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Technology is used to keep track of things and timing and make the best decision on the information available. Project Management (PM) has to offer through its various software and technical tools. PM software has always been compared to the traditional ways of libraries. If a library has always been compared to its traditional ways of doing projects, PM brings systematic strategies to the planned project of libraries from beginning to end. The main objective of this research is to find a systematic literature review on the software of PM, the -advantages of using PM in libraries, and the challenges faced by the libraries using PM. This research used the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) to illustrate the systematic literature review. The terms "Project Management" and ("library" OR "libraries") were applied to the advanced research of the five databases: Ebscohost, Emerald, Scopus, WOS, and ScienceDirect were selected databases to ensure relevant studies were captured. The main finding from the research is that using Project Management software in libraries has many advantages, such as clarity of planning and a good understanding of technical tools and software, despite many challenges. It provides end-to-end planning and can be adapted to frequent changes to increase productivity and success. In addition, unlike other studies, this study indicates the missing gap in the literature, and it is hoped that it will guide the researchers. [ABSTRACT FROM AUTHOR]

One developing approach for robotic control is the use of networks of dynamic neurons connected with conductance-based synapses, also known as Synthetic Nervous Systems (SNS). These networks are often developed using cyclic topologies and heterogeneous mixtures of spiking and non-spiking neurons, which is a difficult proposition for existing neural simulation software. Most solutions apply to either one of two extremes, the detailed multi-compartment neural models in small networks, and the large-scale networks of greatly simplified neural models. In this work, we present our open-source Python package SNS-Toolbox, which is capable of simulating hundreds to thousands of spiking and non-spiking neurons in real-time or faster on consumer-grade computer hardware. We describe the neural and synaptic models supported by SNS-Toolbox, and provide performance on multiple software and hardware backends, including GPUs and embedded computing platforms. We also showcase two examples using the software, one for controlling a simulated limb with muscles in the physics simulator Mujoco, and another for a mobile robot using ROS. We hope that the availability of this software will reduce the barrier to entry when designing SNS networks, and will increase the prevalence of SNS networks in the field of robotic control. [ABSTRACT FROM AUTHOR]

The role of restaurants, canteens, other forms of eatery and food service outlets are becoming very big part of most economies at different levels. However improvement in customer experience while making orders plays a very critical role in having a smooth and efficient menu ordering process. This article presents a novel hybrid approach that tackles the challenges associated with menu ordering especially in the post-covid era; both from software and hardware perspectives. First, a web-application - powered by ReactJS and GraphQL, which enables order request anywhere at any time was developed. In addition, is the implementation of hardware with mobility feature on trips to the restaurant using two Arduino microcontrollers (transmitter and receiver). The result allows the user to browse through a catalogue, check-in and out on a Thin-Film-Transistor (TFT) liquid-crystal display (LCD) and deliver a seamless experience to the customer. [ABSTRACT FROM AUTHOR]

The development of the system document models in the field of MR-PYRO was done taking into account the adaptation of the application requirements of this management system, to the organizational and work process particularities specific to the pyrotechnic field, but also with the requirements of the legislation in force in the field of occupational health and safety, namely Law 319/2006 on safety and health at work and the methodological rules for its application, as well as specific legislation (Law 126/1995 on the regime of explosive materials and the Technical Norms of application with subsequent amendments and additions). The scientific article offers an original perspective of solving the problem in the field of management system of pyrotechnic articles (MR-PYRO). The MR-PYRO.EXE 01 software application ensures the operational and procedural development of the MSSM system documents based on the analysis and evaluation of the particular way of occurrence of all aspects of occupational safety and health managed at the level of economic operators working in the field of pyrotechnic articles. [ABSTRACT FROM AUTHOR]

Background: The outbreak of the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) resulted in the global COVID-19 pandemic. The urgency for an effective SARS-CoV-2 vaccine has led to the development of the first series of vaccines at unprecedented speed. The discovery of SARS-CoV-2 spike-glycoprotein mutants, however, and consequentially the potential to escape vaccine-induced protection and increased infectivity, demonstrates the persisting importance of monitoring SARS-CoV-2 mutations to enable early detection and tracking of genomic variants of concern. Results: We developed the CoVigator tool with three components: (1) a knowledge base that collects new SARS-CoV-2 genomic data, processes it and stores its results; (2) a comprehensive variant calling pipeline; (3) an interactive dashboard highlighting the most relevant findings. The knowledge base routinely downloads and processes virus genome assemblies or raw sequencing data from the COVID-19 Data Portal (C19DP) and the European Nucleotide Archive (ENA), respectively. The results of variant calling are visualized through the dashboard in the form of tables and customizable graphs, making it a versatile tool for tracking SARS-CoV-2 variants. We put a special emphasis on the identification of intrahost mutations and make available to the community what is, to the best of our knowledge, the largest dataset on SARS-CoV-2 intrahost mutations. In the spirit of open data, all CoVigator results are available for download. The CoVigator dashboard is accessible via covigator.tron-mainz.de. Conclusions: With increasing demand worldwide in genome surveillance for tracking the spread of SARS-CoV-2, CoVigator will be a valuable resource of an up-to-date list of mutations, which can be incorporated into global efforts. [ABSTRACT FROM AUTHOR]

The increased use of information management systems has enabled companies to manage their operations more efficiently and effectively. The purpose of this research is to examine the utilization realization of the management information system using the software in the application. Systematic literature search (SLR) using reliable journal literature data sources published over the Internet and indexed electronically. Application software testing must be based on variability, data volume, parallelism, message timing, and a fairly complex infrastructure. Therefore, we need to classify the structured knowledge base correctly and use efficient tools to transfer human knowledge to the knowledge base. There are several ways to test software for applications that are suitable for use. B. Run a test case consisting of program inputs and outputs that need to be saved. When testing, you also need to pay attention to data volume, parallelism, message timings, and a fairly complex infrastructure. Most of these studies use system literature search techniques in their applications. [ABSTRACT FROM AUTHOR]

Introduction: Metabolomics analysis based on liquid chromatography-mass spectrometry (LC-MS) has been a prevalent method in the metabolic field. However, accurately quantifying all the metabolites in large metabolomics sample cohorts is challenging. The analysis efficiency is restricted by the abilities of software in many labs, and the lack of spectra for some metabolites also hinders metabolite identification. Objectives: Develop software that performs semi-targeted metabolomics analysis with an optimized workflow to improve quantification accuracy. The software also supports web-based technologies and increases laboratory analysis efficiency. A spectral curation function is provided to promote the prosperity of homemade MS/MS spectral libraries in the metabolomics community. Methods: MetaPro is developed based on an industrial-grade web framework and a computation-oriented MS data format to improve analysis efficiency. Algorithms from mainstream metabolomics software are integrated and optimized for more accurate quantification results. A semi-targeted analysis workflow is designed based on the concept of combining artificial judgment and algorithm inference. Results: MetaPro supports semi-targeted analysis workflow and functions for fast QC inspection and self-made spectral library curation with easy-to-use interfaces. With curated authentic or high-quality spectra, it can improve identification accuracy using different peak identification strategies. It demonstrates practical value in analyzing large amounts of metabolomics samples. Conclusion: We offer MetaPro as a web-based application characterized by fast batch QC inspection and credible spectral curation towards high-throughput metabolomics data. It aims to resolve the analysis difficulty in semi-targeted metabolomics. [ABSTRACT FROM AUTHOR]

