bluuebunny/crossref_metadata_2025 huggingface dataset:
Two-Sample Mendelian Randomization Study Identifies Tissue-Dependent Risk Genes in Autoimmune Diseases
Authors: Chiu Ryan , Ma Li
Published: November 2024

Autoimmune diseases are among the most prevalent diseases across the world with genetic and environmental factors that contribute to their etiology. Because the exact causes of autoimmune diseases are largely unknown, a Mendelian randomization (MR) approach is used here to examine the potential causal association between gene expression levels and disease risk across various tissues. Specifically, this study focuses on six autoimmune diseases including Crohn’s disease, ulcerative colitis, rheumatoid arthritis, multiple sclerosis, type 1 diabetes mellitus, and systemic lupus erythematosus. Several of these diseases are currently treatable with immunosuppressants that target specific genes, such as TNF-alpha, IL-23, CD20, and more. In this study, a two-sample MR analysis is performed with multitissue expression quantitative trait loci (eQTLs) and large-scale genome-wide association studies to investigate how gene expression can influence the risk of developing these diseases. Our results show that genes HLA-DQA1/2, HLA-DRB1/6, HLA-DQB2, C4A, CYP21A2, and HLA-DQB1-AS1 have a high causal effect across several diseases and tissues, and almost all of these findings originate from the major histocompatibility complex (MHC) region on Chromosome 6. Our findings support the current knowledge of genes associated with these diseases while also revealing novel genes that can be used for drug therapies in the future. Although several drug therapies currently exist to treat this selection of autoimmune diseases, we provide further insights into the main, common pathways responsible for autoimmune disease pathogenesis and discuss novel genes that lack research focus.
Establishing a Female Animal Model of Prediabetes Using a High-Carbohydrate, High-Fat Diet
Authors: Nkosi Ayanda , Pather Reveshni , Mshengu Bongeka , Khathi Andile , Ngubane Phikelelani
Published: November 2024

Prediabetes is a condition that often precedes the onset of type 2 diabetes and is characterized by moderate levels of insulin resistance. This condition is well established in male animal models for diabetes; however, few female models exist. There is accumulating evidence that sex variations affect the pathogenesis, treatment, and consequences of numerous diseases, such as type 2 diabetes. Therefore, we sought to develop a diet-induced prediabetic female animal model to better understand prediabetes development and its effects in females. Female Sprague Dawley rats were randomly allocated to one of two groups: the standard diet (SD) group fed a standard diet with normal drinking water, and the high-carbohydrate, high-fat (HCHF) group fed a high-carbohydrate and high-fat diet with drinking water supplemented with fructose. During induction, we measured food intake, body weight, body mass index (BMI), and oral glucose tolerance response (OGT). After the induction period, biochemical analyses were conducted to assess the levels of plasma leptin, ghrelin, insulin, and glycated hemoglobin (HbA1c). Glycogen concentrations were quantified in the liver and skeletal muscles. The HCHF diet-fed group presented higher body weight gain, food intake, and BMI levels, which were accompanied by elevated plasma insulin, ghrelin, and liver and skeletal muscle glycogen levels compared to the SD-fed group. In the HCHF diet-fed group, the HOMA-IR was above 1.9, suggesting the presence of moderate levels of insulin resistance. The OGT response was significantly higher in the HCHF-fed group versus the SD-fed group, suggesting impaired glucose tolerance, thus displaying the signs and symptoms of prediabetes. The HCHF diet with fructose led to the induction of prediabetes in female Sprague Dawley rats. This model could be used to investigate and outline the pathophysiological complications associated with prediabetes in females as a result of the prolonged ingestion of a high carbohydrate, high-fat diet with fructose. The development of this model could also serve as an effort to further bridge the gap regarding the inclusion of females in biomedical research, thus providing advancements in deriving better, specified treatment strategies for women.
Comparison of the Effects of Mucosa Tissue Healing with Chlorhexidine Digluconate and Choline Salicylate in Patients Wearing a Removable Prosthetic Restoration—A RCT
Authors: Sekuła-Kamińska Barbara , Nitecka-Buchta Aleksandra , Wojciechowski Mateusz , Skucha-Nowak Małgorzata , Rymarczyk Małgorzata , Baron Stefan
Published: November 2024

