69,796 results match your criteria neural networks

Development of the fluorescent carbon nanosensor for pH and temperature of liquid media with artificial neural networks.

Spectrochim Acta A Mol Biomol Spectrosc 2021 Apr 21;258:119861. Epub 2021 Apr 21.

Department of Physics, Lomonosov Moscow State University, Moscow 119991, Russia; Skobeltsyn Institute of Nuclear Physics, Lomonosov Moscow State University, Moscow 119991, Russia.

The present study is devoted to the creation of optical nanosensors for pH and temperature of liquid media based on carbon dots (CD) prepared via hydrothermal synthesis. The application of artificial neural networks to the CD fluorescence spectra database provided simultaneous determination of pH and ambient temperature values with an accuracy of 0.005 pH units and 0. Read More

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Wired for insight-recent advances in Caenorhabditis elegans neural circuits.

Curr Opin Neurobiol 2021 May 3;69:159-169. Epub 2021 May 3.

Neurobiology Section, University of California San Diego, La Jolla, CA, 92093, USA; Kavli Institute of Brain and Mind, University of California San Diego, La Jolla, CA, 92093, USA. Electronic address:

The completion of Caenorhabditis elegans connectomics four decades ago has long guided mechanistic investigation of neuronal circuits. Recent technological advances in microscopy and computation programs have aided re-examination of this connectomics, expanding our knowledge by both uncovering previously unreported synaptic connections and also generating models for neural networks underlying behaviors. Combining molecular information from single cell transcriptomes with elegant tools for cell-specific manipulation has further enhanced the ability to precisely investigate individual neurons in behaving animals. Read More

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Autoencoder networks extract latent variables and encode these variables in their connectomes.

Neural Netw 2021 Mar 24;141:330-343. Epub 2021 Mar 24.

Applied Mathematics Department, University of Washington, Seattle, WA, United States of America; Computational Neuroscience Center, University of Washington, Seattle, WA, United States of America; Allen Institute for Brain Science, Seattle, WA, United States of America.

Advances in electron microscopy and data processing techniques are leading to increasingly large and complete microscale connectomes. At the same time, advances in artificial neural networks have produced model systems that perform comparably rich computations with perfectly specified connectivity. This raises an exciting scientific opportunity for the study of both biological and artificial neural networks: to infer the underlying circuit function from the structure of its connectivity. Read More

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Within-Subject Reaction Time Variability: Role of Cortical Networks and Underlying Neurophysiological Mechanisms.

Neuroimage 2021 May 3:118127. Epub 2021 May 3.

National Center for Adaptive Neurotechnologies, Albany, NY, USA; Dept. of Biomedical Science, State University of New York at Albany, Albany, NY, USA. Electronic address:

Variations in reaction time are a ubiquitous characteristic of human behavior. Extensively documented, they have been successfully modeled using parameters of the subject or the task, but the neural basis of behavioral reaction time that varies within the same subject and the same task has been minimally studied. In this paper, we investigate behavioral reaction time variance using 28 datasets of direct cortical recordings in humans who engaged in four different types of simple sensory-motor reaction time tasks. Read More

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Deep Convolutional Neural Networks as a Diagnostic Aid-A Step Toward Minimizing Undetected Scaphoid Fractures on Initial Hand Radiographs.

JAMA Netw Open 2021 May 3;4(5):e216393. Epub 2021 May 3.

Department of Biomedical Data Science, Stanford University, Stanford, California.

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Development and Validation of a Deep Learning Model Using Convolutional Neural Networks to Identify Scaphoid Fractures in Radiographs.

JAMA Netw Open 2021 May 3;4(5):e216096. Epub 2021 May 3.

Section of Plastic Surgery, Department of Surgery, University of Michigan Medical School, Ann Arbor.

Importance: Scaphoid fractures are the most common carpal fracture, but as many as 20% are not visible (ie, occult) in the initial injury radiograph; untreated scaphoid fractures can lead to degenerative wrist arthritis and debilitating pain, detrimentally affecting productivity and quality of life. Occult scaphoid fractures are among the primary causes of scaphoid nonunions, secondary to delayed diagnosis.

