Software Tools Research Articles

Rcsb-api: Python Toolkit for Streamlining Access to RCSB Protein Data Bank APIs.

The Protein Data Bank (PDB) was founded in 1971 as the first open-access digital data resource in biology to serve as the single global archive for three-dimensional (3D) macromolecular structure data. Current PDB holdings exceed 230,000 experimentally determined structures of proteins, nucleic acids, viruses, and macromolecular machines. The RCSB Protein Data Bank RCSB.org research-focused web portal facilitates search, analyses, and visualization of every PDB structure along with more than one million Computed Structure Models from AlphaFold DB and the ModelArchive. It is powered by a set of publicly available Application Programming Interfaces (APIs) that both support RCSB.org users and provide programmatic access to PDB data. Given the breadth and levels of granularity encompassed in this rich data collection, efficiently accessing the information programmatically may be challenging for new users. RCSB PDB has developed a Python software package, rcsb-api, that facilitates easy and efficient use of RCSB PDB APIs within a Python environment. This software tool is designed to streamline access to the extensive corpus of data housed within the PDB, enabling researchers to search, retrieve, and analyze 3D biostructure data seamlessly. Its use will accelerate research in structural biology, molecular biology and biochemistry, drug discovery, and bioinformatics by providing more efficient tools for data integration and analysis. The new toolkit is available on GitHub (github.com/rcsb/py-rcsb-api) and published to the public Python package repository (PyPI) to foster wider usage and support basic and applied research in fundamental biology, biomedicine, and the energy sciences.

Exploring new dimensions: cardiac tomography advances in left atrial appendage wall thickness assessment

Abstract Background/Introduction The left atrial appendage (LAA) is a finger-like extension originating from the main body of the left atrium, characterized by a neck and one or multiple lobes. Closure devices for the LAA are typically implanted at the neck level. Peri-device leaks during LAA closure have different causes, and the LAA thickness and stiffness may play a role in them. Aim This study aimed to assess the feasibility of utilizing a novel software tool, based on cardiac computer tomography (CCT), for measurement of left atrial appendage (LAA) wall thickness. Methods In fifteen patients, ten with a history of atrial fibrillation undergoing LAA closure (study group) and five without structural heart disease (control group), CCT synchronized with ECG-triggered at 75% of the RR cycle was acquired to capture the maximum volume of the left atrium. Using ADAS 3D software (Adas3D Medical S.L., Barcelona, Spain), LAA was segmented into two structures: neck and lobes. The thickness of the neck was analyzed using a novel tool within the software that allows the measurement of thickness of the left atrium wall and may be applicable also for LAA (figure 1). The thickness is represented using a color-coded system (red <1 mm, yellow 1-2 mm, green 2-3 mm, blue 3-4 mm, violet >4 mm). Results Among the 15 patients analyzed, those who underwent LAA closure were significantly older, more hypertensive, and diabetic with a mean history of atrial fibrillation of 7.1 ± 3.2 years. Analyzing a total of 681±177 points per patient, the mean neck wall thickness in the control group was significantly greater than in the group that underwent LAA closure (1.29±0.61 mm vs. 1.18±0.49 mm, p<0.001). (Table 1). Conclusion: Through the use of myocardial thickness quantification software, the feasibility of measuring the left atrial appendage (LAA) wall thickness has been demonstrated. With proof of concept established, further studies with larger sample sizes are needed to determine the clinical implications of LAA wall thickness in optimizing the outcomes of closure procedures.

A Bioinformatician, Computer Scientist, and Geneticist lead bioinformatic tool development—which one is better?