Background and Objectives: A randomized, double-blind clinical trial was conducted based on the CONSORT study protocol for randomized clinical trials (NCT06531720) to compare the effectiveness of oral mucosa healing properties of 0.2% chlorhexidine digluconate (CHX) and 8.7% choline salicylate (CHS), as well as a control group (CON) with no intervention, in patients with delivered partial removable dentures (PRDs). Materials and Methods: Patients (n = 27) who were enrolled in the study were healthy subjects according to the inclusion/exclusion criteria, and they received new PRDs to complement Kennedy’s class III and IV deficiencies. During the process of adaptation to new prosthetic restorations, OMLs were formed and treated with one of two selected preparations, either CHX = 0.2% or CHS = 8.7%, in relation to the control group (CON). The wound surface area (WSA) (mm2) was measured on repeatable intraoral images taken in accordance with the examination protocol on the first control visit on day 1, day 3, day 7, day 10, and day 14 with the assistance of computer software. Results: There were no statistically significant differences between groups. The fastest effect of WSA complete reduction was observed in the CHX group after 7 days (WAS = 0.78, SD = 1.18) in comparison to CHS = 10 days (WAS = 0.44, SD = 0.90) and CON = 14 days (WAS = 0.22, SD = 0.67). The decrease in the WSA after 7 days of observation was 85.1% in the CHX group, 70.1% in the CHS group, and 59.2% in the CON group. Conclusions: The WSA decreased most rapidly after 7 days of treatment with 0.2% chlorhexidine digluconate (CHX), slightly more slowly after 10 days of treatment with 8.7% choline salicylate (CHS), and relatively most slowly in the CON group, who were not treated with any topical medication after 14 days. Oral mucosa lesions (OMLs) therapy during the process of adaptation to new removable prosthetic restorations is a very important element supporting the whole process. Topical medications containing 0.2% chlorhexidine digluconate are indicated as adjunctive therapy in the process of the supportive treatment and disinfection of oral mucosa lesions. However, this does not release the dentist from liability for the careful adjustment of the removable prosthetic restoration.
Influence of Plasma Arc Current and Gas Flow on the Structural and Tribological Properties of TiN Coatings Obtained by Plasma Spraying
Authors: Kengesbekov Aidar
Published: November 2024

This study investigates the development of TiN-based coatings using plasma spraying technology, focusing on how plasma arc current and working gas flow rate affect the coatings’ structural-phase composition and mechanical–tribological properties. The research highlights the potential and effectiveness of plasma spraying for TiN coatings. Results from scanning electron microscopy and nanoindentation tests show that the TiN coatings have a dense microstructure with strong adhesion. Tribological testing demonstrated that coatings deposited at a 250 A arc current displayed the lowest coefficient of dry friction and the lowest porosity (2.13%) compared to those deposited at 350 A and 450 A arc currents, which exhibited higher porosity (up to 10.45%).
Metal Ions’ Dynamic Effect on Metal-Assisted Catalyzed Etching of Silicon in Acid Solution
Authors: Yang Xiaoyu , Liu Ying , Wu Lin , Liao Zhiyuan , Zhang Baoguo , Tembo Tinashe , Wang Yichen , Hu Ya
Published: November 2024

Metal-assisted catalyzed etching (MACE) technology is convenient and efficient for fabricating large-area silicon nanowires at room temperature. However, the mechanism requires further exploration, particularly the dynamic effect of various ions in the acid-etching solution. This paper investigated the MACE of silicon wafers predeposited with metal nanofilms in an HF-M(NO3)x-H2O etching solution (where M(NO3)x is the nitrate of the fourth-period elements of the periodic table). The oxidizing ability of Fe3+ and NO3− was demonstrated, and the dynamic influence of metal ions on the etching process was discussed. The results show that the MACE of silicon can be realized in various HF-M(NO3)x-H2O etching solutions, such as KNO3, Al(NO3)3, Cr(NO3)3, Mn(NO3)2, Ni(NO3)2, Co(NO3)2, HNO3, and Ca(NO3)2. It is confirmed that the concentration and type of cations in the etching solution affect the etching rate and morphology of silicon. Fe3+ and NO3− act as oxidants in catalytic etching. The fastest etching rate is about 5~6 μm/h in Ni(NO3)2, Co(NO3)2, and Ca(NO3)2 etching solutions. However, a high concentration of K+ hinders silicon etching. This study expands the application of MACE etching solution systems.
Numerical Modelling and Experimental Validation of Selective Laser Melting Processes Using a Custom Argon Chamber Setup for 316L Stainless Steel and Ti6AI4V
Authors: Abdelal Gasser , Higgins Daniel , Chan Chi-Wai , Falzon Brian G.
Published: November 2024

Selective Laser Melting (SLM) is an advanced additive manufacturing technique that demands meticulous control over thermal dynamics to maintain the integrity and performance of manufactured parts. This study presents the development and validation of a thermal model designed to enhance the SLM process for 316L stainless steel (316L SS) and titanium alloy Ti6Al4V. A specially constructed Argon Chamber Setup, equipped with a 200 W continuous-wave (CW) fibre laser system, was used to create an SLM-representative environment for 316L SS, enabling precise experimental validation of the model. This validation serves as a robust baseline, facilitating the model’s extension to more complex materials like Ti6Al4V, thereby supporting a cost-efficient and safe approach to initial testing. The rigorously validated thermal model offers a comprehensive link between experimental data and numerical simulations in SLM. It supports process optimisation by accurately predicting thermal behaviours, contributing significantly to additive manufacturing advancements. By fine-tuning processing parameters, this model enhances material characteristics, thereby providing practical insights applicable to industrial production and improving the consistency and quality of SLM-manufactured parts.
Selective Extraction of Terbium Using Functionalized Metal–Organic Framework-Based Solvent-Impregnated Mixed-Matrix Membranes
Authors: Sharaf Maha , Atrees Mohamed S. , Saleh Gehad M. , Mira Hamed I. , Tanaka Shunsuke
Published: November 2024