The availability of phylogenetic data has greatly expanded in recent years. As a result, a new era in phylogenetic analysis is dawning—one in which the methods we use to analyse and assess our data are the bottleneck to producing valuable phylogenetic hypotheses, rather than the need to acquire more data. This makes the ability to accurately appraise and evaluate new methods of phylogenetic analysis and phylogenetic artefact identification more important than ever. Incongruence in phylogenetic reconstructions based on different datasets may be due to two major sources: biological and methodological. Biological sources comprise processes like horizontal gene transfer, hybridization and incomplete lineage sorting, while methodological ones contain falsely assigned data or violations of the assumptions of the underlying model. While the former provides interesting insights into the evolutionary history of the investigated groups, the latter should be avoided or minimized as best as possible. However, errors introduced by methodology must first be excluded or minimized to be able to conclude that biological sources are the cause. Fortunately, a variety of useful tools exist to help detect such misassignments and model violations and to apply ameliorating measurements. Still, the number of methods and their theoretical underpinning can be overwhelming and opaque. Here, we present a practical and comprehensive review of recent developments in techniques to detect artefacts arising from model violations and poorly assigned data. The advantages and disadvantages of the different methods to detect such misleading signals in phylogenetic reconstructions are also discussed. As there is no one‐size‐fits‐all solution, this review can serve as a guide in choosing the most appropriate detection methods depending on both the actual dataset and the computational power available to the researcher. Ultimately, this informed selection will have a positive impact on the broader field, allowing us to better understand the evolutionary history of the group of interest. [ABSTRACT FROM AUTHOR]

• Well-known and advanced feature selection methods within a unified framework have been implemented. • Filter, wrapper, and embedded approaches are considered in the tool. • Essential auxiliary tools for performance evaluation, result visualization, and statistical analysis have been provided. • Researchers can use the tool through its graphical user interface or as a library in their Java codes. • UniFeat has been completely implemented in Java and can be run on a wide range of platforms. The Universal Feature Selection Tool (UniFeat) is an open-source tool developed entirely in Java for performing feature selection processes in various research areas. It provides a set of well-known and advanced feature selection methods within its significant auxiliary tools. This allows users to compare the performance of feature selection methods. Moreover, due to the open-source nature of UniFeat, researchers can use and modify it in their research, which facilitates the rapid development of new feature selection algorithms. [ABSTRACT FROM AUTHOR]

Emergency department (ED) crowding is a well-recognized threat to patient safety and it has been repeatedly associated with increased mortality. Accurate forecasts of future service demand could lead to better resource management and has the potential to improve treatment outcomes. This logic has motivated an increasing number of research articles but there has been little to no effort to move these findings from theory to practice. In this article, we present first results of a prospective crowding early warning software, that was integrated to hospital databases to create real-time predictions every hour over the course of 5 months in a Nordic combined ED using Holt-Winters' seasonal methods. We show that the software could predict next hour crowding with an AUC of 0.94 (95% CI: 0.91-0.97) and 24 hour crowding with an AUC of 0.79 (95% CI: 0.74-0.84) using simple statistical models. Moreover, we suggest that afternoon crowding can be predicted at 1 p.m. with an AUC of 0.84 (95% CI: 0.74-0.91). [ABSTRACT FROM AUTHOR]

Background: Bone age in children is mainly assessed using the Greulich and Pyle (GP) atlas, a validated method with limited interobserver accuracy. While automated methods increase interobserver accuracy, they represent considerable costs and technical requirements. Objective: A proof-of-concept study to create and evaluate an online software program, Boneureka©, based on linear metacarpal length measurements, to assess bone age in healthy children. Materials and methods: The study retrospectively included 434 consecutive children (215 girls) who underwent a left-hand radiograph to rule out trauma between March 2008 and December 2017. Two reviewers measured the second to fourth metacarpal lengths on each radiograph and the distance between the centre of the epiphyses of the second and fifth metacarpals. A single reviewer estimated the bone age using the GP atlas. The automated software assessed the bone age for all radiographs. A mathematical model was developed based on linear regressions to provide the mean bone age and standard deviation based on the estimates. Pearson and intraclass correlation coefficient (ICC) were used to evaluate the correlation and agreement between the estimated bone ages using Boneureka©, the GP atlas and BoneXpert® compared to chronological age. Results: The measure that showed the highest correlation (r2=0.877 for girls and r2=0.834 for boys; P<.001) and the highest ICC (ICC=0.937 for girls and ICC=0.926 for boys; P<0.001) with chronological age was length of the second metacarpal. The GP atlas and the automated software evaluation had excellent ICC with chronological age (ICC>0.95 for both methods and sexes). Using this data, we created an online software program based on the second metacarpal length to obtain bone age estimates, means and standard deviations. Conclusion: The newly created online software Boneureka,© based on the second metacarpal length, is a reliable and user-friendly tool to assess bone age in healthy children. Further studies on a larger population should be performed to validate the developed reference values. [ABSTRACT FROM AUTHOR]

Society is increasingly connected, utilizing more data that demands greater capacity and better channel quality. Furthermore, wireless networks are being inserted into the population's daily lives. Therefore, solutions capable of transferring a high volume of data are increasingly needed. In this context, we present a framework that aims to network planning through data collection, modeling, and routers optimization in the environment. Ziwi framework can simulate wireless networks in indoor and outdoor environments with the main classical propagation models, obtain and calculate metrics and performance parameters. It is possible to measure data by cell phone and send it to the website quickly. Furthermore, it can model the data and compare with different propagation models. Also, optimize them using a genetic algorithm or permutation, choosing whether or not to consider sockets to turn on the routers and how many routers are needed to place in the environment. In addition, have a virtual reality environment aiming at greater interactivity with the data. We analyzed framework results comparing with Close-In propagation model, free space model, and statically using the root mean square error metric. Measurements were made in a real environment using the Ziwi mobile application, inserting data captured on Ziwi website to validate the framework. [ABSTRACT FROM AUTHOR]

Metabolomics experiments generate highly complex datasets, which are time and work-intensive, sometimes even error-prone if inspected manually. Therefore, new methods for automated, fast, reproducible, and accurate data processing and dereplication are required. Here, we present UmetaFlow, a computational workflow for untargeted metabolomics that combines algorithms for data pre-processing, spectral matching, molecular formula and structural predictions, and an integration to the GNPS workflows Feature-Based Molecular Networking and Ion Identity Molecular Networking for downstream analysis. UmetaFlow is implemented as a Snakemake workflow, making it easy to use, scalable, and reproducible. For more interactive computing, visualization, as well as development, the workflow is also implemented in Jupyter notebooks using the Python programming language and a set of Python bindings to the OpenMS algorithms (pyOpenMS). Finally, UmetaFlow is also offered as a web-based Graphical User Interface for parameter optimization and processing of smaller-sized datasets. UmetaFlow was validated with in-house LC–MS/MS datasets of actinomycetes producing known secondary metabolites, as well as commercial standards, and it detected all expected features and accurately annotated 76% of the molecular formulas and 65% of the structures. As a more generic validation, the publicly available MTBLS733 and MTBLS736 datasets were used for benchmarking, and UmetaFlow detected more than 90% of all ground truth features and performed exceptionally well in quantification and discriminating marker selection. We anticipate that UmetaFlow will provide a useful platform for the interpretation of large metabolomics datasets. [ABSTRACT FROM AUTHOR]

While we often think of words as having a fixed meaning that we use to describe a changing world, words are also dynamic and changing. Scientific research can also be remarkably fast-moving, with new concepts or approaches rapidly gaining mind share. We examined scientific writing, both preprint and pre-publication peer-reviewed text, to identify terms that have changed and examine their use. One particular challenge that we faced was that the shift from closed to open access publishing meant that the size of available corpora changed by over an order of magnitude in the last two decades. We developed an approach to evaluate semantic shift by accounting for both intra- and inter-year variability using multiple integrated models. This analysis revealed thousands of change points in both corpora, including for terms such as 'cas9', 'pandemic', and 'sars'. We found that the consistent change-points between pre-publication peer-reviewed and preprinted text are largely related to the COVID-19 pandemic. We also created a web app for exploration that allows users to investigate individual terms (https://greenelab.github.io/word-lapse/). To our knowledge, our research is the first to examine semantic shift in biomedical preprints and pre-publication peer-reviewed text, and provides a foundation for future work to understand how terms acquire new meanings and how peer review affects this process. [ABSTRACT FROM AUTHOR]

