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Neural networks

Bo Zhu, Jeremiah Z Liu, Stephen F Cauley, Bruce R Rosen, Matthew S Rosen
Image reconstruction is essential for imaging applications across the physical and life sciences, including optical and radar systems, magnetic resonance imaging, X-ray computed tomography, positron emission tomography, ultrasound imaging and radio astronomy. During image acquisition, the sensor encodes an intermediate representation of an object in the sensor domain, which is subsequently reconstructed into an image by an inversion of the encoding function. Image reconstruction is challenging because analytic knowledge of the exact inverse transform may not exist a priori, especially in the presence of sensor non-idealities and noise...
March 21, 2018: Nature
D S V Bandara, Jumpei Arata, Kazuo Kiguchi
Robotic prostheses are expected to allow amputees greater freedom and mobility. However, available options to control transhumeral prostheses are reduced with increasing amputation level. In addition, for electromyography-based control of prostheses, the residual muscles alone cannot generate sufficiently different signals for accurate distal arm function. Thus, controlling a multi-degree of freedom (DoF) transhumeral prosthesis is challenging with currently available techniques. In this paper, an electroencephalogram (EEG)-based hierarchical two-stage approach is proposed to achieve multi-DoF control of a transhumeral prosthesis...
March 22, 2018: Bioengineering
Thomas Maier, Daniel Meister, Severin Trösch, Jon Peter Wehrlin
Shooting in biathlon competitions substantially influences final rankings, but the predictability of hits and misses is unknown. The aims of the current study were A) to explore factors influencing biathlon shooting performance and B) to predict future hits and misses. We explored data from 118,300 shots from 4 seasons and trained various machine learning models before predicting 34,340 future shots (in the subsequent season). A) Lower hit rates were discovered in the sprint and pursuit disciplines compared to individual and mass start (P < 0...
March 22, 2018: Journal of Sports Sciences
Reza Rasti, Alireza Mehridehnavi, Hossein Rabbani, Fedra Hajizadeh
The present research intends to propose a fully automatic algorithm for the classification of three-dimensional (3-D) optical coherence tomography (OCT) scans of patients suffering from abnormal macula from normal candidates. The method proposed does not require any denoising, segmentation, retinal alignment processes to assess the intraretinal layers, as well as abnormalities or lesion structures. To classify abnormal cases from the control group, a two-stage scheme was utilized, which consists of automatic subsystems for adaptive feature learning and diagnostic scoring...
March 2018: Journal of Biomedical Optics
Aaron B Simmons, Peter G Fuerst
The retina is a highly organized neural tissue consisting of three neural layers and two synaptic layers. Blood vessels that nourish the mouse and human neural retina mirror this organization consisting of three plexus layers, or plexuses, that run parallel within the retina, connected by interplexus vessels to create a closed vascular network. Here, we describe a methodology to describe this organization that can be used to interrogate factors mediating retinal vessel patterning including: coverage of the vascular plexuses, branching and orientation of the interplexus connections, and digital reconstruction of the retinal vasculature to measure vessel length and density...
2018: Methods in Molecular Biology
Denize Atan
The retina shares its embryological origin with the central nervous system (CNS), so the neural circuitry of the retina has long been considered to be a relatively simple model of the neural networks in the brain, sharing similar morphologies, neurotransmitters, and receptors. Amacrine cells are, by far, the largest group of inhibitory neurons in the retina that also have the most diverse range of phenotypes of any retinal neuron. Here, I describe an approach, using immunolabeling of cryosections, to identify different subclasses of amacrine cell in the mouse retina...
2018: Methods in Molecular Biology
James Deraeve, William H Alexander
Multi-voxel pattern analysis often necessitates feature selection due to the high dimensional nature of neuroimaging data. In this context, feature selection techniques serve the dual purpose of potentially increasing classification accuracy and revealing sets of features that best discriminate between classes. However, feature selection techniques in current, widespread use in the literature suffer from a number of deficits, including the need for extended computational time, lack of consistency in selecting features relevant to classification, and only marginal increases in classifier accuracy...
March 21, 2018: Neuroinformatics
Azadeh Ahmadi, Zahra Fatemi, Sara Nazari
Using the multivariate statistical methods, this study interprets a set of data containing 23 water quality parameters from 10 quality monitoring stations in Karkheh River located in southwest of Iran over 5 years. According to cluster analysis, the stations are classified into three classes of quality, and the most important factors on the whole set of parameters and each class are determined by the help of factor analysis. The results indicate the effects of natural factors, soil weathering and erosion, urban and human wastewater, agricultural and industrial wastewater on water quality at different levels and any location...
