Biology subjects

Zhang, C.

Publications and source records attributed to Zhang, C..

At least 973 records · Page 54Linked to original sources

bioRxiv2019

BEN-solo factors partition active chromatin to ensure proper gene activation in Drosophila

The Drosophila genome encodes three BEN-solo proteins including Insensitive (Insv), Elba1 and Elba2 that possess activities in both transcriptional repression and chromatin insulation. These proteins all have a DNA binding BEN domain. A fourth protein Elba3 bridges Elba1 and Elba2 to form a heterotrimeric complex ELBA. Here we report comprehensive investigation on the in vivo functions of these proteins in Drosophila embryos. We generate ChIP-seq data of all these factors from all cognate and non-cognate mutants to assess common and distinct binding locations of Insv and ELBA, and genetic interdependencies. Notably, while most Elba1 and Elba2 binding requires the full ELBA complex, the adapter protein Elba3 can associate with chromatin and repress gene expression independently of Elba1 and Elba2. We also employ high-resolution ChIP-nexus mapping to show that Insv binds to DNA in a symmetric configuration while the ELBA complex binds asymmetrically in vivo. We observe that motifs of known insulator proteins are enriched in ELBA and Insv ChIP peaks and demonstrate that ELBA collaborates with other insulator factors to regulate developmental patterning in embryos. To differentiate the insulator function of ELBA and Insv from their repressor activity, we determined real-time transcription change in mutant embryos using precision nuclear run-on sequencing. ELBA factor mutants dampen expression differences between pairs of ELBA-bound neighboring genes. Finally, transgenic reporters confirm insulation activity of ELBA- and Insv-bound sites. Altogether, these findings define ELBA and Insv as general insulator proteins in Drosophila and demonstrate the functional importance of insulators in partitioning transcription units.

developmental biology↗

bioRxiv2019

CDC6 regulates both G2/M transition and metaphase-to-anaphase transition during the first meiosis of mouse oocytes

Cell division cycle protein CDC6 is essential for the initiation of DNA replication. CDC6 was recently shown to inhibit the microtubule-organizing activity of the centrosome. Here, we show that CDC6 is localized to the spindle from Pro-MI to MII stages of oocytes, and it plays important roles at two critical steps of oocyte meiotic maturation. CDC6 depletion facilitated the G2/M transition (GV breakdown, GVBD) through regulation of Cdh1 and cyclin B1 expression and CDK1 phosphorylation in a GVBD-inhibiting culture system containing milrinone. Furthermore, GVBD was significantly decreased after knockdown of cyclin B1 in CDC6-depleted oocytes, indicating that the effect of CDC6 loss on GVBD stimulation was mediated, at least in part, by raising cyclin B1. Knockdown of CDC6 also caused abnormal localization of {gamma}-tubulin, resulting in defective spindles, misaligned chromosomes, cyclin B1 accumulation and spindle assembly checkpoint (SAC) activation, leading to significant Pro-MI/MI arrest and PB1 extrusion failure. These phenotypes were also confirmed by time-lapse live cell imaging analysis. The results indicate that CDC6 is indispensable for maintaining G2 arrest of meiosis and functions in G2/M checkpoint regulation in mouse oocytes. Moreover, CDC6 is also a key player regulating meiotic spindle assembly and metaphase-to-anaphase transition in meiotic oocytes.\n\nSummary statementWe show that CDC6 is indispensable for maintaining G2 arrest of mouse oocytes. Moreover, CDC6 is also a key player regulating meiotic spindle assembly and metaphase-to-anaphase transition in meiotic oocytes.

developmental biology↗

bioRxiv2019

Biologically inspired model of associative memory storage with noisy neurons and synapses