SYNCmoss is a software package dedicated to fitting Mössbauer spectra measured with a synchrotron Mössbauer source (SMS). Operation conditions of an SMS can be continuously varied from high resolution and low intensity to the opposite extreme. These variations influence the width and shape of the instrumental function. The main purpose of SYNCmoss is to fit data obtained with instrumental functions of various shapes. SYNCmoss allows the instrumental function to be extracted from the spectrum of a standard absorber, and then to utilize it in the fitting procedures for the samples studied. SYNCmoss is standalone software -- it does not require installations of any software environment. Setting input parameters and the fitting procedure are controlled within a graphical user interface. High-intensity mode of the SMS leads to a short acquisition time, allowing for fast (for example, operando) measurements resulting in a long sequence of spectra with close or smoothly varying parameters. For such cases, SYNCmoss provides an option to fit a series of spectra in automatic mode. SYNCmoss provides fitting models for various cases of hyperfine interaction including the full Hamiltonian for the combined interaction. Finally, for generality, the software has the ability to also fit conventional Mo¨ ssbauer spectra and simple cases of nuclear forward scattering spectra. [ABSTRACT FROM AUTHOR]

A Python package for the analysis of dark-field X-ray microscopy (DFXM) and rocking curve imaging (RCI) data is presented. DFXM is a non-destructive diffraction imaging technique that provides three-dimensional maps of lattice strain and orientation. The darfix package enables fast processing and visualization of these data, providing the user with the essential tools to extract information from the acquired images in a fast and intuitive manner. These data processing and visualization tools can be either imported as library components or accessed through a graphical user interface as an Orange add-on. In the latter case, the different analysis modules can be easily chained to define computational workflows. Operations on larger-than-memory image sets are supported through the implementation of online versions of the data processing algorithms, effectively trading performance for feasibility when the computing resources are limited. The software can automatically extract the relevant instrument angle settings from the input files' metadata. The currently available input file format is EDF and in future releases HDF5 will be incorporated. [ABSTRACT FROM AUTHOR]

Medical image segmentation, whether semi-automatically or manually, is labor-intensive, subjective, and needs specialized personnel. The fully automated segmentation process recently gained importance due to its better design and understanding of CNNs. Considering this, we decided to develop our in-house segmentation software and compare it to the systems of established companies, an inexperienced user, and an expert as ground truth. The companies included in the study have a cloud-based option that performs accurately in clinical routine (dice similarity coefficient of 0.912 to 0.949) with an average segmentation time ranging from 3′54″ to 85′54″. Our in-house model achieved an accuracy of 94.24% compared to the best-performing software and had the shortest mean segmentation time of 2′03″. During the study, developing in-house segmentation software gave us a glimpse into the strenuous work that companies face when offering clinically relevant solutions. All the problems encountered were discussed with the companies and solved, so both parties benefited from this experience. In doing so, we demonstrated that fully automated segmentation needs further research and collaboration between academics and the private sector to achieve full acceptance in clinical routines. [ABSTRACT FROM AUTHOR]

Objectives: To evaluate anatomical and volumetric predictability of a cone beam computed tomography (CBCT)-based virtual parenchymal perfusion (VPP) software for the single-photon-emission computed tomography (SPECT)/CT imaging results during the work-up for transarterial radioembolization (TARE) procedure in patients with hepatocellular carcinoma (HCC). Methods: VPP was evaluated retrospectively on CBCT data of patients treated by TARE for HCC. 99mTc macroaggregated albumin particles (99mTc-MAA) uptake territories on work-up SPECT/CT was used as ground truth for the evaluation. Semi-quantitative evaluation consisted of the ranking of visual consistency of the parenchymal enhancement and portal vein tumoral involvement on VPP and 99mTc-MAA SPECT/CT, using a three-rank scale and two-rank scale, respectively. Inter-reader agreement was evaluated using a kappa coefficient. Quantitative evaluation included absolute volume error calculation and Pearson correlation between volumes enhanced territories on VPP and 99mTc-MAA SPECT/CT. Results: Fifty-two CBCTs were performed in 33 included patients. Semi-quantitative evaluation showed a good concordance between actual 99mTc-MAA uptake and the virtual enhanced territories in 73% and 75% of cases; a mild concordance in 12% and 10% and a poor concordance in 15%, for the two readers. Kappa coefficient was 0.86. Portal vein involvement evaluation showed a good concordance in 58.3% and 66.7% for the two readers, respectively, with a kappa coefficient of 0.82. Quantitative evaluation showed a volume error of 0.46 ± 0.78 mL [0.01–3.55], and Pearson R2 factor at 0.75 with a p value < 0.01. Conclusion: CBCT-based VPP software is accurate and reliable to predict 99mTc-MAA SPECT/CT anatomical and volumetric results in HCC patients during TARE. Key Points: • Virtual parenchymal perfusion (VPP) software is accurate and reliable in the prediction of 99mTc-MAA SPECT volumetric and targeting results in HCC patients during transarterial radioembolization (TARE). • VPP software may be used per-operatively to optimize the microcatheter position for 90Y infusion allowing precise tumor targeting while preserving non-tumoral parenchyma. • Post-operatively, VPP software may allow an accurate estimation of the perfused volume by each arterial branch and, thus, a precise 90Y dosimetry for TARE procedures. [ABSTRACT FROM AUTHOR]

Objectives: To externally validate the performance of a commercial AI software program for interpreting CXRs in a large, consecutive, real-world cohort from primary healthcare centres. Methods: A total of 3047 CXRs were collected from two primary healthcare centres, characterised by low disease prevalence, between January and December 2018. All CXRs were labelled as normal or abnormal according to CT findings. Four radiology residents read all CXRs twice with and without AI assistance. The performances of the AI and readers with and without AI assistance were measured in terms of area under the receiver operating characteristic curve (AUROC), sensitivity, and specificity. Results: The prevalence of clinically significant lesions was 2.2% (68 of 3047). The AUROC, sensitivity, and specificity of the AI were 0.648 (95% confidence interval [CI] 0.630–0.665), 35.3% (CI, 24.7–47.8), and 94.2% (CI, 93.3–95.0), respectively. AI detected 12 of 41 pneumonia, 3 of 5 tuberculosis, and 9 of 22 tumours. AI-undetected lesions tended to be smaller than true-positive lesions. The readers' AUROCs ranged from 0.534–0.676 without AI and 0.571–0.688 with AI (all p values < 0.05). For all readers, the mean reading time was 2.96–10.27 s longer with AI assistance (all p values < 0.05). Conclusions: The performance of commercial AI in these high-volume, low-prevalence settings was poorer than expected, although it modestly boosted the performance of less-experienced readers. The technical prowess of AI demonstrated in experimental settings and approved by regulatory bodies may not directly translate to real-world practice, especially where the demand for AI assistance is highest. Key Points: • This study shows the limited applicability of commercial AI software for detecting abnormalities in CXRs in a health screening population. • When using AI software in a specific clinical setting that differs from the training setting, it is necessary to adjust the threshold or perform additional training with such data that reflects this environment well. • Prospective test accuracy studies, randomised controlled trials, or cohort studies are needed to examine AI software to be implemented in real clinical practice. [ABSTRACT FROM AUTHOR]

