bluuebunny/crossref_metadata_2025_split huggingface dataset:
Association of VEGFR1, VEGFR2 and VEGFR3 Polymorphisms with Esophageal Cancer Risk: A Case- Control Study
Authors: Walia Sukhpreet Kaur , Sambyal Vasudha , Sudan Meena , Uppal Manjit Singh , Guleria Kamlesh 
Published: January 2024

Abstract
Esophageal cancer is the eighth most common cancer with 5.3% cancer related deaths worldwide. Vascular endothelial growth factor (VEGF) and its receptors pathway is a key regulator of angiogenesis and play an important role in carcinogenesis. The aim of current study was to evaluate the association of five VEGFRs polymorphisms with esophageal cancer risk in patients from Punjab, North-West India. This case-control study included 310 esophageal cancer patients and 325 age and gender matched healthy controls. VEGFR1 -710C/T, VEGFR2 -604T/C (rs2071559), VEGFR2 1192 G/A (rs2305948), VEGFR2 1719A/T (rs1870377) and VEGFR3 (rs72816988) polymorphisms were genotyped by using polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP) method. There was a significant association of CT genotype (OR = 0.28; 95%CI, 0.10–0.76; p = 0.01) and T allele (OR = 0.28; 95%CI, 0.10–0.77; p = 0.01) of VEGFR1-710C/T polymorphism with decreased risk of esophageal cancer. TC genotype of VEGFR2 -604T/C (OR = 0.66; 95%CI, 0.44–0.97; p = 0.03) and GA genotype of VEGFR2 1192G/A (OR = 0.54; 95%CI, 0.31–0.95; p = 0.03) polymorphisms were significantly associated with decreased risk of esophageal cancer. There was no significant difference in allele and genotype frequency of VEGFR2 1719A/T and VEGFR3 (rs72816988) polymorphisms between esophageal cancer patients and controls (p > 0.05). Haplotype analysis revealed that haplotype C-604 A1719A1192 was significantly associated with the decreased esophageal cancer risk (OR = 0.44; 95%CI, 0.23–0.84; p = 0.01). In conclusion, VEGFR1 -710C/T, VEGFR2 -604T/C and VEGFR2 1192G/A polymorphisms were associated with the decreased risk of esophageal cancer in patients from Punjab, North-West India.
A Comparative Analysis of Computational Drug Repurposing Approaches – Proposing a Novel Tensor-Matrix-Tensor Factorization Method
Authors: Zabihian Arash , Asghari Javad , Hooshmand Mohsen , Gharaghani Sajjad 
Published: January 2024

Abstract

Graphical Abstract Abstract Computational drug repurposing is a screening method for drug discovery that utilizes a wide spectrum of techniques and tools. It reduces the process cost and time of introducing drugs to the market. No need to mention that drug-related subject is a critical field, therefore, makes screening challenging and an important step for further drug investigations. This work tends to study four approaches of computational drug repurposing, i.e., factorization-based methods, machine learning methods, deep learning methods, and graph neural networks. To achieve this aim, we propose new methods for each approach and discuss their performances, challenges, advantages, and disadvantages. To cover the first approach, we propose a novel tensor-matrix-tensor (TMT) formulation as a new data array method with a gradient-based factorization procedure. To cover the second approach, we present two machine learning algorithms, i.e., SVM and random forest, with a new embedding representation. The paper compares the proposed approaches with two deep-learning methods and a graph attention network model on two datasets and discusses their results. The results show deep learning methods outperform other approaches.
Causal relationship between the gut microbiota and benign colorectal tumors at distinct anatomical sites: a two-sample Mendelian randomization study
Authors: Bai Yaowen , Hei Zhijun , Yang Hailong , Yin Shaojun , Wang Yujia , Hu Junhong , Zhang Zhiyong , Xia Kunkun 
Published: January 2024

