROM AUTHOR]

Objective: This paper describes several automation tools and software that can be considered during evidence synthesis projects and provides guidance for their integration in the conduct of scoping reviews. Study Design and Setting: The guidance presented in this work is adapted from the results of a scoping review and consultations with the JBI Scoping Review Methodology group. Results: This paper describes several reliable, validated automation tools and software that can be used to enhance the conduct of scoping reviews. Developments in the automation of systematic reviews, and more recently scoping reviews, are continuously evolving. We detail several helpful tools in order of the key steps recommended by the JBI's methodological guidance for undertaking scoping reviews including team establishment, protocol development, searching, de‐duplication, screening titles and abstracts, data extraction, data charting, and report writing. While we include several reliable tools and software that can be used for the automation of scoping reviews, there are some limitations to the tools mentioned. For example, some are available in English only and their lack of integration with other tools results in limited interoperability. Conclusion: This paper highlighted several useful automation tools and software programs to use in undertaking each step of a scoping review. This guidance has the potential to inform collaborative efforts aiming at the development of evidence informed, integrated automation tools and software packages for enhancing the conduct of high‐quality scoping reviews. [ABSTRACT FROM AUTHOR]

Software process simulation (SPS) has become an effective tool for software process management and improvement. However, its adoption in industry is less than what the research community expected due to the burden of measurement cost and the high demand for domain knowledge. The difficulty of extracting appropriate metrics with real data from process enactment is one of the great challenges. We aim to provide evidence‐based support of the process metrics for software process (simulation) modeling. A systematic literature review was performed by extending our previous review series to draw a comprehensive understanding of the metrics for process modeling following our proposed ontology of metrics in SPS. We identify 131 process modeling studies that collectively involve 1975 raw metrics and classified them into 21 categories using the coding technique. We found product and process external metrics are not used frequently in SPS modeling while resource external metrics are widely used. We analyze the causal relationships between metrics. We find that the models exhibit significant diversity, as no pairwise relationship between metrics accounts for more than 10% SPS models. We identify 17 data issues may encounter in measurement and 10 coping strategies. The results of this study provide process modelers with an evidence‐based reference of the identification and the use of metrics in SPS modeling and further contribute to the development of the body of knowledge on software metrics in the context of process modeling. Furthermore, this study is not limited to process simulation but can be extended to software process modeling, in general. Taking simulation metrics as standards and references can further motivate and guide software developers to improve the collection, governance, and application of process data in practice. [ABSTRACT FROM AUTHOR]

Background: Complexity metrics are mathematical quantities designed to quantify aspects of radiotherapy treatment plans that may affect both their deliverability and dosimetric accuracy. Despite numerous studies investigating their utility, there remains a notable absence of shared tools for their extraction. Purpose: This study introduces UCoMX (Universal Complexity Metrics Extractor), a software package designed for the extraction of complexity metrics from the DICOM‐RT plan files of radiotherapy treatments. Methods: UCoMX is developed around two extraction engines: VCoMX (VMAT Complexity Metrics Extractor) for VMAT/IMRT plans, and TCoMX (Tomotherapy Complexity Metrics Extractor) tailored for Helical Tomotherapy plans. The software, built using Matlab, is freely available in both Matlab‐based and stand‐alone versions. More than 90 complexity metrics, drawn from relevant literature, are implemented in the package: 43 for VMAT/IMRT and 51 for Helical Tomotherapy. Results: The package is designed to read DICOM‐RT plan files generated by most commercially available Treatment Planning Systems (TPSs), across various treatment units. A reference dataset containing VMAT, IMRT, and Helical Tomotherapy plans is provided to serve as a reference for comparing UCoMX with other in‐house systems available at other centers. Conclusion: UCoMX offers a straightforward solution for extracting complexity metrics from radiotherapy plans. Its versatility is enhanced through different versions, including Matlab‐based and stand‐alone, and its compatibility with a wide range of commercially available TPSs and treatment units. UCoMX presents a free, user‐friendly tool empowering researchers to compute the complexity of treatment plans efficiently. [ABSTRACT FROM AUTHOR]

Background: Geometric distortion is a serious problem in MRI, particularly in MRI guided therapy. A lack of affordable and adaptable tools in this area limits research progress and harmonized quality assurance. Purpose: To develop and test a suite of open‐source hardware and software tools for the measurement, characterization, reporting, and correction of geometric distortion in MRI. Methods: An open‐source python library was developed, comprising modules for parametric phantom design, data processing, spherical harmonics, distortion correction, and interactive reporting. The code was used to design and manufacture a distortion phantom consisting of 618 oil filled markers covering a sphere of radius 150 mm. This phantom was imaged on a CT scanner and a novel split‐bore 1.0 T MRI magnet. The CT images provide distortion‐free dataset. These data were used to test all modules of the open‐source software. Results: All markers were successfully extracted from all images. The distorted MRI markers were mapped to undistorted CT data using an iterative search approach. Spherical harmonics reconstructed the fitted gradient data to 1.0 ± 0.6% of the input data. High resolution data were reconstructed via spherical harmonics and used to generate an interactive report. Finally, distortion correction on an independent data set reduced distortion inside the DSV from 5.5 ± 3.1 to 1.6 ± 0.8 mm. Conclusion: Open‐source hardware and software for the measurement, characterization, reporting, and correction of geometric distortion in MRI have been developed. The utility of these tools has been demonstrated via their application on a novel 1.0 T split bore magnet. [ABSTRACT FROM AUTHOR]

