bluuebunny/crossref_metadata_2025_split huggingface dataset:
Monocyte derived Galectin-9 and PD-L1 differentially impair innate and adaptive immune response in various phases of chronic HBV infection
Authors: Dey Debangana , Biswas Satabdi , Pal Sourina , Nandi Sarthak , Khatun NAJMA , Jha Rambha , Chakraborty Bidhan Chandra , Baidya Ayana , Ghosh Ranajoy , Banerjee Soma , AHAMMED SK MAHIUDDIN , Chowdhury Abhijit , DATTA SIMANTI 
Published: February 2024

Abstract
Background and aims: Patients with chronic HBV infection (CHI) exhibit defective anti-viral immune-response whose underlying mechanisms remain unclear. Monocytes can regulate immunity via interaction with other immune-cells apart from differentiating into macrophages. Immune-checkpoint molecules (ICMs) expressed by different immune-cells, including monocytes negatively regulate immune-responses. We evaluated the expression of ICMs (Gal-9/PD-L1/CTLA-4) on monocytes in different phases of CHI, identified viral/host-factors causing their aberrant expression and investigated their impact during interaction of monocytes with T-/B-/NK-cells and macrophage differentiation. Effect of antiviral-therapy on ICMs was studied.
Methods: Collection of blood/liver-tissue samples/flow-cytometry/cell-sorting/cell-culture/immune-fluorescence were performed.
Results: Gal-9+/PD-L1+-monocytes were significantly increased in HBeAg-positive/HBeAg-negative chronic hepatitis B (CHB) patients than healthy controls (HC). In immune-tolerant (IT) subjects, Gal-9+-monocytes and in inactive carriers (IC), PD-L1+-monocytes were higher than HC while CTLA-4+-monocytes remained comparable among groups. High serum Hepatitis B surface antigen (HBsAg) concentration in IT/CHB and TNF-α in CHB triggered monocytic Gal-9-expression whereas high TNF-α/IL-4 in CHB and IL-1β in CHB/IC potentiated PD-L1 induction. Purified monocytes from CHB/IT having high Gal-9 expression led to expansion of CD4+CD25+FOXP3+-Tregs/CD19+CD27-CD21--atypical memory B-cells/CD19+IL-10+-Bregs and they preferentially differentiated into M2-macrophages. Anti-Gal-9-antibody reversed these phenomena. Parallelly, PD-L1+-monocytes in CHB/IC reduced IL-2/IFN-γ and IL-6-production by HBcAg-specific CD4+/CD8+T-cells and B-cells respectively, which were restored by anti-PD-L1-antibody. Gal-9+-/PD-L1+-monocytes caused decline in IFN-γ+-NK-cells but enhanced IL-10+-NK-cells and HBV-specific-T-cells. Increased intrahepatic CD14+Gal-9+/CD14+PD-L1+-monocytes was noted in CHB patients. One-year tenofovir-therapy failed to reduce monocytic Gal-9/PD-L1-expression and HBsAg/TNF-α/IL-4/IL-1β levels.
Conclusion: Monocyte-derived Gal-9/PD-L1 exert distinct inhibitory effects in different phases of CHI and their therapeutic targeting could boost anti-HBV immunity.
Enhancing Phishing URL Detection Accuracy in Software-Defined Networks (SDNs) through Feature Selection and Machine Learning Techniques
Authors: RUBY A USHA , J George Chellin Chandran 
Published: February 2024

Abstract
Phishing attacks remain an enduring and ever-evolving menace to both networked systems and their users' privacy. In response to this formidable challenge, our research delves into an innovative approach designed to enhance the precision of phishing Uniform Resource Locator (URL) detection within the dynamic and programmable realm of Software-Defined Networks (SDNs). By harnessing feature selection capabilities and adaptive machine learning techniques, our proposed framework aims to fortify security measures in SDNs against these malicious campaigns. Our methodology's core is the deliberate selection of discriminative features from the extensive network data attributes. This feature selection process is meticulously designed to identify the most relevant characteristics associated with phishing URLs, thereby enabling the extraction of invaluable insights for more precise detection. These carefully chosen features then serve as inputs for a dynamic machine learning model, trained to adapt and evolve alongside the constantly changing landscape of phishing attacks. Within the SDN environment, our framework optimizes utilizing network resources and controller processing power. It achieves this by reducing the dimensionality of input data, resulting in improved detection accuracy and a decrease in false positives. The adaptive nature of our machine learning model ensures rapid recognition of emerging phishing tactics, thereby reducing the risk of succumbing to novel and sophisticated attacks. To validate the effectiveness of our approach, we conducted extensive experiments and evaluations within an SDN testbed, utilizing real-world phishing URL datasets. The results consistently demonstrate that our framework surpasses conventional methods, achieving higher detection accuracy and adaptability to evolving threats. In summary, our research represents a significant stride in the ongoing battle against phishing attacks by leveraging the dynamic capabilities of SDNs. The synergy between feature selection and adaptive machine learning techniques empowers SDNs to sustain accurate and effective phishing URL detection, ultimately reinforcing network security and safeguarding user privacy in an ever-evolving threat landscape.
Tumor-stroma ratio can reliably be assessed in pre-treatment biopsies, shows substantial agreement with resections, and is significantly associated with overall and recurrence-free survival in early-stage oral squamous cell carcinomas
Authors: Herber Katrin , Moeckelmann Nikolaus , Muenscher Adrian , Thorns Christoph , Knief Juliana 
Published: February 2024

