bluuebunny/crossref_metadata_2025 huggingface dataset:
Effectiveness of a Nutrition Counseling Intervention on Food Consumption, According to the Degree of Processing: A Community-Based Non-Randomized Trial of Quilombola Communities in South Brazil
Authors: Pacheco Pauline Müller , Bairros Fernanda de Souza , Neutzling Marilda Borges , Nunes Luciana Neves , Knauth Daniela Riva , dos Santos Francine Silva , Drehmer Michele
Published: November 2024

Objectives

To evaluate the effectiveness of a nutrition counseling intervention on food consumption according to the Nova classification that reflects levels of food processing.

Methods

Controlled community trial was conducted in quilombola communities in the South of Brazil. Four communities were allocated to the control group (CG) and the intervention group (IG), two communities comprised each group. A total of 158 individuals (CG = 87; IG = 68) were included in the study. The intervention consisted of six theoretical and practical workshops on food and nutrition education, conducted over a 4-month period. We used a 24-hour recall at baseline and another post-intervention.

Results

There was an increase in the consumption of traditional quilombola food as an effect of the intervention (from 14.5% to 20.7% in the IG, and from 12.7% to 16.0% in the CG, p = 0.05). There was no significant variation in the other Nova food groups according to time and intervention.

Conclusion

An increase in traditional quilombola food indicates a resumption of traditional food intake and appreciation of local culture as an effect of this intervention at the community level.

Clinical Trial Registration:www.clinicaltrials.gov, identifier NCT02489149.
Dual-Metric-Based Assessment and Topology Generation of Urban Airspace with Quadrant Analysis and Pareto Ranking
Authors: Zhang Weizheng , Wu Hua , Liu Yang , Zhou Suyu , Dong Hailong , Liu Huayu
Published: November 2024

In this study, an urban airspace assessment mechanism is proposed and validated using the actual urban building data, offering a systematic approach to airspace selection for unmanned aerial vehicle (UAV) operations. Two metrics are involved to assess the urban airspace accurately, which are the airspace availability and risk to ground population. The former is measured by analyzing the connectivity of the urban airspace which particularly emphasizes the impact of urban features like buildings and obstacles. The latter is quantized by using a previously proposed risk estimation model, with which an urban risk map can be generated. Quadrant analysis and Pareto ranking are then employed to evaluate the available airspace for UAVs. Quadrant analysis maps the urban airspace availability and risk to ground population onto a two-dimensional space. Additionally, Pareto ranking determines a set of Pareto-optimal solutions wherein no objective can be improved without compromising at least one other objective. The topology of urban airspace could be constructed by using the top 50% of grids ranked by Pareto ranking based on the actual building data. A case study is conducted in a densely populated urban area in Changqing District, Jinan, Shandong Province, China. The connectivity of the airspace topology is verified by employing the A-star algorithm to generate a feasible path for UAVs.
A Robust Adaptive PID-like Controller for Quadrotor Unmanned Aerial Vehicle Systems
Authors: Boubakir Ahsene , Souanef Toufik , Labiod Salim , Whidborne James F.
Published: November 2024

This paper introduces a stable adaptive PID-like control scheme for quadrotor Unmanned Aerial Vehicle (UAV) systems. The PID-like controller is designed to closely estimate an ideal controller to meet specific control objectives, with its gains being dynamically adjusted through a stable adaptation process. The adaptation process aims to reduce the discrepancy between the ideal controller and the PID-like controller in use. This method is considered model-free, as it does not require knowledge of the system’s mathematical model. The stability analysis performed using a Lyapunov method demonstrates that every signal in the closed-loop system is Uniformly Ultimately Bounded (UUB). The effectiveness of the proposed PID-like controller is validated through simulations on a quadrotor for path following, ensuring accurate monitoring of the target positions and yaw angle. Simulation results highlight the performance of this control scheme.
Use of Soil Remineralizer to Replace Conventional Fertilizers: Effects on Soil Fertility, Enzymatic Parameters, and Soybean and Sorghum Productivity
Authors: Rodrigues Letícia Nayara Fuzaro , Borges Wander Luis Barbosa , Modesto Viviane Cristina , Ribeiro Naiane Antunes Alves , de Souza Nelson Câmara , Girardi Vitória Almeida Moreira , Matos Aline Marchetti Silva , Silva Bárbara Pereira Christofaro , Galindo Fernando Shintate , Andreotti Marcelo
Published: November 2024

The reliance on soil acidity correctives and mineral fertilizers poses a threat to food production due to the finite nature of these resources and their susceptibility to price volatility from importation. Soil remineralizers have emerged as a sustainable alternative. This study assessed silicate agrominerals as soil remineralizers to replace limestone, gypsum, and conventional fertilizers in a no-tillage system. Conducted in a tropical climate on sandy/medium-textured Ultisol, twelve treatments (combinations of liming, gypsum, mineral fertilization, and remineralizer) were tested for their effects on soybean and sorghum agronomic traits. Applying a remineralizer at 2500 kg ha−1 enhanced soybean productivity by 15% and sorghum by 35% in succession, along with increases in P, S, Ca, Mg, sum of bases (SB), cation exchange capacity (CEC), base saturation (V%) in the 0–0.20 m layer and organic matter in the 0–0.40 m layer, benefiting soil microbiological parameters, with the treatment combining all four products improving soil fertility; however, for better crop productivity, split applications appear to be an alternative to avoid nutrient imbalance. Due to the finer particle size of the remineralizer, which allows faster nutrient release, further research is recommended to investigate the long-term impacts on soil microbiota dynamics, optimal doses and combinations, and economic viability across various soil types and climates.
Proteomic Analysis Unveils the Protective Mechanism of Active Modified Atmosphere Packaging Against Senescence Decay and Respiration in Postharvest Loose-Leaf Lettuce
Authors: Weng Lili , Han Jiyuan , Wu Runyan , Liu Wei , Zhou Jing , Chen Xiangning , Zhang Huijuan
Published: November 2024

