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Based on the engineering background of an iron nine where the perforation and blasting processes are divided,and multiple middle sections and multiple working faces are operated simultaneously, a follow-up survey method was adopted to conclude that the ron mine has difficulty in blasting operation control, untimely feedback from the process,and a small proportion of effective personnel. To solve the above problems, a progressive method including process analysis, problem posing, and risk assessment was used to carry out research on the inteliigent management and control system of underground muti-working face collaborative Wasting. The resutts indicate that there Is a strict sequence between the various processes of underground Wasting operations, the blasting equipment transportation and charging processes are the most dangerous and require precise control, the use of intelHgent software to coordinately supervise the muti--working face Wasting operation process not only achieves high precision control efficiency, but aiso reduces the accident efficiency of mine blasting operations. Finally, comWned wrth the actual operating condiiions of an ron mine, based on the sequential process of simuttaneous Wasting operations on muMpe working faces, an inte^igent management and control software for underground muti-working face collaborative blasting was developed using Python language, and the management and debugging of the Wasting working face was used to prove the control of research and development The software is suttable to assist in the supervision of the ron ore Wasting process. [ABSTRACT FROM AUTHOR]

Background Semi-automated software is essential for planning and prosthesis selection prior transcatheter aortic valve replacement (TAVR). Reliable data on the usability of software programs for planning a TAVR is missing. The aim of this study was to compare software programs ‘Valve Assist 2’ (GE Healthcare) and 3mensio ‘Structural Heart’ (Pie Medical Imaging) regarding usability and accuracy of prosthesis size selection in program-inexperienced users. Methods Thirty-one participants (n=31) were recruited and divided into program-inexperienced users (beginners) (n=22) and experts (n=9). After software training, beginners evaluated 3 patient cases in 129 measurements (n=129) using either Valve Assist 2 (n=11) or Structural Heart (n=11) on 2 test days (T1, T2). System Usability Scale (SUS) and ISONORM 9241/110-S (ISONORM) questionnaire were used after the test. The valve size selected by each begin‑ ner was compared with the valve size selected from expert group. Results Valve Assist 2 had higher SUS Score: median 78.75 (25th, 75th percentile: 67.50, 85.00) compared to Structural Heart: median 65.00 (25th, 75th percentile: 47.50, 73.75), (p<0,001, r=0.557). Also, Valve Assist 2 showed a higher ISONORM score: median 1.05 (25th, 75th percentile:−0.19, 1.71) compared to Structural Heart with a median 0.05 (25th, 75th percentile:−0.49, 0.13), (p=0.036, r=0.454). Correctly selected valve sizes were stable over time using Valve Assist 2: 72.73% to 69.70% compared to Structural Heart program: 93.94% to 40% (χ2 (1)=21.10, p<0.001, φ=0.579). Conclusion The study shows signifcant better usability scores for Valve Assist 2 compared to 3mensio Structural Heart in program-inexperienced users. [ABSTRACT FROM AUTHOR]

We present Svetlana (SuperVised sEgmenTation cLAssifier for NapAri), an open-source Napari plugin dedicated to the manual or automatic classification of segmentation results. A few recent software tools have made it possible to automatically segment complex 2D and 3D objects such as cells in biology with unrivaled performance. However, the subsequent analysis of the results is oftentimes inaccessible to non-specialists. The Svetlana plugin aims at going one step further, by allowing end-users to label the segmented objects and to pick, train and run arbitrary neural network classifiers. The resulting network can then be used for the quantitative analysis of biophysical phenoma. We showcase its performance through challenging problems in 2D and 3D and provide a comprehensive discussion on its strengths and limits. [ABSTRACT FROM AUTHOR]

We aimed to obtain the optimal formula for human milk fat substitute (HMFS) through a combination of software and an evaluation model and further verify its practicability through an animal experiment. The results showed that a total of 33 fatty acid (FA) and 63 triglyceride (TAG) molecular species were detected in vegetable oils. Palmitic acid, oleic acid, linoleic acid, 18:1/16:0/18:1, 18:2/16:0/18:2, 18:1/18:1/18:1 and 18:1/18:2/18:1, were the main molecular species among the FAs and TAGs in the vegetable oils. Based on the HMFS evaluation model, the optimal mixed vegetable oil formula was blended with 21.3% palm oil, 2.8% linseed oil, 2.6% soybean oil, 29.9% rapeseed oil and 43.4% maize oil, with the highest score of 83.146. Moreover, there was no difference in the weight, blood routine indices or calcium and magnesium concentrations in the feces of the mice between the homemade mixed vegetable oil (HMVO) group and the commercial mixed vegetable oil (CMVO) group, while nervonic acid (C24:1) and octanoic acid (C8:0) were absorbed easily in the HMVO group. Therefore, these results demonstrate that the mixing of the different vegetable oils was feasible via a combination of computer software and an evaluation model and provided a new way to produce HMFS. [ABSTRACT FROM AUTHOR]

WormBase has been the major repository and knowledgebase of information about the genome and genetics of Caenorhabditis elegans and other nematodes of experimental interest for over 2 decades. We have 3 goals: to keep current with the fast-paced C. elegans research, to provide better integration with other resources, and to be sustainable. Here, we discuss the current state of WormBase as well as progress and plans for moving core WormBase infrastructure to the Alliance of Genome Resources (the Alliance). As an Alliance member, WormBase will continue to interact with the C. elegans community, develop new features as needed, and curate key information from the literature and large-scale projects. [ABSTRACT FROM AUTHOR]

The Alliance of Genome Resources (Alliance) is an extensible coalition of knowledgebases focused on the genetics and genomics of intensively studied model organisms. The Alliance is organized as individual knowledge centers with strong connections to their research communities and a centralized software infrastructure, discussed here. Model organisms currently represented in the Alliance are budding yeast, Caenorhabditis elegans , Drosophila , zebrafish, frog, laboratory mouse, laboratory rat, and the Gene Ontology Consortium. The project is in a rapid development phase to harmonize knowledge, store it, analyze it, and present it to the community through a web portal, direct downloads, and application programming interfaces (APIs). Here, we focus on developments over the last 2 years. Specifically, we added and enhanced tools for browsing the genome (JBrowse), downloading sequences, mining complex data (AllianceMine), visualizing pathways, full-text searching of the literature (Textpresso), and sequence similarity searching (SequenceServer). We enhanced existing interactive data tables and added an interactive table of paralogs to complement our representation of orthology. To support individual model organism communities, we implemented species-specific "landing pages" and will add disease-specific portals soon; in addition, we support a common community forum implemented in Discourse software. We describe our progress toward a central persistent database to support curation, the data modeling that underpins harmonization, and progress toward a state-of-the-art literature curation system with integrated artificial intelligence and machine learning (AI/ML). [ABSTRACT FROM AUTHOR]