March 22, 2018: Environmental Monitoring and Assessment
U Hassan, R Zhu, R Bashir
Sepsis, as a leading cause of death worldwide, relies on systemic inflammatory response syndrome (SIRS) criteria for its diagnosis. SIRS is highly non-specific as it relies on monitoring of patients' vitals for sepsis diagnosis, which are known to change with many confounding factors. Changes in leukocyte counts and CD64 expression levels are known specific biomarkers of pro-inflammatory host response at the onset of sepsis. Recently, we have developed a biosensor chip that can enumerate the leukocyte counts and quantify the neutrophil CD64 expression levels from a drop of blood...
March 22, 2018: Lab on a Chip
Jun Chen, Xin Xu, Shu Liu, Dong H Zhang
We report here a new global and full dimensional potential energy surface (PES) for the F + CH4 reaction. This PES was constructed by using neural networks (NN) fitting to about 99 000 ab initio energies computed at the UCCSD(T)-F12a/aug-cc-pVTZ level of theory, and the correction terms considering the influence of a larger basis set as well as spin-orbit couplings were further implemented with a hierarchial scheme. This PES, covering both the abstraction and substitution channels, has an overall fitting error of 8...
March 22, 2018: Physical Chemistry Chemical Physics: PCCP
Mark H Myers, Robert Kozma
Simulations of EEG data provide the understanding of how the limbic system exhibits normal and abnormal states of the electrical activity of the brain. While brain activity exhibits a type of homeostasis of excitatory and inhibitory mesoscopic neuron behavior, abnormal neural firings found in the seizure state exhibits brain instability due to runaway oscillatory entrained neural behavior. We utilize a model of mesoscopic brain activity, the KIV model, where each network represents the areas of the limbic system, i...
April 2018: Cognitive Neurodynamics
Zohaib Khan, Vahid Rahimi-Eichi, Stephan Haefele, Trevor Garnett, Stanley J Miklavcic
Background: Unmanned aerial vehicles offer the opportunity for precision agriculture to efficiently monitor agricultural land. A vegetation index (VI) derived from an aerially observed multispectral image (MSI) can quantify crop health, moisture and nutrient content. However, due to the high cost of multispectral sensors, alternate, low-cost solutions have lately received great interest. We present a novel method for model-based estimation of a VI using RGB color images. The non-linear spatio-spectral relationship between the RGB image of vegetation and the index computed by its corresponding MSI is learned through deep neural networks...
2018: Plant Methods
Siyang Qin, Roberto Manduchi
We introduce an algorithm for word-level text spotting that is able to accurately and reliably determine the bounding regions of individual words of text "in the wild". Our system is formed by the cascade of two convolutional neural networks. The first network is fully convolutional and is in charge of detecting areas containing text. This results in a very reliable but possibly inaccurate segmentation of the input image. The second network (inspired by the popular YOLO architecture) analyzes each segment produced in the first stage, and predicts oriented rectangular regions containing individual words...
November 2017: Proceedings of the International Conference on Document Analysis and Recognition
L R Brewster, J J Dale, T L Guttridge, S H Gruber, A C Hansell, M Elliott, I G Cowx, N M Whitney, A C Gleiss
Discerning behaviours of free-ranging animals allows for quantification of their activity budget, providing important insight into ecology. Over recent years, accelerometers have been used to unveil the cryptic lives of animals. The increased ability of accelerometers to store large quantities of high resolution data has prompted a need for automated behavioural classification. We assessed the performance of several machine learning (ML) classifiers to discern five behaviours performed by accelerometer-equipped juvenile lemon sharks ( Negaprion brevirostris ) at Bimini, Bahamas (25°44'N, 79°16'W)...
2018: Marine Biology
Lyle Muller, Frédéric Chavane, John Reynolds, Terrence J Sejnowski
Multichannel recording technologies have revealed travelling waves of neural activity in multiple sensory, motor and cognitive systems. These waves can be spontaneously generated by recurrent circuits or evoked by external stimuli. They travel along brain networks at multiple scales, transiently modulating spiking and excitability as they pass. Here, we review recent experimental findings that have found evidence for travelling waves at single-area (mesoscopic) and whole-brain (macroscopic) scales. We place these findings in the context of the current theoretical understanding of wave generation and propagation in recurrent networks...