Abstract The development of accurate bioinformatic software tools is crucial for the effective analysis of complex biological data. This study examines the relationship between the academic department affiliations of authors and the accuracy of the bioinformatic tools they develop. By analyzing a corpus of previously benchmarked bioinformatic software tools, we mapped bioinformatic tools to the academic fields of the corresponding authors and evaluated tool accuracy by field. Our results suggest that “Medical Informatics” outperforms all other fields in bioinformatic software accuracy, with a mean proportion of wins in accuracy rankings exceeding the null expectation. In contrast, tools developed by authors affiliated with” Bioinformatics” and” Engineering” fields tend to be less accurate. However, after correcting for multiple testing, no result is statistically significant (p > 0.05). Our findings reveal no strong association between academic field and bioinformatic software accuracy. These findings suggest that the development of interdisciplinary software applications can be effectively undertaken by any department with sufficient resources and training.

Impacts of climate change on the suitable habitat of Angelica sinensis and analysis of its drivers in China

Climate change is shifting optimal habitats for medicinal plants, potentially compromising the efficacy and therapeutic value of herbal remedies. Global warming and increased extreme weather events threaten the sustainability and pharmaceutical integrity of Angelica sinensis (Oliv.) Diels (A. sinensis). Despite its importance in traditional herbal medicine, there is limited research on adaptation of A. sinensis to climate challenges. This study systematically collected occurrence data of A. sinensis through field expeditions and online databases, using the Maxent ecological niche modeling tool and ArcGIS software to forecast suitable habitats. A total of 402 species occurrence points and 21 environmental variables were selected for modeling, resulting in 1,160 distribution models, of which only one met the stringent 5% odds ratio (OR) standard. The optimal model exhibited a pROC value of 0, an OR of 0.0196, and an AICc score of 9,287.133. The model, run ten times for robustness, showed an average AUC of 0.980, indicating high accuracy and reliability. Under current climate conditions, suitable habitats for A. sinensis cover approximately 13% of mainland China, primarily in Gansu (73.77%), Qinghai (14.73%), and Sichuan (11.18%) provinces. Environmental factors such as altitude, humidity, and temperature significantly influence the geographical distribution of A. sinensis. The future climate scenario predictions suggest that suitable habitats will generally shift towards higher latitudes, with areas of moderate to high suitability primarily distributed across the provinces of Gansu and Qinghai. The interactions between environmental factors, characterized by mutual and nonlinear enhancement, further influence the spatial differentiation of suitability zones. Overlay analysis with 2020 land cover data indicated that 861,437 km² of arable and forest land are suitable for A. sinensis cultivation. Future predictions under four SSP scenarios show varying changes in suitable habitat areas, with the most significant expansion under SSP370 between 2080 and 2100, covering 14.54% of mainland China. These findings provide critical insights for optimizing A. sinensis cultivation regions and quality assessments in response to climate change.

OCT-based diagnosis of glaucoma and glaucoma stages using explainable machine learning

Glaucoma poses a growing health challenge projected to escalate in the coming decades. However, current automated diagnostic approaches on Glaucoma diagnosis solely rely on black-box deep learning models, lacking explainability and trustworthiness. To address the issue, this study uses optical coherence tomography (OCT) images to develop an explainable artificial intelligence (XAI) tool for diagnosing and staging glaucoma, with a focus on its clinical applicability. A total of 334 normal and 268 glaucomatous eyes (86 early, 72 moderate, 110 advanced) were included, signal processing theory was employed, and model interpretability was rigorously evaluated. Leveraging SHapley Additive exPlanations (SHAP)-based global feature ranking and partial dependency analysis (PDA) estimated decision boundary cut-offs on machine learning (ML) models, a novel algorithm was developed to implement an XAI tool. Using the selected features, ML models produce an AUC of 0.96 (95% CI: 0.95–0.98), 0.98 (95% CI: 0.96–1.00) and 1.00 (95% CI: 1.00–1.00) respectively on differentiating early, moderate and advanced glaucoma patients. Overall, machine outperformed clinicians in the early stage and overall glaucoma diagnosis with 10.4 –11.2% higher accuracy. The developed user-friendly XAI software tool shows potential as a valuable tool for eye care practitioners, offering transparent and interpretable insights to improve decision-making.