Technological advancements have enabled the extended investigation, development, and application of computational approaches in various domains, including health care. A burgeoning number of diagnostic, predictive, prognostic, and monitoring biomarkers are continuously being explored to improve clinical decision making in neuro-oncology. These advancements describe the increasing incorporation of artificial intelligence (AI) algorithms, including the use of radiomics. However, the broad applicability and clinical translation of AI are restricted by concerns about generalisability, reproducibility, scalability, and validation. This Policy Review intends to serve as the leading resource of recommendations for the standardisation and good clinical practice of AI approaches in health care, particularly in neuro-oncology. To this end, we investigate the repeatability, reproducibility, and stability of AI in response assessment in neuro-oncology in studies on factors affecting such computational approaches, and in publicly available open-source data and computational software tools facilitating these goals. The pathway for standardisation and validation of these approaches is discussed with the view of trustworthy AI enabling the next generation of clinical trials. We conclude with an outlook on the future of AI-enabled neuro-oncology. [ABSTRACT FROM AUTHOR]

The information systems supporting business processes of organizations generate and collect a large number of records in event logs that are exploitable in process mining tasks (discovery, conformance and enhancement). Under a Big Data scenario, Process Mining as a Service (PMaaS) can be attractive for organizations to outsource the storage of event logs and the processing resources for process mining tasks to the cloud in the presence of large event logs. However, the Cloud Service Provider (CSP) may be honest but curious, thus posing security and privacy risks when event log data are sensitive or subject to data privacy laws and regulations. In this work, a cryptography-based method is presented that preserves the privacy of event log data outsourced to an untrusted CSP, which executes the process discovery task, the most common task in process mining. The method conveniently encrypts the event log on the data owner's side to enable the CSP to apply access control over the discovered models (encrypted) through proxy re-encryption. The proposed method is implemented as a software tool and validated and evaluated in terms of performance, scalability, and data utility using real medical (sensitive) data logs under recommended security levels. The results demonstrate the feasibility of the proposed approach to support Process Discovery as a Service (PDaaS), which enables privacy preservation and access control. [ABSTRACT FROM AUTHOR]

Creative industries rely on workers who use sampling and remix to produce new content assembled from existing materials. In the process, remix cultures are commodified and reshaped by industrial logics. Rip-o-matic videos provide an example. These scissor reels are used as visual storyboards for television commercials. They are produced by video editors who cut and paste clips found on video sharing platforms. Interviews with rip-o-matic producers show the impact of the industrialization of remix on creative workers who face challenges to their ability to assert their creativity, content ownership, and reputation. Other examples, such as social media and fast fashion, nuance the picture. Industrialization also paves the way for automation by generative "AI." These software tools are based on processes of appropriation and remix that mirror those used by rip-o-matic producers. Remix is in sum at the center of today's corporate cultural production. [ABSTRACT FROM AUTHOR]

The development of detectors for the European Spallation Source is an important parallel element to the effort put into the design and construction of the neutron source and instruments. The 10Boron-film-based detector developments that started over a decade ago as basic detector concepts have now reached technical maturity after intense prototyping work and numerous testing campaigns. Several of the ESS beamlines that will soon enter the commissioning phase started welcoming the detector systems built with the 10B-film converter technology. The real-size demonstrators evolved in the last 2–3 years into a diverse suite of gas proportional counters for use in diffraction, reflectometry and small-angle scattering studies in spite of the operational complexity posed by the requirements for large-area coverage, low material budget and robustness for operation in the high-flux and high-radiation environment of ESS. The detectors are now being shipped to ESS or undergoing the last performance and calibration tests before being handed over to the instrument teams for installation in the host beamlines. A common feature of these detector systems is the very large number of readout channels that they are instrumented with in order to fulfill the demanding requirements for sensitivity, spatial resolution and count-rate capability. Across all of the detector types that will operate at ESS, the integration, testing and commissioning of the read-out technologies and software tools for data reduction, calibration and analysis are the focus of the detector and integration teams. These will yield a wealth of knowledge about their operation as well as initial results on the in-situ performance, a very important asset to ensure rapid commissioning of the detectors when neutrons from the ESS source become available. In this paper we will give an overview of the 10B-film-based detector technologies included in the ESS detector suite that are currently facing the transition from the production phase to installation and integration with the other beamline components, which comes with its own specific challenges of both organizational and technical nature. [ABSTRACT FROM AUTHOR]

Amplicon sequencing has long served as a robust method for characterising microbial communities, and despite inherent resolution limitations, it remains a preferred technique, offering cost‐ and time‐effective insights into bacterial compositions. Here, we introduce ONT‐AmpSeq, a user‐friendly pipeline designed for processing amplicon sequencing data generated from Oxford Nanopore Technology (ONT) devices. Our pipeline enables efficient creation of taxonomically annotated operational taxonomic unit (OTU) tables from ONT sequencing data, with the flexibility to multiplex amplicons on the same barcode. The pipeline encompasses six main steps—statistics, quality filtering, alignment, clustering, polishing, and taxonomic classification—integrating various state‐of‐the‐art software tools. We provide a detailed description of each step, along with performance tests and robustness evaluations using both test data and a ZymoBIOMICS® Microbial Community Standard mock community dataset. Our results demonstrate the ability of ONT‐AmpSeq to effectively process ONT amplicon data, offering valuable insights into microbial community composition. Additionally, we discuss the influence of polishing tools on taxonomic insight and the impact of taxonomic annotation methods on the derived microbial composition. Overall, ONT‐AmpSeq represents a comprehensive solution for analysing ONT amplicon sequencing data, facilitating streamlined and reliable microbial community analysis. The pipeline, along with test data, is freely available for public use. [ABSTRACT FROM AUTHOR]