In this study, physicochemical and proteomic analyses were performed to investigate the effect of modified atmosphere packaging (MAP) on the quality of postharvest loose-leaf lettuce. The results showed that MAP enhanced the sensory characteristics of loose-leaf lettuce and delayed the incidence of postharvest deterioration by suppressing weight loss, electrolyte leakage, and reactive oxygen species levels. MAP-inhibited storage-induced programmed cell death may be attributed to a lower expression of protein disulfide isomerase and a higher expression of oligonucleotide/oligosaccharide binding fold nucleic acid binding site protein and reducing glutamine synthase levels. Also, we explore the potential of MAP to protect against oxidative damage in loose-leaf lettuce by potentially modulating the expression levels of NAC family proteins, which may enhance signaling and the expression of cytochrome c oxidase and membrane-bound pyrophosphate in the oxidative phosphorylation pathway. In addition, MAP potentially delayed postharvest senescence and extended the shelf life of lettuce by regulating key protein metabolic pathways that may reduce respiration rates. These include the NAC family of proteins, enzymes in the oxidative phosphorylation pathway, glutamine synthetize, and other crucial metabolic routes. These findings provide a scientific basis for enhancing the postharvest preservation of leafy vegetables, such as loose-leaf lettuce, through MAP technology.
Combining UAV Multispectral Imaging and PROSAIL Model to Estimate LAI of Potato at Plot Scale
Authors: Li Shuang , Lin Yongxin , Zhu Ping , Jin Liping , Bian Chunsong , Liu Jiangang
Published: November 2024

Accurate and rapid estimation of the leaf area index (LAI) is essential for assessing crop growth and nutritional status, guiding farm management, and providing valuable phenotyping data for plant breeding. Compared to the tedious and time-consuming manual measurements of the LAI, remote sensing has emerged as a valuable tool for rapid and accurate estimation of the LAI; however, the empirical inversion modeling methods face challenges of low efficiency for actual LAI measurements and poor model interpretability. The integration of radiative transfer models (RTMs) can overcome these problems to some extent. The aim of this study was to explore the potential of combining the PROSAIL model with high-resolution unmanned aerial vehicle (UAV) multispectral imaging to estimate the LAI across different growth stages at the plot scale. In this study, four inversion strategies for estimating the LAI were tested. Firstly, two types of lookup tables (LUTs) were built to estimate potato LAI of different varieties across different growth stages. Specifically, LUT1 was based on band reflectance, and LUT2 was based on vegetation index. Secondly, the hybrid models combining LUTs generated by PROSAIL and two machine learning algorithms (random forest (RF), Partial Least Squares Regression (PLSR)) are built to estimate potato LAI. The determination of coefficient (R2) of models for estimating LAI by LUTs ranged from 0.24 to 0.64. The hybrid method that integrates UAV multispectral, PROSAIL, and machine learning significantly improved the accuracy of LAI estimation. Compared to the results based on LUT2, the hybrid model achieved higher accuracy with the R2 of the inversion model improved by 30% to 263%. The LAI retrieval model using the PROSAIL model and PLSR achieved an R2 as high as 0.87, while the R2 using the RF algorithm ranged from 0.33 to 0.81. The proposed hybrid model, integrated with UAV multispectral data, PROSAIL, and PLSR can achieve approximate accuracy compared with the empirical inversion models, which can alleviate the labor-intensive process of handheld LAI measurements for building inversion models. Thus, the hybrid approach provides a feasible and efficient strategy for estimating the LAI of potato varieties across different growth stages at the plot scale.
Ciprofloxacin for the Treatment of Infections Caused by Carbapenemase-Producing Gram-Negative Bacteria
Authors: Rubiñan Pablo , Viñado Belén , Fernández-Hidalgo Nuria , Larrosa Nieves , Sempere Abiu , Campany David , Rodríguez-Pardo Dolors , González-López Juan José , Nuvials Xavier , del Barrio-Tofiño Ester , Escolà-Vergé Laura , Los-Arcos Ibai
Published: November 2024

