bluuebunny/crossref_metadata_2025 huggingface dataset:
Maternal and perinatal outcomes after prenatal or postnatal surgical repair of myelomeningocele
Authors: Callou Rebeca Torquato , Faquini Silvia de Lourdes Dutra Loreto , Santos-Neto Orlando Gomes , Faquini Igor , Furtado Geraldo , Furtado Maria de Fátima Quintas , Souza Gustavo Fonseca de Albuquerque , Lira Marcela Maria Cavalcante , Souza Alex Sandro Rolland
Published: January 2024

Purpose
To evaluate maternal and perinatal outcomes following modified open fetal or postnatal surgery to repair myelomeningocele at a pioneering referral center for prenatal surgery in northeastern Brazil.
Methods
This observational, retrospective cohort study assessed data recorded at the Instituto de Medicina Integral Prof. Fernando Figueira between December 2014 and May 2020. Pregnant women with fetuses diagnosed with myelomeningocele were enrolled. Exclusion criteria consisted of cases for which medical records were unavailable and those for which intrauterine repair was not recommended. Student’s t-test, Welch’s t-test, the chi-square test and Fisher’s exact test were used, as appropriate, in the statistical analysis. Significance level was 5%. Risk ratios (RR) and their 95% confidence intervals (95%CI) were calculated.
Results
Of the 134 pregnant women identified in the database, 45 were included: 26 women underwent fetal surgery and 19 infants were operated after birth. Pregnancy complications were more common in the fetal surgery group, including premature rupture of membranes (RR: 14.0; 95%CI: 0.8-263.5; p = 0.027) and other complications (RR: 5.3; 95%CI: 1.2–22.8; p = 0.025). Fetal surgery was significantly longer than postnatal surgery (mean duration 226 x 71 minutes, respectively; p < 0.001). Prematurity was more common with fetal (79.2%) compared to postnatal surgery (13.3%) (RR: 5.9; 95%CI: 1.4–25.5; p = 0.017). Although ventriculoperitoneal shunts were more common in the postnatal (18.2%) compared to the prenatal group (0%), the two groups were statistically similar (p = 0.50).
Conclusion
Prenatal myelomeningocele repair was associated with more cases of adverse pregnancy outcomes and prematurity. Conversely, no cases of open fetal surgery required ventriculoperitoneal shunting.
The effectiveness of the STOP-X training program on the knee valgus angle and balance in female basketball players with dynamic knee valgus
Authors: Rostami Mohadeseh , Sedaghati Parisa , Daneshmandi Hassan
Published: January 2024

Background: Dynamic knee valgus (DKV) accompanied by poor balance is the cause of anterior cruciate ligament (ACL) injury in athletes, and the identification and correction of these factors are always of interest to researchers. Therefore, the purpose of this research was to investigate the effect of the STOP-X program on the knee valgus angle and on static and dynamic balance in female basketball players with DKV defects.
Methods: The present study was a quasi-experimental study. The samples of this research included 30 female basketball players with DKV defects who were purposefully selected from the statistical population of female basketball players and were randomly assigned to two control (n=15) and experimental (n=15) groups. Dynamic knee valgus status was evaluated with the single-leg landing (SLL) test, static balance status was evaluated with the BASS STICK test, and dynamic balance status was evaluated with the Y-balance test (YBT). As a warm-up program, the experimental group performed the STOP-X program for 25-40 minutes for eight weeks (three times per week), and the control group performed their traditional warm-up program. The data were analyzed by ANCOVA and paired-samples t tests with SPSS version 26.
Results: The results showed that with the use of the STOP-X program, there was a significant difference between the experimental and control groups in terms of the knee valgus angle (η2=0.72, P=0.001), static balance (η2=0.47, P=0.001), and total dynamic balance score (η2 = 0.74, P = 0.001). Therefore, the experimental group performed better than the control group.
Conclusion: In addition to reducing the knee valgus angle, applying STOP-X preventive warm-up exercises can improve static and dynamic balance in female basketball players with DKV defects. Therefore, it can be recommended that sports trainers benefit from these advantages by substituting STOP-X preventive exercises instead of traditional warm-up.
Revealing static and dynamic biomarkers from postprandial metabolomics data through coupled matrix and tensor factorizations
Authors: Li Lu , Yan Shi , Horner David , Rasmussen Morten A. , Smilde Age K. , Acar Evrim
Published: January 2024

