Stand-alone Version Research Articles

The persian version of mhealth app usability questionnaire (MAUQ) for patients: a psychometric assessment study

IntroductionThe growing importance of mobile apps in osteoporosis management highlights the crucial need for evaluating their utility and usability, particularly for Osteoporosis support apps. Addressing this need, the mHealth App Usability Questionnaire (MAUQ) was crafted in four different versions, categorized based on the nature of the app (interactive or standalone) and the intended user (patient or provider). Due to its usage by diverse users with varying languages, this questionnaire requires psychometric assessment in multiple languages. This study aimed to translate and validate the Persian version of MAUQ for patients.MethodAfter translating the standalone and interactive versions of MAUQ into the Persian language, face validity, content validity, and factor analysis were conducted. Ten patients with osteoporosis were involved for face validity, and ten experts in medical informatics and health information technology were invited to assess content validity by completing a questionnaire. A total of 99 patients with osteoporosis participated in the factor analysis. The reliability of the questionnaires was assessed by calculating Cronbach’s alpha.ResultsThe face validity and the content of the Persian version of MAUQ were confirmed. Factor analysis of the standalone version of MAUQ showed 18 items in three dimensions: easy to use (7 items), user interface and satisfaction (6 items), and usefulness (5 items). Factor analysis of the interactive version of MAUQ showed 21 items in two dimensions: easy to use and satisfaction (11 items) and information arrangement and usefulness (10 items). The Cronbach’s alpha of the questionnaire for standalone and interactive applications was 0.90.ConclusionThe psychometric assessment of the Persian MAUQ established its validity and reliability among osteoporosis patients, affirming its efficacy as a robust tool for evaluating mHealth app usability.

Accurate and Fast Prediction of Intrinsic Disorder Using flDPnn.

Intrinsically disordered proteins (IDPs) that include one or more intrinsically disordered regions (IDRs) are abundant across all domains of life and viruses and play numerous functional roles in various cellular processes. Due to a relatively low throughput and high cost of experimental techniques for identifying IDRs, there is a growing need for fast and accurate computational algorithms that accurately predict IDRs/IDPs from protein sequences. We describe one of the leading disorder predictors, flDPnn. Results from a recent community-organized Critical Assessment of Intrinsic Disorder (CAID) experiment show that flDPnn provides fast and state-of-the-art predictions of disorder, which are supplemented with the predictions of several major disorder functions. This chapter provides a practical guide to flDPnn, which includes a brief explanation of its predictive model, descriptions of its web server and standalone versions, and a case study that showcases how to read and understand flDPnn's predictions.

Prediction of Disordered Linkers Using APOD.

Intrinsically disordered linkers (DLs) connect protein domains and structural elements within domains and facilitate allosteric regulation. Computational studies suggest that thousands of proteins have DLs. Since there are only about 250 proteins with manually curated DL annotations (DisProt database ver. 9.3), computational approaches that make accurate predictions of DLs from the protein sequences are essential for reducing this annotation gap. To this end, we recently released the Accurate Predictor Of DLs (APOD) method. Empirical tests show that APOD achieves Area Under the ROC Curve (AUC) of 0.82 and Matthews Correlation Coefficient (MCC) of 0.42 on a low-similarity test dataset. We implement APOD as a freely available and convenient web server at https://yanglab.qd.sdu.edu.cn/APOD/ . This web server takes a protein sequence as the input and outputs an easy-to-parse prediction result, with the entire prediction process done on the server side. We also provide a standalone version of APOD for users who want to process large datasets of sequences. This version must be installed and run locally on the end user's computer. In this chapter, we overview APOD, explain how to locate and use the web server and the standalone implementation, and discuss how to read and interpret APOD's outputs. We also demonstrate utility of APOD based on a case study protein.

