Biology subjects

Parida, S.

Publications and source records attributed to Parida, S..

15 recordsLinked to original sources

bioRxiv2026

A low-dimensional, generalizable encoding manifold for auditory cortex

Neural populations in auditory cortex (AC) perform sensory computations that support stimulus category decoding and flexible behavior. The underlying geometry and generalizability of these computations for natural stimuli remain poorly understood, particularly at the single-neuron level, where neurons display wide-ranging tuning specificity and temporal acuity. To address this gap, we developed ACNet, a foundation model of cortical sound encoding, to predict the time-varying activity of >3000 neurons in AC of ferrets. Training data included 42 hours of natural sounds spanning over 100 categories and were collected from multiple recording sites across multiple animals. The model achieved state-of-the-art response prediction accuracy. Model activity was succinctly captured by a low-dimensional neural manifold, which generalized (>80% of variance) across animals. Analysis of ACNet activations revealed the emergence of rate-based, sparse sound coding across layers, a prominent feature of the auditory cortex. These transformations were concomitant with the emergence of more accurate and neurally aligned auditory category decoding, even though ACNet was not explicitly trained to categorize sounds. The model also revealed tuning differences between anatomically distinct cell types. Taken together, our results demonstrate that foundation models of sensory systems can reveal generalizable computations by large neural populations.

neuroscience↗

bioRxiv2025

Tricotyledonous Seedling in Jute (Corchorus capsularis): First Observation During Tissue Culture Selection

White jute (Corchorus capsularis) is a dicotyledonous, annual, bast fiber-producing crop. Under controlled culture room conditions (27 {+/-} 2{degrees}C, 16 h light/8 h dark photoperiod) and in a 15 mg/L hygromycin-B supplemented nutrient medium, one out of 50 tested seeds (2%) exhibited a rare tricotyledonous seedling phenotype. This is the first documented occurrence of tricotyledon in white jute during tissue culture selection of the cultivar JRC321 (Sonali). Given the potential advantages of polycotyledon in other plant species--such as improved vigor, and biomass accumulation--this finding opens new avenues for investigating the genetic and physiological mechanisms underlying tricotyledon development in jute. Future studies could explore its implications for breeding programs aimed at enhancing fiber yield and production.

plant biology↗

bioRxiv2025

Degraded neural coding of temporal fine structure with age predicts effortful listening in multi-talker environments

Middle age represents a critical window for early detection of neurophysiological decline. Hearing loss is increasingly recognized as both an early marker of neural degeneration and a modifiable risk factor for dementia. Yet many adults report difficulty understanding speech in noise despite normal audiograms, highlighting the limitations of current clinical tests that fail to capture the underlying physiology or effort required for real-world listening. Beyond hearing thresholds, speech comprehension in complex environments depends on precise neural encoding of temporal fine structure (TFS) cues that convey pitch and spatial information. Here, we use a noninvasive EEG-based measure of neural phase-locking (frequency modulation following responses or FMFRs) to quantify TFS encoding in young and middle-aged adults with normal hearing thresholds. Middle-aged listeners exhibited reduced FMFR amplitudes and shallower discriminability slopes, reflecting diminished neural synchrony despite preserved hearing thresholds. Using a multi-talker speech task we further found that pupil-indexed listening effort was significantly greater in middle-aged adults despite matched accuracy across groups. Further, increases in listening effort were predicted by decreases in TFS encoding. Together, these results reveal that degraded neural encoding of TFS underlies subclinical listening difficulties and increased cognitive load, establishing the FMFR as a sensitive biomarker of hidden auditory neural decline. SignificanceUnderstanding speech in noisy environments depends on precise neural encoding of temporal fine structure (TFS) cues. Using a noninvasive EEG metric, the FMFR, we show that neural TFS coding in the peripheral auditory system declines markedly by midlife, even when hearing thresholds and speech performance are normal. These neural deficits predict elevated pupil-indexed listening effort during multi-talker speech perception, revealing that subclinical degradation of temporal coding increases cognitive load in everyday listening. The FMFR thus provides a promising biomarker for early auditory neural decline and its downstream cognitive consequences.

neuroscience↗

bioRxiv2025

Systematic analysis of lectin gene family reveals dynamic modes of paralogue evolution and immune regulatory functions in tomato

