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LASTU is a tool for searching for Finnish language stimulus words for psycholinguistic studies. The tool allows the user to query a number of properties, including forms, lemmas, frequencies, and morphological features. It also includes two new measures for quantifying lemma and form ambiguity. The tool is written in Python and is available for Windows and macOS platforms. It is available at https://osf.io/j8v6b/. Included with the tool is a database based on a massive corpus of dependency-parsed Finnish language data crawled from the Internet (over 5 billion tokens). While LASTU has been developed for researchers working on the Finnish language, the openly available implementation can also be applied to other languages. [ABSTRACT FROM AUTHOR]

This study evaluated the positive predictive value (PPV) of artificial intelligence (AI) in detecting pneumothorax on chest radiographs (CXRs) and its affecting factors. Patients determined to have pneumothorax on CXR by a commercial AI software from March to December 2021 were included retrospectively. The PPV was evaluated according to the true-positive (TP) and false-positive (FP) diagnosis determined by radiologists. To know the factors that might influence the results, logistic regression with generalized estimating equation was used. Among a total of 87,658 CXRs, 308 CXRs with 331 pneumothoraces from 283 patients were finally included. The overall PPV of AI about pneumothorax was 41.1% (TF:FP = 136:195). The PA view (odds ratio [OR], 29.837; 95% confidence interval [CI], 15.062–59.107), high abnormality score (OR, 1.081; 95% CI, 1.066–1.097), large amount of pneumothorax (OR, 1.005; 95% CI, 1.003–1.007), presence of ipsilateral atelectasis (OR, 3.508; 95% CI, 1.509–8.156) and a small amount of ipsilateral pleural effusion (OR, 5.277; 95% CI, 2.55–10.919) had significant effects on the increasing PPV. Therefore, PPV for pneumothorax diagnosis using AI can vary based on patients' factors, image-acquisition protocols, and the presence of concurrent lesions on CXR. [ABSTRACT FROM AUTHOR]

Glycans participate in a vast number of recognition systems in diverse organisms in health and in disease. However, glycans cannot be sequenced because there is no sequencer technology that can fully characterize them. There is no "template" for replicating glycans as there are for amino acids and nucleic acids. Instead, glycans are synthesized by a complicated orchestration of multitudes of glycosyltransferases and glycosidases. Thus glycans can vary greatly in structure, but they are not genetically reproducible and are usually isolated in minute amounts. To characterize (sequence) the glycome (defined as the glycans in a particular organism, tissue, cell, or protein), glycosylation pathway prediction using in silico methods based on glycogene expression data, and glycosylation simulations have been attempted. Since many of the mammalian glycogenes have been identified and cloned, it has become possible to predict the glycan biosynthesis pathway in these systems. By then incorporating systems biology and bioprocessing technologies to these pathway models, given the right enzymatic parameters including enzyme and substrate concentrations and kinetic reaction parameters, it is possible to predict the potentially synthesized glycans in the pathway. This review presents information on the data resources that are currently available to enable in silico simulations of glycosylation and related pathways. Then some of the software tools that have been developed in the past to simulate and analyze glycosylation pathways will be described, followed by a summary and vision for the future developments and research directions in this area. [ABSTRACT FROM AUTHOR]

The generalized hyperbolic distributions are fast becoming the most popular models for financial returns. In recent years, many variants of generalized hyperbolic distributions have been proposed in the literature. This paper provides a review of generalized hyperbolic and related distributions, including software available for them. A simulation study and real data applications are presented to compare some of the reviewed distributions. [ABSTRACT FROM AUTHOR]

Morphometry is fundamental for studying and correlating neuronal morphology with brain functions. With increasing computational power, it is possible to extract morphometric characteristics automatically, including features such as length, volume, and number of neuron branches. However, to the best of our knowledge, there is no mapping of morphometric tools yet. In this context, we conducted a systematic search and review to identify and analyze tools within the scope of neuron analysis. Thus, the work followed a well-defined protocol and sought to answer the following research questions: What open-source tools are available for neuronal morphometric analysis? What morphometric characteristics are extracted by these tools? For this, aiming for greater robustness and coverage, the study was based on the paper analysis as well as the study of documentation and tests with the tools available in repositories. We analyzed 1,586 papers and mapped 23 tools, where NeuroM, L-Measure, and NeuroMorphoVis extract the most features. Furthermore, we contribute to the body of knowledge with the unprecedented presentation of 150 unique morphometric features whose terminologies were categorized and standardized. Overall, the study contributes to advancing the understanding of the complex mechanisms underlying the brain. [ABSTRACT FROM AUTHOR]

Introduction: Human metabolism is sustained by functional networks that operate at diverse scales. Capturing local and global dynamics in the human body by hierarchically bridging multi-scale functional networks is a major challenge in physiological modeling. Objectives: To develop an interactive, user-friendly web application that facilitates the simulation and visualization of advection-dispersion transport in three-dimensional (3D) microvascular networks, biochemical exchange, and metabolic reactions in the tissue layer surrounding the vasculature. Methods: To help modelers combine and simulate biochemical processes occurring at multiple scales, KiPhyNet deploys our discrete graph-based modeling framework that bridges functional networks existing at diverse scales. KiPhyNet is implemented in Python based on Apache web server using MATLAB as the simulator engine. KiPhyNet provides the functionality to assimilate multi-omics data from clinical and experimental studies as well as vascular data from imaging studies to investigate the role of structural changes in vascular topology on the functional response of the tissue. Results: With the network topology, its biophysical attributes, values of initial and boundary conditions, parameterized kinetic constants, biochemical species-specific transport properties such as diffusivity as inputs, a user can use our application to simulate and view the simulation results. The results of steady-state velocity and pressure fields and dynamic concentration fields can be interactively examined. Conclusion: KiPhyNet provides barrier-free access to perform time-course simulation experiments by building multi-scale models of microvascular networks in physiology, using a discrete modeling framework. KiPhyNet is freely accessible at http://pallab.cds.iisc.ac.in/kiphynet/ and the documentation is available at https://deepamahm.github.io/kiphynet_docs/. [ABSTRACT FROM AUTHOR]

