bluuebunny/crossref_metadata_2025_split huggingface dataset:
Optimizing Radio Access in 5G Vehicle Networks Using Novel Machine Learning-Driven Resource Management
Authors: Natarajan Rajesh , Mahadev Natesh , Alfurhood Badria Sulaiman , Ranjith Christodoss Prasanna , Zaki John , MN Manu 
Published: August 2023

Abstract
The huge crossover of vehicle network needs cannot be met by present cellular technologies and vehicular networks. To achieve desired results in a vehicle context, resource management has evolved into a complicated task. The 5G wireless network claims to provide communications that are ultra-fast, delayed less, and dependable. Software-defined networking (SDN) is one of the major technologies that will enable 5G. Additionally, it guarantees increased performance all around. Managing the increased mobility of vehicles and maintaining smooth transfer between base stations are the main concerns. Additionally, providing safety-critical applications like autonomous driving requires very low latency and great dependability. Due to the limited amount of spectrum available and the dynamic nature of vehicular communication, effective resource allocation strategies are required. To maintain network efficiency, interference, and channel congestion must be reduced, and priority techniques are required to meet the needs of various services. In this study, we proposed a traffic classification using Magnified Recurrent Neural Network (MRNN) and optimized the radio access in 5g vehicle networks using Boosting Ant Colony Optimization (BACO) for the resource allocation procedure. The BACO-MRNN algorithm produced the best results based on several performance metrics, including accuracy of 98.10%, precision of 97.23%, recall of 98.15%, F1-Score of 98.45%, and RMSE of 30.10%. Additionally, the BACO-MRNN classification revealed a very advanced capacity for discrimination. The entire potential of connected vehicles and the fulfillment of smart transportation systems depend on the effective resolution of radio access difficulties in 5G vehicle networks.
Drug-Protein Interaction Prediction via Multi-View Variational Autoencoder and Cascade Deep Forests
Authors: Xia Liang-Yong , Wu Yu , Zhao Longfei , Chen Leying , Zhang Shiyi , Wang Mengdi , Luo Jie 
Published: August 2023

Abstract
Accurate prediction of Drug-Protein Interactions (DPIs) is crucial for drug discovery and precision medicine. Despite recent progress, the high-dimensionality representation drug and protein characteristics and their interactions poses a significant challenge. In this study, we propose a novel deep learning framework, Multi-view Variational Auto-Encoder embedded Deep Forest (MVAE-DFDPnet) for DPI prediction. This framework learns low-dimensional drug/protein embeddings from heterogeneous drug/protein networks by multi-view variational auto-encoder, then feeds drug-protein embeddings into a cascade deep forest to predict yet unknown DPIs. Our experiments show that the MVAE-DFDPnet achieves 98.6% out-of-sample accuracy in predicting DPIs on benchmark datasets, outperforming state-of-the-art methods with much lower embedding dimensions. We further demonstrate the robustness and generalizability of the method by testing on unseen drugs and proteins as well as on unseen drug classes, where the model still maintains high prediction accuracy. We have also successfully validated a number of novel DPIs by experimental evidence found in literature, demonstrating the potential of MVAE-DFDPnet in real-world applications.
Analysis of the Factors Influencing the Predictive Learning Performance Using Federated Learning
Authors: Farooq Umer , Naseem Shahid , Li Jianqiang , Mahmood Tariq , Rehman Amjad , Saba Tanzila , Mustafa Luqman 
Published: August 2023