The psychometric assessment of the provider version of mHealth App Usability Questionnaire (MAUQ) in persian language

IntroductionmHealth apps are widely utilized in healthcare. To guarantee their usefulness and usability, it is crucial to assess them using a reliable scale tailored specifically for mHealth apps and their users.ObjectiveThe aim of this study is the psychometric assessment of the provider version of mHealth App Usability Questionnaire (MAUQ) in Persian language.MethodThe Persian translations of standalone and interactive versions of the MAUQ for healthcare providers underwent validation. Face validity, content validity, and factor analysis were conducted to validate these two versions. Ten nurses evaluated face validity, while ten nursing and psychometric analysis experts assessed content validity. Factor analysis involved 98 nurses. The reliability of the questionnaires was measured using Cronbach’s alpha.ResultsThe translated questionnaires were validated, confirming both face validity (impact score ≥ 2.40) and content validity (k*≥0.66). The Persian version of the MAUQ for standalone applications had 18 items across two dimensions: easy to use and usefulness (11 items) and user interface and satisfaction (7 items). The Persian version of the MAUQ for interactive applications had 21 items across three dimensions: easy to use (4 items), usefulness (5 items), and user interface and satisfaction (12 items). Both standalone and interactive versions demonstrated high internal consistency with a Cronbach’s alpha of 0.96.ConclusionsThe psychometric assessment of the provider version of MAUQ in Persian language has the reliability and validity required to assess mHealth applications usability.

The Short Form 6 Dimensions (SF-6D): Development and Evolution.

This paper considers the development and evolution of the short-form 6 dimensions (SF-6D), a generic preference-weighted measure consisting of a health classification with accompanying value set that was developed from one of the widest used health related quality of life measures, the SF-36 health survey. This enabled health state utility values to be directly generated from SF-36 and SF-12 data for a range of purposes, including to produce quality adjusted life years for use in economic evaluation of healthcare interventions across a range of different conditions and treatments. This paper considers the rationale for the development of the measure, the development process, performance and how the SF-6D has evolved since its conception. This includes the development of an updated version, SF-6D version 2 (SF-6Dv2), which was generated to deal with some criticisms of the first version, and now includes a standalone version for inclusion in studies without relying on use of SF-36 or SF-12. Valuation methods have also evolved, from standard gamble in-person interviews to online discrete choice experiment surveys. International work related to the SF-6Dv1 and SF-6Dv2 is considered. We also consider recommendations for use, highlighting key psychometric evidence and reimbursement agency recommendations.

Impacts of Parameterizing Estuary Mixing on the Large-Scale Circulations in the Community Earth System Model

Abstract Riverine outflow between the land surface/cryosphere and the ocean undergoes intricate physical and biogeochemical transformations in estuaries before it finally merges with oceanic waters. To enhance our understanding of these transformations, estuary box models (EBMs) are being incorporated into comprehensive Earth system models. These models aim to refine our knowledge of both physical and biogeochemical processes. In our study, we conducted simulations using the Community Earth System Model, version 2, both with and without the inclusion of an EBM that was jointly developed by the University of Connecticut and the National Center for Atmospheric Research and by default included in the climate model. The objective was to examine the influence of these modifications on global climate patterns. We performed these simulations under fixed atmospheric and runoff conditions, using a standalone version of the ocean/sea ice components of the model. Additionally, we conducted a fully coupled Earth system model simulation at a 2° atmosphere and 1° ocean resolution. The implementation of the EBM into the ocean component of the model resulted in regional variations and noticeable improvements in the salinity distribution on the Siberian shelves and at the Amazon outflow. Interestingly, our findings revealed that the tropical Atlantic Ocean plays a significant role in controlling the global salinity distribution. Due to the tropical Atlantic circulation, which redirects thermocline water southward while allowing surface waters to continue northward, the improved vertical mixing in the EBM leads to an accumulation of salt in the North Atlantic and a freshening of other ocean basins. This shift subsequently results in an intensification of the Atlantic meridional overturning circulation and a northward shift of tropical precipitation patterns. Significance Statement This research investigates the impact of riverine outflow interactions with ocean waters on global climate dynamics. Implementing an enhanced representation of river runoff within a global climate model reveals that the tropical Atlantic region is pivotal in regulating the salinity distribution across the world’s oceans. This dynamic leads to an increased salt concentration in the North Atlantic and extends its influence to other ocean basins. The resulting shifts in salinity have profound consequences, as they alter rainfall patterns in the tropics. The insights gained from this study contribute to the advancement of climate projections, offering society a valuable tool to anticipate and adapt to the impending effects of climate change more effectively.