This study focuses on energy saving for a Taiwan high-tech DRAM factory as the primary research subject. Collecting operational parameters related to various facility systems and process equipment is initially performed by using the developed energy conversion factors (ECF) calculator. Moreover, innovative fab energy simulation (FES) software has been designed by Taipei Tech. This software is designed for high-tech fab energy consumption analysis. The annual energy consumption data for fabs can be calculated. This data is then converted into carbon dioxide emissions using the power carbon emission coefficient provided by the Bureau of Energy, Ministry of Economic Affairs Taiwan. In this study, five different energy-saving strategies were proposed. The energy consumption and carbon emissions distribution were evaluated to assess the benefits of those different techniques. The findings show that among the existing operational facilities, the use of an exhaust air conditioning unit with reduced enthalpy value setting, with lowered supply air temperature, demonstrates the highest energy-saving. This technique has the potential to annually reduce carbon emissions by approximately 623,158 kg CO2 and operational costs by NT ${\$}$ 6,005,764 (189,602 U.S. ${\$}$). This can reduce the overall manufacturing cost and is also beneficial for the environment. [ABSTRACT FROM AUTHOR]

Objectives Recent work has demonstrated that automated fluorescence flow cytometry (FLC) is a potential alternative for the detection and quantification of Plasmodium parasites. The objective of this study was to apply this novel FLC method to detect and quantify Babesia parasites in venous blood and compare results to light microscopy and polymerase chain reaction methods. Methods An automated hematology/malaria analyzer (XN-31; Sysmex) was used to detect and quantify B microti –infected red blood cells from residual venous blood samples (n = 250: Babesia positive, n = 170; Babesia negative, n = 80). As no instrument software currently exists for Babesia, qualitative and quantitative machine learning (ML) algorithms were developed to facilitate analysis. Results Performance of the ML models was verified against the XN-31 software using P falciparum –infected samples. When applied to Babesia -infected samples, the qualitative ML model demonstrated an area under the curve (AUC) of 0.956 (sensitivity, 95.9%; specificity, 83.3%) relative to polymerase chain reaction. For valid scattergrams, the qualitive model achieved an AUC of 1.0 (sensitivity and specificity, 100%), while the quantitative model demonstrated an AUC of 0.986 (sensitivity, 94.4%; specificity, 100%). Conclusions This investigation demonstrates that Babesia parasites can be detected and quantified directly from venous blood using FLC. Although promising, opportunities remain to improve the general applicability of the method. [ABSTRACT FROM AUTHOR]

Diffuse scattering is a promising method to gain additional insight into protein dynamics from macromolecular crystallography experiments. Bragg intensities yield the average electron density, while the diffuse scattering can be processed to obtain a three‐dimensional reciprocal‐space map that is further analyzed to determine correlated motion. To make diffuse scattering techniques more accessible, software for data processing called mdx2 has been created that is both convenient to use and simple to extend and modify. mdx2 is written in Python, and it interfaces with DIALS to implement self‐contained data‐reduction workflows. Data are stored in NeXus format for software interchange and convenient visualization. mdx2 can be run on the command line or imported as a package, for instance to encapsulate a complete workflow in a Jupyter notebook for reproducible computing and education. Here, mdx2 version 1.0 is described, a new release incorporating state‐of‐the‐art techniques for data reduction. The implementation of a complete multi‐crystal scaling and merging workflow is described, and the methods are tested using a high‐redundancy data set from cubic insulin. It is shown that redundancy can be leveraged during scaling to correct systematic errors and obtain accurate and reproducible measurements of weak diffuse signals. [ABSTRACT FROM AUTHOR]

The Swiss Light Source facilitates fragment‐based drug‐discovery campaigns for academic and industrial users through the Fast Fragment and Compound Screening (FFCS) software suite. This framework is further enriched by the option to utilize the Smart Digital User (SDU) software for automated data collection across the PXI, PXII and PXIII beamlines. In this work, the newly developed HEIDI webpage (https://heidi.psi.ch) is introduced: a platform crafted using state‐of‐the‐art software architecture and web technologies for sample management of rotational data experiments. The HEIDI webpage features a data‐review tab for enhanced result visualization and provides programmatic access through a representational state transfer application programming interface (REST API). The migration of the local FFCS MongoDB instance to the cloud is highlighted and detailed. This transition ensures secure, encrypted and consistently accessible data through a robust and reliable REST API tailored for the FFCS software suite. Collectively, these advancements not only significantly elevate the user experience, but also pave the way for future expansions and improvements in the capabilities of the system. [ABSTRACT FROM AUTHOR]

This paper considers the possible benefits and substantial risks of 'Rules as Code', the parallel drafting of legislation and codification in software, which has been the subject of attention from policy-makers and pilot studies in some jurisdictions. It highlights the long history of these approaches, and the challenges of ossification, mis-translation of rules, and separation of powers problems. It also examines in the detail the Australian Pintarich case, which demonstrates the inadequacy of conventional judicial review of automated decision-making. It outlines some possible solutions to these issues — two 'internal' to development processes (greater transparency, and literate pair programming) and two 'external' (expanding the capacity of judicial review to look beyond a specific citizen/state interaction and consider the design and development of the controlling software system, and greater cross-disciplinary awareness by lawyers). [ABSTRACT FROM AUTHOR]

Preparation of DNA polymorphism datasets for analysis is an important step in evolutionary genetic and molecular ecology studies. Ever‐growing dataset sizes make this step time consuming, but few convenient software tools are available to facilitate processing of large‐scale datasets including thousands of sequence alignments. Here I report “processor of sequences v4” (proSeq4)—a user‐friendly multiplatform software for preparation and evolutionary genetic analyses of genome‐ or transcriptome‐scale sequence polymorphism datasets. The program has an easy‐to‐use graphic user interface and is designed to process and analyse many thousands of datasets. It supports over two dozen file formats, includes a flexible sequence editor and various tools for data visualization, quality control and most commonly used evolutionary genetic analyses, such as NJ‐phylogeny reconstruction, DNA polymorphism analyses and coalescent simulations. Command line tools (e.g. vcf2fasta) are also provided for easier integration into bioinformatic pipelines. Apart of molecular ecology and evolution research, proSeq4 may be useful for teaching, e.g. for visual illustration of different shapes of phylogenies generated with coalescent simulations in different scenarios. ProSeq4 source code and binaries for Windows, MacOS and Ubuntu are available from https://sourceforge.net/projects/proseq/. [ABSTRACT FROM AUTHOR]