Neural networks in the brain can function reliably despite various sources of errors and noise present at every step of signal transmission. These sources include errors in the presynaptic inputs to the neurons, noise in synaptic transmission, and fluctuations in the neurons postsynaptic potentials. Collectively they lead to errors in the neurons outputs which are, in turn, injected into the network. Does unreliable network activity hinder fundamental functions of the brain, such as learning and memory retrieval? To explore this question, this article examines the effects of errors and noise on properties of biologically constrained networks of inhibitory and excitatory neurons involved in associative sequence learning. The associative learning problem is solved analytically and numerically, and it is also shown how memory sequences can be loaded into the network with a more biologically plausible perceptron-type learning rule. Interestingly, the results reveal that errors and noise during learning increase the probability of memory recall. There is a tradeoff between the capacity and reliability of stored memories, and, noise during learning is required for optimal retrieval of stored information. What is more, networks loaded with associative memories to capacity display many structural and dynamical features observed in local cortical circuits. Due to the similarities between the associative and brain networks, this article predicts that the connections originating from unreliable inhibitory and excitatory neurons or neuron classes in the cortex must be depressed or eliminated during learning, while the connections onto noisy neurons or neuron classes must have lower probabilities and higher weights.

neuroscience↗

bioRxiv2019

Evolutionary patterns of the chimerical retrogenes in Oryza

Chimerical retroposition delineate a process by which RNA reverse transcribed integration into genome accompanied with recruiting flanking sequence, which is asserted to play essential roles and drive genome evolution. Although chimerical retrogenes hold high origination rate in plant genome, the evolutionary pattern of retrogenes and their parental genes are not well understood in rice genome. In this study, using maximum likelihood method, we evaluated the substitution ratio along lineages of 24 retrogenes and parental gene pairs to retrospect the evolutionary patterns. The results indicate that some specific lineages in 7 pairs underwent positive selection. Besides the rapid evolution in the initial stage of new chimerical retrogene evolution, an unexpected pattern was revealed: soon or some uncertain period after the origination of new chimerical retrogenes, their parental genes evolved rapidly under positive selection, rather than the rapid evolution of the new chimerical retrogenes themselves. This result lend support to the hypothesis that the new copy assistant the function evolution among parental gene and retrogene. Transcriptionally, we also found that one retrogene (RCG3) have a high expression at the period of calli infection which supported by chip data while its parental gene doesnt have. Finally, by calibration to Ka/Ks analysis results in other species including Apis mellifera, we concluded that chimerical retrogenes are higher proportionally positive selected than the regular genes in the rice genome.

evolutionary biology↗

bioRxiv2019

Massive computational acceleration by using neural networks to emulate mechanism-based biological models

Mechanism-based mathematical models are the foundation for diverse applications. It is often critical to explore the massive parametric space for each model. However, for many applications, such as agent-based models, partial differential equations, and stochastic differential equations, this practice can impose a prohibitive computational demand. To overcome this limitation, we present a fundamentally new framework to improve computational efficiency by orders of magnitude. The key concept is to train an artificial neural network using a limited number of simulations generated by a mechanistic model. This number is small enough such that the simulations can be completed in a short time frame but large enough to enable reliable training of the neural network. The trained neural network can then be used to explore the system dynamics of a much larger parametric space. We demonstrate this notion by training neural networks to predict self-organized pattern formation and stochastic gene expression. With this framework, we can predict not only the 1-D distribution in space (for partial differential equation models) and probability density function (for stochastic differential equation models) of variables of interest with high accuracy, but also novel system dynamics not present in the training sets. We further demonstrate that using an ensemble of neural networks enables the self-contained evaluation of the quality of each prediction. Our work can potentially be a platform for faster parametric space screening of biological models with user defined objectives.

systems biology↗

bioRxiv2019

Targeted mutagenesis of the SUMO protease, Overly Tolerant to Salt1 in rice through CRISPR/Cas9-mediated genome editing reveals a major role of this SUMO protease in salt tolerance.