Using asynchronous programming to improve computer simulation performance in energy systems

Due to the progressing complexity of modern energy systems, the need to forecast energy consumption and generation, optimise processes and develop new technologies in the energy sector, analyse scenarios for the development of energy systems and elaborate a strategy for their development, modelling and simulation is of particular relevance in this industry. The growing need to improve the productivity of computer simulation in the energy industry is effectively addressed by utilising modern computer architectures and advanced software tools that provide acceleration for computationally intensive tasks. Research presented in this paper focuses on enhancing the performance of computationally intensive algorithms using the Thomas algorithm by employing modern asynchronous programming techniques. The work implements classical and develops and implements asynchronous computational algorithms of the sweep method with subsequent assessment of the time and efficiency of their execution for the order of systems of linear equations (SLAEs) up to 5 × 107. The program code was developed using Microsoft Visual Studio C++ and the standard template for asynchronous programming. The numerical experiments showed the possibility of increasing of the implementation speed of the asynchronous algorithm by 1.87–2.91 times. Research results correspond with the literature data and the results previously obtained by the authors in similar studies using alternative parallel programming software. In general, the results of this study determine the potential for further improvement and development of methods and technologies for parallel implementation of computational tasks using the Tridiagonal Matrix Algorithm. These approaches can be extended to developing various computer models of energy processes and systems based on the solution of SLAEs with tridiagonal matrices on computers with multiprocessor or multi-core architectures.

Longitudinal performance trajectories for youth female and male soccer players: 10m-sprint percentile curves adapted to biological age

Introduction Longitudinal performance monitoring is essential in sport science for accurate talent identification and forecasting future performance. The present study applied linear mixed-effects models (LMM) to analyze the longitudinal performance development of youth soccer players, both female and male. In addition to percentile curves based on chronological age, a novel approach incorporating biological age, which accounts for maturation variability during puberty, is introduced. The incorporation of biological age, as compared to chronological age, is intended to enable a more equitable assessment of physical potential and to reduce selection bias. The goal was to create an evidence-based tool for coaches and researchers to establish realistic benchmarks, model predictions, a support athlete development. Methods A total of 33’647 10-meter sprint test results were analyzed, derived from 11’752 male and female soccer players (f = 1’112; m = 10’640) aged 9 to 20 years. To estimate biological age, the Mirwald test was used, with 18’748 measurements utilized for the analysis. These data were extracted from the Swiss Football Association’s online database, covering the period from 2014, respectively 2019 to 2024. LMM were utilized to generate performance trajectories, establish benchmarks, and produce individualized performance predictions. A practical software tool, developed as a Web-Application, was created and made accessible to facilitate individual performance forecasting based on 10-meter sprint times from previous seasons. Results The mixed model approach identified individualized longitudinal performance developments and estimated predictions of future performance based on both chronological and biological age. Percentile curves based on chronological and biological age were developed, differences could be shown and trajectories for early and late developers were established. A Web-Application tool was created and can be used on the soccer pitch to evaluate current and predict future performance. Discussion/Conclusion LMM were utilized to analyze longitudinal sport performance data, enabling the establishment of performance benchmarks and the prediction of future outcomes. By incorporating biological age into the model, the predictions account for individual differences in maturation, enhancing accuracy during critical developmental stages such as puberty. A software tool was developed to support coaches in setting realistic training goals and identifying promising soccer players, providing a more tailored and maturity-sensitive approach to athlete development and identification.

Optical Image Generation Through Digital Terrain Models for Autonomous Lunar Navigation

In recent years, Vision-Based Navigation (VBN) techniques have emerged as a fundamental component to enable autonomous spacecraft operations, particularly in challenging environments such as planetary landings, where ground control may be limited or unavailable. Developing and testing VBN algorithms requires the availability of a large number of realistic images of the application scenario; however, these are rarely available. This paper presents a novel rendering software tool to generate accurate synthetic optical images of the lunar surface by leveraging high-resolution Digital Terrain Models (DTMs). Unlike traditional ray-tracing algorithms, the method iteratively propagates camera rays to determine their intersection with the terrain surface defined by a Digital Elevation Model (DEM). The color information is then retrieved from the corresponding Digital Orthophoto Model (DOM) through the knowledge of the ray impact points, bypassing the need for the costly computation of shadows, reflections, and refractions effects. The rendering performance is demonstrated through a comprehensive selection of images of the lunar surface under different illumination conditions and camera orientations.