Reference Architecture for the Integration of Prescriptive Analytics Use Cases in Smart Factories

Prescriptive analytics plays an important role in decision making in smart factories by utilizing the available data to gain actionable insights. The planning, integration and development of such use cases still poses manifold challenges. Use cases are still being implemented as standalone versions; the existing IT-infrastructure is not fit for integrative bidirectional decision communication, and implementations only reach low technical readiness levels. We propose a reference architecture for the integration of prescriptive analytics use cases in smart factories. The method for the empirically grounded development of reference architectures by Galster and Avgeriou serves as a blueprint. Through the development and validation of a specific IoT-Factory use case, we demonstrate the efficacy of the proposed reference architecture. We expand the given reference architecture for one use case to the integration of a smart factory and its application to multiple use cases. Moreover, we identify the interdependency among multiple use cases within dynamic environments. Our prescriptive reference architecture provides a structured way to improve operational efficiency and optimize resource allocation.

Technical note: A software tool to extract complexity metrics from radiotherapy treatment plans.

Complexity metrics are mathematical quantities designed to quantify aspects of radiotherapy treatment plans that may affect both their deliverability and dosimetric accuracy. Despite numerous studies investigating their utility, there remains a notable absence of shared tools for their extraction. This study introduces UCoMX (Universal Complexity Metrics Extractor), a software package designed for the extraction of complexity metrics from the DICOM-RT plan files of radiotherapy treatments. UCoMX is developed around two extraction engines: VCoMX (VMAT Complexity Metrics Extractor) for VMAT/IMRT plans, and TCoMX (Tomotherapy Complexity Metrics Extractor) tailored for Helical Tomotherapy plans. The software, built using Matlab, is freely available in both Matlab-based and stand-alone versions. More than 90 complexity metrics, drawn from relevant literature, are implemented in the package: 43 for VMAT/IMRT and 51 for Helical Tomotherapy. The package is designed to read DICOM-RT plan files generated by most commercially available Treatment Planning Systems (TPSs), across various treatment units. A reference dataset containing VMAT, IMRT, and Helical Tomotherapy plans is provided to serve as a reference for comparing UCoMX with other in-house systems available at other centers. UCoMX offers a straightforward solution for extracting complexity metrics from radiotherapy plans. Its versatility is enhanced through different versions, including Matlab-based and stand-alone, and its compatibility with a wide range of commercially available TPSs and treatment units. UCoMX presents a free, user-friendly tool empowering researchers to compute the complexity of treatment plans efficiently.

Combined deep-learning optimization predictive models for determining carbon dioxide solubility in ionic liquids

This study explores the development of predictive models for carbon dioxide (CO2) solubility in ionic liquids based on a compiled dataset of 10,116 experimentally measured data points involving four input variables: pressure (P), temperature (T), cation type, and anion type. The deep-learning (DL) predictive models evaluated are standalone and hybrid versions of convolutional neural network (CNN) and long short-term memory (LSTM) algorithms with cuckoo optimization algorithm (COA) and gradient-based optimization (GBO). The laboratory-measured data was separated into training and test categories, and each category was normalized separately to improve the performance of the deep learning algorithms. The Mahalanobis distance-based quantile method was utilized to identify any outliers in the training data. Once identified, the outlier data points were eliminated from the training dataset. The control parameters of the deep learning algorithms were optimized using COA to enhance their efficiency, and the algorithms were hybridized with optimization algorithms to further improve their performance. The resulting models were analyzed to assess their accuracy, degree of overfitting, and the importance of input features. The study found that using 80% of the data for training and 20% for testing results in more accurate and generalizable models. Using the outlier detection method on the training data led to 307 data points being eliminated as outliers. Developing CO2-solubility predictive model showed that, the CNNCOA model had the lowest RMSE and highest R2 among the developed models, indicating high generalizability for data unseen by the trained model. The analysis revealed that using optimization algorithms increased the CO2-solubility prediction performance of DL algorithms and reduced overfitting. T and cation type were the most and least important input features, respectively. Simultaneous changes in cation and anion type on CO2-solubility predictions displayed no systematic pattern. For increases in T, CO2 solubility typically decreased, whereas for increases in P CO2 solubility always increased but at variable rates. The results of this study can be used to develop accurate and generalizable CO2-solubility predictive models for various applications.

