bluuebunny/crossref_metadata_2025_split huggingface dataset:
A biocompatible Polyvinyl Pyrrolidone/Silver-capped Ytrrium oxide ternary nanocomposites for pharmaceutical applications: Antioxidant and Cytotoxicity activities
Authors: MYVIZHI G. , KRISHNA S. K. 
Published: September 2024

Abstract

As a means of addressing medical problems, rare earth nano-oxides are gaining prominence. For the first time, Silver (Ag) and Polyvinylpyrrolidone (PVP) capped Yttrium oxide (Y2O3) ternary nanocomposites (Ag/PVP @ Y2O3 ) were synthesised using a hydrothermal route. In fixed amounts of PVP and Y2O3, different concentrations of Ag (0.5% and 1.5%) were utilised. XRD, FTIR, HRTEM, XPS, UV-VIS, and PL spectroscopy were used to systematically characterise the as-synthesised NCs. By employing the X-ray diffractogram (XRD) technique, the Y2O3 crystals have a rod-shaped cubic structure, with doping causing them to increase from 41 nm to 52 nm in size. Peaks at 563 cm− 1 and 559 cm− 1 in the FTIR spectrum demonstrated the presence of Y2O3 and indicated the formation of the Y-O bond. The charge transfer efficiency of the synthesised nanocomposites was shown by PL spectra. XPS analysis validated the existence of constituent elements (Y2O3) and dopants (Ag, and PVP), as long as only a single element of zero valence silver was present in the system. The Ag/PVP @ Y2O3 NPs showed the most remarkable cytotoxicity with the IC50 value of 32.56 µg/mL against MCF-7 cells which could be a promising agent in the field of nanomedicine.
Optimisation of a microfluidic SNP assay for accurate hybrid class detection in the European Wildcat (Felis silvestris)
Authors: Martin Lina S. , Rolshausen Gregor , Alves Paulo C. , Mattucci Federica , Caniglia Romolo , Randi Ettore , Nowak Carsten , Cocchiararo Berardino 
Published: September 2024

Abstract

Anthropogenic hybridisation between wild and domestic taxa poses a significant threat to species integrity, including the endangered European wildcat. To enable reliable molecular assessment of admixture with domestic cats and to increase the accuracy of hybrid class assignment we optimised an existing reduced microfluidic 96 Single Nucleotide Polymorphism (SNP) panel. We selected SNPs from a genome-wide dataset for maximum FST between both taxa and replaced 60 SNPs from the previous 96 SNP panel. Comparison of both panels based on simulated hybrid genotypes and real-world genotypes proof the higher discriminatory power of the optimised panel, which allows for reliable assignment of F1 and F2 hybrids, as well as 1st and 2nd generation backcrosses. Additionally, we successfully tested the panel for both tissue and non-invasively collected hair samples, demonstrating the suitability of the new panel for implementation in wildcat monitoring programmes.
Dissecting Causal Relationships Between Gut Microbiota, Blood Metabolites, and Postpartum Depression : A Mendelian Randomization Study
Authors: Gao Zhan , Zhou Runze , Chen ZhiQiang , Qian Haotian , Xu Chendong , Gao Mingzhou , Huang Xi 
Published: September 2024

Abstract

Background: There is substantial evidence indicating a significant link between the gut microbiota (GM), blood metabolites and postpartum depression (PPD) . However, the causal relationships underlying these associations have yet to be established.
Methods: This study elucidated the causal relationship among 196 GM taxa, 224 blood metabolites, and PPD from a genetic perspective, employing two-sample Mendelian randomization (MR) and mediation analyses. To validate the relevant findings, we further selected data (GM and blood metabolites) from the the IEU Open GWAS and GWAS Catalog for analysis. Our primary analysis utilized the inverse variance weighted method. To enhance the robustness of our results, we also applied MR-Egger method, weighted median method, Cochran’s Q test, MR-Egger regression, and MR-PRESSO.
Results: MR analysis results revealed a nominal association (p < 0.05) between 13 GM taxa, 6 blood metabolites, and PPD. After Bonferroni correction, only Bifidobacteriales (OR: 0.844, 95% CI: 0.760-0.938; p = 0.0017) showed a significant causal relationship with a lower risk of PPD. In our validation results, the higher level of Alphaproteobacteria (OR: 1.057, 95% CI: 1.024-1.091; Bonferroni-corrected p = 0.0006) retained a strong causal relationship with a higher risk of PPD. Finally, mediation analysis revealed that the impact of Odoribacteron PPD was mediated indirectly through Hyodeoxycholate, with a mediation proportion of 16.8%.
Conclusion: Our findings underscore the importance of elucidating the underlying mechanisms between the GM, blood metabolites, and PPD. These results provide novel insights into microbiome-based therapies and metabolite-targeted interventions for PPD.
Pig production system drivers of antibiotic resistance in Uganda
Authors: Muwonge Adrian , Kakooza Tadeo , Johnson Paul , Kisuule Lawrence , Kimaaga Michael , Kankya Clovice , Bronsvoort Barend , Lembo Tiziana 
Published: September 2024

