C-parameter version of robust bounded one-class support vector classification.

ν-one-class support vector classification (ν-OCSVC) has garnered significant attention for its remarkable performance in handling single-class classification and anomaly detection. Nonetheless, the model does not yield a unique decision boundary, and potentially compromises learning performance when the training data is contaminated by some outliers or mislabeled observations. This paper presents a novel C-parameter version of bounded one-class support vector classification (C-BOCSVC) to determine a unique decision boundary.

Stabilization of small organic molecules on VC and VCO MXenes: first-principles insights into the performance of van der Waals functionals and the effect of oxygen vacancies.

The adsorption of small organic molecules on pristine VC MXene and its derivatives is investigated by first-principles density functional theory calculations. By employing state-of-the-art van der Waals (vdW) density functionals, the binding affinity of studied molecules, , CH, CO, and HO on MXene adsorbents is well described by more recent vdW functionals, , SCAN-rvv10. Although both CH and CO are nonpolar molecules, on pristine and oxygen-vacancy surfaces, they show a different range of adsorption energies, in which CH is more inert and has weaker binding than CO.

Significantly improving the solubility and anti-inflammatory activity of fenofibric acid with native and methyl-substituted beta-cyclodextrins via complexation.

The solubility of commonly used anti-inflammatory drugs has become a significant concern in contemporary medicine. Furthermore, inflammatory arthritis stands out as the most prevalent chronic inflammatory disease globally. The disease's pathology is characterized by heightened inflammation and oxidative stress, culminating in chronic pain and the loss of joint functionality.

Unraveling the Role of Quinoa in Managing Metabolic Disorders: A Comprehensive Review.

Purpose Of Review: The review aims to address the knowledge gap and promote the widespread adoption of quinoa as a functional food for improving metabolic health. By presenting a comprehensive overview of its nutritional profile and bioactive components, the review aims to increase consumers' awareness of the potential therapeutic benefits of incorporating quinoa into diets.

Recent Findings: Recent studies have highlighted the diverse range of bioactive compounds in quinoa, such as phytosterols, saponins, phenolic acids, phytoecdysteroids, and betalains.

Early detection of Alzheimer's disease in structural and functional MRI.

Objectives: To implement state-of-the-art deep learning architectures such as Deep-Residual-U-Net and DeepLabV3+ for precise segmentation of hippocampus and ventricles, in functional magnetic resonance imaging (fMRI). Integrate VGG-16 with Random Forest (VGG-16-RF) and VGG-16 with Support Vector Machine (VGG-16-SVM) to enhance the binary classification accuracy of Alzheimer's disease, comparing their performance against traditional classifiers.

Method: OpenNeuro and Harvard's Data verse provides Alzheimer's coronal functional MRI data.

Particulate air pollution at the time of oocyte retrieval is independently associated with reduced odds of live birth in subsequent frozen embryo transfers.

Study Question: Does exposure to particulate matter (PM) air pollution prior to oocyte retrieval or subsequent frozen embryo transfer (FET) affect the odds of live birth?

Summary Answer: Live birth rates are lower when particulate matter (PM2.5 and PM10) levels are higher prior to oocyte retrieval, regardless of the conditions at the time of embryo transfer.

What Is Known Already: Exposure to air pollution is associated with adverse reproductive outcomes, including reduced fecundity and ovarian reserve, and an increased risk of infertility and pregnancy loss.

HierbaNetV1: a novel feature extraction framework for deep learning-based weed identification.

Extracting the essential features and learning the appropriate patterns are the two core character traits of a convolution neural network (CNN). Leveraging the two traits, this research proposes a novel feature extraction framework code-named 'HierbaNetV1' that retrieves and learns effective features from an input image. Originality is brought by addressing the problem of varying-sized region of interest (ROI) in an image by extracting features using diversified filters.

Coefficient-Shuffled Variable Block Compressed Sensing for Medical Image Compression in Telemedicine Systems.

Medical professionals primarily utilize medical images to detect anomalies within the interior structures and essential organs concealed by the skeletal and dermal layers. The primary purpose of medical imaging is to extract image features for the diagnosis of medical conditions. The processing of these images is indispensable for evaluating a patient's health.

Inpainting of damaged temple murals using edge- and line-guided diffusion patch GAN.

Mural paintings are vital cultural expressions, enriching our lives by beautifying spaces, conveying messages, telling stories, and evoking emotions. Ancient temple murals degrade over time due to natural aging, physical damage, etc. Preserving these cultural treasures is challenging.

Metagenomics approaches in the discovery and development of new bioactive compound of 8-demethoxy-10-deoxysteffimycin from mangrove sediments.

A metagenomic library consisting of 15,000 clones was constructed from the mangrove sediment. An antimicrobially active clone from the metagenomic library PS49 was identified by function- based screening. This paper presents the results of the biochemical characterization and metagenomic library screening of the marine-derived antibiotic, 8-demethoxy-10-deoxysteffimycin.

