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Purpose/Objectives: This article introduces the Key Electronic Assessment Platform (KEAP), an electronic-based assessment platform created in-house at the College of Dentistry (COD), King Saud bin Abdulaziz University for Health Sciences (KSAU-HS), Riyadh, Saudi Arabia. It highlights the platform’s integral value and demand within the educational system. Methods: The article introduces the KEAP system design and architecture, describes its features, tests, and implementation in addition to its maintenance and security. It also presents the outcomes of KEAP utilization and the level of users’ satisfaction. Results: The KEAP item bank has been expanded by more than 26,500 questions over the last four academic years and 1,255 exams have been administered. KEAP provides a highly secure structured framework for assessment planning, conduction, and evaluation. More than 90% of students and faculty are satisfied or very satisfied with their experience using the KEAP system indicating that it is well accepted by the end users. Conclusions: The development of KEAP is based on contextual factors in COD, KSAU-HS with the aim of creating a process for assessment that is valid, efficient, standardized, and highly secure. KEAP’s design and successful implementation can provide a successful example of implementing electronic-based assessment in higher education.

Quantitative susceptibility mapping (QSM) is a rising MRI-based technology and quite a few QSM-related algorithms have been proposed to reconstruct maps of tissue susceptibility distribution from phase images. In this paper, we develop a comprehensive susceptibility imaging process and analysis studio (SIPAS) that can accomplish reliable QSM processing and offer a standardized evaluation system. Specifically, SIPAS integrates multiple methods for each step, enabling users to select algorithm combinations according to data conditions, and QSM maps could be evaluated by two aspects, including image quality indicators within all voxels and region-of-interest (ROI) analysis. Through a sophisticated design of user-friendly interfaces, the results of each procedure are able to be exhibited in axial, coronal, and sagittal views in real-time, meanwhile ROIs can be displayed in 3D rendering visualization. The accuracy and compatibility of SIPAS are demonstrated by experiments on multiple in vivo human brain datasets acquired from 3T, 5T, and 7T MRI scanners of different manufacturers. We also validate the QSM maps obtained by various algorithm combinations in SIPAS, among which the combination of iRSHARP and SFCR achieves the best results on its evaluation system. SIPAS is a comprehensive, sophisticated, and reliable toolkit that may prompt the QSM application in scientific research and clinical practice.

This paper presents an innovative ‘strain acceleration method’ for determining the onset of diffuse necking in sheet forming tests using data obtained from digital image correlation (DIC). The method identifies the onset time of diffuse necking and provides the corresponding in-plane principal strain values by detecting a local extreme in the second derivative of the minor principal in-plane strain with respect to time at the edges of the sheet surface region where diffuse necking occurs. Results obtained from applying the method to tensile testing on two different materials and comparisons with available methods based on force-time or principal strain rate evolutions confirm its accuracy and validity. The new method was implemented in a computer software to be used for research and education that also enables determination of localized necking and fracture and plotting the strain loading paths in principal strain space.

Background: 3D printing is one of the fastest-growing technologies in medicine, but it is essential to have a system for 3D printing documentation that is accessible for not only clinical engineers and surgeons, but also quality managers and data-privacy officers in hospitals. Dedicated software such as product lifecycle management (PLM) software could enable comprehensive management and traceability of all data relevant to 3D printing tasks in a hospital and would highly beneficial. Therefore, customizable software called 3Diamond was developed for 3D printing in medicine. Methods: The software development process involved several stages, including setting specifications based on end-user requirements, design, implementation, and testing. In order to ensure the software's long-term success and smooth operation, critical phases were also considered, such as deployment and maintenance. Results: The developed software provides immediate and complete traceability of all preparations and controls, as well as management of reports, orders, stock, and post-operative follow-up of tasks related to 3D printing in a hospital. Based on user requirements, software testing is provided automatically with each release. The software was implemented in a natural clinical environment with a developed 3D printing center. Conclusion: Although 3D printing has potential for innovation in the medical profession, it is nevertheless subject to regulations. Even though there are exemptions for patient-specific products, the effects of their local legal implementations related to 3D printing cannot be fully overseen. To this end, 3Diamond provides a robust system for 3D printing documentation that is accessible to different personnel in hospitals.

KairoSight-3.0 is a recently released Python-based, open-source software for cardiac optical mapping analysis. Addressing challenges in high-resolution electrophysiological data analysis, KairoSight-3.0 facilitates comprehensive studies of cardiac conduction and excitation-contraction coupling. We compared its performance with ElectroMap, focusing on action potential duration and conduction velocity measurements in mouse heart models subjected to ischaemia and flecainide treatment. Our findings reveal that while both software are effective, inherent methodological differences impact measurement outcomes. KairoSight-3.0's robust analysis capabilities make it a valuable tool in cardiac research. Additionally, future directions for KairoSight-3.0 and other mapping analysis tools are explored. Statement of importance: Open-source methods for analysis of cardiac optical mapping are vital tools in electrophysiological research. Our work directly evaluates the latest version of KarioSight, recently published in JMCC plus, with ElectroMap, an established and widely used tool. Our results show both software are effective in analysis of changes in both conduction and repolarisation. Considering the new features of KairoSight-3.0 and python implementation, our study importantly demonstrates the effectiveness of the software, highlights potential discrepancies between it and ElectroMap, and provides a perspective on future directions for KairoSight-3.0 and other software.

As digital systems continue to grow in popularity, they also become more vulnerable to various forms of attacks with various motives, including financial gain and political influence. In response, engineers must consider system security from the design phase. However, defining security requirements at this stage can be challenging. To address this challenge, we propose E-SCORE, a web-based tool that streamlines the security requirements engineering process. E-SCORE implements the SCORE (Security Criteria Ontology for security Requirements Engineering) (i) to suggest security mechanisms and additional criteria to enhance security coverage and (ii) to facilitate security analysis of the system. An example of banking system usage is provided. Through our approach, we could define ten additional security requirements for a single requirement. Therefore, E-SCORE offers a valuable resource for engineers to ensure the security of digital systems across various domains.