Abstract
Objectives
Early-stage oral squamous cell carcinomas (OSCC) are a heterogeneous group of tumors in terms of patients’ outcomes, and the current TNM classification is often insufficient to predict the clinical course. Recently, tumor-stroma ratio has been shown to better stratify patients into low- and high-risk groups. This study aims to assess whether pre-treatment biopsies can be used for this purpose and correlates findings with resections as well as survival.
Materials and Methods
66 resection specimens of early-stage OSCC (UICC stages I and II) and 50 corresponding pre-treatment biopsies were analyzed. Tumor-stroma ratio was determined with a cut-off of 50%. The Kappa correlation coefficient was used to determine the agreement between resections and biopsies. Kaplan-Meier curves for overall and recurrence-free survival were created, and a p-value < 0.05 indicated a significant correlation in all analyses. Additionally, cox proportional hazard analysis (univariate) was performed to test for independence.
Results
There was substantial agreement between preoperative biopsies and surgical specimens (Kappa correlation coefficient: 0.655). Stroma-rich tumors were associated with significantly decreased overall and recurrence-free survival in both resections and biopsies (p-values < 0.0001 and 0.039). Cox proportional hazard analysis revealed tumor-stroma ratio as an independent prognostic factor, with hazard ratios between 2.81 and 12.46 (p-values 0.002 and 0.049).
Cross-neutralizing antibody against emerging Omicron subvariants of SARS-CoV-2 in infection-naïve individuals with homologous BNT162b2 or BNT162b2(WT+ BA.4/5) bivalent booster vaccination
Authors: Cheng Samuel M.S. , Mok Chris K.P. , Li John K.C. , Chan Ken K.P. , Luk Kristine S. , Lee Ben H.W. , Gu Haogao , Chan Karl C.K. , Tsang Leo C.H. , Yiu Karen Y.S. , Ling Ken K.C. , Tang Yun Sang , Luk Leo L.H. , Yu Jennifer K.M. , Pekosz Andrew , Webby Richard J. , Cowling Benjamin J. , Hui David S.C. , Peiris Malik 
Published: February 2024

Abstract
Since the emergence of SARS-CoV-2, different variants and subvariants successively emerged to dominate global virus circulation as a result of immune evasion, replication fitness or both. COVID-19 vaccines continue to be updated in response to the emergence of antigenically divergent viruses, the first being the bivalent RNA vaccines that encodes for both the Wuhan-like and Omicron BA.5 subvariant spike proteins. Repeated infections and vaccine breakthrough infections have led to complex immune landscapes in populations making it increasingly difficult to assess the intrinsic neutralizing antibody responses elicited by the vaccines. Hong Kong’s intensive COVID-19 containment policy through 2020–2021 permitted us to identify sera from a small number of infection naïve individuals who received 3 doses RNA vaccine BNT162b2 of vaccines encoding the Wuhan-like spike who were boosted with a fourth dose monovalent Wuhan-like (WT) vaccine or the bivalent Wuhan-like and BA.4/5 spike (WT + BA.4/5) expressing vaccine. While neutralizing antibody to wild-type virus was comparable in both vaccine groups, BNT162b2 bivalent vaccine elicited significantly higher plaque neutralizing antibodies to Omicron subvariants BA.5, XBB.1.5, XBB.1.16, XBB.1.9.1, XBB.2.3.2, EG.5.1, HK.3, BA.2.86 and JN.1, compared to BNT162b2 monovalent vaccine. The single amino acid substitution that differentiates the spike of JN.1 from BA.2.86 resulted in a profound antigenic change.
Cattaneo-Christov and Darcy-Forchheimer Heat Flux on Reiner-Philippoff Fluid with Velocity and Thermal Slip Boundary Condition Under Heat Sink/Source
Authors: Warraich Sanaullah , Ayub Nadia , Qadeer Fatima , Umar Irfan 
Published: February 2024

