Synergistic integration of Multi-View Brain Networks and advanced machine learning techniques for auditory disorders diagnostics.

In the field of audiology, achieving accurate discrimination of auditory impairments remains a formidable challenge. Conditions such as deafness and tinnitus exert a substantial impact on patients' overall quality of life, emphasizing the urgent need for precise and efficient classification methods. This study introduces an innovative approach, utilizing Multi-View Brain Network data acquired from three distinct cohorts: 51 deaf patients, 54 with tinnitus, and 42 normal controls. Electroencephalogram (EEG) recording data were meticulously collected, focusing on 70 electrodes attached to an end-to-end key with 10 regions of interest (ROI). This data is synergistically integrated with machine learning algorithms. To tackle the inherently high-dimensional nature of brain connectivity data, principal component analysis (PCA) is employed for feature reduction, enhancing interpretability. The proposed approach undergoes evaluation using ensemble learning techniques, including Random Forest, Extra Trees, Gradient Boosting, and CatBoost. The performance of the proposed models is scrutinized across a comprehensive set of metrics, encompassing cross-validation accuracy (CVA), precision, recall, F1-score, Kappa, and Matthews correlation coefficient (MCC). The proposed models demonstrate statistical significance and effectively diagnose auditory disorders, contributing to early detection and personalized treatment, thereby enhancing patient outcomes and quality of life. Notably, they exhibit reliability and robustness, characterized by high Kappa and MCC values. This research represents a significant advancement in the intersection of audiology, neuroimaging, and machine learning, with transformative implications for clinical practice and care.

HDSNE a new unsupervised multiple image database fusion learning algorithm with flexible and crispy production of one database: a proof case study of lung infection diagnose In chest X-ray images.

Continuous release of image databases with fully or partially identical inner categories dramatically deteriorates the production of autonomous Computer-Aided Diagnostics (CAD) systems for true comprehensive medical diagnostics. The first challenge is the frequent massive bulk release of medical image databases, which often suffer from two common drawbacks: image duplication and corruption. The many subsequent releases of the same data with the same classes or categories come with no clear evidence of success in the concatenation of those identical classes among image databases. This issue stands as a stumbling block in the path of hypothesis-based experiments for the production of a single learning model that can successfully classify all of them correctly. Removing redundant data, enhancing performance, and optimizing energy resources are among the most challenging aspects. In this article, we propose a global data aggregation scale model that incorporates six image databases selected from specific global resources. The proposed valid learner is based on training all the unique patterns within any given data release, thereby creating a unique dataset hypothetically. The Hash MD5 algorithm (MD5) generates a unique hash value for each image, making it suitable for duplication removal. The T-Distributed Stochastic Neighbor Embedding (t-SNE), with a tunable perplexity parameter, can represent data dimensions. Both the Hash MD5 and t-SNE algorithms are applied recursively, producing a balanced and uniform database containing equal samples per category: normal, pneumonia, and Coronavirus Disease of 2019 (COVID-19). We evaluated the performance of all proposed data and the new automated version using the Inception V3 pre-trained model with various evaluation metrics. The performance outcome of the proposed scale model showed more respectable results than traditional data aggregation, achieving a high accuracy of 98.48%, along with high precision, recall, and F1-score. The results have been proved through a statistical t-test, yielding t-values and p-values. It's important to emphasize that all t-values are undeniably significant, and the p-values provide irrefutable evidence against the null hypothesis. Furthermore, it's noteworthy that the Final dataset outperformed all other datasets across all metric values when diagnosing various lung infections with the same factors.

Novel Fluorinated Biphenyl Compounds Synthesized via Pd(0)-Catalyzed Reactions: Experimental and Computational Studies.