Lectins are a structurally diverse class of carbohydrate-binding proteins implicated in plant development and stress responses. In this study, we present a comprehensive structural, evolutionary, and functional analysis of the lectin gene family in tomato (Solanum lycopersicum cv. Heinz). We identified 247 lectin genes across eight major families, exhibiting diverse domain architectures, including merolectins, hololectins, and chimerolectins. Gene ontology enrichment, tissue-specific expression profiling, and stress-responsive transcriptomics highlighted key roles of lectin genes in development and pathogen defense. Comparative genomic analyses across five Solanum species and ancestral outgroups (Vitis vinifera and Amborella trichopoda) revealed both conserved and lineage-specific expansions driven by whole-genome and small-scale duplications. Evolutionary modeling indicated that most lectin gene duplications are under purifying selection, with some genes exhibit signatures of adaptive evolution. Expression and co-expression variance among duplicated paralogues revealed three distinct evolutionary fates -balance, dominance, and divergence. Notably, small-scale duplications frequently led to functional divergence, whereas whole-genome duplicates largely retained ancestral expression patterns. Functional validation through virus-induced gene silencing (VIGS) of two GNA chimerolectins, Solyc04g077390 and Solyc07g063700, revealed their role as negative regulators of immunity. Silencing these genes enhanced resistance to Ralstonia solanacearum, reduced bacterial colonization, and triggered stronger induction of defense-related genes. Together, these findings highlight the structural innovation, evolutionary dynamics, and immune regulatory roles of lectin genes in tomato, and identifies promising targets for breeding disease-resistant cultivars in Solanum lycopersicum and related crops.

plant biology↗

bioRxiv2024

Convolutional neural network models describe the encoding subspace of local circuits in auditory cortex

Convolutional neural networks (CNNs) provide powerful models of neural sensory encoding, but their complexity makes it difficult to discern computations that support their performance. To address this limitation, we developed a linear-nonlinear subspace model that identifies the most informative sensory dimensions captured by a CNN. A CNN was trained on single-neuron data recorded from auditory cortex of ferrets during presentation of a large natural sound set. Each neurons linear tuning subspace was computed by applying dimensionality reduction to the gradient of CNN output relative to input. Subspace projections were combined nonlinearly to predict neural activity. The resulting model was functionally equivalent to the CNN. Analysis of trained models showed that responses of local neural populations sparsely tiled a shared stimulus subspace. Encoding properties also differed between cell types and layers, reflecting their position in the cortical circuit. More generally, these results establish a framework for interpreting deep learning-based encoding models. Significance statementAuditory cortex mediates the representation and discrimination of complex sound features. Many models have been proposed for cortical sound encoding, varying in their generality, interpretability, and ease of fitting. It has been difficult to determine if and what different functional properties are captured by different models. This study shows that two families of encoding models, convolutional neural networks (CNNs) and tuning subspace models, account for the same functional properties, providing an important analytical link between accurate models that are easy to fit (CNNs) and models that are straightforward to interpret (tuning subspace).

neuroscience↗

bioRxiv2024

Genome-wide Identification of the Laccase Gene Family in White Jute (Corchorus capsularis): Potential Targets for Lignin Engineering in Bast Fiber

Jute (Corchorus spp.) is an important industrial bast fibre crop valued for its lignocellulosic fibres, yet the molecular basis of fibre lignification remains unexplored. Laccase (EC 1.10.3.2) is a key enzyme catalysing the final steps of lignin polymerisation. A genome-wide analysis of white jute (Corchorus capsularis) identified 32 putative laccase genes (CcaLACs) that were phylogenetically grouped into six clades. Expression profiling revealed predominant expression in phloem tissue (16 genes), followed by leaf (8) and xylem/root tissues (4). Several CcaLACs showed progressive upregulation from early growth to harvest stages. Homology with Arabidopsis laccases highlighted candidate genes involved in lignification, which were further supported by transcriptomic and qRT-PCR analyses. Notably, key CcaLACs showed significantly reduced expression in dlpf (deficient lignified phloem fibre), a low-lignin white jute mutant. CcaLAC expression was also responsive to abiotic stresses, including abscisic acid and copper. MicroRNA target prediction identified Ath-miR397a and Ath-miR397b as potential regulators of multiple CcaLACs. Structural and subcellular analyses revealed conserved motifs, transmembrane domains, and diverse cellular localisation. Gene ontology analysis linked CcaLACs to lignin and phenylpropanoid biosynthesis. Among them, CcaLAC28 and CcaLAC32 emerged as strong candidate genes associated with phloem fibre lignification, representing promising targets for future functional validation towards developing low-lignin jute varieties.