The article presents a structure of the algorithm and mathematical and software systems. The general characteristics of the algorithm for determining heat losses in the hearth of a blast furnace based on the use of the heat balance of the lower zone are presented. External heat losses in the process of melting occurring through the tuyere belt, barrel, and bosh, are determined by the difference between the input and output components of the zonal heat balance for the lower part of heat transfer. The determination of heat losses by the heat balance in the lower heat transfer part allows their value to be estimated according to the current information about the operation of the furnace under specific raw materials and operating parameters. The information support of the system is presented, the software architecture is described, the characteristics of the software modules are given, and fragments of its work are illustrated. The information modeling system is distinguished by taking into account the basic physical and chemical laws of the processes of the control object, the principles of system analysis, and the use of modern principles for the development and construction of mathematical models and software. Heat loss control is necessary to assess the condition of the blast furnace refractory lining and rational gas distribution, as well as to adjust the coke rate. [ABSTRACT FROM AUTHOR]

The aim of the article is to reveal the level of problematic smartphone use (PSU) among young people, with an additional aim being to diagnose the level of use of applications offering ready access to popular social networks (SNS—e.g. Facebook, Instagram, Snapchat, TikTok, Pinterest). The text fills a gap related to the scale and mechanisms of PSUs. The research was conducted in Bosnia and Herzegovina in 2022, and is the first study of its kind in the country. A total of 1,024 young people participated in the study (age range 14–19 years; Female 627, Male 397). From the data collected, it was noted that: 1) Loss of control of the duration of smartphone use is the most common symptom of PSU; 2) Around 40% of respondents report having problems with self-control of duration of smartphone use; 3) 17.19% of the sample have high intensity of almost all diagnostic criteria of PSU; 4) About 1/3 of respondents declare no symptoms of PSU; 5) High intensity of PSU is more common in girls than in boys; 6) Instagram and TikTok are the most frequently used applications on smartphones; 7) More than half of the respondents use Instagram software constantly or almost constantly during the day, while less than 40% use TikTok-enabled software; 8) Girls are slightly more likely to use Instagram-enabled and TikTok-enabled software on smartphones; 9) Frequency of use of SNS software on smartphones is a poor predictor of PSU rates. [ABSTRACT FROM AUTHOR]

CT angiography prior to endovascular aortic surgery is the standard non-invasive imaging method for evaluation of aortic dimensions and access sites. A detailed report is crucial to a proper planning. We assessed Artificial Intelligence (AI)-algorithm accuracy to measure vessels diameters at CT prior to transcatheter aortic valve implantation (TAVI). CT scans of 50 patients were included. Two Radiologists with experience in vascular imaging together manually assessed diameters at nine landmark positions according to the American Heart Association guidelines: 450 values were obtained. We implemented TOST (Two One-Sided Test) to determine whether the measurements were equivalent to the values obtained from the AI algorithm. When the equivalence bound was a range of ± 2 mm the test showed equivalence for every point; if the range was equal to ± 1 mm the two measurements were not equivalent in 6 points out of 9 (p-value > 0.05), close to the aortic valve. The time for automatic evaluation (average 1 min 47 s) was significantly lower compared with manual measurements (5 min 41 s) (p < 0.01). In conclusion, our results indicate that AI-algorithms can measure aortic diameters at CT prior to endovascular surgery with high accuracy. AI-assisted reporting promises high efficiency, reduced inter-reader variabilities and time saving. In order to perform optimal TAVI procedure planning aortic root analysis could be improved, including annulus dimensions. [ABSTRACT FROM AUTHOR]

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

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

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

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

Understanding porous media flow is inherently a multi-scale challenge, where at the core lies the aggregation of pore-level processes to a continuum, or Darcy-scale, description. This challenge is directly mirrored in image processing, where pore-scale grains and interfaces may be clearly visible in the image, yet continuous Darcy-scale parameters may be what are desirable to quantify. Classical image processing is poorly adapted to this setting, as most techniques do not explicitly utilize the fact that the image contains explicit physical processes. Here, we extend classical image processing concepts to what we define as "physical images" of porous materials and processes within them. This is realized through the development of a new open-source image analysis toolbox specifically adapted to time-series of images of porous materials. [ABSTRACT FROM AUTHOR]

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

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

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

Background: Computer software for absorbed dose quantification has been used widely in nuclear medicine. Different software tools have been written to improve the dose assessment, especially in therapeutic nuclear medicine. Some software tools focusing on computational phantom models from the international commission of radiation protection and units (ICRP) whilst others on Monte Carlo simulated models. While many studies have investigated therapeutic nuclear medicine dosimetry. The authors have noticed that very few papers compare the therapeutic software tools to each other, hence a doctor of philosophy study was embarked on. The aim of our study was therefore to validate our in-house developed software tool Masterdose using the commercial software OLINDA/EXM 1.0 that was available in our department. Methods: Methodology was based on clinical patient data treated for neuroendocrine tumours with 177Lutetium (Lu)-DOTATATE at a South African hospital. All patients underwent the same SPECT acquisition protocol and were corrected for scatter, partial volume, collimator-detector response, gamma camera calibration and attenuation. Correction factors were applied to images to convert counts to activity. The first cycle of peptide receptor radionuclide therapy (PRRT) for 11 single photon emission computed tomography (SPECT) patients were compared on the Masterdose and OLINDA/EXM 1.0 software tools at 1, 24, 72 and 168 h. Cumulated activity and the absorbed dose were compared for the two software tools. The absorbed dose difference was then compared using statistical Bland-Altman analysis. Results: Masterdose and OLINDA/EXM 1.0 had different peptide receptor radionuclide therapy methodologies. This led to different results obtained for the software tools. Cumulated activities of Masterdose and DTK was 10.5% and 10.9% for the kidneys and tumours respectively. On average tumour absorbed doses were nine-times that of the kidneys. Bland–Altman analysis show a non-systematic difference between the two software. Conclusion: On average the relative percentage difference between the cumulated activities and absorbed dose of the two software were 10.7%. [ABSTRACT FROM AUTHOR]