Introduction. Longitudinal metabolomics data from a meal challenge test contains both fasting and dynamic signals, that may be related to metabolic health and diseases. Recent work has explored the multiway structure of time-resolved metabolomics data by arranging it as a three-way array with modes: subjects, metabolites, and time. The analysis of such dynamic data (where the fasting data is subtracted from postprandial states) reveals dynamic markers of various phenotypes, and differences between fasting and dynamic states. However, there is still limited success in terms of extracting static and dynamic biomarkers for the same subject stratifications.
Objectives. Through joint analysis of fasting and dynamic metabolomics data, our goal is to capture static and dynamic biomarkers of a phenotype for the same subject stratifications providing a complete picture, that will be more effective for precision health.
Methods. We jointly analyze fasting and dynamic metabolomics data collected during a meal challenge test from the COPSAC2000 cohort using coupled matrix and tensor factorizations (CMTF), where the dynamic data (subjects by metabolites by time) is coupled with the fasting data (subjects by metabolites) in the subjects mode.
Results. The proposed data fusion approach extracts shared subject stratifications in terms of BMI (body mass index) from fasting and dynamic signals as well as the static and dynamic metabolic biomarker patterns corresponding to those stratifications. Specifically, we observe a subject stratification showing the positive association with all fasting VLDLs and higher BMI. For the same subject stratification, a subset of dynamic VLDLs (mainly the smaller sizes) correlates negatively with higher BMI. Higher correlations of the subject quantifications with the phenotype of interest are observed using such a data fusion approach compared to individual analyses of the fasting and postprandial state.
Conclusion. The CMTF-based approach provides a complete picture of static and dynamic biomarkers for the same subject stratifications - when markers are present in both fasting and dynamic states.
Sparse Friendly Distillation Using Feature Decoupling
Authors: He WeiHong , Fu YuLi , Xiang Youjun
Published: January 2024

In our paper, we introduce the sparse-friendly distillation framework as an effective training strategy for knowledge distillation. While model sparsity techniques have been widely adopted to reduce training overhead, sparse student models often struggle to achieve good performance in knowledge distillation. To address this issue, our framework leverages the observation that sparse student models exhibit different behaviors in foreground and background features. We separate these features using different pooling techniques and apply separate mean squared error (MSE) feature distillation. Furthermore, we dynamically adjust the weights of the two loss components to optimize performance. Experimental results on CIFAR-10 and CIFAR-100 benchmarks demonstrate significant performance improvements, validating the effectiveness of our methodology. Additionally, we provide a comprehensive analysis of our experimental results, further validating the effectiveness of our approach.
Maternal smoking around birth and psychiatric disorders of offspring: A Mendelian Randomization Study
Authors: Zhang Zheng , Luo Huirong , Wang Xueqian , Zhang Yinlin , Li Junyao , Hu Renqin , Guo Yanwei , Tan Jinglan , Li Jiazheng , Wang Yating , Luo Qinghua
Published: January 2024

Objective: To investigate the causal effect of maternal smoking around birth on the risk of four psychiatric disorders in offspring using Mendelian randomization (MR);
Methods Genome-wide association studies (GWAS) data were extracted from published data on maternal maternal smoking around birth and four psychiatric disorders, including attention deficit hyperactivity disorder (ADHD), autism spectrum disorder (ASD), bipolar disorder (BD) and major depressive disorder (MDD). Inverse variance weighting (IVW), weighted median (WM), and MR-PRESSO methods were applied, and a comprehensive MR analysis and sensitivity analysis were conducted to explore the causal relationship between maternal smoking around birth and the risk of developing the four psychiatric disorders, using the dominance ratio (OR) as an evaluation index;
Results Smoking before and after delivery was associated with ADHD (IVW: OR=12.299, 95% CI=3.341-45.27, P<0.001) and MDD (IVW: OR=5.789, 95% CI=2.576-13.011, P<0.001);
Conclusion Our study is based on genetic data, revealing a potential causal relationship between maternal smoking before and after birth and the incidence of attention deficit hyperactivity disorder and severe depression in offspring.
Wind farm impact on climate, based on weather observation in two European locations.
Authors: Kuca Mikolaj
Published: January 2024