SUMO proteases are encoded by a large gene family in rice and are a potential source of specificity within the SUMO system that is responding to different environmental cues. We previously demonstrated a critical role of OsOTS class of SUMO proteases in salt and drought stress in rice by silencing several family members collectively via RNAi methods. However, to date it has not been possible to assign a role to specific family members due to limitations of RNAi mediated off target silencing across several members of the gene family. The clustered regularly interspaced short palindromic repeats (CRISPR)-associated endonuclease 9 (CRISPR/Cas9) system has emerged as a promising technology for specific gene editing in crop plants. Here, we demonstrate targeted mutagenesis of OsOTS1 in rice using the CRISPR/Cas9 gene editing system in the rice cultivar Kitaake. Guide RNA mediated mutations in OsOTS1 was highly efficient with almost 95% of T0 transgenics showing the desired effect with no off-target mutations. The OsOTS1 mutations observed in T0 generation were heritable in subsequent generations. OsOTS1 CRISPR lines show enhanced sensitivity to salt with reduced root and shoot biomass indicating that OsOTS1 has a major role in salt stress tolerance in rice. This unexpected finding indicates that precise and effective genome editing can be used to characterise specificity within the SUMO system in rice.

plant biology↗

bioRxiv2019

Spatiotemporal dissection of the cell cycle regulated human proteome

Cellular division is a fundamental source of cell-to-cell variability, and studies of transcript and protein abundances have revealed several hundred genes that are regulated by the cell cycle1-8. However, none of these studies provide single-cell resolution of protein expression, leaving an incomplete understanding of cell-to-cell heterogeneity and the roles of cycling transcripts and proteins. Here, we present the first comprehensive map of spatiotemporal heterogeneity of the human proteome by integrating proteomics at subcellular resolution, single-cell transcriptomics, and pseudotime measurements of individual cells within the cell cycle. We identify that 17% of the human proteome displays cell-to-cell variability, of which 26% is correlated to cell cycle progression, and we present the first evidence of cell cycle association for 235 proteins. Only 15% of proteomic cell cycle regulation is due to transcriptomic cycling, which points to other means of regulation such as post-translational modifications. For proteins regulated at the transcript level, we observe a 7.7 hour delay between peak expression of transcript and protein on average. This spatially resolved proteomic map of the cell cycle has been integrated into the Human Protein Atlas and serves as a valuable resource for accelerating molecular studies of the human cell cycle and cell proliferation.

cell biology↗

bioRxiv2019

VEF: a Variant Filtering tool based on Ensemble methods

MotivationVariant discovery is crucial in medical and clinical research, especially in the setting of personalized medicine. As such, precision in variant identification is paramount. However, variants identified by current genomic analysis pipelines contain many false positives (i.e., incorrectly called variants). These can be potentially eliminated by applying state-of-the-art filtering tools, such as the Variant Quality Score Recalibration (VQSR) or the Hard Filtering (HF), both proposed by GATK. However, these methods are very user-dependent and fail to run in some cases. We propose VEF, a variant filtering tool based on ensemble methods that overcomes the main drawbacks of VQSR and the HF. Contrary to these methods, we treat filtering as a supervised learning problem. This is possible by using for training variant call data for which the set of "true" variants is known, i.e., a gold standard exists. Hence, we can classify each variant in the training VCF file as true or false using the gold standard, and further use the annotations of each variant as features for the classification problem. Once trained, VEF can be directly applied to filter the variants contained in a given VCF file. Analysis of several ensemble methods revealed random forest as offering the best performance, and hence VEF uses a random forest for the classification task. ResultsAfter training VEF on a Whole Genome Sequencing (WGS) Human dataset of sample NA12878, we tested its performance on a WGS Human dataset of sample NA24385. For these two samples, the set of high-confident variants has been produced and made available. Results show that the proposed filtering tool VEF consistently outperforms VQSR and HF. In addition, we show that VEF generalizes well even when some features have missing values, and when the training and testing datasets differ either in coverage or in the sequencing machine that was used to generate the data. Finally, since the training needs to be performed only once, there is a significant saving in running time when compared to VQSR (50 minutes versus 4 minutes approximately for filtering the SNPs of WGS Human sample NA24385). Code and scripts available at: github.com/ChuanyiZ/vef.