A simple MATLAB toolbox for analyzing calcium imaging data in vitro and in vivo

BackgroundFluorescence imaging of calcium dynamics in neuronal populations is powerful because it offers a way of relating the activity of individual cells to the broader population of nearby cells. The method’s growth across neuroscience has particularly been driven by the introduction of sophisticated mathematical techniques related to motion correction, image registration, cell detection, spike estimation, and population characterization. However, for many researchers, making good use of these techniques has been difficult because they have been devised by different workers and impose differing – and sometimes stringent – technical requirements on those who seek to use them. New methodWe have built a simple toolbox of analysis routines that encompass the complete workflow for analyzing calcium imaging data. The workflow begins with preprocessing of data, includes motion correction and longitudinal image registration, detects active cells using constrained non-negative matrix factorization, and offers multiple options for estimating spike times and characterizing population activity. The routines can be navigated through a simple graphical user interface. Although written in MATLAB, a standalone version for researchers who do not have access to MATLAB is included. ResultsWe have used the toolbox on two very different preparations: spontaneously active brain slices and microendoscopic imaging from deep structures in awake behaving mice. In both cases, the toolbox offered a seamless flow from raw data all the way through to prepared graphs. ConclusionThe field of calcium imaging has benefited from the development of numerous innovative mathematical techniques. Here we offer a simple toolbox that allows ordinary researchers to fully exploit these techniques.

In silico assessment of biocompatibility and toxicity: molecular docking and dynamics simulation of PMMA-based dental materials for interim prosthetic restorations

Aim: This study aimed to comprehensively assess the biocompatibility and toxicity profiles of poly(methyl methacrylate) (PMMA) and its monomeric unit, methyl methacrylate (MMA), crucial components in dental materials for interim prosthetic restorations. Methodology: Molecular docking was employed to predict the binding affinities, energetics, and steric features of MMA and PMMA with selected receptors involved in bone metabolism and tissue development, including RANKL, Fibronectin, BMP9, NOTCH2, and other related receptors. The HADDOCK standalone version was utilized for docking calculations, employing a Lamarckian genetic algorithm to explore the conformational space of ligand-receptor interactions. Furthermore, molecular dynamics (MD) simulations over 100 nanoseconds were conducted using the GROMACS package to evaluate dynamic actions and structural stability. The LigandScout was utilized for pharmacophore modeling, which employs a shape-based screening approach to identify potential ligand binding sites on protein targets. Results: The molecular docking studies elucidated promising interactions between PMMA and MMA with key biomolecular targets relevant to dental applications. MD simulation results provided strong evidence supporting the structural stability of PMMA complexes over time. Pharmacophore modeling highlighted the significance of carbonyl and hydroxyl groups as pharmacophoric features, indicating compounds with favorable biocompatibility profiles. Conclusion: This study underscores the potential of PMMA in dental applications, emphasizing its structural stability, molecular interactions, and safety considerations. These findings lay a foundation for future advancements in dental biomaterials, guiding the design and optimization of materials for enhanced biocompatibility. Future directions include experimental validation of computational findings and the development of PMMA-based dental materials with improved biocompatibility and clinical performance.Graphical

Tremors—A Software App for the Analysis of the Completeness Magnitude

This paper introduces a software tool developed within the MATLAB environment, called Tremors, aimed at streamlining the pre-processing and analysis of seismic catalogues, with a particular emphasis on determining the Magnitude of Completeness. It will outline the criteria for event selection, as well as various techniques to derive the Magnitude of Completeness values, including the recent and widely used Lilliefors statistical method. The study also addresses the important issue of short-term aftershock incompleteness and proposes solutions for managing it. Moreover, the software generates high-quality, customizable figures, and georeferenced raster images in .tif format as output. A standalone version of the App is also available (i.e., the users do not need a MATLAB license on their PC/laptop).