The unprecedented scale of wind farm development on the coasts of Europe and America overlaps with observable climate change in the context of declining summer precipitation. In search of methods to trace this correlation and in an effort to precisely assess the impact, I chose specific locations where the phenomenon is observable in the form of altered meteorological data. Similarly to the increases in atmospheric CO2, the effect is very small, but its long-term impact will eventually lead to significant climate devastation in these densely populated areas. Minor changes are difficult to grasp. When standing on the seashore, it's not easy to notice any deviations, those must be looked for inland. My research is the first attempt of estimating the impact of the widely erected offshore wind farms, and it is being conveyed in order to start a debate on the environmental viability of such projects. Based on the analysis of meteorological data in these precisely selected European locations, I am presenting the results indicating the degrading impact of the offshore wind farms on climate in the form of inland dehydration leading to desertification in particular.
Investigating the efficacy of purified tannin extracts from underutilized temperate forages in reducing enteric methane emissions in vitro
Authors: Verma Supriya , Akpensuen Tersur Theophilus , Wolffram Siegfried , Salminen Juha-Pekka , Taube Friedhelm , Blank Ralf , Kluß Christof , Malisch Carsten Stefan
Published: January 2024

The study investigated how the concentration and composition of purified tannin extracts, at various inclusion rates, affect the ruminal in vitro fermentation parameters. Tannin extracts were isolated from four different forage species, birdsfoot trefoil (Lotus corniculatus), sulla (Hedysarum coronarium), big trefoil (Lotus pedunculatus), and salad burnet (Sanguisorba minor). Plants extracts were purified by Sephadex LH-20 gel chromatography and analyzed by UPLC-ESI-MS/ MS. The results showed a large variation among the extracts from different species in terms of tannin composition and structural features. The extracts from salad burnet were dominated by hydrolysable tannins (HTs), comprising mainly ellagitannins. The extracts derived from sulla and big trefoil contained predominantly proanthocyanidins (PA), primarily composed of prodelphinidins (PDs) with high mean degree of polymerisation (mDP). Birdsfoot trefoil extracts comprised procyanidin-rich PAs with low mDP. To determine whether the combined presence of tannins and flavonoid together lead to synergistic or antagonistic effects, the tannin extracts were incubated both with or without rutin at concentrations of 10, 20, and 30 g/kg DM, using a base substrate of perennial ryegrass (Lolium perenne, control). In general, all the tannin extracts decreased methane (CH4) production compared to the control, while no significant effect of rutin was observed on both gas (GP) and CH4 production, neither pure, nor in the simultaneous presence of tannins. The highest CH4 reduction (15%, at 30g/kg DM) was observed from sulla and big trefoil extracts compared to control, but this was also supplemented with a concomitant reduction in GP (11%) indicating a reduction in feed digestibility. The extracts from birdsfoot trefoil and salad burnet reduced CH4 up to 12% without significantly reducing GP (4%), indicating the importance of tannin composition on ruminal fermentation.
IGF-1R mediates crosstalk between nasopharyngeal carcinoma cells and osteoclasts and promotes tumor bone metastasis
Authors: Yang Kaifan , Hu Yanjun , Feng Yuanyuan , Li Kaiqun , Zhu Ziyan , Liu Shuyi , Lin Yanling , Yu Bin
Published: January 2024