Background: There is no experience with ciprofloxacin for the treatment of carbapenemase-producing Gram-negative bacteria (CP-GNB) infections. Methods: This is a retrospective single-centre study where we describe the clinical evolution of all consecutive adult patients who received ciprofloxacin monotherapy for the treatment of CP-GNB infections. Primary outcomes were clinical failure (defined as death, lack of clinical improvement or a switch to another drug) at day 14 and 30-day all-cause mortality. Results: Nineteen patients were included. Fifteen (79%) were men, the median age was 74 years (IQR 66–79) and the median Charlson comorbidity index was five (IQR 3–6.5). The most frequent infections were: nine complicated urinary tract infections, three soft tissue infections and three intra-abdominal infections. Twenty CP-GNBs were isolated (one patient had a coinfection): nine VIM-type-producing Enterobacterales, nine OXA-48-type-producing Enterobacterales and two VIM-type-producing Pseudomonas aeruginosa. Six (32%) patients had positive blood cultures, and one presented with septic shock. The median duration of ciprofloxacin treatment was 14 days (IQR 10–15). One patient presented with clinical failure at day 14. There was no 30-day mortality. Two patients exhibited microbiological recurrence at day 90. There were no reported adverse effects. Conclusions: Monotherapy with ciprofloxacin may be an alternative treatment for selected, clinically stable patients with ciprofloxacin-susceptible CP-GNB infections.
Antimicrobial Resistance in African Great Apes
Authors: Tanga Coch Tanguy Floyde , Makouloutou-Nzassi Patrice , Mbehang Nguema Pierre Philippe , Düx Ariane , Lendzele Sevidzem Silas , Mavoungou Jacques François , Leendertz Fabian H. , Mintsa-Nguema Rodrigue
Published: November 2024

Background/Objectives: Antibiotic-resistant bacteria pose a significant global public health threat that demands serious attention. The proliferation of antimicrobial resistance (AMR) is primarily attributed to the overuse of antibiotics in humans, livestock, and the agro-industry. However, it is worth noting that antibiotic-resistant genes (ARGs) can be found in all ecosystems, even in environments where antibiotics have never been utilized. African great apes (AGAs) are our closest living relatives and are known to be susceptible to many of the same pathogens (and other microorganisms) as humans. AGAs could therefore serve as sentinels for human-induced AMR spread into the environment. They can potentially also serve as reservoirs for AMR. AGAs inhabit a range of environments from remote areas with little anthropogenic impact, over habitats that are co-used by AGAs and humans, to captive settings with close human–animal contacts like zoos and sanctuaries. This provides opportunities to study AMR in relation to human interaction. This review examines the literature on AMR in AGAs, identifying knowledge gaps. Results: Of the 16 articles reviewed, 13 focused on wild AGAs in habitats with different degrees of human presence, 2 compared wild and captive apes, and 1 study tested captive apes alone. Ten studies included humans working with or living close to AGA habitats. Despite different methodologies, all studies detected AMR in AGAs. Resistance to beta-lactams was the most common (36%), followed by resistance to aminoglycosides (22%), tetracyclines (15%), fluoroquinolones (10%), sulphonamides (5%), trimethoprim (5%), macrolide (3%), phenicoles (2%) and fosfomycin (1%). Conclusions: While several studies suggest a correlation between increased human contact and higher AMR in AGAs, resistance was also found in relatively pristine habitats. While AGAs clearly encounter bacteria resistant to diverse antibiotics, significant gaps remain in understanding the underlying processes. Comparative studies using standardized methods across different sites would enhance our understanding of the origin and distribution of AMR in AGAs.
Current Knowledge on CRISPR Strategies Against Antimicrobial-Resistant Bacteria
Authors: de la Fuente Tagarro Carlos , Martín-González Diego , De Lucas Andrea , Bordel Sergio , Santos-Beneit Fernando
Published: November 2024

CRISPR/Cas systems have emerged as valuable tools to approach the problem of antimicrobial resistance by either sensitizing or lysing resistant bacteria or by aiding in antibiotic development, with successful applications across diverse organisms, including bacteria and fungi. CRISPR/Cas systems can target plasmids or the bacterial chromosome of AMR-bacteria, and it is especially necessary to have an efficient entry into the target cells, which can be achieved through nanoparticles or bacteriophages. Regarding antibiotic development and production, though the use of CRISPR/Cas in this field is still modest, there is an untapped reservoir of bacterial and fungal natural products, with over 95% yet to be characterized. In Streptomyces, a key antibiotic-producing bacterial genus, CRISPR/Cas has been successfully used to activate silent biosynthetic gene clusters, leading to the discovery of new antibiotics. CRISPR/Cas is also applicable to non-model bacteria and different species of fungi, making it a versatile tool for natural products discovery. Moreover, CRISPR/Cas-based studies offer insights into metabolic regulation and biosynthetic pathways in both bacteria and fungi, highlighting its utility in understanding genetic regulation and improving industrial strains. In this work, we review ongoing innovations on ways to treat antimicrobial resistances and on antibiotic discovery using CRISPR/Cas platforms, highlighting the role of bacteria and fungi in these processes.
Coffee: Fuel for Your Day or Foe for Your Arteries
Authors: Buelna-Chontal Mabel
Published: November 2024