Association of autistic personality traits with the EEG scores in non-clinical subjects during the facial video viewing

A software information module of the experimental computer platform “EEG_Self-Construct” was developed and tested in the framework of this study. This module can be applied for identification of neurophysiological markers of self-referential processes based on the joint use of EEG and facial video recording to induce the brain’s functional states associated with participants’ personality traits. This module was tested on a group of non-clinical participants with varying degrees of severity of autistic personality traits (APT) according to the Broad Autism Phenotype Questionnaire. The degree of individual severity of APT is a quantitative characteristic of difficulties that a person has when communicating with other people. Each person has some individual degree of severity of such traits. Patients with autism are found to have high rates of autistic traits. However, some individuals with high rates of autistic traits are not accompanied by clinical symptoms. Our module allows inducing the brain’s functional states, in which the EEG indicators of people with different levels of APT significantly differ. In addition, the module includes a set of software tools for recording and analyzing brain activity indices. We have found that relationships between brain activity and the individual level of severity of APT in non-clinical subjects can be identified in resting-state conditions following recognition of self-referential information, while recognition of socially neutral information does not induce processes associated with APT. It has been shown that people with high scores of APT have increased spectral density in the delta and theta ranges of rhythms in the frontal cortical areas of both hemispheres compared to people with lower scores of APT. This could hypothetically be interpreted as an index of reduced brain activity associated with recognition of self-referential information in people with higher scores of autistic traits. The software module we are developing can be integrated with modules that allow identifying molecular genetic markers of personality traits, including traits that determine the predisposition to mental pathologies.

Impact of Conservation Agriculture on Soil Quality and Cotton–Maize System Yield in Semi-Arid India

Intensive agriculture is the chief cause of soil degradation, particularly in regions with low soil organic carbon status, such as semi-arid southern India. In the quest to attain sustainable yield and improved soil quality, conservation agriculture (CA) is being advocated and adopted globally, including in India. In this experiment, CA was implemented to investigate the synergistic impacts of tillage and weed management on soil quality index and system yield and to identify a remunerative treatment combination that can sustain system yield and enhance soil quality. Contrasting tillage practices (main plots) included the T1: conventional tillage with cotton–conventional tillage with maize–fallow, i.e., no Sesbania rostrata (Farmers’ practice), T2: conventional tillage with cotton–zero tillage with maize–zero tillage with Sesbania rostrata and T3: zero tillage with cotton + Sesbania rostrata residues–zero tillage with maize + cotton residues–zero tillage with Sesbania rostrata + maize stubbles. Weed management tactics (sub-plots) were W1: chemical weed control, W2: herbicide rotation, W3: integrated weed management and W4: single hand-weeded control in a split-plot design with cotton–maize–Sesbania cropping system over 3 years, in a split-plot design. Principal component analysis (PCA) was performed using the soil quality index (SQI)-CAL Version 1.0 software tool to extract minimum datasets from measured soil properties. A total of 40 soil variables were analyzed at 60 DAS and after the maize harvest, then subjected to principal component analysis (PCA) and subjected to PCA in soil quality index (SQI)-CAL software as to choose variables, minimum dataset and obtain soil quality index. The following soil properties, soil organic carbon (SOC), silt fraction, available soil zinc (Zn), iron (Fe), potassium (K), nitrogen (N), pH, electrical conductivity (EC), soil carbon to nitrogen (C:N) and cation exchange capacity (CEC), were selected as indicators based on correlations, calculated PCA and adept opinions on texture and lime concretions of experimental soil. The soil quality index improved by 23.34% in the T3W4 compared to T1W1. The system yield was 51.79% higher with the adoption of T3W3 compared to T3W4 combinations. Therefore, considering both system yield and soil quality index, T3 and W3 were remunerative and the best treatment combination among all others to sustain both soil and crop productivity in this region.