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

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

Background: A six year collaboration between academics, community pharmacists and informaticians, led to the development of nine guidelines for a clinical decision support system, enhancing community pharmacists' ability to address drug-related problems and improve care. Aim: The objective of this study was to assess the effectiveness of clinical decision support system rules in enhancing medication management within the community pharmacy setting. This was achieved through retrospective monitoring of real-world usage and measuring the pharmacotherapeutic impact of the rules. Method: In 2019, a retrospective observational evaluation appraised the acceptance rate of the clinical decision support system components in 490 Belgian pharmacies. Among these, 51 pharmacies underwent a longitudinal analysis involving (i) co-prescription of methotrexate and folic acid, (ii) gastroprotection with non-steroidal anti-inflammatory drugs, and (iii) drug combinations causing QT prolongation. The study period spanned one year pre-launch, one year post-launch, and two years post-launch. Results: Of the targeted pharmacies, 80% used 7 of the 9 rules. After four years, methotrexate-folic acid co-prescription increased 4%, reaching 79.8%. Gastroprotection improved by 3% among older patients and 7.47% in younger individuals (< 70 year) with multiple risk factors. The QT prolongation rules faced implementation difficulties. Conclusion: Pharmacists' acceptance of the developed rules was high and coincided with a decline in drug-related problems, holding potential public health impact. This real-world data can inform the future implementation of such systems, as it demonstrated the need for more detailed data-gathering and more intensive training of pharmacists in the handling of more complex problems such as QT prolongation. [ABSTRACT FROM AUTHOR]

Software quality is becoming a momentous challenge in software engineering processes, and software testing has a pivotal role in its measurements. Nature-inspired metaheuristic methods play an essential role in software testing, in which various studies have been conducted in this field; however, due to a lack of wide-ranging papers reviewing these methods, conducting a comprehensive systematic review to examine an array of crucial mechanisms in this field has become a necessity. This study aims to present a detailed analysis and taxonomically classifies the metaheuristic methods inspired by nature. This paper compromises a systematic literature review of 65 chosen studies published between 2015 and 2022. Genetic algorithm-based, hybrid, ant colony optimization-based, cuckoo search-based, firefly algorithm-based, artificial bee colony-based, and other metaheuristic methods constitute this systematic study's stratification. Evaluation methods, applied tools, merits, and demerits of each reviewed article are investigated. Additionally, future directions and open issues are addressed. This conducted paper not only expounds on software testing strengths, open issues, and future works, but also recognizes the quest for optimizing the insufficient metrics in software testing, such as mutation score, complexity, and scalability, which would be the propulsion of the testing process if consummated. [ABSTRACT FROM AUTHOR]

Introduction and hypothesis: Transperineal ultrasound (TPUS) is an effective tool for evaluating the integrity of the levator ani muscle (LAM). Several operating steps are required to obtain the standard multi-slice image of the LAM, which is experience dependent and time consuming. This study was aimed at evaluating the feasibility and reproducibility of the built-in software, Smart-pelvic™, in reconstructing standard tomographic images of LAM from 3D/4D TPUS volumes. Methods: This study was conducted at a tertiary teaching hospital, enrolling women who underwent TPUS. Tomographic images of the LAM were automatically reconstructed by Smart-pelvicTM and rated by two experienced observers as standard or nonstandard. The anteroposterior diameter (APD) of the levator hiatus was also measured on the mid-sagittal plane of the automatically and manually reconstructed images. The APD measurements of each approach were compared using Bland–Altman plots, and interclass correlation coefficient (ICC) was used to evaluate intra- and inter-observer reproducibility. Meanwhile, the time taken for the reconstruction process of both methods was also recorded. Results: The ultrasound volume of a total of 104 patients were included in this study. Using Smart-pelvicTM, the overall success rate of the tomographic image reconstruction was 98%. Regarding measurements of APD, the ICC between the automatic and manual reconstruction methods was 0.99 (0.98, 0.99). The average time taken for reconstruction per case was 2.65 ± 0.52 s and 22.08 ± 3.45 s, respectively. Conclusions: Using Smart-pelvicTM to reconstruct tomographic images of LAM is feasible, and it can promote TPUS by reducing operator dependence and improving examination efficiency in a clinical setting. [ABSTRACT FROM AUTHOR]

Background: Neurological disorders remain a worldwide concern due to their increasing prevalence and mortality, combined with the lack of available treatment, in most cases. Exploring protective and risk factors associated with the development of neurological disorders will allow for improving prevention strategies. However, ascertaining neurological outcomes in population-based studies can be both complex and costly. The application of eHealth tools in research may contribute to lowering the costs and increase accessibility. The aim of this systematic review is to map existing eHealth tools assessing neurological signs and/or symptoms for epidemiological research. Methods: Four search engines (PubMed, Web of Science, Scopus & EBSCOHost) were used to retrieve articles on the development, validation, or implementation of eHealth tools to assess neurological signs and/or symptoms. The clinical and technical properties of the software tools were summarised. Due to high numbers, only software tools are presented here. Findings: A total of 42 tools were retrieved. These captured signs and/or symptoms belonging to four neurological domains: cognitive function, motor function, cranial nerves, and gait and coordination. An additional fifth category of composite tools was added. Most of the tools were available in English and were developed for smartphone device, with the remaining tools being available as web-based platforms. Less than half of the captured tools were fully validated, and only approximately half were still active at the time of data collection. Interpretation: The identified tools often presented limitations either due to language barriers or lack of proper validation. Maintenance and durability of most tools were low. The present mapping exercise offers a detailed guide for epidemiologists to identify the most appropriate eHealth tool for their research. Funding: The current study was funded by a PhD position at the University of Groningen. No additional funding was acquired. [ABSTRACT FROM AUTHOR]