The contrast‐variation method in small‐angle neutron scattering (SANS) is a uniquely powerful technique for determining the structure of individual components in biomolecular systems containing regions of different neutron scattering length density ρ. By altering the ρ of the target solute and the solvent through judicious incorporation of deuterium, the scattering of desired solute features can be highlighted. Most contrast‐variation methods focus on highlighting specific bulk solute elements, but not on how the scattering at specific scattering vectors q, which are associated with specific structural distances, changes with contrast. Indeed, many systems exhibit q‐dependent contrast effects. Here, a method is presented for calculating both bulk contrast‐match points and q‐dependent contrast using 3D models with explicit solute and solvent atoms and SASSENA, an explicit‐atom SANS calculator. The method calculates the bulk contrast‐match points within 2.4% solvent D2O accuracy for test protein–nucleic acid and lipid nanodisc systems. The method incorporates a general model for the incorporation of deuterium at non‐exchangeable sites that was derived by performing mass spectrometry on green fluorescent protein. The method also decomposes the scattering profile into its component parts and identifies structural features that change with contrast. The method is readily applicable to a variety of systems, will expand the understanding of q‐dependent contrast matching and will aid in the optimization of next‐generation neutron scattering experiments. [ABSTRACT FROM AUTHOR]

The parallel performance of the three-level fast and robust overlapping Schwarz (FROSch) preconditioners is investigated for linear elasticity. The FROSch framework is part of the Trilinos software library and contains a parallel implementation of different preconditioners with energy minimizing coarse spaces of generalized Dryja--Smith--Widlund type. The three-level extension is constructed by a recursive application of the FROSch preconditioner to the coarse problem. In this paper, the additional steps in the implementation in order to apply the FROSch preconditioner recursively are described in detail. Furthermore, it is shown that no explicit geometric information is needed in the recursive application of the preconditioner. In particular, the rigid body modes, including the rotations, can be interpolated on the coarse level without additional geometric information. Parallel results for a three-dimensional linear elasticity problem obtained on the Theta supercomputer (Argonne Leadership Computing Facility, Argonne, IL) using up to 220 000 cores are discussed and compared to results obtained on the SuperMUC-NG supercomputer (Leibniz Supercomputing Centre, Garching, Germany). Notably, it can be observed that a hierarchical communication operation in FROSch related to the coarse operator starts to dominate the computing time on Theta, which has a dragonfly interconnect, for 100 000 message passing interface (MPI) ranks or more. The same operation, however, scales well and stays within the order of a second in all experiments performed on SuperMUC-NG, which uses a fat tree network. Using hybrid MPI/OpenMP parallelization, the onset of the MPI communication problem on Theta can be delayed. Further analysis of the performance of FROSch on large supercomputers with dragonfly interconnects will be necessary. [ABSTRACT FROM AUTHOR]

This article presents the experimental design of ClaimCheck, an artificial intelligence tool for detecting repeated falsehoods in political discourse using a semantic similarity model developed by the fact-checking organization Newtral in collaboration with ABC Australia. The study reviews the state of the art in algorithmic fact-checking and proposes a definition of claim matching. Additionally, it outlines the scheme for annotating similar sentences and presents the results of experiments conducted with the tool. [ABSTRACT FROM AUTHOR]

Spatial wombling is an approach for detecting edges within a defined two‐dimensional landscape. This is achieved by calculating the rate and direction of change through the interpolation of points. This not only gives an approximation as to the shape of the landscape but can also be used to identify candidate boundaries cells that delimit a shift from one state to another within the landscape. Here we introduce the SpatialBoundaries.jl package for Julia, which has been developed to implement the wombling algorithm for datasets that are spatially referenced for both uniformly or randomly sampled landscapes. From a practical perspective, the wombling functionality allow the user to answer two questions: how much and in which direction does the variable of interest change within the landscape? Whereas the boundaries functionality identifies candidate boundary cells. We conclude by providing a working example of the package using the various woody plant layers for Britain and Ireland from the EarthEnv database. [ABSTRACT FROM AUTHOR]

A reference interval, or an interval in which a prespecified proportion of measurements from a healthy population are expected to fall, is used to determine whether a person's measurement is typical of a healthy individual. For a specific biomarker, multiple published studies may provide data collected from healthy participants. A reference interval estimated by combining the data across these studies is typically more generalizable than a reference interval based on a single study. Methods for estimating reference intervals from random effects meta‐analysis and fixed‐effects meta‐analysis have been recently proposed and implemented using R software. We present an R Shiny tool, RIMeta, implementing these methods, which allows users not proficient in R to estimate a reference interval from a meta‐analysis using aggregate data (mean, standard deviation, and sample size) from each study. RIMeta (https://cers.shinyapps.io/RIMeta/) provides users a convenient way to estimate a reference interval from a meta‐analysis and to generate the reference interval plot to visualize the results. The use of this web‐based R Shiny tool does not require the installation of R or any background knowledge of programming. We explain all functions of the R Shiny tool and illustrate how to use it with a real data example. [ABSTRACT FROM AUTHOR]

The unambiguous identification of lipids is a critical component of lipidomics studies and greatly impacts the interpretation and significance of analyses as well as the ultimate biological understandings derived from measurements. The level of structural detail that is available for lipid identifications is largely determined by the analytical platform being used. Mass spectrometry (MS) coupled with liquid chromatography (LC) is the predominant combination of analytical techniques used for lipidomics studies, and these methods can provide fairly detailed lipid identification. More recently, ion mobility spectrometry (IMS) has begun to see greater adoption in lipidomics studies thanks to the additional dimension of separation that it provides and the added structural information that can support lipid identification. At present, relatively few software tools are available for IMS-MS lipidomics data analysis, which reflects the still limited adoption of IMS as well as the limited software support. This fact is even more pronounced for isomer identifications, such as the determination of double bond positions or integration with MS-based imaging. In this review, we survey the landscape of software tools that are available for the analysis of IMS-MS-based lipidomics data and we evaluate lipid identifications produced by these tools using open-access data sourced from the peer-reviewed lipidomics literature. [ABSTRACT FROM AUTHOR]

The subjective analysis of conventional radiography represents the principal method for bone diagnostics in endoprosthetics. Alternative objective quantitative methods are described but not commonly used. Therefore, semi-quantitative methods are tested using digital computation and artificial intelligence to standardize, simplify, and ultimately improve the assessment. This study aimed to evaluate the correlation between relative density progressions and clinical outcomes. Radiographs and clinical examinations before and 24 and 48 weeks after surgery were obtained from sixty-eight patients with a modular hip stem. For the calculation of the relative bone density, the modal gray values of the Gruen zones were measured using ImageJ and were normalized by gray values of the highest and lowest ROI. The clinical outcomes were measured according to the Harris hip score before evaluating them for correlations. Analyses were performed separately for subgroups and bone regions. The Harris hip score increased from 44.15 ± 15.00 pre-operatively to 66.20 ± 13.87 at the latest follow-up. The relative bone density adjustment of Gruen zone 7 showed a significant correlation to its clinical outcome. Other bone adaptations could be realistically reproduced and differences by regional zones and patients' histories visualized. Next to the simplicity and that no additional examination is required, the method provides good semi-quantitative results and visualizes adaptations, which make it suitable for use. [ABSTRACT FROM AUTHOR]

