Syntheses and Characterization of BaSiTe and BaSiTe(Te) Structures with Complex Te-Te Interactions.

Multinary tellurides with complex structures and narrow bandgaps are potential candidates for thermoelectric applications. Herein, we report the syntheses of two new ternary polytellurides, BaSiTe and BaSiTe(Te). Both title structures adopt unprecedented structure types.

Effect of the First Laparoscopy in an Adolescent and Young Adult Female Population and Its Association With Chronic Pelvic Pain: A Randomised Controlled Trial.

Background: Chronic pelvic pain (CPP) is a common and debilitating presentation for adolescent and young adult females. Medical management is often utilised as first line therapy with surgical management considered if medical treatment has been unsuccessful. Laparoscopy in this young population remains controversial due to the high recurrence rate of pain, requirement for repeat surgeries and surgical risks.

SrZrCu ( = S and Se): two novel layered quaternary mixed transition metal chalcogenides.

Depending on their bandgaps, mixed metal layered chalcogenides are potential candidates for thermoelectric and photovoltaic applications. Herein, we reported the exploratory synthesis of Sr-Zr-Cu- ( = S/Se) systems, resulting in the identification of two novel quaternary chalcogenides: SrZrCuS and SrZrCuSe. These isoelectronic compounds (SrZrCu) crystallized in two different structural types.

Few-shot learning for inference in medical imaging with subspace feature representations.

Unlike in the field of visual scene recognition, where tremendous advances have taken place due to the availability of very large datasets to train deep neural networks, inference from medical images is often hampered by the fact that only small amounts of data may be available. When working with very small dataset problems, of the order of a few hundred items of data, the power of deep learning may still be exploited by using a pre-trained model as a feature extractor and carrying out classic pattern recognition techniques in this feature space, the so-called few-shot learning problem. However, medical images are highly complex and variable, making it difficult for few-shot learning to fully capture and model these features.

Assessment of the effect of premedication on the success of inferior alveolar nerve block in tobacco chewing patients with symptomatic irreversible pulpitis: a randomized control trial.

Background: This study aimed to evaluate and compare the efficacy of oral premedication with ibuprofen on the anesthetic efficacy of inferior alveolar nerve block (IANB) using 2% lignocaine and 1:100000 epinephrine in tobacco-chewing (TC) and non-tobacco-chewing (NTC) patients with symptomatic irreversible pulpitis (SIP) during nonsurgical endodontic intervention (NEI).

Methods: This multicenter, prospective, double-blind, two-arm parallel-group randomized controlled trial involving 160 patients was conducted for a period of 9 months. The patients were classified into the study (TC patients) and control (NTC patients) groups, which were subdivided into two subgroups 1 hour before the procedure based on oral premedication with tab ibuprofen 600 mg.

The Genus : Insights on Taxonomy, Diversity and Distribution.

is the third largest rust genus of the order with more than 200 described species. It is an important rust genus that has undergone tremendous taxonomic changes. This genus produces teliospores united into a head on a compound pedicel composed of two to several hyphae with autoecious, macro-, demi- to hemi-, and, more rarely, microcyclic modes of their life cycle which provide it a unique identity and have proved helpful in the identification of the genus.

Discrepancy-based diffusion models for lesion detection in brain MRI.

Diffusion probabilistic models (DPMs) have exhibited significant effectiveness in computer vision tasks, particularly in image generation. However, their notable performance heavily relies on labelled datasets, which limits their application in medical images due to the associated high-cost annotations. Current DPM-related methods for lesion detection in medical imaging, which can be categorized into two distinct approaches, primarily rely on image-level annotations.

BaZrTe: a new complex ternary telluride structure with low thermal conductivity.

Heavier metal-based tellurides with complex structures are of great interest for thermoelectric () applications. Herein, we report the synthesis of a new telluride BaZrTe using high-temperature reactions of elements. Our single-crystal X-ray diffraction study reveals that it crystallizes in the space group 3̄ of the trigonal crystal system and is isostructural to its Se analogue BaZrSe complex.