Abstract
Numerous educational institutions utilize data mining techniques to manage student records, particularly those related to academic achievements , which are essential in improving learning experiences and overall 1 Springer Nature 2021 L A T E X template outcomes. Educational Data Mining (EDM) is a thriving research field that employs data mining and machine learning methods to extract valuable insights from educational databases, primarily focused on predicting students’ academic performance. This study proposes a novel Federated Learning (FL) standard that ensures the confidentiality of the dataset and allows predicting student grades, categorized into four levels: Low, Good, Average, and Drop. To enhance model precision, optimized features are incorporated into the training process. This study evaluates the optimized dataset using five machine learning (ML) algorithms , namely Support Vector Machine (SVM), Decision Tree, Nã A¯ve Bayes, K-Nearest Neighbours, and the proposed Federated Learning Model. The models’ performance is assessed regarding accuracy, precision , recall, and F1 score, followed by a comprehensive comparative analysis. The results reveal that FL and SVM outperform the alternative models, demonstrating superior predictive performance for student grade classification. This study showcases the potential of Federated Learning in effectively utilizing educational data from various institutes while maintaining data privacy, contributing to educational data mining and machine learning advancements for student performance prediction.
Combination of a MIP3α-antigen fusion therapeutic DNA vaccine with treatments of IFNα and 5-Aza-2’Deoxycytidine enhances activated effector CD8+ T cells expressing CD11c in the B16F10 melanoma model
Authors: Fessler Kaitlyn , Gordy James T. , Sandhu Avinaash K. , Hui Yinan , Kapoor Aakanksha R. , Ayeh Samuel K. , Karanika Styliani , Karakousis Petros C. , Markham Richard B. 
Published: August 2023

Abstract

Previous studies in the B16F10 mouse melanoma model have demonstrated that combining a DNA vaccine comprised of regions of gp100 and tyrosinase-related protein 2 fused to Macrophage-inflammatory protein 3-alpha (MIP3α) with recombinant Interferon alpha (IFN) and 5-Aza-2’-Deoxycytidine (5Aza) treatments resulted in significantly greater anti-tumor activity and immunogenicity in the tumor microenvironment (TME). This brief report details that the combination of vaccine with treatments IFN and 5Aza results in both the upregulation of genes expressing CD11c-interacting proteins and an increase in the TME of a distinct CD11c+ CD8+ T cell population. This cell population correlates with tumor size, is primarily comprised of effector or effector memory T cells, and has a more robust response to ex vivo stimulation as compared to CD11c- CD8+ T cells as measured by surface activation markers 4-1BB (CD137) and KLRG1 (Killer cell lectin-like receptor G1) and intracellular IFNγ production. In conclusion, this combination therapy results in greater presence of highly active effector CD8+ T-cells expressing CD11c in the TME that correlate with and are likely primary contributors to treatment efficacy.
Efficacy and safety of immunotherapy combinations in advanced esophageal and gastric cancers: A systematic review and bayesian network meta-Analysis
Authors: Wang Yan , Zhou Chunyang , Wu Xue , Tang Xiufeng , Liu Chengxin 
Published: August 2023

Abstract
Background Immune checkpoint inhibitors (ICIs) have yielded a revolution in the treatment of esophageal, gastroesophageal junction, and gastric cancers cancers. We made a Bayesian network meta-analysis to systematically compare the efficacy and safety of various immunotherapies, chemotherapy, and supportive treatment, and to further identify the best benefitting subgroup.
Methods We systematically searched Pubmed/MEDLINE, Embase, Cochrane Library and the conference abstracts of oncology conferences up to July 25, 2022. The Bayesian network meta-analysis was performed with a random-effect model using R, package gemtc and package JAGS. The primary outcomes including overall survival (OS), progression-free survival (PFS), objective response rate (ORR) and grade 3-5 adverse events (AEs).
Results Totally 11719 patients from 20 RCTs (19/20 were phase 3) were included, which were divided into 12 treatment regimens. Immune monotherapies yield superior OS benefit (HR = 0.89, 95% CI: 0.83–0.95), but inferior PFS (HR = 0.75, 95% CI: 0.7–0.8) compared with chemotherapy. Except for Nivo_5FCisp (HR = 0.8, 95% CI: 0.49–1.32), Nivo_S1Oxal (HR = 0.9, 95% CI: 0.75–1.08), Tori_PacCisp (HR = 0.91, 95% CI: 0.77–1.07), the other ICIs combined chemotherapy (IO) had a significant OS benefit compared with chemotherapy. Except for Nivo_5FCisp, the other IO regimens had significant PFS benefits than chemotherapy. There were no significantly statistical differences of ORR and grade 3-5 AEs. Sin_PacCisp and Cam_PacCisp achieved an effective balance between efficacy and safety. The former regimen ranked second for OS (48.3%), PFS (29.5%), ORR (18.4%), and sixth for grade 3-5 AEs (7.2%), the later one ranked third for OS and PFS, fifth for ORR and ninth for grade 3-5 AEs. Esophagogastric cancers with higher PD-L1 expression levels might benefit more from immunotherapies.
Conclusion We found immunotherapis were more likely to provide OS and PFS superiority compared with chemotherapy, with no significantly statistical differences of ORR and grade 3-5 AEs. Sin_PacCisp and Cam_PacCisp achieved an effective balance between efficacy and safety. Esophagogastric cancers with higher PD-L1 expression levels might benefit more from immunotherapies.
PTEN decreases NR2F1 expression to inhibit ciliogenesis during EGFRL858R-induced lung cancer progression
Authors: Hung Jan-Jong , Tran Thi Thanh Truc 
Published: August 2023