Abstract
The causal relationship between the gut microbiota and benign colorectal tumors remains enigmatic. This study aims to thoroughly assess the causal impact of the gut microbiome on benign tumors at different anatomical sites in the colorectum. Leveraging microbiota data from the MiBioGen consortium and benign colorectal tumor data from the FinnGen study, Mendelian randomization studies were conducted. Methodological measures were implemented to address biases stemming from linkage disequilibrium and weak instrumental variables. The results from the Inverse Variance Weighting (IVW) analysis conspicuously demonstrate a definitive causal correlation between the gut microbiota and genus Bifidobacterium. This finding not only enhances our nuanced understanding of the mechanistic underpinnings of benign colorectal tumors, particularly concerning anatomical variations but also holds promise for the formulation of avant-garde therapeutic strategies tailored to specific anatomical domains. This, in turn, augurs a more sophisticated and targeted approach to individualized prevention and treatment.
MM165 - a small hybrid molecule modulates the kynurenine pathway and attenuates lipopolysaccharide-induced memory deficits and inflammation
Authors: Kotańska Magdalena , Łanocha Michał , Bednarski Marek , Marcinkowska Monika 
Published: January 2024

Abstract
Cognitive dysfunctions are now recognized as core symptoms of various psychiatric disorders e.g., major depressive disorder. Sustained immune activation may leads to cognitive dysfunctions. Proinflammatory cytokines shunt the metabolism of tryptophan towards kynurenine and quinolinic acid may accumulate at toxic concentrations. This acid triggers an increase in neuronal nitric oxide synthase function and promotes oxidative stress. The searching for small molecules that can regulate tryptophan metabolites produced in the kynurenic pathway has become an important goal in developing treatments for various central nervous system diseases with an inflammatory component. Previously we have identified a small hybrid molecule – MM165 which significantly reduces depressive-like symptoms caused by inflammation induced by lipopolysaccharide administration. In the present study, we investigated whether this compound would mitigate cognitive deficits induced by lipopolysaccharide administration and whether treatment with it would affect the plasma or brain levels of quinolinic acid and kynurenic acid. Neuroinflammation was induced in rats by administering lipopolysaccharide at a dose of 0.5 mg/kg body weight for 10 days. We conducted two tests: novel object recognition and object location, to assess the effect on memory impairment in animals previously treated with lipopolysaccharide. In plasma collected from rats, the concentrations of C-reactive protein and tumor necrosis factor alfa were determined. The concentrations of kynurenic acid and quinolinic acid were determined in plasma and homogenates obtained from the cerebral cortex of rats. Interleukin 6 in the cerebral cortex of rats was determined. Additionally, the body mass and spontaneous activity were measured in rats. Our study shows that MM165 may mitigate cognitive deficits induced by inflammation after administration of lipopolysaccharide and alter the concentrations of tryptophan metabolites in the brain. Compounds exhibiting a mechanism of action analogous to that of MM165 may serve as foundational structures for the development of a new class of antidepressants.
Network Pharmacology-based Mechanistic Study on Prunellae Spica for the Treatment of Endometrial Cancer
Authors: zhang zhenyun , Wang Jiajia , Wei Zhili , Zhang Xiaodong , Wei Qiuju , Lu Donglin , Huang Weixia , Wang Guoquan , Gan Xiaodong , Shi Guiling , Wang Junli 
Published: January 2024

Abstract
Ethnopharmacological relevance:
Prunellae spica belongs to the Labiatae family and it clears the liver and purges fire, improves vision, and disperses swelling. Clinically, Prunellae spica plays diverse antibacterial, antiviral, and anti-cancer roles and is involved in immune regulation but its mechanism of action is unknown for exerting the therapeutic effects. This study comprehensively analyzes the mechanism of Prunellae Spica for Endometrial cancer (EC) based on multiple databases such as Network Pharmacology and TCGA, which will help to provide new ideas for targeted therapy for EC.
Materials and methods
Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP), together with Therapeutic Target Database (TTD), Online Mendelian Inheritance in Man (OMIM), and GeneCards were employed to retrieve Prunellae spica-related active components and corresponding targets, along with the target genes related to EC. A protein-protein interaction (PPI) network was constructed after the shared target genes were screened. Subsequently, functional annotations were obtained through Gene Ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed. A drug component-target-disease network was established to visualize the overall pharmacological effects exerted by Prunellae spica on EC. Bioinformatics analyses for the genes of interest and common genes targeted by traditional Chinese medicine (TCM) were performed. Molecular docking inferred the possibility of treating EC using Prunellae spica.
Results
Eleven active components related to Prunellae spica were identified from the screen. By converting their names in the UniProt database, 185 target genes of Prunellae spica were retrieved, 83 of which could interact with EC cells. Bioinformatics analysis inferred that the pharmacological effects of Prunellae spica on EC involved the positive and negative regulation of apoptosis, positive regulation of gene expression, and cell cycle regulation, along with a strong association with DNA synthesis. Quercetin, kaempferol, and luteolin were the active components of Prunellae spica, which could bind well to TP53, AKT1, JUN, BCL2, Myc, TNF, and CASP3, according to the molecular docking study.
Conclusions
The mechanistic underpinning of Prunellae spica for treating EC involves multiple targets and signaling pathways. In summary, the plausible mechanisms of Prunellae spica demonstrated pharmacologically in this study provide a rationale for TCM as EC treatment. However, experiments are further needed to strengthen the evidence for using Prunellae spica, a promising candidate for treating EC.
Analysis of Emerging Trends in Artificial Intelligence in Education in Nigeria
Authors: Bali Bulus 
Published: January 2024