Objectives: To map the clinical use of CE-marked artificial intelligence (AI)–based software in radiology departments in the Netherlands (n = 69) between 2020 and 2022. Materials and methods: Our AI network (one radiologist or AI representative per Dutch hospital organization) received a questionnaire each spring from 2020 to 2022 about AI product usage, financing, and obstacles to adoption. Products that were not listed on www.AIforRadiology.com by July 2022 were excluded from the analysis. Results: The number of respondents was 43 in 2020, 36 in 2021, and 33 in 2022. The number of departments using AI has been growing steadily (2020: 14, 2021: 19, 2022: 23). The diversity (2020: 7, 2021: 18, 2022: 34) and the number of total implementations (2020: 19, 2021: 38, 2022: 68) has rapidly increased. Seven implementations were discontinued in 2022. Four hospital organizations said to use an AI platform or marketplace for the deployment of AI solutions. AI is mostly used to support chest CT (17), neuro CT (17), and musculoskeletal radiograph (12) analysis. The budget for AI was reserved in 13 of the responding centers in both 2021 and 2022. The most important obstacles to the adoption of AI remained costs and IT integration. Of the respondents, 28% stated that the implemented AI products realized health improvement and 32% assumed both health improvement and cost savings. Conclusion: The adoption of AI products in radiology departments in the Netherlands is showing common signs of a developing market. The major obstacles to reaching widespread adoption are a lack of financial resources and IT integration difficulties. Clinical relevance statement: The clinical impact of AI starts with its adoption in daily clinical practice. Increased transparency around AI products being adopted, implementation obstacles, and impact may inspire increased collaboration and improved decision-making around the implementation and financing of AI products. Key Points: • The adoption of artificial intelligence products for radiology has steadily increased since 2020 to at least a third of the centers using AI in clinical practice in the Netherlands in 2022. • The main areas in which artificial intelligence products are used are lung nodule detection on CT, aided stroke diagnosis, and bone age prediction. • The majority of respondents experienced added value (decreased costs and/or improved outcomes) from using artificial intelligence–based software; however, major obstacles to adoption remain the costs and IT-related difficulties. [ABSTRACT FROM AUTHOR]

Purpose: To evaluate the detection rate of feeding arteries in renal cell carcinoma with automated feeder-detection software and determine the optimal imaging phase for accurate feeder detection with transarterial time-resolved computed tomography angiography. Materials and Methods: The performance of automated feeder-detection software was retrospectively evaluated using transarterial renal time-resolved computed tomography angiography images of 15 renal cell carcinomas (mean size, 22.1 mm); the images were obtained via the renal artery using a hybrid angio-CT system with 320-row computed tomography, across nine phases with 0.5-s intervals over a contrast delay time of 1.0–5.0 s. Automated feeder-detection software was applied to each phase in all tumors (135 image series in total). The feeder-detection rate (i.e., sensitivity) in each phase was evaluated, and the number of false feeders demonstrated by the software was counted for each tumor. Results: A total of 22 feeders were identified. The feeder-detection rate was the highest (95.5% [21/22]) at delay times of 1.5 s and 2.0 s and lower in later phases. At delay times of 1.0 s and 1.5 s, the software demonstrated no or only a few (≤ 3) false feeders in 93.3% (14/15) of the tumors. In later phases, however, many (≥ 4) false feeders were observed in > 50% of tumors. Conclusion: The automated feeder-detection software showed a favorable feeder-detection rate and may be useful in transarterial embolization for renal cell carcinoma. The optimal delay time to avoid the demonstration of false feeders and achieve a high detection accuracy was 1.5 s. Level of Evidence IV: Case Series. [ABSTRACT FROM AUTHOR]

The introduction of information systems and appropriate software at enterprises is considered, by means of various assessment methods. The benefits of information systems for manufacturing enterprises are outlined. Economic methods and indices are discussed. Criteria for assessing the effectiveness of software packages are proposed. [ABSTRACT FROM AUTHOR]

Practical work results on the development of an experimental stand for wear tests of millstones for grinding of grain are presented. When designing the stand, the method of testing for abrasive wear during friction against loosely fixed abrasive particles was used. A feature of the test bench is that the movable and fixed millstones are made cone-shaped and during testing are in oscillatory motion relative to the stationary stand platform. The test bench is equipped with a drive shaft speed sensor, a temperature sensor and a strain gauge force sensor. The Arduino IDE microcontroller software is used to process the incoming data. The software allows one to display the current values of the measured parameters on the screen of a personal computer monitor. All data are saved to files for further processing, which significantly reduces the time for experimental studies. [ABSTRACT FROM AUTHOR]

Analysis of chemical exchange saturation transfer (CEST) MRI data requires sophisticated methods to obtain reliable results about metabolites in the tissue under study. CEST generates z-spectra with multiple components, each originating from individual molecular groups. The individual lines with Lorentzian line shape are mostly overlapping and disturbed by various effects. We present an elaborate method based on an adaptive nonlinear least squares algorithm that provides robust quantification of z-spectra and incorporates prior knowledge in the fitting process. To disseminate CEST to the research community, we developed software as part of this study that runs on the Microsoft Windows operating system and will be made freely available to the community. Special attention has been paid to establish a low entrance threshold and high usability, so that even less experienced users can successfully analyze CEST data. [ABSTRACT FROM AUTHOR]