Objective: To describe the challenges and perspectives of the automation of pain assessment in the Neonatal Intensive Care Unit. Data sources: A search for scientific articles published in the last 10 years on automated neonatal pain assessment was conducted in the main Databases of the Health Area and Engineering Journal Portals, using the descriptors: Pain Measurement, Newborn, Artificial Intelligence, Computer Systems, Software, Automated Facial Recognition. Summary of findings: Fifteen articles were selected and allowed a broad reflection on first, the literature search did not return the various automatic methods that exist to date, and those that exist are not effective enough to replace the human eye; second, computational methods are not yet able to automatically detect pain on partially covered faces and need to be tested during the natural movement of the neonate and with different light intensities; third, for research to advance in this area, databases are needed with more neonatal facial images available for the study of computational methods. Conclusion: There is still a gap between computational methods developed for automated neonatal pain assessment and a practical application that can be used at the bedside in real-time, that is sensitive, specific, and with good accuracy. The studies reviewed described limitations that could be minimized with the development of a tool that identifies pain by analyzing only free facial regions, and the creation and feasibility of a synthetic database of neonatal facial images that is freely available to researchers.

Background/purpose: World Health Organization (WHO) oral health survey manual is the main guideline for most oral health surveys worldwide. It has been updated several times since 1971; however, using papers and pens for data recording remains unchanged. This study aimed to develop the Vietnam Oral Survey Electronics Recorder (VOSER) to record dental caries data based on the WHO 2013 criteria and assess its reliability and efficiency. Materials and methods: VOSER was developed and tested for reliability and efficiency by performing clinical examinations on 365 school children in three key indicator age groups: 120 5-year-old students with primary dentition, 123 8-year-old students with mixed dentition, and 122 12-year-old students with permanent dentition. One gold standard examiner and two trained clerks examined these children using either WHO's paper survey form or VOSER's digital form for dental caries. Recording time, spreadsheet time, DMFT/S, and dmft/s were analyzed to compare the efficiency of VOSER to the paper form. Cohen's Kappa, intraclass correlation coefficient, and Wilcoxon signed-rank test were adopted in the data analyses. Results: Median time of using VOSER was significantly shorter than the standard time in all three dentitions (P

Stepped fishways are the main alternative to allow fish migration in river obstacles. Their design is a multidisciplinary process, where civil engineering meets biology. This can bias the fishway design towards one discipline, which may cause low efficiencies or inadequate solutions. Likewise, it is often challenging to incorporate new discoveries into well-established design principles. To solve these problems, we have developed a novel tool named “Escalas”. Escalas is a multipurpose platform for the assisted design, 1D simulation, assessment, and correction of stepped fishways. Escalas architecture allows fishway assessment during different hydraulic scenarios in the river (i.e., different water levels and discharges in the river), automatic dimensioning considering fish's physical needs, the study of any type of stepped fishway, to test solutions for malfunctioning or to assess fishway retrofitting. This is achieved by a modular variable definition during fishway design or definition, which allows multiple combinations of connections within and/or between cross-walls and independent discharge equation definition. This work aims to introduce Escalas to the research and engineering community, describe its algorithms, and show and validate its performance by its use in real and practical cases. Among others, results demonstrate how the tool can reproduce uniform and non-uniform performances on stepped fishways and allows fishway retrofitting to make hydraulic conditions compatible with fish usage during different river scenarios. Therefore, this work represents a step forward in the fishway engineering discipline by applying methods of engineering informatics and providing a technical and scientific base to make engineering decision-making more reliable and accessible as well as to incorporate new advances in fishway research into the engineering design process.

Background: Deep brain stimulation (DBS) devices with neural recording capabilities are commercially available and may potentially improve clinical care and advance research. However, tools, to visualize neural recording data have been limited. These tools in general, require custom-made software for processing and analysis. The development of new tools will be critical for clinicians and researchers to fully leverage the latest device capabilities. Objective: There is an urgent need for a user-friendly tool for in-depth visualization and analysis of brain signals and of DBS data. Methods and results: The Brain Recording Analysis and Visualization Online (BRAVO) platform was developed to easily import, visualize, and analyze brain signals. This Python-based web interface has been designed and implemented on a Linux server. The tool processes the session files from DBS programming generated by a clinical ‘programming’ tablet. The platform is capable of parsing and organizing neural recordings for longitudinal analysis. We present the platform and cases exemplifying its application and use. Conclusion: The BRAVO platform is an accessible easy-to-use, open-source web interface for clinicians and researchers to apply for analysis of longitudinal neural recording data. The tool can be used for both clinical and research applications.

Multiple criteria decision-making (MCDM) is an effective technique for evaluating and selecting alternative(s) based on a set of criteria/sub-criteria. Most MCDM techniques use cardinal data as input data. However, in real-world situations, decision-makers face difficulties, while providingexact values for comparing alternatives/criteria. While “Ordinal Data” is more tangible and straightforward to use by human. Considering human ability in ordinal thinking and expressing preferences, the Ordinal Priority Approach (OPA) was proposed in 2020 to solve the MCDM problems with the ordinal data as input. In the current study, a novel web-based software is developed using JavaScript to streamline and facilitate solving the OPA model. It (i) supports individual and group decision-making, (ii) calculates the weights of the experts, alternatives, and criteria, and (iii) determines confidence level value and uncertainty in the group decision-making problems to check the quality of the input and result. Promisingly, this software is user-friendly for users, time-efficient, accurate for solving MCDM problems, and practical with significant impacts on the academic and industrial worlds.

Most packages for the analysis of fMRI-based functional connectivity (FC) and genomic data are used with a programming language interface, lacking an easy-to-navigate GUI frontend. This exacerbates two problems found in these types of data: demographic confounds and quality control in the face of high dimensionality of features. The reason is that it is too slow and cumbersome to use a programming interface to create all the necessary visualizations required to identify all correlations, confounding effects, or quality control problems in a dataset. FC in particular usually contains tens of thousands of features per subject, and can only be summarized and efficiently explored using visualizations. To remedy this situation, we have developed ImageNomer, a data visualization and analysis tool that allows inspection of both subject-level and cohort-level demographic, genomic, and imaging features. The software is Python-based, runs in a self-contained Docker image, and contains a browser-based GUI frontend. We demonstrate the usefulness of ImageNomer by identifying an unexpected race confound when predicting achievement scores in the Philadelphia Neurodevelopmental Cohort (PNC) dataset, which contains multitask fMRI and single nucleotide polymorphism (SNP) data of healthy adolescents. In the past, many studies have attempted to use FC to identify achievement-related features in fMRI. Using ImageNomer to visualize trends in achievement scores between races, we find a clear potential for confounding effects if race can be predicted using FC. Using correlation analysis in the ImageNomer software, we show that FCs correlated with Wide Range Achievement Test (WRAT) score are in fact more highly correlated with race. Investigating further, we find that whereas both FC and SNP (genomic) features can account for 10–15% of WRAT score variation, this predictive ability disappears when controlling for race. We also use ImageNomer to investigate race-FC correlation in the Bipolar and Schizophrenia Network for Intermediate Phenotypes (BSNIP) dataset. In this work, we demonstrate the advantage of our ImageNomer GUI tool in data exploration and confound detection. Additionally, this work identifies race as a strong confound in FC data and casts doubt on the possibility of finding unbiased achievement-related features in fMRI and SNP data of healthy adolescents.

