Software Tools Research Articles

Ionic Liquid-Based Green Solvents for Extraction and Purification of Natural Plant Products

Introduction: This research paper explores the environmental sustainability of ionic liquid-based green solvents in the extraction and purification of natural plant products, with a focus on their entire life cycle. The objectives of the study were to assess the environmental impact of ionic liquid synthesis, energy consumption, water usage, emissions, recycling rates, policy effects, and stakeholder perceptions. Method: Methodologically, we conducted a comprehensive Life Cycle Assessment (LCA) that involved primary data collection through field surveys and interviews with key stakeholders in the ionic liquid production and usage industry across various regions in India. The data were analyzed using specialized LCA software tools to quantify environmental impacts. Key findings include the identification of synthesis as a major contributor to environmental impact, emphasizing the need for greener synthesis methods. Result: The study revealed the significant carbon footprint, energy consumption, and water usage during production, highlighting opportunities for improvement. Emissions data underscored the importance of emission control measures, particularly for greenhouse gases and volatile organic compounds. Recycling and reuse were identified as environmentally friendly disposal methods. Policy compliance varied among stakeholders, indicating room for stricter regulations. Stakeholder perceptions varied, with researchers having the most positive outlook. Implications of the findings extend to sustainable chemistry practices, emphasizing interdisciplinary collaboration and the importance of considering the entire life cycle of chemical processes. Conclusion: This research contributes to a deeper understanding of green solvents and provides a foundation for promoting sustainable practices in industrial processes in India and globally

An interoperable software system to store, associate, visualize, and publish global open science data of earth surface system

Multi-source heterogeneous, multi-modal, and multi-type open scientific data (e.g., thematic sharing sites, metadata, journal articles, etc.) on earth surface systems (EES) provide important data sources for knowledge mining, discovery, and accurate recommendations, and also pose increasing challenges, resulting in the need to develop appropriate tools to address these challenges and support decision-making. This paper constructs an interoperable software system to store, visualize, and publish open science data of ESS. Utilizing an open scientific data catalogue repository encompassing EES information as foundational input and employing an integrated modeling methodology, this system endeavors to synthesize heterogeneous surface data of diverse linguistic, sourced, and typological origins. The objective is to facilitate multidimensional data retrieval and precise data auto-recommendation, thereby fostering the dissemination of scientific data and facilitating value-added services within EES domain. The tool may be used by stakeholders including researchers, data analysts, policymakers and national authorities to support decision-making on questions ranging from locating the location of open data related to the topic, to discovering high-quality data, selecting the data with the better overall evaluation. Along with a description of the system/platform design process, its structure, and the constituent models, key results are presented relating to the user interface, and several application examples. Software systems can help modelers to use the best features of a single software tool to answer open scientific data-related questions that seek to discovery, use, comparison or synthesis within or across topics of ESS.

Gamification improves learning: Experience in a training activity of computer programming in higher education

AbstractBackgroundEffective learning in computer programming courses has been a constant challenge for university teachers and has become a relevant competence for current professionals. The literature on gamification in learning presents mixed results, mainly due to problems in instructional design and inconsistency in gamification. Studies with positive empirical results on computer programming formative activities and showing under what conditions gamification is effective still need to be included.ObjectivesThis study aimed to quantify the effects of gamification techniques in software tools that support computer programming learning applied to data‐structures university students. The study focused on determining whether there are differences in learning when using gamification techniques in online exercising formative activities. The effects of gamification on learning objectives with diverse complexity are studied.MethodsThe study was conducted on computer engineering university students following a pretest‐intervention‐posttest design. They were divided into control and experimental groups, where both used a bank of exercises in Moodle to support learning. The experimental group considered gamification techniques, including points, leaderboards, and badges.Results and ConclusionsGamification techniques significantly outperformed in learning gains in exercising activities without gamification. Regarding learning complexity, increasing taxonomic complexity did not negatively affect gamification learning effects.

