Software Tools Research Articles

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.

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.

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

Understanding how the population is distributed geographically helps governments and organizations allocate resources more efficiently. For example, they can allocate funds for infrastructure development, healthcare facilities, schools, and public services based on the population's concentration and distribution. City planners can use population distribution data to design cities and urban areas effectively. They consider factors like housing needs, transportation infrastructure, and zoning regulations based on the population's spatial distribution. This study takes Chengdu as an example, and the article analyzes the spatial structure distribution of the population in Chengdu in the last decade based on the census data of 2012, 2017, and 2021, using analysis methods such as population distribution structure index (PDSI), mean pointer center, and population center of gravity, combined with Geographic Information System (GIS) technology. According to the analysis, it can be concluded that the population distribution pattern in Chengdu has not changed much, but the city area has been expanded. Besides, the policy adjustment has a certain impact on the population center of gravity but not as much as other factors. At last, topography, transportation, and economic development all have an impact on the population distribution 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

This paper proposes a novel approach to cancer diagnosis and prognosis, integrating pathology and molecular data. A machine learning algorithm is developed, trained, and validated on extensive datasets. A clinical software tool will facilitate clinicians in delivering accurate, personalized diagnoses and prognoses, potentially revolutionizing cancer care.

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.

An advanced fast method for the evaluation of multiple immunolabelling using gold nanoparticles based on low-energy STEM

We present a powerful method for the simultaneous detection of Au nanoparticles located on both sides of ultrathin sections. The method employs a high-resolution scanning electron microscope (HRSEM) operating in scanning transmission electron microscopy (STEM) mode in combination with the detection of backscattered electrons (BSE). The images are recorded simultaneously during STEM and BSE imaging at the precisely selected accelerating voltage. Under proper imaging conditions, the positions of Au nanoparticles on the top or bottom sides can be clearly differentiated, hence showing this method to be suitable for multiple immunolabelling using Au nanoparticles (NPs) as markers. The difference between the upper and lower Au NPs is so large that it is possible to apply common software tools (such as ImageJ) to enable their automatic differentiation. The effects of the section thickness, detector settings and accelerating voltage on the resulting image are shown. Our experimental results correspond to the results modelled in silico by Monte Carlo (MC) simulations.

The water energy food nexus: A multi-objective optimization tool

Many methods and tools are used to model and optimize various aspects of the water energy food nexus (WEFN). This work presents a novel software tool for the multi-objective optimization of the WEFN (MOO-WEFN). The model underlying the tool captures all three nexus sectors and the interlinkages between them. In addition, the model imposes constraints that mimic forces affecting the nexus, such as economics, environment, health, and nutrition. The software tool couples Excel with Python, which facilitates the tool's accessibility and ease of use due to its widespread usage and availability. A case study application demonstrates the tool's execution and provides examples of how changes can be made to the data to simulate different nexus developments. Five scenarios are applied to reflect such changes with results demonstrating the overall impact on resource production and consumption. Removing one of the less water-intensive food items results in a 20% increase in water consumption. Limiting the availability of surface water results in a reallocation of water resources such that 80% of water needs are satisfied from surface water and groundwater and 20% from wastewater, compared to the baseline of 100% surface water. Moreover, upon introduction of the conventional activated sludge treatment technology for wastewater (compared to the incumbent membrane bioreactor technology), energy consumption decreased by 10%. These results show changes beyond the immediate sector of change and reflect the necessity for integrated modeling that captures the impact of one change on the nexus as a whole, in terms of resource consumption and allocation. Thus, the tool supports the capability of testing how new resources, technologies, and limitations will impact nexus interactions. Furthermore, the tool facilitates carrying out sensitivity and Pareto analyses for data generation and result evaluation.

Casting of nickel powder based MMC using microwave energy

In this paper, an experimental setup is developed to cast a metal matrix composite (MMC) specimen, by using microwave energy as a heat source. A MMC (Nickel and 15 % of SiC by weight percentage) specimen of size 140 × 30 × 2 mm3 is in-situ cast by using a domestic microwave applicator at 2.45 GHz frequency, and 900 W, power output. As per selected process parameters, the exposure time to cast MMC specimen, inside an electromagnetic environment is optimized as 33 min. A COMSOL Multiphysics software tool is also used to model the in-situ microwave-assisted casting setup. Numerical simulations are performed to visualize the microwave heating effects within the applicator cavity. Microwave heating effects are analyzed in terms of electric field distribution, resistive heating and temperature profile. As per the simulation study results, maximum electric field intensity (4.46 × 104 (V/m)) is observed around the mold. Resistive heating losses are maximum (4 .9× 109 W/m3) in the susceptor zone. It is observed from the simulation study, that uniform heating of the specimen is observed throughout the cross-section and the melting temperature of the specimen is achieved in 38 min. A good correlation is observed between experimental and simulation study results in terms of exposure time of cast specimen and microwave heating effects within the applicator cavity

Constructing a CeFeO3/LaFeO3/ZnIn2S4 double Z-scheme heterojunction for photocatalytic atrazine degradation under visible light irradiation

