bluuebunny/crossref_metadata_2025_split huggingface dataset:
Electroanalytical analysis of phenol oxidation using bacteria immobilized by a polycaprolactone coating on the copper electrode surface
Authors: Moutcine Abdelaziz , Laghlimi Charaf , Ziat Younes , Isaad Jalal , El Bahraoui Soumia , Chtaini Abdelilah 
Published: June 2024

AbstractThe copper electrode modified by bacteria immobilised by a polycaprolactone film was successfully developed by electropolymerisation for the purpose of determining the presence of phenol. Electrochemical techniques such as square-wave voltammetry (SWV), cyclic voltammetry (CV) and electrochemical impedance spectroscopy (EIS) were used to characterize the electrochemical properties of the Cu-polymer/bacteria electrode. The results show that the intensity of the phenol oxidation peak increases with concentration, allowing us to obtain good analytical results with DL of 2.156 × 10–7 M and QL which is 7.2 × 10–7 M , confirming that the biosensor has excellent electroanalytical activity for phenol oxidation, with good stability and a wide linear range. Our electrode is based on a easily available and inexpensive material, as well as on its simple preparation, which has demonstrated high performance for phenol.
Impacts of thyme and/or garlic oils on growth, immunity, antioxidant and net farm income in Damascus goats
Authors: Imbabi Tharwat , Hassan Tamer M. M. , Osman Ali , El Aziz Ayman H. Abd , Tantawi Abuelkassem A. , Nasr Mohammed A. F. 
Published: June 2024

AbstractThis study aimed to evaluate the impact of thyme and/or garlic oil administration on growth performance, immunity, antioxidant, biochemical parameters, and net farm income of Damascus goats. Forty weaned Damascus goats were allocated into four groups. The first group was the control without oral administration, while the 2nd (Th), 3rd (Gr), and 4th (ThGr) groups were orally administrated by (2 ml/goat/day) of thyme oil, garlic oil and their mixture (1:1), respectively during the whole experiment period. The final body weight of goats orally administered oil mixture was the heaviest group, it was 10, 4.5 and 3.5% than the control, Th. and Gr. groups, respectively with better feed conversion ratio and high net farm income. Goats of ThGr. group revealed the best immunity, antioxidant and general health condition than the control group with 50% reduction of MDA. Liver (AST, 33% and ALT, 38%) and kidney (creatinine, 88%) functions improved by oils mixtures orally administration compared with the control group. LDL, triglyceride and cholesterol were reduced by 47, 33 and 21% compared with the control group, respectively. Thus, mixture oil administration (thyme and garlic at the ratio of 1:1, 2 ml/goat/day) improved growth (10%), antioxidant status (MDA 50%), liver (AST, 33% and ALT, 38%), kidney function (creatinine, 88%), the FCR (17.4%) and net farm income (21%), of Damascus goats.
Enhanced efficiency of bifacial perovskite solar cells using computational study
Authors: Hossain Mohammad Istiaque , Chelvanathan Puvaneswaran , Khandakar Amith , Thomas Kevin , Rahman Ahasanur , Mansour Said 
Published: June 2024

AbstractThe most rapidly expanding type of solar cells are the Perovskite Solar Cells (PSCs), because of its high device performance, ease of synthesis, high open-circuit voltage, and affordability. Despite these advantages, the development of perovskite-based solar cells continues to be impeded by the issues with perovskite stability and the utilization of the hazardous heavy element lead (Pb). The study emphasizes on the bifacial structure that maintains the conventional absorber layer and electron transport layer (ETL) in the optimized PSC structure. This study employs SCAPS software for device simulation to comprehensively analyze how various parameters affect the performance of solar cells. Additionally, doping concentration variation in both ETL and HTL are explored. The simulation reveals that changing device structure from monofacial to bifacial significantly influences PSC performance, demonstrating that optimizing individual layers effectively enhances overall solar cell performance. The optimized structure achieves impressive PSC performance metrics through parametric analysis, such as voltage (VOC) of 1.18 V, fill factor (FF) of 82.24%, current density (JSC) of 27.12 mA/cm2, power conversion efficiency (PCE) of 27.90% for an incident solar spectrum from the ETL side, and power conversion efficiency (PCE) of 19.86% for an incident solar spectrum from the HTL side, the calculated bifaciality factor (BF) for this structure is 71.18%.
BCG priming followed by a novel interleukin combination activates Natural Killer cells to selectively proliferate and become anti-tumour long-lived effectors
Authors: Felgueres María-José , Esteso Gloria , García-Jiménez Álvaro F. , Dopazo Ana , Aguiló Nacho , Mestre-Durán Carmen , Martínez-Piñeiro Luis , Pérez-Martínez Antonio , Reyburn Hugh T. , Valés-Gómez Mar 
Published: June 2024