Abstract
In the domain of education, the integration of Artificial Intelligence (AI) has ushered in a paradigm shift towards a more technologically-driven landscape, demonstrating its efficacy as an emergency strategy. The pervasive influence of computer technology has catalyzed a surge in online learning within the country, yielding positive educational outcomes. Despite these advancements, a considerable number of educational institutions in Nigeria have yet to leverage AI technologies. Recognizing the expanding significance of AI in education, this study seeks to align with this trajectory by aggregating instances of AI implementation in education from developed countries. The methodology employed involves a comprehensive review of current advancements in AI applications within the Nigerian educational context. The review process, spanning papers retrieved from four digital libraries published between 2008 and 2022, culminated in the inclusion of 73 papers. These selected papers demonstrated the utilization of AI software tools and technologies, adhering to predefined exclusion and inclusion criteria. The findings of the study reveal a prevalent use of AI technologies in education in Nigeria, encompassing evolutionary software modelling, student performance prediction, multimedia e-learning platforms and frameworks, and the incorporation of Moodle learning. This discernible trend indicates a growing demand for the application of AI technology in the educational landscape of Nigeria. However, the study also highlights a discrepancy wherein more sophisticated AI techniques, such as intelligent tutoring systems, learnable robots or agents, web-based educational systems, and learning management systems explored extensively in other nations were infrequently applied in the Nigerian context. In light of these observations, the study proposes that educational institutions in Nigeria should consider adopting AI practices from more advanced nations. This strategic alignment is posited as a means to augment student learning opportunities and bridge the existing gap between the current state of AI integration in Nigerian education and the more advanced applications witnessed globally.
Embedded Cyber-physical System for Physiological Control of Ventricular Assist Devices
Authors: Santos Bruno , Leao Tarcisio , Barboza Marcelo , Drigo Evandro , Bock Eduardo 
Published: January 2024

Abstract
Left Ventricular Assist Devices (LVADs) serve as both a bridge to transplantation and a destination therapy for managing congestive heart failure (CHF). However, the existing control strategy's inability to automatically adapt to hemodynamic changes can significantly impact patient survival rates and quality of life. Researchers have proposed various approaches to achieve Physiological Control over LVADs (PC-LVAD). Despite promising results, implementing these strategies on computers faces limitations due to substantial differences in controller characteristics between computers and embedded systems. An embedded cyber-physical system (CPS) was proposed to address this challenge, integrating real-time data processing, reconfigurable architecture, and communication protocols based on Health 4.0 concepts. To evaluate this embedded CPS, a multi-objective PC-LVAD was utilized in a hybrid cardiovascular simulator with an implanted LVAD, testing symmetry between the original proposal of the PC-LVAD and the version running on the embedded CPS. The embedded CPS demonstrated comparable results to the control executed on a computer, maintaining mean arterial pressure and cardiac output at physiological levels, even amid variations in ejection fraction. Furthermore, the pump's rotational speed was dynamically adjusted based on simulated clinical conditions, and no aortic reflux to the ventricle occurred through the LVAD during testing. These outcomes affirm the embedded CPS's satisfactory control performance. However, additional testing is imperative to validate its enhanced physiological strategies in embedded CPS during in vivo experiments.
Optimizing FPGA-Based Target Detection: A Hybrid Winograd-GEMM Accelerator with Enhanced Resource Efficiency and Throughput
Authors: Wei Yingjie , Wang Rugang , Wang Yuanyuan , Zhou Feng , Gou Naihong 
Published: January 2024