Research on internet of things (IoT) indexed in Web of Science from 2011–2022

Academic publications have increased geometrically in the rapidly developing field of Internet of Things (IoT). Using the keywords “Internet of Things” in titles and abstracts, a large collection of scientific publications from 2011 to 2022 that were indexed in the Web of Science were examined in this study. Several software tools, including VOSviewer, ScientoPy, Biblioshiny and MS Excel, were used to analyze the data. According to the study's findings, the majority of publications in the field of IoT are written in English, and publications in this area are steadily rising. Rodrigues, Joel J. P. C. is the most prolific author. The most productive nation was found to be China, the Beijing University Posts & Telecommunication, a thriving institution. After the US and India, China has produced the greatest number of articles and received the greatest number of citations. The IEEE was found to be the most prolific publishing source, and the IEEE Internet of Things Journal was the most productive journal.

Lifetime assessment of deep-sea manned cabin with PMMA pressure-resistant hull prior to micro-cracks

Fully-transparent cabin type, utilized in manned submersibles, is typically constructed using viscoelastic PMMA (polymethyl methacrylate) material. Accurate fatigue life assessment of manned cabin is of paramount importance to ensure structural safety. In this study, a calculation process for evaluating the fatigue life of PMMA manned cabins based on a continuum damage mechanics model combined with the viscoelastic constitutive relationship of the PMMA material in response to the absence of related specification is proposed. Fatigue tests of PMMA material are performed for determination of damage evolution model parameters and the calculation process is validated by the fatigue test data. Tailored subroutines are then developed by using ABAQUS software tool, respectively for structural analysis of a typical manned cabin with PMMA pressure-resistant hull, openings, hatches and penetration plates and simulating trapezoidal loading history acting on manned cabin. Subroutine coupling is accordingly employed to conduct parametric analysis on fatigue crack initiation life of the fully-transparent manned cabin with consideration of different outside pressure level and operation time and valuable results are obtained for identifying key areas of concern. This study intends to contribute to further cabins safety enhancement, as well as to design optimization and fatigue analysis of underwater structures involving PMMA materials.

New Scientific Field for Modelling Complex Dynamical Systems: The Cybernetics Artificial Intelligence (CAI)

Artificial Intelligence (AI) has been considered a revolutionary and world-changing science, although it is still a young field and has a long way to go before it can be established as a viable theory. Every day, new knowledge is created at an unthinkable speed, and the Big Data Driven World is already upon us. AI has developed a wide range of theories and software tools that have shown remarkable success in addressing difficult and challenging societal problems. However, the field also faces many challenges and drawbacks that have led some people to view AI with skepticism. One of the main challenges facing AI is the difference between correlation and causation, which plays an important role in AI studies. Additionally, although the term Cybernetics should be a part of AI, it was ignored for many years in AI studies. To address these issues, the Cybernetic Artificial Intelligence (CAI) field has been proposed and analyzed here for the first time. Despite the optimism and enthusiasm surrounding AI, its future may turn out to be a “catastrophic Winter” for the whole world, depending on who controls its development. The only hope for the survival of the planet lies in the quick development of Cybernetic Artificial Intelligence and the Wise Anthropocentric Revolution. The text proposes specific solutions for achieving these two goals. Furthermore, the importance of differentiating between professional/personal ethics and eternal values is highlighted, and their importance in future AI applications is emphasized for solving challenging societal problems. Ultimately, the future of AI heavily depends on accepting certain ethical values.

Role of N6-methyladenosine-related lncRnas in pseudoexfoliation glaucoma

ABSTRACT To explore the role of lncRNA m6A methylation modification in aqueous humour (AH) of patients with pseudoexfoliation glaucoma (PXG). Patients with open-angle PXG under surgery from June 2021 to December 2021 were selected. Age- and gender-matched patients with age-related cataract (ARC) were chosen as control. Patients underwent detailed ophthalmic examinations. 0.05–0.1 ml AH were extracted during surgery for MeRIP-Seq and RNA-Seq. Joint analysis was used to screen lncRNAs with differential m6A methylation modification and expression. Online software tools were used to draw lncRNA-miRNA-mRNA network (ceRNA). Expression of lncRNAs and mRNAs was confirmed using quantitative real-time PCR. A total of 4151 lncRNAs and 4386 associated m6A methylation modified peaks were identified in the PXG group. Similarly, 2490 lncRNAs and 2595 associated m6A methylation modified peaks were detected in the control. Compared to the ARC group, the PXG group had 234 hypermethylated and 402 hypomethylated m6A peaks, with statistically significant differences (| Fold Change (FC) |≥2, p < 0.05). Bioinformatic analysis revealed that these differentially methylated lncRNA enriched in extracellular matrix formation, tight adhesion, TGF- β signalling pathway, AMPK signalling pathway, and MAPK signalling pathway. Joint analysis identified 10 lncRNAs with differential m6A methylation and expression simultaneously. Among them, the expression of ENST000000485383 and ROCK1 were confirmed downregulated in the PXG group by RT-qPCR. m6A methylation modification may affect the expression of lncRNA and participate in the pathogenesis of PXG through the ceRNA network. ENST000000485383-hsa miR592-ROCK1 May be a potential target pathway for further investigation in PXG m6A methylation.