In this article, we present a software tool developed in Python, named T-WorkFlow. It has been devised to meet some of the design needs of Tatuus Racing S.p.a., a leading company in the design and production of racing cars for the FIA Formula 3 Regional and Formula 4 categories. The software leverages the open-source tools OpenFOAM and FreeCAD to fully automate the fluid dynamics simulation process within car radiators. The goal of T-WorkFlow is to provide designers with precise and easily interpretable results that facilitate the identification of the geometry, ensuring optimal flow distribution in the radiator channels. T-WorkFlow requires the radiator's geometry files in .stp and .stl formats, along with additional user inputs provided through a graphical interface. For mesh generation, the software leverages the OpenFOAM tools blockMesh and snappyHexMesh. To ensure uniform mesh quality across different configurations, and thus, comparable numerical results, various pre-processing operations on the specific geometry files are needed. After generating the mesh, T-WorkFlow automatically defines a control surface for each radiator channel to monitor the volumetric flow rate distribution. This is achieved by combining the OpenFOAM command topoSet with specific geometric information directly obtained from the radiator's CAD through FreeCAD. During the simulation, the software provides various outputs that automate the main post-processing operations, enabling quick and easy identification of the configuration that ensures the desired performance. [ABSTRACT FROM AUTHOR]

Abnormal crowd behavior recognition is one of the research hotspots in computer vision. Its goal is to use computer vision technology and abnormal behavior detection models to accurately perceive, predict, and intervene in potential abnormal behaviors of the crowd and monitor the status of the crowd system in public places in real time, to effectively prevent and deal with public security risks and ensure public life safety and social order. To this end, focusing on the abnormal crowd behavior recognition technology in the computer vision system, a systematic review study of its theory and cutting-edge technology is conducted. First, the crowd level and abnormal behaviors in public places are defined, and the challenges faced by abnormal crowd behavior recognition are expounded. Then, from the dimensions based on traditional methods and based on deep learning, the mainstream technologies of abnormal behavior recognition are discussed, and the design ideas, advantages, and limitations of various methods are analyzed. Next, the mainstream software tools are introduced to provide a comprehensive reference for the technical framework. Secondly, typical abnormal behavior datasets at home and abroad are sorted out, and the characteristics of these datasets are compared in detail from multiple perspectives such as scale, characteristics, and uses, and the performance indicators of different algorithms on the datasets are compared and analyzed. Finally, the full text is summarized and the future development direction of abnormal crowd behavior recognition technology is prospected. [ABSTRACT FROM AUTHOR]

Computer tools allow to ease the daily life of everyone, especially for people with disabilities. The optimal choice of pointing interfaces or types of settings can be difficult to achieve. Although there exist a few tests to compare and evaluate the performance of computer access technology (CAT), most of them only provide results as qualitative terms. Besides, the current choice is based mainly on clinical observations or non-standardized tests. To objectify the recommendations in CAT, support funding for people with disabilities and measure the functional repercussions of a therapeutic action, it is necessary to carry out comparative tests with measurable criteria. This paper presents the development of a personalized, free, and dedicated evaluation platform of pointing interfaces and aids. The quantitative evaluation process is described in detail, including the configuration of different exercises with settable parameters that permits to build a customized evaluation process with increased difficulties, the definition of performance indicators and the statistical analysis methods for quantified comparative tests. Finally, we present the assessment results of four people with different disabilities using multiple pointing interfaces, which verify the effectivity of this evaluation platform with the help of occupational therapists. [ABSTRACT FROM AUTHOR]

In recent years, crowd behavior detection has posed significant challenges in the realm of public safety and security, even with the advancements in surveillance technologies. The ability to perform real-time surveillance and accurately identify crowd behavior by considering factors such as crowd size and violence levels can avert potential crowd-related disasters and hazards to a considerable extent. However, most existing approaches are not viable to deal with the complexities of crowd dynamics and fail to distinguish different violence levels within crowds. Moreover, the prevailing approach to crowd behavior recognition, which solely relies on the analysis of closed-circuit television (CCTV) footage and overlooks the integration of online social media video content, leads to a primarily reactive methodology. This paper proposes a crowd behavior detection framework based on the swin transformer architecture, which leverages crowd counting maps and optical flow maps to detect crowd behavior across various sizes and violence levels. To support this framework, we created a dataset comprising videos capable of recognizing crowd behaviors based on size and violence levels sourced from CCTV camera footage and online videos. Experimental analysis conducted on benchmark datasets and our proposed dataset substantiates the superiority of our proposed approach over existing state-of-the-art methods, showcasing its ability to effectively distinguish crowd behaviors concerning size and violence level. Our method's validation through Nvidia's DeepStream Software Development Kit (SDK) highlights its competitive performance and potential for real-time intelligent surveillance applications. [ABSTRACT FROM AUTHOR]

More commonly today, research ethics and misconduct are ideas that are frequently violated. The availability of information sources and the dissemination of awareness among researchers can help to reduce this kind of violation. This study highlights how YouTube can be used to promote discussions of research misconduct and ethics. The study looked into how many videos there are on research ethics and misconduct, which colleges actively provide such videos, and how satisfied viewers are with the available videos by analyzing comments. Various software tools, including Webometric Analyst, R-studio, and Microsoft Excel, were applied for data collection and analysis. On 01-24-2023, 515 videos and 6984 comments were retrieved using the correct search queries that is "Research ethics" OR "Research misconduct" OR "Research conduct" OR "Scientific integrity" OR "Research integrity" OR "Scientific misconduct." Results indicate that 2020 was the most significant year, since the most videos (241) were posted in this year. The channels titled "PPIRCPSC, ABRIZAH A, and ALHOORI H" upload 10, 9, and 8 videos respectively, placing them in the first, second, and third positions. By analyzing viewer comments, it was determined that the majority of comments were favorable, indicating that viewers are generally pleased with the available videos. [ABSTRACT FROM AUTHOR]

