Biology subjects

Bi, H.

Publications and source records attributed to Bi, H..

18 recordsLinked to original sources

bioRxiv2026

Stromal Gasdermin D-mediated Pyroptosis Drives Maladaptive CD4⁺ T-cell Remodeling in Tet2-Deficient Hematopoiesis

An inflammatory bone marrow microenvironment is increasingly recognized as critical in myeloid disease evolution, yet how stromal inflammation interfaces with adaptive immunity remains poorly defined. Here, we show that stromal pyroptosis drives mutation-specific myeloid expansion by coordinating monocytic remodeling and CD4 T-cell activation. Genetic ablation of gasdermin D in the bone marrow stroma suppressed stromal pyroptosis and attenuated Tet2-deficient myeloid expansion. Tet2 deficiency skewed monocyte and macrophage differentiation toward an activated, antigen-presenting state that interacted with pyroptotic stromal cells to promote expansion of a distinct CD4 T-cell population. These cells expressed canonical T follicular helper markers (Bcl6, Cxcr5, Il21, and Cd40l) together with interferon-responsive and tissue-interaction programs, consistent with an inflammation-adapted TFH-like state. CD40L produced by these cells reinforced the expansion of Tet2-deficient monocytes and macrophages, establishing a feed-forward stromal-immune circuit. Disruption of this axis through stromal gasdermin D deficiency or CD40L blockade attenuated myeloid expansion in vivo. Consistent with these findings, patients with isolated TET2 loss-of-function mutations exhibited CD4 T-cell skewing and CD40L+ T-cell-rich tertiary lymphoid structures in the bone marrow. Together, these data identify a pyroptosis-dependent stromal-immune axis that links early myeloid inflammation to maladaptive remodeling of adaptive immunity and reveals a context-dependent therapeutic vulnerability in Tet2-deficient hematopoiesis.

cancer biology↗

bioRxiv2026

Binary-SPA: A Reference-Free Method for Cell Annotation in High-Resolution Spatial Transcriptomics

Accurate cell type annotation remains a major challenge in high-resolution spatial transcriptomics analysis. Current approaches primarily rely on label transfer from single-cell RNA sequencing (scRNA-seq) reference datasets or marker-based annotation of transcriptionally defined clusters. While these methods are widely used, they have critical limitations. Label transfer approaches depend on the availability of a well-matched scRNA-seq reference. Marker-based annotation methods often suffer from accuracy and limited coverage. To address these challenges, we developed Binary-SPA, a computational framework for cell-type annotation of high-resolution spatial transcriptomics data. Binary-SPA performs annotation in two stages. First, a binary classification step identifies high-confidence cells using predefined marker sets. These confidently annotated cells are then used as an internal reference for anchor-based label transfer in the second stage. Binary-SPA outperforms conventional marker-based annotation approaches and existing label transfer methods across multiple high-resolution spatial transcriptomics platforms, preservation methods, and tissue types. While label transfer methods achieve high accuracy only when same-tissue scRNA-seq references are available, and decline substantially when relying on independent datasets, Binary-SPA matches this performance with 100% annotation coverage while eliminating the need for external reference data entirely. Binary-SPA demonstrates robust performance even in challenging specimens such as bone marrow biopsies, and validation against matched COMET protein expression data confirmed strong concordance between transcriptomic- and protein-based cell identities. Binary-SPA thus provides a robust, reference-free solution for spatial transcriptomics annotation with broad applicability to research and clinical specimens.

genomics↗

bioRxiv2024

Dairy DigiD ~ A Deep Learning-Based, Non-Invasive Biometric Identification System for Dairy Cattle Using Detectron2

