Stand-alone Version Research Articles

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.

Delimiting Species with Single-Locus DNA Sequences.

DNA sequences are increasingly used for large-scale biodiversity inventories. Because these genetic data avoid the time-consuming initial sorting of specimens based on their phenotypic attributes, they have been recently incorporated into taxonomic workflows for overlooked and diverse taxa. Major statistical developments have accompanied this new practice, and several models have been proposed to delimit species with single-locus DNA sequences. However, proposed approaches to date make different assumptions regarding taxon lineage history, leading to strong discordance whenever comparisons are made among methods. Distance-based methods, such as Automatic Barcode Gap Discovery (ABGD) and Assemble Species by Automatic Partitioning (ASAP), rely on the detection of a barcode gap (i.e., the lack of overlap in the distributions of intraspecific and interspecific genetic distances) and the associated threshold in genetic distances. Network-based methods, as exemplified by the REfined Single Linkage (RESL) algorithm for the generation of Barcode Index Numbers (BINs), use connectivity statistics to hierarchically cluster-related haplotypes into molecular operational taxonomic units (MOTUs) which serve as species proxies. Tree-based methods, including Poisson Tree Processes (PTP) and the General Mixed Yule Coalescent (GMYC), fit statistical models to phylogenetic trees by maximum likelihood or Bayesian frameworks.Multiple webservers and stand-alone versions of these methods are now available, complicating decision-making regarding the most appropriate approach to use for a given taxon of interest. For instance, tree-based methods require an initial phylogenetic reconstruction, and multiple options are now available for this purpose such as RAxML and BEAST. Across all examined species delimitation methods, judicious parameter setting is paramount, as different model parameterizations can lead to differing conclusions. The objective of this chapter is to guide users step-by-step through all the procedures involved for each of these methods, while aggregating all necessary information required to conduct these analyses. The "Materials" section details how to prepare and format input files, including options to align sequences and conduct tree reconstruction with Maximum Likelihood and Bayesian inference. The Methods section presents the procedure and options available to conduct species delimitation analyses, including distance-, network-, and tree-based models. Finally, limits and future developments are discussed in the Notes section. Most importantly, species delimitation methods discussed herein are categorized based on five indicators: reliability, availability, scalability, understandability, and usability, all of which are fundamental properties needed for any approach to gain unanimous adoption within the DNA barcoding community moving forward.

FRAILTY ASSESSMENT FROM PAPER TO ONLINE: THE E-FI-CGA WEB APP

Abstract Background Early frailty assessment is crucial for improving care for older adults through enhancing frailty-informed care planning and execution. To increase the access and use of the established electronic Frailty Index based on the Comprehensive Geriatric Assessment tool, we developed an online software application: the web-based eFI-CGA. Methods End-user requirements primarily adapted the standalone version of the eFI-CGA and covered assessment competency, interface familiarity, accessibility and convenient use by front-line health professionals. Web app-bounded features included user-account authentication and authorization, user-space management, record-search and retrieval, and functions for data management, analysis and display. The web app development used the Microsoft Azure web server with the ASP.NET framework, combined Webforms and Model-View-Controller architecture, and C# programming language with standard client-side libraries incorporating scripting and mark-up languages. Results The web-based eFI-CGA was technically released and accessible online (efi-cga). Tests on the web pages for all the web pages (e.g., Home, Signup Login, Assessment, Search, and Analysis) using large sizes of systematically designed test data and mocked patient cohorts showed that the web software functions meet the requirements with 100% accuracy. Conclusion The web-based eFI-CGA provides a valuable remote frailty assessment and information retrieval method for healthcare professionals, promoting effective multidisciplinary integrated care of older adults. Further work is needed to allow the safe use of the web app. We are also planning a patient-oriented frailty assessment tool for community-dwelling older adults.

A deep learning approach to the automatic detection of alignment errors in cryo-electron tomographic reconstructions

Electron tomography is an imaging technique that allows for the elucidation of three-dimensional structural information of biological specimens in a very general context, including cellular in situ observations. The approach starts by collecting a set of images at different projection directions by tilting the specimen stage inside the microscope. Therefore, a crucial preliminary step is to precisely define the acquisition geometry by aligning all the tilt images to a common reference. Errors introduced in this step will lead to the appearance of artifacts in the tomographic reconstruction, rendering them unsuitable for the sample study. Focusing on fiducial-based acquisition strategies, this work proposes a deep-learning algorithm to detect misalignment artifacts in tomographic reconstructions by analyzing the characteristics of these fiducial markers in the tomogram. In addition, we propose an algorithm designed to detect fiducial markers in the tomogram with which to feed the classification algorithm in case the alignment algorithm does not provide the location of the markers. This open-source software is available as part of the Xmipp software package inside of the Scipion framework, and also through the command-line in the standalone version of Xmipp.

LncRTPred: Predicting RNA-RNA mode of interaction mediated by lncRNA.

Long non-coding RNAs (lncRNAs) play a significant role in various biological processes. Hence, it is utmost important to elucidate their functions in order to understand the molecular mechanism of a complex biological system. This versatile RNA molecule has diverse modes of interaction, one of which constitutes lncRNA-mRNA interaction. Hence, identifying its target mRNA is essential to understand the function of an lncRNA explicitly. Existing lncRNA target prediction tools mainly adopt thermodynamics approach. Large execution time and inability to perform real-time prediction limit their usage. Further, lack of negative training dataset has been a hindrance in the path of developing machine learning (ML) based lncRNA target prediction tools. In this work, we have developed a ML-based lncRNA-mRNA target prediction model- 'LncRTPred'. Here we have addressed the existing problems by generating reliable negative dataset and creating robust ML models. We have identified the non-interacting lncRNA and mRNAs from the unlabelled dataset using BLAT. It is further filtered to get a reliable set of outliers. LncRTPred provides a cumulative_model_score as the final output against each query. In terms of prediction accuracy, LncRTPred outperforms other popular target prediction protocols like LncTar. Further, we have tested its performance against experimentally validated disease-specific lncRNA-mRNA interactions. Overall, performance of LncRTPred is heavily dependent on the size of the training dataset, which is highly reflected by the difference in its performance for human and mouse species. Its performance for human species shows better as compared to that for mouse when applied on an unknown data due to smaller size of the training dataset in case of mouse compared to that of human. Availability of increased number of lncRNA-mRNA interaction data for mouse will improve the performance of LncRTPred in future. Both webserver and standalone versions of LncRTPred are available. Web server link: http://bicresources.jcbose.ac.in/zhumur/lncrtpred/index.html. Github Link: https://github.com/zglabDIB/LncRTPred.