Computational Techniques Research Articles

Protein feature engineering framework for AMPylation site prediction.

AMPylation is a biologically significant yet understudied post-translational modification where an adenosine monophosphate (AMP) group is added toTyrosine and Threonine residues primarily. While recent work has illuminated the prevalence and functional impacts of AMPylation, experimental identification of AMPylation sites remains challenging. Computational prediction techniques provide a faster alternative approach. The predictive performance of machine learning models is highly dependent on the features used to represent the raw amino acid sequences. In this work, we introduce a novel feature extraction pipeline to encode the key properties relevant to AMPylation site prediction. We utilize a recently published dataset of curated AMPylation sites to develop our feature generation framework. We demonstrate the utility of our extracted features by training various machine learning classifiers, on various numerical representations of the raw sequences extracted with the help of our framework. Tenfold cross-validation is used to evaluate the model's capability to distinguish between AMPylated and non-AMPylated sites. The top-performing set of features extracted achieved MCC score of 0.58, Accuracy of 0.8, AUC-ROC of 0.85 and F1 score of 0.73. Further, we elucidate the behaviour of the model on the set of features consisting of monogram and bigram counts for various representations using SHapley Additive exPlanations.

Integrated analysis of gut metabolome, microbiome, and exfoliome data in an equine model of intestinal injury.

The equine gastrointestinal (GI) microbiome has been described in the context of various diseases. The observed changes, however, have not been linked to host function and therefore it remains unclear how specific changes in the microbiome alter cellular and molecular pathways within the GI tract. Further, non-invasive techniques to examine the host gene expression profile of the GI mucosa have been described in horses but not evaluated in response to interventions. Therefore, the objectives of our study were to (1) profile gene expression and metabolomic changes in an equine model of non-steroidal anti-inflammatory drug (NSAID)-induced intestinal inflammation and (2) apply computational data integration methods to examine host-microbiota interactions. Twenty horses were randomly assigned to 1 of 2 groups (n = 10): control (placebo paste) or NSAID (phenylbutazone 4.4mg/kg orally once daily for 9days). Fecal samples were collected on days 0 and 10 and analyzed with respect to microbiota (16S rDNA gene sequencing), metabolomic (untargeted metabolites), and host exfoliated cell transcriptomic (exfoliome) changes. Data were analyzed and integrated using a variety of computational techniques, and underlying regulatory mechanisms were inferred from features that were commonly identified by all computational approaches. Phenylbutazone induced alterations in the microbiota, metabolome, and host transcriptome. Data integration identified correlation of specific bacterial genera with expression of several genes and metabolites that were linked to oxidative stress. Concomitant microbiota and metabolite changes resulted in the initiation of endoplasmic reticulum stress and unfolded protein response within the intestinal mucosa. Results of integrative analysis identified an important role for oxidative stress, and subsequent cell signaling responses, in a large animal model of GI inflammation. The computational approaches for combining non-invasive platforms for unbiased assessment of host GI responses (e.g., exfoliomics) with metabolomic and microbiota changes have broad application for the field of gastroenterology. Video Abstract.

Chemometric modeling of pharmaceuticals for partitioning between sludge and aqueous phase during the wastewater treatment process.

Computational techniques, such as quantitative structure-property relationships (QSPRs), can play a significant role in exploring the important chemical features essential for the degree of sorption or sludge/water partition coefficient (Kd) towards sewage sludge of wastewater treatment process to evaluate the environmental consequence and risk of pharmaceuticals. The current research work aims to construct a predictive QSPR model for the sorption of 148 diverse active pharmaceutical ingredients (APIs) in sewage sludge during wastewater treatment. For the development of the model, we employed easily computable 2D descriptors as independent variables. The model has been developed following the Organization for Economic Cooperation and Development's (OECD) guidelines. It has undergone internal and external validation using a variety of methodologies, as well as been tested for its applicability domain. A measure of hydrophobicity, i.e., MLOGP2, showed the most promising contribution in modeling the sorption coefficient of APIs. Among other parameters, the number of tertiary aromatic amines, the presence of electronegative atoms like N, O, and Cl, the size of a molecule, the number of aromatic hydroxyl groups, the presence of substituted aromatic nitrogen atoms and alkyl-substituted tertiary carbon atoms were also found to be influential for the regulation of solid water partition coefficient of APIs during the wastewater treatment process. The statistical validity tests performed on the developed partial least squares (PLS) model showed that it is statistically evident, robust, and predictive (R2Train = 0.750, Q2LOO = 0.683, Q2F1 = 0.655, Q2F2 (or R2Test) = 0.651). In addition, the predictivity of the constructed model was further inspected by using the "prediction reliability indicator" tool for 14 external APIs.