Abstract

As population-driven demand for protein grows, shifts to livestock intensification in low- and middle-income countries (LMICs) increase risks of antibiotic overuse and antibiotic resistance (ABR). Focusing on Uganda as a fast-growing pig producer in Africa, we examined the potential impact of this transformation. Pigs reared in semi-intensive systems carry six times more antibiotic-resistant bacteria and significantly more ABR encoding genes in their guts than free-range pigs. ABR levels in farmers and pigs in semi-intensive systems were similarly high(22.5%), whereas in free-range pigs they were half those of the farmers who managed them (9% and 21%, respectively). ABR increased at an average rate of 0.7% during the study. Pig-Farmer transmission was ~3 times more likely on semi-intensive farms and associated with differences in abundance of ABR encoding genes in the gut of farmers and their pigs. Shifts to intensified livestock farming require managing enhanced risks of ABR emergence and cross-species transmission.
Impact of Rule 11 on the European medical software landscape: analysis of EUDAMED and further databases three years after MDR implementation
Authors: Schmitz Arndt A , Font-Nieves Miriam , Doucoure Toumani , Podhaisky Hans-Peter 
Published: September 2024

Abstract

Introduction:
Medicine is increasingly supported by software, with digital health technologies offering innovative ways to capture insights and drive therapies. Globally, medical device software must follow regulatory processes based on risk classification. The introduction of MDR represents a significant shift in risk-based qualification for Medical Devices in Europe, including classification Rule 11 for software, which has caused significant discussions among European regulators.
Materials and Methods
Three years after implementation, we conducted a systematic impact assessment of MDR classification Rule 11 for MDSW through a qualitative and quantitative analysis of over 2,000 software entries from the European Medical Device database, complemented by data from other public databases such as the German DiGa directory and mHealthBELGIUM.
Results and Discussion
Our results indicate a shift of the per-default classification of MDSW in Europe after the implementation of the MDR: while most of legacy software in EUDAMED falls in the lowest risk category as MDD Class I (53%), the situation reverses for the MDR conform entries with the most entries in Class IIa (55%). Analyzing the legacy MDD patient apps in Germany implies that three quarters will have to re-classify as MDR Class IIa at the end of the transition period in 2028. A comparison of the European and US regulatory landscapes, along with a systematic review of software features for Class I vs. Class IIa products, explains our findings and enables us to recommend a regulatory strategy for developing MDSW compliant with MDR Class I rules, ensuring fast access to the European market.
Mitigating the Impact of Adversarial Fine-tuning on Large Language Models through Perturbation Attenuation
Authors: Jejesi Zainab , McBride Aaron , Kovacic Felix , O'Connor Leah 
Published: September 2024

Abstract

The increasing complexity and scale of machine learning models have rendered them vulnerable to adversarial manipulation, particularly during fine-tuning processes where subtle perturbations can lead to significant degradation in model performance. Addressing this vulnerability, the proposed approach introduces a novel perturbation attenuation mechanism that integrates noise injection and gradient clipping during training, offering a substantial improvement in model resilience against adversarial attacks. Through a detailed evaluation of the Mistral Large model, the research demonstrates how the application of such attenuation techniques preserves model accuracy while mitigating the adverse effects of adversarial fine-tuning, significantly enhancing robustness without imposing excessive computational overhead. Experiments comparing the baseline model, adversarially fine-tuned model, and the model equipped with the proposed attenuation mechanism reveal that the latter successfully restores stability across various tasks, confirming the efficacy of the strategy in real-world settings. This work contributes to the growing need for robust defenses against adversarial inputs, illustrating how targeted interventions can maintain the integrity and reliability of machine learning systems even in adversarial environments. The findings offer valuable insights into how to counteract adversarial strategies in machine learning models while preserving their performance, providing a practical framework for improving model robustness in critical applications.
Response of Onion (Allium cepa L.) Varieties to Blended NPS Fertilizer at Haramaya, eastern Ethiopia
Authors: Adem Johar , Mihretu Fikreyohannes Gedamu , Alemayehu Yibekal 
Published: September 2024