Abstract
Lung cancer is the major cause of death worldwide. Activation of oncogenes or inhibition of tumour suppressors causes cancer formation. Previous studies have indicated that Pten, as a tumour suppressor, inhibits cancer formation. In this study, we studied the role of Pten in EGFRL858R-induced lung cancer in vivo. Interestingly, loss of Pten increased bronchial cell hyperplasia but decreased alveolar cell hyperplasia in EGFRL858R*Pten−/−-induced lung cancer. Syatematic analysis of gene expression by RNA-seq showed that a number of genes related to ciliogenesis were upregulated in EGFRL858R*Pten−/−-induced lung cancer and subsequently showed that bronchial ciliated cells were hyperplastic. Several critical ciliogenesis-related genes, such as mucin5a, DNAI2 and DNAI3, were found to be regulated by NR2F1. Next, NR2F1 was found to be inhibited by overexpression of Pten, indicating that Pten negatively regulates NR2F1, thereby inhibiting the expression of ciliogenesis-related genes and leading to the inhibition of bronchial cell hyperplasia during EGFRL858R-induced lung cancer progression. In addition, we also found that Pten decreased Akt phosphorylation in A549, Kras mutant, and H1299 cells but increased Akt phosphorylation in PC9, EGFRL858R, and H1299L858R cells, suggesting that Pten may function as a tumour suppressor and an oncogene in lung cancers with Kras mutation and EGFR mutation, respectively. PTEN acts as a double-edged sword that differentially regulates EGFRL858R-induced lung cancer progression in different genomic backgrounds. Understanding the Pten in lung cancer with different genetic backgrounds will be beneficial for therapy in the future.
Prevalence of depression and associated factors among university students in Ethiopia: Protocol for a systematic review and meta-analysis
Authors: Bitew Sintayehu Tsegaye , Hailu Habtamu Belay , Weldekidan Fekede , Dejene Asnake Fantu 
Published: August 2023

Abstract
Background
Depression is one of the most commonly occurring mental disorders globally and it is increasingly prevalent among university students. This systematic review and meta-analysis will provide the pooled prevalence of depression and associated factors among university students in Ethiopia.
Method
The studies will be identified from data base of PubMed, Google scholar, EMBASE and reference lists of previous studies. All cross-sectional studies reporting the prevalence of depression among university students in Ethiopia will be included. In order to transform the prevalence of extracted studies we will use Freeman-Tukey transformation (arcsine square root transformation and the pooled proportion will be calculated using the back-transformation of the weighted mean of the transformed proportions, using inverse arcsine variance weights for the fixed-effects model and DerSimonian-Laird weights for the random-effects model. The Cochrane Q test and I2 test statistic will be used to test heterogeneity across studies. Meta regression and Subgroup analyses will be performed to explore source of Heterogeneity between studies and evidence of publication bias will also be assessed.
Discussion
Estimating the prevalence of depression among university student will help policy-makers and other stakeholders in developing evidence-based mental health promotion and prevention programme in university settings.
Systematic review registration: PROSPERO CRD42023418350
Investigating the immunological function of alpha-2-glycoprotein 1, zinc-binding in regulating tumor response in the breast cancer microenvironment
Authors: Hanamura Toru , Yokoyama Kozue , Kitano Shigehisa , Kagamu Hiroshi , Yamashita Makiko , Terao Mayako , Okamura Takuho , Kumaki Nobue , Hozumi Katsuto , Iwamoto Takayuki , Honda Chikako , Kurozumi Sasagu , Richer Jennifer K , Niikura Naoki 
Published: August 2023