Motivation Investigating the aggregate burden of environmental factors on human traits and diseases requires consideration of the entire 'exposome'. However, current studies primarily focus on a single exposure or a handful of exposures at a time, without considering how multiple exposures may be simultaneously associated with each other or with the phenotype. Polyexposure risk scores (PXS) have been shown to predict and stratify risk for disease beyond or complementary to genetic and clinical risk. PXStools provides an analytical package to standardize exposome-wide studies as well as derive and validate polyexposure risk scores. Implementation PXStools is a package for the statistical R. General features The package allows users to (i) conduct exposure-wide association studies; (ii) derive and validate polyexposure risk scores with and without accounting for exposure interactions, using new approaches in regression modelling (hierarchical lasso);(iii) compare goodness of fit between models with and without multiple exposures; and (iv) visualize results. A data frame with a unique identifier, phenotype and exposures is needed as the only input. Various customizations are allowed including data preprocessing (removing missing or unwanted responses), covariates adjustment, multiple hypothesis correction and model specification (linear, logistic, survival). Availability The PXStools source code is freely available on Github at [ https://github.com/yixuanh/PXStools ]. [ABSTRACT FROM AUTHOR]

This work presents the MarketPlace Agent and Expert Ontology (MAEO), an ontology for modeling experts, expertise, and more broadly, knowledge providers and knowledge seekers for the subject areas of Materials Modeling. MAEO had its inception within the "MarketPlace" European project, whose purpose is to bring about a single entry point for gathering scientific and industrial stakeholders in Materials Modeling. As such, this project aimed to build an online platform where experts and knowledge providers can be searched, found and brought into contact with users, or knowledge seekers, and with one another. MAEO was developed in order to fulfill the requirements of this online platform and thus support it, but is also part of a wider ecosystem of Materials Modeling-related ontologies, at whose core lies the Elementary Multiperspective Material Ontology (EMMO). MAEO is thus an EMMO-compliant application ontology, and has been loosely aligned with a number of existing ontologies, including Friend-Of-A-Friend (FOAF) and five recently-developed EMMO-based domain ontologies for the classification of materials, models, manufacturing processes, characterization methods and software products related to Materials Modeling. Here, a detailed description of the axiomatization of MAEO and its interconnected ontologies is provided, along with results coming from its deployment and experimentation in a StarDog triplestore. Availability. The axiomatization of the ontology is stored in a GitHub repository available at: https://github.com/emmo-repo/MAEO-Ontology , and is published at the following URL: http://emmo.info/emmo/application/maeo/experts. [ABSTRACT FROM AUTHOR]

During a Futures Study, researchers analyze a significant quantity of information dispersed across multiple document databases to gather conjectures about future events, making it challenging for researchers to retrieve all predicted events described in publications quickly. Generating a timeline of future events is time-consuming and prone to errors, requiring a group of experts to execute appropriately. This work introduces NERMAP, a system capable of semi-automating the process of discovering future events, organizing them in a timeline through Named Entity Recognition supported by machine learning, and gathering up to 83% of future events found in documents when compared to humans. The system identified future events that we failed to detect during the tests. Using the system allows researchers to perform the analysis in significantly less time, thus reducing costs. Therefore, the proposed approach enables a small group of researchers to efficiently process and analyze a large volume of documents, enhancing their capability to identify and comprehend information in a timeline while minimizing costs. [ABSTRACT FROM AUTHOR]

Whole‐genome alignment allows researchers to understand the genomic structure and variation among genomes. Approaches based on direct pairwise comparisons of DNA sequences require large computational capacities. As a consequence, pipelines combining tools for orthologous gene identification and synteny have been developed. In this manuscript, we present the latest functionalities implemented in NGSEP 4, to identify orthogroups and perform whole genome alignments. NGSEP implements functionalities for identification of clusters of homologus genes, synteny analysis and whole genome alignment. Our results showed that the NGSEP algorithm for orthogroups identification has competitive accuracy and efficiency in comparison to commonly used tools. The implementation also includes a visualization of the whole genome alignment based on synteny of the orthogroups that were identified, and a reconstruction of the pangenome based on frequencies of the orthogroups among the genomes. NGSEP 4 also includes a new graphical user interface based on the JavaFX technology. We expect that these new developments will be very useful for several studies in evolutionary biology and population genomics. [ABSTRACT FROM AUTHOR]

Data-reduction software used at neutron-scattering facilities around the world, Mantid and Scipp, ignore correlations when propagating uncertainties in arithmetic operations. Normalization terms applied during data-reduction frequently have a lower dimensionality than the quantities being normalized. We show how the lower dimensionality introduces correlations, which the software does not take into account in subsequent data-reduction steps such as histogramming, summation, or fitting. As a consequence, any uncertainties in the normalization terms are strongly suppressed and thus effectively ignored. This can lead to erroneous attribution of significance to deviations that are actually pure noise, or to overestimation of significance in final data-reduction results that are used for further data analysis. We analyze this flaw for a number of different cases as they occur in practice. For the two concrete experiments that are comprised in these case studies the underestimation turns out to be of negligible size. There is however no reason to assume that this generalizes to other measurements at the same or at different neutron-scattering beamlines. We describe and implement a potential solution that yields not only corrected error estimates but also the full variance-covariance matrix of the reduced result with minor additional computational cost. [ABSTRACT FROM AUTHOR]

Cloud computing, a computational model based on computer networks similar the Internet, revolutionizes the provision, consumption, and delivery of computing services by leveraging network utilization. The objective of this study is to comprehensively analyze the existing research literature regarding the implementation of cloud computing in enhancing higher education in the field of medical sciences. Conducted as a systematic review, this study extensively utilized indexed articles sourced from renowned scientific databases such as SID, Google Scholar, PubMed, Scopus, and Science Direct. The selected research texts spanned the period from 2012 to the end of 2022. The search process focused on key terms and phrases including cloud computing, development of higher education, and epidemics as they appeared in the titles of the articles. By categorizing nine influential factors into three divisions based on library resources, namely technological infrastructure (including reliability, security, accessibility, and adaptability), outsourcing (involving cloud service providers and negotiations of service-level agreements), and internal management (comprising guidelines, policies, planning teams, and superior management), the study offers valuable insights into the implementation of cloud computing. Furthermore, future studies that validate these factors across various higher education institutions can significantly enhance the meaningfulness of examining cloud computing based on university experience. Finally, this study has the possible to serve as an appreciated resource for embracing cloud computing within the realm of mobile learning or mobile computing in the domain of medical sciences. [ABSTRACT FROM AUTHOR]

For improving the energy efficiency level in industrial systems, the service specialists should take appropriate measures to improve the efficiency in a timely manner. A necessity arises to provide adequate training for current and future specialists and practitioners. This training can be based on the digital technologies taking into account the information revolution we are witnessing and the future trends in this field. In this paper, a unified training methodology and laboratory equipment on energy efficiency investigation in induction motor drives is developed based on a developed automated computer system. The methodology is provided with created software products based on developed complex algorithms. The methodology procedure and the corresponding learning processes are organized into six main stages. In order to implement the method in the training of specialists, a computer laboratory bench is designed and implemented. The training is provided with three elaborated textbooks. The education conducted using the methodology improves the competence of the trained persons and gives them the opportunity to make justified and correct decisions and take relevant actions. The proposed development is used for educating number of students in some master programs in the University of Ruse. A major part of them already works in the field. [ABSTRACT FROM AUTHOR]