Precision livestock farming demands accurate and reliable individual animal identification for optimizing health monitoring, resource allocation, and overall herd productivity. Conventional identification methods--such as ear tagging and wearable sensors--are often invasive, potentially causing animal discomfort and affecting natural behaviors. To address these challenges, we introduce Dairy DigiD, a non-invasive biometric classification system for dairy cattle using facial image analysis. Leveraging deep learning and the Detectron2 framework, Dairy DigiD classifies cattle into four categories--young (<2 years), mature milking, pregnant, and old--using over 2,500 high-resolution facial images. A DenseNet121 model achieved a classification accuracy of 97%, showcasing its strong discriminative capability. Further, Detectron2 attained an average recall of 87% in category identification, an average precision of 96% in cattle detection, and an overall detection accuracy of 93%, demonstrating its robustness and adaptability to varied environmental conditions. Unlike traditional approaches that rely on fixed facial landmarks and can be sensitive to environmental variability, Dairy DigiDs flexible deep learning architecture enables stable and scalable facial feature extraction. This non-invasive method minimizes animal stress, enhances data integrity, and facilitates improved herd management and welfare monitoring. In comparison to existing convolutional neural network (CNN)-based strategies, Dairy DigiD exhibits superior adaptability to real-world farm conditions, including variable lighting and complex poses. This work highlights the potential of integrating advanced computer vision and deep learning methodologies into precision dairy farming, offering an ethically aligned, efficient, and scalable approach to modern livestock management.

animal behavior and cognition↗

bioRxiv2024

DDX41 dissolves G-quadruplexes to maintain erythroid genome integrity and prevent cGAS-mediated cell death

Deleterious germline DDX41 variants constitute the most common inherited predisposition disorder linked to myeloid neoplasms (MNs). The role of DDX41 in hematopoiesis and how its germline and somatic mutations contribute to MNs remain unclear. Here we show that DDX41 is essential for erythropoiesis but dispensable for the development of other hematopoietic lineages. Using stage-specific Cre models for erythropoiesis, we reveal that Ddx41 knockout in early erythropoiesis is embryonically lethal, while knockout in late-stage terminal erythropoiesis allows mice to survive with normal blood counts. DDX41 deficiency induces a significant upregulation of G-quadruplexes (G4), noncanonical DNA structures that tend to accumulate in the early stages of erythroid precursors. We show that DDX41 co-localizes with G4 on the erythroid genome. DDX41 directly binds to and dissolves G4, which is significantly compromised in MN-associated DDX41 mutants. Accumulation of G4 by DDX41 deficiency induces erythroid genome instability, defects in ribosomal biogenesis, and upregulation of p53. However, p53 deficiency does not rescue the embryonic death of Ddx41 hematopoietic-specific knockout mice. In parallel, genome instability also activates the cGas-Sting pathway, which is detrimental to survival since cGas-deficient and hematopoietic-specific Ddx41 knockout mice are viable without detectable hematologic phenotypes, although these mice continue to show erythroid ribosomal defects and upregulation of p53. These findings are further supported by data from a DDX41 mutated MN patient and human iPSC-derived bone marrow organoids. Our study establishes DDX41 as a G4 dissolver, essential for erythroid genome stability and suppressing the cGAS-STING pathway.

cancer biology↗

bioRxiv2024

Can we predict sleep health based on brain features? A large-scale machine learning study

BackgroundsSeveral correlational or group comparison evidence highlighted robust associations between sleep health and macro-scale brain organization. However, inter-individual variability is critical in such interplay. Therefore, in this study, we aimed to investigate the role of brain imaging features in predicting diverse sleep health-related characteristics at the individual subject level using the Machine Learning (ML) approach. MethodsA sample of 28,088 participants from the UK Biobank was employed to calculate 4677 structural and functional neuroimaging markers. Then, we employed them to predict self-reported insomnia symptoms, sleep duration, easiness of getting up in the morning, chronotype, daily nap, daytime sleepiness, and snoring. To assess the predictability of brain features, we built seven different linear and nonlinear ML models for each sleep health-related characteristic. ResultsWe performed extensive ML analyses that involved more than 19 years of compute time. We observed relatively low performance in predicting all sleep health-related characteristics from brain images (e.g., balanced accuracy ranging between 0.50-0.59). Across all models, the best performance achieved was 0.59, using a linear ML model to predict the ease of getting up in the morning. In fact, a similar performance was achieved with models trained solely on age and sex, indicating that these demographic factors might be the ones driving the predictions. ConclusionsThe low capability of multimodal neuroimaging markers in predicting sleep health-related characteristics, even under extensive ML optimization in a large population sample, suggests a complex relationship between sleep health and brain organization.