Multi-heme cytochromes (MHCs), together with accessory proteins like porins and periplasmic cytochromes, enable microbes to transport electrons between the cytoplasmic membrane and extracellular substrates (e.g., minerals, electrodes, other cells). Extracellular electron transfer (EET) has been described in multiple systems; yet, the broad phylogenetic and mechanistic diversity of these pathways is less clear. One commonality in EET-capable systems is the involvement of MHCs, in the form of porin-cytochrome complexes, pililike cytochrome polymers, and lipid-anchored extracellular cytochromes. Here, we put forth MHCscan—a software tool for identifying MHCs and identifying potential EET capability. Using MHCscan, we scanned ∼60,000 bacterial and 2,000 archaeal assemblies, and identify a diversity of MHCs, many of which represent enzymes with no known function, and many found within organisms not previously known to be electroactive. In total, our scan identified ∼1,400 unique enzymes, each encoding more than 10 heme-binding motifs. In our analysis, we also find evidence for modularity and flexibility in MHC-dependent EET pathways, and suggest that MHCs may be far more common than previously recognized, with many facets yet to be discovered. [ABSTRACT FROM AUTHOR]

Establishing suitable in vitro culture conditions for microorganisms is crucial for dissecting their biology and empowering potential applications. However, a significant number of bacterial and fungal species, including Pneumocystis jirovecii, remain unculturable, hampering research efforts. P. jirovecii is a deadly pathogen of humans that causes life-threatening pneumonia in immunocompromised individuals and transplant patients. Despite the major impact of Pneumocystis on human health, limited progress has been made in dissecting the pathobiology of this fungus. This is largely due to the fact that its experimental dissection has been constrained by the inability to culture the organism in vitro. We present a comprehensive in silico genome-scale metabolic model of Pneumocystis growth and metabolism, to identify metabolic requirements and imbalances that hinder growth in vitro. We utilise recently published genome data and available information in the literature as well as bioinformatics and software tools to develop and validate the model. In addition, we employ relaxed Flux Balance Analysis and Reinforcement Learning approaches to make predictions regarding metabolic fluxes and to identify critical components of the Pneumocystis growth medium. Our findings offer insights into the biology of Pneumocystis and provide a novel strategy to overcome the longstanding challenge of culturing this pathogen in vitro. Author summary: Pneumocystis jirovecii is a human pathogen that causes life-threatening pneumonia in hundreds of thousands of immunocompromised individuals each year. Neither this fungus nor its close relative, the mouse pathogen Pneumocystis murina, can be cultured in vitro, and this is significantly hindering scientific progress. Therefore, we developed a comprehensive genome-scale metabolic model for P. murina using bioinformatics, software tools, and recently published genome data, and we used this metabolic model to predict critical components required for growth of the fungus. Our findings suggest that a subset of amino acids and specific lipids are essential for Pneumocystis survival. Additionally, we employed non-classical Flux Balance Analysis and Reinforcement Learning approaches to optimise ingredients for a Pneumocystis growth medium. This novel methodology has provided new insights into Pneumocystis metabolism and offers a potential approach to overcoming the challenge of culturing this pathogen in vitro, which would accelerate progress towards the development of improved diagnostics and therapies. [ABSTRACT FROM AUTHOR]

The educational landscape of toxicology is increasingly integrating computational methodologies due to ethical concerns about animal testing and advancements in biotechnological and data analysis tools. This paper examines the evolution and significance of the Toxicology in the 21st century (Tox21) initiative and its impact on computational toxicology education. It contrasts computational toxicology with traditional methods, highlighting the limitations of conventional approaches and the new perspectives offered by computational techniques. The study emphasizes the importance of incorporating computational toxicology into curricula, including case studies that demonstrate how this integration enhances students' problem-solving abilities, real-time data analysis skills, and innovation capabilities. Furthermore, it outlines effective teaching content and methods, including software tools, online resources, and academic literature. The paper also addresses the challenges and limitations faced in this educational shift and explores prospects for advancing computational toxicology education. By documenting these developments, the study aims to clarify the current advancements in toxicology education and the preparedness of students to address global chemical safety challenges with innovative solutions. [ABSTRACT FROM AUTHOR]

LiDAR technology introduced the possibility of indirectly estimating various tree parameters. In 2020, Apple incorporated LiDAR sensors into their iPhone 12 Pro. This brings an opportunity to make data collection and plot scanning incredibly convenient. The main aim is to search for a tool and software to estimate the most accurate Diameter at Breast Height (DBH) for individual trees. Therefore, this study focuses on using an iPhone 12 Pro for data collection of individual trees. The performance of three software tools (ForestScanner, rTLS, RANSAC) for the estimation of DBH with field-estimated DBH was compared. The investigation of the significance of the estimation of DBH for each tree species was performed using the three algorithms. In this context, the scanning of 123 trees comprising six species: pine, oak, beech, hornbeam, spruce, and fir was done. DBH was estimated of the scanned tree point cloud using a built-in algorithm of ForestScanner application in the iPhone 12 Pro, RANSAC (CloudCompare plugin), and rTLS (an R-based package). In this study, ForestScanner showed the best results. Therefore, ForestScanner can be used as a reliable tool for measuring DBH. Inferences confirmed by ANOVA and Tukey post-hoc tests show that species significantly influence DBH. Accurate DBH estimation is crucial, and using lightweight devices like the iPhone can revolutionize the forest inventory sector in terms of the estimation of DBH. [ABSTRACT FROM AUTHOR]