Objective: To develop and validate a deep convolutional neural network (DCNN) that can reliably detect both apparent and occult scaphoid fractures from radiographic images. Read More

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Use of Machine Learning Algorithms to Predict the Understandability of Health Education Materials: Development and Evaluation Study.

JMIR Med Inform 2021 May 6;9(5):e28413. Epub 2021 May 6.

Department of English, The Hong Kong Polytechnic University, Hong Kong, Hong Kong.

Background: Improving the understandability of health information can significantly increase the cost-effectiveness and efficiency of health education programs for vulnerable populations. There is a pressing need to develop clinically informed computerized tools to enable rapid, reliable assessment of the linguistic understandability of specialized health and medical education resources. This paper fills a critical gap in current patient-oriented health resource development, which requires reliable and accurate evaluation instruments to increase the efficiency and cost-effectiveness of health education resource evaluation. Read More

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An artificial neural network-pharmacokinetic (ANN-PK) model and its interpretation using Shapley additive explanations.

CPT Pharmacometrics Syst Pharmacol 2021 May 6. Epub 2021 May 6.

Department of Computer Engineering, College of Science and Technology, Nihon University, 7-24-1 Narashinodai, Funabashi, Chiba, 274-8501, Japan.

We developed a method to apply artificial neural networks (ANN) for predicting time-series pharmacokinetics, and an interpretable the ANN-pharmacokinetic (ANN-PK) model which can explain the evidence of prediction by applying Shapley additive explanations (SHAP). A previous population pharmacokinetic (popPK) model of cyclosporine A was used as the comparison model. The patients' data were used for the ANN-PK model input, and the output by ANN was the clearance (CL). Read More

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Increased segregation of structural brain networks underpins enhanced broad cognitive abilities of cognitive training.

Hum Brain Mapp 2021 May 6. Epub 2021 May 6.

Elaine C. Hubbard Center for Nursing Research on Aging, School of Nursing, University of Rochester Medical Center, Rochester, New York, USA.

A major challenge in the cognitive training field is inducing broad, far-transfer training effects. Thus far, little is known about the neural mechanisms underlying broad training effects. Here, we tested a set of competitive hypotheses regarding the role of brain integration versus segregation underlying the broad training effect. Read More

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Fluorescence microscopy datasets for training deep neural networks.

Gigascience 2021 May;10(5)

Department of Computer Science and Software Engineering, California Polytechnic State University, San Luis Obispo, CA 93407, USA.

Background: Fluorescence microscopy is an important technique in many areas of biological research. Two factors that limit the usefulness and performance of fluorescence microscopy are photobleaching of fluorescent probes during imaging and, when imaging live cells, phototoxicity caused by light exposure. Recently developed methods in machine learning are able to greatly improve the signal-to-noise ratio of acquired images. Read More

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Strategy to improve the accuracy of convolutional neural network architectures applied to digital image steganalysis in the spatial domain.

PeerJ Comput Sci 2021 9;7:e451. Epub 2021 Apr 9.

Department of Systems Engineering, Universidad de Antioquia, Medellín, Antioquia, Colombia.

In recent years, Deep Learning techniques applied to steganalysis have surpassed the traditional two-stage approach by unifying feature extraction and classification in a single model, the Convolutional Neural Network (CNN). Several CNN architectures have been proposed to solve this task, improving steganographic images' detection accuracy, but it is unclear which computational elements are relevant. Here we present a strategy to improve accuracy, convergence, and stability during training. Read More

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An ensemble approach based on transformation functions for natural gas price forecasting considering optimal time delays.

PeerJ Comput Sci 2021 7;7:e409. Epub 2021 Apr 7.

Faculty of Industrial Engineering, Urmia University of Technology, Urmia, Iran.

Natural gas, known as the cleanest fossil fuel, plays a vital role in the economies of producing and consuming countries. Understanding and tracking the drivers of natural gas prices are of significant interest to the many economic sectors. Hence, accurately forecasting the price is very important not only for providing an effective factor for implementing energy policy but also for playing an extremely significant role in government strategic planning. Read More

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Migraine Triggers: An Overview of the Pharmacology, Biochemistry, Atmospherics, and Their Effects on Neural Networks.