GameTable COST action: kickoff report

The GameTable COST Action kickoff, focusing on “Computational Techniques for Tabletop Games Heritage,” took place at Leiden University in the Pieter de la Court Building from January 29th to 30th, 2024. This event aimed to convene researchers from diverse backgrounds involved in the Action, offering an opportunity to present an overview of the key research areas, share concrete case studies, and facilitate discussions and idea exchanges across fields that may not typically intersect, thereby enhancing the organization of the Action. This report provides a summary of the organization and discussions of the event, and future plans for GameTable.

A comprehensive exploration of twist1 to identify a biomarker for tumor immunity and prognosis in pan-cancer.

Twist1 has been identified as a critical gene in tumor, but current study of this gene remains limitative. This study aims to investigate its roles and potential mechanisms across pan-cancer. The study used various databases and computational techniques to analyze twist's RNA expression, clinical data, gene mutations, tumor stemness, tumor microenvironment, immune regulation. Furthermore, the experimental method of fluorescence staining was carried out to identify twist1 expression in various tumor masses. After analyzing the protein-protein interaction of TWIST, enrichment analysis and predictive potential drugs were performed, and molecular docking was conducted to validate. We found that twist1 expression was significantly higher in various types of cancer and associated with tumor stage, grade, and poor prognosis in multiple cancers. Differential expression of twist1 was linked to gene mutation, RNA modifications, and tumor stemness. Additionally, twist1 expression was positively associated with tumor immunoregulation and immune checkpoint. Salinomycin, klugline, isocephaelince, manassantin B, and pimonidazole are predictive potential drugs targeting TWIST1. This study revealed that twist1 plays an important role in tumor, and might be a curial marker in tumor diagnose and prognosis. The study also highlighted twist1 as a promising therapeutic target for cancer treatment and provided a foundation for future research.

Advancing blast fragmentation simulation of RC slabs: A graph neural network approach

Accurate prediction of blast-induced fragmentation in reinforced concrete (RC) structures is pivotal for structural debris hazard assessment in an explosion event. This assessment is essential for designing effective measures for protection of people and structure in the vicinity. While conventional computational techniques are proficient, they are computationally very demanding and may not be readily adaptable in practical applications. This study proposes a novel approach, the Fragment Graph Network (FGN), a specialized variant of graph neural networks (GNNs), to simulate complex interactions between macro-level particles of RC structures subjected to close-in blast loads. The RC structure is discretised into particles, with the model predicting structural responses and fragmentation based on states of these particles. Utilizing an autoregressive prediction mechanism, our model can accurately simulate spatiotemporal responses of RC slabs against blast loads. The efficacy of the model was tested on a dataset consisting of 149 simulated cases, encompassing around 180,000 states each, covering a wide range of blasting scenarios and structural design parameters including TNT charge weight, standoff distance, slab dimension, concrete strength, and reinforcement ratio and layout. Notably, the proposed model delivered accurate predictions, achieving relative errors under 10% and R2 values exceeding 0.93 for both standard and extrapolated test scenarios as compared to the SPH simulations. Remarkably, our model dramatically outpaced traditional numerical approaches, reducing simulation times from hours to minutes on CPUs and seconds on GPUs. This pioneering effort paves the way for efficient and accurate simulations of RC blast fragmentations, offering a rapid alternative to standard numerical approaches.