Five new difluorinated biphenyl compounds, 4'-(tert-butyl)-3,4-difluoro-1,1'-biphenyl (TBDFBP), 1-(3',4'-difluoro-[1,1'-biphenyl]-4-yl)ethanone (DFBPE), 3',4'-difluoro-2,5-dimethoxy-1,1'-biphenyl (DFDMBP), 3,4-difluoro-3'-nitro-1,1'-biphenyl (DFNBP), and (3',4'-difluoro-[1,1'-biphenyl]-3-yl)(methyl)sulfane (DFBPMS), have been successfully synthesized by the well-known Suzuki-Miyaura coupling with excellent yields averaging 78%. UV-visible, Fourier transform infrared ,and 13C NMR and 1H NMR spectroscopies along with single-crystal X-ray diffraction (SC-XRD) analysis (for TBDFBP and DFBPE) were used for the structure elucidation of the synthesized compounds. The SC-XRD results demonstrated the crystal systems of the studied compounds, TBDFBP and DFBPE, to be monoclinic, and their space groups were found to be P21/c. Also, a detailed density functional theory study was performed. The calculated structures for TBDFBP and DFBPE were found to agree quite well with the experimental results. The natural bonding orbital charge analysis suggested that molecules of these compounds should interact quite noticeably with each other in the solid phase and with polar solvent molecules. Molecular electrostatic potential analysis suggests that accumulation of positive and negative potential implies possibility of quite significant dipole-dipole intermolecular interactions in crystals of these compounds, as well as quite strong interactions with polar solvent molecules. The global reactivity parameters analysis suggests all compounds to be quite stable in redox reactions, with the compound DFNBP being relatively more reactive and the compounds TBDFBP and DFDMBP being relatively more stable.

Atomistic to Mesoscopic Modelling of Thermophysical Properties of Graphene-Reinforced Epoxy Nanocomposites.

This research addresses the need for a multiscale model for the determination of the thermophysical properties of nanofiller-enhanced thermoset polymer composites. Specifically, we analyzed the thermophysical properties of an epoxy resin containing bisphenol-A diglyceryl ether (DGEBA) as an epoxy monomer and dicyandiamide (DICY) and diethylene triamine (DETA) as cross-linking agents. The cross-linking process occurs at the atomistic scale through the formation of bonds among the reactive particles within the epoxy and hardener molecules. To derive the interatomic coarse-grained potential for the mesoscopic model and match the density of the material studied through atomic simulations, we employed the iterative Boltzmann inversion method. The newly developed coarse-grained molecular dynamics model effectively reproduces various thermophysical properties of the DGEBA-DICY-DETA resin system. Furthermore, we simulated nanocomposites made of the considered epoxy additivated with graphene nanofillers at the mesoscopic level and verified them against continuum approaches. Our results demonstrate that a moderate amount of nanofillers (up to 2 wt.%) increases the elastic modulus and thermal conductivity of the epoxy resin while decreasing the Poisson's ratio. For the first time, we present a coarse-grained model of DGEBA-DICY-DETA/graphene materials, which can facilitate the design and development of composites with tunable thermophysical properties for a potentially wide range of applications, e.g., automotive, aerospace, biomedical, or energy ones.

The impact of physicochemical features of carbon electrodes on the capacitive performance of supercapacitors: a machine learning approach.

Hybrid electric vehicles and portable electronic systems use supercapacitors for energy storage owing to their fast charging/discharging rates, long life cycle, and low maintenance. Specific capacitance is regarded as one of the most important performance-related characteristics of a supercapacitor's electrode. In the current study, Machine Learning (ML) algorithms were used to determine the impact of various physicochemical properties of carbon-based materials on the capacitive performance of electric double-layer capacitors. Published experimental datasets from 147 references (4899 data entries) were extracted and then used to train and test the ML models, to determine the relative importance of electrode material features on specific capacitance. These features include current density, pore volume, pore size, presence of defects, potential window, specific surface area, oxygen, and nitrogen content of the carbon-based electrode material. Additionally, categorical variables as the testing method, electrolyte, and carbon structure of the electrodes are considered as well. Among five applied regression models, an extreme gradient boosting model was found to best correlate those features with the capacitive performance, highlighting that the specific surface area, the presence of nitrogen doping, and the potential window are the most significant descriptors for the specific capacitance. These findings are summarized in a modular and open-source application for estimating the capacitance of supercapacitors given, as only inputs, the features of their carbon-based electrodes, the electrolyte and testing method. In perspective, this work introduces a new wide dataset of carbon electrodes for supercapacitors extracted from the experimental literature, also giving an instance of how electrochemical technology can benefit from ML models.