We introduce Shennong, a Python toolbox and command-line utility for audio speech features extraction. It implements a wide range of well-established state-of-the-art algorithms: spectro-temporal filters such as Mel-Frequency Cepstral Filterbank or Predictive Linear Filters, pre-trained neural networks, pitch estimators, speaker normalization methods, and post-processing algorithms. Shennong is an open source, reliable and extensible framework built on top of the popular Kaldi speech processing library. The Python implementation makes it easy to use by non-technical users and integrates with third-party speech modeling and machine learning tools from the Python ecosystem. This paper describes the Shennong software architecture, its core components, and implemented algorithms. Then, three applications illustrate its use. We first present a benchmark of speech features extraction algorithms available in Shennong on a phone discrimination task. We then analyze the performances of a speaker normalization model as a function of the speech duration used for training. We finally compare pitch estimation algorithms on speech under various noise conditions. [ABSTRACT FROM AUTHOR]

Camera traps have revolutionized how ecologists monitor wildlife, but their full potential is realized only when the hundreds of thousands of collected images can be readily classified with minimal human intervention. Deep learning classification models have allowed extraordinary progress towards this end, but trained models remain rare and are only now emerging for European fauna. We report on the first milestone of the DeepFaune initiative (https://www.deepfaune.cnrs.fr), a large-scale collaboration between more than 50 partners involved in wildlife research, conservation and management in France. We developed a classification model trained to recognize 26 species or higher-level taxa that are common in Europe, with an emphasis on mammals. The classification model achieved 0.97 validation accuracy and often > 0.95 precision and recall for many classes. These performances were generally higher than 0.90 when tested on independent out-of-sample datasets for which we used image redundancy contained in sequences of images. We implemented our model in a software to classify images stored locally on a personal computer, so as to provide a free, user-friendly, and high-performance tool for wildlife practitioners to automatically classify camera trap images. The DeepFaune initiative is an ongoing project, with new partners joining regularly, which allows us to continuously add new species to the classification model. [ABSTRACT FROM AUTHOR]

This review paper surveys recent development in software implementations for extreme value analyses since the publication of Stephenson and Gilleland (Extremes 8:87–109, 2006) and Gilleland et al. (Extremes 16(1):103–119, 2013). We provide a comparative review by topic and highlight differences in existing numerical routines, along with listing areas where software development is lacking. The online supplement contains two vignettes comparing implementations of frequentist and Bayesian estimation of univariate extreme value models. [ABSTRACT FROM AUTHOR]

The design of spacecraft components by means of digital technology is analyzed. The concept of digital space is adopted as the basis; the role of this concept in the life cycle of aerospace components is considered. Such research is prompted by the increasing production of aerospace systems and the simultaneous tightening of the requirements on product quality and reliability. The consequences of employing digital models are discussed, at different stages of the product life cycle. Problems that arise in aerospace applications of digital technology to aerospace systems are studied. Software for the creation and use of digital models and digital twins is reviewed. As a practical example, software for modeling the properties of polymer composite in the design of aerospace components is considered. A polymer composite consists of layers with very different properties. Because the layers may be combined by various methods, it is difficult to determine the physical properties of the composite. The best approach is virtual testing of a digital twin of the material. The mechanical properties of a unidirectional composite are also determined and compared with an existing analytical solution. [ABSTRACT FROM AUTHOR]

The birds (pigeons) are greatest gift to mankind. Pigeons have been used for sending messages to loved ones. These birds are found living with human surroundings. Due to presence of protein (criptochrome4) in retina of eye, these birds have navigational capabilities. Most of the pigeons birds breed and stay at one place for longer duration. Their droppings have been much cause of concern, since it is unhygienic and may spread diseases in the neighbourhoods. Sometimes, pigeon menace may not allow new-born babies to sleep at home, due to cooing and flapping sounds in the balcony. As per research available, their ear hearing frequency falls between 0.5 and 20 Hz (infrasonic frequency spectrum) and 4–6 kHz(sonic or audio frequency range). This paper proposed a solution to stop pigeon nuisances in domestic areas with short-range electronic device. An electronic system is proposed to operate at 15 Hz. The electronic system generates infrasonic signals and emitted towards area of interest at a level of 90–115 dB. The electronic device infrasonic emissions will keep pigeons away from designated area due to irritation or discomfort to the birds. Also, these infrasonic signals are not audible to humans. Hence, electronic device is silent in operation. In this device, 5 kHz test signal is also generated. This is used as audible test signal to ascertain its serviceability at the fields. The work done is purely intended to keep the pigeons away. The design of infrasonic electronic system is indigenous, safe, programmable and cost effective. The design and implementation of infrasonic pigeon away concept is novel concept with a mix of software and Hardware. [ABSTRACT FROM AUTHOR]

Basic ICT competence beliefs consist of a range of competence dimensions, including communication and collaboration and safety and security. Yet, unclear is if (student) teachers need to overall feel competence in using technology, or is feeling competent in specific competence dimensions sufficient for the use of technology. This study addressed this research lack and examined (a) basic ICT competence beliefs and technology use in different stages of teacher education and (b) the association between basic ICT competence beliefs and various hardware and software used. Data included 338 teachers and 343 student teachers from Germany and were analyzed using bifactor exploratory structural equation models. These models allow us to simultaneously account for general basic ICT competence beliefs (G-Factor) and specific basic ICT competence beliefs (S-Factors). The study found that teachers, on average, reported higher basic ICT competence beliefs than student teachers. For both student teachers and teachers, the use of wikis and forum was linked to higher general ICT competence beliefs. The use of LMS was linked to basic ICT competence beliefs in safety and security and problem solving. The study also identified unique links in both groups, such as wiki and weblog use being linked to higher general ICT competence beliefs for teachers. In various teacher education phases, we can emphasize the need to promote basic ICT competence beliefs to empower teachers in the digital transformation. By interpreting these results, however, it should be noted that our analysis only focused on utilizing particular hardware and software; replications of findings are also needed. [ABSTRACT FROM AUTHOR]