Computational Analysis and Classification of Hernia Repairs

Problems related to ventral hernia repairs (VHR) are very common, and evaluating them using computational methods can assist in selecting the most appropriate treatment. This study is based upon data from 3339 patients from different European countries observed during the last 12 years (2012–2023), which were collected by specialists in hernia surgery. Most patients underwent standard surgical procedures, with a growing trend towards laparoscopic surgery. This paper focuses on statistically evaluating the treatment methods in relation to patient age, body mass index (BMI), and the type of repair. Appropriate mathematical methods are employed to extract and classify the selected features, with emphasis on computational and machine-learning techniques. The paper presents surgical hernia treatment statistics related to patient age, BMI, and repair methods. The main conclusions point to mean groin hernia repair (GHR) complications of 19% for patients in the database. The accuracy of separating GHR mesh surgery with and without postoperative complications reached 74.4% using a two-layer neural network classification. Robotic surgeries represent 22.9% of all the evaluated hernia repairs. The proposed methodology suggests both an interdisciplinary approach and the utilization of computational intelligence in hernia surgery, potentially applicable in a clinical setting.

Establishment of a prognostic model for gastric cancer patients who underwent radical gastrectomy using machine learning: a two-center study

ObjectiveGastric cancer is a prevalent gastrointestinal malignancy worldwide. In this study, a prognostic model was developed for gastric cancer patients who underwent radical gastrectomy using machine learning, employing advanced computational techniques to investigate postoperative mortality risk factors in such patients.MethodsData of 295 patients with gastric cancer who underwent radical gastrectomy at the Department of General Surgery of Affiliated Hospital of Xuzhou Medical University (Xuzhou, China) between March 2016 and November 2019 were retrospectively analyzed as the training group. Additionally, 109 patients who underwent radical gastrectomy at the Department of General Surgery Affiliated to Jining First People’s Hospital (Jining, China) were included for external validation. Four machine learning models, including logistic regression (LR), decision tree (DT), random forest (RF), and gradient boosting machine (GBM), were utilized. Model performance was assessed by comparing the area under the curve (AUC) for each model. An LR-based nomogram model was constructed to assess patients’ clinical prognosis.ResultsLasso regression identified eight associated factors: age, sex, maximum tumor diameter, nerve or vascular invasion, TNM stage, gastrectomy type, lymphocyte count, and carcinoembryonic antigen (CEA) level. The performance of these models was evaluated using the AUC. In the training group, the AUC values were 0.795, 0.759, 0.873, and 0.853 for LR, DT, RF, and GBM, respectively. In the validation group, the AUC values were 0.734, 0.708, 0.746, and 0.707 for LR, DT, RF, and GBM, respectively. The nomogram model, constructed based on LR, demonstrated excellent clinical prognostic evaluation capabilities.ConclusionMachine learning algorithms are robust performance assessment tools for evaluating the prognosis of gastric cancer patients who have undergone radical gastrectomy. The LR-based nomogram model can aid clinicians in making more reliable clinical decisions.

Sodium acetate increases the productivity of HEK293 cells expressing the ECD-Her1 protein in batch cultures: experimental results and metabolic flux analysis

Human Embryonic Kidney cells (HEK293) are a popular host for recombinant protein expression and production in the biotechnological industry. This has driven within both, the scientific and the engineering communities, the search for strategies to increase their protein productivity. The present work is inserted into this search exploring the impact of adding sodium acetate (NaAc) into a batch culture of HEK293 cells. We monitored, as a function of time, the cell density, many external metabolites, and the supernatant concentration of the heterologous extra-cellular domain ECD-Her1 protein, a protein used to produce a candidate prostate cancer vaccine. We observed that by adding different concentrations of NaAc (0, 4, 6 and 8 mM), the production of ECD-Her1 protein increases consistently with increasing concentration, whereas the carrying capacity of the medium decreases. To understand these results we exploited a combination of experimental and computational techniques. Metabolic Flux Analysis (MFA) was used to infer intracellular metabolic fluxes from the concentration of external metabolites. Moreover, we measured independently the extracellular acidification rate and oxygen consumption rate of the cells. Both approaches support the idea that the addition of NaAc to the culture has a significant impact on the metabolism of the HEK293 cells and that, if properly tuned, enhances the productivity of the heterologous ECD-Her1 protein.