Atherosclerosis, a major cause of cardiovascular diseases, is influenced by modifiable factors such as adiposity and blood cholesterol. Diet is crucial in these areas, particularly regarding antioxidant, inflammatory, and obesity effects. Coffee, a globally popular stimulant beverage, has garnered significant attention for its potential impact on cardiovascular diseases. Recent insights reinforce the need to re-examine the relationship between coffee consumption and atherosclerosis progression. Coffee’s complex composition includes polyphenols, renowned for their antioxidant and anti-inflammatory properties as well as potential weight-reducing effects. In addition, studies have demonstrated that certain coffee compounds such as chlorogenic acid, caffeic, p-coumaric, and ferulic acid can prevent atherogenesis by preventing the oxidation of low-density lipoproteins. Conversely, diterpenes, found in some coffee brews, can elevate cholesterol levels, posing a risk to coronary health. Notably, coffee intake has been shown to influence gut microbiota diversity, potentially contributing to anti-obesity effects. This review explores the insights from preclinical and clinical studies investigating the potential mechanisms through which coffee consumption may reduce the risk of atherosclerosis—highlighting the potential benefits of moderate filtered coffee consumption and the potential risks associated with excessive coffee consumption. Understanding this relationship is crucial for informing public health recommendations and guiding future research.
Fibrinogen Structural Changes and Their Potential Role in Endometriosis-Related Thrombosis
Authors: Fini Eleonora , Argento Flavia Rita , Borghi Serena , Giurranna Elvira , Nencini Francesca , Cirillo Michela , Fatini Cinzia , Taddei Niccolò , Coccia Maria Elisabetta , Fiorillo Claudia , Becatti Matteo
Published: November 2024

Endometriosis (EM), a chronic inflammatory condition predominantly affecting women of reproductive age, has been linked to an elevated risk of thrombosis, though its underlying molecular mechanisms remain incompletely understood. In this case-control study, involving 71 EM patients and 71 matched controls, we explored the structural and functional changes in fibrinogen and their potential role in thrombosis. Key oxidative stress markers, such as reactive oxygen species (ROS) levels in blood lymphocytes, monocytes, and granulocytes, along with plasma lipid peroxidation markers and total antioxidant capacity, were measured. Fibrinogen structure was examined using circular dichroism spectroscopy and intrinsic fluorescence, while functional properties were evaluated by analyzing thrombin-mediated polymerization and plasmin-induced lysis. Compared to controls, EM patients exhibited elevated ROS production and systemic oxidative stress, leading to notable fibrinogen oxidation and structural alterations. These changes were associated with impaired fibrin polymerization and enhanced resistance to plasmin-induced lysis, which are indicative of a pro-thrombotic state. These findings suggest that oxidative stress-driven fibrinogen modifications may contribute to the heightened thrombotic risk in women with EM, highlighting a potential therapeutic target to mitigate cardiovascular complications.
POI Recommendation Scheme Based on User Activity Patterns and Category Similarity
Authors: Lim Jongtae , Lee Seoheui , Li He , Bok Kyoungsoo , Yoo Jaesoo
Published: November 2024

The utilization of location-based social networks to provide point-of-interest (POI) recommendation services has been the subject of extensive research in recent years. Various factors that can enhance the precision of POI recommendations were examined in previous studies. However, the factors of a user, including the location and time, were not considered. In this paper, we proposed a POI recommendation scheme in which user activity patterns and the similarity of categories are considered. The proposed scheme is used to organize users based on the activity level and to take into account the characteristics of both the user and location. Furthermore, it provides personalized recommendations by considering the category similarity, time, and location data that were collected from users. We evaluated the performance of the proposed scheme and compared it with that of a currently used scheme. The proposed scheme exhibits precision that is approximately 16% greater than that of the existing scheme.
Real-Time Short-Circuit Current Calculation in Electrical Distribution Systems Considering the Uncertainty of Renewable Resources and Electricity Loads
Authors: Liu Dan , Xiong Ping , Tang Jinrui , Li Lie , Wang Shiyao , Cao Yunyu
Published: November 2024

Existing short-circuit calculation methods for distribution networks with renewable energy sources ignore the fluctuation of renewable sources and cannot reflect the impact of renewable sources and load changes on short-circuit current in real time at all times of the day and in extreme scenarios. A real-time short-circuit current calculation method is proposed to take into account the stochastic nature of distributed generators (DGs) and electricity loads. Firstly, the continuous power flow of distribution networks is calculated based on the real-time renewable energy output and electricity loads. And then, equivalent DG models with low-voltage ride through (LVRT) strategies are substituted into the iterative calculation method to obtain the short-circuit currents of all main branches in real time. The effects of different renewable energy output curves on distribution network short-circuit currents are quantitatively analyzed during the fluctuation in distributed power output, which can provide an important basis for the setting calculation of distribution network relay protection and the study of new principles of protection.
Deep Learning Classification of Traffic-Related Tweets: An Advanced Framework Using Deep Learning for Contextual Understanding and Traffic-Related Short Text Classification
Authors: Melhem Wasen Yahya , Abdi Asad , Meziane Farid
Published: November 2024

Classifying social media (SM) messages into relevant or irrelevant categories is challenging due to data sparsity, imbalance, and ambiguity. This study aims to improve Intelligent Transport Systems (ITS) by enhancing short text classification of traffic-related SM data. Deep learning methods such as RNNs, CNNs, and BERT are effective at capturing context, but they can be computationally expensive, struggle with very short texts, and perform poorly with rare words. On the other hand, transfer learning leverages pre-trained knowledge but may be biased towards the pre-training domain. To address these challenges, we propose DLCTC, a novel system combining character-level, word-level, and context features with BiLSTM and TextCNN-based attention. By utilizing external knowledge, DLCTC ensures an accurate understanding of concepts and abbreviations in traffic-related short texts. BiLSTM captures context and term correlations; TextCNN captures local patterns. Multi-level attention focuses on important features across character, word, and concept levels. Experimental studies demonstrate DLCTC’s effectiveness over well-known short-text classification approaches based on CNN, RNN, and BERT.
Design, Simulation, and Comparison of Advanced Control Strategies for a 3-Degree-of-Freedom Robot
Authors: Urrea Claudio , Kern John , Torres Víctor
Published: November 2024