bioinformatics↗

bioRxiv2019

Identification of transgene-free CRISPR edited plants of rice and tomato by monitoring DsRED fluorescence in dry seeds

CRISPR/Cas technology for specific and precise modification of genomes has transformed molecular biology. However, quick elimination of the transgene remains a challenge in plant biotechnology after genome edition, especially for crops due to their long life cycle and multiploidy, not only to avoid transgene position effects and to minimize the probability of off-target mutation appearance, but also to deliver end users with edited plants free of the recombinant gene editing machinery. Counter selection based on resistance marker genes are inconvenient in the case of CRISPR/Cas applications because plants lacking the transgene cannot survive the selection, and thus two more generations must be screened to evaluate the presence of the transgene. In the case of some crops, generations can last between 6 months and a few years, and the workload may be a limiting factor because transgene detection by PCR requires germination of seeds, and selected plants must be grown until a new generation can be harvested. The expression of fluorescent proteins as selective marker has been successfully used in Arabidopsis thaliana (Stuitje et al., 2003) as a fast method for transgene presence detection prior to seed germination. Despite this clear advantage, it has not been tested in other species yet, because of the special requirements of in vitro transformation protocols. To overcome the above mentioned inconvenient, we have adapted fluorescence-dependent monitoring of transgene to genome editing approaches in tomato and rice with the goal of obtaining transgene-free homozygous edited crop plants in two generations.

plant biology↗

bioRxiv2019

Primary Studies on the Effect of Microbially Coalbed Methane Enhancement in Suit in the Qinshui Basin

Methods used to yield bio-methane with coal to increase coalbed methane reserves had researched, thus providing a means for improving gas drainage efficiency. One such method utilized to convert coal into gas involves coal biodegradation technology. In order to confirm the practical application of this technology, the experiments were conducted in wells, Z-159, Z-163, Z-167, and Z-7H, in the Qinshui Basin in China, and the duration of the experiments was 32 months. Cl- ion tracer, number changes of Methanogen sp., and coal bed biome evolution indicated that the culture medium diffused in the Z-159 and Z-7H wells. These wells resumed gas production separately. Gasification of coal lasted 635 and 799 days, and yielded 74817 m3 and 251754 m3 coalbed methane in Z-159 and Z-7H wells, respectively. Results demonstrate that coalbed methane enhancement with biogasification of coal is a potential technical to achieve the productivity improvement of coalbed methane wells.

bioengineering↗

bioRxiv2019

Mapping of transgenic alleles in plants using a Nanopore-based sequencing strategy

Transgenic technology was developed to introduce transgenes into various organisms to validate gene function and add genetic variation for the development of beneficial input or output trait over 40 years ago. However, the identification of the transgene insertion position in the genome, while doable, can be cumbersome in the organisms with complex genomes. Here, we report a Nanopore-based sequencing method to rapidly map transgenic alleles in the soybean genome. This strategy is high-throughput, convenient, reliable, and cost-efficient. The transgenic allele mapping protocol outlined herein can be easily translated to other higher eukaryotes with complex genomes.

plant biology↗

bioRxiv2019

The genetic basis of cis-regulatory divergence between the subspecies of cultivated rice (Oryza sativa)