AbstractThe short-lived nature and heterogeneity of Natural Killer (NK) cells limit the development of NK cell-based therapies, despite their proven safety and efficacy against cancer. Here, we describe the biological basis, detailed phenotype and function of long-lived anti-tumour human NK cells (CD56highCD16+), obtained without cell sorting or feeder cells, after priming of peripheral blood cells with Bacillus Calmette-Guérin (BCG). Further, we demonstrate that survival doses of a cytokine combination, excluding IL18, administered just weekly to BCG-primed NK cells avoids innate lymphocyte exhaustion and leads to specific long-term proliferation of innate cells that exert potent cytotoxic function against a broad range of solid tumours, mainly through NKG2D. Strikingly, a NKG2C+CD57-FcεRIγ+ NK cell population expands after BCG and cytokine stimulation, independently of HCMV serology. This strategy was exploited to rescue anti-tumour NK cells even from the suppressor environment of cancer patients’ bone marrow, demonstrating that BCG confers durable anti-tumour features to NK cells.
A novel smart hybrid multimorph piezoelectric spherical shell cloak for broadband near-perfect underwater acoustic camouflage applications
Authors: Hasheminejad Seyyed M. , Kasaeisani Ali 
Published: June 2024

AbstractNon-visual auditory camouflage plays a major role in the art of underwater deception. In this work, a hybrid active/semi-active omnidirectional cloaking shell structure composed of alternate complementary piezoelectric and smart viscoelastic (PZT/SVE) actuator layers is proposed that can effectively conceal a three dimensional underwater macroscopic object from broadband incident sound waves. The smart hybrid structure incorporates a finite sequence of fully active parallel-connected multimorph PZT constraining layers inter-stacked with semi-active SVE core layers both of which are collaboratively operative in the framework of a Particle Swarm Optimized (PSO) multiple-input multiple-output active damping control (MIMO-ADC) scheme. The elasto-acoustic modeling of the problem is conducted by coupling the spatial state space methodology based on the classical three-dimensional exact piezoelasticity theory with the wave equations for the inner and outer acoustic domains. The acoustic cloaking performance of proposed configuration is evaluated for four distinct classes of highly functional SVE interlayer materials with tunable (field-dependent) rheological properties, namely, magnetorheological elastomer (MRE), shape memory polymer (SMP), electrorheological fluid (ERF), and magnetorheological shear thickening polishing fluid (MRSTPF). Extensive numerical results reveal significant broadband reductions of the far-field backscattering amplitude in the ($$\left|{f}_{\infty }\left(\theta =\pi ,{k}_{\text{ex}}{R}_{\text{ex }}\right)\right|)$$

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by incorporating a sufficient number of smart multimorph PZT/SVE material layers. Furthermore, it is concluded that comparable low frequency acoustic cloaking effects is possible without expenditure of any external energy just by employing the entirely inactive MRSTPF-based cloak as an alternative to the semiactive or fully active multimorph PZT/SVE cloaks. The outcome of proposed study can advantageously serve as the first step towards practical development and experimental implementation of future high performance smart acoustic cloaking devices with expanded broadband near-perfect omnidirectional invisibility for three dimensional objects of diverse geometries.
Unveiling bacterial communication with a MATLAB GUI implementing the diffusion-based quorum sensing model
Authors: Singh Urvashi , Saifi Zeeshan , Tirumalai Prem Saran , Krishnananda Soami Daya 
Published: June 2024

