Browse Articles

Fault classification in the architecture of virtual machine using deep learning

These alien planets are astronomers’ favourites: here’s why

Dynamic assessment of listening effort by EEG alpha oscillations during an adaptive speech-in-noise test

Abstract The task of speech recognition in noisy environments can be cognitively demanding for both hearing-impaired and normal-hearing individuals. Recent research emphasized brain oscillations, particularly alpha rhythms (8-13 Hz) from the electroencephalogram (EEG), as potential markers of processing auditory information in challenging listening contexts. However, most studies examined fixed listening demand with meaningful speech. Our study explores how alpha rhythms may quantify listening effort during an adaptive speech-in-noise task with reduced semantic complexity. For analysis, 11 participants with normal hearing were selected, and the individual speech recognition threshold was used to define two conditions with different task demands. Within each condition, multiple auditory stimuli were presented at different signal-to-noise ratios. The difference in listening demand between these conditions was reflected by reaction time and speech recognition performance. A cluster-based permutation test on EEG time-frequency data identified a fronto-central cluster in the alpha band. In-depth analysis of EEG dynamics revealed a significant difference in post-stimulus alpha event-related synchronization in a fronto-central cluster and a near-significant difference at central electrodes, providing preliminary support to the inhibition hypothesis. These findings further validate EEG-derived measures towards an accurate quantitative assessment of short-term neural responses in the context of a simple adaptive speech-in-noise test.

Indobufen alleviates apoptosis by the PI3K/Akt/eNOS pathway in myocardial ischemia‒reperfusion (I/R) injury

Model organism databases face budget cuts and closures

The impact of vaping behavior on functional changes within the subgingival microbiome

Memorable, distinctive, not too ‘sciencey’: why we named our biotech firm Anocca

Comparative multi-index analysis of existing drought typology and environmental droughts in a climate-stressed region

Daily briefing: ‘A messenger of hope’ — Jane Goodall’s impact on science

Time-resolved fluoroimmunoassay improves sensitivity in the diagnosis of primary membranous nephropathy with low phospholipase A2 receptor antibody titer

Scooped by a cupcake business: why we called our green-cement company Sublime Systems

Publisher Correction: TCF1 and LEF1 promote B-1a cell homeostasis and regulatory function

Optimization of the real-time PCR platform method using high-resolution melting analysis and comparison with sequencing and phylogenetic analysis for developing optimal malaria diagnostic methods

Preclinical models of melanoma leptomeningeal disease to assess intrathecal checkpoint blockade

Cross-modal deep learning enhanced mixed reality accelerates construction skill transfer from experts to students

Childhood eczema linked to mother’s stress during pregnancy

Noise-augmented contrastive learning with attention for knowledge-aware collaborative recommendation

Metabolite profiling and free radical scavenging activity studies of alkaloids from Erythrina crista-galli twigs through in vitro and in silico analysis

Abstract Erythrina crista-galli is among 130 species of genus Erythrina which widely distributed in Indonesia, Australia, Argentina, Uruguay, Paraguay, and Brazil. Among the botanical parts used for the exploration of alkaloids from E. crista-galli (cockspur coral, ceibo, corticeira), the twig being one that rarely explored. This study aims to explore the alkaloid content from the twig of E. crista-galli as well as to evaluate their free radical scavenging activities through in vitro and in silico studies. The metabolite profile of the ethanol extract from Erythrina crista-galli twigs was characterized using Ultra-Performance Liquid Chromatography coupled with Tandem Mass Spectrometry (UPLC-MS/MS). The structures of the isolated alkaloids were determined through Nuclear Magnetic Resonance (NMR) and Mass Spectrometry (MS). Their free radical scavenging activities were evaluated using the 1,1-diphenyl-2-picrylhydrazyl (DPPH) free radical scavenging assay, while their electronic properties were assessed via Density Functional Theory (DFT). Twelve erythrina type alkaloids were tentatively identified from the twig of E. crista-galli. Four among these alkaloids, namely crystamidine (1), 8-oxoerythraline (2), erythrinine (3), erythraline (4) were isolated and their structures were confirmed through NMR and MS spectroscpy. Among the isolated alkaloids, erythraline (4) show highest free radical scavenging activity with IC50 of 182.5 ± 5.3 µg/mL, whereas 8-oxoerythraline (1) show the lowest free radical scavenging activity with IC50 of 868.2 ± 0.26 µg/mL. Structure–Activity relationship study shows that any modification in erythraline base skeleton, would decrease the antioxidant activity of erythraline, Furthermore, DFT calculation revealed that erythrinine (3), erythraline (4) tend to act as electron donors, whereas crystamidine (1), 8-oxoerythraline (2) tend to act as electron acceptors.

Therapeutic efficacy of Rosa damascena Mill. on oxidative stress parameters and cyclooxygenase-2 gene expression in estradiol valerate-induced polycystic ovarian syndrome in Wistar rats

Radiometer calibration using machine learning

Abstract Radiometers are crucial instruments in radio astronomy, forming the primary component of nearly all radio telescopes. They measure the intensity of electromagnetic radiation, converting this radiation into electrical signals. A radiometer’s primary components are an antenna and a Low Noise Amplifier (LNA), which is the core of the “receiver” chain. Instrumental effects introduced by the receiver are typically corrected or removed during calibration. However, impedance mismatches between the antenna and receiver can introduce unwanted signal reflections and distortions. Traditional calibration methods, such as Dicke switching, alternate the receiver input between the antenna and a well-characterised reference source to mitigate errors by comparison. Recent advances in Machine Learning (ML) offer promising alternatives. Neural networks, which are trained using known signal sources, provide a powerful means to model and calibrate complex systems where traditional analytical approaches struggle. These methods are especially relevant for detecting the faint sky-averaged 21-cm signal from atomic hydrogen at high redshifts. This is one of the main challenges in observational Cosmology today. Here, for the first time, we introduce and test a machine learning-based calibration framework capable of achieving the precision required for radiometric experiments aiming to detect the 21-cm line.