Cultivated rice consists of two subspecies, Indica and Japonica, that exhibit well-characterized differences at the morphological and genetic levels. However, the differences between these subspecies at the transcriptome level remains largely unexamined. Here, we provide a comprehensive characterization of transcriptome divergence and cis-regulatory variation within rice using transcriptome data from 91 accessions from a rice diversity panel (RDP1). The transcriptomes of the two subspecies of rice are highly divergent. The expression and genetic diversity was significantly lower within Japonica relative to Indica, which is consistent with the known population bottleneck during Japonica domestication. Moreover, 1,860 and 1,325 genes showed differences in heritability in the broad and narrow sense respectively, between the subspecies, which was driven largely by environmental and genetic effects rather than differences in phenotypic variability. We leveraged high-density genotypic data and transcript levels to identify cis-regulatory variants that may explain the genetic divergence between the subspecies. We identified significantly more eQTL that were specific to the Indica subspecies compared to Japonica, suggesting that the observed differences in expression and genetic variability also extends to cis-regulatory variation. We next explored the potential causes of this cis-regulatory divergence by assessing local genetic diversity for cis-eQTL. Local genetic diversity around subspecies-specific cis-eQTL was significantly lower than genome-wide averages in subspecies lacking the eQTL, suggesting that selective pressures may have shaped regulatory variation in each subspecies. This study provides the first comprehensive characterization of transcriptional and cis-regulatory variation in cultivated rice, and could be an important resource for future studies.

genomics↗

bioRxiv2019

Combined Transcriptomics and Proteomics Forecast Analysis for Potential Genes Regulating the Columbian Plumage Color in Chickens

BackgroundColoration is one of the most recognizable characteristics in chickens, and clarifying the coloration mechanisms will help us understand feather color formation. \"Yufen I\" is an commercial egg-laying chicken breed in China, that was developed by a three-line cross using lines H, N and D. Columbian plumage is a typical feather character of the \"Yufen I\" H line. To elucidate the molecular mechanism underlying pigmentation of Columbian plumage, this study utilizes the technology of high-throughput sequencing to compare the transcriptome and proteome differences in different feather follicular tissue, including the dorsal neck with black and white striped feather follicles (Group A) and the ventral neck with white feather follicles (Group B) in the \"Yufen I\" H line.\n\nResultsIn this study, we identified a total of 21,306 genes and 5203 proteins in chicken feather follicles. Among these, 209 genes and 382 proteins were differentially expressed in two different locations, Group A and Group B, respectively. A total of 8 differentially expressed genes (DEGs) and 9 differentially expressed proteins (DEPs) were found to be involved in the melanogenesis pathway. Besides, a specifically expressed MED23 gene and a differential expressed GNAQ protein were involved in melanin synthesis. Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis mapped 190 DEGs and 322 DEPs, to 175 and 242 pathways, respectively, and there were 166 pathways correlated with both DEGs and DEPs. 49 DEPs/DEGs overlapped and were enriched for 12 pathways. Transcriptomic and proteomic analyses revealed that the following pathways were activated: melanogenesis, cardiomycete adrenergic, calcium and the cGMP-PKG. The expression of DEGs was validated by real-time quantitative polymerase chain reaction (qRT-PCR) that was similar to that of RNA-seq. In addition, we found that MED23, FZD10, WNT7B and WNT11 genes expression peaked at approximately 8 weeks in the \"Yufen I\" H line, which is consistent with the molting cycle. As both the groups showed significant differences in terms of expression of the genes studied, this study opens up avenues for study in the future to assess their exact function in color of plumage.\n\nConclusionThese common DEGs and DEPs were enriched in the melanogenesis pathway. The MED23 and GNAQ were also reported to have a crucial part synthesis of melanin. In addition, this study is the first to reveal variations in gene and protein in the \"Yufen I\" H line during Columbian feather color development, and discover principal genes and proteins that would aid in the functional genomics studies in future. The results of the present study provide a significant conceptual basis for the \"Yufen I\" H line future breeding schemes and provide a basis for research on the mechanisms of feather pigmentation.

molecular biology↗

bioRxiv2019

Exploring Frequency-dependent Brain Networks from ongoing EEG using Spatial ICA during music listening