Design of Nickel Supported Hierarchical ZSM-5/USY Zeolite Bifunctional Catalysts for One-Pot Menthol Synthesis via Liquid-Phase Citral Hydrogenation.

Nickel-supported hierarchical zeolite catalysts were prepared through a desilication reassembly process under optimized conditions and applied in one-pot menthol synthesis. In this work, the hierarchical zeolite-supported metal bifunctional catalysts were prepared with the help of desilication re-assembly and wetness impregnation techniques and applied in menthol synthesis via citral hydrogenation. The prepared catalysts were characterized using PXRD, BET, FE-TEM, NH3-TPD, H2-TPR, pyridine adsorption, and ICP-OES techniques. As a result, the physicochemical and acidic properties, such as mesopore surface area, metal dispersion, acidity, catalytic activity, and strong Lewis acid sites of pure microporous ZSM-5/USY zeolites, were significantly improved. Consequently, with the occurrence of superior physicochemical and acidic properties, the Ni/HZ-0.5 M catalyst exhibited outstanding catalytic activity (100% conversion, TOF 7.12 h-1) and menthol selectivity (83%, 4 h) with uniform stability at 100 °C, 1.0 MPa hydrogen. Similarly, the cracking rate decreased with the decrease in Bronsted acid sites.

Genetic characterization of hepatitis B virus genotypes among patients with chronic infection in Sulaimaniyah city, Iraq.

Background: Hepatitis B virus (HBV) genotypes are distributed unevenly throughout the world's regions. The researchers' goal in this study was to find out which HBV genotypes are now prevalent in the blood of chronic HBV patients in Iraq's Kurdistan Region's Sulaimaniyah governorate. Methods: Genotyping was carried out utilizing Polymerase Chain Reaction (PCR) type-specified primers. Thirty-three chronic HBV patients were included in the HBV genotyping assay. Phylogenic trees of Pre-S1/Pre S2/S genes' nucleotide sequences were constructed using 36 HBV isolates. Results: All the patients had HBV genotype D. Additionally, two samples were further analyzed by sequencing and deposited in GenBank as HBV/Sul-1/2021 accession numbers MZ077051 and HBV/Sul-2/2021 accession numbers MZ077052. Phylogenic analysis indicated that the HBV isolates belong to sub-genotype D1/serotype ayw2. The HBV/Sul-2/2021 had two sequence deletion mutations from G61del-T87del, which accounted for 27 amino acid deletions, and ten other mutations were identified in the carboxylic terminus of the pre-S1 from Q104del-R113del. Accordingly, 37 amino acids were deleted in the S promoter region. Several other substitution mutations were recorded in both HBV isolates. Conclusion: Patients with chronic HBV were found to have the HBV sub-genotype D1/subtype ayw2 with no mixed genotypes. HBV/Sul-1/2022, a new strain with a 37-amino acid mutation, was found to be distinct from any previously known HBV isolates.

Mesoscopic Modeling and Experimental Validation of Thermal and Mechanical Properties of Polypropylene Nanocomposites Reinforced By Graphene-Based Fillers.

The development of nanocomposites relies on structure-property relations, which necessitate multiscale modeling approaches. This study presents a modeling framework that exploits mesoscopic models to predict the thermal and mechanical properties of nanocomposites starting from their molecular structure. In detail, mesoscopic models of polypropylene (PP)- and graphene-based nanofillers (graphene (Gr), graphene oxide (GO), and reduced graphene oxide (rGO)) are considered. The newly developed mesoscopic model for the PP/Gr nanocomposite provides mechanistic information on the thermal and mechanical properties at the filler-matrix interface, which can then be exploited to enhance the prediction accuracy of traditional continuum simulations by calibrating the thermal and mechanical properties of the filler-matrix interface. Once validated through a dedicated experimental campaign, this multiscale model demonstrates that with the modest addition of nanofillers (up to 2 wt %), the Young's modulus and thermal conductivity show up to 35 and 25% enhancement, respectively, whereas the Poisson's ratio slightly decreases. Among the different combinations tested, the PP/Gr nanocomposite shows the best mechanical properties, whereas PP/rGO demonstrates the best thermal conductivity. This validated mesoscopic model can contribute to the development of smart materials with enhanced mechanical and thermal properties based on polypropylene, especially for mechanical, energy storage, and sensing applications.