Advancements in membrane separation techniques will expand the applications and requirements for highly specialized, inventive, efficient, and resistant separation materials. The selective separation of rare earth elements (REEs) is one of the expanding applications of membrane-based techniques, as their use is becoming more widespread. Membrane techniques are becoming increasingly desired as environmentally friendly, straightforward methods for treating wastewater and separating metals. For the separation of REEs, an innovative impregnated mixed-matrix membrane (IMMM) technique was developed in this study. It provides a selective, efficient, and reusable method that is suitable for industrial applications. Terbium was selectively adsorbed from other REEs using organophosphorus IMMM with a loading capacity of 113.2 mg/g in 3 h and was reused three times without destroying the initial membrane. Solvent impregnation is thought to offer specific chelation sites that are selective for terbium separation.
Advanced Computational Pipeline for FAK Inhibitor Discovery: Combining Multiple Docking Methods with MD and QSAR for Cancer Therapy
Authors: Siyah Pinar
Published: November 2024

Synthetic lethality, involving the simultaneous deactivation of two genes, disrupts cellular functions or induces cell death. This study examines its role in cancer, focusing on focal adhesion kinase and Neurofibromin 2. Inhibiting FAK, crucial for synthetic lethality with NF2/Merlin, offers significant cancer treatment potential. No FAK inhibitor has been clinically approved, underscoring the need for new, effective inhibitors. The small-molecule FAK inhibitors identified in this study show promise, with SP docking, IFD, QPLD, and MD simulations revealing intricate interactions. Based on the comprehensive analysis, the MM/GBSA scores from SP docking for amprenavir, bosutinib, ferric derisomaltose, flavin adenine dinucleotide, lactulose, and tafluprost were determined as −72.81, −71.84, −76.70, −69.09, −74.86, and −65.77 kcal/mol, respectively. The MMGBSA results following IFD docking MD identified the top-performing compounds with scores of −84.0518, −75.2591, −71.8943, −84.2638, −56.3019, and −75.3873 kcal/mol, respectively. The MMGBSA results from QPLD docking MD identified the leading compounds with scores of −77.8486, −69.5773, −71.9171, N/A, −62.5716, and −66.8067 kcal/mol, respectively. In conclusion, based on the MMGBSA scores obtained using the three docking methods and the 100 ns MD simulations, and considering the combined evaluation of these methods, amprenavir, ferric derisomaltose, and bosutinib are proposed as the most promising candidates.
The Performance of an Alfalfa–Sainfoin Mixture in the Semiarid Southern High Plains of the USA
Authors: Lauriault Leonard M. , Marsalis Mark
Published: November 2024

While alfalfa (Medicago sativa) is widely used as forage because of its many positive attributes, there are concerns regarding bloat in grazing ruminants, but tannin-containing legumes, such as sainfoin (Onobrychis viciifolia), are non-bloating. Based on interest elsewhere for growing alfalfa–sainfoin mixtures to address bloat concerns, the objectives of a study conducted at Tucumcari, NM, USA, were to compare an alfalfa–sainfoin mixture to the respective monocultures for stand persistence and dry matter yield over three production years in three randomized complete blocks. The onset of drought limited irrigation water availability shortly after the study’s initiation, which limited sainfoin persistence to the second year after seeding. The resulting percentage decline in forage mass in June from year 1 to year 2 was alfalfa > alfalfa–sainfoin > sainfoin. That said, the alfalfa mass was considerably greater than the sainfoin mass, and the June mass of both monocultures was consistent with that in previous studies in the region when fully irrigated (5.57 vs. 2.00 Mg ha−1 for monoculture alfalfa and sainfoin, respectively; p < 0.0004). Alfalfa persistence was greater than that of sainfoin after 3 years. Alfalfa–sainfoin mixtures may have value in the Southern High Plains of the USA and similar semiarid environments when fully irrigated. Additional studies should evaluate mixture seeding ratios and stand performance under fully irrigated conditions with sainfoin reseeding.
Effects of Implementing the Digital Storytelling Strategy on Improving the Use of Various Forms of the Passive Voice in Undergraduate EFL Students’ Oral Skills at the University Level
Authors: Gutiérrez-Colón Mar , Alameh Sahar Abboud
Published: November 2024

This pilot study explores the effectiveness of digital storytelling in improving the oral use of the passive voice among Lebanese undergraduate EFL students. Conducted during the 2021/2022 spring semester amidst Lebanon’s ongoing economic and social crises, the study involved an experimental group using a digital storytelling strategy and a control group receiving traditional instruction. The research employed a quantitative approach, utilizing a pretest and a posttest to assess grammatical accuracy and fluency, and qualitative interviews to gauge student perceptions. The findings indicate that digital storytelling significantly enhances students’ ability to use the passive voice in oral communication, fostering greater engagement and a deeper understanding of grammatical structures. Despite the challenges posed by the COVID-19 pandemic and Lebanon’s economic difficulties, students in the experimental group demonstrated marked improvement over those in the control group. The study’s limitations include its small sample size and the specific context of a private Lebanese university, which may limit generalizability. However, the results offer promising insights into the benefits of digital storytelling as a pedagogical tool, suggesting its potential for broader application in EFL education. This research contributes to the growing body of literature on technology-enhanced language learning and underscores the need for further exploration in diverse educational settings.
Improving the Effectiveness of a Stock Simulation Trading Course via Blockchain and Social Networking: A Taiwanese Study
Authors: Chang Shuchih Ernest , Luo Hueimin , Tseng Liwen
Published: November 2024