This study presents the design, simulation, and comparative analysis of three advanced control strategies applied to a 3-Degree-of-Freedom (DoF) robot manipulator. The controllers investigated are a variant from the Computed Torque Control family, a Proportional–Derivative–Integral with fuzzy logic (PD-PI + fuzzy) controller, and a Model Predictive Control (MPC) scheme. The controller performance is evaluated through the tracking of predefined trajectories in the three-dimensional space. The results are analyzed through XYZ coordinate motion graphs and 3D trajectories. To quantify performance, three error indicators are employed: Residual Mean Square (RMS) with a value of 0.0720 for the Computed Torque Controller, Residual Standard Deviation (RSD), and Index of Agreement (IA). The results demonstrate that the proposed controllers achieve accurate trajectory tracking, with IA values close to unity, demonstrating a high degree of concordance between the desired and executed trajectories.
Effect of Design Parameters of Supporting Structure on the Energy Evolution Characteristic of Surrounding Rock
Authors: Chen Ying , Da Qi , Zhang Lei , Li Danli , Dai Bing
Published: November 2024

A reasonable support parameter design scheme is the key to ensuring the stability of the roadway. This study established 217 models using FLAC3D to analyze the evolution characteristics of elastic strain energy and plastic dissipation energy of surrounding rock under different shotcrete and rockbolt support structures. Additionally, five single models (BP, DT, ELM, RF, SVM) were introduced to explore the application of machine learning in predicting the stability of the roadway. The study found that in the parameters of the shotcrete layer support structure, the energy evolution of the surrounding rock is more sensitive to isotropic and thickness; in the parameters of the anchor rod support structure, the energy evolution of the surrounding rock is more sensitive to Young’s modulus, cross-sectional area young, and grout stiffness. Additionally, the parameters of the shotcrete layer support structure are not necessarily the larger the better. When isotropic is 100 GPa, both the dissipated energy and the elastic strain energy are higher than that at 25 GPa. The results of the single model test indicate that machine learning is relatively accurate in predicting different shotcrete and anchor support structures. The runtime difference between traditional methods and machine learning models highlights the potential advantages of machine learning.
Fault Detection Algorithms in Medium-Voltage Direct-Current (MVDC) Grids
Authors: Blázquez Aitor , Pérez-Molina María José , Larruskain Dunixe Marene , Iturregi Araitz , Eguia Pablo
Published: November 2024

Developing reliable protection systems is critical for the advancement of medium-voltage direct-current (MVDC) grids. This paper highlights the significance of fault detection in MVDC grids, especially in ensuring the reliability and efficiency of renewable energy systems. This paper provides a comparative analysis of fault detection algorithms, including overcurrent, undervoltage, rate of change of current (ROCOC), rate of change of voltage (ROCOV), differential, and inductor voltage derivative methods. The performance of these algorithms is quantified by metrics such as the detection speed, accuracy, and sensitivity under diverse scenarios. The authors assess these algorithms within a multi-terminal MVDC grid designed for renewable hydrogen production, evaluating the detection speed across various fault types (bus and link faults) and conditions, including the variation in the fault location and resistance. The results reveal that the UV, ROCOC, ROCOV and LIVRD methods achieve detection speeds as high as 0.01 ms, outperforming other techniques under low-resistance fault conditions. By using uniform fault scenarios, we identify the most effective algorithms for rapid fault detection, aiming to enhance the protection strategies for MVDC grids. These findings underline critical performance differences between methods, guiding the design of tailored protection schemes that address specific fault challenges in renewable-powered grids. Additionally, the practical implications of these findings for designing resilient protection schemes in renewable-powered grids are discussed. Simulations are conducted using PSCAD/EMTDC V4.6 software, ensuring the consistency and accuracy of the performance comparison. The insights gained provide a concrete understanding of each algorithm’s trade-offs, enabling informed decisions when selecting optimal fault detection methods to ensure MVDC grid reliability.
Improving Web Readability Using Video Content: A Relevance-Based Approach
Authors: Elahi Ehsan , Morato Jorge , Iglesias Ana
Published: November 2024

With the increasing integration of multimedia elements into webpages, videos have emerged as a popular medium for enhancing user engagement and knowledge retention. However, irrelevant or poorly placed videos can hinder readability and distract users from the core content of a webpage. This paper proposes a novel approach leveraging natural language processing (NLP) techniques to assess the relevance of video content on educational websites, thereby enhancing readability and user engagement. By using a cosine similarity-based relevance scoring method, we measured the alignment between video transcripts and webpage text, aiming to improve the user’s comprehension of complex topics presented on educational platforms. Our results demonstrated a strong correlation between automated relevance scores and user ratings, with an improvement of over 35% in relevance alignment. The methodology was evaluated across 50 educational websites representing diverse subjects, including science, mathematics, and language learning. We conducted a two-phase evaluation process: an automated scoring phase using cosine similarity, followed by a user study with 100 participants who rated the relevance of videos to webpage content. The findings support the significance of integrating NLP-driven video relevance assessments for enhanced readability on educational websites, highlighting the potential for broader applications in e-learning.
Relations of Harmonic Starlike Function Subclasses with Mittag–Leffler Function
Authors: Taşar Naci , Sakar Fethiye Müge , Aydoğan Seher Melike , Oros Georgia Irina
Published: November 2024