Background: Nasopharyngeal carcinoma (NPC) poses a significant health burden in specific Asian regions, with radiotherapy substantially improving early-stage survival. However, bone metastasis remains a formidable challenge. This study aims to unravel the complex role of insulin-like growth factor 1 receptor (IGF-1R) in NPC bone metastasis, offering insights into potential therapeutic targets.
Methods: We assessed IGF-1R expression in NPC cells and explored its correlation with bone metastasis. Experiments investigated the impact of osteoclast-secreted IGF-1 on the IGF-1R/AKT/S6 pathway in promoting NPC cell proliferation within the bone marrow. Additionally, the reciprocal influence of tumor-secreted Granulocyte-macrophage colony-stimulating factor (GM-CSF) on osteoclast differentiation and bone resorption was examined. The effects of IGF-1 neutralizing antibody, IGF-1R specific inhibitor (NVP-AEW541) and mTORC inhibitor (rapamycin) on nasopharyngeal carcinoma bone metastasis were also explored in animal experiments.
Results: Elevated IGF-1R expression in NPC cells correlated with an increased tendency for bone metastasis. IGF-1, secreted by osteoclasts, activated the IGF-1R/AKT/S6 pathway, promoting NPC cell proliferation in the bone marrow. Tumor-secreted GM-CSF further stimulated osteoclast differentiation, exacerbating bone resorption. The IGF-1 neutralizing antibody, NVP-AEW541 and rapamycin were respectively effective in slowing down the rate of bone metastasis and reducing bone destruction.
Conclusion: The intricate interplay among IGF-1R, IGF-1, and GM-CSF highlights potential therapeutic targets for precise control of NPC bone metastasis, providing valuable insights for developing targeted interventions.
Multi-label Text Classification on TextCNN fused BiLSTM with Attention Mechanism
Authors: Duan Aihua , RAGA RODOLFO C.
Published: January 2024

In order to effectively manage and utilize the network text information and realize the automatic labeling of text content, this paper proposes to use a variety of deep learning models to study multi-label text classification. In this paper, GloVe is used to obtain the semantic features of text data, and the convolutional neural network and BiLSTM neural network are fused. The latter introduces the Attention mechanism to form a parallel neural network model of TextCNN and BiLSTM_Attention. Experimental results show that TextCNN and BiLSTM_Attention model structures combine the advantages of convolutional neural network model and recurrent neural network, and can better understand local and global semantic information. The Attention mechanism is more reasonable for text feature extraction, so that the model focuses its attention on the features that contribute more to the text classification task, and the classification effect is better.
Application of endoscopic ultrasound in the treatment of mesenchymal tumors of digestive tract by endoscopic submucosal dissection
Authors: Li Linzhen , Yin Hui , Cao Huiru , Wang Jing
Published: January 2024

Background and purpose
In cinical, some mesenchymal tumors of the digestive tract require endoscopic submucosal dissection (ESD). Endoscopic ultrasound (EUS) is required to evaluate tumor nature, size, and origin layer before ESD. The purpose of this study was to investigate the accuracy of EUS in evaluating the nature of tumors, and the relationship between tumor size, origin layer and ESD-related complications.
Patients and Methods:
All the 207 patients should undergo EUS before ESD. EUS should record the nature, location, origin layer and size of the tumor. ESD-related complications should also be recorded.
Results
In the 207 patients, EUS accurately identified the tumor nature of 158 (76.33%) patients. Thirty patients had perforation. Twenty-five (83.33%) patients occurred in stomach, 3 (10%) patients in esophagus, and 2 (6.67%) patients in rectum. The lesions of 28 (93.33%) perforated patients were located in muscularis propria under EUS, 2 (6.67%) patients were located in submucosa. In order to study the relationship between perforation and tumer size, 207 patients were divided into perforation group and non-perforation group. There was a significant difference in tumor size between the two groups (P = 0.047). A total of 11 patients had tumors larger than 20mm, 6 (54.55%) of them had perforation, and most of the perforated patients had tumors located in the muscularis propria.
Conclusions
The accuracy of EUS in evaluating mesenchymal tumors of the digestive tract is high. It is necessary to evaluate the layer and size of tumors by EUS before ESD.
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

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

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

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

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

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

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

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

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

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

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

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

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.
Target Informed Client Recruitment for Efficient Federated Learning in Healthcare
Authors: Scheltjens Vincent , Momo Lyse Naomi Wamba , Verbeke Wouter , De Moor Bart
Published: January 2024