The Galclaim software is designed to identify association between astrophysical transient sources and host galaxy by computing the probability of chance alignment. It is distributed as an open source Python software. It is already used to identify, confirm or reject host galaxy candidates of GRBs and to validate or invalidate transient candidates in astrophysical observations. Such tools are also very useful to characterise archived transient candidates in large sky survey telescopes.

The requirement for high-quality, inexpensive software that can be maintained is being driven by the rise in demand for automated online software systems. Early defect identification in SDLCs (Software Development Life Cycles) results in early adjustments and eventually on-time delivery of maintainable software, satisfying the client and fostering his trust in the development team. Many MLTs (Machine Learning Techniques) have been put out in the last ten years to increase SDP accuracy. Most of the suggested SDPs frameworks and models employ ANNs (Artificial Neural Networks), which are a popular MLTs. Software defect data are hampered by a number of problems, including duplication, correlation, feature relevance, and missing samples. However, because to the under/over fitting issues, most existing SDPs utilising ANNs have low accuracy. SDPET (Software Defect Predictions Ensemble Technique), an ensemble learning technique to produce accurate SDPs based on the AdaBoost algorithm, is proposed. The proposed schema's efficacy against RFs (Random Forests) and GBs(Gradient Boosts) for needed values through experiments. The experiment results verify that the suggested SDPET has good accuracy in training and better accuracy in test datasets when compared with other methods. The original obtained dataset was cleaned of unnecessary features, converted to csv, and is stored as dataset. csv.

The climate change impact and adaptation communities need future scenarios with sufficient high resolution, which are frequently achieved by applying Statistical Downscaling Models (SDMs) over global climate models. A large variety of SDMs exists, and some can be more suitable than others for each specific purpose. For this reason, it is important to develop tools to facilitate the evaluation and generation of downscaled scenarios following different approaches. In this paper we present a service, ‘pyClim-SDM’, which allows users to generate and evaluate their own downscaled scenarios with a very simple and user-friendly graphical interface. This tool includes a large collection of state-of-the-art methods belonging to different families to downscale daily data of the following surface variables: temperature, precipitation, wind, relative humidity and cloud coverage. Additionally, the software is prepared to be applied over any other user-defined target variable. Thus, multivariable indexes can be tackled as target variables themselves, instead of being calculated from the downscaled primary variables. With this possibility, potential intervariable inconsistencies are avoided. An application example for a Fire Weather Index, dependent on temperature, wind, humidity and precipitation, is shown. The service here presented -mainly based on a new downscaling software and a user-friendly graphical interface- is an essential piece for evaluating and generating high-resolution projection data within the Spanish national climate change adaptation strategy which includes, among other elements, a common database for all sectors, viewer and data distribution portal, etc.

As open science principles continue to gain traction, striking a balance between patient privacy and data accessibility has become more crucial in medical research than ever before. Encryption-based pseudonymization is a powerful tool to ensure compliance with data protection regulations from both local institutional guidelines and broader regional regulations, such as the General Data Protection Regulation of the European Union. Employing this type of pseudonymization protects the privacy and security of research participants, and allows researchers to effortlessly comply with data security regulations. The pseudonymization workflow however, can vary significantly across research projects, limiting the usability of supporting software tools. Here we present ALIIAS, a customizable pseudonymization framework that allows easy and flexible deployment of custom pseudonymization software, dedicated to the specific ethical and experimental requirements of individual research projects. Features include compatibility with hardware security tokens paired with two-factor authentication, integration to the survey web application LimeSurvey, as well as custom-format pseudonyms and automatic barcode generation. Collectively, these features make ALIIAS suitable for integration into various research infrastructures and lower the initial barrier to incorporating cutting-edge encryption-based pseudonymization in translational and clinical research practices.

Antibiotic resistance is an emerging issue that requires a track on antimicrobial resistance pattern in an institution to prescribe appropriate empirical therapy. Antibiogram that's an aggregate of antimicrobial susceptibility profile within an institution provides a better understanding of empirical and prophylactic use of antimicrobials. It also focuses on M39 guidelines published by Clinical and Laboratory Standard Institute for developing accurate and reliable data of antibiograms. The data for this review has been extracted from literature databases such as Medline/PubMed, Scopus, Web of Science, Embase and the Cochrane Library. This literature highlights the importance of clinical pharmacists in developing antibiogram by preventing antimicrobial resistance, types of antibiogram, history and development, need, role of hospital and microbiological laboratory and Clinical and Laboratory Standard Institute guidelines. Additionally, the significance of WHONET software for developing and analysis of results rather than using old/traditional methods, limitations, and future considerations of antibiogram has also been described.

This paper gives insight into the development and utilization of a computer software that uses raw experimental data from the load cells and DIC systems to obtain the instant of time at fracture tf, the loading paths in principal strain space ε1=f(ε2), and their conversion into the space of effective strain vs. stress triaxiality ε‾=f(η). Special emphasis is given to the different assumptions and stress triaxiality measures that can be used to convert the loading paths from principal strain space into the space of effective strain vs. stress triaxiality. Results for double-action radial extrusion show the differences of treating the loading paths as linear or non-linear from beginning until the onset of failure by fracture. Results also allow concluding on the importance of accounting for the stress triaxiality derived from individual experimental measurements in an average sense over the entire loading paths, to avoid overestimation and mislocation of the fracture forming limits. The applicability of the software for education and training of students in formability is also discussed.

Mixed methods research relies on the integration of quantitative and qualitative data to develop meta-inferences. Despite an increase in techniques to facilitate integration at the methods and reporting levels, there is less practical guidance on how to conduct mixed methods integrative data analysis. Qualitative data analysis software is rapidly advancing to facilitate integrative analysis in mixed methods research studies. However, examples on how these features support analysis are relatively limited. The purpose of this article is to promote the use of qualitative data analysis software for integrative analysis in mixed methods research by describing the value of the major features available in software (i.e., code queries, crosstabulations, and quote matrices). In addition to a brief overview of these features, this article demonstrates the use of MAXQDA's Interactive Quote Matrix with a real-life example of a completed mixed methods study and delineates the step-by-step process on how to use this feature to develop meta-inferences.