Background: The growth and development of organism were dependent on the effect of genetic, environment, and their interaction. In recent decades, lots of candidate additive genetic markers and genes had been detected by using genome-widely association study (GWAS). However, restricted to computing power and practical tool, the interactive effect of markers and genes were not revealed clearly. And utilization of these interactive markers is difficult in the breeding and prediction, such as genome selection (GS). Results: Through the Power-FDR curve, the GbyE algorithm can detect more significant genetic loci at different levels of genetic correlation and heritability, especially at low heritability levels. The additive effect of GbyE exhibits high significance on certain chromosomes, while the interactive effect detects more significant sites on other chromosomes, which were not detected in the first two parts. In prediction accuracy testing, in most cases of heritability and genetic correlation, the majority of prediction accuracy of GbyE is significantly higher than that of the mean method, regardless of whether the rrBLUP model or BGLR model is used for statistics. The GbyE algorithm improves the prediction accuracy of the three Bayesian models BRR, BayesA, and BayesLASSO using information from genetic by environmental interaction (G × E) and increases the prediction accuracy by 9.4%, 9.1%, and 11%, respectively, relative to the Mean value method. The GbyE algorithm is significantly superior to the mean method in the absence of a single environment, regardless of the combination of heritability and genetic correlation, especially in the case of high genetic correlation and heritability. Conclusions: Therefore, this study constructed a new genotype design model program (GbyE) for GWAS and GS using Kronecker product. which was able to clearly estimate the additive and interactive effects separately. The results showed that GbyE can provide higher statistical power for the GWAS and more prediction accuracy of the GS models. In addition, GbyE gives varying degrees of improvement of prediction accuracy in three Bayesian models (BRR, BayesA, and BayesCpi). Whatever the phenotype were missed in the single environment or multiple environments, the GbyE also makes better prediction for inference population set. This study helps us understand the interactive relationship between genomic and environment in the complex traits. The GbyE source code is available at the GitHub website (https://github.com/liu-xinrui/GbyE). [ABSTRACT FROM AUTHOR]

The Internet has become an important part of our lives and an increasing number of researchers use eye-tracking technology to examine attention and behavior in online environments. Researchers, however, face a significant challenge in mapping eye-tracking data from scrollable web pages. We describe the R package eyeScrollR for mapping eye-tracking data from scrollable content such as web pages. The package re-maps eye-tracking gaze coordinates to full-page coordinates with a deterministic algorithm based on mouse scroll data. The package includes options for handling common situations, such as sticky menus or ads that remain visible when the user scrolls. We test the package's validity in different hardware and software settings and on different web pages and show that it is highly accurate when tested against manual coding. Compared to current methods, eyeScrollR provides a more reproducible and reliable approach for mapping eye-tracking data from scrollable web pages. With its open code and free availability, we recommend eyeScrollR as an essential tool for eye-tracking researchers, particularly those who adhere to open-science principles. The eyeScrollR package offers a valuable contribution to the field of eye-tracking research, facilitating accurate and standardized analysis of eye-tracking data in web scrolling contexts. [ABSTRACT FROM AUTHOR]

Lesion localization has been an important aspect of neurosurgery and has advanced significantly with technological evolution. The journey started from the localization of lesion based on clinical findings to the current era where neuronavigation and virtual reality are being used for the purpose. However, the financial implications of these advanced equipments have made them inaccessible for patients in the majority of low- and middle-income countries. The authors describe techniques to use software, which are cost effective and can be used effectively for the localization of a lesion of the brain. [ABSTRACT FROM AUTHOR]

This study was carried out with the aim of developing a software program that will enable the breeder to decide easily during the preparation of the feasibility for livestock enterprises. For this purpose, 63 enterprises in Gaziantep and Sanliurfa provinces/Turkey were visited between the years 2021-2022 and all the data obtained were evaluated. The "C#" programming language was used in the development of the software program. "Microsoft SQL Server" database was used to store the obtained data. This feasibility program is a software program where productivity checks are performed for enterprises and their personnel. It is a program that can be used easily from a small-capacity enterprises to a large-capacity enterprises. The cost calculations are not included in the program due to the economic conditions of the market. As a result, this program, which was prepared by taking into account software engineering techniques, will provide great advantages and conveniences for enterprises. Risk factors will be determined and alternatives will be presented with this software program that performs enterprises efficiency testing. It has been concluded that this software will be a program that can be preferred by the breeder since it can be used on all computers and offers different alternatives in enterprises establishments. [ABSTRACT FROM AUTHOR]

Purpose: Bone age (BA) is needed to assess developmental status and growth disorders. We evaluated the clinical performance of a deep-learning-based BA software to estimate the chronological age (CA) of healthy Korean children. Methods: This retrospective study included 371 healthy children (217 boys, 154 girls), aged between 4 and 17 years, who visited the Department of Pediatrics for health check-ups between January 2017 and December 2018. A total of 553 left-hand radiographs from 371 healthy Korean children were evaluated using a commercial deep-learning-based BA software (BoneAge, Vuno, Seoul, Korea). The clinical performance of the deep learning (DL) software was determined using the concordance rate and Bland-Altman analysis via comparison with the CA. Results: A 2-sample t-test (P<0.001) and Fisher exact test (P=0.011) showed a significant difference between the normal CA and the BA estimated by the DL software. There was good correlation between the 2 variables (r=0.96, P<0.001); however, the root mean square error was 15.4 months. With a 12-month cutoff, the concordance rate was 58.8%. The Bland-Altman plot showed that the DL software tended to underestimate the BA compared with the CA, especially in children under the age of 8.3 years. Conclusion: The DL-based BA software showed a low concordance rate and a tendency to underestimate the BA in healthy Korean children. [ABSTRACT FROM AUTHOR]

Software for realistically simulating complex population genomic processes is revolutionizing our understanding of evolutionary processes, and providing novel opportunities for integrating empirical data with simulations. However, the integration between standalone simulation software and R is currently not well developed. Here, we present slimr, an R package designed to create a seamless link between standalone software SLiM >3.0, one of the most powerful population genomic simulation frameworks, and the R development environment, with its powerful data manipulation and analysis tools. We show how slimr facilitates smooth integration between genetic data, ecological data and simulation in a single environment. The package enables pipelines that begin with data reading, cleaning and manipulation, proceed to constructing empirically based parameters and initial conditions for simulations, then to running numerical simulations and finally to retrieving simulation results in a format suitable for comparisons with empirical data – aided by advanced analysis and visualization tools provided by R. We demonstrate the use of slimr with an example from our own work on the landscape population genomics of desert mammals, highlighting the advantage of having a single integrated tool for both data analysis and simulation. slimr makes the powerful simulation ability of SLiM directly accessible to R users, allowing integrated simulation projects that incorporate empirical data without the need to switch between software environments. This should provide more opportunities for evolutionary biologists and ecologists to use realistic simulations to better understand the interplay between ecological and evolutionary processes. [ABSTRACT FROM AUTHOR]

Despite progress in computer automated solutions, constitutional isomer verification by NMR using one‐ and two‐dimensional data sets is still, in the main, a manual, user‐intensive activity that is challenging for a number of reasons. These include the problem of simultaneously keeping track of the information from a number of separate NMR experiments and the difficulty of another researcher subsequently verifying the assignments made without having to independently repeat the whole analysis. This paper describes a graphical interactive approach that overcomes some of these problems. By using concepts used to visualise graph networks, we have been able to represent the NMR data in a manner that highlights directly the link between the different NMR experiments and the molecule of interest. Furthermore, by making the graph networks interactive, a user can easily validate and correct the assignment and understand the decisions made in arriving at the solution. We have developed a usable proof‐of‐concept computer program, 'simpleNMR', written in Python to illustrate the ideas and approach. [ABSTRACT FROM AUTHOR]

The article offers information on the implementation of iMDsoft's Metavision as Boston Children's Hospital's anesthesia information management system (AIMS) software in 2012. It discusses the development of a "low-flow" interface for the system by 2015 to enhance teaching and patient safety during the delivery of low flow anesthesia.