Machine learning-based virtual screening of multi-target anti-obesity compounds from medicinal and edible plants: A combined in silico and in vitro study

In response to the limited effectiveness of existing weight loss food products, we sought to apply machine learning-based virtual screening methods to identify potential anti-obesity functional compounds from medicinal and edible plants and validate their in vitro activities. Firstly, we construct and evaluate the machine learning (ML) screening models using Multilayer Perceptron (MLP) and Random Forest (RF) algorithms. The receiver operating characteristic (ROC) curve demonstrates the high accuracy of MLP and RF models in screening for obese-related targets PL (pancreatic lipase) and AMPK (Adenosine 5′-monophosphate activated protein kinase). Subsequently, the tested ML models are employed to screen the constructed database, and Gypenoside LXVI (GYP) and alisol-b-23-acetate (ALI) are identified as compounds exhibiting favorable activity against both targets. The hit compounds are tested for their impact on lipase activity and lipid accumulation. The test results show that GYP and ALI have favorable inhibitory effects on pancreatic lipase (PL), with IC50 of 359.7 and 433.8 μg/mL. Furthermore, both GYP and ALI significantly reduced cellular lipid accumulation by 72.89% and 79.01% with the concentration increase to 40 μg/mL. The molecular docking results indicate that GYP and ALI can interact with several amino acid residues on the two target proteins, thereby affecting the activity of the target proteins. In conclusion, GYP and ALI can prevent and alleviate obesity by inhibiting PL activity and regulating AMPK signaling factors. We innovatively applied virtual screening based on ML to discover functional factors in food for anti-obesity purposes. This novel computational screening technique holds significant potential in the development of dietary supplements to combat obesity.

Application and progress of water colour remote sensing technology in monitoring chlorophyll concentration changes in seawater

This paper provides an in-depth discussion on the application and progress of water colour remote sensing technology in monitoring changes in chlorophyll concentration in seawater. The hydrochromatic remote sensing technique uses spectral data acquired by remote sensing satellites and airborne platforms to monitor and analyse the distribution of marine chlorophyll, which is of great significance for understanding and protecting marine ecosystems. The article first introduces the basic principles and development history of this technology, and then discusses in detail its applications in global ocean monitoring, including resource management, environmental assessment, and ecological protection. In the discussion, we highlight the advantages of water colour remote sensing technology in providing large-scale and efficient ocean monitoring, while also pointing out the challenges it faces in terms of data accuracy, atmospheric disturbance handling and algorithm development. In particular, these challenges are gradually being overcome with the application of advanced computational techniques, such as machine learning and artificial intelligence, resulting in significant improvements in monitoring accuracy and efficiency. Finally, this paper looks at the future direction of water colour remote sensing technology, including technological innovation, data integration and interdisciplinary collaboration, highlighting the potential value of this technology in addressing global climate change and marine environmental protection. Through this comprehensive analysis, we have gained a deeper understanding and appreciation of the important role of water colour remote sensing technology in global ocean monitoring and management.