Abstract

Reiner–Philippoff (RP) fluid flow above a heated sheet concluded the model of Cattaneo–Christov heat flux for Darcy-Forchheimer is implemented in this work. The influences of thermal radiation, heat source/sink, velocity, and thermal slip boundary conditions are also deliberated. The transformations are used to convert obtained PDEs into a set of ordinary differential equations, and they are solved numerically using the shooting method (RK-4) solver with the help of the computational software MATLAB. The dimensionless temperature and velocity numbers are further developed. More engineering curiosity of local Nusselt and Skin frictions are tabulated, depicted, and interpreted.
Agricultural practices influence soil microbiome assembly and interactions at different depths identified by machine learning
Authors: Mo Yujie , Bier Raven , Li Xiaolin , Daniels Melinda , Smith Andrew , Yu Lei , Kan Jinjun 
Published: February 2024

Abstract

Background:Soil microbiomes are critical to soil health and sustainable agriculture. Microbiome structures differ across agricultural soils that experience different management practices, such as tillage, fertilizers, and cover crops. Environmental factors, including those related to soil management, and microbiome-interactions alter these microbial communities. However, the full extent of these influences and relationships is not comprehensively understood. Here, we applied machine learning-based methods to identify prokaryotic and fungal assembly and distribution across various soil depths from fields under different agricultural practices.Results:Fertility source was the most pronounced agricultural factor for microbial assembly, especially for fungal communities, and this impact lessened as soil depth increased. Furthermore, fertility source shaped co-occurrence patterns within the microbial community, leading to fertility source-sensitive modules dominated by fungi down to 30 cm soil depth. Tillage predominantly affected microbial communities in soil depths of 0-20 cm, and not only enhanced microorganism dispersal but also amplified stochastic processes, especially in the prokaryotic community. However, tillage potentially jeopardized interactions within the microbiome. In contrast to the fertility source and tillage, cover crops had a less pronounced effect on microbial communities, with no clear depth-dependent patterns; however, they still affect microbial co-occurrence patterns to some extent. Additionally, microorganisms at varying depths, specifically 0-20 cm for tillage and cover crops and 0-60 cm for fertility sources, were indicative of different agricultural practices.Conclusions:Our result showed that the impact of agricultural practices on microbial communities is multifaceted, including the assembly process, interactions, and recruitment of specific microorganisms. We also demonstrated that machine learning models can overcome certain limitations of traditional methods, offering enhanced insights into the mechanisms underlying microbial assembly and distribution in agriculture soils.
Kinetics and Mechanism of Acid Orange AO7 Photodegradation Using Ag-Doped ZnO Nanoparticles
Authors: Alsaad Ahmad , Al-Hmoud Mohannad , Rababah Taha M. , Marashdeh Mohammad W. , Aljaafreh Mamduh J. , Alrub Sharif Abu , Binzowaimil Ayed , Aldaghri Osamah , Al-Aqeel Muneerah. A. M. , Telfah Ahmad D. 
Published: February 2024

Abstract
The photocatalyst degradation process of the azobenzene derivatives is complicated process due to the existence of many interaction combinations. Among these are intermolecular interactions including London dispersion forces (LDF), dipole interactions, and hydrogen bonding. In addition to investigating the mechanism of the photoisomerization process, UV-Vis spectroscopy, FTIR spectroscopy, pH and conductivity measurements were used to investigate and clarify the photocatalytic mechanism and degradation pathway of AO7 using Ag doped ZnO NPs upon long-wavelength UV-irradiation. The kinetic rate constant is divided into two components. The first-rate and second-rate constants are obtained to be 0.014 min-1 and 0.022 min-1, respectively. The average rate constant of the AO7 degradation is a superposition of the rate constants of all frequency bands. This confirms that photocatalyst degradation process of the azobenzene derivatives is sophisticated process.
Coupling and coordination of rainfall erosivity and aerosols in the Pearl River Basin under multiple shared socio-economic pathways
Authors: Cao Zhen , Zhu Dayun , Zhao Yingshan , Xiao Hua , Li Ronghan , Fu Linjing 
Published: February 2024