Global navigation satellite system-acoustic (GNSS-A) positioning is an important geodetic observation technique for detecting seafloor crustal deformation. After the 2011 Tohoku–Oki earthquake, GNSS-A observational networks were extended along various subduction zones, and observational systems have been improved, especially for sea surface platforms, such as the introduction of an unmanned vehicle, the Wave Glider. The aforementioned development of GNSS-A observations has provided a large amount of observational data. Furthermore, GNSS-A positioning methods were recently developed considering the lateral heterogeneity of the sound speed structure. Thus, it is important to develop a software that makes it easy for widespread use of the latest GNSS-A positioning methods. However, there is currently only one open-source GNSS-A positioning software, which may hinder the entry of various researchers into GNSS-A positioning analyses. Here, we developed a new GNSS-A positioning software, henceforth called "SeaGap" (Software of enhanced analyses for GNSS-acoustic positioning), that executes various positioning methods from the conventional kinematic positioning technique to the latest Markov Chain Monte Carlo (MCMC)-based static positioning technique. We introduce their methodology and demonstrate its application to actual observational data. The software newly added optional prior distributions to the unknown parameters expressing the heterogeneity of a sound speed structure into the MCMC-based static positioning method, and we also applied the new method to actual observational data. In addition to the positioning functions, the software contains various auxiliary functions, including drawing. The developed software is written using the "Julia" language and is distributed as an open-source software. [ABSTRACT FROM AUTHOR]

An investigation related to the technological innovation was carried out in the Health Nursing-Technology Faculty from Santiago de Cuba, during the school course 2019-2020, aimed at designing an educational software for the subject Stratification of Environmental Risks, directed to medium technician students in Surveillance and Vector Control. The theoretical methods used were: analysis-synthesis, historicallogical, modelation, systemic-structural and inductive-deductive; and the empiric methods were: observation and documental analysis. The sample had 44 students and 6 professors chosen at random. It was concluded that the proposed software is feasible, pertinent and necessary as teaching aid; provides up-to-date information, it is easy and interesting to surf internet, and allows the self-appraisal of students in the interaction with themselves, what contributes to improve the independent work. [ABSTRACT FROM AUTHOR]

NTT COMWARE supports the systems that make up NTT's telecommunications infrastructure and systems related to service operations from the software side. As a member of the NTT DOCOMO Group, it is also developing systems that will serve as the foundation for expanding the Group's business. We interviewed Toshio Kashiwagi, senior executive vice president and executive officer (chief information officer/chief digital officer) of NTT COMWARE, which is committed to providing new value to society beyond the social infrastructure of telecommunications, about the company's strengths as a group of software professionals and his attitude as a top executive. [ABSTRACT FROM AUTHOR]

Single case experimental designs are an important research design in behavioral and medical research. Although there are design standards prescribed by the What Works Clearinghouse for single case experimental designs, these standards do not include statistically derived power computations. Recently we derived the equations for computing power for (AB)k designs. However, these computations and the software code in R may not be accessible to applied researchers who are most likely to want to compute power for their studies. Therefore, we have developed an (AB)k power calculator Shiny App () that researchers can use with no software training. These power computations assume that the researcher would be interested in fitting multilevel models with autocorrelations or conduct similar analyses. The purpose of this software contribution is to briefly explain how power is derived for balanced (AB)k designs and to elaborate on how to use the Shiny App. The app works well on not just computers but mobile phones without installing the R program. We believe this can be a valuable tool for practitioners and applied researchers who want to plan their single case studies with sufficient power to detect appropriate effect sizes. [ABSTRACT FROM AUTHOR]

In recent years, there has been an increasing need to understand the complex interdependencies between critical infrastructure systems, for example, electric power, natural gas, and potable water. Whereas open-source and commercial tools for the independent simulation of these systems are well established, frameworks for cosimulation with other systems are nascent and tools for co-optimization are scarce—the major challenge being the hidden combinatorics that arise when connecting multiple-infrastructure system models. Building toward a comprehensive solution for modeling interdependent infrastructure systems, this work presents InfrastructureModels, an extensible, open-source mathematical programming framework for co-optimizing multiple interdependent infrastructures. This work provides new insights into methods and programming abstractions that make state-of-the-art independent infrastructure models composable with minimal additional effort. To that end, this paper presents the design of the InfrastructureModels framework, documents key components of the software's implementation, and demonstrates its effectiveness with three case studies on canonical co-optimization tasks arising in interdependent infrastructure systems. History: Accepted by Ted Ralphs, Area Editor for Software Tools. Funding: The work was funded by Los Alamos National Laboratory's Directed Research and Development project "The Optimization of Machine Learning: Imposing Requirements on Artificial Intelligence" and the U.S. Department of Energy's Office of Electricity Advanced Grid Modeling projects "Joint Power System and Natural Gas Pipeline Optimal Expansion Planning" and "Coordinated Planning and Operation of Water and Power Infrastructures for Increased Resilience and Reliability." This work was carried out under the U.S. DOE contract no. [DE-AC52-06NA25396]. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information (https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2022.0118) as well as from the IJOC GitHub software repository (https://github.com/INFORMSJoC/2022.0118). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/. [ABSTRACT FROM AUTHOR]

In the realm of studying supramolecular polymers using computer simulations, the task of generating appropriate initial structures poses a significant challenge, primarily owing to the extensive range of potential configurations. In this study, we introduce StackGen, an open-source framework designed to efficiently create energy-optimized one-dimensional supramolecular polymer structures with minimal computational overhead. This tool utilizes the particle swarm optimization (PSO) algorithm in conjunction with a semiempirical quantum mechanical approach to identify low-energy supramolecular stack configurations from a diverse set of possibilities. These configurations result from the translational and rotational adjustments of adjacent molecules around monomers along various axes. The tool also considers various structural factors, including the presence of functional side groups and the extent of intermolecular π – π stacking interactions. Extensive testing across different molecules demonstrates StackGen's ability to produce low-energy structures with negligible computational costs. Additionally, the tool incorporates features for optimizing PSO hyperparameters in real-time, thus improving convergence. The tool provides a convenient means of generating structures suitable for both molecular simulations and quantum mechanical calculations. [ABSTRACT FROM AUTHOR]

Real-time magnetic resonance imaging (rtMRI) is a technique that provides high-contrast videographic data of human anatomy in motion. Applied to the vocal tract, it is a powerful method for capturing the dynamics of speech and other vocal behaviours by imaging structures internal to the mouth and throat. These images provide a means of studying the physiological basis for speech, singing, expressions of emotion, and swallowing that are otherwise not accessible for external observation. However, taking quantitative measurements from these images is notoriously difficult. We introduce a signal processing pipeline that produces outlines of the vocal tract from the lips to the larynx as a quantification of the dynamic morphology of the vocal tract. Our approach performs simple tissue classification, but constrained to a researcher-specified region of interest. This combination facilitates feature extraction while retaining the domain-specific expertise of a human analyst. We demonstrate that this pipeline generalises well across datasets covering behaviours such as speech, vocal size exaggeration, laughter, and whistling, as well as producing reliable outcomes across analysts, particularly among users with domain-specific expertise. With this article, we make this pipeline available for immediate use by the research community, and further suggest that it may contribute to the continued development of fully automated methods based on deep learning algorithms. [ABSTRACT FROM AUTHOR]