plant biology↗

bioRxiv2024

Rapid and objective assessment of auditory temporal processing using dynamic amplitude-modulated stimuli

Auditory neural coding of speech-relevant temporal cues can be noninvasively probed using envelope following responses (EFRs), neural ensemble responses phase-locked to the stimulus amplitude envelope. EFRs emphasize different neural generators, such as the auditory brainstem or auditory cortex, by altering the temporal modulation rate of the stimulus. EFRs can be an important diagnostic tool to assess auditory neural coding deficits that go beyond traditional audiometric estimations. Existing approaches to measure EFRs use discrete amplitude modulated (AM) tones of varying modulation frequencies, which is time consuming and inefficient, impeding clinical translation. Here we present a faster and more efficient framework to measure EFRs across a range of AM frequencies using stimuli that dynamically vary in modulation rates, combined with spectrally specific analyses that offer optimal spectrotemporal resolution. EFRs obtained from several species (humans, Mongolian gerbils, Fischer-344 rats, and Cba/CaJ mice) showed robust, high-SNR tracking of dynamic AM trajectories (up to 800Hz in humans, and 1.4 kHz in rodents), with a fivefold decrease in recording time and thirtyfold increase in spectrotemporal resolution. EFR amplitudes between dynamic AM stimuli and traditional discrete AM tokens within the same subjects were highly correlated (94% variance explained) across species. Hence, we establish a time-efficient and spectrally specific approach to measure EFRs. These results could yield novel clinical diagnostics for precision audiology approaches by enabling rapid, objective assessment of temporal processing along the entire auditory neuraxis.

neuroscience↗

bioRxiv2022

Adaptive mechanisms facilitate robust performance in noise and in reverberation in an auditory categorization model

For robust vocalization perception, the auditory system must generalize over variability in vocalization production as well as variability arising from the listening environment (e.g., noise and reverberation). We previously demonstrated that a hierarchical model generalized over production variability by detecting sparse intermediate-complexity features that are maximally informative about vocalization category from a dense spectrotemporal input representation. Here, we explore three biologically feasible model extensions to generalize over environmental variability: (1) training in degraded conditions, (2) adaptation to sound statistics in the spectrotemporal stage and (3) sensitivity adjustment at the feature detection stage. All mechanisms improved vocalization categorization performance, but improvement trends varied across degradation type and vocalization type. One or both adaptive mechanisms were required for model performance to approach the behavioral performance of guinea pigs on a vocalization categorization task. These results highlight the contributions of adaptive mechanisms at multiple auditory processing stages to achieve robust auditory categorization.

neuroscience↗

bioRxiv2022

Estimation of cochlear frequency selectivity using a convolution model of forward-masked compound action potentials

Frequency selectivity is a fundamental property of the peripheral auditory system; however, the invasiveness of auditory nerve (AN) experiments limits its study in the human ear. Compound action potentials (CAPs) associated with forward-masking have been suggested as an alternative means to assess cochlear frequency selectivity. Previous methods relied on an empirical comparison of AN and CAP tuning curves in animal models, arguably not taking full advantage of the information contained in forward-masked CAPs. In this work, we seek to provide a direct estimate of the quality factor characterizing AN frequency tuning using many forward-masked CAP responses. The method is based on a convolution model of the CAP that takes into account the masking of AN populations induced by notched-noise maskers with various notch widths and attenuations. The model produces masking patterns that, once convolved by a unitary response, predict forward-masked CAP waveforms. Model parameters, including those characterizing frequency selectivity, are fine-tuned by minimizing waveform prediction errors across the different masking conditions, yielding robust estimates. The method was applied to click-evoked CAPs at the round window of anesthetized chinchillas. The estimated quality factor Q10 as a function of center frequency is shown to closely match the average quality factor obtained from AN-fiber tuning curves, without the need for an empirical correction factor. Beyond the estimation of frequency selectivity, the proposed model proves to be accurate in predicting forward-masked CAP responses, and therefore could be extended to study more complex aspects of cochlear signal processing using a similar experimental approach.