Cureus 2021 Apr 1;13(4):e14243. Epub 2021 Apr 1.

Neurology, Flowers Medical Group, Dothan, USA.

We define a migraine trigger to be an endogenous agent or agency such as the menses or an exogenous agent or agency such as red wine or a drop in barometric pressure, and their ability to reduce the threshold of a migraine attack in those predisposed to migraine. This definition excludes agents with idiosyncratic mechanisms that may trigger a migrainous (migraine-like) headache in non-migraineurs such as benign cough headaches or headaches due to altitude-sickness. We also assume as axiomatic that migraine has as its basis the activation of the trigeminovascular pathway (TVP) and the key role of serotonin and the calcitonin gene-related peptide (CGRP). Read More

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Enhancing deep-learning training for phase identification in powder X-ray diffractograms.

IUCrJ 2021 May 1;8(Pt 3):408-420. Epub 2021 Apr 1.

Institute for Automation and Applied Informatics, Karlsruhe Institute of Technology, Germany.

Within the domain of analyzing powder X-ray diffraction (XRD) scans, manual examination of the recorded data is still the most popular method, but it requires some expertise and is time consuming. The usual workflow for the phase-identification task involves software for searching databases of known compounds and matching lists of spacings and related intensities to the measured data. Most automated approaches apply some iterative procedure for the search/match process but fail to be generally reliable yet without the manual validation step of an expert. Read More

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Comparison of artificial neural networks and multiple linear regression for prediction of dairy cow locomotion score.

Vet Res Forum 2021 15;12(1):33-37. Epub 2021 Mar 15.

Department of Soft Computing, Faculty of Mathematical Sciences, Shahid Beheshti University, Tehran, Iran.

In this study, artificial neural networks (ANNs) were employed to investigate the relationship between locomotion score and production traits. A total number of 123 dairy cows from a free-stall housing farm were used in this study. To compare the effectiveness of the ANNs for the prediction of locomotion score, the multiple linear regression (MLR) model was developed using the eight production traits, body condition score, parity, days in milk, daily milk yield, milk fat percent, milk protein percent, daily milk fat yield, and daily milk protein yield as input variables to predict the locomotion score. Read More

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Deep direct likelihood knockoffs.

Adv Neural Inf Process Syst 2020 Dec;33:5036-5046

Courant Institute of Mathematical Sciences, Center for Data Science, New York University.

Predictive modeling often uses black box machine learning methods, such as deep neural networks, to achieve state-of-the-art performance. In scientific domains, the scientist often wishes to discover which features are actually important for making the predictions. These discoveries may lead to costly follow-up experiments and as such it is important that the error rate on discoveries is not too high. Read More

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December 2020

Deep convolution stack for waveform in underwater acoustic target recognition.

Sci Rep 2021 May 5;11(1):9614. Epub 2021 May 5.

Guangzhou Key Laboratory of Multilingual Intelligent Processing, Guangdong University of Foreign Studies, Guangzhou, 510006, China.

In underwater acoustic target recognition, deep learning methods have been proved to be effective on recognizing original signal waveform. Previous methods often utilize large convolutional kernels to extract features at the beginning of neural networks. It leads to a lack of depth and structural imbalance of networks. Read More

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Inverse renormalization group based on image super-resolution using deep convolutional networks.

Sci Rep 2021 May 5;11(1):9617. Epub 2021 May 5.

Department of Physics, Tokyo Metropolitan University, Hachioji, Tokyo, 192-0397, Japan.

The inverse renormalization group is studied based on the image super-resolution using the deep convolutional neural networks. We consider the improved correlation configuration instead of spin configuration for the spin models, such as the two-dimensional Ising and three-state Potts models. We propose a block-cluster transformation as an alternative to the block-spin transformation in dealing with the improved estimators. Read More

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Co-occupancy identifies transcription factor co-operation for axon growth.

Nat Commun 2021 05 5;12(1):2555. Epub 2021 May 5.