Solvent flashcards: a visualisation tool for sustainable chemistry

Selecting greener solvents during experiment design is imperative for greener chemistry. While many solvent selection guides are currently used in the pharmaceutical industry, these are often paper-based guides which can make it difficult to identify and compare specific solvents. This work presents a stand-alone version of the solvent flashcards that were developed as part of the AI4Green electronic laboratory notebook. The functionality is an intuitive and interactive interface for the visualisation of data from CHEM21, a pharmaceutical solvent selection guide that categorises solvents according to “greenness”. This open-source software is written in Python, JavaScript, HTML and CSS and allows users to directly contrast and compare specific solvents by generating colour-coded flashcards. It can be installed locally using pip, or alternatively the source code is available on GitHub: https://github.com/AI4Green/solvent_flashcards. The documentation can also be found on GitHub or on the corresponding Python Package Index webpage: https://pypi.org/project/solvent-guide/.Scientific ContributionThis simple and easy-to-use digital tool provides a visualisation of solvent greenness data through a novel intuitive interface and encourages green chemistry. It offers numerous advantages over traditional solvent selection guides, allowing users to directly customise the solvent list and generate side-by-side comparisons of only the most important solvents. The release as a standalone package will maximise the benefit of this software.Graphical

#703 Usability of a mobile health app for peritoneal dialysis patients: a pilot study

Abstract Background and Aims Technology has changed how people interact with the world, so health apps may be useful for patients with long term conditions, facilitating adherence and enabling remote monitoring. Usability is a critical factor when evaluating the quality of a mobile health app, as it indicates the ease with which a user can use it to achieve specific clinically orientated goals with effectiveness, efficiency, and satisfaction. Method The mHealth App Usability Questionnaire (MAUQ), a reliable and validated tool, was used to evaluate the MyPD® mobile health app's usability for adult peritoneal dialysis (PD) patients. The arm using the My PD app in a pilot randomized clinical study in six renal clinics in Colombia from September 2022 to August 2023 was evaluated. The MAUQ questionnaire includes 18 items assessing 3 domains related to ease of use (5 items), interface and satisfaction (7 items) and usefulness (6 items). Patients rated each item on a seven-point Likert scale, with one (1) strongly disagreeing and seven (7) strongly agreeing. The overall rating is the mean of eighteen items (standalone version); closer to seven (7), higher usability. The socio-demographic and clinical characteristics of all patients were summarized descriptively. Results MyPD app allows patients to collect and view their clinical information and dialysis treatment. Figure 1 shows a general overview of the different navigation windows. A total of 65 patients were included in this analysis; the mean age was 52.2 years, 44.6% were in CAPD, and 21.5 % achieved elementary school education or were illiterate (Table 1). The overall usability was 6.5 (SD 0.7), the maximum possible score is 7. The items related to satisfaction and interface have a mean of 6.8 (SD 0.5). Regarding ease of use, the mean was 6.6 (SD 0.8), a very high figure given the socio-cultural context of a middle-income country. In relation to usefulness, the mean was 6.1 (SD 1.5). Patients rated the overall satisfaction item with the app as 6.8 (SD 0.6). See details in Table 2. These results are better than those reported by other authors (Table 3). Conclusion The usability of the health app MyPD® was rated very high by patients, which will allow this application to be used widely as a tool that supports the outpatient care of PD patients.

Enhancing cladding mechanical modelling during DBA/LOCA accidents with FRAPTRAN: The TUmech one-dimensional model

The analysis of the cladding mechanical performance is essential for predicting rod failure during the heat-up phase of a loss-of-coolant accident (LOCA). Previous modelling and simulation exercises have shown that there is room for improvement in the accuracy of fuel performance simulations under such conditions. In this work, TUmech, a simplified standalone version of the mechanical model in TRANSURANUS, has been developed and coupled with FRAPTRAN-2.0. After highlighting the most significant differences between both approaches, particularly in their mechanical treatment and material property correlations, simulations have been performed for IFA-650.2 (fresh fuel) and IFA-650.10 (high burnup fuel) of the Halden LOCA test series. The results obtained from both FRAPTRAN versions have been compared with experimental data and benchmarked against TRANSURANUS estimates, focusing on rod inner gas pressure, post-test cladding outer diameter, and burst time. In both scenarios, FRAPTRAN-TUmech has proven to enhance the accuracy in predicting these three variables compared to the FRAPTRAN default version. However, further validation is required before drawing definitive conclusions based on the encouraging results.