March 22, 2018: Nature Reviews. Neuroscience
Woosuk Kim, Myunggyu Kim
In sports motion analysis, observation is a prerequisite for understanding the quality of motions. This paper introduces a novel approach to detect and segment sports motions using a wearable sensor for supporting systematic observation. The main goal is, for convenient analysis, to automatically provide motion data, which are temporally classified according to the phase definition. For explicit segmentation, a motion model is defined as a sequence of sub-motions with boundary states. A sequence classifier based on deep neural networks is designed to detect sports motions from continuous sensor inputs...
March 19, 2018: Sensors
Fabrizia Caiazzo, Alessandra Caggiano
Laser direct metal deposition is an advanced additive manufacturing technology suitably applicable in maintenance, repair, and overhaul of high-cost products, allowing for minimal distortion of the workpiece, reduced heat affected zones, and superior surface quality. Special interest is growing for the repair and coating of 2024 aluminum alloy parts, extensively utilized for a wide range of applications in the automotive, military, and aerospace sectors due to its excellent plasticity, corrosion resistance, electric conductivity, and strength-to-weight ratio...
March 19, 2018: Materials
Tian Tian, Chang Li, Jinkang Xu, Jiayi Ma
Detecting urban areas from very high resolution (VHR) remote sensing images plays an important role in the field of Earth observation. The recently-developed deep convolutional neural networks (DCNNs), which can extract rich features from training data automatically, have achieved outstanding performance on many image classification databases. Motivated by this fact, we propose a new urban area detection method based on DCNNs in this paper. The proposed method mainly includes three steps: (i) a visual dictionary is obtained based on the deep features extracted by pre-trained DCNNs; (ii) urban words are learned from labeled images; (iii) the urban regions are detected in a new image based on the nearest dictionary word criterion...
March 18, 2018: Sensors
Yifu Xu, Bin Yan, Jian Chen, Lei Zeng, Lei Li
BACKGROUND: Dual-energy computed tomography (DECT) has been widely used to improve identification of substances from different spectral information. Decomposition of the mixed test samples into two materials relies on a well-calibrated material decomposition function. OBJECTIVE: This work aims to establish and validate a data-driven algorithm for estimation of the decomposition function. METHODS: A deep neural network (DNN) consisting of two sub-nets is proposed to solve the projection decomposition problem...
March 15, 2018: Journal of X-ray Science and Technology
Harald Hampel, Nicola Toschi, Claudio Babiloni, Filippo Baldacci, Keith L Black, Arun L W Bokde, René S Bun, Francesco Cacciola, Enrica Cavedo, Patrizia A Chiesa, Olivier Colliot, Cristina-Maria Coman, Bruno Dubois, Andrea Duggento, Stanley Durrleman, Maria-Teresa Ferretti, Nathalie George, Remy Genthon, Marie-Odile Habert, Karl Herholz, Yosef Koronyo, Maya Koronyo-Hamaoui, Foudil Lamari, Todd Langevin, Stéphane Lehéricy, Jean Lorenceau, Christian Neri, Robert Nisticò, Francis Nyasse-Messene, Craig Ritchie, Simone Rossi, Emiliano Santarnecchi, Olaf Sporns, Steven R Verdooner, Andrea Vergallo, Nicolas Villain, Erfan Younesi, Francesco Garaci, Simone Lista
The Precision Neurology development process implements systems theory with system biology and neurophysiology in a parallel, bidirectional research path: a combined hypothesis-driven investigation of systems dysfunction within distinct molecular, cellular, and large-scale neural network systems in both animal models as well as through tests for the usefulness of these candidate dynamic systems biomarkers in different diseases and subgroups at different stages of pathophysiological progression. This translational research path is paralleled by an "omics"-based, hypothesis-free, exploratory research pathway, which will collect multimodal data from progressing asymptomatic, preclinical, and clinical neurodegenerative disease (ND) populations, within the wide continuous biological and clinical spectrum of ND, applying high-throughput and high-content technologies combined with powerful computational and statistical modeling tools, aimed at identifying novel dysfunctional systems and predictive marker signatures associated with ND...
March 16, 2018: Journal of Alzheimer's Disease: JAD
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