The Relationship Between Fear-Avoidance Beliefs, Disability, and Physical Capacity in Patients with Chronic Low Back Pain.

Objective: The objective of this study was to document the multi-dimensional profile of patients referring with chronic low back pain (CLBP) and determine the relationship among fear-avoidance belief and disability and physical capacity in the Pakistani population. Methods: A cross-sectional survey was conducted at the Physiotherapy Department of the Institute of Physical Medicine and Rehabilitation, Dow University of Health Sciences. Consecutive sampling was used among the population of CLBP, and objective measures were fear-avoidance beliefs (FABQ-total) and Roland Morris questionnaire (RMDQ) and pain. Each participant performed a physical capacity test, which included a 6-minute walk test (6MWT), abdominal muscular endurance (AME), hand grip strength (HGS), and Functional reach test (FRT). Result: Of the 136 participants, 70(51.5%) were males. The mean and SD of the tests performed were: 6MWT (487.97±51.46), AME (9.31±4.68), FRT (35.14±2.79), HGS (33.31±14.55), VAS (5.51±1.27), RMDQ (18.25±2.17), FABQ-total (48.18±5.31). Multivariate regression revealed that symptom duration, FRT, AME, HGS, VAS, RMDQ were all found to be associated with fear-avoidance belief, accounting for 60% (adjusted R2 = 0.60) of variability. Likewise, only 6-MWT, FRT, FABQ-total were the significant predictors of disability (adjusted R2=0.282). Conclusion: It has been demonstrated that sub-maximal exercise capacity, strength, endurance, pain and flexibility are the contributing factors for the change in disability and fear-avoidance beliefs among the individuals with CLBP. The findings of this study could have inference for increasing productivity both at individual and organizational levels by formulating effective strategies to manage CLBP.

Genetic characterization of highly pathogenic avian influenza A (H5N8) virus isolated from domestic geese in Iraq, 2018.

BACKGROUND: Influenza viruses are a continuous threat to avian and mammalian species, causing epidemics and pandemics. After the circulation of H5N1 in 2006, 2015, and 2016 in Iraq, an H5N8 influenza virus emerged in domestic geese in Sulaymaniyah Province, Iraq. This study analyzed the genetic characteristics of the Iraqi H5N8 viruses. RESULTS: An HPAI virus subtype H5N8 was identified from domestic backyard geese in the Kurdistan Region, north Iraq. Phylogenic analyses of the hemagglutinin (HA) and neuraminidase (NA) genes indicated that Iraq H5N8 viruses belonged to clade 2.3.4.4 group B and clustered with isolates from Iran, Israel, and Belgium. Genetic analysis of the HA gene indicated molecular markers for avian-type receptors. Characterization of the NA gene showed that the virus had sensitive molecular markers for antiviral drugs. CONCLUSIONS: This is the first study ever on H5N8 in Iraq, and it is crucial to understand the epidemiology of the viruses in Iraq and the Middle East. The results suggest a possible role of migratory birds in the introduction of HPAI subtype H5N8 into Iraq.

Experimental investigations of arsenic adsorption from contaminated water using chemically activated hematite (Fe2O3) iron ore.

