Browse Articles

Effect of blended NPSB fertilizer and seeding rates on yield and yield components of bread Wheat (Triticum aestivum L.) in Banja District, Northwestern Ethiopia

Maternal trans-vaccenic acid shapes neonatal T cell development and early-life immune imprinting

How maternal nutrition influences neonatal immune development and imprinting through breastfeeding remains largely unclear. We report that maternal supplementation with trans-vaccenic acid (TVA), the predominant naturally occurring trans-fatty acid in human breast milk, promoted neonatal T cell development in mice. Neonates fed by mothers on a TVA-enriched diet showed an expanded naïve cluster of differentiation 4 (CD4 + ) T cell population and enhanced adaptive immunity against infection. TVA reprogrammed neonatal naïve CD4 + T cells through a G protein–coupled receptor–CCCTC-binding factor axis and promoted T helper cells (Th1)–skewing by cooperating with the transcription factor TBX21. Early-life exposure to maternal TVA via breastfeeding supported long-lasting antiviral immunity in adulthood. Our findings establish the multifaceted benefits of maternal nutrition and breastfeeding via TVA in promoting infant immune homeostasis and protective immunity.

High-resolution photovoltaic power forecasting using machine learning models under seasonal and stress conditions

Tracking the roots of zoonoses

Ancient pathogen DNA from animals elucidates the origin and evolution of infectious diseases

Grey wolf optimized neural network with hybrid feature extraction for heart disease prediction

Abstract With an increasing mortality rate due to heart diseases, there is a critical need for early and reliable cardiovascular disease prediction. However, when it comes to health records data, traditional approaches have primarily utilized a single prediction model. This study proposes an advanced approach that leverages Support Vector Machine (SVM) in independent parallel streams with Convolutional Neural Networks (CNN) for extracting features, combined with an ANN enhanced via Grey Wolf Optimization (GWO) for predictive classification. First, the input data is passed into the SVM and CNN simultaneously to extract relevant and important features by each method. Both extractors receive identically preprocessed inputs simultaneously; their outputs are concatenated post-extraction. The SVM extracts probability-based discriminative features reflecting class separation confidence, while CNN extracts hierarchical convolutional features capturing local patterns and non-linear attribute interactions. Then, the attributes extracted by SVM and the attributes extracted by CNN are concatenated to get a comprehensive set of features. The concatenation enriches the feature space with diverse representational characteristics: SVM contributions provide regularized decision confidence scores, while CNN contributions offer multi-scale pattern descriptors. This heterogeneous feature integration enables the ANN to learn more robust classification boundaries by accessing both discriminative and descriptive feature domains simultaneously. Second, the concatenated features are sent to the ANN for prediction. The GWO algorithm is used to configure ANN parameters like channel size, drop-out rate, and learning rate. The fitness function maximizes stratified 5-fold cross-validation accuracy. The experiments were conducted on the Heart Disease dataset collection, collected from the UCI Machine Learning Repository (Cleveland, Hungarian, Switzerland, and Long Beach), which includes 14 variables for heart disease prediction. All results represent mean ± standard deviation from nested cross-validation. The results demonstrate that the proposed SVM–CNN+ANN-GWO model achieves a high classification accuracy of 91.80% ± 1.2% with the Cleveland, 91.53% ± 1.5% with Hungarian, 96.08% ± 0.8% with Switzerland, and 87.69% ± 2.1% with VA Long Beach databases, respectively. Statistical significance was confirmed via paired Wilcoxon tests ( $$p < 0.05$$ ). It outperforms existing baseline approaches and other recent approaches from previous studies based on predictive accuracy. It achieves promising performance in heart disease prediction across all four databases and hence generalizes well on different datasets.