Online courses in higher education became prevalent during the COVID-19 pandemic; however, their application requires technology to be fully integrated into the curriculum. This study explores the integration of a blockchain-based platform in a private online stock simulation trading course during the COVID-19 pandemic. Using a pre–post experimental design with 142 college students, it assessed learning behaviors and outcomes. Students collaborated with teaching assistants via LINE groups, fostering discussion and engagement. They received cryptocurrency rewards, which enhanced motivation and connected the course to their career goals. The findings suggest that combining blockchain and social networking is an effective approach to improving online education. This contributes to the literature on educational technology and online learning by exploring the integration of blockchain and social networking in higher education, specifically within the context of stock simulation trading courses, and demonstrates its impact on student motivation and learning outcomes.
Developing a Model to Study Walking and Public Transport to Attractive Green Spaces for Equitable Access to Health and Socializing Opportunities as a Response to Climate Change: Testing the Model in Pu’er City, China
Authors: Xu Chengdong , Zhang Jianpeng , Xu Yi , Wang Zhenji
Published: November 2024

Green space is not always equitably located in cities, and the attractiveness of green space varies, leaving some residents with easy access to high-quality parks and others with little or no access or access to under-maintained parks. To remedy these inequities, this study identified attractive and well-utilized recreational green spaces and developed a model to measure the likelihood of using these recreational green spaces (PSG). The goal was to reduce the travel time and cost of walking or using public transportation to get to green spaces and to design all green spaces to be attractive. The data come from the perspective of the city’s public transportation system and residents’ personal choices. First, the attractiveness of recreational green spaces was calculated from big data on the geolocation of cell phones, measuring the level of provision of recreational green spaces and the trip rates of urban residents. After that, the travel cost to reach recreational green space in residential areas was calculated according to residents’ travel habits. Finally, the probability of all recreational green spaces in the city being used was calculated by combining the population size of residential areas. Taking Pu’er City in China as an example, the attractiveness and utilization rates of recreational green spaces were calculated by PSG, and the results of the study showed that the probability of residents choosing to use the recreational green spaces that are closer to the residential area, with a larger population capacity, and with a higher attractiveness is the highest. The results of the study help promote equitable access to health and socialization opportunities for individuals and communities, thereby promoting environmental justice to help mitigate and respond to climate change.
Visualisation Design Ideation with AI: A New Framework, Vocabulary, and Tool
Authors: Owen Aron E. , Roberts Jonathan C.
Published: November 2024

This paper introduces an innovative framework for visualisation design ideation, which includes a collection of terms for creative visualisation design, the five-step process, and an implementation called VisAlchemy. Throughout the visualisation ideation process, individuals engage in exploring various concepts, brainstorming, sketching ideas, prototyping, and experimenting with different methods to visually represent data or information. Sometimes, designers feel incapable of sketching, and the ideation process can be quite lengthy. In such cases, generative AI can provide assistance. However, even with AI, it can be difficult to know which vocabulary to use and how to strategically approach the design process. Our strategy prompts imaginative and structured narratives for generative AI use, facilitating the generation and refinement of visualisation design ideas. We aim to inspire fresh and innovative ideas, encouraging creativity and exploring unconventional concepts. VisAlchemy is a five-step framework: a methodical approach to defining, exploring, and refining prompts to enhance the generative AI process. The framework blends design elements and aesthetics with context and application. In addition, we present a vocabulary set of 300 words, underpinned from a corpus of visualisation design and art papers, along with a demonstration tool called VisAlchemy. The interactive interface of the VisAlchemy tool allows users to adhere to the framework and generate innovative visualisation design concepts. It is built using the SDXL Turbo language model. Finally, we demonstrate its use through case studies and examples and show the transformative power of the framework to create inspired and exciting design ideas through refinement, re-ordering, weighting of words and word rephrasing.
Fast-Time Simulations to Study the Capacity of a Traffic Network Aimed at Urban Air Mobility
Authors: Di Mascio Paola , Celesti Matteo , Sabatini Matteo , Moretti Laura
Published: November 2024

This article investigates viable solutions to implement an Urban Air Mobility network in Milan, Italy, and analyzes its influence on the airspace capacity. The network comprises eight vertiports for passenger transport among two main airports in the area and the city using electric vertical take-off and landing aircraft (eVTOLs). A Fast-Time Simulation (FTS) model with the software AirTOp (Air Traffic Optimization) allowed the evaluation of the ideal capacity of the network by varying two configurations, which differ from each other in terms of the number of Final Approach and Takeoff areas (FATOs). The results show how it is possible to reach high hourly capacities (in the order of one hundred), thus allowing the use of the service for about 4% of the total passengers passing through the two airports during the reference day chosen for this study. However, the results are ideal due to the strong idealism of the system, which overlooks several factors, and they should be considered as the maximum limit that can be obtained. Despite this, the method presented in this article can also be adapted for other urban areas with high population densities. In addition, the use of a simulation tool of this type allows, in addition to a numerical analysis, a qualitative analysis of the network behavior in terms of traffic, thus highlighting the criticalities of the proposed systems.
Mapping Rosenwald Schools for African Americans in South Carolina: A Geographic Analysis of Spatial Patterns
Authors: Morgan Grayson R. , Otterstrom Samuel M. , Stevenson Lane , Otterstrom Allen C.
Published: November 2024