Sicurpest: A Prototype of a User-Friendly Tool for Preventive Risk Assessment of Pesticide Use in Agriculture

In recent years, there has been a significant increase in attention towards the use of pesticides, as evidenced by the introduction of regulations aimed at ensuring their safe and environmentally responsible application. Although this stricter legislative framework offers potential benefits, the challenges faced by farmers, particularly those in small-scale operations, in complying with occupational health and safety (OHS) requirements are considerable. To address this issue, a research project was promoted by the Italian Workers’ Compensation Authority (INAIL) aimed at developing a user-friendly software tool to support farmers in planning pesticide applications in safe conditions. This study summarizes the results of the research, which consisted of three main phases: the definition of the main parameters that characterize the farmers’ exposure based on the analysis of the literature; the development of a risk assessment model that integrates these determinants with data provided by producers for each authorized pesticide; and the development of software tool (called SICURPEST) for a preliminary risk assessment when using pesticides. This tool serves as a preliminary risk assessment method, offering a simplified model for practical use by farmers. Its initial verification was conducted through a case study and the results represent a step towards promoting safer pesticide practices, providing a solid basis for further validation.

Literature Review on Road Damage Detection and Severity Recognition: Leveraging Computer Vision

The increasing demand for quick and precise road maintenance has therefore been underscored by extensive research that was created over the last years, to find automated systems in which computer vision can work — harnessing deep learning methodologies to detect defects on roads & figure out how severe these actually are. It provides a discussion of different methodologies and technologies in these pipelines such as Convolutional Neural Networks (CNNs), YOLO models, ensembling learning etc. They have a good performance in various kinds of road damage: cracks, potholes, and surface deformations identification with high accuracy. They also use methods like image tiling, transfer learning, and multiple spectral data to increase their robustness so that models can be used effectively in different environmental conditions.But the avoidance of problems is still a long way off. Nonetheless, the disparate road scenarios across geographies and absence of labeled high-quality datasets remained as some common bottlenecks in addition to real-time-driven requirements that mandate significant compute powers. Although much more headway has been made in recognizing severity (using segmentation approaches and multimodal data, in particular), the combination of detection and classification altogether within a single joint pipeline is significantly harder.A comprehensive analysis of state‑of-the‑art software tools, recent challenges, and advances to improve the detection/ severity identification capabilities of road damage systems. It emphasizes how to address data problems, enhance model generalization capability in unseen scenes(a.k.a novel), and minimize latency. One final takeaway is the valuable reminder of the need to create operational standards for classifier performance required to drive practice adoption and scale in larger deployment settings ideally incentivizing public-private partnerships.

Research on the Construction and Teaching Application of the STEP Engineering Education Model in Cultivating Excellent Engineers

Objective: To address the urgent demand for high-level engineering talents in the new era, and in response to engineering education reform trends and accreditation requirements, this paper focuses on the educational philosophies of world-class universities, particularly drawing from the globally recognized CDIO (Conceive, Design, Implement, Operate) framework. Based on the characteristics and practical experiences of a university’s engineering education, we propose the STEP education model (i.e., Software, Theory, Experiment, Project) to achieve systematic enhancement in engineering education and effectively cultivate the comprehensive abilities of engineers. Methods: The STEP model is structured around project-driven learning, seamless integration of theoretical and experimental teaching, and a curriculum structure supported by software tools. It aims to enhance students’ learning outcomes through innovative teaching approaches, including science-education integration, virtual-real fusion, and competition-driven learning. Results: Practical applications of the STEP education model in several course systems at the university demonstrate its effectiveness in enhancing students' engineering practice abilities and innovative thinking. Conclusion: Providing strong support for the cultivation of excellent engineering talent, the STEP education model has proven effective in enhancing students' engineering practice abilities and innovative thinking, which are essential for meeting the demands of the new era and the trends in engineering education reform and accreditation requirements.