Abstract

Onion (Allium cepa L.) is one of the most predominantly produced bulb crops in East Hararghe.The productivity of onion in the study area is mainly below the national average due to a number of production constraints, lack of site specific fertilizer recommendations and improved onion varieties are among others.Therefore, this research was conducted to determine the effect of blended NPS fertilizer rates on yield components, yield and bulb quality of onion varieties at Haramaya, East Hararghe. The experiment was arranged in factorial combination of six rates of NPS fertilizer (0, 50, 100, 150,200, 250 kg ha-1) and three onion varieties (Nasik Red, Sirius F1 and Red coach F1) in a Randomized Complete Block Design with three replications. The analysis of variance revealed that variety and NPS fertilizer had significant effect on onion yield components and bulb yield. The interaction of variety and blended NPS fertilizer showed significant differences on bulb weight, total dry biomass yield, marketable, unmarketable and total bulb yield, TSS and percentage bulb dry matter. The highest bulb weight (219.93 g), bulb dry matter percentage (16.42% ), marketable bulb yield (57.23 t ha-1) and total bulb yield (58.35 t ha-1) were obtained from Red coach F1 at rate of 200 kg ha-1 NPS fertilizer. Thus, production of Red Coach F1 with application of 200 kg ha-1NPS fertilizer could be recommend for onion growers at Haramaya as well as other areas having similar agro-ecologies.
Digital Twin-Based Learning Analytics with Fog Computing and LLAMA
Authors: Abdelaziz Abdelkawy A. , EL-Ghany Sameh Abd , Tolba A. S. 
Published: September 2024

Abstract

The growing incorporation of digital technologies into education settings has transformed learning analytics to initiate personalized and adaptive learning. This paper presents the “Learner's Digital Twin," a framework for real-time learning analytics enabled by Fog Computing and Meta LLAMA (Large Language Model with Localized Adaptation) model to interpret multimodal data. The proposed new system aims to improve the learning process by providing students with instant, personalized alerts and feedback and insights for educators that can be acted upon. Fog Computing processes data at the network edge to reduce latency and deliver timely feedback, LLAMA can do context-aware text analysis of student’s processed data. This study would apply Linear Regression model for predictive analytics, and K-means clustering to cluster students based on learning patterns. Extensive empirical evaluation shows improved feedback relevance, system responsiveness and overall performance of the students. The findings demonstrate the ability of our Learner's Digital Twin framework to effectuate data-driven and adaptive learning environments enabling more efficient personalized learning with great fidelity as applied across a variety of educational settings.
Assessing the Effectiveness of Machine Learning in Enhancing Quantum Approximate Optimization Algorithm Performance
Authors: Ajlouni Naim 
Published: September 2024

Abstract

Quantum computing has recently emerged as a promising approach to solve complex combinatorial optimization problems beyond the reach of classical algorithms. This study explores the integration of machine learning techniques with the Quantum Approximate Optimization Algorithm (QAOA) to improve its effectiveness in graph-based optimization tasks, with a particular focus on the Max-Cut problem. We propose a hybrid framework that utilizes a neural network to dynamically adjust QAOA parameters, leveraging historical data to enhance performance. We conduct a comparative analysis between the machine learning-augmented QAOA and a traditional QAOA implementation. Through extensive simulations on random graphs with up to 2000 nodes, the machine learning-enhanced QAOA demonstrates superior performance, reducing training loss significantly and achieving a final loss of approximately 3.112 x 10-33 compared to 2.34 x 10-15 for the standard QAOA. Statistical analysis reveals a moderate negative correlation between training epochs and loss, highlighting the benefits of continued training while acknowledging other influencing factors. This research highlights the potential of combining quantum algorithms with machine learning to solve real-world optimization problems, offering promising applications in fields such as logistics, finance, and network design.
Influence of biodiesel and water dilution on ZDDP antiwear performance: an in-situ AFM study combined with macroscopic experiments
Authors: Jiang Shaoli , Yamana Théo , Yuan Yuyang , Adachi Koshi , Murashima Motoyuki 
Published: September 2024