Background: Modern machine learning and deep learning methods have been widely incorporated in decision making processes in healthcare in the form of decision support mechanisms. In healthcare, data are abundant but typically not centrally available and, therefore, require some form of aggregation to facilitate training procedures. Aggregating sensitive data poses a significant privacy risk, which is why, both in Europe and the United States, legal frameworks regulate the treatment of such data. Whilst these measures protect the individual behind the data, they pose a significant challenge that results in extensive legal administration related to data sharing efforts. Federated learning (FL) offers a way to mitigate these challenges by allowing to learn models in distributed fashion, eliminating the need to aggregate data for the purpose of training. However, FL comes with a new set of challenges related to communication overhead, client selection and efficiency of the FL training procedure, among others.
Methods: In this work, we extend on a previously proposed client recruitment approach by incorporating knowledge on the local hardware such that it becomes possible to recruit a subset of clients for the federation based on the construct of client-level representativeness, which is expressed in terms of the local target distribution divergence, sample size, and the underlying hardware.
Results: We show that, for prominent, medical regression and classification tasks, the recruitment approach yields results that are on par, or better, compared to the central and federated approaches. The proposed approach requires a mere fraction of the data for training and reduces the training time by a factor of 3-4. In addition, we show that excluded clients can still significantly benefit from the resulting federated model through local fine-tuning.
Conclusions: By expressing the representativeness of clients in function of the deviation in the local target distribution, the sample size and efficiency of the underlying hardware, we are able to define a recruitment approach that yields a subset of clients for the federation resulting in significantly reduced training time, without harming predictive performance, whilst improving the privacy preserving characteristics compared to the standard FL and central approaches.
A Thangka cultural element classification model based on self-supervised contrastive learning and MS-Triplet Attention
Authors: Tang Wenjing , Xie Qing
Published: January 2024

Being a significant repository of Buddhist imagery, Thangka images are valuable historical materials of Tibetan studies, which covers many domains such as Tibetan history, politics, culture, social life and even traditional medicine and astronomy. Thangka cultural element images are the essence of Thangka images. Hence Thangka cultural element images classification is one of the most important work of knowledge representation and mining in the field of Thangka, and is the foundation of digital protection of Thangka images. However, due to the limited quantity, high complexity and the intricate textures of Thangka images, the classification of Thangka images is limited to a small number of categories and coarse granularity. Thus a novel fusion texture feature dual-branch Thangka cultural elements classification model based on the attention mechanism and self-supervised contrastive learning has been proposed in this paper. Specifically, to address the issue of insufficient labeled samples and improve the classification performance, this method utilizes a large amount of unlabeled irrelevant data to pre-train the feature extractor through self-supervised learning. During the fine-tuning stage of the downstream task, a dual-branch feature extraction structure incorporating texture features has been designed, and MS-Triplet Attetnion proposed by us is used for the integration of important features. Additionally, to address the problem of sample imbalance and the existence of a large number of difficult samples in the Thangka cultural element data set, the Gradient Harmonizing Mechanism Loss has been adopted, and it has been improved by introducing a self designed adaptive mechanism. The experimental results on Thangka cultural elements dataset prove the superiority of the proposed method over the state-of-the-art methods.The source code of our proposed algorithm and the related datasets is available at https://github.com/WiniTang/MS-BiCLR.
Watch the Moon, Learn the Moon: Lunar Geology Research at School Level with Telescope and Open Source Data
Authors: Luke Kevin , Mishra Abhinav , Ghare Vihaan , Chanyal Shaurya , Shukla Priyamvada , Pandey Anushreya , Rane Vaishnavi , Pathan Ashadieeyah , Vaja Parv , Gogate Sai , Tiwari Shreyansh , Singh Jagruti , Dhawda Dhruv
Published: January 2024

Science-AI Symbiotic Group at Seven Square Academy, Naigaon was formed in 2023 with the purpose of bringing school students to the forefronts of science research by involving them in hands on research. In October 2023 a new project was started with the goal of studying the lunar surface by real-time observations and open source data. Twelve students/members from grades 8, 9, 10 participated in this research attempt wherein each student filled an observation metric by observing the Moon on various days with a Bresser Messier 150mm/1200mm reflector Newtonian telescope. After the observations were done, the members were assigned various zones on the lunar near side for analysis of geological features. Then a data analysis metric was filled by each of students with the help of Lunar Reconnaissance Orbiter Camera's/ LROC's quickmap open access data hosted by Arizona State University. In this short paper a brief overview of this project is given. One example each of observation metric and data analysis metric is presented. This kind of project has high impact for school science education with minimal costs. This project can also serve as an interesting science outreach program for organisations looking forward to popularise planetary sciences research at school level.