A Microsoft® Visual Basic software, called WinSpingc, has been developed to calculate and classify wet chemical and electron-microprobe spinel supergroup mineral analyses based on the New Minerals, Nomenclature and Classification (CNMMN) of the International Mineralogical Association (IMA-19) nomenclature scheme. The program evaluates the 60 approved species according to the dominant valance and constituents in the general AB2X4 formula for the spinel, ulvöspinel, carrollite, linnaeite, tyrellite and bornhardtite subgroups that belong to the oxyspinel, thiospinel and selenospinel groups. Mineral analyses of the oxyspinel group are calculated based on 3 cations and 4 oxygen atoms per formula unit, whereas the formulae of thiospinel and selenospinel analyses are on the basis of 7 atoms per formula unit. Employing the anions of spinel supergroup mineral analyses, the program first assigns three groups on the basis of dominant X anion, including O2-, S2- and Se2-, determines subgroups according to the cation charge arrangement combinations and then defines the spinel species in each subgroup based on the dominant valance and constituents. The Fe3+ and Fe2+, as well as the Mn3+, Mn2+, Co3+ and Co2+ contents from microprobe-derived total FeO, MnO and CoO (wt. %) amounts, are estimated by stoichiometric constraints. WinSpingc allows the users to enter total 57 input variables for groups as well as to type and load the multiple spinel supergroup compositions in the data entry section, to edit and load the Microsoft® Excel files in calculating, classifying and naming the spinel species, and to store all the calculated parameters in the Microsoft® Excel file for further evaluations. The program is distributed as a self-extracting setup file, including the necessary support files used by the program, a help file, and representative sample data files. [ABSTRACT FROM AUTHOR]

We present here the signal-to-noise (S/N) calculator developed for the Colorado Ultraviolet Transit Experiment (CUTE) mission. CUTE is a 6U CubeSat operating in the near-ultraviolet (NUV) observing exoplanetary transits to study their upper atmospheres. CUTE was launched into a low-Earth orbit in September 2021 and it is currently gathering scientific data. As part of the S/N calculator, we also present the error propagation for computing transit depth uncertainties starting from the S/N of the original spectroscopic observations. The CUTE S/N calculator is currently extensively used for target selection and scheduling. The modular construction of the CUTE S/N calculator enables its adaptation and can be used also for other missions and instruments. [ABSTRACT FROM AUTHOR]

Cataloging is an essential activity of a children's hospital library used not only to identify and retrieve bibliographic resources, but mainly to protect its users: hospitalized child and accompanying adult. The child is in a situation of extreme vulnerability, in which reading a mis-indicated book may harm him/her, while an appropriate book may help his/her recovery. In this paper, metadata is introduced to indicate the most appropriate user profile for each book. In addition, the design and cataloging process in the children's hospital library of a Brazilian public hospital are detailed, including specific software and experimental results for cataloging. [ABSTRACT FROM AUTHOR]

STUDY QUESTION Can early pregnancies be accurately and cost-effectively diagnosed and managed using a new medical computerized tool? SUMMARY ANSWER Compared to the standard clinical approach, retrospective implementation of the new medical software in a gynaecological emergency unit was correlated with more accurate diagnosis and more cost-effective management. WHAT IS KNOWN ALREADY Early pregnancy complications are responsible for a large percentage of consultations, mostly in emergency units, with guidelines becoming complex and poorly known/misunderstood by practitioners. STUDY DESIGN, SIZE, DURATION A total of 780 gynaecological emergency consultations (446 patients), recorded between November 2018 and June 2019 in a tertiary university hospital, were retrospectively encoded in a new medical computerized tool. The inclusion criteria were a positive hCG test result, ultrasonographical visualization of gestational sac, and/or embryo corresponding to a gestational age of 14 weeks or less. Diagnosis and management suggested by the new computerized tool are named eDiagnoses, while those provided by a gynaecologist member of the emergency department staff are called medDiagnoses. PARTICIPANTS/MATERIALS, SETTING, METHODS Usability was the primary endpoint, with accuracy and cost reduction, respectively, as secondary and tertiary endpoints. Identical eDiagnoses/medDiagnoses were considered as accurate. During follow-up visits, if the updated eDiagnoses and medDiagnoses became both identical to a previously discrepant eDiagnosis or medDiagnosis, this previous eDiagnosis or medDiagnosis was also considered as correct. Four double-blinded experts reviewed persistent discrepancies, determining the accurate diagnoses. eDiagnoses/medDiagnoses accuracies were compared using McNemar's Chi square test, sensitivity, specificity, and predictive values. MAIN RESULTS AND THE ROLE OF CHANCE Only 1 (0.1%) from 780 registered medical records lacked data for full encoding. Out of the 779 remaining consultations, 675 eDiagnoses were identical to the medDiagnoses (86.6%) and 104 were discrepant (13.4%). From these 104, 60 reached an agreement during follow-up consultations, with 59 medDiagnoses ultimately changing into the initial eDiagnoses (98%) and only one discrepant eDiagnosis turning later into the initial medDiagnosis (2%). Finally, 24 remained discrepant at all subsequent checks and 20 were not re-evaluated. Out of these 44, the majority of experts agreed on 38 eDiagnoses (86%) and 5 medDiagnoses (11%, including four twin pregnancies whose twinness was the only discrepancy). No majority was reached for one discrepant eDiagnosis/medDiagnosis (2%). In total, the accuracy of eDiagnoses was 99.1% (675 + 59 + 38 = 772 eDiagnoses out of 779), versus 87.4% (675 + 1 + 5 = 681) for medDiagnoses (P  < 0.0001). Calculating all basic costs of extra consultations, extra-medications, extra-surgeries, and extra-hospitalizations induced by incorrect medDiagnoses versus eDiagnoses, the new medical computerized tool would have saved 3623.75 Euros per month. Retrospectively, the medical computerized tool was usable in almost all the recorded cases (99.9%), globally more accurate (99.1% versus 87.4%), and for all diagnoses except twinning reports, and it was more cost-effective than the standard clinical approach. LIMITATIONS, REASONS FOR CAUTION The retrospective study design is a limitation. Some observed improvements with the medical software could derive from the encoding by a rested and/or more experienced physician who had a better ultrasound interpretation. This software cannot replace clinical and ultrasonographical skills but may improve the compliance to published guidelines. WIDER IMPLICATIONS OF THE FINDINGS This medical computerized tool is improving. A new version considers diagnosis and management of multiple pregnancies with their specificities (potentially multiple locations, chorioamnionicity). Prospective evaluations will be required. Further developmental steps are planned, including software incorporation into ultrasound devices and integration of previously published predictive/prognostic factors (e.g. serum progesterone, corpus luteum scoring). STUDY FUNDING/COMPETING INTEREST(S) No external funding was obtained for this study. F.B. and D.G. created the new medical software. TRIAL REGISTRATION NUMBER NCT03993015 [ABSTRACT FROM AUTHOR]

Objectives: To establish a novel quantitative method that automatically excludes the red bone marrow and accurately quantifies the tumor volume on whole‐body magnetic resonance imaging using updated imaging software. To also evaluate the association between the quantified tumor volume and the prognosis of patients with metastatic prostate cancer. Methods: This prospective analysis included patients diagnosed with metastatic hormone‐sensitive or metastatic castration‐resistant prostate cancer between 2017 and 2022. We developed an imaging software (Attractive BD_Score) that analyzed whole‐body diffusion‐weighted and in‐phase and opposed‐phase T1‐weighted images to automatically exclude the red bone marrow. The quantified tumor volume was compared with that quantified by traditional whole‐body diffusion‐weighted imaging without red bone marrow exclusion. Prostate‐specific antigen progression‐free survival, time‐to‐pain progression, and overall survival were evaluated to assess the prognostic value of the quantified tumor volume. Results: The quantified tumor volume was significantly smaller than that quantified by the traditional method in metastatic hormone‐sensitive (median: 81.0 ml vs. 149.4 ml) and metastatic castration‐resistant (median: 29.4 ml vs. 63.5 ml) prostate cancer. A highly quantified tumor volume was associated with prostate‐specific antigen progression‐free survival (p = 0.030), time‐to‐pain progression (p = 0.003), and overall survival (p = 0.005) in patients with metastatic hormone‐sensitive prostate cancer and with poor prostate‐specific antigen progression‐free survival (p = 0.001) and time‐to‐pain progression (p = 0.005) in patients with metastatic castration‐resistant prostate cancer. Conclusions: Our imaging method could accurately quantify the tumor volume in patients with metastatic prostate cancer. The quantified tumor volume can be clinically applied as a new prognostic biomarker for metastatic prostate cancer. [ABSTRACT FROM AUTHOR]