neuroscience↗

bioRxiv2024

The T-type voltage-gated Ca2+ channel CaV3.1 as a candidate receptor for Pasteurella multocida toxin and contributes to the disruption of respiratory epithelial barrier induced by the toxin

Pasteurella multocida toxin (PMT) is an exotoxin produced by several members of the zoonotic respiratory pathogen P. multocida. The role of PMT in disrupting the mammalian respiratory barrier remains to be elucidated. In this study, we discovered that inoculation of recombinantly expressed PMT increased the permeability of the respiratory epithelial barrier in mouse and respiratory cell models. This was evidenced by a decreased expression of tight junctions (ZO-1, occludin) and adherens junctions ({beta}-catenin, E-cadherin), as well as enhanced cytoskeletal rearrangement. In mechanism, we demonstrated that PMT inoculation induced cytoplasmic Ca2+ inflow, leading to an imbalance of cellular Ca2+ homeostasis and endoplasmic reticulum stress. This process further stimulated the RhoA/ROCK signaling, promoting cytoskeletal rearrangement and reducing the expression of tight junctions and adherens junctions. Notably, the T-type voltage-gated Ca2+ channel CaV3.1 was found to participate in PMT-induced cytoplasmic Ca2+ inflow. Knocking out CaV3.1 significantly reduced the cytotoxicity induced by PMT on swine respiratory epithelial cells and mitigated cytoplasmic Ca2+ inflow stimulated by PMT. Further analysis identified Ser (aa92), Glu (aa155), Tyr (aa167), and Leu (aa448) as crucial sites utilized by PMT to interact with CaV3.1. These findings suggest CaV3.1 serves as an important host receptor of PMT and contributes to PMT-induced respiratory epithelial barrier disruption. ImportancePMT is a significant toxin produced by the zoonotic respiratory pathogen P. multocida, yet little is known about its pathogenesis beyond causing progressive atrophic rhinitis in pigs. In our study, we have discovered that PMT has the capacity to disrupt the integrity of the mammalian respiratory epithelial barrier. This disruption involves an imbalance in cellular Ca2+ homeostasis, endoplasmic reticulum stress, and activation of the RhoA/ROCK signaling pathway induced by PMT. Importantly, we have identified CaV3.1 as a pivotal receptor that plays a crucial role in the pathogenic effects of PMT. Our findings highlight the potential of CaV3.1 as a target for intervention strategies aimed at combating the detrimental effects of PMT.

microbiology↗

bioRxiv2024

Lower motor performance is linked with poor sleep quality, depressive symptoms, and grey matter volume alterations

Motor performance (MP) is essential for functional independence and well-being, particularly in later life. However, the relationship between behavioural aspects such as sleep quality and depressive symptoms, which contribute to MP, and the underlying structural brain substrates of their interplay remains unclear. This study used three population-based cohorts of younger and older adults (n=1,950) from the Human Connectome Project-Young Adult (HCP-YA), HCP-Aging (HCP-A), and enhanced Nathan Kline Institute-Rockland sample (eNKI-RS). Several canonical correlation analyses were computed within a machine learning framework to assess the associations between each of the three domains (sleep quality, depressive symptoms, grey matter volume (GMV)) and MP. The HCP-YA analyses showed progressively stronger associations between MP and each domain: depressive symptoms (unexpectedly positive, r=0.13, SD=0.06), sleep quality (r=0.17, SD=0.05), and GMV (r=0.19, SD=0.06). Combining sleep and depressive symptoms significantly improved the canonical correlations (r=0.25, SD=0.05), while the addition of GMV exhibited no further increase (r=0.23, SD=0.06). In young adults, better sleep quality, mild depressive symptoms, and GMV of several brain regions were associated with better MP. This was conceptually replicated in young adults from the eNKI-RS cohort. In HCP-Aging, better sleep quality, fewer depressive symptoms, and increased GMV were associated with MP. Robust multivariate associations were observed between sleep quality, depressive symptoms and GMV with MP, as well as age-related variations in these factors. Future studies should further explore these associations and consider interventions targeting sleep and mental health to test the potential effects on MP across the lifespan.