Bioinformatics has been playing a crucial role in the scientific progress to fight against the pandemic of the coronavirus disease 2019 (COVID-19) caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The advances in novel algorithms, mega data technology, artificial intelligence and deep learning assisted the development of novel bioinformatics tools to analyze daily increasing SARS-CoV-2 data in the past years. These tools were applied in genomic analyses, evolutionary tracking, epidemiological analyses, protein structure interpretation, studies in virus-host interaction and clinical performance. To promote the in-silico analysis in the future, we conducted a review which summarized the databases, web services and software applied in SARS-CoV-2 research. Those digital resources applied in SARS-CoV-2 research may also potentially contribute to the research in other coronavirus and non-coronavirus viruses.

AnIMAGE is a MATLAB-based software tool that empowers designers to create microstrip antennas with complex shapes, enhancing their performance and expanding design possibilities. With its user-friendly interface, the software simplifies the design process and streamlines it by automating calculations. Furthermore, it offers the functionality to export the shapes of the images, enabling CST Studio to simulate their electromagnetic behavior effectively. By leveraging AnIMAGE, designers gain the capability to fabricate antennas with complex forms, such as fractals or structures derived from photographs. As a valuable resource for antenna designers, AnIMAGE introduces innovative approaches to the development of complex microstrip antennas.

“Can you tell me where San Jose is located?” “Uh! Do you know that there are more than 1700 locations named San Jose in the world?” The official name of a location is often not the name with which we are familiar. Spatial named entity (SNE) disambiguation is the process of identifying and assigning precise coordinates to a place name that can be identified in a text. This task is not always straightforward, especially when the place name in question is ambiguous for various reasons. In this context, we are interested in the disambiguation of spatial named entities that can be identified in a textual document on a country level. The solution that we propose is based on a set of techniques that allow us to disambiguate the spatial entity considering the context in which it is mentioned from a certain number of characteristics that are specific to it. The solution uses as input a textual document and extricates the named entities identified therein while associating them with the correct coordinates. SNE disambiguation is designed to support the process of fast exploration of spatiotemporal data analysis, most often for event tracking. The proposed approach was tested on 1360 SNEs extracted from the GeoVirus dataset. The results show that SNEToolkit outperformed the baseline, the standard Geonames geocoder, with a recall value of 0.911 against a recall value of 0.871 for the baseline. A flexible Python package is provided for end users.

In order to achieve visionary concepts such as Society 5.0 and Industry 5.0, there is a growing need for people who are able to create innovative robotic technologies. Training students to become such skilled professionals requires transitioning from often toy-like educational platforms with significant hardware limitations to costly research robots with full ROS (Robot Operating System) support. To aid in this transition, we propose Robotont – an open-source omnidirectional mobile robot platform with both physical hardware and a digital twin. Robotont enables robotics education with professional tools as well as provides researchers with a capable mobility platform for validating and demonstrating scientific results. Robotont has successfully been used for university teaching, professional education, and online courses about ROS and robotics.

Medullated fibres, due to their higher resistance to bending and pressure, constitute a problem for the textile industry. Thus, having practical instruments to identify them is essential. Therefore, the aim of this research was to develop and validate a novel, swift, automatic system (referred to as S-Fiber Med) for medullation and diameter assessment of animal fibres based on artificial intelligence. The medullation of 88 samples of alpaca, llama and mohair fibres (41, 43 and 4, respectively) was evaluated. Additionally, 269 samples of alpacas were considered for average fibre diameter (AFD) and the results were compared with the Portable Fiber Tester (PFT) and Optical Fibre Diameter Analyser (OFDA) methods (72 and 197 samples, respectively). The preparation of each sample to be analysed followed the procedure described in IWTO-8-2011. Version 5 of “You Only Look Once” and DenseNet models were used to recognise the type of medullation and diameter of the fibres, respectively. Within each image (n = 661 for alpaca), all fibres were labelled (as Non-Medullated, Fragmented Medulla, Uncontinuous Medulla, Continuous Medulla and Strongly Medullated) using the LabelImg tool. Data augmentation technique was applied to obtain 3 966 images. Such data set was divided into 3 576 and 390 images for training and test data, respectively. For mohair samples (n = 321), a similar process was carried out. The data to train the model used to infer the diameter contained 16 446 fibres labelled with his respective AFD. A complementary hardware composed of three subsystems (mechanical, electronic, and optical) was developed for evaluation purposes. T-test, Pearson and Concordance correlation, Bland-Altman plot and linear regression analyses were used to validate and compare the S-Fiber Med with other methods. Results indicate that there was no significant difference between medullation percentage obtained with the projection microscope and the S-Fiber Med. The Pearson and Concordance correlation analysis shows a strong, high and significant relationship (P-value 0.172), and they have a strong, high and significant relationship between them, given the high Pearson correlation value (r ≥ 0.96 with P-value

ABSTRACT: Diffusion MRI is the dominant non-invasive imaging method used to characterize white matter organization in health and disease. Increasingly, fiber-specific properties within a voxel are analyzed using fixels. While tools for conducting statistical analyses of fixel-wise data exist, currently available tools support only a limited number of statistical models. Here we introduce ModelArray, an R package for mass-univariate statistical analysis of fixel-wise data. At present, ModelArray supports linear models as well as generalized additive models (GAMs), which are particularly useful for studying nonlinear effects in lifespan data. In addition, ModelArray also aims for scalable analysis. With only several lines of code, even large fixel-wise datasets can be analyzed using a standard personal computer. Detailed memory profiling revealed that ModelArray required only limited memory even for large datasets. As an example, we applied ModelArray to fixel-wise data derived from diffusion images acquired as part of the Philadelphia Neurodevelopmental Cohort (n = 938). ModelArray revealed anticipated nonlinear developmental effects in white matter. Moving forward, ModelArray is supported by an open-source software development model that can incorporate additional statistical models and other imaging data types. Taken together, ModelArray provides a flexible and efficient platform for statistical analysis of fixel-wise data.