Abstract

When compression-ignition (CI) engines are fueled by biodiesel, the engine oil is prone to the dilution of biodiesel and water due to the incomplete combustion, resulted by a higher boiling point than traditional petrodiesel. The dilution affects the performance of widely used oil additives, e.g., zinc dialkyldithiophosphate (ZDDP). In this study, its influence on tribological performance of ZDDP was investigated using two different scale tests: ball-on-disc friction tests of 1000 m sliding distance at 75 ℃ under boundary lubrication regime at macroscopic scale, atomic force microscopy (AFM) experiments with the in situ measurement on tribofilms formation at microscopic scale. Three main compositions of biodiesel: methyl oleate, methyl laurate and methyl palmitate were blended with ZDDP containing polyalphaolefin (PAO) base oil separately. Two water-diluted lubricants, namely oil-in-water (OW) and water-in-oil (WO) emulsions were also prepared. Results of ball-on-disc tests show that these fatty acid methyl esters (FAMEs) gave rise to improved lubricity and hence the reduction in both friction and wear. Despite no obvious difference in friction, OW had high volumes of wear while WO produced the wear track of high surface roughness. Results of AFM tests revealed that both FAMEs and water hindered solid-like ZDDP tribofilm formation and the lowest growth rate was observed in WO emulsion. Young’s modulus of all measured tribofilms were in a range of 50-150 GPa and the film formed in the presence of methyl palmitate showed significantly improved stiffness.
Boiling heat transfer by phase-field method
Authors: Alessio  
Published: September 2024

Abstract

In this work, we propose and test the validity of a phase-field method tailored specifically for modeling boiling phenomena. The method relies on numerical solutions of the Navier-Stokes equations coupled with a phase-field method and the energy equation. The continuity and Navier-Stokes equations have been modified introducing a source term that accounts for phase change. Likewise, in the con- servative Allen-Cahn equation (phase-field method) a source term that accounts for the volume is introduced. The system of governing equations is solved using a projection-correction method and equations are discretized using second-order finite difference approach. Thanks to the type of discretization employed, a con- stant coefficient Poisson equation for the pressure is obtained, which can be efficiently solved using FFT-based direct solvers. The proposed method is vali- dated against different benchmarks: an interface undergoing phase change at a constant rate, the Stefan problem, the adsorption problem and the growth of a 2D vapor bubble. For all the benchmarks, the present method well matches with analytical and archival literature results for a wide range of vapor-to-liquid den- sity ratios, from ρv /ρl = 1 down to ρv /ρl ≃ 5 × 10−4 (where ρv identifies the vapor density and ρl the liquid density).
Enhanced Inverse Design of Thin-Film High Reflectors Using a Mixture Density Network and Optimization Framework
Authors: Kumar Adna , Gupta Shrakajana , Patel Isha , Sharma Aarav , Desai Rohan 
Published: September 2024

Abstract

Deep learning (DL) has emerged as a promising tool for photonic inverse design. Despite initial successes in retrieving spectra of modest complexity with near-instantaneous readout, DL-assisted design methods often exhibit lower accuracy compared to advanced optimization techniques and have yet to demonstrate competitiveness in handling spectra of practical relevance. In this work, we introduce a tandem optimization model that integrates a mixture density network (MDN) and a fully connected (FC) network to perform inverse design of practical thin-film high reflectors. The multimodal nature of the MDN provides access to a vast array of candidate designs described by probability distributions, which are iteratively sampled and evaluated by the FC network to facilitate rapid optimization. We demonstrate that the proposed model effectively retrieves the reflectance spectra of 20-layer thin-film structures. Notably, it reproduces high reflectors with periodic structures derived from physical principles with high precision, despite the absence of such information in the training data. Additionally, the model is capable of producing improved designs with extended high-reflectance zones. Our approach combines the high-efficiency advantages of DL with optimization-enabled performance improvements, making it an efficient and on-demand solution for practical inverse design applications.
Hydro-abrasive erosion in Kaplan turbines: a case study
Authors: Arora Naman , Kumar Arun , Singal Sunil Kumar 
Published: September 2024