The limited light absorption range and rapid recombination of interfacial charges were deemed to be the main restriction of the photocatalysts for atrazine (ATZ) degradation, and the construction of Z-scheme heterojunction was an effective strategy. Herein, a CeFeO3/LaFeO3/ZnIn2S4 (CFO/LFO/ZIS) double Z-scheme ternary heterojunction photocatalyst was fabricated by the in-situ precipitation and hydrothermal method. The photocatalytic ATZ degradation efficiency of optimized heterojunction reached 52% within 175min under visible light irradiation, which was 3.1, 3.7 and 1.7 times higher than that of CFO, LFO and ZIS, respectively. The enhanced photocatalytic activity was due to the excellent light capture capacity and improved photogenerated charge separation in CFO/LFO/ZIS double Z-scheme heterojunction photocatalyst. Meanwhile, some environmental factors on the influence of photocatalytic ATZ degradation performance were systematically investigated. Quenching experiments confirmed that ·OH, h+ and 1O2 were the main active species during the photocatalytic ATZ degradation process. The possible photogenerated charge separation route was proposed via the analysis of the IEF effects between CFO, LFO and ZIS. Finaly, the possible ATZ degradation pathway was deduced by LC-MS measurement and toxicity assessment of the intermediates were also evaluated by the Toxicity Estimation Software Tool (TEST). This work not only provided a potential magnetic recyclable ferrate based double Z-scheme heterojunction photocatalyst for enhancing photogenerated charge separation and light absorption, but also evaluated the intermediate toxicity and related environmental factors on the activity of photocatalyst, which was facilitating the practical application for the degradation of ATZ.

Tanshinone IIA alleviates pulmonary fibrosis by modulating glutamine metabolic reprogramming based on [U-13C5]-glutamine metabolic flux analysis

IntroductionGlutamine metabolic reprogramming, mediated by glutaminase (GLS), is an important signal during pulmonary fibrosis (PF) progression. Tanshinone IIA (Tan IIA) is a naturally lipophilic diterpene with antioxidant and antifibrotic properties. However, the potential mechanisms of Tan IIA for regulating glutamine metabolic reprogramming are not yet clear. ObjectivesThis study aimed was to evaluate the role of Tan IIA in intervening in glutamine metabolic reprogramming to exert anti-PF and to explore the potential new mechanisms of metabolic regulation. MethodsFibrotic characteristics was detected via immunofluorescence and western blotting analysis. Cell proliferation was examined with EdU Assay. Cell metabolites were labeled by using stable isotope [U-13C5]-glutamine. By utilizing 100% 13C glutamine tracers and employing network analysis to investigate the activation of metabolic pathways in fibroblasts, as well as evaluating the impact of Tan IIA on these pathways, we accurately quantified the absolute flux of glutaminolysis, proline synthesis, and the TCA cycle pathway using isotopomer network compartmental analysis (INCA), a user-friendly software tool for 13C metabolic flux analysis (13C-MFA). Molecular docking was used for identifying the binding of Tan IIA with target protein. ResultsTan IIA ameliorate TGF-β1-induced myofibroblast proliferation, reduce collagen I and III and α-SMA protein expression in MRC-5 and NIH-3 T3 cells. Furthermore, Tan IIA regulate mitochondrial energy metabolism by modulating TGF-β1-stimulated glutamine metabolic reprogramming in NIH-3 T3 cells and inhibiting GLS1 expression, which reduced the metabolic flux of glutamine into mitochondria in myofibroblasts, and also targeted inhibited the expression of Δ1-pyrroline-5-carboxylate synthase (P5CS), P5C reductase 1 (PYCR1), and phosphoserine aminotransferase 1 (PSAT1), and reduced proline hydroxylation and blocked the collagen synthesis pathway. ConclusionTan IIA reverses glutamine metabolic reprogramming, reduces mitochondrial energy expenditure, and inhibits collagen matrix synthesis by modulating potential targets in glutamine metabolism. This novel perspective sheds light on the essential role of glutamine metabolic reprogramming in PF.

Assessing the effectiveness of data mining tools in classifying and predicting road traffic congestion

Traffic congestion is a significant issue in cities, impacting the environment, commuters, and the economy. Predicting congestion is crucial for efficient network operation, but high-quality data and computational techniques are challenging for scientists and engineers. The revolution of data mining and machine learning has enabled the development of effective prediction methods. Machine learning (ML) approaches have shown potential in predicting traffic congestion, with classification being a key area of study. Open-source software tools WEKA and Orange are used to predict and classify traffic congestion. However, there is no single best strategy for every situation. This study compared the effectiveness of both data mining tools for predicting congestion in one of the areas of the capital of the Hashemite Kingdom of Jordan, Amman, by testing several classifiers including support vector machine (SVM), K-nearest neighbors (KNN), logistic regression (LR), and random forest (RF) classifications. The results showed that the Orange mining tool was superior in predicting traffic congestion, with a prediction accuracy of 100% for Random forest, logistic regression, and 99.8% for KNN. On the other hand, results were better in WEKA for the SVM classifier with an accuracy of 99.7%.