Over the past two decades, extensive studies, particularly in cancer analysis through large datasets like The Cancer Genome Atlas (TCGA), have aimed at improving patient therapies and precision medicine. However, limited overlap and inconsistencies among gene signatures across different cohorts pose challenges. The dynamic nature of the transcriptome, encompassing diverse RNA species and functional complexities at gene and isoform levels, introduces intricacies, and current gene signatures face reproducibility issues due to the unique transcriptomic landscape of each patient. In this context, discrepancies arising from diverse sequencing technologies, data analysis algorithms, and software tools further hinder consistency. While careful experimental design, analytical strategies, and standardized protocols could enhance reproducibility, future prospects lie in multiomics data integration, machine learning techniques, open science practices, and collaborative efforts. Standardized metrics, quality control measures, and advancements in single-cell RNA-seq will contribute to unbiased gene signature identification. In this perspective article, we outline some thoughts and insights addressing challenges, standardized practices, and advanced methodologies enhancing the reliability of gene signatures in disease transcriptomic research. [ABSTRACT FROM AUTHOR]

The syntheses of 3,4-dihydropyrimidin-2(1H)-ones by one-pot, three-component condensation of aldehydes, β-ketoesters and urea or thiourea have been made more simple and efficient by using 20 mol% hydrazine sulfate as catalyst. Aldehydes, β-ketoesters and urea are cyclocondensed in the presence of hydrazine sulfate to produce dihydropyrimidines in ethanol under reflux conditions. The advantages of using hydrazine sulfate as a catalyst over the traditional Biginelli reaction conditions include outstanding yields (80–91%) and a shorter (10–15 hours) reaction time. In order to evaluate the antibacterial efficiencies of the synthesized compounds, we have studied the inhibitions of microbial proliferation of both Gram-positive (Bacillus brevis) and Gram-negative (E. coli) bacterial strains in comparison to a control group. The microbial inhibitions occur in the range of 40–98% by different derivatives of dihydropyrimidinones. Molecular docking studies of the synthesized compounds have also been done using software tools such as SwissADME. [ABSTRACT FROM AUTHOR]

Background: Cardiovascular diseases (CVDs) remain the leading cause of mortality, especially in low‐ and middle‐income countries, many of which are in the Arab region. We aimed to conduct a bibliometric analysis to assess the scientific output concerning CVDs in the Arab region and the Middle East over the past decade. Methods: A bibliometric search was conducted on PubMed and Scopus between 2012 and 2022. The study analyzed the number of publications, countries, institutional sources, authors, journals, and keywords. Visualization analysis was executed using various software tools, including R, VOSviewer, and CiteSpace. Results: PubMed and Scopus yielded 5340 documents related to CVDs at the time of data collection. A total of 1263 documents were retrieved after screening based on specific inclusion criteria that guarantee the inclusion only for Arab region studies and authors. There has been a consistent increase in annual publications in recent years. The countries that contributed the most to research in this field were Egypt, Saudi Arabia, and the UAE. Although Egypt stood out as the most productive country, the institute with the highest number of documents was in Qatar. In addition, the Top 3 authors hailed from Qatar. Saudi Medical Journal leads the field as the most productive journal, followed closely by PLoS One and Angiology. Research topics of significant interest in the realm of CVDs include specific diseases such as heart failure, risk factors related to CVDs, genetic aspects of CVDs, and congenital disorders in infant's cardiac health. Conclusion: This study provides a comprehensive overview of the current status of CVD research in the Arab region. It includes research hotspots that can guide authors in determining the necessary future investigations within this field. There is a clear call for further exploration of various aspects of CVDs in the Arab world. Strengthening cooperation between countries and institutes is needed. [ABSTRACT FROM AUTHOR]

This work presents the development of an embedded platform using Field Programmable Gate Arrays (FPGAs) for real-time simulation of dynamic systems in industrial plants. The platform, Real-Time Simulator for Dynamic Systems in FPGA (RTSDS-FPGA), is designed for industrial and academic applications. In industrial contexts, the RTSDS-FPGA facilitates the optimization and tuning of embedded control algorithms, while in academia, it supports research on new embedded solutions in automation and control. It is also an educational tool for undergraduate and postgraduate students developing embedded control system projects. Additionally, the simulator accelerates the simulation of slow dynamic systems, significantly reducing overall simulation time. Experimental results demonstrate the platform's capability to perform real-time simulations effectively, validating its accuracy and performance through comparative analyses with established software tools such as Matlab/Simulink. [ABSTRACT FROM AUTHOR]

This article discusses the software tool (Simscape—Multibody program of MATLAB) primarily intended for dynamic and kinematic processes with practical applications in static calculations. Currently, there are few published scientific works utilizing this tool for tasks like basic static calculations of truss systems. We were interested in comparing the calculation using the tools we use in our work and research activities for theoretical calculation; the potential reliance on simulations in the future could help to avoid the necessity of complex theoretical calculations, which can be time-consuming and prone to errors. Despite the fact that the structure may appear simple, in practice, there may not always be time for a verification calculation in the theoretical field (proper model creation, inclusion of all conditions, etc.). The beam system is intentionally both externally and internally statically indeterminate. For this reason, it is logically necessary to also consider deformation conditions. The achieved results were interesting in terms of accuracy compared to SOLIDWORKS, which was used for computation verification. Through very simple optimization, we were able to further increase the calculation accuracy without complicating other parameters. [ABSTRACT FROM AUTHOR]