Abstract

To store the constant transformation matrix while using the classic Winograd algorithm on FPGAs, a large amount of on-chip resources needs to be used, which will reduce the model’s throughput and on-chip resource usage. To address these issues, this paper designs a hardware accelerator that combines the Winograd algorithm and GEMM. In this accelerator, a Winograd-GEMM-Shared Processing Element (WGS PE) is designed to share on-chip computing resources, switch between different convolutional cores flexibly, and reuse the data in the buffer. Meanwhile, this paper adopts dynamic 16-bit fixed-point quantization, multi-level line cache pipelining, and im2col+GEMM optimization strategies for the WGS PE, which significantly improves the model's throughput and resource efficiency. In the experiment, the YOLOv2 algorithm was implemented and tested using the COCO dataset on the FPGA. The detection latency of a single image was decreased to 168.68ms, and the detection accuracy was 80.94\%; meanwhile, the efficiency of DSP resources usage was 0.61, showing 1.4x DSP usage efficiency compared to other research results; besides, the throughput was 65.98 GOP/S, and the total power consumption is 3.26 W. The experimental results demonstrate that the designed hardware accelerator can fully utilize on-chip computing resources and has great potential deployment on FPGAs to accelerate target detection.
Thermal properties of NaF-KF and NaF-KF-MgF2 molten eutectic mixtures: experiment and simulation
Authors: Rudenko Alexey , Redkin Alexander , Galashev Alexander , Abramova Kseniya , Rakhmanova Oksana , Il’ina Eugeniya , Pershina Svetlana , Zaikov Yurii 
Published: January 2024

Abstract
Molten fluoride salts are under development for use as fuel coolant and thermal storage in industrial nuclear energy production. This study focuses on the experimental and molecular dynamic investigation of thermal conductivity and ion diffusion in the eutectic molten salts of NaF-KF and NaF-KF-MgF2. Experimental and calculated data demonstrate that the temperature-dependent thermal conductivity can be accurately represented as a decaying linear function for both melts. The significant diffusion coefficient of fluorine ions in the NaF-KF molten system can be attributed to the considerable number of Coulomb repulsions among the abundant negative ions in the irregular system. The findings of this study provide insights into the behavior of NaF-KF and NaF-KF-MgF2 molten salt mixtures under operating conditions in high-temperature power plants.
Implementation of a robust optimisation genetic algorithm for HDR prostate brachytherapy including all major uncertainties
Authors: Kennedy Andrew C. , Douglass Michael J. J. , Santos Alexandre M. C. 
Published: January 2024

Abstract
Introduction
In high-dose-rate prostate brachytherapy, uncertainties cause a deviation from the nominal treatment plan, leading to a possible failure of clinical objectives in the delivered scenario. Robust optimisation has the potential to maximise the probability that these objectives are met during treatment.
Method
A computationally efficient probabilistic robust optimisation algorithm was developed and evaluated comprehensively on one patient by comparing it to the treatment-planning-systems (TPS) optimised plan. Three objective functions were maximised within a genetic algorithm (NSGA-ii), each an approximation for robustness against uncertainty for three clinical objectives: the minimum dose to the hottest 90% of the prostate target, \({\text{D}}_{90}^{\text{P}}\), and the maximum doses to the urethra, \({\text{D}}_{0.01 \text{c}\text{c}}^{\text{U}}\), and the rectum, \({\text{D}}_{0.1 \text{c}\text{c}}^{\text{R}}\). The approximations are derived from a probabilistic robust evaluation algorithm incorporating 14 major planning and treatment uncertainties. The robustness of a plan was quantified as a pass-rate from 500 probabilistic uncertainty scenarios for \({\text{D}}_{90}^{\text{P}}, {\text{D}}_{0.01 \text{c}\text{c}}^{\text{U}}\), and\({\text{D}}_{0.1 \text{c}\text{c}}^{\text{R}}\). Two hundred robust-optimised plans are generated that are the best trade-off between the three-competing DVH metric pass-rates.
Results
The robust-optimised plans on average (mean) increased in overall robustness by 58.5 ± 3.0% (SD: 7.1%, min: 34.1%, max: 67.7%) compared to the TPS-optimised plan. The robustness increase for the \({\text{D}}_{90}^{\text{P}}\) pass-rate was 31.2 ± 2.2% (SD: 15.6%, min: -5.1%, max: 46.7%), for the \({\text{D}}_{0.01 \text{c}\text{c}}^{\text{U}}\) pass-rate, the increase was 48.2 ± 2.6% (SD: 11.9%, min: 26.9%, max: 67.7%), and for the \({\text{D}}_{0.1 \text{c}\text{c}}^{\text{R}}\) pass-rate, the change was 0.0 ± 1.1% (SD: 0.72%, min: -2.6%, max: 0.4%).
Conclusion
The robust optimisation algorithm was demonstrated to produce more robust plans than the TPS, in an increased probability of target coverage and organs-at-risk sparing within a clinically reasonable time.
Estimation of Heterosis in Cotton Genotypes for Yield Performance at Multan Location PR China
Authors: Wahid Muhammad Atif , Mahmood Zahid , Zhongxulin  
Published: January 2024