neuroscience↗

bioRxiv2024

Coral hosts actively accumulate inorganic carbon for zooxanthellae to maintain symbiotic relationships

High primary productivity of coral reefs is widely attributed to the mutualistic symbiosis between coral hosts and their microalgal partners (Symbiodiniaceae). Although the mechanisms maintaining this symbiosis have been considerable investigated, how the symbiont microalgae within coral get sufficient CO2 for photosynthesis still remains inadquately explored. Here, we hypothesized that corals may actively accumulate dissolved inorganic carbon (DIC) for microalgae to maintain the symbiosis. Carbon (HCO3- and glucose) supply and consumption were evaluated in the scleractinian coral (Goniopora lobata) and its symbiont under light and dark conditions. Results suggest that Symbiodiniaceae were high DIC consumers, requiring about 2-3 fold more DIC than free-living species. The corals were high DIC producers, with internal concentrations up to 4.2 fold higher than in the surrounding seawater. In the absence of microalgae utilization, the excess DIC they produced appeared detrimental to their own growth. Moreover, transcriptomic analysis identified several DIC enrichment pathways are evolved in corals to attact the Symbiodiniaceae, including CO2 concentrating mechanisms, respiration, calcification and phosphoenolpyruvate carboxylase. Increased the CO2 dosage in seawater may induce coral symbiosis bleaching. Our findings can deeper reveal on the mechanisms sustaining coral symbiosis, and may help to predict how some corals respond to DIC imbalance under climate changes.

ecology↗

bioRxiv2024

Artificial Intelligence driven Benchmarking Tool for Emission Reduction in Canadian Dairy Farms

This study develops an Artificial Intelligence-driven benchmarking tool to reduce methane emissions in Canadian dairy farms, responding to the urgent need to mitigate environmental impacts from agriculture. Utilizing a comprehensive dataset from over 1000 dairy farms and processors across Canada, combined with satellite-driven methane emission data, we apply advanced machine learning technologies and data analytics, including geospatial analysis and time series forecasting. This approach identifies critical emission hotspots and temporal trends. We tested several predictive models--ARIMA, LSTM, GBR, and PROPHET--with the LSTM model showing the greatest accuracy in forecasting emissions, demonstrated by the lowest Root Mean Squared Error (RMSE) of 15.40. Our results highlight the transformative potential of AI tools in agricultural environmental management by providing dairy farmers and policymakers with precise, real-time emission insights. This facilitates informed decision-making and the implementation of effective emission reduction strategies. This study not only advances understanding of emission dynamics in dairy farming but also underscores the role of technology in sustainable agricultural practices and achieving environmental targets consistent with global agreements.

bioengineering↗

bioRxiv2022

Taming the data deluge: a novel end-to-end deep learning framework for classifying marine biological and environmental images