In early twentieth-century America, many Blacks in the south had poor access to schooling. Philanthropist Julius Rosenwald and educator Booker T. Washington collaborated to help build schools in the south for Blacks in the 1920s and 1930s. This paper analyzes the geographic patterns of the schools that were built using funds donated by Rosenwald in the state of South Carolina. Some 446 of the 500 Rosenwald schools were located and mapped using a multi-step process, and then data related to these schools were analyzed in four state subregions. Rosenwald schools were built around the state, with relatively more schools built in the northern part of the state compared with the south, where a larger Black population in the south potentially points to less access to schools for Blacks at that time. Rosenwald funds required contributions from other sources, and the state, Blacks, and Whites all contributed. Interestingly, in the south, where there was a higher percentage of Blacks, Whites contributed a larger share towards the building of these schools in comparison with other state regions. The paper thus shows both the process and value of mapping these Rosenwald schools and analyzing this important historical time in South Carolina within a spatial construct.
Improving Retention and Success of African American Students in Computer Science: A Mixed-Method Case Study at an HBCU
Authors: Hur Jung Won , Thomas Cassandra , Huang Li , Chang Xiao
Published: November 2024

This case study introduces the STARS (Supporting Talented African American Undergraduates for Retention and Success) project, designed to foster the retention and success of academically talented African American computer science students from low-income backgrounds at Historically Black Colleges and Universities (HBCUs) in the U.S. The STARS program employs a holistic approach, integrating four primary pillars of support: academic, social, career, and financial. Specific support provided includes near-peer mentoring, technical skill development seminars, undergraduate research, and high school outreach activities. To explore the program’s effectiveness and areas of improvement, a mixed-method evaluation study was conducted, collecting data through surveys, observations, individual interviews, and focus group interviews. The findings revealed that the STARS program contributed to high levels of retention among its scholars, and the mentoring program provided valuable networking opportunities. The study suggests that the program’s comprehensive approach, tailored to scholars’ needs, and combined with a culturally affirming learning environment, facilitates the retention and success of talented African American students in computer science.
Antimicrobial Activity of Diffusible Substances Produced by Lactococcus lactis Against Bacillus cereus in a Non-Contact Co-Culture Model
Authors: Han Yuting , Akinsemolu Adenike A. , Anumudu Christian K. , Miri Taghi , Onyeaka Helen
Published: November 2024

The symptoms of foodborne illness caused by Bacillus cereus often go unreported, complicating the effectiveness of conventional chemical and physical methods used to inhibit its growth in food production. This challenge, combined with the increasing use of lactic acid bacteria (LAB) in the food industry and consumer preference for minimally processed products, prompted this study. The antibacterial activity of diffusible substances produced by Lactococcus lactis ATCC 11454 against Bacillus cereus NC11143 and Escherichia coli K-12 MG1655 was investigated using a non-contact co-culture model utilising deMann Rogosa and Sharpe broth, with glucose as a carbon source. This study employed plate counting and flow cytometry to assess the impact of these substances on bacterial growth and to analyse their composition and antimicrobial efficacy. The co-culture of Lactococcus lactis ATCC 11454 resulted in the production of a stable antimicrobial peptide, which was heat resistant and acid tolerant. Purification was achieved via ammonium sulphate precipitation and preparative HPLC, yielding a peptide with a molecular mass of 3.3 kDa, with daughter ion fractions similar to nisin A. Antimicrobial activity studies demonstrated that the diffusible substances effectively inhibited B. cereus growth over a period of eight days and exhibited bactericidal activity, killing 99% of the B. cereus cells. Additionally, these substances also inhibited Escherichia coli K-12 MG1655 grown under similar conditions. Comparative analysis revealed that in the co-culture assay, L. lactis produced a 50% higher yield of the antimicrobial peptides compared to pure cultures. Similarly, the specific growth rate of L. lactis was four times higher. With respect to protein purification and concentration, ammonium sulphate precipitation coupled with solid phase extraction was most effective in the purification and concentration of the diffusible substances. The findings provide a basis for utilising bacteriocin-producing strains as a preservation method, offering an alternative to traditional chemical and physical control approaches especially for the food industry.
Exploring Sentiment Analysis for the Indonesian Presidential Election Through Online Reviews Using Multi-Label Classification with a Deep Learning Algorithm
Authors: Ma’aly Ahmad Nahid , Pramesti Dita , Fathurahman Ariadani Dwi , Fakhrurroja Hanif
Published: November 2024