With only a few exceptions, U.S. copyright law does not allow copyrights for software created by the U.S. federal agencies' government-operated laboratories. This paper explains the public policy concern that the inability to copyright the software causes a barrier to the transfer of government software to the private sector. Using a survey of developers and managers of custom software developed within the federal agencies, the paper presents and interprets new evidence supporting the view that allowing copyright protection would dramatically increase the transfer of the technology. [ABSTRACT FROM AUTHOR]

Background: Different volume of interest (VOI) sizes influence radiomic features. This study examined if translating images into feature maps before feature sampling could compensate for these effects in liver magnetic resonance imaging (MRI). Methods: T1- and T2-weighted sequences from three different scanners (two 3-T scanners, one 1.5-T scanner) of 66 patients with normal abdominal MRI were included retrospectively. Three differently sized VOIs (10, 20, and 30 mm in diameter) were drawn in the liver parenchyma (right lobe), excluding adjacent structures. Ninety-three features were extracted conventionally using PyRadiomics. All images were also converted to 93 parametric feature maps using a pretested software. Agreement between the three VOI sizes was assessed with overall concordance correlation coefficients (OCCCs), while OCCCs > 0.85 were rated reproducible. OCCCs were calculated twice: for the VOI sizes of 10, 20, and 30 mm and for those of 20 and 30 mm. Results: When extracted from original images, only 4 out of the 93 features were reproducible across all VOI sizes in T1- and T2-weighted images. When the smallest VOI was excluded, 5 features (T1-weighted) and 7 features (T2-weighted) were reproducible. Extraction from parametric maps increased the number of reproducible features to 9 (T1- and T2-weighted) across all VOIs. Excluding the 10-mm VOI, reproducibility improved to 16 (T1-weighted) and 55 features (T2-weighted). The stability of all other features also increased in feature maps. Conclusions: Translating images into parametric maps before feature extraction improves reproducibility across different VOI sizes in normal liver MRI. Relevance statement: The size of the segmented VOI influences the feature quantity of radiomics, while software-based conversion of images into parametric feature maps before feature sampling improves reproducibility across different VOI sizes in MRI of normal liver tissue. Key points: • Parametric feature maps can compensate for different VOI sizes. • The effect seems dependent on the VOI sizes and the MRI sequence. • Feature maps can visualize features throughout the entire image stack. [ABSTRACT FROM AUTHOR]

Applications running in a large and complex manycore system can significantly benefit from adopting the dataflow model of computation. In a dataflow execution environment, a thread can run only if all its required inputs are available. While the potential benefits are large, it is not trivial to improve resource utilization and energy efficiency by focusing on dataflow thread execution models (i.e., the ways specifying how the threads adhering to a dataflow model of computation execute on a given compute/communication architecture). This paper proposes and implements a hardware-software co-design-based dataflow threads management framework. It works at the Network-on-Chip (NoC) level and consists of three stages. The first stage focuses on a fast and effective thread distribution policy. The next stage proposes an approach that adds reconfigurability to a 2D mesh NoC via customized instructions to manage the dataflow thread distribution. Finally, a 2D mesh and ring-based hybrid NoC is proposed for better scalability and higher performance. This work can be considered a primary reference framework from which extensions can be carried out. [ABSTRACT FROM AUTHOR]

Purpose: Precise preoperative planning is mandatory when a double-level osteotomy (DLO) is required to correct a severe knee deformity. Literature does not report a validated planning method regarding DLO that could be performed directly on digital radiographs using simple measurement tools. This study aims to validate a novel DLO planning method called New Mikulicz-Joint Line (NM-JL) based on essential measurement tools, in which the correction angles are induced by the predicted post-operative joint line obliquity (JLO). Methods: Twenty-three patients who satisfied the inclusion criteria were enrolled. NM-JL planning method was performed using basic measurement tools to detect corrective angles and gaps. The correction was then simulated using a Virtual Segmentation Software method to obtain the osteotomy fragments. Both planning procedures were performed independently and later repeated by two orthopaedic surgeons to assess the inter and intra-observer reliability. Results: The intraclass correlation coefficient (ICC) regarding corrective angles and gaps showed a significant positive correlation between the values determined using the two procedures by both raters (p < 0.05). Pearson's correlation analysis revealed a significant correlation between the measured results of the two planning methods. (p < 0.05). Finally, the Bland–Altman analysis showed an excellent agreement (p < 0.05) for all measurements performed. Conclusions: The NM-JL method showed high values of intra and inter-rater reliability. The procedure is built up starting from the predicted value of post-operative joint line obliquity, allowing to maintain this parameter fixed. Other advantages include the quickness, adaptability, and possibility to be performed on any Digital Imaging and Communication in Medicine (DICOM) viewer. Level of evidence: Level IV. [ABSTRACT FROM AUTHOR]