Key Clinical Message: This research investigates the trueness and precision of 3D printing technology in dental applications, specifically focusing on dimensional variations observed in models printed at different angles. The methodology involved importing a dental model into slicing software, adjusting its orientation, and implementing support structures for stability. Subsequently, the model underwent 3D printing five times for each orientation using appropriate equipment and underwent post‐processing steps, including cleaning, washing, and UV‐light post‐curing. The printed models were then scanned using a specialized desktop scanner for further analysis. Accuracy assessment was carried out using dedicated software, employing an algorithm for precise alignment by comparing the scanned files. Color deviation maps were utilized to visually represent variations, aiming to evaluate how positioning during printing influences the trueness and precision of 3D‐printed dental models. Trueness and precision analyses involved the Shapiro–Wilk test for normality and a one‐way ANOVA to compare means of three independent groups, with statistical analyses conducted using IBM SPSS Statistics software. The color maps derived from 3D comparisons revealed positive and negative deviations, represented by distinct colors. Comparative results indicated that models positioned at 0° exhibited the least dimensional deviation, whereas those at 90° showed the highest. Regarding precision, models printed at 0° demonstrated the highest reproducibility, while those at 15° exhibited the lowest. Based on the desired level of precision, it is recommended that printed models be produced at an inclination angle of 0°. [ABSTRACT FROM AUTHOR]

Software development effort estimation is regarded as a crucial activity for managing project cost, time, and quality, as well as for the software development life cycle. As a result, proper estimating is crucial to the success of projects and to lower risks. Software effort estimation has drawn much research interest recently and has become a problem for the software industry. When results are inaccurate, an effort may be over- or under-estimated, which can disastrously affect project resources. In the sector, machine learning methods are becoming more and more prominent. Therefore, in this paper, we propose a Modified Jaya algorithm to improve the effectiveness of the estimated model; Modified JOA selects the ideal subset of components from an extensive feature collection. Then, the ensemble machine learning-based Enhanced Extreme gradient boosting algorithm and Ensemble Learning machine approach are employed to estimate the software effort. On the PROMISE SDEE repository, the proposed methodologies are empirically assessed. In this approach, applying machine learning techniques to the effort estimation process increases the likelihood that the time and cost estimates will be accurate. The proposed approach yields a greater performance. The key benefit of this approach is that it lowers the computational cost. This approach can also inspire the development of a tool that could reliably, effectively, and accurately estimate the effort required to complete different software projects. [ABSTRACT FROM AUTHOR]

Nonlinear curve fitting is an important process in laboratory medicine, particularly with the increased use of highly sensitive antibody-based assays. Although the process is often automated in commercially available software, it is important that clinical scientists and physicians recognize the limitations of the various approaches used and are able to select the most appropriate model. This article summarizes the key nonlinear functions and demonstrates their application to common laboratory data. Following this, a basic overview of the statistical comparison of models is presented and then a discussion of important algorithms used in nonlinear curve fitting. An accompanying Microsoft Excel workbook is available that can be used to explore the content of this article. [ABSTRACT FROM AUTHOR]

Simple Summary: Quantitative assessment of the therapy response in oncological patients undergoing chemo- or immunotherapy is becoming increasingly important not only in the context of clinical studies but also in clinical routine. To facilitate the sometimes complex and time-consuming oncological response assessment, dedicated software solutions, e.g., according to (i)RECIST, have been developed. Considering the higher degree of complexity of iRECIST, we investigated the benefits of software-assisted assessments compared to manual approaches with respect to reader agreement, error rate, and reading time. iRECIST assessments were more feasible and reliable when supported by dedicated software. We conclude that oncologic response assessment in clinical trials should be performed software-assisted rather than manually. Objectives: To compare the feasibility and reliability of manual versus software-assisted assessments of computed tomography scans according to iRECIST in patients undergoing immune-based cancer treatment. Methods: Computed tomography scans of 30 tumor patients undergoing cancer treatment were evaluated by four independent radiologists at baseline (BL) and two follow-ups (FU), resulting in a total of 360 tumor assessments (120 each at BL/FU1/FU2). After image interpretation, tumor burden and response status were either calculated manually or semi-automatically as defined by software, respectively. The reading time, calculated sum of longest diameter (SLD), and tumor response (e.g., "iStable Disease") were determined for each assessment. After complete data collection, a consensus reading among the four readers was performed to establish a reference standard for the correct response assignments. The reading times, error rates, and inter-reader agreement on SLDs were statistically compared between the manual versus software-assisted approaches. Results: The reading time was significantly longer for the manual versus software-assisted assessments at both follow-ups (median [interquartile range] FU1: 4.00 min [2.17 min] vs. 2.50 min [1.00 min]; FU2: 3.75 min [1.88 min] vs. 2.00 min [1.50 min]; both p < 0.001). Regarding reliability, 2.5% of all the response assessments were incorrect at FU1 (3.3% manual; 0% software-assisted), which increased to 5.8% at FU2 (10% manual; 1.7% software-assisted), demonstrating higher error rates for manual readings. Quantitative SLD inter-reader agreement was inferior for the manual compared to the software-assisted assessments at both FUs (FU1: ICC = 0.91 vs. 0.93; FU2: ICC = 0.75 vs. 0.86). Conclusions: Software-assisted assessments may facilitate the iRECIST response evaluation of cancer patients in clinical routine by decreasing the reading time and reducing response misclassifications. [ABSTRACT FROM AUTHOR]

Objectives: The purpose of this study was to assess the diagnostic accuracy and the inter‐rater agreement among general dentists when staging and grading periodontitis cases with the aid of a software application (SA) developed by the Italian Society of Periodontology and Implantology. Materials and methods: Ten general dentists were asked to independently assess 25 periodontitis cases using the SA. Accuracy was estimated using quadratic weighted kappa and examiners' percentage of agreement with a reference diagnosis provided by a gold standard examiner. Inter‐rater agreement was evaluated using Fleiss kappa statistics. Results: The overall case definition agreed with the reference diagnosis in 53.6% of cases. The agreements for each general dentist's pairwise comparisons against the reference definition were at least substantial in 100% of cases for stage, in 70% of cases for grade and in none of the cases for extent. Fleiss kappa was 0.818, 0.608, and 0.632 for stage, extent, and grade, respectively. The study recognized possible reasons that could lead to decreased accuracy using the SA. Conclusions: Supported by the SA, general dentists have reached substantial inter‐rater agreement and highly accurate assignments of stage and grade. However, complete case definitions were correctly diagnosed in slightly over half of the cases. [ABSTRACT FROM AUTHOR]