Comparative Analysis of Weather Forecasting Using LSTM, BiLSTM, and CLSTM Networks

Weather prediction plays vital role in the evaluation of various processes and phenomenon for the betterment of society. Weather forecasting depends on many parameters hence, it’s a multivariable prediction problem. Behavior of data involved in the prediction are in majority of time stochastics hence, conventional mathematical approaches are failed to predict accurate data. Classical computational methods fail in predicting chaotic data due to its limitation to have know-how about behavior of data. Artificial Intelligence and Data Processing techniques is appealing investigators due to its efficiency to predict data based on its learning capability, without any know-how about the behavior of data. In this paper, Artificial Intelligence based approach LSTM, BiLSTM, and CLSTM are used to predict time series weather data prediction. Data considered are Pressure, Humidity, Temperature, and Maximum wind speed as input data. Neural Network based approaches are used to predict Average wind speed based on above inputs. The performance of above Artificial based approaches is compared to predict time series Average wind speed. MATLAB is used as software tool to implement Artificial Intelligence approaches. It has been observed that CLSTM algorithm outperformed other two algorithms LSTM and BiLSTM algorithm.

Sustainable-Driven Renovation of Existing Residential Buildings in China: A Systematic Exploration Based on Review and Solution Approaches

Under the backdrop of China’s national strategy to achieve carbon neutrality by 2060, efforts are underway across governmental, corporate, societal, and individual sectors to actively explore energy-saving renovations in existing buildings. Given that residential buildings constitute a significant proportion of the total energy consumption throughout the lifecycle of buildings in China, sustainable renovation of residential structures can contribute significantly to implementing China’s carbon emission reduction policies. While there exists a plethora of technological means in the market aimed at improving the energy performance of residential buildings, there still needs to be a more systematic discussion on the framework for sustainable renovation of existing Chinese residential buildings, with knowledge dissemination still needing to be more cohesive. In this context, this paper provides a comprehensive review of the field, utilizing bibliometric methods. Through a systematic review of selected peer-reviewed literature from the Web of Science and Scopus databases, the study focuses on the sustainable renovation of existing Chinese residential buildings, categorizing the process into three main stages: sustainable renovation, building performance simulation and suitability assessment. The paper also reviews the research methods adopted by previous researchers in the renovation, simulation and assessment stages, considering various optimization algorithms, variables, objectives, and software tools. Subsequently, the paper synthesizes a research framework comprising these three stages combined for different research objectives, aiming to assist policymakers, designers, and researchers in gaining a comprehensive understanding of the implementation status of sustainable renovation in existing Chinese residential buildings, identifying barriers to implementation, and formulating more efficient renovation policies and strategies for the future.

Regulatory landscape enrichment analysis (RLEA): a computational toolkit for non-coding variant enrichment and cell type prioritization

BackgroundAs genomic studies continue to implicate non-coding sequences in disease, testing the roles of these variants requires insights into the cell type(s) in which they are likely to be mediating their effects. Prior methods for associating non-coding variants with cell types have involved approaches using linkage disequilibrium or ontological associations, incurring significant processing requirements. GaiaAssociation is a freely available, open-source software that enables thousands of genomic loci implicated in a phenotype to be tested for enrichment at regulatory loci of multiple cell types in minutes, permitting insights into the cell type(s) mediating the studied phenotype.ResultsIn this work, we present Regulatory Landscape Enrichment Analysis (RLEA) by GaiaAssociation and demonstrate its capability to test the enrichment of 12,133 variants across the cis-regulatory regions of 44 cell types. This analysis was completed in 134.0 ± 2.3 s, highlighting the efficient processing provided by GaiaAssociation. The intuitive interface requires only four inputs, offers a collection of customizable functions, and visualizes variant enrichment in cell-type regulatory regions through a heatmap matrix. GaiaAssociation is available on PyPi for download as a command line tool or Python package and the source code can also be installed from GitHub at https://github.com/GreallyLab/gaiaAssociation.ConclusionsGaiaAssociation is a novel package that provides an intuitive and efficient resource to understand the enrichment of non-coding variants across the cis-regulatory regions of different cells, empowering studies seeking to identify disease-mediating cell types.