Marine underwater imaging facilitates non-destructive sampling of species at frequencies, durations, and accuracies that are unattainable by conventional sampling methods. These systems necessitate complex automated processes to identify organisms efficiently, however, current frameworks struggle to disentangle ecological foreground components from their dispensable background content. Underwater image processing relies on common architecture: namely image binarization for segmenting potential targets, prior to information extraction and classification by deep learning models. While intuitive, this infrastructure underperforms as it has difficulty in handling: high concentrations of biotic and abiotic particles, rapid changes in dominant taxa, and target sizes that vary by several orders of magnitude. To overcome these issues, a new framework is presented that begins with a scene classifier to capture large within-image variation, such as disparities in particle concentration and dominant taxa. Following scene classification, scene-specific regional convolutional neural network (Mask R-CNN) models were trained to separate target objects into different taxonomic groups. The procedure allows information to be extracted from different image types, while minimizing potential bias for commonly occurring features. Using in situ coastal PlanktonScope images, we compared the scene-specific models to the Mask R-CNN model including all scene categories without scene classification, defined as the full model, and found that the scene-specific approach outperformed the full model with >20% accuracy in noisy images. The full model missed up to 78% of the dominant taxonomic groups, such as Lyngbya, Noctiluca, and Phaeocystis colonies. This performance improvement is due to the scene classifier, which reduces the variation among images and allows an improved match between the observed taxonomic groups and the taxonomic groups in pre-trained models. We further tested the framework on images from a benthic video camera and an imaging sonar system. Results demonstrate that the procedure is applicable to different types of underwater images and achieves significantly more accurate results than the full model. Given that the unified framework is neither instrument nor ecosystem-specific, the proposed model facilitates deployment throughout the marine biome.

ecology↗

bioRxiv2022

Uni-Fold: An Open-Source Platform for Developing Protein Folding Models beyond AlphaFold

Recent breakthroughs on protein structure prediction, namely AlphaFold, have led to unprecedented new possibilities in related areas. However, the lack of training utilities in its current open-source code hinders the community from further developing or adapting the model. Here we present Uni-Fold as a thoroughly open-source platform for developing protein folding models beyond AlphaFold. We reimplemented AlphaFold and AlphaFold-Multimer in the PyTorch framework, and reproduced their from-scratch training processes with equivalent or better accuracy. Based on various optimizations, Uni-Fold achieves about 2.2 times training acceleration compared with AlphaFold under similar hardware configuration. On a benchmark of recently released multimeric protein structures, Uni-Fold outperforms AlphaFold-Multimer by approximately 2% on the TM-Score. Uni-Fold is currently the only open-source repository that supports both training and inference of multimeric protein models. The source code, model parameters, test data, and web server of Uni-Fold are publicly available3.

bioinformatics↗

bioRxiv2022

Low mutation load in a supergene underpinning alternative male mating strategies in ruff

Ruffs are shorebirds with an elaborate lekking behavior involving three male morphs with different mating strategies: Independents, Satellites, and Faeders1,2. The latter two are heterozygous for different versions of a supergene maintained by an inversion that were estimated to have occurred about 4 million years ago3. Faeders carry an intact inversion while the Satellite allele is recombinant, both of which are expected to accumulate high mutational load because they are recessive lethals. Here we have constructed a highly contiguous genome assembly of the inversion region for both the Independent and Satellite haplotypes. The recombination event(s) between an inverted and non-inverted chromosome creating the Satellite allele must have occurred recently (within the last 100,000 years) based on the minute sequence divergence between the Satellite and Independent alleles in the recombinant regions. Contrary to expectations4,5, we find no expansion of repeats and only a very modest mutation load on the Satellite allele in the nonrecombinant region despite high sequence divergence (1.46%). The essential centromere protein CENPN gene is disrupted by the inversion, and surprisingly is as well conserved on the inversion haplotypes as on the noninversion haplotype. The results suggest that the inversion may be much younger than previously thought. The lack of mutation load despite recessive lethality can be explained by the introgression of the inversion from a now extinct lineage.