Background: Infarct volume measured from 2,3,5-triphenyltetrazolium chloride (TTC)-stained brain slices is critical to in vivo stroke models. In this study, we developed an interactive, tunable, software that automatically computes whole-brain infarct metrics from serial TTC-stained brain sections. Methods: Three rat ischemic stroke cohorts were used in this study (Total n = 91 rats; Cohort 1 n = 21, Cohort 2 n = 40, Cohort 3 n = 30). For each, brains were serially-sliced, stained with TTC and scanned on both anterior and posterior sides. Ground truth annotation and infarct morphometric analysis (e.g., brain-Vbrain, infarct-Vinfarct, and non-infarct-Vnon-infarct volumes) were completed by domain experts. We used Cohort 1 for brain and infarct segmentation model development (n = 3 training cases with 36 slices [18 anterior and posterior faces], n = 18 testing cases with 218 slices [109 anterior and posterior faces]), as well as infarct morphometrics automation. The infarct quantification pipeline and pre-trained model were packaged as a standalone software and applied to Cohort 2, an internal validation dataset. Finally, software and model trainability were tested as a use-case with Cohort 3, a dataset from a separate institute. Results: Both high segmentation and statistically significant quantification performance (correlation between manual and software) were observed across all datasets. Segmentation performance: Cohort 1 brain accuracy = 0.95/f1-score = 0.90, infarct accuracy = 0.96/f1-score = 0.89; Cohort 2 brain accuracy = 0.97/f1-score = 0.90, infarct accuracy = 0.97/f1-score = 0.80; Cohort 3 brain accuracy = 0.96/f1-score = 0.92, infarct accuracy = 0.95/f1-score = 0.82. Infarct quantification (cohort average): Vbrain (ρ = 0.87, p

We present a computational framework to simultaneously perform image acquisition, reconstruction, and analysis in the context of open-source microscopy automation. The setup features multiple computer units intersecting software with hardware devices and achieves automation using python scripts. In practice, script files are executed in the acquisition computer and can perform any experiment by modifying the state of the hardware devices and accessing experimental data. The presented framework achieves concurrency by using multiple instances of ImSwitch and napari working simultaneously. ImSwitch is a flexible and modular open-source software package for microscope control, and napari is a multidimensional image viewer for scientific image analysis.The presented framework implements a system based on file watching, where multiple units monitor a filesystem that acts as the synchronization primitive. The proposed solution is valid for any microscope setup, supporting various biological applications. The only necessary element is a shared filesystem, common in any standard laboratory, even in resource-constrained settings. The file watcher functionality in Python can be easily integrated into other python-based software.We demonstrate the proposed solution by performing tiling experiments using the molecular nanoscale live imaging with sectioning ability (MoNaLISA) microscope, a high-throughput super-resolution microscope based on reversible saturable optical fluorescence transitions (RESOLFT).

An open source tool is introduced that provides a versatile environment to meet the needs of researchers in developing deep learning (DL) algorithms for single image super-resolution reconstruction (SISR). The processes of SISR were carefully studied, unified and integrated to create software that can be used by the community for any type of imaging method such as aerial, medical, optical, etc. DeepSR allows easy implementation of SISR application with rapidly prototyped DL models, and detailed reporting and recording of the results. The entire experiment can be done with simple command line scripts. It can be easily extended by user-defined metrics, augmentations, callbacks, etc.

Due to climate change and the COVID-19 pandemic, the number of malaria cases and deaths, caused by the Plasmodium genus, of which P. falciparum is the most common and lethal to humans, increased between 2019 and 2020. Reversing this trend and eliminating malaria worldwide requires improvements in malaria diagnosis, in which artificial intelligence (AI) has recently been demonstrated to have a great potential. One of the main reasons for the use of neural networks (NNs) is the time saving through automatising the process and the elimination of human error. When classifying with two-dimensional images of red blood cells (RBCs), the number of parameters fitted by the NN for the classification of RBCs is extremely high, which strongly influences the performance of the network, especially for training sets of moderate size. The complicated handling of malaria culturing and sample preparation does not only limit the efficiency of NNs due to small training sets, but also because of the uneven distribution of red blood cell (RBC) categories. To boost the performance of microscopy techniques in malaria diagnosis, our approach aims at resolving these drawbacks by reducing the dimension of the input data and by data augmentation, respectively. We assess the performance of our approach on images recorded by light (LM), atomic force (AFM), and fluorescence microscopy (FM). Our tool, the Malaria Stage Classifier, provides a fast, high-accuracy recognition by (1) identifying individual RBCs in multi-cell microscopy images, (2) extracting characteristic one-dimensional cross-sections from individual RBC images. These cross-sections are selected by a simple algorithm to contain key information about the status of the RBCs and are used to (3) classify the malaria blood stages. We demonstrate that our method is applicable to images recorded by various microscopy techniques and available as a software package. • Identifying individual RBCs in multi-cell microscopy images. • Extracting characteristic one-dimensional cross-sections from individual RBC images. These cross-sections are selected by a simple algorithm to contain key information about the status of the RBCs and are used to. • Classify the malaria blood stages. We demonstrate that our method is applicable to images recorded by various microscopy techniques and available as a software package.

Nonribosomal peptides are a class of secondary metabolites synthesized by multimodular enzymes named nonribosomal peptide synthetases and mainly produced by bacteria and fungi. NMR, LC-MS/MS and other analytical methods allow to determine a peptide structure precisely, but it is often not a trivial task to find natural producers of them. There are cases when potential producers should be found among hundreds of strains, for instance, when analyzing metagenomic data. We have developed BioCAT, a tool designed for finding biosynthetic gene clusters which may produce a given nonribosomal peptide when the structure of an interesting nonribosomal peptide has already been found. BioCAT unites the antiSMASH software and the rBAN retrosynthesis tool but some improvements were added to both gene cluster and peptide structure analysis. The main feature of the method is an implementation of a position-specific score matrix to store specificities of nonribosomal peptide synthetase modules, which has increased the alignment sensitivity in comparison with more strict approaches developed earlier. We tested the method on a manually curated nonribosomal peptide producers database and compared it with competing tools GARLIC and Nerpa. Finally, we showed the method’s applicability on several external examples.

A supply chain ecosystem is a collection of complex asynchronous events. Blockchain has already found commercial applications in the SC domain, particularly in product tracing and verification. However, there is a lack of uniformity in these approaches. Application-generated data cannot be accessed across the supply chain ecosystem, resulting in data silos. Data silos reduce the opportunity for supply chain process optimizations. This paper does not propose any supply chain solution but a generic framework primarily aimed at reducing the communication gaps among the stakeholders and application developer(s) to build quality solutions. The ideal readers are who want to blockchanify their existing supply chain applications. The proposed framework can add real value to the organization by developing effective SC solutions satisfying application requirements. The framework consists of four stages. In the first stage, it extracts the application requirements and then maps on blockchain following an asynchronous mode of communication among the stakeholders and application developer(s). Next, it discusses how it can combine technologies to achieve the requirements stated in the first stage. Later, it discusses how to perform effective data management. Finally, it proposes a four-stage software build method that can lead to an efficient SC solution. The primary aim of this framework is to reduce communication gaps during solution development and ensure smooth operational data movement across the SC ecosystem, thanks to blockchain. The software development process also embeds eight essential features for a quality solution. The paper is concluded by discussing the technical challenges.