Recently, exploring brain activity based on functional networks during naturalistic stimuli especially music and video represents an attractive challenge because of the low signal-to-noise ratio in collected brain data. Although most efforts focusing on exploring the listening brain have been made through functional magnetic resonance imaging (fMRI), sensor-level electro- or magnetoencephalography (EEG/MEG) technique, little is known about how neural rhythms are involved in the brain network activity under naturalistic stimuli. This study exploited cortical oscillations through analysis of ongoing EEG and musical feature during free-listening to music. We used a data-driven method that combined music information retrieval with spatial Independent Components Analysis (ICA) to probe the interplay between the spatial profiles and the spectral patterns. We projected the sensor data into cortical space using a minimum-norm estimate and applied the Short Time Fourier Transform (STFT) to obtain frequency information. Then, spatial ICA was made to extract spatial-spectral-temporal information of brain activity in source space and five long-term musical features were computationally extracted from the naturalistic stimuli. The spatial profiles of the components whose temporal courses were significantly correlated with musical feature time series were clustered to identify reproducible brain networks across the participants. Using the proposed approach, we found brain networks of musical feature processing are frequency-dependent and three plausible frequency-dependent networks were identified; the proposed method seems valuable for characterizing the large-scale frequency-dependent brain activity engaged in musical feature processing.

neuroscience↗

bioRxiv2018

Bi-clustering based biological and clinical characterization of colorectal cancer in complementary to CMS classification

In light of the marked differences in the intrinsic biological underpinnings and prognostic outcomes among different subtypes, Consensus Molecular Subtype (CMS) classification provides a new taxonomy of colorectal cancer (CRC) solely based on transcriptomics data and has been accepted as a standard rule for CRC stratification. Even though CMS was built on highly cancer relevant features, it suffers from limitations in capturing the promiscuous mechanisms in a clinical setting. There are at least two facts about using transcriptomic data for prognosis prediction: the engagement of genes or pathways that execute the clinical response pathway are highly dynamic and interactive with others; and a predefined patient stratification not only largely decrease the statistical analysis power, but also excludes the fact that clusters of patients that confer similar clinical outcomes may or may not overlap with a pre-defined subgrouping. To enable a flexible and prospective stratified exploration, we here present a novel computational framework based on bi-clustering aiming to identify gene regulatory mechanisms associated with various biological, clinical and drug-resistance features, with full recognition of the transiency of transcriptional regulation and complicacies of patients subgrouping with regards to different biological and clinical settings. Our analysis on multiple large scale CRC transcriptomics data sets using a bi-clustering based formulation suggests that the detected local low rank modules can not only generate new biological understanding coherent to CMS stratification, but also identify predictive markers for prognosis that are general to CRC or CMS dependent, as well as novel alternative drug resistance mechanisms. Our key results include: (1) a comprehensive annotation of the local low rank module landscape of CRC; (2) a mechanistic relationship between different clinical subtypes and outcomes, as well as their characteristic biological underpinnings, visible through a novel consensus map; and (3) a few (novel) resistance mechanisms of Oxaliplatin, 5-Fluorouracil, and the FOLFOX therapy are revealed, some of which are validated on independent datasets.

bioinformatics↗

bioRxiv2018

Genome assembly of the A-group Wolbachia in Nasonia oneida and phylogenomic analysis of Wolbachia strains revealed genome evolution and lateral gene transfer