AbstractBacteria employ quorum sensing as a remarkable mechanism for coordinating behaviors and communicating within their communities. In this study, we introduce a MATLAB Graphical User Interface (GUI) that offers a versatile platform for exploring the dynamics of quorum sensing. Our computational framework allows for the assessment of quorum sensing, the investigation of parameter dependencies, and the prediction of minimum biofilm thickness required for its initiation. A pivotal observation from our simulations underscores the pivotal role of the diffusion coefficient in quorum sensing, surpassing the influence of bacterial cell dimensions. Varying the diffusion coefficient reveals significant fluctuations in autoinducer concentration, highlighting its centrality in shaping bacterial communication. Additionally, our GUI facilitates the prediction of the minimum biofilm thickness necessary to trigger quorum sensing, a parameter contingent on the diffusion coefficient. This feature provides valuable insights into spatial constraints governing quorum sensing initiation. The interplay between production rates and cell concentrations emerges as another critical facet of our study. We observe that higher production rates or cell concentrations expedite quorum sensing, underscoring the intricate relationship between cell communication and population dynamics in bacterial communities. While our simulations align with mathematical models reported in the literature, we acknowledge the complexity of living organisms, emphasizing the value of our GUI for standardizing results and facilitating early assessments of quorum sensing. This computational approach offers a window into the environmental conditions conducive to quorum sensing initiation, encompassing parameters such as the diffusion coefficient, cell concentration, and biofilm thickness. In conclusion, our MATLAB GUI serves as a versatile tool for understanding the diverse aspects of quorum sensing especially for non-biologists. The insights gained from this computational framework advance our understanding of bacterial communication, providing researchers with the means to explore diverse ecological contexts where quorum sensing plays a pivotal role.
Developing a three stage coordinated approach to enhance efficiency and reliability of virtual power plants
Authors: Amissah Jeremiah , Abdel-Rahim Omar , Mansour Diaa-Eldin A. , Bajaj Mohit , Zaitsev Ievgen , Abdelkader Sobhy 
Published: June 2024

AbstractA Virtual Power Plant (VPP) is a centralized energy system that manages, and coordinates distributed energy resources, integrating them into a unified entity. While the physical assets may be dispersed across various locations, the VPP integrates them into a virtual unified entity capable of responding to grid demands and market signals. This paper presents a tri-level hierarchical coordinated operational framework of VPP. Firstly, an Improved Pelican Optimization Algorithm (IPOA) is introduced to optimally schedule Distributed Energy Resources (DERs) within the VPP, resulting in a significant reduction in generation costs. Comparative analysis against conventional algorithms such as Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) demonstrates IPOA's superior performance, achieving an average reduction of 8.5% in generation costs across various case studies. The second stage focuses on securing the optimized generation data from rising cyber threats, employing the capabilities of machine learning, preferably, a convolutional autoencoder to learn the normal patterns of the optimized data to detect deviations from the optimized generation data to prevent suboptimal decisions. The model exhibits exceptional performance in detecting manipulated data, with a False Positive Rate (FPR) of 1.92% and a Detection Accuracy (DA) of 98.06%, outperforming traditional detection techniques. Lastly, the paper delves into the dynamic nature of the day ahead market that the VPP participates in. In responding to the grid by selling its optimized generated power via the day-ahead market, the VPP employs the Prophet model, another machine learning technique to forecast the spot market price for the day-ahead to mitigate the adverse effects of price volatility. By utilizing Prophet forecasts, the VPP achieves an average revenue increase of 15.3% compared to scenarios without price prediction, emphasizing the critical role of predictive analytics in optimizing economic gains. This tri-level coordinated approach adopted addresses key challenges in the energy sector, facilitating progress towards achieving universal access to clean and affordable energy.
Auditory gamma-band entrainment enhances default mode network connectivity in dementia patients
Authors: Lahijanian Mojtaba , Aghajan Hamid , Vahabi Zahra 
Published: June 2024