Presidential elections are an important political event that often trigger intense debate. With more than 139 million users, YouTube serves as a significant platform for understanding public opinion through sentiment analysis. This study aimed to implement deep learning techniques for a multi-label sentiment analysis of comments on YouTube videos related to the 2024 Indonesian presidential election. Offering a fresh perspective compared to previous research that primarily employed traditional classification methods, this study classifies comments into eight emotional labels: anger, anticipation, disgust, joy, fear, sadness, surprise, and trust. By focusing on the emotional spectrum, this study provides a more nuanced understanding of public sentiment towards presidential candidates. The CRISP-DM method is applied, encompassing stages of business understanding, data understanding, data preparation, modeling, evaluation, and deployment, ensuring a systematic and comprehensive approach. This study employs a dataset comprising 32,000 comments, obtained via YouTube Data API, from the KPU and Najwa Shihab channels. The analysis is specifically centered on comments related to presidential candidate debates. Three deep learning models—Convolutional Neural Network (CNN), Bidirectional Long Short-Term Memory (Bi-LSTM), and a hybrid model combining CNN and Bi-LSTM—are assessed using confusion matrix, Area Under the Curve (AUC), and Hamming loss metrics. The evaluation results demonstrate that the Bi-LSTM model achieved the highest accuracy with an AUC value of 0.91 and a Hamming loss of 0.08, indicating an excellent ability to classify sentiment with high precision and a low error rate. This innovative approach to multi-label sentiment analysis in the context of the 2024 Indonesian presidential election expands the insights into public sentiment towards candidates, offering valuable implications for political campaign strategies. Additionally, this research contributes to the fields of natural language processing and data mining by addressing the challenges associated with multi-label sentiment analysis.
What Is the Role of the Gut Microbiota in Anastomotic Leakage After Colorectal Resection? A Scoping Review of Clinical and Experimental Studies
Authors: Lianos Georgios D. , Frountzas Maximos , Kyrochristou Ilektra D. , Sakarellos Panagiotis , Tatsis Vasileios , Kyrochristou Gerasimia D. , Bali Christina D. , Gazouli Maria , Mitsis Michail , Schizas Dimitrios
Published: November 2024

Background: Anastomotic leakage (AL) still remains a common complication after colorectal anastomosis that leads to increased morbidity and mortality. The gut microbiota has been hypothesized as one of the risk factors associated with anastomotic leakage. The aim of the present study was to summarize all existing clinical and experimental studies that evaluate the impact of intestinal microbiota on anastomotic leakage after colorectal resection. Methods: The present scoping review was designed according to PRISMA recommendations and a systematic search in Medline, Scopus, EMBASE, Clinicaltrials.gov, Google Scholar, and CENTRAL was conducted until September 2024. Results: Overall, 7 clinical and 5 experimental studies were included. A diminished α-diversity of the gut microbiota in patients suffering from AL was demonstrated. Specific microbe genera, such as Lachnospiraceae, Bacteroidaceae, Bifidobacterium, Acinetobacter, Fusobacterium, Dielma, Elusimicronium, Prevotella, and Faecalibacterium, seem to be associated with AL. However, specific genera, like Prevotella, Streptococcus, Eubacterium, Enterobacteriaceae, Klebsiella, Actinobacteria, Gordonibacter, Phocaeicola, and Ruminococcus2, seem to be protective against AL. Experimental studies highlighted that the Western diet seems to affect microbiota diversity and increases the AL rate, whereas anastomotic healing seems to be impaired by high metalloproteinase production and increased collagenase activity. Conclusions: The intestinal microbiota seems to play an important role in anastomotic leakage after colorectal resection. Specific interventions targeting the microbiota’s composition and the pathophysiological mechanisms by which it impairs anastomotic healing could diminish the risk for anastomotic leakage and improve clinical outcomes. However, future studies should be based on prospective design and eliminate heterogeneity.
Clinical, Histological, and HPV-Related Factors Associated to Diffuse Presentation of Exophytic Nasal Papillomas
Authors: Fulla Marta , Quiros Beatriz , Clavero Omar , Gomà Montse , de Andrés-Pablo Álvaro , Pavon Miquel Àngel , Penella Anna , Alemany Laia , González-Compta Xavier , Mena Marisa
Published: November 2024

Background: Sinonasal exophytic papillomas (SNEP) are benign tumours arising from nasal mucosa. Human papillomavirus (HPV) infection seems to be related to the aetiology of a fraction of SNEP cases. SNEP presentation can be focal (FSNEP) or diffuse (DSNEP), but factors related to focal or diffuse presentation have not yet been well ascertained. This study aimed to analyse clinical, histological, and HPV-related differences between FSNEP and DSNEP. Methods: A retrospective cohort of 18 patients with SNEP from our centre were evaluated. Demographic, clinical and follow-up data were collected. All samples were subject to histopathological evaluation, DNA quality control, HPV-DNA detection, and viral load assessment. Univariate analyses were performed to evaluate differences between FSNEP and DSNEP. Results: Twelve SNEP patients were included in the final analysis. Seven patients had a diffuse nasal presentation, being younger than patients affected with FSNEP (42.7 years vs. 65.2 years, p = 0.019). The nasal septum was significantly more affected in DSNEP than in FSNEP (85.7% vs. 20%, p = 0.029). HPV-DNA was detected more frequently (100%) in DSNEP (HPV11 in six cases, HPV6 in one case) than in FSNEP (40%, p = 0.045, HPV6 in two cases). The median viral load among HPV6-positive samples was 626.8 virus/cell for FSNEP and 80.2 for DSNEP, and among HPV11-positive samples was 1673.7 for DSNEP. Recurrences were more frequent in the diffuse than in the focal group (85.7% vs. 20%, p = 0.029). Conclusions: The diffuse presentation of SNEP seems to be related to younger patients, nasal septum involvement, HPV infection, mostly HPV11, and a higher risk of recurrence.
Contemporary Single-Center Experience of Complete Aortic Arch Replacement Employing the Frozen Elephant Trunk Technique in Patients with Extensive Aortic Disease
Authors: Schoeberl Armin-Kai , Huber Florian , Schachner Bruno , Preinfalk Valentina , Zierer Andreas
Published: November 2024