The Brain Imaging Data Structure (BIDS) is a specification accompanied by a software ecosystem that was designed to create reproducible and automated workflows for processing neuroimaging data. BIDS Apps flexibly build workflows based on the metadata detected in a dataset. However, even BIDS valid metadata can include incorrect values or omissions that result in inconsistent processing across sessions. Additionally, in large-scale, heterogeneous neuroimaging datasets, hidden variability in metadata is difficult to detect and classify. To address these challenges, we created a Python-based software package titled “Curation of BIDS” (CuBIDS), which provides an intuitive workflow that helps users validate and manage the curation of their neuroimaging datasets. CuBIDS includes a robust implementation of BIDS validation that scales to large samples and incorporates DataLad––a version control software package for data––as an optional dependency to ensure reproducibility and provenance tracking throughout the entire curation process. CuBIDS provides tools to help users perform quality control on their images’ metadata and identify unique combinations of imaging parameters. Users can then execute BIDS Apps on a subset of participants that represent the full range of acquisition parameters that are present, accelerating pipeline testing on large datasets.

Standard indicators and measurements are key to cross-scale agroecosystem assessments. While the need to standardize indicators is broadly recognized, inconsistencies in indicator calculations from common measurements may yield discrepancies in indicator meaning and consequently reduce the accuracy of agroecosystem assessments. Here we present a framework that provides a mechanism for standardizing indicator vegetation and ground cover calculations across datasets and agroecosystems yet also provides flexibility for local or process-specific indicators. This framework, demonstrated in an R package terradactyl, 1) harmonizes standardized agroecosystem measurements into analysis-friendly datasets, 2) produces a standard, yet flexible indicator calculation approach, 3) aggregates indicators into commonly used sets of indicators or data models. This paper provides a workflow that can be applied across agroecosystem measurements and indicators globally to produce scale-able indicators that are relevant for land managers, conservation planners, and policymakers alike.

Accurate models of protein tertiary structures are now available from numerous advanced prediction methods, although the accuracy of each method often varies depending on the specific protein target. Additionally, many models may still contain significant local errors. Therefore, reliable, independent model quality estimates are essential both for identifying errors and selecting the very best models for further biological investigations. ModFOLD9 is a leading independent server for detecting the local errors in models produced by any method, and it can accurately discriminate between high-quality models from multiple alternative approaches. ModFOLD9 incorporates several new scores from deep learning-based approaches, leading to greatly improved prediction accuracy compared with earlier versions of the server. ModFOLD9 is continuously independently benchmarked, and it is shown to be highly competitive with other public servers. ModFOLD9 is freely available at https://www.reading.ac.uk/bioinf/ModFOLD/., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

Protein interactions are essential for cellular processes. In recent years there has been significant progress in computational prediction of 3D structures of individual protein chains, with the best-performing algorithms reaching sub-Ångström accuracy. These techniques are now finding their way into the prediction of protein interactions, adding to the existing modeling approaches. The community-wide Critical Assessment of Predicted Interactions (CAPRI) has been a catalyst for the development of procedures for the structural modeling of protein assemblies by organizing blind prediction experiments. The predicted structures are assessed against unpublished experimentally determined structures using a set of metrics with proven robustness that have been established in the CAPRI community. In addition, several advanced benchmarking databases provide targets against which users can test docking and assembly modeling software. These include the Protein-Protein Docking Benchmark, the CAPRI Scoreset, and the Dockground database, all developed by members of the CAPRI community. Here we present CAPRI-Q, a stand-alone model quality assessment tool, which can be freely downloaded or used via a publicly available web server. This tool applies the CAPRI metrics to assess the quality of query structures against given target structures, along with other popular quality metrics such as DockQ, TM-score and l-DDT, and classifies the models according to the CAPRI model quality criteria. The tool can handle a variety of protein complex types including those involving peptides, nucleic acids, and oligosaccharides. The source code is freely available from https://gitlab.in2p3.fr/cmsb-public/CAPRI-Q and its web interface through the Dockground resource at https://dockground.compbio.ku.edu/assessment/., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

RiboVision2 is a web server designed to visualize phylogenetic, structural, and evolutionary properties of ribosomal RNAs simultaneously at the levels of primary, secondary, and three-dimensional structure and in the context of full ribosomal complexes. RiboVision2 instantly computes and displays a broad variety of data; it has no login requirements, is open-source, free for all users, and available at https://ribovision2.chemistry.gatech.edu., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Anticancer peptides (ACPs), naturally occurring molecules with remarkable potential to target and kill cancer cells. However, identifying ACPs based solely from their primary amino acid sequences remains a major hurdle in immunoinformatics. In the past, several web-based machine learning (ML) tools have been proposed to assist researchers in identifying potential ACPs for further testing. Notably, our meta-approach method, mACPpred, introduced in 2019, has significantly advanced the field of ACP research. Given the exponential growth in the number of characterized ACPs, there is now a pressing need to create an updated version of mACPpred. To develop mACPpred 2.0, we constructed an up-to-date benchmarking dataset by integrating all publicly available ACP datasets. We employed a large-scale of feature descriptors, encompassing both conventional feature descriptors and advanced pre-trained natural language processing (NLP)-based embeddings. We evaluated their ability to discriminate between ACPs and non-ACPs using eleven different classifiers. Subsequently, we employed a stacked deep learning (SDL) approach, incorporating 1D convolutional neural network (1D CNN) blocks and hybrid features. These features included the top seven performing NLP-based features and 90 probabilistic features, allowing us to identify hidden patterns within these diverse features and improve the accuracy of our ACP prediction model. This is the first study to integrate spatial and probabilistic feature representations for predicting ACPs. Rigorous cross-validation and independent tests conclusively demonstrated that mACPpred 2.0 not only surpassed its predecessor (mACPpred) but also outperformed the existing state-of-the-art predictors, highlighting the importance of advanced feature representation capabilities attained through SDL. To facilitate widespread use and accessibility, we have developed a user-friendly for mACPpred 2.0, available at https://balalab-skku.org/mACPpred2/., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