The paper presents a description of the information modeling system for the distribution of hot blast and natural gas through the tuyeres of a blast furnace. The developed system is distinguished by considering the main physical and chemical laws and ongoing processes in the control object. The system is based on the method of determining the blast flow through the air tuyeres using the thermal power of the flow passing through the tuyere and the value of the heat removal taken from this tuyere. Based on the calculation results, the system determines the main parameters of the blast mode of the blast furnace and the parameters of individual tuyere hearths, including the theoretical combustion temperature, the blast velocity from the tuyere, the kinetic energy of the blast, the length of the oxygen and carbon dioxide combustion zones of the tuyere hearth, and others. The required natural gas consumption through the blast furnace tuyeres is calculated, which ensures the maintenance of the theoretical combustion temperature in the blast furnace tuyeres at a given level in the design mode. The architecture of the software is described, the characteristics of the modules are presented, and its operation is illustrated. The developed software can be used by the technological personnel of the blast-furnace process to improve the efficiency of blast-furnace smelting. [ABSTRACT FROM AUTHOR]

Purpose: Deep brain stimulation (DBS) relies on precise targeting of key structures such as the subthalamic nucleus (STN) for Parkinson's disease (PD) and the ventro-intermedius nucleus of the thalamus (Vim) for essential tremor (ET). Segmentation software, such as GuideXT© and Suretune©, are commercially available for atlas-based identification of deep brain structures. However, no study has compared the concordance of the segmentation results between the two software. Methods: We retrospectively compared the concordance of segmentation of GuideXT© and Suretune© software by comparing the position of the segmented key structures with clinically predicted targets obtained using the newly developed RebrAIn© software as a reference. Results: We targeted the STN in 44 MRI from PD patients (88 hemispheres) and the Vim in 31 MRI from ET patients (62 hemispheres) who were elected for DBS. In 22 STN targeting (25%), the target positioning was not correlating between GuideXT© and Suretune©. Regarding the Vim, targets were located in the segmented Vim in 37%, the posterior subthalamic area (PSA) in 60%, and the STN in 3% of the cases using GuideXT©; the proportions were 34%, 60%, and 6%, respectively, using Suretune©. The mean distance from the centre of the RebrAIn© targeting to the segmented Vim by Suretune© was closer (0.64 mm) than with GuideXT© (0.96 mm; p = 0.0004). Conclusion: While there is some level of concordance in the segmentation results of key structures for DBS treatment among software models, differences persist. Therefore, such software should still be considered as tools and should not replace clinician experience in DBS planning. [ABSTRACT FROM AUTHOR]

This paper presents the architecture and features of RiskScape software. RiskScape is an open-source software with a flexible modelling engine for multi-hazard risk analysis. The RiskScape engine implements modeller-defined risk quantification workflows as 'model pipelines'. Model pipeline steps and functions analyse hazard, exposure, and vulnerability data across different spatio-temporal domains using geoprocessing and spatial sampling operations. The RiskScape engine supports deterministic and probabilistic risk quantification, with several probabilistic-based modes described in this paper. RiskScape advances modelling software for multi-hazard risk analysis through several implementation features. The RiskScape engine operates model pipelines independent of system prescribed model input data classifications or standards. Multiple hazard types, metric intensities, and temporal occurrence information is geometry processed and sampled to create coverage data of simultaneous or sequenced multi-hazard events at object-exposure locations. Escalating multi-hazard event impacts are then determined for object-exposures using scripted conditional or nested statements that apply vulnerability functions in a logical sequence of temporal hazard and impact occurrence. These model features, supported by open geospatial consortium standard geospatial data files and operations, expedite RiskScape for modelling multi-hazard risk at any geographical location or scale. [ABSTRACT FROM AUTHOR]

In civil engineering, the analysis and characterisation of structural phenomena is key for the deviation of mechanical models and strongly depends on experimental studies. For this reason, the development of new measurement techniques plays a crucial role in research to allow capturing the mechanical behaviour of structures. In this context, distributed fibre optic sensing (DFOS) gained attention during the last years. Advantageous properties such as minimal invasiveness and quasi-continuous strain measurement enable new possibilities in structural monitoring. DFOS is able to sense minimal strain variations, yet this often results in recording unwanted anomalies. Thus, to generate the best results from raw measurement data, it is crucial to use a robust and reliable post-processing procedure. In order to meet this requirement, the software solution FOS Evaluator was developed using the Python programming language, targeting the most important aspects that may occur when using DFOS. Those aspects comprise a high data volume, disturbances and anomalies in the measured data and the necessity to carry out calculations with acquired data sets. In this paper, functionality and background of FOS Evaluator are presented and discussed. Furthermore, several methods for post-processing and evaluating DFOS measurements are presented, consisting of multiple functions for reducing, cleaning, or smoothing strain data, and calculating various mechanical properties from filtered measurements. Finally, the functional scope of FOS Evaluator is illustrated by various application examples in structural concrete. [ABSTRACT FROM AUTHOR]

This paper conceptualizes the main principles of comprehensive software verification for an onboard spacecraft control system. An optimal comprehensive verification strategy for onboard software is selected by rigorously stating and solving the corresponding optimization problem. Software verification methods with functional correctness indicators are proposed. [ABSTRACT FROM AUTHOR]

It is a priority today to develop financial models adequate to the commercialization of the outcome of research and development at industrial enterprises. Given the diversity of outcomes, it is a challenge to establish a set of adequate models [1, 2]. Such models must also take account of the type of results, legal constraints, significant design variants, and repayment requirements. In the present work, principles are outlined for formulating financial models that may be used in the commercialization of innovative research aimed at developing a system for automated monitoring of power plant equipment. Specifically, the use of drones for that purpose is considered. Possible sales plans, system options, and competitive pricing are considered. The efficiency of the research expenditures is assessed, taking account of the profits on sales. The relation between the prices of system options and the expected net discounted income is plotted. [ABSTRACT FROM AUTHOR]

The influence of the shaping control strategy for the blades of gas-turbine engines in belt grinding on CNC machines is investigated. Software is developed for different control strategies at different stages of machining, with the goal of eliminating the errors arising in the preceding operation and maximizing the productivity, while ensuring specified accuracy and roughness of the machined surface. [ABSTRACT FROM AUTHOR]