An experimental approach for dimensional tolerance analysis using 3D printed parts

In 2D or 3D tolerance analysis, a dimension chain involves a complex propagation of random deviations from individual parts to the controlled assembly requirement. This can be calculated by setting up complex mathematical models, possibly with the help of CAD-based software tools. The paper studies the feasibility of an alternative approach, which uses measurements on assemblies of 3D printed parts to estimate the coefficients of a linearized error propagation model. The analysis is structured as a designed experiment, where each dimension is varied in two levels resulting in two different printed parts. The experimental plan consists of building assemblies from selected combinations of parts and measuring the assembly dimension on each combination. Among the possible designs of the plan, the paper compares two methods based on finite differences and least squares. The comparison includes both simulations with randomly generated dimension chains and physical tests on simple cases. The results show that a least squares plan allows a significant improvement in the accuracy on the estimation of model coefficients compared to a finite difference plan, with just a marginally higher number of assembly combinations. However, dimensional and geometric deviations in the 3D printing process are shown to play a critical role, and criteria are suggested to reduce or compensate them.

Global Research Trends in Performance-Based Structural Design: A Comprehensive Bibliometric Analysis

In the context of seismic hazard assessment and engineering design, a comprehensive understanding of local geological and geophysical factors is essential. However, previous studies have lacked crucial components such as local soil condition, ground response analysis, topographic influences, active fault characteristics, slip rates, groundwater behaviour, and slope considerations. To ensure the accuracy of the seismic hazard map of a country for the safe and cost-effective design of engineering structures in urban areas, a detailed analysis of these factors is imperative. Moreover, multidisciplinary investigations, such as logic-tree considerations, are needed to enhance seismic hazard maps. As a result, adopting a performance-based approach in structural design has become an essential priority. A performance-based approach allows engineers to design buildings to specified performance levels (IO, LS, CP) even without a reliable seismic hazard map. This approach is akin to a miracle for countries that do not have a reliable seismic hazard map. This study presents a systematic and comprehensive bibliometric analysis of the academic literature pertaining to performance-based design (PBD). By fostering collaborative efforts and expanding research networks, we aim to facilitate the development of coordinated initiatives within the field. Preferred journals, leading countries, leading organisations, and international institutions were identified utilizing the Scopus database. This study examined 3456 PBD-related publications spanning from 1969 to 2023 using VOSviewer version 1.6.19, a bibliometric mapping and visualization software tool. The analysis of co-citations revealed that performance-based design serves as the primary theoretical foundation for structural design and analysis. Furthermore, through a co-word analysis, we tracked the evolution of research topics within the PBD domain over time. This investigation uncovered noteworthy trends, including the steady growth of research output, the increasing prominence of the term “PBD”, and a focus on various types of performance-based analyses.

Temperature Prediction for the Sirius-2c Nuclear Propulsion Fuel Experiment Conducted at the TREAT Facility

This study presents a predictive transient model developed for simulating experiments at the Transient Reactor Test (TREAT) Facility and its application to the Sirius-2c experiments. We introduce an innovative approach to predict control rod motion, reactor power, and specimen temperature during transient experiments using Griffin, BISON, and the stochastic tool packages of the Multiphysics Object-Oriented Simulation Environment. The model provides a blind prediction of the specimen temperature evolution during transient tests given a desired power signal. Initially, our model accurately predicts the transient motion of the control rods to achieve the desired reactor power profile. Subsequently, it predicts the specimen temperature based on the power deposited within the specimen. The simulation results show strong agreement with the measured reactor power and control rod positions; however, the specimen temperature is slightly overpredicted, primarily because of assumptions about surface emissivities. When considering a more accurate set of specimen emissivities, the predicted temperature aligns exceptionally well with the measured values according to post-analysis results. The simulation results will support the design and analysis of the National Aeronautics and Space Administration sponsored Sirius series of experiments for nuclear thermal propulsion applications, and the model can be utilized to assist in the design and optimization of new experiments to be conducted at the TREAT Facility.