Background and purpose: It is challenging to assess essential blepharospasm (EB) patients objectively because they exhibit chaotic patterns of abnormal eyelid movements. Previously employed objective approaches used systems with low levels of accuracy systems or were too complex to be applied in routine clinical practice. We aimed to develop a practical tool using a smartphone camera and custom‐made software to objectively assess the therapeutic effects of botulinum toxin in blepharospasm patients. Methods: Thirty‐four patients with EB were evaluated before and 15 days after receiving onabotulinumtoxinA injections. The control group was composed of 19 age‐matched healthy individuals. A smartphone (iPhone 6 S; Apple) was used to record spontaneous eyelid movements for 3 min, after which eyelid movement frequency was analysed using custom‐made software. Results: Before treatment, eyelid movement frequency was significantly higher in the EB group (21.55 ± 13.30 movements/min) compared to the control group (8.26 ± 8.89 eyelid movements/min; p < 0.001). The frequency of spontaneous eyelid movements was significantly reduced after treatment in the EB patients (8.46 ± 6.32 eyelid movements/min; p < 0.001). After treatment, no statistically significant difference in eyelid movement rate was observed between the EB patients and the control group (p = 0.32). Conclusions: Assessment of the spontaneous eyelid movements obtained with the smartphone camera and analysed with the custom‐made software enabled us to objectively measure the therapeutic effects of botulinum toxin in patients with blepharospasm. Further refinement of this system could enable customized and fine adjustments to botulinum toxin doses based on each patient's needs. [ABSTRACT FROM AUTHOR]

The United Kingdom National Data Centre, on behalf of the UK government, is responsible for handling the analysis and assessment of radionuclide data from the International Monitoring System—the verification system operated and maintained by the Comprehensive Nuclear-Test-Ban Treaty Organisation. A series of custom-developed software packages have been implemented for the analysis of both Noble Gas and Particulate sampling data and the subsequent source parameter estimation of radionuclide detection events. These include a radionuclide pipeline for the download, analysis, sorting and archiving of radionuclide data, as well as an atmospheric transport modelling pipeline for performing simulations at varying degrees of fidelity. A new software package, CARVE, provides the capability to interrogate radionuclide detection events and assess their relevance in the context of the Treaty using interactive mapping. [ABSTRACT FROM AUTHOR]

Background: Due to rapid urbanization, there is an increase in population in slums often lacking access to basic health services. Objective: The purpose of this study was to assess the utilization of reproductive and child health services by recently delivered women and their children residing in catchment areas of urban PHCs, to evaluate their level of satisfaction and to determine the factors associated with utilization of these services by recently delivered women. Settings and Design: The study was conducted in the catchment areas of urban primary health centre of Lucknow. Methods and Material: A cross-sectional study was conducted among 320 women having children less than 24 months in the slums covered under selected U-PHCs, sampled through a multi-stage random sampling technique using a semi-structured questionnaire. Statistical analysis used: SPSS-26 and MedCalc software were used for data analysis. Results: Utilization of RCH services by recently delivered women from UPHC was 39%. About 24% of recently delivered women availed ANC services from UPHC and 56.7% utilized family planning services and 95.8% immunization services for children. Child care services for diarrhea and ARI were availed by 9.9 and 3.1% of women, respectively. Satisfaction regarding maternal care and immunization services was seen in 84 and 95.8% of women. Poor satisfaction regarding child care was seen in 56.5% of women. Conclusions: The utilization of RCH services was found to be poor in the study population. Most recently delivered women were satisfied with all services except child care. [ABSTRACT FROM AUTHOR]

Purpose: To determine the level of awareness, beliefs and experience of healthcare professionals (HCPs) regarding mobile applications for self-management of type 2 diabetes mellitus. Methods: This review was done by searching the literature using three databases viz: PubMed, Web of Science and Scopus. Assessment of quality of studies was carried out using the scale of the Agency for Healthcare Research and Quality. The results were presented in accordance with the PRISMA 2020 guidelines. Results: The search strategy identified 725 unique research papers, 4 of which were included. A survey among pharmacists showed that 56 % of participants were aware of the existence of mobile health applications, and that 60 % of those who were aware recommended their use. In the multi-HCPs setting, depending on the study, apps recommendation rate varied from 45.5 to 62 %. Most of the participants (88 %) agreed that a mobile app would help strengthen their treatment recommendations to the patients (88 %), and 84 % also agreed that it would help patients manage diabetes better. Conclusion: Not much research has been done on this topic. Available data suggest that the awareness of HCPs regarding mobile applications is unsatisfactory, and that those familiar with these apps find them useful and are ready to recommend them to patients. There is need for further research and measures to increase awareness and knowledge of HCPs about available mobile applications, in order to ensure adequate support to patients with diabetes. [ABSTRACT FROM AUTHOR]

Background: High-throughput and selective detection of organelles in immunofluorescence images is an important but demanding task in cell biology. The centriole organelle is critical for fundamental cellular processes, and its accurate detection is key for analysing centriole function in health and disease. Centriole detection in human tissue culture cells has been achieved typically by manual determination of organelle number per cell. However, manual cell scoring of centrioles has a low throughput and is not reproducible. Published semi-automated methods tally the centrosome surrounding centrioles and not centrioles themselves. Furthermore, such methods rely on hard-coded parameters or require a multichannel input for cross-correlation. Therefore, there is a need for developing an efficient and versatile pipeline for the automatic detection of centrioles in single channel immunofluorescence datasets. Results: We developed a deep-learning pipeline termed CenFind that automatically scores cells for centriole numbers in immunofluorescence images of human cells. CenFind relies on the multi-scale convolution neural network SpotNet, which allows the accurate detection of sparse and minute foci in high resolution images. We built a dataset using different experimental settings and used it to train the model and evaluate existing detection methods. The resulting average F1-score achieved by CenFind is > 90% across the test set, demonstrating the robustness of the pipeline. Moreover, using the StarDist-based nucleus detector, we link the centrioles and procentrioles detected with CenFind to the cell containing them, overall enabling automatic scoring of centriole numbers per cell. Conclusions: Efficient, accurate, channel-intrinsic and reproducible detection of centrioles is an important unmet need in the field. Existing methods are either not discriminative enough or focus on a fixed multi-channel input. To fill this methodological gap, we developed CenFind, a command line interface pipeline that automates cell scoring of centrioles, thereby enabling channel-intrinsic, accurate and reproducible detection across experimental modalities. Moreover, the modular nature of CenFind enables its integration in other pipelines. Overall, we anticipate CenFind to prove critical for accelerating discoveries in the field. [ABSTRACT FROM AUTHOR]

Early effort estimation is important for efficiently planning the use of resources in an Information Technology (IT) project. However, limited research has been conducted on the topic of effort estimation in agile software development using artificial intelligence. This research project contributes to strengthening the use of hybrid models composed of algorithmic models and learning oriented techniques as a project-level effort estimation method in agile frameworks. Effort estimation in agile methods such as Scrum uses a story point approach that measures, using an arithmetic scale, the effort required to complete a release of the system. This project relied on labeled historical data to estimate the completion time measured in days and the total cost of a project set in Pakistani rupees (PKR). using a decision tree, random forest and AdaBoost to improve the accuracy of predictions. Models were trained using 10-fold cross-validation and the relative error was used as a comparison with literature results. The bootstrap aggregation (bagging) ensemble made of the three techniques provides the highest accuracy, and project classification also improves the estimates. [ABSTRACT FROM AUTHOR]