Abstract
The present study was conducted with the aim to find out the best general and specific combiner, type of their gene action, hetterotic effects, correlation and path coefficient among morphological, yield and yield contributing and fiber traits through line x tester mating design. In this research work 6 line parents and 6 tester parents along with their 36 F1 generation were studied. The traits i.e. days to first flower, flower shedding (%), boll formation, boll opening, 1st monopodial node number plant-1, 1st sympodial nodes number plant-1, number of monopodial branches plant-1, boll shedding (% age), number of leaves plant-1, plant height, total number of fruiting points plant-1, number of bolls harvested plant-1, boll weight, seed index, harvest index, biological weight plant-1, GOT %, fibre length (mm), fibre strength, micronaire value, fibre mean length, upper half means length and fibre uniformity index (%age) were studied. Results of gene action revealed that flower shedding, number of sympodial branches plant-1 and number of leaves indicated greater value than 1 revealing over dominance type of gene action at both locations. Total number of fruiting point, number of harvest bolls, flower shedding, number of sympodial branches, boll opening, GOT% and fiber strength showed a greater GCA at both Multan; Pakistan and location. Maximum SCA effects were observed in traits i.e. boll formation, boll opening, number of monopodial branches, number of leaves, plant height, fruiting points, boll weight, seed cotton yield, biological weight and staple length. Heterosis results showed that days to first flower, flower shedding, boll formation, sympodial node number, plant height, fruiting points, number of bolls, seed cotton yield, GOT% and staple length had maximum significant mid heterosis at both locations. The traits including flower shedding, boll formation, sympodial node numbers, number of sympodial branches, boll shedding, plant height, fruiting points, number of bolls harvested, boll weight, seed cotton yield and upper half mean length indicated maximum standard heterosis at both locations. Correlation results indicated that boll shedding, boll formation, boll opening, sympodial node number, number of bolls harvested, boll weight, plant height, GOT%, fiber strength, micronaire value, seed index and seed cotton yield had positive correlation at both locations.
Improvement of the whale optimization algorithm and its application to engineering design problems
Authors: Binbin Tu , Fei Fynn , Yan Huo , Xiaotian Wang 
Published: January 2024

Abstract
Aiming at the problems of insufficient global exploration ability, low convergence accuracy and slow speed of the standard whale optimization algorithm, the paper proposes a dimension-based neighborhood search strategy, which constructs a neighborhood for each search agent during iteration, and the search agents in this neighborhood can share the search information; considering that the motion of the search agent is a kind of jumping movement assuming successive jumps, which may cause the search agent to prematurely fall into local optimum, so adaptive weights are added to regulate the position update. The improved whale optimization algorithm (notated as: DWOA) is mainly used to solve global optimization and engineering design problems. DWOA and other excellent whale optimization algorithm improvement schemes are evaluated by 23 benchmark test functions and 5 engineering design problems, and the experimental results show that DWOA has strong competitiveness in terms of global exploration ability, local exploitation ability, convergence speed and convergence accuracy. Meanwhile, the improved algorithm has obvious advantages in solving engineering design problems, which also proves its effectiveness and applicability.