PHYLOGENETIC ANALYSIS OF TWELVE BULGARIAN SEQUENCES BASED ON PARTIAL OPEN READING FRAME 2 GENOME FRAGMENT OF HEPATITIS E VIRUS

Background: Hepatitis E virus (HEV) causes both acute and chronic liver inflammation. HEV is transmitted through the fecal-oral mechanism and infects both animals and humans. The virus belongs to the Hepeviridae family and its genome is a single stranded RNA molecule. Thanks to molecular sequencing methods different genotypes and subgenotypes have been established. The aim of the present study was to identify and characterize Bulgarian HEV sequences by applying Sanger sequencing technique for a genome fragment in Open Reading Frame 2 (ORF2 region). Material and methods: Twelve retrospective samples from patients with serologically confirmed HEV infection (anti-HEV IgM and IgG positive) were sequenced by Sanger sequencing. Sequencing data were analysed by BioEdit, MEGA11 and NCBI Genbank software tools. Results: The results revealed that all isolates assign to species Paslahepevirus balayani. Phylogenetic analysis showed that HEV isolates were characterized with considerable genetic diversity. The sequences were sub-clustered into the following subgenotypes: HEV-3e, 3m, 3f and 3c. Conclusion: We successfully applied the Sanger method for hepatitis E virus RNA sequencing. The established heterogeneity of subgenotypes requires further study in order to determine the circulation of all possible subgenotypes of HEV in the country.

Population analysis using GIS software tools A case study of Chengdu

Population analysis using GIS software tools A case study of Chengdu

BeST-Graft viewer, a new system to improve the bone allograft–recipient matching process

Introduction:Tissue establishments are responsible for processing, testing, preserving, storing, and distributing allografts from donors to be transplanted into recipients. In some situations, a matching process is required to determine the allograft that best fits the recipient. Allograft morphology is a key consideration for the matching process. The manual procedures applied to obtain these parameters make the process error-prone. Material and Methods:A new system to manage bone allograft–recipient matching for tissue establishments is proposed. The system requires bone allografts to be digitalized and the resulting images to be stored in a DICOM file. The system provides functionalities to: (i) manage DICOM files (registered in the PACs) from both allografts and recipients; (ii) reconstruct 3D models from DICOM images; (iii) explore 3D models using 2D, 3D, and multiplanar reconstructions; (iv) take allograft and recipient measurements; and (v) visualize and interact with recipient and allograft data simultaneously. The system has been installed in the Barcelona Tissue Bank (Banc de Sang i Teixits), which has digitalized the bone allografts to test the system. Results:A use case with a femur is presented to test all the viewer functionalities. In addition, the recipient–allograft workflow is evaluated to show the steps of the procedure where the viewer can be used. Conclusions:The bone allograft–recipient matching procedure can be optimized using software tools with functionalities to visualize, interact, and take measurements.

In-Depth Analysis of the Selection of PBPK Modeling Tools: Bibliometric and Social Network Analysis of the Open Systems Pharmacology Community.

Since the Open Source Initiative laid the foundation for the open source software environment in 1998, the popularity of free and open source software has been steadily increasing. Model-informed drug discovery and development (MID3), a key component of pharmaceutical research and development, heavily makes use of computational models which can be developed using various software including the Open Systems Pharmacology (OSP) software (PK-Sim/MoBi), a free and open source software tool for physiologically based pharmacokinetic (PBPK) modeling. In this study, we aimed to investigate the impact, application areas, and reach of the OSP software as well as the relationships and collaboration patterns between organizations having published OSP-related articles between 2017 and 2023. Therefore, we conducted a bibliometric analysis of OSP-related publications and a social network analysis of the organizations with which authors of OSP-related publications were affiliated. On several levels, we found evidence for a significant growth in the size of the OSP community as well as its visibility in the MID3 community since OSP's establishment in 2017. Specifically, the annual publication rate of PubMed-indexed PBPK-related articles using the OSP software outpaced that of PBPK-related articles using any software. Our bibliometric analysis and network analysis demonstrated that the expansion of the OSP community was predominantly driven by new authors and organizations without prior connections to the community involving the generation of research clusters de novo and an overall diversification of the network. These findings suggest an ongoing evolution of the OSP community toward a more segmented, diverse, and inclusive network.