Indigenous hematite iron ore was chemically activated as a function of various hydrogen peroxide concentrations (0.5, 1, 1.5, and 2.0 M), activation time, and iron ore size. Adsorption potential was evaluated at various initial arsenic concentrations, contact time, adsorbent dose, and particle size. Maximum 95% removal efficiency was achieved at 600-µm size of iron ore, activated with 0.5 M concentration of hydrogen peroxide at 24 h of activation time. The experimental data were further evaluated through Langmuir and Freundlich isotherms. The maximum 14.46 mg/g of adsorption capacity was observed through Langmuir isotherm. Moreover, adsorption kinetics was evaluated using pseudo-first-order and pseudo-second-order kinetics, and the intra-particle diffusion model. The kinetics of arsenic adsorption was best described by using the pseudo-first-order kinetics with a kinetic rate of 0.621 min-1. The hematite iron ore before and after arsenic adsorption was characterized by XRD, SEM, and EDX.

Aminolevulinic acid and nitric oxide regulate oxidative defense and secondary metabolisms in canola (Brassica napus L.) under drought stress.

To minimize the damaging effects of stresses, plant growth regulators (PGRs) are widely used to sustain the plant life under stress-prone environments. So, a study was carried out to evaluate the response of two canola (Brassica napus L.) cultivars, Dunkeld and Cyclone, to foliar-applied two potential PGRs, nitric oxide (NO) and 5-aminolevulinic acid, under water deficit conditions. In this study, the levels of NO and ALA used were 0.02 and 0.895 mM, respectively. Plants of both canola cultivars were subjected to control (100% field capacity) and water deficit (60% field capacity). Drought stress significantly decreased growth, chlorophyll pigments, relative water contents (RWC), and soluble proteins, while it increased relative membrane permeability (RMP), proline, glycinebetaine (GB), malondialdehyde (MDA), total phenolics, and activities of catalase (CAT) and peroxidase (POD) enzymes in both cultivars. Foliar application of PGRs improved growth, chlorophyll a, GB, total phenolics, CAT activity, and total soluble proteins, while it decreased RMP, MDA, and POD activity in both canola cultivars. Other physio-biochemical attributes such as chlorophyll b, RWC, hydrogen peroxide (H2O2) and proline contents as well as superoxide dismutase (SOD) activity remained unaffected due to application of PGRs. So, the results of the present study suggest that exogenous application of NO and ALA could be useful to enhance the drought tolerance of canola plants by up-regulating the oxidative defense system, osmoprotectant accumulation, and minimizing the lipid peroxidation.

Preventing Peer Violence Against Children: Methods and Baseline Data of a Cluster Randomized Controlled Trial in Pakistan.

BACKGROUND: Violence against and among children is a global public health problem that annually affects 50% of youth worldwide with major impacts on child development, education, and health including increased probability of major causes of morbidity and mortality in adulthood. It is also associated with the experience of and perpetration of later violence against women. The aim of this article is to describe the intervention, study design, methods, and baseline findings of a cluster randomized controlled trial underway in Pakistan to evaluate a school-based play intervention aiming to reduce peer violence and enhance mental health. METHODS: A cluster randomized controlled design is being conducted with boys and girls in grade 6 in 40 schools in Hyderabad, Pakistan, over a period of 2 years. The Multidimensional Peer-Victimization and Peer Perpetration Scales and the Children's Depression Inventory 2 (CDI 2) are being used to measure the primary outcomes while investigator-derived scales are being used to assess domestic violence within the family. Specifics of the intervention, field logistics, ethical, and fidelity management issues employed to test the program's impact on school age youth in a volatile and politically unstable country form this report. BASELINE RESULTS: A total of 1,752 school-age youth were enrolled and interviewed at baseline. Over the preceding 4 weeks, 94% of the boys and 85% of the girls reported 1 or more occurrences of victimization, and 85% of the boys and 66% of the girls reported 1 or more acts of perpetration. Boys reported more depression compared with girls, as well as higher negative mood and self-esteem scores and more interpersonal and emotional problems. INTERPRETATION: Globally, prevalence of youth violence perpetration and victimization is high and associated with poor physical and emotional health. Applying a randomized controlled design to evaluate a peer violence prevention program built on a firm infrastructure and that is ready for scale-up and sustainability will make an important contribution to identifying evidence-informed interventions that can reduce youth victimization and perpetration.