ABSTRACT
Sediment in the flowing water causes hydro-abrasive erosion of hydraulic turbines and other underwater components. In the present study, erosion quantification in the Kaplan turbine of a low-head hydropower plant (HPP) on the Upper Ganga Canal, is considered in the study. The study uses the guidelines of the International Electrotechnical Commission IEC 62364: 2019 for quantifying erosion in the Kaplan turbine. This work extends the research of Rai and Kumar (2016) and uses their primary details of some suspended sediment and operating parameters. The shape and mineral composition of the suspended sediment were quantified using dynamic imaging and X-ray diffraction techniques, respectively. The annual average particle load at the study HPP is obtained to be 356.87 kg·h/m3. Based on the analysis, it is estimated that the erosion depth varies from 0.88 to 1.37 mm/year at the blade tip, whereas from 2.40 to 3.74 mm/year at the blade outlet. At the runner chamber, the erosion depth varies from 1.27 to 1.98 mm/year. The erosion depth varies from 0.01 to 1.53 mm/year in the guide vanes. The calibration factor corresponding to the maximum erosion in guide vanes is estimated to be 1.75 × 10−5.
Biocatalytic Hydroxylation of Progesterone by Penicillium aculeatum
Authors: Nickavar Bahman , Mehmannavaz Maryam 
Published: September 2024

Background:
Biotransformation is a powerful process for producing steroid compounds,
and fungi are commonly regarded as effective biological agents for this purpose. They facilitate
reactions that are difficult to perform via conventional chemical methods.

Objective:
In the current study, the ability of Penicillium aculeatum to biotransform progesterone
was studied.

Methods:
Forty-eight hours after the incubation of active P. aculeatum with substrate (progesterone),
the reaction medium was extracted and chromatography methods isolated metabolites. The
chemical structures of the products were characterized by various spectroscopic techniques.

Results:
Two main hydroxylated products, 14α-hydroxyprogesterone, and 7α,14α-dihydroxyprogesterone,
were finally identified.

Conclusion:
P. aculeatum may be considered a functional biocatalyst for some biotransformation
processes.
Splenectomy as a Risk Factor for Graft Rejection Following Endothelial Transplantation: Retrospective Study
Authors: Kammrath Betancor Paola , Böhringer Daniel , Maier Philip , Lapp Thabo , Reinhard Thomas 
Published: September 2024

Background
Anterior chamber–associated immune deviation (ACAID) is an active immunotolerance mechanism, which is induced by placing antigen into the anterior eye chamber as long as a major surgical trauma is avoided. For this reason, ACAID may be a major contributor to the favorable immunologic outcomes in Descemet membrane endothelial keratoplasty (DMEK). Rodent models have demonstrated the importance of a functional spleen for the development of an ACAID.

Objective
This study aimed to investigate whether splenectomy leads to increased rejection rates after DMEK in humans.

Methods
A retrospective evaluation was conducted on the course following DMEK at the Eye Center, Medical Center, University of Freiburg, for patients with a self-reported history of splenectomy compared to patients without this condition. Potential study patients were contacted by mail. A questionnaire to self-report splenectomy and the time thereof was sent out. The medical records of all consenting patients at the Eye Center were reviewed for graft survival and immune reactions.

Results
We asked 1818 patients after DMEK to report their history of splenectomy. A total of 1340 patients responded and were included in the study. Of these 1340 patients, 16 (1.2%) reported a history of splenectomy (ie, 26 DMEKs, with 10 patients being transplanted in both eyes and 6 patients being transplanted in 1 eye; median age at surgery 73.7, range 66.7-76.1 y). The remaining patients (1324 patients, ie, 1941 eyes) served as controls, with 1941 DMEKs (median age at surgery 71.5, range 64.1-77.2 y). Five (19%) out of the 26 eyes from the splenectomy group required a second transplant due to dislocation (n=2.8%), failure (n=2.8%), and rejection (n=1.4%). Kaplan-Meier analysis revealed no relevant difference compared with controls.

Conclusions
Our results suggest that splenectomy has no major effect on the outcome following DMEK. Subsequent, ACAID may not be the main reason for the favorable immunological outcomes in DMEK, or the camero-splenic axis may be subordinate in humans. However, we only included 16 patients who underwent splenectomy, so it might be possible that we missed a minor effect.