Improvement of Smart Grid Stability Based on Artificial Intelligence with Fusion Methods

It is crucial to evaluate and anticipate stability under various conditions, as the ability to stabilize a smart grid (SG) is one of its key features for assessing the effectiveness of its design. Intelligent approaches to stability forecasting are necessary to mitigate inadvertent instability in SG design. This is particularly crucial with the expansion of residential and commercial infrastructures, along with the growing integration of renewable energies into these grids. Predicting the stability of SGs is currently a major challenge. The concept of an SG encompasses a broad range of emerging technologies in which artificial intelligence (AI) plays a crucial role and is increasingly being utilized in light of the limitations of conventional methods. It empowers informed decision-making and adaptable responses to fluctuations in customer energy needs, unexpected power outages, rapid changes in renewable energy generation, or any unforeseen crises within an SG system. In this paper, we propose a symmetric approach to enhance SG stability by integrating various machine learning (ML) and deep learning (DL) algorithms, where symmetry is observed in the balanced application of these diverse computational techniques to predict and ensure the grid’s stability. These algorithms utilized a dataset containing the simulation results of the SG stability. The learning phase of these algorithms is based on imprecise and unreliable data. To overcome this limitation, the fusion of classifiers can be a powerful approach to modeling inaccurate and uncertain data, providing more robust and reliable predictions than individual classifiers. Voting and Dempster–Shafer (DS) methods, two commonly used techniques in ensemble learning, were employed and compared. The results show that the use of the fusion of distinct classifiers with voting theory achieves an accuracy of 99.8% and outperforms several other methods including the DS method.

Sentiments of Public Opinion

Sentiment analysis, also known as opinion mining, is a computational technique that aims to identify and extract subjective information from text data, allowing us to understand and categorize the underlying sentiment expressed by individuals. With the exponential growth of social media, online reviews, and user generated content, sentiment analysis has gained significant attention as a valuable tool for businesses, researchers, and decision-makers to gain insights into public opinion, customers satisfaction, and market trends. The project is about searching the web for public opinion about people, movies, places, companies or any entities. The user of this software can type in a query that is any entity and the system mines the web for public opinion about that entity and generates a sentiment score. A positive score represents a positive public sentiment about that entity and negative score represents a negative sentiment or opinion.

Analysis of natural language processing in engineering documentations based on Schweizer-Sklar prioritized aggregation operators for bipolar complex hesitant fuzzy uncertainty

In the context of fuzzy relations, symmetry refers to a property where the relationship between two elements remains the same regardless of the order in which they are considered. Natural language processing (NLP) in engineering documentation discusses the application of computational methods or techniques to robotically investigate, analyze, and produce natural language information for manufacturing contents. The NLP plays an essential role in dealing with large amounts of textual data normally recovered in engineering documents. In this paper, we expose the idea of a bipolar complex hesitant fuzzy (BCHF) set by combining the bipolar fuzzy set (BFS) and the complex hesitant fuzzy set (CHFS). Further, we evaluate some algebraic and Schweizer-Sklar operational laws under the presence of BCHF numbers (BCHFNs). Additionally, using the above information as well as the idea of prioritized (PR) operators, we derive the idea of BCHF Schweizer-Sklar PR weighted averaging (BCHFSSPRWA) operator, BCHF Schweizer-Sklar PR ordered weighted averaging (BCHFSSPROWA) operator, BCHF Schweizer-Sklar PR weighted geometric (BCHFSSPRWG) operator, and BCHF Schweizer-Sklar PR ordered weighted geometric (BCHFSSPROWG) operator. Basic properties for the above operators are also discussed in detail, such as idempotency, monotonicity, and boundedness. Moreover, we evaluate the best way in which NLP can be applied to engineering documentations with the help of the proposed operators. Therefore, we illustrate the major technique of multi-attribute decision-making (MADM) problems based on these derived operators. Finally, we use some existing operators and try to compare their ranking results with our proposed ranking results to show the supremacy and validity of the investigated theory.