Abstract
Background: The immunosuppressive function of androgen receptor (AR) in breast cancer is known; however, the mechanism remains unclear. Elucidating the immunoregulatory mechanisms in the breast cancer microenvironment will help develop novel treatment strategies. Here, we focused on AR-dependent secreted proteins encoded by the AZGP1 gene in breast cancer: alpha-2-glycoprotein 1, zinc-binding (ZAG), which is structurally similar to HLA class I and is implicated in immune regulation.
Methods: We performed a gene set enrichment analysis (GSEA) to screen the biological processes associated with AZGP1 expression using a gene expression profile dataset of the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC). Subsequently, we analyzed the correlation between AZGP1 expression and the immune cell composition in breast cancer tissues estimated with the CIBERSORTx using METABRIC and another dataset of The Sweden Cancerome Analysis Network- Breast. In our previous study of 45 breast cancer tissues, we evaluated the infiltration of 11 types of immune cells using flow cytometry (FCM). ZAG expression was further evaluated by immunohistochemistry, and the relationship between ZAG and the immune cells was analyzed. The action of ZAG in M1/M2 polarization models constructed using primary culture of human peripheral blood mononuclear cells (PBMC)-derived macrophage (Mφ) based on M1/M2 differentiation markers (Cluster of Differentiation (CD) 86, CD80/CD163, MRC1) and HLA class I/II expression evaluated by FCM (n=15 each).
Results: GSEA demonstrated that AZGP1 expression was negatively correlated with multiple gene sets representing immunological processes. AZGP1 expression was inversely correlated with Mφ M1 infiltration estimated by CIBERSORTx. ZAG expression was associated with decreased infiltration of monocytes/macrophages, non-classical monocytes, and myeloid-derived suppressor cells in breast cancer tissues assessed by FCM. In in vitro analyses, ZAG decreased the expression of CD80, CD163, MRC1, and HLA classes I and II in the M1 polarization model and the expression of CD163 and MRC1 in the M2 polarization model. Conclusion: ZAG is a novel immunoregulatory factor affecting Mφ phenotype and possibly function in breast cancer tissues. Our findings lay the foundation for future studies to elucidate the immunological role of ZAG in breast cancer.
Ultra-thin magnetic metal with TMOKE-enhanced high performance sensing
Authors: Lei Fugui , Li Penglei , Liu Yufang 
Published: August 2023

Abstract
Magneto-plasmon sensing structure based on surface plasmon resonance (SPR) and magneto-optical (MO) effect has become a hot research topic in recent years. In this work, we propose a magneto-plasmon crystal consisting of an Au/Co double-layer nanoporous structure with optically thick Au film as a substrate. Due to the excitation of high-quality SPR, a Fano-like transverse magneto-optical Kerr (TMOKE) spectrum with subnanometer bandwidth is realized. A sensitivity of up to 677 nm/RIU is detected under subtle refractive index changes in air, and all the calculated figure of merits (FOM) are higher than 104 RIU-1. In addition, in order to highlight the superiority of the sensing performance of the magneto-optical plasmon (MOSPR) sensor, we compare the MOSPR sensor with the SPR sensor and calculate their FOM in different refractive index environments. The results show that the FOM of the MOSPR sensor is two orders of magnitude higher than that of the SPR sensor, which proves that the performance of the MOSPR sensor is significantly better than that of the SPR sensor and provides a theoretical basis for biosensing and gas detection based on magnetoplasmaon.
Arabic Opinion Mining System For E-Services Based on Ontology and Machine Learning
Authors: mansour maha 
Published: August 2023