In this study, the connection between certain subfamilies of harmonic univalent functions is established by utilizing a convolution operator involving the Mittag–Leffler function. The investigation reveals inclusion relations concerning harmonic γ-uniformly starlike mappings in the open unit disc, harmonic starlike functions and harmonic convex functions, highlighting the improvements given by the results presented here on previously published outcomes.
A B-Polynomial Approach to Approximate Solutions of PDEs with Multiple Initial Conditions
Authors: Bhatti Muhammad I. , Rahman Md. Habibur
Published: November 2024

In this article, we present a novel B-Polynomial Approach for approximating solutions to partial differential equations (PDEs), addressing the multiple initial conditions. Our method stands out by utilizing two-dimensional Bernstein polynomials (B-polynomials) in conjunction with their operational matrices to effectively manage the complexity associated with PDEs. This approach not only enhances the accuracy of solutions but also provides a structured framework for tackling various boundary conditions. The PDE is transformed into a system of algebraic equations, which are then solved to approximate the PDE solution. The process is divided into two main steps: First, the PDE is integrated to incorporate all initial and boundary conditions. Second, we express the approximate solution using B-polynomials and determine the unknown expansion coefficients via the Galerkin finite element method. The accuracy of the solution is assessed by adjusting the number of B-polynomials used in the expansion. The absolute error is estimated by comparing the exact and semi-numerical solutions. We apply this method to several examples, presenting results in tables and visualizing them with graphs. The approach demonstrates improved accuracy as the number of B-polynomials increases, with CPU time increasing linearly. Additionally, we compare our results with other methods, highlighting that our approach is both simple and effective for solving multidimensional PDEs imposed with multiple initial and boundary conditions.
The Association Between the Hemodynamics in Anomalous Origins of Coronary Arteries and Atherosclerosis: A Preliminary Case Study Based on Computational Fluid Dynamics
Authors: Wei Yuhao , Cao Haoyao , Zheng Tinghui
Published: November 2024

Patients with anomalous coronary artery origins (AOCA) exhibit a higher risk of atherosclerosis, where even minimal stenosis may lead to adverse cardiovascular events. However, the factors contributing to this heightened risk in AOCA patients remain unclear. This study aimed to investigate whether an AOCA patient is more prone to stenosis occurrence and its progression in view of hemodynamics. A patient whose left circumflex artery originated from the right coronary sinus with a mild stenosis in the left anterior descending (LAD) artery and a healthy individual were included in this study. Two additional models were developed by removing stenosis from the patient model and adding a corresponding stenosis to the healthy model. Additionally, the inlet flow waveforms for the left and right coronary arteries were swapped in both the patient and healthy models. Results indicated that the AOCA patient without stenosis demonstrated higher wall pressure (LAD: 95.57 ± 0.73 vs. 93.86 ± 0.50 mmHg; LCX: 94.97 ± 0.98 vs. 93.47 ± 0.56 mmHg; RCA: 96.23 ± 0.30 vs. 93.86 ± 0.46 mmHg) and TAWSS (LAD: 24.41 ± 19.53 vs. 13.82 ± 9.87 dyne/cm2, p < 0.0001; LCX: 27.21 ± 14.51 vs. 19.33 ± 8.78 dyne/cm2) compared to the healthy individual, with similar trends also observed in stenotic conditions. Significant changes in the LCX flow distribution were also noted under varying pulsatile conditions (LCX: 18.28% vs. 9.16%) compared to the healthy individual. The high-pressure, high-shear hemodynamic environment in AOCA patients predisposes them to atherosclerosis, and the unique geometry exacerbates hemodynamic abnormalities when stenosis occurs. Clinicians should closely monitor AOCA patients with stenosis to prevent adverse cardiovascular events.
3D Culture of MSCs for Clinical Application
Authors: Gao Qi , Cekuc Mehmet Sertac , Ergul Yasemin Sude , Pius Alexa K. , Shinohara Issei , Murayama Masatoshi , Susuki Yosuke , Ma Chao , Morita Mayu , Chow Simon Kwoon-Ho , Goodman Stuart B.
Published: November 2024

Mesenchymal stem cells (MSCs) play an important role in regenerative medicine and drug discovery due to their multipotential differentiation capabilities and immunomodulatory effects. Compared with traditional 2D cultures of MSCs, 3D cultures of MSCs have emerged as an effective approach to enhance cell viability, proliferation, and functionality, and provide a more relevant physiological environment. Here, we review the therapeutic potential of 3D-cultured MSCs, highlighting their roles in tissue regeneration and repair and drug screening. We further summarize successful cases that apply 3D MSCs in modeling disease states, enabling the identification of novel therapeutic strategies. Despite these promising applications, we discuss challenges that remain in the clinical translation of 3D MSC technologies, including stability, cell heterogeneity, and regulatory issues. We conclude by addressing these obstacles and emphasizing the need for further research to fully exploit the potential of 3D MSCs in clinical practice.
Dynamics of Marenzelleria spp. Biomass and Environmental Variability: A Case Study in the Neva Estuary (The Easternmost Baltic Sea)
Authors: Golubkov Sergey M. , Golubkov Mikhail S.
Published: November 2024