Department of Biomedical Sciences, Marquette University, Milwaukee, WI, USA.

Transcription factors (TFs) act as powerful levers to regulate neural physiology and can be targeted to improve cellular responses to injury or disease. Because TFs often depend on cooperative activity, a major challenge is to identify and deploy optimal sets. Here we developed a bioinformatics pipeline, centered on TF co-occupancy of regulatory DNA, and used it to predict factors that potentiate the effects of pro-regenerative Klf6 in vitro. Read More

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Synaptic metaplasticity in binarized neural networks.

Nat Commun 2021 05 5;12(1):2549. Epub 2021 May 5.

Université Paris-Saclay, CNRS, Centre de Nanosciences et de Nanotechnologies, Palaiseau, France.

While deep neural networks have surpassed human performance in multiple situations, they are prone to catastrophic forgetting: upon training a new task, they rapidly forget previously learned ones. Neuroscience studies, based on idealized tasks, suggest that in the brain, synapses overcome this issue by adjusting their plasticity depending on their past history. However, such "metaplastic" behaviors do not transfer directly to mitigate catastrophic forgetting in deep neural networks. Read More

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Phase imaging with an untrained neural network.

Light Sci Appl 2020 May 6;9(1):77. Epub 2020 May 6.

Shanghai Institute of Optics and Fine Mechanics, Chinese Academy of Sciences, 201800, Shanghai, China.

Most of the neural networks proposed so far for computational imaging (CI) in optics employ a supervised training strategy, and thus need a large training set to optimize their weights and biases. Setting aside the requirements of environmental and system stability during many hours of data acquisition, in many practical applications, it is unlikely to be possible to obtain sufficient numbers of ground-truth images for training. Here, we propose to overcome this limitation by incorporating into a conventional deep neural network a complete physical model that represents the process of image formation. Read More

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Digital synthesis of histological stains using micro-structured and multiplexed virtual staining of label-free tissue.

Light Sci Appl 2020 May 6;9(1):78. Epub 2020 May 6.

Electrical and Computer Engineering Department, University of California, Los Angeles, CA, 90095, USA.

Histological staining is a vital step in diagnosing various diseases and has been used for more than a century to provide contrast in tissue sections, rendering the tissue constituents visible for microscopic analysis by medical experts. However, this process is time consuming, labour intensive, expensive and destructive to the specimen. Recently, the ability to virtually stain unlabelled tissue sections, entirely avoiding the histochemical staining step, has been demonstrated using tissue-stain-specific deep neural networks. Read More

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High-throughput soybean seeds phenotyping with convolutional neural networks and transfer learning.

Plant Methods 2021 May 5;17(1):50. Epub 2021 May 5.

College of Information and Electrical Engineering, China Agricultural University, Beijing, 100083, China.

Background: Effective soybean seed phenotyping demands large-scale accurate quantities of morphological parameters. The traditional manual acquisition of soybean seed morphological phenotype information is error-prone, and time-consuming, which is not feasible for large-scale collection. The segmentation of individual soybean seed is the prerequisite step for obtaining phenotypic traits such as seed length and seed width. Read More

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Tongue image quality assessment based on a deep convolutional neural network.

BMC Med Inform Decis Mak 2021 May 5;21(1):147. Epub 2021 May 5.

Basic Medical College Shanghai University of Traditional Chinese Medicine, 1200 Cailun Road, Pudong New Area, Shanghai, 201203, China.

Background: Tongue diagnosis is an important research field of TCM diagnostic technology modernization. The quality of tongue images is the basis for constructing a standard dataset in the field of tongue diagnosis. To establish a standard tongue image database in the TCM industry, we need to evaluate the quality of a massive number of tongue images and add qualified images to the database. Read More

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DeepNetBim: deep learning model for predicting HLA-epitope interactions based on network analysis by harnessing binding and immunogenicity information.

BMC Bioinformatics 2021 May 5;22(1):231. Epub 2021 May 5.

Department of Bioinformatics and Biostatistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China.

