Improving inner speech decoding by hybridisation of bimodal EEG and fMRI data.

Decoding inner speech from the brain via the hybridisation of fMRI and EEG data is explored to investigate the performance benefits over unimodal models. Two different fusion approaches are examined: concatenation of probability vectors from unimodal fMRI and EEG machine learning models, and data fusion with feature engineering. Same-task inner speech data are recorded from four participants, and different processing strategies are compared and contrasted to previously-employed hybridisation efforts.

Lineage-Specific Class-A GPCR Dynamics Reflect Diverse Chemosensory Adaptations in Lophotrochozoa.

Sensing external chemosensory cues via Class-A G protein-coupled receptors (GPCRs) is crucial for a multitude of behavioral and biological functions, influencing animal evolution and ecological adaptations. While extensively studied in vertebrates and echinoderms, the role of GPCR-mediated chemoreception in major protostome clades like Lophotrochozoa remains obscure despite their remarkable ecological adaptations across diverse aquatic and terrestrial environments. Utilizing 238 lophotrochozoan genomes across eight phyla, we conducted a large-scale comparative genomics analysis to identify lineage-specific expansions of Class-A GPCR subsets that are likely adapted for chemoreception.

Adopting a model of antimicrobial stewardship program to anti-tubercular treatment stewardship: A single-centre experience from a private tertiary care hospital in South India.

Tuberculosis (TB) remains a significant public health challenge in Low- and Middle-Income Countries (LMIC). Inappropriate use of Anti-Tubercular Treatment (ATT) undermines treatment efficacy and could contribute to drug resistance. While antimicrobial stewardship programs (AMSP) are well established, anti-tubercular treatment stewardship programs (ATTSP) in private hospitals do not have an established model.

SignEEG v1.0: Multimodal Dataset with Electroencephalography and Hand-written Signature for Biometric Systems.

Handwritten signatures in biometric authentication leverage unique individual characteristics for identification, offering high specificity through dynamic and static properties. However, this modality faces significant challenges from sophisticated forgery attempts, underscoring the need for enhanced security measures in common applications. To address forgery in signature-based biometric systems, integrating a forgery-resistant modality, namely, noninvasive electroencephalography (EEG), which captures unique brain activity patterns, can significantly enhance system robustness by leveraging multimodality's strengths.