Virtual surgical planning and three-dimensional (D) printing are rapidly becoming essential for challenging and complex surgeries around the world. An Ibero-American survey reported a lack of awareness of technology benefits and scarce financial resources as the two main barriers to widespread adoption of 3-D technologies. The Pereira Rossell Hospital Center is a publicly funded maternal and pediatric academic clinical center in Uruguay, a low-resource Latin American country, that successfully created and has been running a 3-D unit for 4 years. The present work is a step-by-step review of the 3-D technology implementation process in a hospital with minimal financial investment. References to training, software, hardware, and the management of human resources are included. Difficulties throughout the process and future challenges are also discussed. [ABSTRACT FROM AUTHOR]

Population‐adjusted indirect comparison (PAIC) is an increasingly used technique for estimating the comparative effectiveness of different treatments for the health technology assessments when head‐to‐head trials are unavailable. Three commonly used PAIC methods include matching‐adjusted indirect comparison (MAIC), simulated treatment comparison (STC), and multilevel network meta‐regression (ML‐NMR). MAIC enables researchers to achieve balanced covariate distribution across two independent trials when individual participant data are only available in one trial. In this article, we provide a comprehensive review of the MAIC methods, including their theoretical derivation, implicit assumptions, and connection to calibration estimation in survey sampling. We discuss the nuances between anchored and unanchored MAIC, as well as their required assumptions. Furthermore, we implement various MAIC methods in a user‐friendly R Shiny application Shiny‐MAIC. To our knowledge, it is the first Shiny application that implements various MAIC methods. The Shiny‐MAIC application offers choice between anchored or unanchored MAIC, choice among different types of covariates and outcomes, and two variance estimators including bootstrap and robust standard errors. An example with simulated data is provided to demonstrate the utility of the Shiny‐MAIC application, enabling a user‐friendly approach conducting MAIC for healthcare decision‐making. The Shiny‐MAIC is freely available through the link: https://ziren.shinyapps.io/Shiny_MAIC/. [ABSTRACT FROM AUTHOR]

Uncrewed-Aerial Vehicles (UAVs) and hyperspectral sensors are emerging as effective alternatives for monitoring water quality on-demand. However, image mosaicking for largely featureless coastal water surfaces or open seas has shown to be challenging. Another pertinent issue observed is the systematic image misalignment between adjacent flight lines due to the time delay between the UAV-borne sensor and the GNSS system. To overcome these challenges, this study introduces a workflow that entails a GPS-based image mosaicking method for push-broom hyperspectral images, together with a correction method to address the aforementioned systematic image misalignment. An open-source toolkit, CoastalWQL, was developed to facilitate the workflow, which includes essential pre-processing procedures for improving the image mosaic's quality, such as radiometric correction, de-striping, sun glint correction, and object masking classification. For validation, UAV-based push-broom hyperspectral imaging surveys were conducted to monitor coastal turbidity in Singapore, and the implementation of CoastalWQL's pre-processing workflow was evaluated at each step via turbidity retrieval. Overall, the results confirm that the image mosaicking of the push-broom hyperspectral imagery over featureless water surface using CoastalWQL with time delay correction enabled better localisation of the turbidity plume. Radiometric correction and de-striping were also found to be the most important pre-processing procedures, which improved turbidity prediction by 46.5%. [ABSTRACT FROM AUTHOR]

The statistical tool eCerto was developed for the evaluation of measurement data to assign property values and associated uncertainties of reference materials. The analysis is based on collaborative studies of expert laboratories and was implemented using the R software environment. Emphasis was put on comparability of eCerto with SoftCRM, a statistical tool based on the certification strategy of the former Community Bureau of Reference. Additionally, special attention was directed towards easy usability from data collection through processing, archiving, and reporting. While the effects of outlier removal can be flexibly explored, eCerto always retains the original data set and any manipulation such as outlier removal is (graphically and tabularly) documented adequately in the report. As a major reference materials producer, the Bundesanstalt für Materialforschung und -prüfung (BAM) developed and will maintain a tool to meet the needs of modern data processing, documentation requirements, and emerging fields of RM activity. The main features of eCerto are discussed using previously certified reference materials. [ABSTRACT FROM AUTHOR]

BACKGROUND: Premature newborns have a higher risk of abnormal visual development and visual impairment. OBJECTIVE: To develop a computational methodology to help assess functional vision in premature infants by tracking iris distances. METHODS: This experimental study was carried out with children up to two years old. A pattern of image capture with the visual stimulus was proposed to evaluate visual functions of vertical and horizontal visual tracking, visual field, vestibulo-ocular reflex, and fixation. The participants' visual responses were filmed to compose a dataset and develop a detection algorithm using the OpenCV library allied with FaceMesh for the detection and selection of the face, detection of specific facial points and tracking of the iris positions is done. A feasibility study was also conducted from the videos processed by the software. RESULTS: Forty-one children of different ages and diagnoses participated in the experimental study, forming a robust dataset. The software resulted in the tracking of iris positions during visual function evaluation stimuli. Furthermore, in the feasibility study, 8 children participated, divided into Pre-term and Term groups. There was no statistical difference in any visual variable analyzed in the comparison between groups. CONCLUSION: The computational methodology developed was able to track the distances traveled by the iris, and thus can be used to help assess visual function in children. [ABSTRACT FROM AUTHOR]

Purpose: Changes in the cross-sectional area (CSA) and functional cross-sectional area (FCSA) of the lumbar multifidus (MF) and erector spinae muscles (ES) are factors that can contribute to low back pain. For the assessment of muscle CSA and composition there are various software and threshold methods used for tissue segmentation in quantitative analysis. However, there is currently no gold standard for software as well as muscle segmentation. This study aims to analyze the measurement error between different image processing software and different threshold methods for muscle segmentation. Methods: Magnetic resonance images (MRI) of 60 patients were evaluated. Muscle CSA and FCSA measurements were acquired from axial T2-weighted MRI of the MF and ES at L4/L5 and L5/S1. CSA, FCSA, and FCSA/CSA ratio were measured independently by two observers. The MRI images were measured using two different software programs (ImageJ and Amira) and with two threshold methods (Circle/Overlap method) for each software to evaluate FCSA and FCSA/CSA ratio. Results: Inter-software comparisons revealed high inter-rater reliability. However, poor inter-rater reliability were obtained with different threshold methods. CSA, FCSA, and FCSA/CSA showed excellent inter-software agreement of 0.75–0.99 regardless of the threshold segmentation method. The inter-rater reliability between the two observers ranged between 0.75 and 0.99. Comparison of the two segmentation methods revealed agreement between 0.19 and 0.84. FCSA and FCSA/CSA measured via the Overlap method were significantly higher than those measured via the Circle method (P < 0.01). Conclusion: The present study showed a high degree of reliability with very good agreement between the two software programs. However, study results based on different threshold methods should not be directly compared. [ABSTRACT FROM AUTHOR]