Objective: This study aimed to examine contemporary results of the frozen elephant trunk (FET) procedure in an all-comers patient cohort. Methods: Between January 2017 and May 2024, a total of 132 consecutive patients with either aortic aneurysm (n = 32), acute aortic dissection (n = 32), or chronic aortic dissection (n = 68) underwent total aortic arch replacement employing the FET technique. In-hospital data were collected prospectively and included preoperative characteristics, intraoperative data, and follow-up results. Results: The median cardiopulmonary bypass time, cardiac ischemia time, and selective antegrade cerebral perfusion time were 180 (161–205), 89 (70–113), and 45 (38–54) min, respectively. Total 30-day mortality rate was 7.6% (n = 10). The rate of major postoperative neurological complications was 6.8% (n = 9) for perioperative stroke and 2.3% (n = 3) for permanent spinal cord injury. Five patients (3.8%) required hemofiltration at the time of discharge due to postoperative kidney injury. Rates of subsequent endovascular and open aortic repair following primary FET were 40.9% (n = 54) and 3.8% (n = 5), respectively. The median time to reintervention was 86 (30–439) days. The median follow-up time was 25 (8–52) months, and overall survival rates at 1, 2, and 3 years were 89%, 89%, and 87%, respectively. Conclusions: Our data are consistent with current reports, indicating that the FET technique is a valuable adjunct in treating extensive aortic arch pathologies. The procedure provides an increasingly safe and effective option for complete aortic arch replacement, even in patients requiring a redo procedure.
Molecular Pathogenesis of Renal Neoplasms in Patients with Birt–Hogg–Dubé Syndrome
Authors: Nezami Behtash G. , Teh Bin Tean , Lin Xiaoqi , Yang Ximing J.
Published: November 2024

Birt–Hogg–Dubé syndrome (BHDS) is an autosomal dominant disease characterized by skin, lung, and renal manifestations. This syndrome is caused by a germline mutation in the FLCN gene, which leads to disruption in multiple downstream pathways. Renal cell carcinomas are one of the serious clinical manifestations of the disease, which usually presents as bilateral and multiple tumors. Morphologically, most of these tumors are classified as hybrid oncocytic tumors. Recent advances in molecular techniques have shed light on the pathogenesis of these renal tumors. In this review, we evaluate and summarize the current knowledge of BHDS, pathologic changes, and its molecular basis with the focus on the renal hybrid oncocytic tumor (HOT), their pathogenesis, and molecular underpinning.
AI-Driven Business Model: How AI-Powered Try-On Technology Is Refining the Luxury Shopping Experience and Customer Satisfaction
Authors: Song Xin , Bonanni Carole
Published: November 2024

Artificial Intelligence (AI) has revolutionized interactive marketing, creating dynamic and personalized customer experiences. To the best of our knowledge, no studies have ventured into how firms in the luxury sector can leverage AI marketing activities to innovate their business model and boost the development of future digital marketing to enhance the luxury shopping experience (LSE). Building on the existing LSE literature and adopting a business model innovation (BMI) lens, we conducted an experimental study to identify how AI-powered try-on technology (ATT) can contribute to LSEs and create customer value proxied by customer satisfaction. In addition, we determined the specific dimensions of the LSE that are most affected by AI marketing efforts. Furthermore, our findings explored the role of AI in driving BMI and the interrelationship between enhanced customer satisfaction and BMI. This research contributes to understanding the crucial role of AI in shaping the future of interactive marketing in the luxury context.
Adapting and Verifying the Liming Index for Enhanced Rock Weathering Minerals as an Alternative Liming Approach
Authors: Araujo Francisco S. M. , Chacon Andrea G. M. , Porto Raphael F. , Cavalcante Jaime P. L. , Chiang Yi Wai , Santos Rafael M.
Published: November 2024

Acidic soils limit plant nutrient availability, leading to deficiencies and reduced crop yields. Agricultural liming agents address these issues and are crucial for deploying silicate amendments used in enhanced rock weathering (ERW) for carbon sequestration and emission reduction. Grower recommendations for liming agents are based on the liming index (LI), which combines the neutralizing value (NV) and fineness rating (FR) to predict a mineral’s acidity neutralization relative to pure calcite. However, the LI was originally developed for carbonate minerals, and its applicability to silicates remains uncertain, with studies often yielding inconclusive results on soil carbon and liming efficiency. This study aims to evaluate the liming efficiency of silicates. We determined the LI of five candidate ERW minerals (basalt, olivine, wollastonite, kimberlite, and montmorillonite) and compared them to pure calcite. Post-NV acid digestion, we characterized the minerals and soils, applying nonparametric statistical tests (Wilcoxon, Kendall) to correlate liming results with LI, dosage, and amendment methods. We developed an empirical model incorporating mineralogy and kinetics to explain silicate behavior in liming, considering soil, climate, and crop factors.
MMS-EF: A Multi-Scale Modular Extraction Framework for Enhancing Deep Learning Models in Remote Sensing
Authors: Yu Hang , Song Weidong , Zhang Bing , Zhu Hongbo , Dai Jiguang , Zhang Jichao
Published: November 2024