Abstract
With the growing use of E-Services in the Arab world, there is a need for efficient methods to analyze customer feedback and opinions. This research is proposing an Arabic opinion mining system for E-Services based on ontology and machine learning techniques. The system utilizes an Arabic ontology to capture domain-specific vocabulary and relationships between features and a machine learning model to classify customer feedback into positive, negative, or neutral sentiments. By evaluating the performance of the system using several domains datasets for Arabic customer reviews of various e-services, and the results show that the system achieves a high accuracy level. Comparing the performance of the proposed system to traditional polarity and subjectivity approaches
Stochastic Evaluation of Serial Multi-Purpose Solar System Thru Gumbel-Hougaard Family Copula
Authors: Ismail Abdulkareem Lado , Aminu Abdulhadi , Yusuf Ibrahim 
Published: August 2023

Abstract
Considering the high cost and safety risks linked with traditional energy sources such as fossil fuels, hydroelectricity, thermal electricity etc. this made it mandatory for the globe to investigate in renewable energy sector. In line with above mentioned issues, the present study concentrated on a multi-purpose solar system consisting of four subsystems: a solar panel, a controller, two USB charge points and four electric bulb points all merged together in a series-parallel conformation. Components/units failure is constant and in line with exponential function, copula and general repairs are available for the repair of partial and entire system failure. The transition diagram of the system is used to derived the partial differential equations of order one, and solved using supplementary variable and Laplace transformation procedures. Maple software package is used to generate expressions for availability, reliability, mean time to failure (MTTF), sensitivity and cost. Results are justified using particular examples and presented in Tables and Figures.
Some Rigidity Theorems for Anosov Geodesic Flows in Manifolds of Finite Volume
Authors: Ibarra Sergio Augusto Romaña , Melo Ítalo Dowell 
Published: August 2023

Abstract
In this paper, we prove that if the geodesic flow of a complete manifold without conjugate points with sectional curvatures bounded below by - c2 is of Anosov type, then the constant of contraction of the flow is greater than e-c . Moreover, if M has finite volume, the equality holds if and only if the sectional curvature is constant. We also apply this result to get a certain rigidity for bi-Holder, bi-Lipschitz, and consequently, for C1-conjugacy between two geodesic flows.
Simple image deconvolution based on reverse image convolution and backpropagation algorithm
Authors: Nguyen Loc 
Published: August 2023

Abstract
Deconvolution task is not important in convolutional neural network (CNN) because it is not imperative to recover convoluted image when convolutional layer is important to extract features. However, the deconvolution task is useful in some cases of inspecting and reflecting a convolutional filter as well as trying to improve a generated image when information loss is not serious with regard to trade-off of information loss and specific features such as edge detection and sharpening. This research proposes a duplicated and reverse process of recovering a filtered image. Firstly, source layer and target layer are reversed in accordance with traditional image convolution so as to train the convolutional filter. Secondly, the trained filter is reversed again to derive a deconvolutional operator for recovering the filtered image. The reverse process is associated with backpropagation algorithm which is most popular in learning neural network. Experimental results show that the proposed technique in this research is better to learn the filters that focus on discovering pixel differences. Therefore, the main contribution of this research is to inspect convolutional filters from data.
Endosymbiosis change under the stress of omethoate and four plant allelochemicals in cotton aphid, Aphis gossypii Glover (Hemiptera: Aphididae)
Authors: Liu Ying , Chen Chengyu , Li Ren , Liang Pingzhuo , Gu Shaohua , Zhang Lei , Gao Xiwu 
Published: August 2023

Abstract
Aphids feed on plant phloem-sap and are currently model taxa for studying insect-microbe interactions as they host several beneficial bacterial endosymbionts. In this study, the second generation of high-throughput sequencing was employed to identify the microbiome of cotton aphids, Aphis gossypii Glover. We found that the communities of endosymbiotic microorganisms could affected by the treatments of omethoate or plant allelochemicals. The low-dose and high-dose of omethoate could decreased the bacteria abundance in the cotton aphid. The tannic acid and quercetin slightly reduced the diversity of the community while 2-tridenone and gossypol significantly enhanced the abundance of the communities. Especially, the abundance of both Buchnera and Serratia obviously increased in the cotton aphid after exposure to gossypol. This is the first insight of endosymbiont variation under the induction of omethoate and plant allelochemicals. These results should be useful to understand the adaptation of the cotton aphid to xenobiotics.