Computer-aided diagnosis of early-stage Retinopathy of Prematurity in neonatal fundus images using artificial intelligence.

Retinopathy of Prematurity (ROP) is a retinal disorder affecting preterm babies, which can lead to permanent blindness without treatment. Early-stage ROP diagnosis is vital in providing optimal therapy for the neonates. The proposed study predicts early-stage ROP from neonatal fundus images using Machine Learning (ML) classifiers and Convolutional Neural Networks (CNN) based pre-trained networks.

Triple acquisition mass spectrometry (TRAM) combining targeted and non-targeted metabolomics in a single run.

Background: We introduce TRAM, a triple acquisition strategy on a high-speed quadrupole time-of-flight mass spectrometer for merging non-targeted and targeted metabolomics into one run. TRAM stands for "quasi-simultaneous" acquisition of (1) a full scan MS1, (2) top 30 data-dependent MS2 (DDA), and (3) targeted scheduled MS2 for multiple reaction monitoring (MRM) within measurement cycles of ∼1 s. TRAM combines the selectivity and sensitivity of state-of-the-art targeted MRM-based methods with the full scope of non-targeted analysis enabled by high-resolution mass spectrometry.

Unleashing the future: The revolutionary role of machine learning and artificial intelligence in drug discovery.

Drug discovery is a complex and multifaceted process aimed at identifying new therapeutic compounds with the potential to treat various diseases. Traditional methods of drug discovery are often time-consuming, expensive, and characterized by low success rates. Because of this, there is an urgent need to improve the drug development process using new technologies.

Fruit Extract Mediated Formation of Luminescent Titanium Dioxide Nanometer-Sized Particles: An Innovative Strategy in Domain of Photodecomposition and Germicidal Properties.

Discoveries in nanotechnologies are placing a great deal of attention on greener strategies that use harmless substances and moderated reactions to promote healthy development. This work used a straightforward, innovative, and cost-effective sustainable approach to produce bio-augmented TiO nanometer-sized particles (NMSP) by applying a water-based extract of the star fruit as a stabilization and reduction agent. A variety of techniques, comprising UV-Vis, XRD, FT-IR, FE-SEM with EDAX, and TEM, have been employed to investigate the formed TiO NMSP.

Fabrication of Luminescent Titanium Dioxide Nanoparticles in a Biocompatible Approach: Its Potential for Photodecomposition and Germicidal Effects.

The fabrication of the green strategy of metallic oxide creation provides considerable profits via a minimum of effort, making it an acceptable substitute to the most laborious and challenging conventional processes. The suggested approach involved the creation of titanium dioxide (TiO) nanoparticles through Orthosiphon stamineus extracts of leaves. The synthesized nanoparticles (NPs) were then characterized using XRD, FT-IR, FE-SEM with EDX, and UV-visible spectroscopy.

Sustainable Fabrication of Luminescent Zinc Oxide Nanoparticles From Cucumis melo Fruit Peel Extract as Prospective Photocatalytic and Antigermicidal Agent.

The focus of current advances in nanotechnology has shifted significantly towards environmentally conscious methods that use harmless ingredients and moderated reaction circumstances to promote equitable development. Zinc oxide nanoparticles (NPs) currently grabbed attention of multiple medical fields owing to their unique ability to safeguard against cellular damage and alleviate serious human diseases via processes related to metabolism. This work focused on the generation of ZnO NPs using the peel of Cucumis melo fruit.

Impact of diethyl phthalate on freshwater planarian behaviour, regeneration, and antioxidant defence.

Diethyl phthalate (DEP) has been widely used as a plasticiser in various consumer products, including cosmetics, personal care items, and pharmaceuticals, and recent studies reported a higher abundance of this priority phthalate in the aquatic environment. DEP is a potential endocrine disruptor, affecting immune systems in humans and wildlife even at low-level chronic exposure. As concern over phthalates increases globally, regulatory bodies focus more on their environmental impact.

Assessing the impact of climate change on reproductive health: A content analysis of climate journalism coverage.

complications, infectious diseases, maternal and infant health in disasters, gender-based violence, healthcare access inequities, mental health impacts, and food security issues. Findings reveal an uneven distribution of coverage across continents, with potential language bias in English-dominated sources. Acknowledging limitations, future research directions emphasize a more inclusive approach, incorporating diverse linguistic perspectives and qualitative exploration of community experiences.

Super learner model for classifying leukemia through gene expression monitoring.

Leukemia is a form of cancer that affects the bone marrow and lymphatic system, and it requires complex treatment strategies that vary with each subtype. Due to the subtle morphological differences among these types, monitoring gene expressions is crucial for accurate classification. Manual or pathological testing can be time-consuming and expensive.