Biology subjects

Rakesh, S.

Publications and source records attributed to Rakesh, S..

4 recordsLinked to original sources

bioRxiv2025

Expanding the Landscape of BREX Diversity: Uncovering Multi-Layered Functional Frameworks and Identification of Novel BREX-Related Defense Systems

Despite extensive scrutiny of BREX systems, several overarching questions persist regarding the functional modalities of individual components and the collective mechanistic framework underlying their defense responses. Using comparative genomics and sequence-structure analyses, we comprehensively map the phyletic distribution and domain-level functional annotations of BREX proteins across all subtypes. Our analysis uncovers numerous previously uncharacterized domains with key functional implications and demonstrates that BrxA- and BrxB-like homologs are universally present across all subtypes, thereby redefining the core machinery of BREX. Our survey strikingly expands the BREX landscape by characterizing three newly defined RM-like systems, which we term BREX-related (BR) systems, and establishes them as new subtypes that share multiple core components with BREX. Furthermore, we identified a novel composite anti-invader system that intriguingly integrates the BREX machinery with components derived from HerA/FtsK-based capture and Dpd defense systems, forming a unique multifaceted defense machinery. Notably, we identify an extensive repertoire of auxiliary effectors recruited alongside the primary effectors across all BREX and BR systems, functioning to reinforce initial restriction responses and counter phage anti-defense mechanisms. Based on these findings, we propose a unified model organized around a multi-modal "sensing-modifying-restricting" functional architecture, defining the fundamental basis of the multi-layered BREX defense systems.

genomics↗

bioRxiv2024

Uncovering the Structural and Functional Implications of Uncharacterized NSPs and Variations in the Molecular Toolkit Across Mammalian and Non-Mammalian Arteriviruses

Despite considerable scrutiny of mammalian arterivirus genomes, their genomic architecture remains incomplete, with several unannotated non-structural proteins (NSPs) and the puzzling absence of methyltransferase (MTase) domains. Additionally, the host range of arteriviruses has expanded to include seven newly sequenced genomes from non-mammalian hosts, which remain largely unannotated and await detailed comparisons alongside mammalian isolates. Utilizing comparative genomics approaches and comprehensive sequence-structure analysis, we provide enhanced genomic architecture and annotations for arterivirus genomes. We identify the previously unannotated C-terminal domain of NSP3 as a winged helix-turn-helix domain and classified NSP7 as a new small {beta}-barrel domain, both likely involved in interactions with viral RNA. NSP12 is identified as a derived variant of the N7-MTase-like Rossmann fold domain, showing structural alignment with N7-MTases in Nidovirales, yet it likely lacks enzymatic functionality due to the erosion of catalytic residues, indicating a unique role-specific to mammalian arteriviruses. In contrast, non-mammalian arteriviruses sporadically retain a 2' O-MTase and an exonuclease domain, which are typically absent in mammalian arteriviruses, highlighting contrasting evolutionary trends and variations in their molecular armory. Similar lineage-specific paterns are observed in the diversification of papain-like proteases and structural proteins. Overall, the study extends our knowledge of arterivirus genomic diversity and evolution.

evolutionary biology↗

bioRxiv2023

SignEEG v1.0 : Multimodal Electroencephalography and Signature Database 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 multimodalitys strengths. By combining EEG, a physiological modality, with handwritten signatures, a behavioral modality, our approach capitalizes on the strengths of both, significantly fortifying the robustness of biometric systems through this multimodal integration. In addition, EEGs resistance to replication offers a high-security level, making it a robust addition to user identification and verification. This study presents a new multimodal SignEEG v1.0 dataset based on EEG and hand-drawn signatures from 70 subjects. EEG signals and hand-drawn signatures have been collected with Emotiv Insight and Wacom One sensors, respectively. The multimodal data consists of three paradigms based on mental, & motor imagery, and physical execution: i) thinking of the signatures image, (ii) drawing the signature mentally, and (iii) drawing a signature physically. Extensive experiments have been conducted to establish a baseline with machine learning classifiers. The results demonstrate that multimodality in biometric systems significantly enhances robustness, achieving high reliability even with limited sample sizes. We release the raw, pre-processed data and easy-to-follow implementation details.

neuroscience↗

bioRxiv2022

Bimodal pilot study on inner speech decoding reveals the potential of combining EEG and fMRI

The recognition of inner speech, which could give a voice to patients that have no ability to speak or move, is a challenge for brain-computer interfaces (BCIs). A shortcoming of the available datasets is that they do not combine modalities to increase the performance of inner speech recognition. Multimodal datasets of brain data enable the fusion of neuroimaging modalities with complimentary properties, such as the high spatial resolution of functional magnetic resonance imaging (fMRI) and the temporal resolution of electroencephalography (EEG), and therefore are promising for decoding inner speech. This paper presents the first publicly available bimodal dataset containing EEG and fMRI data acquired nonsimultaneously during inner-speech production. Data were obtained from four healthy, right-handed participants during an inner-speech task with words in either a social or numerical category. Each of the 8-word stimuli were assessed with 40 trials, resulting in 320 trials in each modality for each participant. The aim of this work is to provide a publicly available bimodal dataset on inner speech, contributing towards speech prostheses.

neuroscience↗