AI may raise the bar and thin the pipeline

Air pollution and cause-specific mortality in EU: a review integrating meta-analysis and meta-regression

Managing the pitfalls of private money

A two-stage deep-learning model using MobileNetV2 and U-Net for CT-based muscle volume assessment in total hip arthroplasty

An HIV vaccine blueprint

The infection of rhesus macaques with a chimeric virus uncovers a recipe for broadly neutralizing antibodies

LiWO-SRDN-based EV charging coordination for stable smart grid systems using a single-switch high step-up zeta converter

Scientific computing in an AI world

Scientific computing must integrate AI with simulation and focus on energy-efficient methods and systems

Socioeconomic and clinical determinants of mental health service use: a population-based cohort study exploring frailty as a potential mediator

Preserve EEOC data collection

Uropathogenic profiles and antibiotic resistance in gynecological cases: a microbial surveillance study from Northeast India

A metastable solid solution transforms into complex nanostructures

Instability in a high-entropy alloy drives structural changes that strengthen the alloy

The EU needs to back its ambition to end animal testing with cash

Human-inspired hyperparameter optimization for long-horizon forecasting of freshwater and desalination per-capita dynamics

Brain-wide topographic coordination of rotating waves

Patterns of brain activity moving in waves occur across brain regions and species, yet their spatial organization, anatomical basis, and brain-wide distribution remain unclear. Using cortex-wide imaging and electrophysiology in awake mice, we revealed a prominent wave motif across spatial scales. Waves frequently formed rotational patterns centered on somatosensory cortex and sweeping across somatotopic maps. Axonal architecture within sensory cortex exhibited a matching circular arrangement. Rotating waves were mirrored between hemispheres and between sensory and motor cortex and were coordinated with subcortical spiking. Bilaterally cutting the circular circuitry diminished rotating waves. Rotating waves were modulated across behavioral states, evoked by sensory inputs, and recruited during correct visuomotor performance. These results establish that rotating waves are sculpted by axonal architecture across diverse brain systems and behavioral contexts.

Investigation of the lower block rows in the King’s Chamber of the Great Pyramid using ultrasonic testing with shear wave arrays

Abstract This study investigates the internal geometry of the granite block rows in the King’s Chamber (KC) of the Great Pyramid of Giza using Non-destructive testing (NDT) with ultrasonic testing (UST) using shear wave echo arrays. As part of the ScanPyramids (SP) project measurement campaigns in 2022, the lower two block rows of all four walls were examined using ultrasonic shear-wave echo arrays (PD8000, Screening Eagle Technologies). The measurement strategy initially included rapid overview acquisitions using an 8-channel UST device to identify potential areas of interest. High-resolution, detailed measurements were then performed at these locations, particularly along the north wall (NW) and in the area of block labeled NW9, using a 16-channel configuration built from two UST devices. The data acquired during these campaigns were processed using a Fourier Transform Synthetic Aperture Focusing Technique (FT-SAFT) algorithm based on the general Synthetic Aperture Focusing Technique (SAFT), creating two-dimensional (2D) cross-sectional images that reveal internal reflectors. A reliable determination of reflector depth requires a precise measurement of the shear wave velocity, which was determined from measurements on the surfaces of 45 accessible granite blocks of the two lower block rows of the KC. The whole study was performed on these 45 blocks. The chamber-wide mean value of 3035 m/s ± 90 m/s results in a remaining depth uncertainty of only a few centimeters, arising from variations in stone properties and measurement uncertainties (up to ± 70 mm even for deep reflectors). Images reconstructed using an FT-SAFT enabled the determination of block thickness (i.e., the locations of backwalls), block joints, and the identification of additional internal reflectors at depths of up to approximately 3 m. The 16-channel UST device exhibited an improved signal-to-noise ratio (SNR) and greater penetration depth than the 8-channel UST device, enabling hyperbolic reflectors and anomalies behind individual blocks to be visualized more distinctly. From the reconstructed 2D images of the measurement profiles, a three-dimensional model of the lower two block rows was derived, providing for the first time a systematic view of the internal structure of the granite lining of the two lower block rows of the KC’s walls. The results illustrate the potential of UST in combination with FT-SAFT for the NDT of massive historic stone structures, establishing a basis for subsequent multi-modal NDT analyses and structural evaluations of the Great Pyramid.