AbstractDementia, and in particular Alzheimer’s disease (AD), can be characterized by disrupted functional connectivity in the brain caused by beta-amyloid deposition in neural links. Non-pharmaceutical treatments for dementia have recently explored interventions involving the stimulation of neuronal populations in the gamma band. These interventions aim to restore brain network functionality by synchronizing rhythmic energy through various stimulation modalities. Entrainment, a newly proposed non-invasive sensory stimulation method, has shown promise in improving cognitive functions in dementia patients. This study investigates the effectiveness of entrainment in terms of promoting neural synchrony and spatial connectivity across the cortex. EEG signals were recorded during a 40 Hz auditory entrainment session conducted with a group of elderly participants with dementia. Phase locking value (PLV) between different intraregional and interregional sites was examined as an attribute of network synchronization, and connectivity of local and distant links were compared during the stimulation and rest trials. Our findings demonstrate enhanced neural synchrony between the frontal and parietal regions, which are key components of the brain’s default mode network (DMN). The DMN operation is known to be impacted by dementia’s progression, leading to reduced functional connectivity across the parieto-frontal pathways. Notably, entrainment alone significantly improves synchrony between these DMN components, suggesting its potential for restoring functional connectivity.
A fully automated classification of third molar development stages using deep learning
Authors: Milani Omid Halimi , Atici Salih Furkan , Allareddy Veerasathpurush , Ramachandran Vinitha , Ansari Rashid , Cetin Ahmet Enis , Elnagar Mohammed H. 
Published: June 2024

AbstractAccurate classification of tooth development stages from orthopantomograms (OPG) is crucial for dental diagnosis, treatment planning, age assessment, and forensic applications. This study aims to develop an automated method for classifying third molar development stages using OPGs. Initially, our data consisted of 3422 OPG images, each classified and curated by expert evaluators. The dataset includes images from both Q3 (lower jaw left side) and Q4 (lower right side) regions extracted from panoramic images, resulting in a total of 6624 images for analysis. Following data collection, the methodology employs region of interest extraction, pre-filtering, and extensive data augmentation techniques to enhance classification accuracy. The deep neural network model, including architectures such as EfficientNet, EfficientNetV2, MobileNet Large, MobileNet Small, ResNet18, and ShuffleNet, is optimized for this task. Our findings indicate that EfficientNet achieved the highest classification accuracy at 83.7%. Other architectures achieved accuracies ranging from 71.57 to 82.03%. The variation in performance across architectures highlights the influence of model complexity and task-specific features on classification accuracy. This research introduces a novel machine learning model designed to accurately estimate the development stages of lower wisdom teeth in OPG images, contributing to the fields of dental diagnostics and treatment planning.
Study on the ability of indoor plants to absorb and purify benzene pollution
Authors: Li Donghe , Wang Han , Gao Qingyu , Lu Min 
Published: June 2024

AbstractThe ability of indoor plants to purify benzene pollution is the basic basis for the selection of plants for ecological remediation of indoor benzene pollution. In this study, the purification rate and the purification amount per unit leaf area of 13 test plants at three benzene concentrations were determined by indoor fumigation experiments, and the benzene absorption and purification abilityability of indoor plants were comprehensively evaluated. The results showed that (1) there was a significant correlation between benzene concentration and purification rate and purification amount per unit leaf area. (2) At the three concentrations, Spathiphyllum floribundum showed the highest purification rate and Sansevieria trifasciata var. laurentii showed the highest purification per unit leaf area. (3) The combined results showed that Sansevieria trifasciata var. laurentii, Spathiphyllum floribundum and Aloe arborescens were the strongest absorbers and purifiers, while Podocarpus nagi and Anthurium andraeanum ‘Pink champin’ had the weakest absorption and purification capacity. The results of this study provide a theoretical basis and reference for the selection of plants with strong capacities to adsorb and purify benzene pollution in indoor air.
Saccharomyces boulardii combined with triple therapy alter the microbiota in the eradication of Helicobacter pylori infection
Authors: Zhang Yan , Lu Bingyun , Dong Yu , Zhang Yifeng , Du Qianming , Chen Ye , Zhang Zhenyu 
Published: June 2024