We introduce XGR-model (or XGRm), a web server made accessible at http://www.xgrm.pro, with the aim of meeting the increasing demand for effectively interpreting summary-level genomic data in model organisms. Currently, it hosts two enrichment analysers and two subnetwork analysers to support enrichment and subnetwork analyses for user-input mouse genomic data, whether gene-centric or genomic region-centric. The enrichment analysers identify ontology term enrichments for input genes (GElyser) or for genes linked from input genomic regions (RElyser). The subnetwork analysers rely on our previously established network algorithm to identify gene subnetworks from input gene-centric summary data (GSlyser) or from input region-centric summary data (RSlyser), leveraging network information about either functional interactions or pathway-derived interactions. Collectively, XGRm offers an all-in-one solution for gaining systems biology insights into summary-level genomic data in mice, underpinned by our commitment to regular updates as well as natural extensions to other model organisms., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Prediction of the intrinsic disorder in protein sequences is an active research area, with well over 100 predictors that were released to date. These efforts are motivated by the functional importance and high levels of abundance of intrinsic disorder, combined with relatively low amounts of experimental annotations. The disorder predictors are periodically evaluated by independent assessors in the Critical Assessment of protein Intrinsic Disorder prediction (CAID) experiments. The recently completed CAID2 experiment assessed close to 40 state-of-the-art methods demonstrating that some of them produce accurate results. In particular, flDPnn2 method, which is the successor of flDPnn that performed well in the CAID1 experiment, secured the overall most accurate results on the Disorder-NOX dataset in CAID2. flDPnn2 implements a number of improvements when compared to its predecessor including changes to the inputs, increased size of the deep network model that we retrained on a larger training set, and addition of an alignment module. Using results from CAID2, we show that flDPnn2 produces accurate predictions very quickly, modestly improving over the accuracy of flDPnn and reducing the runtime by half, to about 27 s per protein. flDPnn2 is freely available as a convenient web server at http://biomine.cs.vcu.edu/servers/flDPnn2/., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

The DockThor-VS platform (https://dockthor.lncc.br/v2/) is a free protein-ligand docking server conceptualized to facilitate and assist drug discovery projects to perform docking-based virtual screening experiments accurately and using high-performance computing. The DockThor docking engine is a grid-based method designed for flexible-ligand and rigid-receptor docking. It employs a multiple-solution genetic algorithm and the MMFF94S molecular force field scoring function for pose prediction. This engine was engineered to handle highly flexible ligands, such as peptides. Affinity prediction and ranking of protein-ligand complexes are performed with the linear empirical scoring function DockTScore. The main steps of the ligand and protein preparation are available on the DockThor Portal, making it possible to change the protonation states of the amino acid residues, and include cofactors as rigid entities. The user can also customize and visualize the main parameters of the grid box. The results of docking experiments are automatically clustered and ordered, providing users with a diverse array of meaningful binding modes. The platform DockThor-VS offers a user-friendly interface and powerful algorithms, enabling researchers to conduct virtual screening experiments efficiently and accurately. The DockThor Portal utilizes the computational strength of the Brazilian high-performance platform SDumont, further amplifying the efficiency and speed of docking experiments. Additionally, the web server facilitates and enhances virtual screening experiments by offering curated structures of potential targets and compound datasets, such as proteins related to COVID-19 and FDA-approved drugs for repurposing studies. In summary, DockThor-VS is a dynamic and evolving solution for docking-based virtual screening to be applied in drug discovery projects., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Here we present TPPU_DSF (https://maciasnmr.net/tppu_dsf/). This is a free and open-source web application that opens, converts, fits, and calculates the thermodynamic parameters of protein unfolding from standard differential scanning fluorimetry (DSF) data in an automated manner. The software has several applications. In the context of screening compound libraries for protein binders, obtaining thermodynamic parameters provides a more robust approach to detecting hits than the changes in the melting temperature (T m ) alone, thereby helping to increase the number of positive hits in screening campaigns. Moreover, changes in ΔG u o indicate protein response to binding at lower compound concentrations than those in the T m comparison, the software can contribute to the optimization of protein constructs and buffer conditions, a common practice before structural and functional projects.m comparison, the software can contribute to the optimization of protein constructs and buffer conditions, a common practice before structural and functional projects., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Knowledge of protein-ligand complexes is essential for efficient drug design. Virtual docking can bring important information on putative complexes but it is still far from being simultaneously fast and accurate. Receptors are flexible and adapt to the incoming small molecules while docking is highly sensitive to small conformational deviations. Conformation ensemble is providing a mean to simulate protein flexibility. However, modeling multiple protein structures for many targets is seldom connected to ligand screening in an efficient and straightforward manner. @TOME-3 is an updated version of our former pipeline @TOME-2, in which protein structure modeling is now directly interfaced with flexible ligand docking. Sequence-sequence profile comparisons identify suitable PDB templates for structure modeling and ligands from these templates are used to deduce binding sites to be screened. In addition, bound ligand can be used as pharmacophoric restraint during the virtual docking. The latter is performed by PLANTS while the docking poses are analysed through multiple chemoinformatics functions. This unique combination of tools allows rapid and efficient ligand docking on multiple receptor conformations in parallel. @TOME-3 is freely available on the web at https://atome.cbs.cnrs.fr., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

Knowledge of the solvent accessibility of residues in a protein is essential for different applications, including the identification of interacting surfaces in protein-protein interactions and the characterization of variations. We describe E-pRSA, a novel web server to estimate Relative Solvent Accessibility values (RSAs) of residues directly from a protein sequence. The method exploits two complementary Protein Language Models to provide fast and accurate predictions. When benchmarked on different blind test sets, E-pRSA scores at the state-of-the-art, and outperforms a previous method we developed, DeepREx, which was based on sequence profiles after Multiple Sequence Alignments. The E-pRSA web server is freely available at https://e-prsa.biocomp.unibo.it/main/ where users can submit single-sequence and batch jobs., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier Ltd.)