GWASTool: A web pipeline for detecting SNP-phenotype associations

The genome-wide association study (GWAS) aims to detect associations between individual single nucleotide polymorphisms (SNPs) or SNP interactions and phenotypes to decipher the genetic mechanism. Existing GWAS analysis tools have different focuses and advantages, but suffer a series of tedious and heterogeneous configurations for computation. It is inconvenient for researchers to simply choose and apply these tools, statistically and biologically analyze their results for different usages. To address these issues, we develop a user friendly web pipeline GWASTool for detecting associations, which includes simulation data generation, associated loci detection, result visualization, analysis and comparison. GWASTool provides a unified and plugin-able framework to encapsulate the heterogeneity of GWAS algorithms, simplifies the analysis steps and energizes GWAS tasks. GWASTool is implemented in Java and is freely available for public use at http://www.sdu-idea.cn/GWASTool. The website hosts a comprehensive collection of resources, including a user manual, description of integrated algorithms, data examples and standalone version for download.

Free and open-source QSAR-ready workflow for automated standardization of chemical structures in support of QSAR modeling

The rapid increase of publicly available chemical structures and associated experimental data presents a valuable opportunity to build robust QSAR models for applications in different fields. However, the common concern is the quality of both the chemical structure information and associated experimental data. This is especially true when those data are collected from multiple sources as chemical substance mappings can contain many duplicate structures and molecular inconsistencies. Such issues can impact the resulting molecular descriptors and their mappings to experimental data and, subsequently, the quality of the derived models in terms of accuracy, repeatability, and reliability. Herein we describe the development of an automated workflow to standardize chemical structures according to a set of standard rules and generate two and/or three-dimensional “QSAR-ready” forms prior to the calculation of molecular descriptors. The workflow was designed in the KNIME workflow environment and consists of three high-level steps. First, a structure encoding is read, and then the resulting in-memory representation is cross-referenced with any existing identifiers for consistency. Finally, the structure is standardized using a series of operations including desalting, stripping of stereochemistry (for two-dimensional structures), standardization of tautomers and nitro groups, valence correction, neutralization when possible, and then removal of duplicates. This workflow was initially developed to support collaborative modeling QSAR projects to ensure consistency of the results from the different participants. It was then updated and generalized for other modeling applications. This included modification of the “QSAR-ready” workflow to generate “MS-ready structures” to support the generation of substance mappings and searches for software applications related to non-targeted analysis mass spectrometry. Both QSAR and MS-ready workflows are freely available in KNIME, via standalone versions on GitHub, and as docker container resources for the scientific community. Scientific contribution: This work pioneers an automated workflow in KNIME, systematically standardizing chemical structures to ensure their readiness for QSAR modeling and broader scientific applications. By addressing data quality concerns through desalting, stereochemistry stripping, and normalization, it optimizes molecular descriptors' accuracy and reliability. The freely available resources in KNIME, GitHub, and docker containers democratize access, benefiting collaborative research and advancing diverse modeling endeavors in chemistry and mass spectrometry.

Yearly intrasubject variability of hematological biomarkers in elite athletes for the Athlete Biological Passport.