Background: Epitope prediction is a useful approach in cancer immunology and immunotherapy. Many computational methods, including machine learning and network analysis, have been developed quickly for such purposes. However, regarding clinical applications, the existing tools are insufficient because few of the predicted binding molecules are immunogenic. Read More

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MG-BERT: leveraging unsupervised atomic representation learning for molecular property prediction.

Brief Bioinform 2021 May 5. Epub 2021 May 5.

Xiangya School of Pharmaceutical Sciences, Central South University, China.

Motivation: Accurate and efficient prediction of molecular properties is one of the fundamental issues in drug design and discovery pipelines. Traditional feature engineering-based approaches require extensive expertise in the feature design and selection process. With the development of artificial intelligence (AI) technologies, data-driven methods exhibit unparalleled advantages over the feature engineering-based methods in various domains. Read More

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Personalising left-ventricular biophysical models of the heart using parametric physics-informed neural networks.

Med Image Anal 2021 Apr 20;71:102066. Epub 2021 Apr 20.

Institute for Biomedical Engineering, ETH Zurich and University Zurich, Zurich, Switzerland.

We present a parametric physics-informed neural network for the simulation of personalised left-ventricular biomechanics. The neural network is constrained to the biophysical problem in two ways: (i) the network output is restricted to a subspace built from radial basis functions capturing characteristic deformations of left ventricles and (ii) the cost function used for training is the energy potential functional specifically tailored for hyperelastic, anisotropic, nearly-incompressible active materials. The radial bases are generated from the results of a nonlinear Finite Element model coupled with an anatomical shape model derived from high-resolution cardiac images. Read More

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Association between metabolic syndrome and resting-state functional brain connectivity.

Neurobiol Aging 2021 Apr 1;104:1-9. Epub 2021 Apr 1.

Neuroimaging Research for Veterans Center (NeRVe), Geriatric Research Education and Clinical Center (GRECC), VA Boston Healthcare System, Boston, MA, USA; Harvard Medical School, Boston, MA, USA.

The objective of this study is to examine whether metabolic syndrome (MetS), the clustering of 3 or more cardiovascular risk factors, disrupts the resting-state functional connectivity (FC) of the large-scale cortical brain networks. Resting-state functional magnetic resonance imaging data were collected from seventy-eight middle-aged and older adults living with and without MetS (27 MetS; 51 non-MetS). FC maps were derived from the time series of intrinsic activity in the large-scale brain networks by correlating the spatially averaged time series with all brain voxels using a whole-brain seed-based FC approach. Read More

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The developing relations between networks of cortical myelin and neurophysiological connectivity.

Neuroimage 2021 May 2:118142. Epub 2021 May 2.

Department of Diagnostic Imaging, Hospital for Sick Children, Toronto, M5G 0A4, Canada; Program in Neurosciences and Mental Health, Hospital for Sick Children, Toronto, M5G 0A4, Canada; Department of Psychology, University of Toronto, Toronto, M5G 0A4 Canada; Department of Medical Imaging, University of Toronto, Toronto, M5G 0A4, Canada.

Recent work identified that patterns of distributed brain regions sharing similar myeloarchitecture are related to underlying functional connectivity, demonstrating cortical myelin's plasticity to changes in functional demand. However, the changing relations between functional and structural architecture throughout child and adulthood are poorly understood. We show that structural covariance connectivity (T1-weighted/T2-weighted ratio) and functional connectivity (magnetoencephalography) exhibit nonlinear developmental changes. Read More

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Explaining face representation in the primate brain using different computational models.

Curr Biol 2021 Apr 29. Epub 2021 Apr 29.

Division of Biology and Biological Engineering, Computation and Neural Systems, Caltech, Pasadena, CA 91125, USA; Howard Hughes Medical Institute, Pasadena, CA 91125, USA. Electronic address:

Understanding how the brain represents the identity of complex objects is a central challenge of visual neuroscience. The principles governing object processing have been extensively studied in the macaque face patch system, a sub-network of inferotemporal (IT) cortex specialized for face processing. A previous study reported that single face patch neurons encode axes of a generative model called the "active appearance" model, which transforms 50D feature vectors separately representing facial shape and facial texture into facial images. Read More

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