Search results for "Neural"

TALPID3/KIAA0586 Regulates Multiple Aspects of Neuromuscular Patterning During Gastrointestinal Development in Animal Models and Human

2021

TALPID3/KIAA0586 is an evolutionary conserved protein, which plays an essential role in protein trafficking. Its role during gastrointestinal (GI) and enteric nervous system (ENS) development has not been studied previously. Here, we analyzed chicken, mouse and human embryonic GI tissues with TALPID3 mutations. The GI tract of TALPID3 chicken embryos was shortened and malformed. Histologically, the gut smooth muscle was mispatterned and enteric neural crest cells were scattered throughout the gut wall. Analysis of the Hedgehog pathway and gut extracellular matrix provided causative reasons for these defects. Interestingly, chicken intra-species grafting experiments and a conditional knockou…

Deep-learning based reconstruction of the shower maximum X max using the water-Cherenkov detectors of the Pierre Auger Observatory

2021

The atmospheric depth of the air shower maximum $X_{\mathrm{max}}$ is an observable commonly used for the determination of the nuclear mass composition of ultra-high energy cosmic rays. Direct measurements of $X_{\mathrm{max}}$ are performed using observations of the longitudinal shower development with fluorescence telescopes. At the same time, several methods have been proposed for an indirect estimation of $X_{\mathrm{max}}$ from the characteristics of the shower particles registered with surface detector arrays. In this paper, we present a deep neural network (DNN) for the estimation of $X_{\mathrm{max}}$. The reconstruction relies on the signals induced by shower particles in the groun…

Sequenziamento temporale ed elaborazione neurale: una prospettiva psicofisiologica

2011

SingleChannelNet : A model for automatic sleep stage classification with raw single-channel EEG

2022

In diagnosing sleep disorders, sleep stage classification is a very essential yet time-consuming process. Various existing state-of-the-art approaches rely on hand-crafted features and multi-modality polysomnography (PSG) data, where prior knowledge is compulsory and high computation cost can be expected. Besides, it is a big challenge to handle the task with raw single-channel electroencephalogram (EEG). To overcome these shortcomings, this paper proposes an end-to-end framework with a deep neural network, namely SingleChannelNet, for automatic sleep stage classification based on raw single-channel EEG. The proposed model utilizes a 90s epoch as the textual input and employs two multi-conv…

Comparison of Machine Learning Methods in Stochastic Skin Optical Model Inversion

2020

In this study, we compare six different machine learning methods in the inversion of a stochastic model for light propagation in layered media, and use the inverse models to estimate four parameters of the skin from the simulated data: melanin concentration, hemoglobin volume fraction, and thicknesses of epidermis and dermis. The aim of this study is to determine the best methods for stochastic model inversion in order to improve current methods in skin related cancer diagnostics and in the future develop a non-invasive way to measure the physical parameters of the skin based partially on the results of the study. Of the compared methods, which are convolutional neural network, multi-layer …

Alleviating Class Imbalance Problem in Automatic Sleep Stage Classification

2022

For real-world automatic sleep-stage classification tasks, various existing deep learning-based models are biased toward the majority with a high proportion. Because of the unique sleep structure, most of the current polysomnography (PSG) datasets suffer an inherent class imbalance problem (CIP), in which the number of each sleep stage is severely unequal. In this study, we first define the class imbalance factor (CIF) to describe the level of CIP quantitatively. Afterward, we propose two balancing methods to alleviate this problem from the dataset quantity and the relationship between the class distribution and the applied model, respectively. The first one is to employ the data augmentati…

Measuring the Task Induced Oscillatory Brain Activity Using Tensor Decomposition

2019

The characterization of dynamic electrophysiological brain activity, which form and dissolve in order to support ongoing cognitive function, is one of the most important goals in neuroscience. Here, we introduce a method with tensor decomposition for measuring the task-induced oscillations in the human brain using electroencephalography (EEG). The time frequency representation of source-reconstructed singletrail EEG data constructed a third-order tensor with three factors of time ∗ trails, frequency and source points. We then used a non-negative Canonical Polyadic decomposition (NCPD) to identify the temporal, spectral and spatial changes in electrophysiological brain activity. We validate …

SCOPE-Based Emulators for Fast Generation of Synthetic Canopy Reflectance and Sun-Induced Fluorescence Spectra

2017

Progress in advanced radiative transfer models (RTMs) led to an improved understanding of reflectance (R) and sun-induced chlorophyll fluorescence (SIF) emission throughout the leaf and canopy. Among advanced canopy RTMs that have been recently modified to deliver SIF spectral outputs are the energy balance model SCOPE and the 3D models DART and FLIGHT. The downside of these RTMs is that they are computationally expensive, which makes them impractical in routine processing, such as scene generation and retrieval applications. To bypass their computational burden, a computationally effective technique has been proposed by only using a limited number of model runs, called emulation. The idea …

Application of neural networks in diagnostics of chemical compounds based on their infrared spectra

2017

Abstract The paper presents possibilities of using the so-called „finger-print“ identification method and artificial neural network (ANN) for diagnosis of chemical compounds. The construction of a tool specifically developed for this purpose and the ANN, as well as the required conditions for its proper functioning were described. The identification of chemical compounds was tested in two different ways for proving correctness of the assumptions. First of all, initial studies were carried out with the objective to verify the proper functioning of the developed procedure for IR spectrum interpretation. The second research stage was to find out how the properties of artificial neural networks…

Auditory cortical and hippocampal-system mismatch responses to duration deviants in urethane-anesthetized rats

2013

Any change in the invariant aspects of the auditory environment is of potential importance. The human brain preattentively or automatically detects such changes. The mismatch negativity (MMN) of event-related potentials (ERPs) reflects this initial stage of auditory change detection. The origin of MMN is held to be cortical. The hippocampus is associated with a later generated P3a of ERPs reflecting involuntarily attention switches towards auditory changes that are high in magnitude. The evidence for this cortico-hippocampal dichotomy is scarce, however. To shed further light on this issue, auditory cortical and hippocampal-system (CA1, dentate gyrus, subiculum) local-field potentials were …