neuroscience↗

bioRxiv2022

Enhanced envelope coding following acoustic trauma is detrimental to neural coding of speech in noise

People with hearing loss struggle understanding speech in noisy backgrounds. Speech-intelligibility models highlight the importance of slow-varying envelopes of speech and noise; however, the physiological basis of impaired perception remains unclear. We provide neurophysiological evidence that, although acoustic trauma enhances both speech and noise envelopes, disruptions in cochlear tonotopicity preferentially enhance neural responses to noise in noisy speech. These results provide mechanistic insights into everyday-communication challenges created by hearing loss.

neuroscience↗

bioRxiv2022

Vocalization categorization behavior explained by a feature-based auditory categorization model

Vocal animals produce multiple categories of calls with high between- and within-subject variability, over which listeners must generalize to accomplish call categorization. The behavioral strategies and neural mechanisms that support this ability to generalize are largely unexplored. We previously proposed a theoretical model that accomplished call categorization by detecting features of intermediate complexity that best contrasted each call category from all other categories. We further demonstrated that some neural responses in the primary auditory cortex were consistent with such a model. Here, we asked whether a feature-based model could predict call categorization behavior. We trained both the model and guinea pigs on call categorization tasks using natural calls. We then tested categorization by the model and guinea pigs using temporally and spectrally altered calls. Both the model and guinea pigs were surprisingly resilient to temporal manipulations, but sensitive to moderate frequency shifts. Critically, model performance quantitatively matched guinea pig behavior to a remarkable degree. By adopting different model training strategies and examining features that contributed to solving specific tasks, we could gain insight into possible strategies used by animals to categorize calls. Our results validate a model that uses the detection of intermediate-complexity contrastive features to accomplish call categorization.

neuroscience↗

bioRxiv2021

Distorted tonotopy severely degrades neural representations of natural speech in noise following acoustic trauma

Listeners with sensorineural hearing loss (SNHL) struggle to understand speech, especially in noise, despite audibility compensation. These real-world suprathreshold deficits are hypothesized to arise from degraded frequency tuning and reduced temporal-coding precision; however, peripheral neurophysiological studies testing these hypotheses have been largely limited to in-quiet artificial vowels. Here, we measured single auditory-nerve-fiber responses to a natural speech sentence in noise from anesthetized chinchillas with normal hearing (NH) or noise-induced hearing loss (NIHL). Our results demonstrate that temporal precision was not degraded, and broader tuning was not the major factor affecting peripheral coding of natural speech in noise. Rather, the loss of cochlear tonotopy, a hallmark of normal hearing, had the most significant effects (both on vowels and consonants). Because distorted tonotopy varies in degree across etiologies (e.g., noise exposure, age), these results have important implications for understanding and treating individual differences in speech perception for people suffering from SNHL.

neuroscience↗

bioRxiv2020

Spectrally specific temporal analyses of spike-train responses to complex sounds: A unifying framework