Predicting which non-indigenous species (NISs) will establish persistent invasive populations and cause significant ecosystem changes remains an important environmental challenge. We analyzed the spatial and temporal dynamics of the entire zoobenthos and the biomass of Marenzelleria spp., one of the most successful invaders in the Baltic Sea, in the Neva estuary in 2014–2023. A considerable decrease in Marenzelleria biomass was observed in the second half of the study period, which was accompanied by a sharp increase in the dominance of opportunistic oligochaete and chironomid species. Our one-way analysis of variance showed that communities with high Marenzelleria biomass had significantly higher diversity and biomass of native benthic crustaceans compared to communities with low alien polychaetes biomass. A high biomass of Marenzelleria was observed in biotopes characterized by low temperatures, high salinity, low plankton primary production and chlorophyll concentration. The results of PCA and one-way ANOVA indicated that these factors significantly influenced the spatial and temporal dynamics of the polychaete biomass. More detailed studies of the responses of NISs to environmental variables are needed to better understand and anticipate their dynamics in different regions of the Baltic Sea in relation to climate warming and anthropogenic impacts.
Green Synthesis of Terminalia ferdinandiana Exell-Mediated Silver Nanoparticles and Evaluation of Antibacterial Performance
Authors: Alwis W. Hansi S. , Murthy Vinuthaa , Wang Hao , Khandanlou Roshanak , Mandal Pappu Kumar
Published: November 2024

This study uses a novel method in which extracts from different parts of a single plant are used to synthesize well-defined silver nanoparticles (AgNPs) to address the lack of capping agents in certain plant extracts. We focused on synthesizing AgNPs with enhanced biomedical activity using aqueous leaves and fruit extracts of Terminalia ferdinandiana Exell, a plant native to northern Australia that is known for its high phenolic content and associated health benefits. The impact of using parameters such as the Ag+ ion-to-extract ratio and pH on AgNP synthesis was examined. The formation of AgNPs was confirmed using UV–visible spectrophotometry, transmission electron microscopy, and dynamic light scattering. The AgNPs synthesized at a pH of 8 and 1:25 Ag+/extract ratio exhibited the lowest particle size and polydispersity index. The AgNPs synthesized with leaf extract (AgKL) were monodisperse and exhibited a smaller hydrodynamic diameter (37 nm) compared to the fruit extract nanoparticles (AgKP), which were polydisperse and larger (147 nm). Phytochemicals in T. ferdinandiana aqueous leaf extract act as effective capping and stabilizing agents, enabling the synthesis of small-sized and homogenous AgNPs, which the fruit extract alone could not achieve. The in vitro bioactivity was evaluated using antioxidant and antibacterial assays and compared with the crude extract. Both the AgNPs and T. ferdinandiana extracts demonstrated strong 2,2 diphenyl-1-picrylhydrazyl radical scavenging activity. However, only AgKL showed excellent antibacterial activity against Gram-negative and Gram-positive bacteria based on minimum inhibitory and bactericidal results. Mixing 50% leaf extract with fruit extract resulted in well-stabilized NPs (AgKPL) with a hydrodynamic diameter of 33.4 nm and superior antibacterial properties. These results indicate that AgKL and AgKPL have significant potential for pharmaceutical and biomedical applications.
Multifaceted Role of Specialized Neuropeptide-Intensive Neurons on the Selective Vulnerability to Alzheimer’s Disease in the Human Brain
Authors: Li Manci , Flack Nicole , Larsen Peter A.
Published: November 2024

Regarding Alzheimer’s disease (AD), specific neuronal populations and brain regions exhibit selective vulnerability. Understanding the basis of this selective neuronal and regional vulnerability is essential to elucidate the molecular mechanisms underlying AD pathology. However, progress in this area is currently hindered by the incomplete understanding of the intricate functional and spatial diversity of neuronal subtypes in the human brain. Previous studies have demonstrated that neuronal subpopulations with high neuropeptide (NP) co-expression are disproportionately absent in the entorhinal cortex of AD brains at the single-cell level, and there is a significant decline in hippocampal NP expression in naturally aging human brains. Given the role of NPs in neuroprotection and the maintenance of microenvironments, we hypothesize that neurons expressing higher levels of NPs (HNP neurons) possess unique functional characteristics that predispose them to cellular abnormalities, which can manifest as degeneration in AD with aging. To test this hypothesis, multiscale and spatiotemporal transcriptome data from ~1900 human brain samples were analyzed using publicly available datasets. The results indicate that HNP neurons experienced greater metabolic burden and were more prone to protein misfolding. The observed decrease in neuronal abundance during stages associated with a higher risk of AD, coupled with the age-related decline in the expression of AD-associated neuropeptides (ADNPs), provides temporal evidence supporting the role of NPs in the progression of AD. Additionally, the localization of ADNP-producing HNP neurons in AD-associated brain regions provides neuroanatomical support for the concept that cellular/neuronal composition is a key factor in regional AD vulnerability. This study offers novel insights into the molecular and cellular basis of selective neuronal and regional vulnerability to AD in human brains.
Generation of Transcript Length Variants and Reprogramming of mRNA Splicing During Atherosclerosis Progression in ApoE-Deficient Mice
Authors: Hueso Miguel , Mallén Adrián , Navarro Estanis
Published: November 2024