PATHOMIC FUSION: CANCER DIAGNOSIS AND PROGNOSIS INTEGRATION

PATHOMIC FUSION: CANCER DIAGNOSIS AND PROGNOSIS INTEGRATION

Structural Analysis of an Off-Road Vehicle Suspension System Using CATIA and ANSYS

This project delves into the structural analysis of an off-road vehicle suspension system, employing cutting-edge software tools such as CATIA and ANSYS. Off-road vehicles necessitate robust suspension systems capable of withstanding harsh terrain conditions while ensuring vehicle stability, manoeuvrability, and passenger comfort. The study focuses on evaluating the structural integrity, performance, and behaviour of the suspension system under diverse load conditions and terrain scenarios. This project focuses on conducting a comprehensive structural analysis of an off-road vehicle suspension system using advanced engineering software tools, specifically CATIA and ANSYS. CATIA is a powerful computer-aided design (CAD) software known for its capabilities in creating detailed 3D models, while ANSYS is a leading finite element analysis (FEA) software used for simulating structural behaviour and analyzing mechanical systems. Keywords: suspension system, CATIA v5, ANSYS, iterations, FEA.

Topological indices & Hirshfeld pseudo-surfaces synergy to understand the radiation attenuation in materials: Case of study spinel mineral crystals

The present work deals with the radiation attenuation characteristics of spinel minerals, a unique class of minerals with diverse crystalline structures and chemical compositions. We analyze the MAC and LAC in various photon energy spectrum using XCOM and Phys-X software tools. In addition to traditional methods, we employ topological indices (TIs) and Hirshfeld pseudo-surfaces (HPSs) to explore the atomic interactions and electron density distributions within these minerals to pinpointed their effects on the on these γ-radiation attenuation coefficients. Our findings reveal that MAC values decrease with increasing photon energy, a trend observed across all studied spinel minerals. The use of the HPSs provides visual insights into electron density distributions and intermolecular interactions mastered by 2D fingerprints which are correlated with the γ-attenuation of these spinels crystal. Linear regression correlations between TIs and MAC, LAC values demonstrate strong relationships, with the first Zagreb coindex and the Forgotten Index (F-index) emerging as reliable predictors of γ-rays radiations shield properties.

Predicting metabolic modules in incomplete bacterial genomes with MetaPathPredict.

The reconstruction of complete microbial metabolic pathways using 'omics data from environmental samples remains challenging. Computational pipelines for pathway reconstruction that utilize machine learning methods to predict the presence or absence of KEGG modules in incomplete genomes are lacking. Here, we present MetaPathPredict, a software tool that incorporates machine learning models to predict the presence of complete KEGG modules within bacterial genomic datasets. Using gene annotation data and information from the KEGG module database, MetaPathPredict employs deep learning models to predict the presence of KEGG modules in a genome. MetaPathPredict can be used as a command line tool or as a Python module, and both options are designed to be run locally or on a compute cluster. Benchmarks show that MetaPathPredict makes robust predictions of KEGG module presence within highly incomplete genomes.