Wolbachia are obligate intracellular bacteria which commonly infect various nematode and arthropod species. Based on depth differences, we assembled the genome of Wolbachia in the parasitoid jewel wasp species Nasonia oneida (wOne), using 10X Genomics Chromium linked-read technology. The final draft assembly consists of 1,293,406 bp in 47 scaffolds with 1,114 coding genes and 97.01% genome completeness assessed by checkM. wOne is the A1 strain previously reported in N. oneida, and pyrosequencing confirms that the wasp strain lacks A2 and B types, which were likely lost during laboratory culturing. Polymorphisms identified in the wOneA1 genome have elevated read depths, indicating recent gene duplications rather that strain variation. These polymorphisms are enriched in nonsynonymous changes in 27 coding genes, including phase baseplate assembly proteins and transporter activity related genes. wOneA1 is more closely grouped with A-Wolbachia in the Drosophila simulans (wHa) than A-Wolbachia found in wasps. Genome variation was next evaluated in 34 published Wolbachia genomes for 211 single ortholog genes, and revealed six supergroup discordant trees, indicating recombination events not only between A and B supergroups, but also between A and E supergroups. Comparisons of strain divergence using the five genes of the Multi Locus Strain Typing (MLST) system show a high correlation (rho=0.98) between MLST and whole genome divergences, indicating that MLST is a reliable method for identifying related strains when whole genome data are not available. Assembling bacterial genomes from host genome projects can provide an effective method for sequencing Wolbachia genomes and characterizing their diversity.\n\nAuthor SummaryMore than half of the arthropod species are infected by the obligated intracellular bacteria Wolbachia. As one of the most widespread parasitic microbes, Wolbachia mediate important biological processes such as cytoplasmic incompatibility and lateral gene transfer in insects. Their evolutionary relationship has been characterize using five protein-coding and 16S rRNA genes. In this work, we identified 211 conserved single copies genes in 34 genome sequenced Wolbachia strains, and we discovered that they maintain the supergroup relationship classified previously based on selected genes. We constructed phylogenetic trees for individual genes and found only six genes display discordant tree structure between supergroups, due to lateral gene transfer and homologous recombination events. But these events are not common (3%) in Wolbachia genomes, at least in these conserved single copy genes. In addition to known lateral gene transfer events between A and B supergroups, we identified transfers between A and E supergroups for the first time. Selective maintenance of such transfers suggests possible roles in Wolbachia infection related functions. We also found enriched nonsynonymous polymorphisms in Nasonia oneida Wobachia genome, and their differences are more likely to result from gene duplications within the strain, rather than strain variation within the parasitoid.

evolutionary biology↗

bioRxiv2018

Loud music and the specific sound stress open the blood-brain barrier: new fundamental, biomedical, and social aspects

The blood-brain barrier (BBB) poses a significant challenge for drug brain delivery. The limitation of our knowledge about the nature of BBB explains the slow progress in the therapy of brain diseases and absence of methods for drug brain delivery in the clinical practice.\n\nHere we show that BBB opens for low/high weight molecules and nanocarriers after exposure of loud music/sound of 90 dB and 100 dB (regardless its frequency) as being easily produced by MP3/MP4 players, kitchen appliances, loudspeakers at concerts. The role of sound, sound-induced stress and molecular mechanisms behind is discussed in the framework of BBB opening as an informative platform for a novel fundamental knowledge about the nature of BBB and for the development of a non-invasive brain drug delivery technology.\n\nSocial aspects of music/sound-induced opening of BBB provide completely new information about noise and healthy life conditions that will stimulate new research in this field.

neuroscience↗

bioRxiv2018

Anticorrelated inter-network electrophysiological activity varies dynamically with attentional performance and behavioral states

The default mode network (DMN) is thought to exhibit infraslow anticorrelated activity with dorsal attention (DAN) and salience (SN) networks across various behavioral states. To investigate the dynamics of activity across these networks on a finer timescale, we used human intracranial electroencephalography with simultaneous recordings within core nodes of the three networks. During attentional task performance, the three sites showed dissociable profiles of high-frequency broadband activity. Anticorrelated infraslow fluctuations of this activity were found across networks during task performance but also intermittently emerged during rest and sleep in concert with the expression of task-like network-level topographic patterns. Critically, on a finer timescale, DAN and SN activations preceded DMN deactivations by hundreds of milliseconds. Moreover, greater lagged, but not zero-lag, anticorrelation between DAN and DMN activity was associated with better attentional performance. These findings have implications for interpreting antagonistic network relationships and confirm the behavioral importance of time-lagged inter-network interactions.

neuroscience↗