Bayesian p-curve mixture models as a tool to dissociate effect size and effect prevalence

Much research in the behavioral sciences aims to characterize the “typical” person. A statistically significant group-averaged effect size is often interpreted as evidence that the typical person shows an effect, but that is only true under certain distributional assumptions for which explicit evidence is rarely presented. Mean effect size varies with both within-participant effect size and population prevalence (proportion of population showing effect). Few studies consider how prevalence affects mean effect size estimates and existing estimators of prevalence are, conversely, confounded by uncertainty about effect size. We introduce a widely applicable Bayesian method, the p-curve mixture model, that jointly estimates prevalence and effect size by probabilistically clustering participant-level data based on their likelihood under a null distribution. Our approach, for which we provide a software tool, outperforms existing prevalence estimation methods when effect size is uncertain and is sensitive to differences in prevalence or effect size across groups or conditions.

OP12 Therapeutic antibody clearance better predicts endoscopic outcomes than trough concentrations in patients with Crohn’s disease

Abstract Background Monitoring of monoclonal antibody (mAb) clearance has been suggested for prediction and follow-up of treatment outcomes in patients with inflammatory bowel diseases. We investigated clearance monitoring in patients with Crohn’s disease (CD) using data from clinical trials. Methods Data from patients with moderate-to-severe CD starting infliximab (n=108; TAILORIX trial [1]) and ustekinumab (n=80; a real-life cohort study [2]) therapy were repurposed. Endoscopic remission (CD Endoscopic Index of Severity <3) was assessed at week (w)12 and w54 of infliximab therapy. Endoscopic response (≥50% reduction in Simple Endoscopic Score for CD) was assessed at w24 of ustekinumab therapy. We performed Bayesian forecasting to estimate time-varying mAb clearance using measured drug concentrations, covariates, and population pharmacokinetics models (NONMEM 7.5). A novel software tool for clearance monitoring of infliximab and ustekinumab was developed using the Shiny and mapbayr packages in R. Results Baseline demographics and clinical characteristics of the patients are summarized in Table 1. Patients achieving endoscopic remission at w12 had significantly lower infliximab clearance and higher infliximab serum trough concentrations (Ctrough) at w2 and w6 of standard 5 mg/kg infliximab induction therapy, than patients without endoscopic remission (p<0.05). During infliximab maintenance therapy (with eventual dose optimisations), patients achieving endoscopic remission at w54 had significantly lower infliximab clearance (p<0.001) but not higher infliximab Ctrough (p=0.92) than patients without endoscopic remission. Moreover, infliximab clearance during maintenance had similar discriminative ability as faecal calprotectin (area under the receiver operating characteristics curve 0.63 [95% confidence interval 0.57–0.69] vs. 0.67 [0.61–0.73], respectively) (Figure 1). Ustekinumab clearance (p<0.05), but not Ctrough, was significantly different between endoscopic responders and non-responders during standard ustekinumab induction and maintenance therapy. We developed an evidence-based interactive software tool that can predict the probability of achieving endoscopic outcomes in a clinical setting based on the estimated clearance of infliximab and ustekinumab (https://lpmx.shinyapps.io/clearance_monitoring/). Conclusion While Ctrough loses its ability to predict treatment response when doses are optimised or not administered by bodyweight, mAb clearance remains a reliable predictor. Our interactive clearance monitoring tool can forecast the chance of achieving endoscopic outcomes and distinguish between exposure-driven and mechanistic failure when patients with CD are dose-optimised.