Background: Deep Inferior Epigastric Perforator Flap (DIEP) surgical procedures have benefited in recent years from the introduction of 3D printed models, yet new technologies are expanding design opportunities which promise to improve patient specific care. Numerous studies, utilizing 3D printed models for DIEP, have shown a reduction of surgical time and complications when used in addition to the review of standard CT imaging. A DIEP free flap procedure requires locating the inferior epigastric perforator vessels traversing and perforating the rectus abdominis muscle, perfusing the abdominal skin and fatty tissue. The goal of dissecting the inferior epigastric perforator vessels is complicated by the opacity of the fatty tissue and muscle. Previous attempts to 3D print patient specific models for DIEP free flap cases from CT imaging has shown a wide range of designs which only show variations of perforator arteries, fatty tissue, and the abdominis rectus muscle. Methods: To remedy this limitation, we have leveraged a voxel-based modeling environment to composite complex modeling elements and incorporate a ruled grid upon the muscle providing effortless 'booleaning' and measured guidance. Results: A limitation of digital surface-based modeling tools has led to existing models lacking the ability to composite critical anatomical features, such as differentiation of vessels through different tissues, coherently into one model, providing information more akin to the surgical challenge. Conclusion: With new technology, highly detailed multi-material 3D printed models are allowing more of the information from medical imaging to be expressed in 3D printed models. This additional data, coupled with advanced digital modeling tools harnessing both voxel- and mesh-based modeling environments, is allowing for an expanded library of modeling techniques which create a wealth of concepts surgeons can use to assemble a presurgical planning model tailored to their setting, equipment, and needs. Trial registration: COMIRB 21–3135, ClinicalTrials.gov ID: NCT05144620. [ABSTRACT FROM AUTHOR]

Exploitation of the ever-increasing volume of observational data has become a major challenge for real-world evidence (RWE). Causal inference can be viewed as emulations of clinical trials (ECTs) from real-world data (RWD). This requires in-depth knowledge of data management and modern statistical models, which evolve considerably. We have developed Plug-Stat® to facilitate these analyses. Plug-Stat® is a user-friendly graphical interface of R software implemented with JavaScript and VB.net. The implementation of Plug-Stat® for a specific database consists of two main tasks. The first concerns the automatic importation of data to link the software with the data and to avoid the repetition of data management activities for each study. The second is to define the outcomes and the associated possible confounders. To perform an ECT, we proposed the use of inverse probability weighting, flexible parametric estimation of the propensity score model with B-splines, and robust estimation of the variance. The statistical modeling steps are automatized to direct the user toward the correct methodology without data management. We illustrated the usefulness of Plug-Stat® with several recently published ECTs. In this paper, we presented the outcome comparison of preemptive deceased-donor kidney transplantations versus transplantations after dialysis. The interactive interfaces assist the user in conducting causal research based on ECT, and tutorials are available, offering self-training opportunities. Plug-Stat® reduces the time and, consequently, the cost necessary for different studies from the same cohort. [ABSTRACT FROM AUTHOR]

Genotyping individuals using forensic or non-invasive samples such as hair or fecal samples increases the risk of allelic amplification failure (dropout) due to the low quality and quantity of DNA. One way to decrease genotyping errors is to increase the number of replicates per sample. Here, we have developed the software SNP+ to estimate the dropout probability and the subsequent required number of replicates to obtain the reliable genotype with probability 95%. Moreover, the software predicts the minor allele frequency and compares two competing models assuming equal or allele-specific dropout probabilities by Bayes factor. The software handles data from one SNP to high density arrays (e.g., 100,000 SNPs). [ABSTRACT FROM AUTHOR]

Objectives: To provide radiologists and physicists with methodological tools to improve patient management after vascular fluoroscopically guided intervention (FGI) by providing optimized thresholds (OT) values that could be used as a surrogate to the thresholds classically proposed by the National Council on Radiation Protection (NCRP) or could be useful to adapt their own substantial radiation dose levels (SRDL) values. Methods: PSD of 2000–4000 mGy after FGI were calculated for 258 patients with dedicated software. Overall, the kerma and KAP 3D-ROC curves were used to assess the sensitivity (SEN) and specificity (SPE) of NCRP thresholds and OT for each PSD. Kiviat diagram and density curves were plotted for the best SEN/SPE pair of 3D-ROC curves and compared to the NCRP thresholds. Results: OT for both kerma and KAP generating the best SEN/SPE couple for PSD of 2000–4000 mGy were obtained. The SEN/SPE couple of each OT was always better than that obtained using NCRP ones. The best OT among all those calculated providing the highest SEN/SPE values for kerma (3020.5 mGy) and KAP (741.02 Gy.cm2) were obtained when PSD was equal to 3300 mGy. Conclusions: We have calculated OT in terms of kerma and KAP based on 3D-ROC curves analysis and peak skin dose calculations that can be obtained to better predict high skin dose. The use of OT that predicted PSD greater than 3000 mGy is likely to improve patient follow-up. The methodology developed in this work could be adapted to other institutions in order to better define their own SRDL. Key Points: • Optimized dose thresholds in terms of kerma and KAP based on 3D-ROC curves analysis and peak skin dose calculations between 2000 and 4000 mGy can be obtained to better predict high skin dose. • Patients receiving a peak skin dose between 2000 and 4000 mGy have their follow-up enhanced by using the optimized thresholds instead of the NCRP thresholds. • The best-optimized thresholds, corresponding to 3020.5 mGy and 741.02 Gy.cm2 for kerma and KAP respectively can be used instead of NRCP ones to trigger patient follow-up after fluoroscopically guided vascular interventions. [ABSTRACT FROM AUTHOR]