Abstract

Aerosols can influence the size, quantity, and other characteristics of raindrops, subsequently altering the precipitation pattern and modifying rainfall erosivity (RE). To explore the relationship between RE and aerosols, this study analyzed the spatiotemporal distribution characteristics of RE and aerosols in the Pearl River Basin. Utilizing GPM rainfall data from 2001 to 2022, future 100-year rainfall prediction data, and aerosol data, the study investigated the potential relationship between these indicators. The findings are as follows: (1) RE exhibited a spatial distribution decreasing from southeast to northwest, showing a declining trend; Aerosol Optical Depth (AOD) demonstrated a similar decreasing spatial distribution and trend. (2) RE and AOD displayed a highly significant positive correlation, with a coordinated distribution within the basin, as indicated by a multi-year average coupling coordination index of 0.7054. (3) Over the next century, RE in various scenarios remained lower than current RE and exhibited a highly significant negative correlation with current AOD. (4) Aerosol particle size emerged as the most significant indicator correlated with RE, and among all RE indicators, rainfall quantity exhibited the strongest correlation with aerosols. (5) RE for rainfall across all magnitudes demonstrated a highly significant correlation with aerosols, with the strongest correlation observed in heavy rain. This study unveils the interrelationship between RE and aerosols, providing a theoretical basis for land managers and governments to formulate measures aimed at reducing the impact of rainfall erosion on farmland and ecosystems.
The Impact of Health Disparities on Peripheral Vascular Access Outcomes in Hospitalized Patients: An Observational Study
Authors: Mielke Nicholas , O’Sullivan Charlotte , Xing Yuying , Bahl Amit 
Published: February 2024

Abstract

Background
Placement of peripheral intravenous catheters (PIVC) is a routine procedure in hospital settings. The primary objective is to explore the relationship between healthcare inequities and PIVC outcomes.
Methods
This study is a multicenter, observational analysis of adults with PIVC access established in the emergency department requiring inpatient admission between January 1st, 2021, and January 31st, 2023 in metro Detroit, Michigan, United States. Epidemiological, demographic, therapeutic, clinical, and outcomes data were collected. Health disparities are defined by the National Institute on Minority Health and Health Disparities. The primary outcome is the proportion of PIVC dwell time to hospitalization length of stay, which is expressed as the proportion of dwell time (hours) to hospital stay (hours) x 100%. Multivariable linear regression and a machine learning model were used for variable selection. Subsequently, a multivariate linear regression analysis was utilized to adjust for confounders and best estimate the true effect of each variable.
Results
Between January 1st, 2021, and January 31st, 2023, our study analyzed 144,524 ED encounters, with an average patient age of 65.7 years and 53.4% female. Racial demographics showed 67.2% White, 27.0% Black, with the remaining identifying as Asian, American Indian or Alaska Native, or other races. The median proportion of PIVC dwell time to hospital length of stay was 0.88, with individuals identifying as Asian having the highest ratio (0.94) and Black individuals the lowest (0.82). Black females had a median dwell time to stay ratio of 0.76, significantly lower than White males at 0.93 (p < 0.001). After controlling for confounder variables, a multivariable linear regression demonstrated that Black males and White males had a 10.0% and 19.6% greater proportion of dwell to stay, respectively, compared to Black females (p < 0.001).
Conclusions
Black females face the highest risk of compromised PIVC functionality, resulting in approximately one full day less of reliable PIVC access than White males. To comprehensively address and rectify these disparities, further research is imperative to improve understanding of the clinical impact of healthcare inequities on PIVC access. Moreover, it is essential to formulate effective strategies aimed at mitigating these disparities and ensuring equitable healthcare outcomes for all individuals.
LMagNet: a Lightweight Magnetic Convolutional Neural Network on Microcontrollers for Hidden Corrosion Detection in Aircraft Structures
Authors: Vu Thuy Phuong , Luong Van Su , Le Minhhuy 
Published: February 2024