Delineating maternal influence in regulation of variance in major economic traits of White Leghorns: Bayesian insights.

Proper variance partitioning and estimation of genetic parameters at appropriate time interval is crucial for understanding the dynamics of trait variance and genetic correlations and for deciding the future breeding strategy of the population. This study was conducted on the same premise to estimate genetic parameters of major economic traits in a White Leghorn strain IWH using Bayesian approach and to identify the role of maternal effects in the regulation of trait variance. Three different models incorporating the direct additive effect (Model 1), direct additive and maternal genetic effect (Model 2) and direct additive, maternal genetic and maternal permanent environmental effects (Model 3) were tried to estimate the genetic parameters for body weight traits (birth weight, body weight at 16, 20, 40 and 52 weeks), Age at sexual maturity (ASM), egg production traits (egg production up to 24, 28, 40, 52, 64 and 72 weeks) and egg weight traits (egg weight at 28, 40 and 52 weeks).

Polarized Raman, infrared and dielectric spectra of lead-free KNaNbOpiezoelectric system: insights fromtheoretical and experimental studies.

The KNaNbO(KNN) system has emerged as one of the most promising lead-free piezoelectric over the years. In this work, we perform a comprehensive investigation of electronic structure, lattice dynamics and dielectric properties of room temperature phase of KNN by combiningDFT based theoretical analysis and experimental characterization. We assign the symmetry labels to KNN vibrational modes and obtainpolarized Raman spectra, Infrared reflectivity, Born-effective charge tensors, oscillator strengths etc.

Machine Learning to Predict Prostate Artery Embolization Outcomes.

Purpose: This study leverages pre-procedural data and machine learning (ML) techniques to predict outcomes at one year following prostate artery embolization (PAE).

Materials And Methods: This retrospective analysis combines data from the UK-ROPE registry and patients that underwent PAE at our institution between 2012 and 2023. Traditional ML approaches, including linear regression, lasso regression, ridge regression, decision trees and random forests, were used with leave-one-out cross-validation to predict international prostate symptom score (IPSS) at baseline and change at 1 year.

Integrated plasma proteomics identifies tuberculosis-specific diagnostic biomarkers.

BACKGROUNDNovel biomarkers to identify infectious patients transmitting Mycobacterium tuberculosis are urgently needed to control the global tuberculosis (TB) pandemic. We hypothesized that proteins released into the plasma in active pulmonary TB are clinically useful biomarkers to distinguish TB cases from healthy individuals and patients with other respiratory infections.METHODSWe applied a highly sensitive non-depletion tandem mass spectrometry discovery approach to investigate plasma protein expression in pulmonary TB cases compared to healthy controls in South African and Peruvian cohorts.

Influence of temperature on bandgap shifts, optical properties and photovoltaic parameters of GaAs/AlAs and GaAs/AlSbheterojunctions: insights fromDFT + NEGF studies.

The III-V group semiconductors are highly promising absorbers for heterojunctions based solar cell devices due to their high conversion efficiency. In this work, we explore the solar cell properties and the role of electron-phonon coupling (EPC) on the solar cell parameters of GaAs/AlSb and GaAs/AlAsheterojunctions using non-equilibrium Green function method (NEGF) in combination ofdensity functional theory (DFT). In addition, the band offsets at the heterointerfaces, optical absorption and bandgap shifts (BGSs) due to temperature are estimated using DFT + NEGF approach.

Protein language models meet reduced amino acid alphabets.

Motivation: Protein language models (PLMs), which borrowed ideas for modelling and inference from natural language processing, have demonstrated the ability to extract meaningful representations in an unsupervised way. This led to significant performance improvement in several downstream tasks. Clustering amino acids based on their physical-chemical properties to achieve reduced alphabets has been of interest in past research, but their application to PLMs or folding models is unexplored.

Causal Analysis of Physiological Sleep Data Using Granger Causality and Score-Based Structure Learning.