The analysis of land cover using deep learning techniques plays a pivotal role in understanding land use dynamics, which is crucial for land management, urban planning, and cartography. However, due to the complexity of remote sensing images, deep learning models face practical challenges in the preprocessing stage, such as incomplete extraction of large-scale geographic features, loss of fine details, and misalignment issues in image stitching. To address these issues, this paper introduces the Multi-Scale Modular Extraction Framework (MMS-EF) specifically designed to enhance deep learning models in remote sensing applications. The framework incorporates three key components: (1) a multiscale overlapping segmentation module that captures comprehensive geographical information through multi-channel and multiscale processing, ensuring the integrity of large-scale features; (2) a multiscale feature fusion module that integrates local and global features, facilitating seamless image stitching and improving classification accuracy; and (3) a detail enhancement module that refines the extraction of small-scale features, enriching the semantic information of the imagery. Extensive experiments were conducted across various deep learning models, and the framework was validated on two public datasets. The results demonstrate that the proposed approach effectively mitigates the limitations of traditional preprocessing methods, significantly improving feature extraction accuracy and exhibiting strong adaptability across different datasets.
Integration and Analysis of Omics Data Using Genome-Scale Metabolic Models
Authors: Moškon Miha , Režen Tadeja
Published: November 2024

Constraint-based modelling and genome-scale metabolic models (GEMs) have been used extensively to analyze omics data, providing a mechanistic perspective on complex metabolic systems and networks [...]
Analysis and Optimization of Laser Beam Welding Parameters for Aluminium Composite (Al-Zn-Cu Alloy) by Grey Relational Optimization
Authors: Kumar Singh Nitish , Sethuraman Balaguru , Gupta Manoj
Published: November 2024

Aluminium and its composites are widely used in production to enhance the strength of lightweight objects. In this study, an AA7075/SiC composite was fabricated using a stir casting route. Multi-objective optimization and finite element analysis were performed with various process parameters on a manufactured aluminium composite (AA7075 + SiC) undergoing a laser beam welding process. Four welding parameters, i.e., pulse frequency, power, welding speed (transverse), and wire size were taken for laser welding as per the L-9 orthogonal array for experimental study. Tensile strength, deflection, temperature distribution, Rockwell hardness (fusion zone), and Rockwell hardness (heat affected zone) were taken as output parameters after welding. The standard deviation objective weighting–grey relational optimization method optimized the process parameter. ANSYS APDL 23 software was utilized to simulate the entire laser welding method with a cylindrical heat source to predict the temperature distribution in the butt-welded plates. This software uses finite element analysis and gives a deviation of only 5.85% for temperature distribution with experimental results. This study helps to understand the effect of various parameters on the welding strength of the aluminium composite.
Effect of In Vitro Gastrointestinal Digestion on the Polyphenol Bioaccessibility and Bioavailability of Processed Sorghum (Sorghum bicolor L. Moench)
Authors: Collins Aduba , Francis Nidhish , Chinkwo Kenneth , Santhakumar Abishek Bommannan , Blanchard Christopher
Published: November 2024

Sorghum is a significant source of polyphenols, whose content, antioxidant properties and bioaccessibility may be modulated by digestion. Studies have reported sorghum polyphenol changes after simulated digestion. However, the effects of simulated digestion on processed, pigmented sorghum are unknown. This study investigated the bioaccessibility and bioavailability of black (BlackSs and BlackSb), red (RedBa1, RedBu1, RedBa2, RedBu2) and white (WhiteLi2 and White Li2) sorghum samples using a Caco-2 in vitro model. Ultra high performance liquid chromatography—online 2,2′-azino-bis (3-ethylbenzothiazoline-6-sulfonic acid) (UHPLC–online ABTS)—and quadrupole time-of-flight liquid chromatography mass spectra (QTOF LC–MS) facilitated the identification of digested and transported compounds. Simulated digestion showed increased bioaccessibility and total phenolic content (TPC) for BlackSs by 2-fold. BlackSs and BlackSb exhibited high antioxidant capacities, with variations dependent on processing in other varieties. Kaempferol-3-O-xyloside exhibited a 4-fold increase in TPC following digestion of processed BlackSs and BlackSb but was absent in the others. BlackSs, BlackSb, and RedBu1 revealed twelve bioaccessible and Caco-2 transported compounds not previously reported in sorghum, including trans-pinostilbene, tryptophan and maackin a. This study demonstrates that in vitro digestion increases the bioaccessiblity of sorghum polyphenols through the process of cellular biotransformation, possibly improving transport and bioactivity in vivo.
Progress in MXenes and Their Composites as Electrode Materials for Electrochemical Sensing and Dye-Sensitized Solar Cells
Authors: Suganthi Sanjeevamuthu , Ahmad Khursheed , Oh Tae Hwan
Published: November 2024

In the present mini-review article, we have compiled the previously reported literature on the fabrication of MXenes and their hybrid composite materials based electrochemical sensors for the determination of phenolic compounds and counter electrodes for platinum (Pt)-free dye-sensitized solar cells (DSSCs). MXenes are two-dimensional (2D) materials with excellent optoelectronic and physicochemical properties. MXenes and their composite materials have been extensively used in the construction of electrochemical sensors and solar cell applications. In this paper, we have reviewed and compiled the progress in the construction of phenolic sensors based on MXenes and their composite materials. In addition, co1.unter electrodes based on MXenes and their composites have been reviewed for the development of Pt-free DSSCs. We believe that the present review article will be beneficial for the researchers working towards the development of phenolic sensors and DSSCs using MXenes and their composites as electrode materials.