Bioacoustics increasingly relies on large datasets and computational methods. The need to batch-process large amounts of data and the increased focus on algorithmic processing require software tools. To optimally assist in a bioacoustician's workflow, software tools need to be as simple and effective as possible. Five years ago, the Python package Parselmouth was released to provide easy and intuitive access to all functionality in the Praat software. Whereas Praat is principally designed for phonetics and speech processing, plenty of bioacoustics studies have used its advanced acoustic algorithms. Here, we evaluate existing usage of Parselmouth and discuss in detail several studies which used the software library. We argue that Parselmouth has the potential to be used even more in bioacoustics research, and suggest future directions to be pursued with the help of Parselmouth. [ABSTRACT FROM AUTHOR]

Colour pattern variation provides biological information in fields ranging from disease ecology to speciation dynamics. Comparing colour pattern geometries across images requires colour segmentation, where pixels in an image are assigned to one of a set of colour classes shared by all images. Manual methods for colour segmentation are slow and subjective, while automated methods can struggle with high technical variation in aggregate image sets. We present recolorize, an R package toolbox for human‐subjective colour segmentation with functions for batch‐processing low‐variation image sets and additional tools for handling images from diverse (high‐variation) sources. The package also includes export options for a variety of formats and colour analysis packages. This paper illustrates recolorize for three example datasets, including high variation, batch processing and combining with reflectance spectra, and demonstrates the downstream use of methods that rely on this output. [ABSTRACT FROM AUTHOR]

Objectives Leukemia diagnosis in Vietnam is limited by a lack of hematopathology training and expert consultation as well as the cost of high-magnification digitization of hematology slides. Screen-sharing software allows international collaboration with experienced hematopathologists for improved diagnostic accuracy. Methods A hematopathology education and consultation program was proposed for Vietnam hospitals. By appointment, pathologists in Vietnam with access to a microscope camera, imaging software, and high-speed internet were invited to review slides and data with a volunteer board-certified hematopathologist in the United States using secure videoconferencing software. A single hospital in southern Vietnam assigned a pathologist proficient in English to access this service. All consultations from this site with clinicopathologic information were logged. After a 2-year period of online consultation, case slides for selected diagnoses were reviewed under the microscope in Vietnam to assess concordance. Results In total, 135 consultations were logged, 53 of which were for blood and bone marrow. T-cell large granular lymphocytic leukemia (T-LGLL) was 1 of the most frequent bone marrow consultation-related diagnoses; all diagnoses of this entity were confirmed by in-person microscopy (100% concordance). A records search and physician surveys found no prior documented diagnoses of T-LGLL made in Vietnam before this education and consultation program. Conclusions Our virtual consultation model has improved patient care in Vietnam by providing correct diagnoses to inform best practices in treatment. As a result of our program, the first Vietnam diagnoses of T-LGLL were made and may help expand on the literature in this area. This model could provide cost-effective, real-time consultation and education services for pathologists in underserved communities. [ABSTRACT FROM AUTHOR]

Achieving the UN Sustainable Development Goals is impossible without sustainable oil and gas production, both in terms of ensuring equal access to cheap energy and preserving the environment, as well as caring for non-renewable fossil energy sources. This actualizes the need for the digital 3D modeling of wells, which allows one to assess their current condition and predict their future condition as well as determine the feasibility of investing in their reconstruction and the expansion of the well network. This is possible due to the fact that the visualization of a well, reflecting its physical and technical parameters, gives engineers, designers and ecologists the opportunity to recognize the defects of the well (cracks, cavities, behind casing and interlayer leaks, etc.) and predict their appearance and proliferation on which the service life, hydrocarbon flow rate, pollution, operating and capital costs directly depend. The software required for this must be versatile enough to cover different types of logging and fit different operating systems. The goal of the study is to develop a software for creating visual 3D models of wells based on acoustic and various types of radioactive logging data, capable of integrating drilling rig parameters and geophysical survey data with modern 3D modeling and programming methods. The developed software meets the requirements of various operating systems and the specifics of different types of logging, which is designed to help in increasing the productivity of oil and gas wells, save energy consumption and reduce groundwater pollution from chemicals used in hydrocarbon production. This is achievable by ensuring trouble-free execution and the operation of well systems, minimizing the risks of collapses and the destruction of well walls through accurate monitoring and forecasting their dynamic condition in real time using 3D models, which is not available for static 2D models. [ABSTRACT FROM AUTHOR]

Head motion correction is particularly challenging in diffusion‐weighted MRI (dMRI) scans due to the dramatic changes in image contrast at different gradient strengths and directions. Head motion correction is typically performed using a Gaussian Process model implemented in FSL's Eddy. Recently, the 3dSHORE‐based SHORELine method was introduced that does not require shell‐based acquisitions, but it has not been previously benchmarked. Here we perform a comprehensive evaluation of both methods on realistic simulations of a software fiber phantom that provides known ground‐truth head motion. We demonstrate that both methods perform remarkably well, but that performance can be impacted by sampling scheme and the extent of head motion and the denoising strategy applied before head motion correction. Furthermore, we find Eddy benefits from denoising the data first with MP‐PCA. In sum, we provide the most extensive known benchmarking of dMRI head motion correction, together with extensive simulation data and a reproducible workflow. Practitioner points: Both Eddy and SHORELine head motion correction methods performed quite well on a large variety of simulated data.Denoising with MP‐PCA can improve head motion correction performance when Eddy is used.SHORELine effectively corrects motion in non‐shelled diffusion spectrum imaging data. [ABSTRACT FROM AUTHOR]

Summary: The aim of this study was to evaluate the safety and efficacy of digital therapeutic application of Sleep Index‐Based Treatment for Insomnia (dSIBT‐I) and compare them with those of digital application of Cognitive Behavioural Therapy for Insomnia (dCBT‐I). This randomised prospective pilot study was conducted at the Asan Medical Center. A total of 50 patients with insomnia were recruited between December 2022 and January 2023 and randomly allocated to the dSIBT‐I or dCBT‐I group. The study was carried out for one month. The primary outcome was the significant reduction in Insomnia Severity Index score at Week 4 compared to baseline, while the secondary outcome was proportion of participants whose Insomnia Severity Index scores were reduced to <15 at Week 4. We performed linear mixed model and generalised estimating equation analyses. Both dSIBT‐I and dCBT‐I groups showed significant improvements in Insomnia Severity Index scores at Week 4. There was no significant difference between two groups in terms of Insomnia Severity Index scores at Week 4 (group × time effect, F = 1.07, p = 0.382) and proportion of participants whose Insomnia Severity Index scores were reduced to <15 at Week 4 (group × time effects, F = 1.80, p = 0.615). However, at Week 2, the dSIBT‐I group showed better results than the dCBT‐I group in terms of both Insomnia Severity Index scores (p = 0.044) and proportion of participants whose Insomnia Severity Index scores were reduced to <15 (82.6% vs. 48.0%, p = 0.017). No treatment‐emergent adverse events were reported in either group. The dSIBT‐I is a safe and effective therapy for insomnia, with rapid treatment effects. [ABSTRACT FROM AUTHOR]

We extensively survey applications of Clifford Geometric algebra in recent years (mainly 2019–2022). This includes engineering; electric engineering; optical fibers; geographic information systems; geometry; molecular geometry; protein structure; neural networks; artificial intelligence; encryption; physics; signal, image, and video processing; and software. [ABSTRACT FROM AUTHOR]