AbstractTo assess the effectiveness and safety of combining Saccharomyces boulardii powder with triple therapy as a primary approach for eradicating H. pylori infection, a total of 144 patients who tested positive for H. pylori and diagnosed with non-ulcer dyspepsia underwent endoscopy at two national centers between June 2017 and March 2019 were included. The patients were categorized into three groups using a subsection randomization method and received initial H. pylori eradication treatments. Microbial composition, eradication rates, symptom alleviation, and adverse reactions were monitored on the 14th and 44th days post-treatment. According to PP analysis showed the eradication rates for the SRAC group was 75%, BRAC was 93.18% and RAC was 65.2%. Group BRAC exhibited a marginally higher eradication rate compared to other groups. However, patients receiving Saccharomyces boulardii treatment exhibited an overall reduction in initial dyspepsia symptoms by the end of the treatment period. When employed as a primary strategy, the combination of Saccharomyces boulardii powder with triple therapy displayed notable efficacy and smaller gastrointestinal side effects in eradicating initial H. pylori infections among non-ulcer dyspepsia patients. Moreover, this approach demonstrated advantages in alleviating symptoms, exhibited favorable tolerance, and maintained a high level of clinical safety.
Assessment of sustainable baits for passive fishing gears through automatic fish behavior recognition
Authors: Abangan Alexa Sugpatan , Bürgi Kilian , Méhault Sonia , Deroiné Morgan , Kopp Dorothée , Faillettaz Robin 
Published: June 2024

AbstractLow-impact fishing gear, such as fish pots, could help reduce human’s impact on coastal marine ecosystems in fisheries but catch rates remain low and the harvest of resources used for baiting increases their environmental cost. Using black seabreams (Spondyliosoma cantharus) as target species in the Bay of Biscay, we developed and assessed the efficiency of biodegradable biopolymer-based baits (hereafter bio-baits) made of cockles (Cerastoderma edule) and different biopolymer concentrations. Through a suite of deep and machine learning models, we automatized both the tracking and behavior classification of seabreams based on quantitative metrics describing fish motion. The models were used to predict the interest behavior of seabream towards the bait over 127 h of video. All behavior predictions categorized as interested to the bait were validated, highlighting that bio-baits have a much weaker attractive power than natural bait yet with higher activity after 4 h once natural baits have been consumed. We also show that even with imperfect tracking models, fine behavioral information can be robustly extracted from video footage through classical machine learning methods, dramatically lifting the constraints related to monitoring fish behavior. This work therefore offers new perspectives both for the improvement of bio-baits and automatic fish behavior recognition.
Weighted single-step genome-wide association study for direct and maternal genetic effects associated with birth and weaning weights in sheep
Authors: Khazaei-Koohpar Hava , Gholizadeh Mohsen , Hafezian Seyed Hasan , Esmaeili-Fard Seyed Mehdi 
Published: June 2024

AbstractBody weight is an important economic trait for sheep meat production, and its genetic improvement is considered one of the main goals in the sheep breeding program. Identifying genomic regions that are associated with growth-related traits accelerates the process of animal breeding through marker-assisted selection, which leads to increased response to selection. In this study, we conducted a weighted single-step genome-wide association study (WssGWAS) to identify potential candidate genes for direct and maternal genetic effects associated with birth weight (BW) and weaning weight (WW) in Baluchi sheep. The data used in this research included 13,408 birth and 13,170 weaning records collected at Abbas-Abad Baluchi Sheep Breeding Station, Mashhad-Iran. Genotypic data of 94 lambs genotyped by Illumina 50K SNP BeadChip for 54,241 markers were used. The proportion of variance explained by genomic windows was calculated by summing the variance of SNPs within 1 megabase (Mb). The top 10 window genomic regions explaining the highest percentages of additive and maternal genetic variances were selected as candidate window genomic regions associated with body weights. Our findings showed that for BW, the top-ranked genomic regions (1 Mb windows) explained 4.30 and 4.92% of the direct additive and maternal genetic variances, respectively. The direct additive genetic variance explained by the genomic window regions varied from 0.31 on chromosome 1 to 0.59 on chromosome 8. The highest (0.84%) and lowest (0.32%) maternal genetic variances were explained by genomic windows on chromosome 10 and 17, respectively. For WW, the top 10 genomic regions explained 6.38 and 5.76% of the direct additive and maternal genetic variances, respectively. The highest and lowest contribution of direct additive genetic variances were 1.37% and 0.42%, respectively, both explained by genomic regions on chromosome 2. For maternal effects on WW, the highest (1.38%) and lowest (0.41%) genetic variances were explained by genomic windows on chromosome 2. Further investigation of these regions identified several possible candidate genes associated with body weight. Gene ontology analysis using the DAVID database identified several functional terms, such as translation repressor activity, nucleic acid binding, dehydroascorbic acid transporter activity, growth factor activity and SH2 domain binding.