The use and adoption of quantum computers by the wider computing community is diminished by the need to adopt new programming techniques. These techniques involve moving from a high-level language where the programmer can define and manipulate objects, to a quantum model where the programmer defines and configures the circuits at a gate level. Previous work by the authors aimed to ease this transition through the use of a software development kit (Qx86 SDK) that emulates a traditional CPU for execution on a quantum computer, but only delivered a raw capability. The current work now presents a number of new methods that extends and improves the SDK's capability. These methods include optimizing traditional logic gate emulation, multiple gate simplification methods, reducing the number of required qubits and alternative optimized techniques for many CPU instructions. A quantum machine code mapping method is described that enhances the emulation of a traditional/quantum hybrid CPU prototype. While still orders of magnitude slower than the performance of a traditional CPU in terms of arithmetic, logic and bitwise operations, execution speed is shown to be markedly improved (in some cases by more than 1,000%) and without introducing any unrealistic requirements (that is, all execution can be performed utilizing less than 32 qubits). The usefulness of the SDK has now been enhanced as a reference guide, where the programmer/researcher can contrast traditional methods versus multiple quantum methods of execution. [ABSTRACT FROM AUTHOR]

Motivation Single cell profiling by cytometry has emerged as a key technology in biology, immunology and clinical-translational medicine. The correct annotation, which refers to the identification of clusters as specific cell populations based on their marker expression, of clustered high-dimensional cytometry data, is a critical step of the analysis. Its accuracy determines the correct interpretation of the biological data. Despite the progress in various clustering algorithms, the annotation of clustered data still remains a manual, time consuming and error-prone task. We developed a user-friendly cluster annotation and differential abundance detection tool that can be applied on data generated with Self Organizing Map clustering algorithms, thus simplifying the annotation process of datasets that consist of hundreds or thousands of clusters. Results We present Cytometry Cluster Annotation and Differential Abundance Suite (CyCadas), a semi-automated software tool that facilitates cluster annotation in cytometry data by offering both visual and computational guidance. CyCadas addresses the critical need for efficient and accurate annotation of high-resolution clustered cytometry data, significantly reducing the time needed to perform the analysis compared to both manual gating approaches and manual annotation of clustered data. The tool features a user-friendly interface, visual tools enabling data exploration and automated threshold estimation to separate negative and positive marker expression. It facilitates the definition and annotation of cell phenotypes among multiple clusters in a tree-based data structure. Finally, it calculates the abundance of various cell populations across the conditions with statistical interpretation. It is an ideal resource for researchers aiming to streamline their cytometry workflow. Availability and implementation CyCadas is available as open source at: https://github.com/DII-LIH-Luxembourg/cycadas. [ABSTRACT FROM AUTHOR]

Background and Objective: Tacrolimus is an immunosuppressant commonly administered after solid organ transplantation. It is characterized by a narrow therapeutic window and high variability in exposure, demanding personalized dosing. In recent years, population pharmacokinetic models have been suggested to guide model-informed precision dosing of tacrolimus. We aimed to provide a comprehensive overview of population pharmacokinetic models and model-informed precision dosing software modules of tacrolimus in all solid organ transplant settings, including a simulation-based investigation of the impact of covariates on exposure and target attainment. Methods: We performed a systematic literature search to identify population pharmacokinetic models of tacrolimus in solid organ transplant recipients. We integrated selected population pharmacokinetic models into an interactive software tool that allows dosing simulations, Bayesian forecasting, and investigation of the impact of covariates on exposure and target attainment. We conducted a web survey amongst model-informed precision dosing software tool providers and benchmarked publicly available tools in terms of models, target populations, and clinical integration. Results: We identified 80 population pharmacokinetic models, including 44 one-compartment and 36 two-compartment models. The most frequently retained covariates on clearance and distribution parameters were cytochrome P450 3A5 polymorphisms and body weight, respectively. Our simulation tool, hosted at https://lpmx.shinyapps.io/tacrolimus/, allows thorough investigation of the impact of covariates on exposure and target attainment. We identified 15 model-informed precision dosing software tool providers, of which ten offer a tacrolimus solution and nine completed the survey. Conclusions: Our work provides a comprehensive overview of the landscape of available tacrolimus population pharmacokinetic models and model-informed precision dosing software modules. Our simulation tool allows an interactive thorough exploration of covariates on exposure and target attainment. [ABSTRACT FROM AUTHOR]

This paper deals with the study of torsional energy (total squared torsion) at infinitesimal bending of curves and knots in three dimensional Euclidean space. During bending, the curve is subject to change, and its properties are changed. The effect that deformation has on the curve is measured by variations. Here, we observe the infinitesimal bending of the second order and variations of the first and the second order that occur in this occasion. The subjects of study are curves and knots, in particular torus knots. We analyze various examples both analytically and graphically, using our own calculation and visualization software tool. [ABSTRACT FROM AUTHOR]

Background: Genomic medicine has transformed clinical genetics by utilizing high‐throughput sequencing technologies to analyze genetic variants associated with diseases. Accurate variant classification is crucial for diagnosis and treatment decisions, and various tools and software such as the Ion Reporter Software and the Illumina Nirvana Software often used in a clinical setting utilize information from the ClinVar database/archive to aid in variant interpretation. However, these existing annotation tools may lack access to the latest ClinVar data, necessitating manual variant inspection. Aims: To address this gap in developing a tool providing the latest ClinVar data for variant annotation in clinical and research settings. Materials and Methods: We introduce CANVAR, a Python‐based script that efficiently annotates variants identified from next‐generation sequencing in a clinical or research context, offering comprehensive information from the latest ClinVar database. Results: CANVAR provides accurate, up‐to‐date variant annotations, streamlining variant analysis. Discussion: The rise in genomic data requires accurate variant annotation for clinical decision‐making. Misclassification poses risks, and current tools may not always access the latest data, challenging variant interpretation. Conclusion: CANVAR contributes to enhancing variant annotation by offering comprehensive information from the latest ClinVar database for genetic variants identified through next‐generation sequencing. [ABSTRACT FROM AUTHOR]