Abstract

Detection of hidden corrosion within aircraft structures poses a significant challenge in nondestructive testing (NDT) methodologies, particularly in electromagnetic testing (ET) method. Corrosions often manifest in the recesses of rivet fastener holes, complicating their identification. In this paper, we propose an approach to enhance ET system detectability by introducing a Lightweight Magnetic Convolutional Neural Network (LMagNet) model tailored for the efficient processing of electromagnetic signals from corrosion. We aim to develop a lightweight model that could be deployed on low-resource microcontrollers ensuring its practical applicability in aircraft inspection scenarios. The proposed LMagNet model archives better performance compared to other lightweight models such as ShuffleNet, SqueezeNet, and MobileNet structures. The model archives an accuracy of 92% with only 16K parameters when being evaluated on hidden corrosion of aircraft structure, having volumes from 2.8-195.4 mm3. The model size is about 224×, 67×, 96× smaller compared to the ShuffleNet G1, SqueezeNet Complex, and MobileNet V3 model. When deploying on low-resource microcontrollers (i.e., STM32 MCUs), the LMagNet model requires only 90kB of flash and 36kB of RAM allowing to run within 40ms per prediction. In addition, we employed explainable techniques to interpret how the decision-making process of the model is made to achieve reliable results.
Enhanced slime mould algorithm with backtracking search algorithm: global optimization and feature selection
Authors: Wang Jian , Chen Yi , Zou Huilai , Lu Chenglang , Heidari Ali Asghar , Liu Lei , Chen Huiling , Liang Guoxi 
Published: February 2024

Abstract

The Slime Mould Algorithm (SMA), renowned for its swarm-based approach, encounters challenges, particularly in maintaining a balance between exploration and exploitation, leading to a trade-off that impacts its optimization performance. The simple structure and limited hyperparameters of SMA contribute to difficulties in effectively navigating the exploration-exploitation trade-off, with a drawback being its poor ability for exploration. To address these challenges and enhance SMA, this paper introduces BSSMA, an improved variant that incorporates the Backtracking Search Algorithm (BSA). The introduction of the \(phaseratio\) parameter aims to synergize BSA and SMA, capitalizing on the strengths of both algorithms while mitigating their individual drawbacks, including SMA's poor exploration ability. BSA facilitates a thorough exploration, dispersing search agents widely across the solution space, ensuring significant diversity. These search agents then transition to SMA to further refine the search for optimal solutions while addressing SMA's exploration limitations. Evaluating the performance of BSSMA involves comparisons with 12 other meta-heuristic algorithms (MAs) and 10 advanced MAs using the CEC2017 benchmark functions. Experimental results showcase that the enhanced BSSMA outperforms SMA in terms of convergence speed and accuracy, specifically addressing the challenges associated with balancing exploration and exploitation trade-offs, including SMA's poor exploration ability. Additionally, to demonstrate BSSMA's effectiveness in practical engineering applications, a binary version (bBSSMA) is developed for feature selection (FS) using a V-shaped transfer function. Comparative experiments with seven other binary MA variants reveal that bBSSMA selects fewer features, attains higher classification accuracy, and demands less computational time. These results affirm the effectiveness of bBSSMA for practical feature selection applications.
Optimization of deamidation of casein by protein-glutaminase and its effect on structural and functional properties
Authors: Jiang Deming , Wei Ouyang , Huang Lingling , Niu Jinjin , Zhang Zheng , Jin Congli , Gu Siyi , Liu Mengmeng , Chang Zhongyi , Niu Yanning , Zou Chunjing , Huang Jing , Jia Caifeng , Tang Lihua , Gao Hongliang 
Published: February 2024

Abstract

Casein is a commonly used protein in the food industry, and its related products are abundant, such as beverages, desserts, but the further application of casein is limited by its solubility and stability. This study aimed to optimize the deamidation of casein using Protein-glutaminase (PG) and investigate its impact on the structure, solubility and stability of casein. Through center composite experiments, the optimal conditions for PG deamidation were determined to be at pH 6.0, E/S 15 U/g, and a temperature 45°C. The deamidation process alters the secondary structure of casein, resulting in a decrease in α-helix structure and an increase in β-sheet structure. The modification of casein improved emulsifying activity at pH 8.0 and pH 10.0, and significantly enhanced solubility at 5.0 to 6.0. Furthermore, the deamidation of casein leads to an increase in zeta potential and a decrease in particle size, resulting in improved stability of the protein solution due to reduced particle aggregation. The 3% deamidated casein based beverage with carrageenan exhibited reduced precipitation rates compared to the control after sterilization at 121°C for 15 min. In summary, PG deamidation offers a promising method to modify casein, enhancing its functional properties, including solubility, stability and emulsifying activity, thereby expanding the use of casein in the food industry.
RETRACTED: Empirical analysis of link prediction methods based on the topological structure of the COVID-19 network
Authors: Solangi Shauban Ali , Mahesar Abdul Waheed , Dhomeja Lachhman Das , Khoumbati Khalil-ur-Rehman , Memon Bisharat Rasool 
Published: February 2024

Abstract

The authors have requested that this preprint be removed from Research Square.