Understanding how the human body works during sleep and how this varies in the population is a task with significant implications for medicine. Polysomnographic studies, or sleep studies, are a common diagnostic method that produces a significant quantity of time-series sensor data. This study seeks to learn the causal structure from data from polysomnographic studies carried out on 600 adult volunteers in the United States.

Quantum annealing-based clustering of single cell RNA-seq data.

Cluster analysis is a crucial stage in the analysis and interpretation of single-cell gene expression (scRNA-seq) data. It is an inherently ill-posed problem whose solutions depend heavily on hyper-parameter and algorithmic choice. The popular approach of K-means clustering, for example, depends heavily on the choice of K and the convergence of the expectation-maximization algorithm to local minima of the objective.

BaSiSbTe(Te): hypervalent Te in a new structure type with low thermal conductivity.

Heavier pnictogen (Sb, Bi) containing chalcogenides are well known for their complex structures and semiconducting properties for numerous applications, particularly thermoelectric materials. Here, we report the syntheses of single crystals and polycrystalline phases of a new complex quaternary polytelluride, BaSiSbTe(Te), a high-temperature reaction of elements. A single-crystal X-ray diffraction study showed that it crystallizes in an unprecedented structure type with monoclinic symmetry (space group: 2/).

Insights into Diversity, Distribution, and Systematics of Rust Genus .

, which comprises 4000 species, is the largest genus of rust fungi and one of the destructive plant pathogenic rust genera that are reported to infect both agricultural and nonagricultural plants with severe illnesses. The presence of bi-celled teliospores is one of the major features of these rust fungi that differentiated them from , which is another largest genus of rust fungi. In the present study, an overview of the current knowledge on the general taxonomy and ecology of the rust genus is presented.

Withania somnifera inhibits photorefractoriness which triggers neuronal apoptosis in both pre-optic and paraventricular hypothalamic area of Coturnix coturnix japonica: involvement of oxidative stress induced p53 dependent Caspase-3 mediated low immunoreactivity of estrogen receptor alpha.

Light has a very important function in the regulation of the normal physiology including the neuroendocrine system, biological rhythms, cognitive behavior, etc. The variation in photoperiod acts as a stressor due to imbalance in endogenous hormones. Estrogen and its receptors ER alpha and beta play a vital role in the control of stress response in birds.

An Improved AlexNet Model and Cepstral Coefficient-Based Classification of Autism Using EEG.

Autism is a neurodevelopmental disorder that cannot be completely cured, but early intervention during childhood can improve outcomes. Identifying autism spectrum disorder (ASD) has relied on subjective detection methods that involve questionnaires, medical professionals, and therapists and are subject to observer variability. The need for early diagnosis and the limitations of subjective detection methods has led researchers to explore machine learning-based approaches, such as Random Forests, K-Nearest Neighbors, Naive Bayes, and Support Vector Machines, to predict ASD meltdowns.

Bichromatic state-dependent disordered potential for Anderson localization of ultracold atoms.

Abstract: The ability to load ultracold atoms at a well-defined energy in a disordered potential is a crucial tool to study quantum transport, and in particular Anderson localization. In this paper, we present a new method for achieving that goal by rf transfer of atoms in an atomic Bose-Einstein condensate from a disorder-insensitive state to a disorder-sensitive state. It is based on a bichromatic laser speckle pattern, produced by two lasers whose frequencies are chosen so that their light-shifts cancel each other in the first state and add up in the second state.

Machine learning applied to HR-pQCT images improves fracture discrimination provided by DXA and clinical risk factors.

Background: Traditional analysis of High Resolution peripheral Quantitative Computed Tomography (HR-pQCT) images results in a multitude of cortical and trabecular parameters which would be potentially cumbersome to interpret for clinicians compared to user-friendly tools utilising clinical parameters. A computer vision approach (by which the entire scan is 'read' by a computer algorithm) to ascertain fracture risk, would be far simpler. We therefore investigated whether a computer vision and machine learning technique could improve upon selected clinical parameters in assessing fracture risk.