Search results for "Neural"
showing 10 items of 2783 documents
Recurrent Deep Neural Networks for Nucleosome Classification
2020
Nucleosomes are the fundamental repeating unit of chromatin. A nucleosome is an 8 histone proteins complex, in which approximately 147–150 pairs of DNA bases bind. Several biological studies have clearly stated that the regulation of cell type-specific gene activities are influenced by nucleosome positioning. Bioinformatic studies have improved those results showing proof of sequence specificity in nucleosomes’ DNA fragment. In this work, we present a recurrent neural network that uses nucleosome sequence features representation for their classification. In particular, we implement an architecture which stacks convolutional and long short-term memory layers, with the main purpose to avoid t…
Retrieving infinite numbers of patterns in a spin-glass model of immune networks
2013
The similarity between neural and immune networks has been known for decades, but so far we did not understand the mechanism that allows the immune system, unlike associative neural networks, to recall and execute a large number of memorized defense strategies {\em in parallel}. The explanation turns out to lie in the network topology. Neurons interact typically with a large number of other neurons, whereas interactions among lymphocytes in immune networks are very specific, and described by graphs with finite connectivity. In this paper we use replica techniques to solve a statistical mechanical immune network model with `coordinator branches' (T-cells) and `effector branches' (B-cells), a…
Analyzing the feasibility of time correlated spectral entropy for the assessment of neuronal synchrony
2016
In this paper, we study neuronal network analysis based on microelectrode measurements. We search for potential relations between time correlated changes in spectral distributions and synchrony for neuronal network activity. Spectral distribution is quantified by spectral entropy as a measure of uniformity/complexity and this measure is calculated as a function of time for the recorded neuronal signals, i.e., time variant spectral entropy. Time variant correlations in the spectral distributions between different parts of a neuronal network, i.e., of concurrent measurements via different microelectrodes, are calculated to express the relation with a single scalar. We demonstrate these relati…
Cellular Response to Spinal Cord Injury in Regenerative and Non-Regenerative Stages in Xenopus Laevis
2020
Abstract Background The efficient regenerative abilities at larvae stages followed by a non-regenerative response after metamorphosis in froglets makes Xenopus an ideal model organism to understand the cellular responses leading to spinal cord regeneration. Methods We compared the cellular response to spinal cord injury between the regenerative and non-regenerative stages of Xenopus laevis. For this analysis, we used electron microscopy, immunofluorescence and histological staining of the extracellular matrix. We generated two transgenic lines: i) the reporter line with the zebrafish GFAP regulatory regions driving the expression of EGFP, and ii) a cell specific inducible ablation line with…
Partitioned learning of deep Boltzmann machines for SNP data.
2016
Abstract Motivation Learning the joint distributions of measurements, and in particular identification of an appropriate low-dimensional manifold, has been found to be a powerful ingredient of deep leaning approaches. Yet, such approaches have hardly been applied to single nucleotide polymorphism (SNP) data, probably due to the high number of features typically exceeding the number of studied individuals. Results After a brief overview of how deep Boltzmann machines (DBMs), a deep learning approach, can be adapted to SNP data in principle, we specifically present a way to alleviate the dimensionality problem by partitioned learning. We propose a sparse regression approach to coarsely screen…
Deep learning models for bacteria taxonomic classification of metagenomic data.
2018
Background An open challenge in translational bioinformatics is the analysis of sequenced metagenomes from various environmental samples. Of course, several studies demonstrated the 16S ribosomal RNA could be considered as a barcode for bacteria classification at the genus level, but till now it is hard to identify the correct composition of metagenomic data from RNA-seq short-read data. 16S short-read data are generated using two next generation sequencing technologies, i.e. whole genome shotgun (WGS) and amplicon (AMP); typically, the former is filtered to obtain short-reads belonging to a 16S shotgun (SG), whereas the latter take into account only some specific 16S hypervariable regions.…
Adult Neurogenesis Is Sustained by Symmetric Self-Renewal and Differentiation
2018
Somatic stem cells have been identified in multiple adult tissues. Whether self-renewal occurs symmetrically or asymmetrically is key to understanding long-term stem cell maintenance and generation of progeny for cell replacement. In the adult mouse brain, neural stem cells (NSCs) (B1 cells) are retained in the walls of the lateral ventricles (ventricular-subventricular zone [V-SVZ]). The mechanism of B1 cell retention into adulthood for lifelong neurogenesis is unknown. Using multiple clonal labeling techniques, we show that the vast majority of B1 cells divide symmetrically. Whereas 20%-30% symmetrically self-renew and can remain in the niche for several months before generating neurons, …
11q Deletion or ALK Activity Curbs DLG2 Expression to Maintain an Undifferentiated State in Neuroblastoma
2020
High-risk 11q deleted neuroblastomas typically display undifferentiated/poorly differentiated morphology. Neuroblastoma is thought to develop from Schwann cell precursors and undifferentiated neural crest (NC) derived cells. It is therefore vital to understand mechanisms involved in the block of differentiation. We identify an important role for oncogenic ALK-ERK1/2-SP1 signaling in maintenance of undifferentiated NC-derived progenitors via repression of DLG2, a tumor suppressor in neuroblastoma. DLG2 is expressed in the ‘bridge signature’ that represents the transcriptional transition state when neural crest cells or Schwann Cell Precursors become chromaffin cells of the adrenal gland. We …
The HSP90 inhibitor, 17AAG, protects the intestinal stem cell niche and inhibits graft versus host disease development.
2016
IF 7.932; International audience; Graft versus host disease (GvHD), which is the primary complication of allogeneic bone marrow transplantation, can alter the intestinal barrier targeted by activated donor T-cells. Chemical inhibition of the stress protein HSP90 was demonstrated in vitro to inhibit T-cell activation and to modulate endoplasmic reticulum (ER) stress to which intestinal cells are highly susceptible. Since the HSP90 inhibitor 17-allylamino-demethoxygeldanamycin (17AAG) is developed in clinics, we explored here its ability to control intestinal acute GvHD in vivo in two mouse GvHD models (C57BL/6 -> BALB/c and FVB/N -> Lgr5-eGFP), ex vivo in intestine organoids and in vitro in …
Selective α-synuclein knockdown in monoamine neurons by intranasal oligonucleotide delivery: potential therapy for parkinson’s disease
2018
Progressive neuronal death in brainstem nuclei and widespread accumulation of α-synuclein are neuropathological hallmarks of Parkinson’s disease (PD). Reduction of α-synuclein levels is therefore a potential therapy for PD. However, because α-synuclein is essential for neuronal development and function, α-synuclein elimination would dramatically impact brain function. We previously developed conjugated small interfering RNA (siRNA) sequences that selectively target serotonin (5-HT) or norepinephrine (NE) neurons after intranasal administration. Here, we used this strategy to conjugate inhibitory oligonucleotides, siRNA and antisense oligonucleotide (ASO), with the triple monoamine reuptake …