Bioinformatics analysis and experimental verification of TIGD1 in non-small cell lung cancer

IntroductionNon-small cell lung cancer (NSCLC) is a prevalent respiratory system tumor. Triggered transposable element derivative 1 (TIGD1) exhibits significant overexpression in various tumor cells and tissues, suggesting its involvement in cancer progression.MethodsClinical data and gene expression profiles of lung adenocarcinoma were collected from TCGA, UCSC XENA, and GEO databases. Computational techniques and empirical studies were employed to analyze the role of TIGD1 in NSCLC. Cellular experiments were conducted using the H1299 cell line, including RNA interference, cell viability assays, quantitative PCR, wound-healing assays, western blotting, and plate clone formation assays.ResultsBioinformatics analysis revealed TIGD1’s potential as a biomarker for diagnosing and predicting lung cancer. It also indicated promise as a target for immune-related therapy and targeted drug therapy. Cellular studies confirmed TIGD1’s involvement in cancer cell proliferation, invasion, and migration. Furthermore, an association between TIGD1 and the PI3K/AKT signaling pathway was suggested.DiscussionThe findings suggest that TIGD1 plays a vital role in NSCLC progression, making it a potential diagnostic biomarker and therapeutic target. The association with the PI3K/AKT signaling pathway provides insights into the underlying molecular mechanisms. Integrating computational analysis with empirical studies enhances our understanding of TIGD1’s significance in NSCLC and opens avenues for further research into targeted therapies.

How different are offline and online diplomacy? A comparative analysis of public statements and SNS posts by delegates to the United Nations

IntroductionThis article investigates the evolving landscape of diplomacy in the digital age, focusing on diplomats at the United Nations (UN) Headquarters in New York. The central inquiry revolves around how diplomatic actors use digital tools to complement or augment traditional face-to-face diplomacy.MethodsWe systematically compare a substantial corpus of X posts (tweets) from UN diplomats with their public statements at the United Nations Security Council (UNSC), employing advanced computational social science techniques. This study applies a range of large-scale text analysis methods, including word embedding, topic modeling, and sentiment analysis, to investigate systematic differences between offline and online communication.ResultsOur analysis reveals that, while the essence of diplomacy remains consistent across both domains, there is strategic selectivity in the use of online platforms by diplomats. Online communication emphasizes non-security topics, ceremonial matters, and prominent policy stances, in contrast to the operational issues common in UNSC deliberations. Additionally, online discourse adopts a less confrontational, more public diplomacy-oriented tone, with variations among countries.DiscussionThis study offers one of the first systematic comparisons between offline and online diplomatic messages. It illuminates how diplomats navigate the digital realm to complement traditional roles. The findings indicate that some elements of public diplomacy and nation branding, directed toward a wider audience far beyond the council chamber, have become an integral part of multilateral diplomacy unfolding at the UNSC.

Exploring the connotation of “Local Community” in ICOMOS discourse: a textual analysis based on authoritative documents

As an authoritative institution in cultural heritage conservation, ICOMOS plays a crucial role in guiding local communities’ participation in heritage conservation. However, its scattered and vague descriptions of local communities pose significant obstacles to further research and practice of community participation in heritage conservation. Given the increasing importance of local communities in heritage conservation, it is essential to systematically explore the connotation of Local Community connotations within ICOMOS discourse. This research employs Natural Language Processing methods to analyze ICOMOS’s descriptions of Local Community. Utilizing computational techniques of word frequency calculation, LDA (Latent Dirichlet Allocation) topic model keyword calculation, and hierarchical clustering calculation, the research uncovers relevant keywords and its thematic clusters of Local Community. These findings are further elucidated by aligning them with the principles outlined in authoritative documents of ICOMOS. The research indicates that ICOMOS’s descriptions of Local Community can be summarized into four main keywords families. These keywords families can be summarized as a comprehensive Local Community “three-level, four-family” keywords system. The “Tourism” keywords family exhibits a close association with Local Community, highlighting ICOMOS’s heightened emphasis on heritage tourism. The “Management-Development” keywords family occupies the second level, emphasizing fundamental principles for local communities’ participation in heritage practices. The “Traditional-Knowledge” and “Social-Economic” Keywords Families, situated in the third level, respectively describe the value attributes and conservation approaches of local communities. Each keywords family formed over different periods, exhibiting varying trends of development. By systematically integrating ICOMOS’s descriptions of Local Community and employing Natural Language Processing for in-depth exploration, This research aims to construct a cognitive understanding of local communities from a new perspective of quantitative text analysis, with the intention of providing theoretical references for subsequent research on local communities.