evolutionary biology↗

bioRxiv2021

Asynchronous and coherent dynamics in balanced excitatory-inhibitory populations

Dynamic excitatory-inhibitory (E-I) balance is a paradigmatic mechanism invoked to explain the irregular low firing activity observed in the cortex. However, we will show that the E-I balance can be at the origin of other regimes observable in the brain. The analysis is performed by combining extensive simulations of sparse E-I networks composed of N spiking neurons with analytical investigations of low dimensional neural mass models. The bifurcation diagrams, derived for the neural mass model, allow to classify the possible asynchronous and coherent behaviours emerging in balanced E-I networks with structural heterogeneity for any finite in-degree K. In the limit N >> K >> 1 both supra and sub-threshold balanced asynchronous regimes can be observed in our system. Due to the heterogeneity the asynchronous states are characterized by the splitting of the neurons in three groups: silent, fluctuation and mean driven. These features are consistent with experimental observations reported for heterogeneous neural circuits. The coherent rhythms observed in our system can range from periodic and quasi-periodic collective oscillations (COs) to coherent chaos. These rhythms are characterized by regular or irregular temporal fluctuations joined to spatial coherence somehow similar to coherent fluctuations observed in the cortex over multiple spatial scales. The COs can emerge due to two different mechanisms. A first mechanism similar to the pyramidal-interneuron gamma (PING) one, usually invoked for the emergence of{gamma} -oscillations. The second mechanism is intimately related to the presence of current fluctuations, which sustain COs characterized by an essentially simultaneous bursting of the two populations. We observe period-doubling cascades involving the PING-like COs finally leading to the appearance of coherent chaos. Fluctuation driven COs are usually observable in our system as quasi-periodic collective motions characterized by two incommensurate frequencies. However, for sufficiently strong current fluctuations we report a novel mechanism of frequency locking among collective rhythms promoted by these intrinsic fluctuations. Our analysis suggest that despite PING-like or fluctuation driven COS are observable for any finite in-degree K, in the limit N >> K >> 1 these solutions finally result in two coexisting balanced regimes: an asynchronous and a fully synchronized one.

neuroscience↗

bioRxiv2021

Transspecies beak color polymorphism in the Darwin's finch radiation

Carotenoid-based polymorphisms are widespread in populations of birds, fish, and reptiles1, but little is known of how they affect fitness and are maintained as species multiply2. We report a combined field and molecular-genetic investigation of a nestling beak color polymorphism in Darwins finches. Beaks are pink or yellow, and yellow is recessive3. Here we show that the polymorphism arose in the Galapagos approximately half a million years ago through a regulatory mutation in the BCO2 gene, and is shared by 14 descendant species. The frequency of the yellow genotype is associated with cactus flower abundance in cactus finches, and is altered by introgressive hybridization. The polymorphism is most likely a balanced polymorphism, maintained by ecological selection pressures associated with diet, and augmented by occasional interspecific introgression. Polymorphisms that are hidden as adults, as here, may contribute to evolutionary diversification in underappreciated ways in other systems.

evolutionary biology↗

bioRxiv2020

Theta-nested gamma oscillations in next generation neural mass models

Theta-nested gamma oscillations have been reported in many areas of the brain and are believed to represent a fundamental mechanism to transfer information across spatial and temporal scales. In a series of recent experiments in vitro it has been possible to replicate with an optogenetic theta frequency stimulation several features of cross-frequency coupling (CFC) among theta and gamma rhythms observed in behaving animals. In order to reproduce the main findings of these experiments we have considered a new class of neural mass models able to reproduce exactly the macroscopic dynamics of spiking neural networks. In this framework, we have examined two set-ups able to support collective gamma oscillations: namely, the pyramidal interneuronal network gamma (PING) and the interneuronal network gamma (ING). In both set-ups we observe the emergence of theta-nested gamma oscillations by driving the system with a sinusoidal theta-forcing in proximity of a Hopf bifurcation. These mixed rhythms display always phase amplitude coupling. However two different types of nested oscillations can be identified: one characterized by a perfect phase locking between theta and gamma rhythms, corresponding to an overall periodic behaviour; another one where the locking is imperfect and the dynamics is quasi-periodic or even chaotic. From our analysis it emerges that the locked states are more frequent in the ING set-up. In agreement with the experiments, we find theta-nested gamma oscillations for forcing frequencies in the range [1:10] Hz, whose amplitudes grow proportionally to the forcing one and which are clearly modulated by the theta phase. Furthermore, analogously to the experiments, the gamma power and the frequency of the gamma-power peak increase with the forcing amplitude. At variance with experimental findings, the gamma-power peak does not shift to higher frequencies by increasing the theta frequency. This effect can be obtained, in or model, only by incrementing, at the same time, also the noise or the forcing amplitude. On the basis of our analysis both the PING and ING mechanisms give rise to theta-nested gamma oscillations with almost identical features.