Simple Summary: Spatial epidemiology, integrating traditional epidemiology, geography, statistics, environmental science, and ecology, provides a comprehensive framework for analyzing the spatial dimensions of health and disease. This interdisciplinary approach enhances the development of effective public health strategies and interventions. However, its multifaceted nature can bring complexities in practical application. Using the case of spatial epidemiology in swine viral diseases (SVDs), we illustrate the objectives, methodologies, and essential considerations for the application of spatial epidemiology, which we hope to offer as a comprehensive reference for researchers in this field. Spatial epidemiology offers a comprehensive framework for analyzing the spatial distribution and transmission of diseases, leveraging advanced technical tools and software, including Geographic Information Systems (GISs), remote sensing technology, statistical and mathematical software, and spatial analysis tools. Despite its increasing application to swine viral diseases (SVDs), certain challenges arise from its interdisciplinary nature. To support novices, frontline veterinarians, and public health policymakers in navigating its complexities, we provide a comprehensive overview of the common applications of spatial epidemiology in SVD. These applications are classified into four categories based on their objectives: visualizing and elucidating spatiotemporal distribution patterns, identifying risk factors, risk mapping, and tracing the spatiotemporal evolution of pathogens. We further elucidate the technical methods, software, and considerations necessary to accomplish these objectives. Additionally, we address critical issues such as the ecological fallacy and hypothesis generation in geographic correlation analysis. Finally, we explore the future prospects of spatial epidemiology in SVD within the One Health framework, offering a valuable reference for researchers engaged in the spatial analysis of SVD and other epidemics. [ABSTRACT FROM AUTHOR]

Although transcriptomics data is typically used to analyze mature spliced mRNA, recent attention has focused on jointly investigating spliced and unspliced (or precursor-) mRNA, which can be used to study gene regulation and changes in gene expression production. Nonetheless, most methods for spliced/unspliced inference (such as RNA velocity tools) focus on individual samples, and rarely allow comparisons between groups of samples (e.g. healthy vs. diseased). Furthermore, this kind of inference is challenging, because spliced and unspliced mRNA abundance is characterized by a high degree of quantification uncertainty, due to the prevalence of multi-mapping reads, ie reads compatible with multiple transcripts (or genes), and/or with both their spliced and unspliced versions. Here, we present DifferentialRegulation , a Bayesian hierarchical method to discover changes between experimental conditions with respect to the relative abundance of unspliced mRNA (over the total mRNA). We model the quantification uncertainty via a latent variable approach, where reads are allocated to their gene/transcript of origin, and to the respective splice version. We designed several benchmarks where our approach shows good performance, in terms of sensitivity and error control, vs. state-of-the-art competitors. Importantly, our tool is flexible, and works with both bulk and single-cell RNA-sequencing data. DifferentialRegulation is distributed as a Bioconductor R package. [ABSTRACT FROM AUTHOR]

FullProfAPP is a software tool for data processing, refinement and visualization of large collections of powder diffraction patterns. Featuring an intuitive graphical user interface, it seamlessly facilitates a variety of tasks. These include conducting full‐profile phase searches, sequential and high‐throughput Rietveld refinements, and managing background (and peak) detection. FullProfAPP also provides convenient interaction with crystallographic databases and supports the visualization and export of high‐quality pixel and vector graphics depicting the refinement results, among other functionalities. FullProfAPP wraps around the refinement program FullProf [Rodríguez‐Carvajal (1993), Physica B, 192, 55–69] and offers the flexibility of user‐defined workflows by accessing and editing FullProf's input files and triggering its execution as necessary. FullProfAPP is distributed as open‐source software and is presently available for Windows and Linux operating systems. [ABSTRACT FROM AUTHOR]

In recent decades, sustained theoretical and software developments have clearly established that representation analysis and magnetic symmetry groups are complementary concepts that should be used together in the investigation and description of magnetic structures. Historically, they were considered alternative approaches, but currently, magnetic space groups and magnetic superspace groups can be routinely used together with representation analysis, aided by state‐of‐the‐art software tools. After exploring the historical antagonism between these two approaches, we emphasize the significant advancements made in understanding and formally describing magnetic structures by embracing their combined use. [ABSTRACT FROM AUTHOR]

Modeling of industrial production processes is becoming increasingly challenging due to their large number of variables. These variables often are highly correlated and introduce nonlinear and complex features. In this paper, we are interested to model an industrial dicalcium phosphate (DCP) drying process. Attempting to solve this issue is motivated by the need to improve several production conditions, such as minimizing the consumption of natural gas and reducing the pollution rate. In fact, applying an advanced control approach requires a dynamic model for the monitored plant. This work proposes a multivariable mathematical model for the DCP dryer within the Tunisian Chemical Group factory. A steady-state model has been reproduced using Aspen Plus software tool to implement the different functionalities of the system as well as involved reactions. Indeed, since the main operation in the drying process is the combustion reaction of the liquified petroleum gas (LPG) in the furnace that produce the necessary heat to reach a target value of temperature at the dryer outlet, we focus on determining a dynamic model for the furnace. To do so, we have proposed two approaches. The first is based on the Aspen dynamic tool. The second is based on the left matrix fraction description (LMFD) identification approach. The obtained results have been successfully validated using real measurements. [ABSTRACT FROM AUTHOR]