Confounding factors including exercise and environments challenge the interpretation of individual Athlete Biological Passports (ABPs). This study aimed to investigate the natural variability of hematological ABP parameters over 1 year in elite athletes compared with healthy control subjects and the validity of a multiparametric model estimating plasma volume (PV) shifts to correct individual ABP thresholds. Blood samples were collected monthly with full blood counts performed by flow cytometry (Sysmex XN analyzers) in 20 elite xc-skiers (ELITE) and 20 moderately trained controls. Individual ABP profiles were generated through Anti-Doping Administration & Management System Training, a standalone version of the ABP's adaptive model developed by the World Anti-Doping Agency. Additionally, eight serum parameters were computed as volume-sensitive biomarkers to run a multiparametric model to estimate PV. Variability in ELITE compared with controls was significantly higher for the Abnormal Blood Profile Scores (P = 0.003). Among 12 Atypical Passport Findings (ATPF) initially reported, six could be removed after correction of PV shifts with the multiparametric modeling. However, several ATPF were additionally generated (n =19). Our study outlines a larger intraindividual variability in elite athletes, likely explained by more frequent exposure to extrinsic factors altering hematological biomarkers. PV correction for individual ABP thresholds allowed to explain most of the atypical findings while generating multiple new ATPF occurrences in the elite population. Overall, accounting for PV shifts in elite athletes was shown to be paramount in this study outlining the opportunity to consider PV variations with novel approaches when interpreting individual ABP profiles.

Graph-Based Imputation Methods and Their Applications to Single Donors and Families.

The outcome of Hematopoietic Stem Cell (HSCT) and organ transplant is strongly affected by the matching of the HLA alleles of the donor and the recipient. However, donors and sometimes recipients are often typed at low resolution, with some alleles either missing or ambiguous. Thus, imputation methods are required to detect the most probably high-resolution HLA haplotypes consistent with a typing. Such imputation algorithms require predefined haplotype frequencies. As such, the phasing of the typing is required for both imputation and frequency generation.We have developed a new approach to HLA haplotype and genotype imputation, where first all candidate phases of a typing are explicated, and then the ambiguity within each phase is solved. This ambiguity is solved through a graph structure of all partial haplotypes and the haplotypes consistent with them.This phasing approach was used to produce an imputation algorithm (GRIMM-Graph Imputation and Matching). GRIMM was then combined with the possibility of combining information from multiple races to produce MR-GRIMM (Multi-Race GRIMM). When family information is available, the phasing of each family member can be restricted by the others. We propose GRAMM (GRaph-bAsed faMily iMputation) to phase alleles in family pedigree HLA typing data and in mother-cord blood unit pairs. Finally, we combined MR-GRIMM with an expectation-maximization (EM) algorithm to estimate haplotype frequencies sharing information between races to produce MR-GRIMME (MR-GRIMM EM).We have shown that these algorithms naturally combine information between races and family members. The accuracy of each of these algorithms is significantly better than its current parallel methods. MR-GRIMM leads to high accuracy in matching predictions. GRAMM better imputes family members than either MR-GRIMM or any existing algorithm and has practically no phasing errors. MR-GRIMME obtains a higher likelihood than existing algorithms.MR-GRIMM, MR-GRIMME, and GRAMM are available as servers or through stand-alone versions in GITHUB and PyPi, as detailed in the appropriate sections.

Evaluating Performance Portability with the CMS Heterogeneous Pixel Reconstruction code

In the past years the landscape of tools for expressing parallel algorithms in a portable way across various compute accelerators has continued to evolve significantly. There are many technologies on the market that provide portability between CPU, GPUs from several vendors, and in some cases even FPGAs. These technologies include C++ libraries such as Alpaka and Kokkos, compiler directives such as OpenMP, the SYCL open specification that can be implemented as a library or in a compiler, and standard C++ where the compiler is solely responsible for the offloading. Given this developing landscape, users have to choose the technology that best fits their applications and constraints. For example, in the CMS experiment the experience so far in heterogeneous reconstruction algorithms suggests that the full application contains a large number of relatively short computational kernels and memory transfer operations. In this work we use a stand-alone version of the CMS heterogeneous pixel reconstruction code as a realistic use case of HEP reconstruction software that is capable of leveraging GPUs effectively. We summarize the experience of porting this code base from CUDA to Alpaka, Kokkos, SYCL, std::par, and OpenMP offloading. We compare the event processing throughput achieved by each version on NVIDIA and AMD GPUs as well as on a CPU, and compare those to what a native version of the code achieves on each platform.