Accounting the Ways of Weighing the Bits of the Data Vector when Constructing a Code with Summation in the Ring of Deductions Modulo M = 4 in the Synthesis of Self-Checking Discrete Devices Based on Boolean Signals Correction

The article shows that in the synthesis of concurrent error-detection circuit based on Boolean signals correction, weighted codes with summation in the ring of deductions modulo M = 4 (weight-based Bose — Lin codes) with various arrays of weighting coefficients can be effectively used. The codes with four data and two test symbols used in organizing the control of calculations in a group of six outputs of the diagnostic object are considered in more detail. Four arrays of weight coefficients are highlighted: [1, 1, 1, 2], [1, 1, 2, 3], [1, 2, 2, 3], [2, 2, 2, 3] — the use of which in the construction of a weight-based Bose — Lin code leads to the formation of for each the test vector has exactly one data vector of the following types: <00ab>, <01 ab>, <10ab>, <11 ab>, where a, b ' {0, 1}. This property allows the use of codes with the specified arrays of weighting coefficients in the synthesis of concurrent error-detection circuit with the conversion of only part of the signals from the diagnostic object — two signals involved in the formation of the lower bits of the data vector. The article presents the results of experimental studies of weight-based Bose — Lin codes in the synthesis of concurrent error-detection circuit based on the Boolean signals correction using two algorithms. The first algorithm is based on the use of converting only those signals from the diagnostic object that are involved in the formation of test symbols. The second algorithm is based on the conversion of only those signals from the diagnostic object that participate in the formation of the two lowest digits of the data vector. It has been experimentally established that self-checking devices synthesized using a code with an array of weighting coefficients have the highest efficiency in terms of structural redundancy [2, 2, 2, 3] (on average, the value of the structural redundancy index has been reached at 73 % of the duplication structure). Next, according to this indicator, there are devices synthesized using a code with an array of weighting coefficients [1, 2, 2, 3] (on average, 76—77 % of the duplication structure). The use of arrays of weighting coefficients [1, 1, 2, 3] and [1, 1, 1, 2] in the construction of the code gives a slightly lower effect (on average 79—81 % from the duplication structure). It is shown that in order to ensure complete self-checking of device structures, it will be necessary to use permutations of outputs within controlled groups of outputs of the initial diagnostic object and between groups. The results of the study can be taken into account when developing self-checking digital devices, as well as software tools for their computer-aided design.

Blood Flow Velocity Analysis in Cerebral Perforating Arteries on 7T 2D Phase Contrast MRI with an Open-Source Software Tool (SELMA)

Blood flow velocity in the cerebral perforating arteries can be quantified in a two-dimensional plane with phase contrast magnetic imaging (2D PC-MRI). The velocity pulsatility index (PI) can inform on the stiffness of these perforating arteries, which is related to several cerebrovascular diseases. Currently, there is no open-source analysis tool for 2D PC-MRI data from these small vessels, impeding the usage of these measurements. In this study we present the Small vessEL MArker (SELMA) analysis software as a novel, user-friendly, open-source tool for velocity analysis in cerebral perforating arteries. The implementation of the analysis algorithm in SELMA was validated against previously published data with a Bland–Altman analysis. The inter-rater reliability of SELMA was assessed on PC-MRI data of sixty participants from three MRI vendors between eight different sites. The mean velocity (vmean) and velocity PI of SELMA was very similar to the original results (vmean: mean difference ± standard deviation: 0.1 ± 0.8 cm/s; velocity PI: mean difference ± standard deviation: 0.01 ± 0.1) despite the slightly higher number of detected vessels in SELMA (Ndetected: mean difference ± standard deviation: 4 ± 9 vessels), which can be explained by the vessel selection paradigm of SELMA. The Dice Similarity Coefficient of drawn regions of interest between two operators using SELMA was 0.91 (range 0.69–0.95) and the overall intra-class coefficient for Ndetected, vmean, and velocity PI were 0.92, 0.84, and 0.85, respectively. The differences in the outcome measures was higher between sites than vendors, indicating the challenges in harmonizing the 2D PC-MRI sequence even across sites with the same vendor. We show that SELMA is a consistent and user-friendly analysis tool for small cerebral vessels.