Abstract PO4-16-12: Patient initiated centricity towards improved communication, side effect management and follow-up care by Real World Evidence Survey from patients for patients – Preview of the Breast BRIDGister Platform

Abstract Background Treatment adherence is crucial for outcome improvement. Still up to 50% of patients interrupt e.g. endocrine treatment or other effective treatment choices due to side effects or miscommunication. Reasons may include limited time spent for patient information in daily routine and the intrinsic barriers to frankly tell the truth to the attending physicians. Therefore, a patient initiated survey has been compiled to comprehensively address all clinical, diagnostic and critical aspects of importance for patients by decentral data capture from patients at home via smart devices in a pseudonymized &amp; interactive fashion to refocus on challenges regarding patients needs, fears and wishes to support evidence based statistical analysis of Real World Data. Materials and Methods Establishing a cloud-based IT Platform enabling nationwide, decentral data entry using Climedo clinical trial software tools without the need of any commercial app utilities and therefore obeying GDPR conform standards to create an interconnected online platform integrating 36 interconnected surveys comprising &amp;gt;1.200 singular questions has been set up between 2020 and 2022 based on patients content elaboration &amp; wishes. The questionnaires address all Real World experiences e.g. at initial diagnosis, during primary treatment and all subsequent lines as well as side effect management with complementary medicine and follow up care realities. After 100 patients a preplanned statistical analysis has been performed to evaluate the feasibility of the patient oriented ePRO approach and demonstrate relevance of proof-of-concept results before entering international trial expansion. Descriptive analysis by Excel as well as Correlation analysis; Chi square and Partitioning tests using the SAS JMP® 9.0.0 software were performed. Results Despite in depth complexity &amp;gt; 85% of patients completed the whole survey. Age distribution of the pilot cohort revealed comparably younger patient age (28% between 30-39 years, 40% between 40-49 years, 24% between 50-59 and 10% being above 60 years) and dominance of Triple Negative Patients (~39%) participating the online survey in the pilot cohort. 97% of patients were treated in certified breast centers (50% in larger centers of bigger towns,15% in central clinics, 23% in university sites, 5% in local hospitals and 7% at office based doctors). Interestingly, 25% of patients did not or only partially understand their diagnosis and upcoming treatment. Time spent for communicating initial breast cancer diagnosis was less than 10 minutes for 25% of patients, while 13% did not remember. Exclusively one treatment option was discussed with 86% of patients. 78% have been informed about their initial breast cancer diagnosis in personal meetings, while 21% were informed by phone calls. 37% of patients had been informed about their patient rights. At preplanned interim analysis 38% of patients were lymphnode positive and 6% metastasized at initial diagnosis. 56% of patients were satisfied with their guideline conform follow-up-care, while 42% did pay for additional follow up care on their own. 85% of patients wished to have a risk-adopted, more individualized follow-up care. Conclusion Despite complexity and lengthiness &amp;gt; 85% of patients did complete the survey. There is an apparent need to have more time for initial confrontation with diagnosis and treatment options. Patients are frequently not informed about alternative treatment options as well as patient rights. Patients frequently feel under pressure in their life threatening situation and suffer substantial lack of information as well as appropriate personal communication. Patient centricity should focus on elaborated communication and information systems that support treatment decision acceptance and adherence. Further details analysis will become available in upcoming subanalysis of the Breast BRIDGister platform survey. Citation Format: Cornelia Stuewe, Ralph Wirtz, Christa Gress, Lena Schmidt, Veronika Schweighart, Sebastian Eidt, Claudia Schumacher, Julia Habraschka, Heidi Lehmann, Katrin Schuere. Patient initiated centricity towards improved communication, side effect management and follow-up care by Real World Evidence Survey from patients for patients – Preview of the Breast BRIDGister Platform [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO4-16-12.

A Workflow for Improved Analysis of Cross-Linking Mass Spectrometry Data Integrating Parallel Accumulation-Serial Fragmentation with MeroX and Skyline.

Cross-linking mass spectrometry (XL-MS) has evolved into a pivotal technique for probing protein interactions. This study describes the implementation of Parallel Accumulation-Serial Fragmentation (PASEF) on timsTOF instruments, enhancing the detection and analysis of protein interactions by XL-MS. Addressing the challenges in XL-MS, such as the interpretation of complex spectra, low abundant cross-linked peptides, and a data acquisition bias, our current study integrates a peptide-centric approach for the analysis of XL-MS data and presents the foundation for integrating data-independent acquisition (DIA) in XL-MS with a vendor-neutral and open-source platform. A novel workflow is described for processing data-dependent acquisition (DDA) of PASEF-derived information. For this, software by Bruker Daltonics is used, enabling the conversion of these data into a format that is compatible with MeroX and Skyline software tools. Our approach significantly improves the identification of cross-linked products from complex mixtures, allowing the XL-MS community to overcome current analytical limitations.