Currently, digital technologies for modeling buildings and infrastructure are successfully used in international and national practice for the implementation of complex construction projects and large-scale programmes. At the same time, the transition to machine-readable standards, implemented in many countries to improve the quality of design documentation and automate its verification, faces serious methodological and technical challenges. First of all, these challenges are due to the complexity of digital models, as well as the variety of requirements formulated in natural languages and imposed on these models at the state, regional, departmental, and corporate levels. Attempts to create catalogs of requirements and software tools for their management and use generally have specific purposes and do not provide necessary completeness, normalization, consistency, interconnectedness, unambiguity, traceability, and validability of requirements description. In this regard, it seems reasonable to use formal methods for specification and verification of requirements that have proven themselves in system and software engineering. This paper provides a comparative analysis of software tools for automated verification of regulatory requirements in the construction domain. There is a growing interest in tools focused on international standards, such as Industry Foundation Classes (IFC) and Information Delivery Specification (IDS), that allow one to control the completeness of the object and attribute composition of models, as well as the clarification of acceptable domains of values values. However, the IDS standard is not formalized and does not enable the specification of requirements expressed in terms of arbitrary algebraic conditions. The EXPRESS object-oriented data modeling language, in which the IFC data schema is specified, seems promising for the formal specification and verification of requirements for digital models used in the construction industry. As a substantiation, it is shown that IDS specifications can be represented by logical expressions and EXPRESS functions, as well as that arbitrary algebraic conditions can be specified as EXPRESS declarative rules. To illustrate the proposed approach, we consider some examples of the formalization of requirements from national construction standards and sets of rules concerning the safety of buildings, structures, and processes. The possibility of harmonizing the proposed formal approach with the IDS standard by defining new facets for the representation of local, uniqueness, and global EXPRESS rules is also discussed. [ABSTRACT FROM AUTHOR]

SoCs with analog‐circuit‐based unsigned weight‐accumulating spiking neural networks (UWA‐SNNs) are a highly promising solution for achieving low‐power AI‐SoCs. This paper addresses the challenges that must be overcome to realize the potential of UWA‐SNNs in low‐power AI‐SoCs: (i) the absence of UWA‐SNN learning methods and the lack of an environment for developing applications based on trained SNN models and (ii) the inherent issue of testing and validating applications on the system being nearly impractical until the final chip is fabricated owing to the mixed‐signal circuit implementation of UWA‐SNN‐based SoCs. This paper argues that, by integrating the proposed solutions, the development of an EDA tool that enables the easy and rapid development of UWA‐SNN‐based SoCs is feasible, and demonstrates this through the development of the SNN eXpress (SNX) tool. The developed SNX automates the generation of RTL code, FPGA prototypes, and a software development kit tailored for UWA‐SNN‐based application development. Comprehensive details of SNX development and the performance evaluation and verification results of two AI‐SoCs developed using SNX are also presented. [ABSTRACT FROM AUTHOR]

Light exposure fundamentally influences human physiology and behavior, with light being the most important zeitgeber of the circadian system. Throughout the day, people are exposed to various scenes differing in light level, spectral composition and spatio-temporal properties. Personalized light exposure can be measured through wearable light loggers and dosimeters, including wrist-worn actimeters containing light sensors, yielding time series of an individual's light exposure. There is growing interest in relating light exposure patterns to health outcomes, requiring analytic techniques to summarize light exposure properties. Building on the previously published Python-based pyActigraphy module, here we introduce the module pyLight. This module allows users to extract light exposure data recordings from a wide range of devices. It also includes software tools to clean and filter the data, and to compute common metrics for quantifying and visualizing light exposure data. For this tutorial, we demonstrate the use of pyLight in one example dataset with the following processing steps: (1) loading, accessing and visual inspection of a publicly available dataset, (2) truncation, masking, filtering and binarization of the dataset, (3) calculation of summary metrics, including time above threshold (TAT) and mean light timing above threshold (MLiT). The pyLight module paves the way for open-source, large-scale automated analyses of light-exposure data. [ABSTRACT FROM AUTHOR]

As one of the two most ancient groups of extant vertebrates, lamprey has become an important model organism in various fields of biology. In this paper, we present a comprehensive tissue-wide spatial metabolomics dataset for lampreys, where 14 distinct tissues were analyzed using liquid chromatography-mass spectrometry (LC-MS) in both positive and negative ion modes. The dataset has been fully validated using internal standard and pooled quality control samples and is readily accessible at the UCSD Metabolomics Workbench. This dataset serves as a valuable resource for researchers using lampreys as a model organism. Additionally, it acts as a benchmark metabolomics dataset for evaluating new algorithms and software tools and comparing them with previously published results. A lamprey spatial metabolomics database is also provided to support studies utilizing lampreys as an animal model, and to complement and validate other spatial metabolomics studies on lampreys conducted with mass spectrometry imaging or other techniques. [ABSTRACT FROM AUTHOR]

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RNA viruses exhibit vast phylogenetic diversity and can significantly impact public health and agriculture. However, current bioinformatics tools for viral discovery from metagenomic data frequently generate false positive virus results, overestimate viral diversity, and misclassify virus sequences. Additionally, current tools often fail to determine virus–host associations, which hampers investigation of the potential threat posed by a newly detected virus. To address these issues we developed VirID, a software tool specifically designed for the discovery and characterization of RNA viruses from metagenomic data. The basis of VirID is a comprehensive RNA-dependent RNA polymerase database to enhance a workflow that includes RNA virus discovery, phylogenetic analysis, and phylogeny-based virus characterization. Benchmark tests on a simulated data set demonstrated that VirID had high accuracy in profiling viruses and estimating viral richness. In evaluations with real-world samples, VirID was able to identify RNA viruses of all types, but also provided accurate estimations of viral genetic diversity and virus classification, as well as comprehensive insights into virus associations with humans, animals, and plants. VirID therefore offers a robust tool for virus discovery and serves as a valuable resource in basic virological studies, pathogen surveillance, and early warning systems for infectious disease outbreaks. [ABSTRACT FROM AUTHOR]

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