Significant scientific and translational questions remain in auditory neuroscience surrounding the neural correlates of perception. Relating perceptual and neural data collected from humans can be useful; however, human-based neural data are typically limited to evoked far-field responses, which lack anatomical and physiological specificity. Laboratory-controlled preclinical animal models offer the advantage of comparing single-unit and evoked responses from the same animals. This ability provides opportunities to develop invaluable insight into proper interpretations of evoked responses, which benefits both basic-science studies of neural mechanisms and translational applications, e.g., diagnostic development. However, these comparisons have been limited by a disconnect between the types of spectrotemporal analyses used with single-unit spike trains and evoked responses, which results because these response types are fundamentally different (point-process versus continuous-valued signals) even though the responses themselves are related. Here, we describe a unifying framework to study temporal coding of complex sounds that allows spike-train and evoked-response data to be analyzed and compared using the same advanced signal-processing techniques. The framework uses alternating-polarity peristimulus-time histograms computed from single-unit spike trains to allow advanced spectral analyses of both slow (envelope) and rapid (temporal fine structure) response components. Demonstrated benefits include: (1) novel spectrally specific temporal-coding measures that are less corrupted by analysis distortions due to hair-cell transduction, synaptic rectification, and neural stochasticity compared to previous metrics, e.g., the correlogram peak-height, (2) spectrally specific analyses of spike-train modulation coding (magnitude and phase), which can be directly compared to modern perceptually based models of speech intelligibility (e.g., that depend on modulation filter banks), and (3) superior spectral resolution in analyzing the neural representation of nonstationary sounds, such as speech and music. This unifying framework significantly expands the potential of preclinical animal models to advance our understanding of the physiological correlates of perceptual deficits in real-world listening following sensorineural hearing loss. Author summaryDespite major technological and computational advances, we remain unable to match human auditory perception using machines, or to restore normal-hearing communication for those with sensorineural hearing loss. An overarching reason for these limitations is that the neural correlates of auditory perception, particularly for complex everyday sounds, remain largely unknown. Although neural responses can be measured in humans noninvasively and compared with perception, these evoked responses lack the anatomical and physiological specificity required to reveal underlying neural mechanisms. Single-unit spike-train responses can be measured from preclinical animal models with well-specified pathology; however, the disparate response types (point-process versus continuous-valued signals) have limited application of the same advanced signal-processing analyses to single-unit and evoked responses required for direct comparison. Here, we fill this gap with a unifying framework for analyzing both spike-train and evoked neural responses using advanced spectral analyses of both the slow and rapid response components that are known to be perceptually relevant for speech and music, particularly in challenging listening environments. Numerous benefits of this framework are demonstrated here, which support its potential to advance the translation of spike-train data from animal models to improve clinical diagnostics and technological development for real-world listening.

neuroscience↗

bioRxiv2020

Phenological, Physiological and yield markers as efficient tools to identify drought tolerant rice genotypes in Eastern India

Rice production is severely threatened by drought stress in Eastern India. To develop drought tolerant varieties, selection of donors for breeding programme is crucial. Twenty one selected rice genotypes including both tolerant and sensitive to drought were grown under well-watered and drought stress conditions in dry seasons of two successive years of 2017 and 2018. Leaf water potential, relative water content displayed significant difference among the genotypes during vegetative screening. At reproductive stage drought screening, days to 50% flowering was delayed in all genotypes except N22 and Anjali (showed early flowering) however grain yield and other yield related traits decreased significantly compared to well watered condition. Correlation analysis of phenological and yield related traits with grain yield revealed that tiller numbers and panicle numbers are highly correlated with grain yield both under well-watered and water stress conditions and contributes maximum towards grain yield. The dendrogram grouped Mahamaya, Sahabhagidhan, Poornima, IBD 1, Hazaridhan, Samleshwari and Danteshwari into one cluster which performed better under water stress conditions and had grain yield more than 1.69 tha-1. Sahabhagidhan, Poornima, Vandana, and N22 displayed tolerance to drought both under vegetative and reproductive conditions which could be a good selection for the breeders to develop drought tolerant rice cultivars for eastern region of India.

plant biology↗

bioRxiv2019

Detection of antibodies against a conserved capsid epitope as the basis of a novel universal serological test for foot-and-mouth disease

Diagnostic tests for foot-and-mouth disease (FMD) include the detection of antibodies against either the viral non-structural proteins or the capsid. The detection of antibodies against the structural proteins (SP) of the capsid can be used to monitor seroconversion in both infected and vaccinated animals. However, SP tests need to be tailored to the individual FMD virus serotype and their sensitivity performances may be affected by antigenic variability within each serotype and mismatching between tests reagents. As a consequence, FMD Reference Laboratories need to maintain contingency to employ multiple type-specific assays for large-scale serological surveillance and post-vaccination monitoring in the event of FMD outbreaks. In this study, a highly conserved region in the N terminus of FMDV capsid protein VP2 (VP2N) was characterised using a panel of intertypic-reactive monoclonal antibodies. This revealed a universal epitope in VP2N which could be used as a peptide antigen to detect FMDV-specific antibodies against all types of the virus. A VP2-peptide ELISA (VP2-ELISA) was optimised using experimental and reference antisera from immunized, convalescent and negative animals (n=172). The VP2-ELISA is universal, simple and provided sensitive (98.6 %) and specific (93%) detection of antibodies to all FMDV strains used in this study. We anticipate that this SP test could have utility for sero-surveillance during virus incursions in FMD-free countries and as an additional screening tool to assess FMD virus circulation in endemic countries.

immunology↗