Sound speed and elastic constants measurements in solids and liquids are commonly performed using the ultrasound pulse-echo technique. Recent advances have expanded the use of this technique at numerous high pressure synchrotron beamlines and offline laboratories. However, the increased experimental throughput has revealed many limitations in existing software for handling the rapid measurement and the subsequent data-reduction. We report the development of a collection of computer programs for sound speed measurements using the ultrasound pulse-echo technique, compatible with stepped multi-frequency, as well as broadband-pulse, couplant-corrected methods. The programs provide a highly interactive graphical interface, enable efficient measurement, exploration and near real-time analysis of the ultrasound data, and contain features useful for working with samples under high pressure and/or high temperature. The included analysis programs can alleviate the time required for data reduction from hours to less than a minute, allowing users to make timely and informed decisions regarding the appropriate experimental parameters. [ABSTRACT FROM AUTHOR]

Bivariate data-driven methods have been widely used in landslide susceptibility analysis. However, the names, principles, and correlations of bivariate methods are still confused. In this paper, the names, principles, and correlations of bivariate methods are first clarified based on a comprehensive and in-depth survey. A total of eleven prevalent bivariate methods are identified, nominated, and elaborated in a general framework, constituting a well-structured bivariate method family. We show that all prevalent bivariate methods depend on empirical conditional probabilities of landslide occurrence to calculate landslide susceptibilities, either exclusively or inclusively. It is clarified that those eight "conditional-probability-based" bivariate methods, which exclusively depend on empirical conditional probabilities, are particularly strongly correlated in principle, and therefore are expected to have a very close or even the same performance. It is also suggested that conditional-probability-based bivariate methods apply to a "classification-free" modification, in which factor classifications are avoided and the result is dominated by a single parameter, "bin width". Then, a general optimization framework for conditional-probability-based bivariate methods, based on the classification-free modification and obtaining optimum results by optimizing the dominant parameter bin width, is proposed. The open software Automatic Landslide Susceptibility Analysis (ALSA) is updated to implement the eight conditional-probability-based bivariate methods and the general optimization framework. Finally, a case study is presented, which confirms the theoretical expectation that different conditional-probability-based bivariate methods have a very close or even the same performance, and shows that optimal bivariate methods perform better than conventional bivariate methods regarding both the prediction rate and the ability to reveal the quasi-continuous varying pattern of sensibilities to landslides for individual predisposing factors. The principles and open software presented in this study provide both theoretical and practical foundations for applications and explorations of bivariate methods in landslide susceptibility analysis. [ABSTRACT FROM AUTHOR]

MQSI is a Fortran 2003 subroutine for constructing monotone quintic spline interpolants to univariate monotone data. Using sharp theoretical monotonicity constraints, first and second derivative estimates at data provided by a quadratic facet model are refined to produce a univariate C2 monotone interpolant. Algorithm and implementation details, complexity and sensitivity analyses, usage information, a brief performance study, and comparisons with other spline approaches are included. [ABSTRACT FROM AUTHOR]

The adaptive immune receptor repertoire (AIRR), consisting of T- and B-cell receptors, is the core component of the immune system. The AIRR sequencing is commonly used in cancer immunotherapy and minimal residual disease (MRD) detection of leukemia and lymphoma. The AIRR is captured by primers and sequenced to yield paired-end (PE) reads. The PE reads could be merged into one sequence by the overlapped region between them. However, the wide range of AIRR data raises the difficulty, so a special tool is required. We developed a software package for IMmune PE reads merger of sequencing data, named IMperm. We used the k -mer-and-vote strategy to pin down the overlapped region rapidly. IMperm could handle all types of PE reads, eliminate adapter contamination and successfully merge low-quality and minor/non-overlapping reads. Compared with existing tools, IMperm performed better in both simulated and sequencing data. Notably, IMperm was well suited to processing the data of MRD detection in leukemia and lymphoma and detected 19 novel MRD clones in 14 patients with leukemia from previously published data. Additionally, IMperm can handle PE reads from other sources, and we demonstrated its effectiveness on two genomic and one cell-free deoxyribonucleic acid datasets. IMperm is implemented in the C programming language and consumes little runtime and memory. It is freely available at https://github.com/zhangwei2015/IMperm. [ABSTRACT FROM AUTHOR]

Objectives: To evaluate commercial deep learning–based software for fully automated coronary artery calcium (CAC) scoring on non-electrocardiogram (ECG)-gated low-dose CT (LDCT) with different slice thicknesses compared with manual ECG-gated calcium-scoring CT (CSCT). Methods: This retrospective study included 567 patients who underwent both LDCT and CSCT. All LDCT images were reconstructed with a 2.5-mm slice thickness (LDCT2.5-mm), and 453 LDCT scans were reconstructed with a 1.0-mm slice thickness (LDCT1.0-mm). Automated CAC scoring was performed on CSCT (CSCTauto), LDCT1.0-mm, and LDCT2.5-mm images. The reliability of CSCTauto, LDCT1.0-mm, and LDCT2.5-mm was compared with manual CSCT scoring (CSCTmanual) using intraclass correlation coefficients (ICCs) and Bland-Altman analysis. Agreement, in CAC severity category, was analyzed using weighted kappa statistics. Diagnostic performance at various Agatston score cutoffs was also calculated. Results: CSCTauto, LDCT1.0-mm, and LDCT2.5-mm demonstrated excellent agreement with CSCTmanual (ICC [95% confidence interval, CI]: 1.000 [1.000, 1.000], 0.937 [0.917, 0.952], and 0.955 [0.946, 0.963], respectively). The mean difference with 95% limits of agreement was lower with LDCT1.0-mm than with LDCT2.5-mm (19.94 [95% CI, −244.0, 283.9] vs. 45.26 [−248.2, 338.7]). Regarding CAC severity, LDCT1.0-mm achieved almost perfect agreement, and LDCT2.5-mm achieved substantial agreement (kappa [95% CI]: 0.809 [0.776, 0.838], 0.776 [0.740, 0.809], respectively). Diagnostic performance for detecting Agatston score ≥ 400 was also higher with LDCT1.0-mm than with LDCT2.5-mm (F1 score, 0.929 vs. 0.855). Conclusions: Fully automated CAC-scoring software with both CSCT and LDCT yielded excellent reliability and agreement with CSCTmanual. LDCT1.0-mm yielded more accurate Agatston scoring than LDCT2.5-mm using fully automated commercial software. Key Points: • Total Agatston scores and all vessels of CSCTauto, LDCT1.0-mm, and LDCT2.5-mmdemonstrated excellent agreement with CSCTmanual(all ICC > 0.85). • The diagnostic performance for detecting all Agatston score cutoffs was better with LDCT1.0-mmthan with LDCT2.5-mm. • This automated software yielded a lower degree of underestimation compared with methods described in previous studies, and the degree of underestimation was lower with LDCT1.0-mmthan with LDCT2.5-mm. [ABSTRACT FROM AUTHOR]

Population studies of diminutive species often present unique challenges associated with methods of identifying individuals in a manner that can be reliable and retained over long periods of time. Small snakes present even more difficulties, as many marking methods rely on the use of either invasive implantation of devices, such as passive integrated transponder (PIT) tags, or through mutilation such as branding or scale clipping. Here I present an alternative method by utilizing a flatbed scanner to obtain consistent images of sufficient quality for use in photographic identification of the Common Sharp-tailed Snake (Contia tenuis) using the unique individual pigmentation patterns on the venter of the head. Additionally, I show that HotSpotter© software consistently scored image pairs of known and assessed matches higher than between pairs of known and assessed mismatches using the flatbed scanner imagery. This method provides a repeatable, reliable, non-invasive, and non-mutilating alternative to more traditional methods of identifying individual snakes. [ABSTRACT FROM AUTHOR]