neuroscience↗

bioRxiv2019

γ-Secretase controls the specification of astrocytes from oligodendrocyte precursor cells via Stat3

Oligodendrocytes (OLs) and astrocytes play critical roles in a variety of brain functions. OL precursor cells (OPCs) are known to give rise to OLs as well as astrocytes. However, little is known about the mechanism by which OPCs determine their specification choice for OLs versus astrocytes in the central nervous system (CNS). Here we show that genetic inhibition of {gamma}-secretase in OPCs reduces OL differentiation but enhances astrocyte specification. Mechanistic analysis reveals that inhibition of {gamma}-secretase results in decreased levels of Hes1, and that Hes1 down-regulates the expression of signal transducer and activator of transcription3 (Stat3) via binding to specific regions of its promoter. We demonstrate that conditional inactivation of Stat3 in OL lineages restores the number of astrocytes in {gamma}-secretase mutant mice. In summary, this study identifies a key mechanism which controls OPCs specification choice for OL versus astrocyte during postnatal development. This {gamma}-secretase-dependent machinery may be essential for the CNS to maintain the population balance between OLs and astrocytes.

neuroscience↗

bioRxiv2019

Coexistence of fast and slow gamma oscillations in one population of inhibitory spiking neurons

Oscillations are a hallmark of neural population activity in various brain regions with a spectrum covering a wide range of frequencies. Within this spectrum gamma oscillations have received particular attention due to their ubiquitous nature and to their correlation with higher brain functions. Recently, it has been reported that gamma oscillations in the hippocampus of behaving rodents are segregated in two distinct frequency bands: slow and fast. These two gamma rhythms correspond to different states of the network, but their origin has been not yet clarified. Here, we show theoretically and numerically that a single inhibitory population can give rise to coexisting slow and fast gamma rhythms corresponding to collective oscillations of a balanced spiking network. The slow and fast gamma rhythms are generated via two different mechanisms: the fast one being driven by the coordinated tonic neural firing and the slow one by endogenous fluctuations due to irregular neural activity. We show that almost instantaneous stimulations can switch the collective gamma oscillations from slow to fast and vice versa. Furthermore, to make a closer contact with the experimental observations, we consider the modulation of the gamma rhythms induced by a slower (theta) rhythm driving the network dynamics. In this context, depending on the strength of the forcing and the noise amplitude, we observe phase-amplitude and phase-phase coupling between the fast and slow gamma oscillations and the theta forcing. Phase-phase coupling reveals on average different theta-phases preferences for the two coexisting gamma rhythms joined to a wide cycle-to-cycle variability.

neuroscience↗

bioRxiv2018

Viral etiology of Acute Respiratory Infections in Hospitalized Children in Novosibirsk City, Russia (2013 - 2017)

Introduction Introduction Materials and methods Results Discussion References Acute respiratory infections (ARIs) pose a significant public health problem worldwide, causing considerable morbidity and mortality among people of all age groups [1]. Children are on average infected two to three times more frequently than adults. [2]. There are more than 200 respiratory viruses that can cause ARIs. Respiratory syncytial virus (RSV), human rhinovirus (HRV), human metapneumovirus (HMPV), human parainfluenza virus (PIV), human enterovirus (EV), influenza virus (IFV), human coronavirus (CoV), adenovirus (ADV), and human bocavirus (BoV) are the most common viral agents associated with ARIs, accounting for around 70 % of ARIs [3, 4]. The frequency of mixed respiratory viral ...

microbiology↗