Comprehensive Summary: Charge transport characterization of single‐molecule junctions is essential for the fundamental research of single‐molecule physical chemistry and the development towards single‐molecule electronic devices and circuits. Among the single‐molecule conductance characterization techniques, the single‐molecule break junction technique is widely used in tens of worldwide research laboratories which can generate a large amount of experimental data from thousands of individual measurement cycles. However, data interpretation is a challenging task for researchers with different research backgrounds, and the different data analysis approaches sometimes lead to the misunderstanding of the measurement data and even reproducibility issues of the measurement. It is thus a necessity to develop a user‐friendly all‐in‐one data analysis tool that automatizes the basic data analysis in a standard and widely accepted way. In this work, we present the XMe Code (Xiamen Molecular Electronics Code), an intelligent all‐in‐one data analysis tool for the comprehensive analysis of single‐molecule break junction data. XMe code provides end‐to‐end data analysis that takes in the original experimental data and returns electronic characteristics and even charge transport mechanisms. We believe that XMe Code will promote the transparency of the data analysis in single‐molecule electronics and the collaborations among scientists with different research backgrounds. [ABSTRACT FROM AUTHOR]

Background: Genome streamlining, the process by which genomes become smaller and encode fewer genes over time, is a common phenomenon among pathogenic bacteria. This reduction is driven by selection for minimized energy expenditure in a nutrient-rich environment. As pathogens evolve to become more reliant on the host, metabolic genes and resulting capabilities are lost in favor of siphoning metabolites from the host. Characterizing genome streamlining, gene loss, and metabolic pathway degradation can be useful in assessing pathogen dependency on host metabolism and identifying potential targets for host-directed therapeutics. Results: PoMeLo (Predictor of Metabolic Loss) is a novel evolutionary genomics-guided computational approach for identifying metabolic gaps in the genomes of pathogenic bacteria. PoMeLo leverages a centralized public database of high-quality genomes and annotations and allows the user to compare an unlimited number of genomes across individual genes and pathways. PoMeLo runs locally using user-friendly prompts in a matter of minutes and generates tabular and visual outputs for users to compare predicted metabolic capacity between groups of bacteria and individual species. Each pathway is assigned a Predicted Metabolic Loss (PML) score to assess the magnitude of genome streamlining. Optionally, PoMeLo places the results in an evolutionary context by including phylogenetic relationships in visual outputs. It can also initially compute phylogenetically-weighted mean genome sizes to identify genome streamlining events. Here, we describe PoMeLo and demonstrate its use in identifying metabolic gaps in genomes of pathogenic Treponema species. Conclusions: PoMeLo represents an advance over existing methods for identifying metabolic gaps in genomic data, allowing comparison across large numbers of genomes and placing the resulting data in a phylogenetic context. PoMeLo is freely available for academic and non-academic use at https://github.com/czbiohub-sf/pomelo. [ABSTRACT FROM AUTHOR]

The disruptive effect of radio frequency interference (RFI) on global navigation satellite system (GNSS) signals is well known, and in the last four decades, many have been investigated as countermeasures. Recently, low-Earth orbit (LEO) satellites have been looked at as a good opportunity for GNSS RFI monitoring, and the last five years have seen the proliferation of many commercial and academic initiatives. In this context, this paper proposes a new spaceborne system to detect, classify, and localize terrestrial GNSS RFI signals, particularly jamming and spoofing, for civil use. This paper presents the implementation of the RFI detection software module to be hosted on a nanosatellite. The whole development work is described, including the selection of both the target platform and the algorithms, the implementation, the detection performance evaluation, and the computational load analysis. Two are the implemented RFI detectors: the chi-square goodness-of-fit (GoF) algorithm for non-GNSS-like interference, e.g., chirp jamming, and the snapshot acquisition for GNSS-like interference, e.g., spoofing. Preliminary testing results in the presence of jamming and spoofing signals reveal promising detection capability in terms of sensitivity and highlight room to optimize the computational load, particularly for the snapshot-acquisition-based RFI detector. [ABSTRACT FROM AUTHOR]

Simple Summary: This scientific study focuses on glioblastoma, a highly aggressive cancer with a poor prognosis. Despite various treatment modalities, including immune checkpoint inhibitors (ICBs), the efficacy of ICBs remains limited, prompting the need for a proactive approach to understand treatment response and resistance. This study involves a thorough analysis of two glioblastoma patient cohorts treated with the Programmed Cell Death Protein 1 (PD-1) blockade. Notably, 60% of the patients exhibited persistent disease progression despite the ICBs. We characterized the immune profiles of these patients with continued cancer progression, revealing multiple defects such as compromised macrophage, monocyte, and T follicular helper responses, impaired antigen presentation, abnormal regulatory T cell (Tregs) responses, and increased expression of immunosuppressive molecules. Using machine learning algorithms, we developed predictive models and software. This computational tool achieved significant success, accurately predicting the progression status of 82.82% of the glioblastoma patients in this study following ICBs based on their unique immune characteristics. In conclusion, this study proposes a personalized approach to immunotherapy in glioblastoma patients. By utilizing patient-specific attributes and computational predictions, we advocate for a paradigm shift towards tailored therapies. This approach has the potential to improve glioblastoma management, offering new possibilities for improved patient care following immunotherapy. Glioblastoma is a highly aggressive cancer associated with a dismal prognosis, with a mere 5% of patients surviving beyond five years post diagnosis. Current therapeutic modalities encompass surgical intervention, radiotherapy, chemotherapy, and immune checkpoint inhibitors (ICBs). However, the efficacy of ICBs remains limited in glioblastoma patients, necessitating a proactive approach to anticipate treatment response and resistance. In this comprehensive study, we conducted a rigorous analysis involving two distinct glioblastoma patient cohorts subjected to PD-1 blockade treatments. Our investigation revealed that a significant portion (60%) of patients exhibit persistent disease progression despite ICB intervention. To elucidate the underpinnings of resistance, we characterized the immune profiles of glioblastoma patients with continued cancer progression following anti-PD1 therapy. These profiles revealed multifaceted defects, encompassing compromised macrophage, monocyte, and T follicular helper responses, impaired antigen presentation, aberrant regulatory T cell (Tregs) responses, and heightened expression of immunosuppressive molecules (TGFB, IL2RA, and CD276). Building upon these resistance profiles, we leveraged cutting-edge machine learning algorithms to develop predictive models and accompanying software. This innovative computational tool achieved remarkable success, accurately forecasting the progression status of 82.82% of the glioblastoma patients in our study following ICBs, based on their unique immune characteristics. In conclusion, our pioneering approach advocates for the personalization of immunotherapy in glioblastoma patients. By harnessing patient-specific attributes and computational predictions, we offer a promising avenue for the enhancement of clinical outcomes in the realm of immunotherapy. This paradigm shift towards tailored therapies underscores the potential to revolutionize the management of glioblastoma, opening new horizons for improved patient care. [ABSTRACT FROM AUTHOR]