In the majority of downstream analysis pipelines for single-cell RNA sequencing (scRNA-seq), techniques like dimensionality reduction and feature selection are employed to address the problem of high-dimensional nature of the data. These approaches involve mapping the data onto a lower-dimensional space, eliminating less informative genes, and pinpointing the most pertinent features. This process ultimately leads to a reduction in the number of dimensions used for downstream analysis, which in turn speeds up the computation of large-scale scRNA-seq data. Most approaches are directed to isolate from biological background the genes characterizing different cells and or the condition under study by establishing lists of differentially expressed or coexpressed genes. Herein, we present scRNA-Explorer an open-source online tool for simplified and rapid scRNA-seq analysis designed with the end user in mind. scRNA-Explorer utilizes: (i) Filtering out uninformative cells in an interactive manner via a web interface, (ii) Gene correlation analysis coupled with an extra step of evaluating the biological importance of these correlations, and (iii) Gene enrichment analysis of correlated genes in order to find gene implication in specific functions. We developed a pipeline to address the above problem. The scRNA-Explorer pipeline allows users to interrogate in an interactive manner scRNA-sequencing data sets to explore via gene expression correlations possible function(s) of a gene of interest. scRNA-Explorer can be accessed at https://bioinformatics.med.uoc.gr/shinyapps/app/scrnaexplorer., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Crosslinking mass spectrometry (MS) has emerged as an important technique for elucidating the in-solution structures of protein complexes and the topology of protein-protein interaction networks. However, the expanding user community lacked an integrated visualisation tool that helped them make use of the crosslinking data for investigating biological mechanisms. We addressed this need by developing xiVIEW, a web-based application designed to streamline crosslinking MS data analysis, which we present here. xiVIEW provides a user-friendly interface for accessing coordinated views of mass spectrometric data, network visualisation, annotations extracted from trusted repositories like UniProtKB, and available 3D structures. In accordance with recent recommendations from the crosslinking MS community, xiVIEW (i) provides a standards compliant parser to improve data integration and (ii) offers accessible visualisation tools. By promoting the adoption of standard file formats and providing a comprehensive visualisation platform, xiVIEW empowers both experimentalists and modellers alike to pursue their respective research interests. We anticipate that xiVIEW will advance crosslinking MS-inspired research, and facilitate broader and more effective investigations into complex biological systems., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Fungal pathogens pose significant threats to plant health by secreting effectors that manipulate plant-host defences. However, identifying effector proteins remains challenging, in part because they lack common sequence motifs. Here, we introduce Fungtion (Fungal effector prediction), a toolkit leveraging a hybrid framework to accurately predict and visualize fungal effectors. By combining global patterns learned from pretrained protein language models with refined information from known effectors, Fungtion achieves state-of-the-art prediction performance. Additionally, the interactive visualizations we have developed enable researchers to explore both sequence- and high-level relationships between the predicted and known effectors, facilitating effector function discovery, annotation, and hypothesis formulation regarding plant-pathogen interactions. We anticipate Fungtion to be a valuable resource for biologists seeking deeper insights into fungal effector functions and for computational biologists aiming to develop future methodologies for fungal effector prediction: https://step3.erc.monash.edu/Fungtion/., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.)

In recent years, advancements in deep learning techniques have significantly expanded the structural coverage of the human proteome. GalaxySagittarius-AF translates these achievements in structure prediction into target prediction for druglike compounds by incorporating predicted structures. This web server searches the database of human protein structures using both similarity- and structure-based approaches, suggesting potential targets for a given druglike compound. In comparison to its predecessor, GalaxySagittarius, GalaxySagittarius-AF utilizes an enlarged structure database, incorporating curated AlphaFold model structures alongside their binding sites and ligands, predicted using an updated version of GalaxySite. GalaxySagittarius-AF covers a large human protein space compared to many other available computational target screening methods. The structure-based prediction method enhances the use of expanded structural information, differentiating it from other target prediction servers that rely on ligand-based methods. Additionally, the web server has undergone enhancements, operating two to three times faster than its predecessor. The updated report page provides comprehensive information on the sequence and structure of the predicted protein targets. GalaxySagittarius-AF is accessible at https://galaxy.seoklab.org/sagittarius_af without the need for registration., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier Ltd.)

Molecular modeling and simulation serve an important role in exploring biological functions of proteins at the molecular level, which is complementary to experiments. CHARMM-GUI (https://www.charmm-gui.org) is a web-based graphical user interface that generates complex molecular simulation systems and input files, and we have been continuously developing and expanding its functionalities to facilitate various complex molecular modeling and make molecular dynamics simulations more accessible to the scientific community. Currently, covalent drug discovery emerges as a popular and important field. Covalent drug forms a chemical bond with specific residues on the target protein, and it has advantages in potency for its prolonged inhibition effects. Even though there are higher demands in modeling PDB protein structures with various covalent ligand types, proper modeling of covalent ligands remains challenging. This work presents a new functionality in CHARMM-GUI PDB Reader & Manipulator that can handle a diversity of ligand-amino acid linkage types, which is validated by a careful benchmark study using over 1,000 covalent ligand structures in RCSB PDB. We hope that this new functionality can boost the modeling and simulation study of covalent ligands., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: W.I. is the co-founder and CEO of MolCube INC., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

There is an increasing need for determining 3D structures of DNAs, e.g., for increasing the efficiency of DNA aptamer selection. Recently, we have proposed a computational method of 3D structure prediction of DNAs, called 3dDNA, which has been integrated into our original web server 3dRNA, now renamed 3dRNA/DNA (http://biophy.hust.edu.cn/new/3dRNA). Currently, 3dDNA can only output the predicted DNA 3D structures for users but cannot rank them as an energy function for assessing DNA 3D structures is still lacking. Here, we first provide a brief introduction to 3dDNA and then introduce a new energy function, 3dDNAscore, for the assessment of DNA 3D structures. 3dDNAscore is an all-atom knowledge-based potential by integrating 86 atomic types from nucleic acids. Benchmarks demonstrate that 3dDNAscore can effectively identify near-native structures from the decoys generated by 3dDNA, thus enhancing the completeness of 3dDNA., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Background: Guideline adherence in the medical field leaves room for improvement. Digitalised decision support helps improve compliance. However, the complex nature of the guidelines makes implementation in clinical practice difficult., Methods: This single-centre prospective study included 204 adult ASA physical status 3-4 patients undergoing elective noncardiac surgery at a German university hospital. Agreement of clearance for surgery between a guideline expert and a digital guideline support tool was investigated. The decision made by the on-duty anaesthetists (standard approach) was assessed for agreement with the expert in a cross-over design. The main outcome was the level of agreement between digital guideline support and the expert., Results: The digital guideline support approach cleared 18.1% of the patients for surgery, the standard approach cleared 74.0%, and the expert approach cleared 47.5%. Agreement of the expert decision with digital guideline support (66.7%) and the standard approach (67.6%) was fair (Cohen's kappa 0.37 [interquartile range 0.26-0.48] vs 0.31 [0.21-0.42], P=0.6). Taking the expert decision as a benchmark, correct clearance using digital guideline support was 50.5%, and correct clearance using the standard approach was 44.6%. Digital guideline support incorrectly asked for additional examinations in 31.4% of the patients, whereas the standard approach did not consider conditions that would have justified additional examinations before surgery in 29.4%., Conclusions: Strict guideline adherence for clearance for surgery through digitalised decision support inadequately considered patients, clinical context. Vague formulations, weak recommendations, and low-quality evidence complicate guideline translation into explicit rules., Clinical Trial Registration: NCT04058769., (Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.)