Background. Variant 3′UTRs provide mRNAs with different binding sites for miRNAs or RNA-binding proteins (RBPs) allowing the establishment of new regulatory environments. Regulation of 3′UTR length impacts on the control of gene expression by regulating accessibility of miRNAs or RBPs to homologous sequences in mRNAs. Objective. Studying the dynamics of mRNA length variations in atherosclerosis (ATS) progression and reversion in ApoE-deficient mice exposed to a high-fat diet and treated with an αCD40-specific siRNA or with a sequence-scrambled siRNA as control. Methods. We gathered microarray mRNA expression data from the aortas of mice after 2 or 16 weeks of treatments, and used these data in a Bioinformatics analysis. Results. Here, we report the lengthening of the 5′UTR/3′UTRs and the shortening of the CDS in downregulated mRNAs during ATS progression. Furthermore, treatment with the αCD40-specific siRNA resulted in the partial reversion of the 3′UTR lengthening. Exon analysis showed that these length variations were actually due to changes in the number of exons embedded in mRNAs, and the further examination of transcripts co-expressed at weeks 2 and 16 in mice treated with the control siRNA revealed a process of mRNA isoform switching in which transcript variants differed in the patterns of alternative splicing or activated latent/cryptic splice sites. Conclusion. We document length variations in the 5′UTR/3′UTR and CDS of mRNAs downregulated during atherosclerosis progression and suggest a role for mRNA splicing reprogramming and transcript isoform switching in the generation of disease-related mRNA sequence diversity and variability.
Rapid and Significant Angioarchitectural Changes in the Carotid Artery and Its Branches After Endovascular Treatment of Acute Hemorrhage Due to a Cerebral Arteriovenous Malformation
Authors: Dobek Adam , Szubert Wojciech , Kurzyk Katarzyna , Zaczkowski Karol , Wiśniewski Karol , Stefańczyk Ludomir
Published: November 2024

Introduction: Cerebral arteriovenous malformations (CAVMs) are rare, with an estimated prevalence of 0.01%. Symptoms typically present in adults under 40, often beginning with hemorrhage in 61% of cases. The annual risk of hemorrhage is between 2–4%, with a mortality rate of 10%, and 50% of survivors may experience permanent neurological deficits. Embolization can induce changes in the angioarchitecture of the affected vessels. Our case uniquely demonstrates a morphological alteration in the carotid artery (CA) and its primary branches, associated with the presence of a CAVM. Detailed Case Presentation: A 52-year-old patient presented to the Emergency Department with weakness, shortness of breath, dizziness, nausea, and vomiting that began earlier that morning. Clinical and radiological evaluations suggested acute bleeding from a ruptured CAVM. The patient was admitted for digital subtraction angiography (DSA) and potential embolization. DSA confirmed the presence of a CAVM, supplied by the middle meningeal and occipital arteries, with distortion of the left CA angioarchitecture. Selective catheterization and embolization using Onyx 18 were successfully performed. After receiving supportive treatment, the patient was discharged in good condition. A follow-up CT scan three months later showed complete resolution of the brain pathologies. A repeat angiogram revealed no recanalization of the CAVM, with normalization of the CA and its branches. The patient is currently asymptomatic. Conclusions: Detection of pathological changes in the head and neck arteries linked to CAVM can predict rupture risk and complicate endovascular access. Identifying these changes early warrants consideration of interventions to prevent hemorrhage, though alternative access routes or strategies may be needed for safe, effective treatment.
Comparative Assessment of the qSOFA, SII, dNLR, and OISS Infection Severity Scores in Diabetic Versus Non-Diabetic Patients with Odontogenic Infections
Authors: Bolos Otilia Cornelia , Sorca Bogdan-Valeriu , Rusu Laura-Cristina , Tapalaga Gianina
Published: November 2024

Background and Objectives: Odontogenic infections (OIs) can progress rapidly and may lead to severe systemic complications, especially in patients with underlying conditions like diabetes mellitus (DM). This study aims to evaluate the predictive value of inflammatory scores—quick Sequential Organ Failure Assessment (qSOFA), Systemic Immune–Inflammation Index (SII), derived Neutrophil-to-Lymphocyte Ratio (dNLR), and Odontogenic Infection Severity Score (OISS)—in assessing the severity of OIs in diabetic versus non-diabetic patients. Materials and Methods: A case–control study was conducted on 123 patients diagnosed with OIs. Patients were divided into two groups: patients with diabetes (n = 42) and patients who were non-diabetic (n = 81). Inflammatory scores were calculated at admission and correlated with clinical outcomes. Statistical analyses included t-tests, chi-square tests, and multivariate logistic regression. Results: The patients with diabetes exhibited significantly higher OISS scores (mean 6.5 ± 2.8) compared to the patients who were non-diabetic (mean 4.8 ± 2.1, p < 0.001). The inflammatory markers qSOFA, SII, and dNLR were significantly elevated in the diabetic group (all p < 0.01). The SII demonstrated the highest predictive accuracy for severe OIs in patients with diabetes, with an area under the curve (AUC) of 0.88 (95% CI: 0.80–0.95). Diabetes mellitus was an independent predictor of severe OIs (OR: 3.2, 95% CI: 1.5–6.8, p = 0.003). Conclusions: Inflammatory scores, particularly SII, are effective in predicting the severity of odontogenic infections in patients with diabetes. Incorporating these scores into clinical practice may enhance the early identification of high-risk patients and improve management strategies.