Dynamic temperature distribution characteristics of a large glasshouse with cooling system during the start-stop stage

Excessive heat accumulation leads to high temperature in the glasshouse, the high temperature directly affects the normal growth of crops. This research based on the cooling system as the cooling method of glasshouse, experimental and computational fluid dynamics technique were used to investigate the cooling effect in the start-stop stage. CFD was used to simulate and optimize the temperature dynamics during the start-stop phase of the glasshouse cooling system in the most common summer climates (rainy and sunny). A glasshouse test platform was built in Wuhan in order to conduct temperature experiments to verify the effectiveness of the simulation. The results show that starting the cooling system in summer can effectively reduce the temperature inside the glasshouse in a short time (when it is rainy, the temperature is reduced by 8.1 °C; when it is sunny, the temperature is reduced by 5 °C). During the start-stop stage, the temperature trend of the horizontal and vertical planes is the same. Until the system is running stably, the indoor temperature is maintained uniform and stable, with little fluctuation. In order to create the best growing environment for crops, the air velocity of the air supply outlet is increased to 6.0 m/s on sunny days, which is helpful to increase crop yield and reduce greenhouse gas emissions.

Large language model enhanced corpus of CO2 reduction electrocatalysts and synthesis procedures

CO2 electroreduction has garnered significant attention from both the academic and industrial communities. Extracting crucial information related to catalysts from domain literature can help scientists find new and effective electrocatalysts. Herein, we used various advanced machine learning, natural language processing techniques and large language models (LLMs) approaches to extract relevant information about the CO2 electrocatalytic reduction process from scientific literature. By applying the extraction pipeline, we present an open-source corpus for electrocatalytic CO2 reduction. The database contains two types of corpus: (1) the benchmark corpus, which is a collection of 6,985 records extracted from 1,081 publications by catalysis postgraduates; and (2) the extended corpus, which consists of content extracted from 5,941 documents using traditional NLP techniques and LLMs techniques. The Extended Corpus I and II contain 77,016 and 30,283 records, respectively. Furthermore, several domain literature fine-tuned LLMs were developed. Overall, this work will contribute to the exploration of new and effective electrocatalysts by leveraging information from domain literature using cutting-edge computer techniques.

Disturbed flow in the juxta-anastomotic area of an arteriovenous fistula correlates with endothelial loss, acute thrombus formation and neointimal hyperplasia.

Clinical failure of arteriovenous fistulae (AVF) is frequently due to juxta-anastomotic neointimal hyperplasia (JANIH). Although the mouse AVF model recapitulates human AVF maturation, previous studies focused on the outflow vein distal to the anastomosis. We hypothesized that the juxta-anastomotic area (JAA) has increased NIH compared to the outflow vein. AVF were created in C57BL/6 mice without or with chronic kidney disease (CKD). Temporal and spatial changes of the JAA were examined using histology and immunofluorescence. Computational techniques were used to model the AVF. RNA-seq and bioinformatic analyses were performed to compare the JAA with the outflow vein. The jugular vein to carotid artery AVF model was created in Wistar rats. The neointima in the JAA shows increased volume compared to the outflow vein. Computational modeling shows increased volume of disturbed flow at the JAA compared to the outflow vein. Endothelial cells are immediately lost from the wall contralateral to the fistula exit, followed by thrombus formation and JANIH. Gene Ontology (GO) enrichment analysis of the 1862 differentially expressed genes (DEG) between the JANIH and the outflow vein identified 525 overexpressed genes. The rat jugular vein to carotid artery AVF showed changes similar to the mouse AVF. Disturbed flow through the JAA correlates with rapid endothelial cell loss, thrombus formation, and JANIH; late endothelialization of the JAA channel correlates with late AVF patency. Early thrombus formation in the JAA may influence later development of JANIH.