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neural network

Piotr Klukowski, Michal Augoff, Maciej Zieba, Maciej Drwal, Adam Gonczarek, Michal J Walczak
Motivation: Automated selection of signals in protein NMR spectra, known as peak picking, has been studied for over 20 years, nevertheless existing peak picking methods are still largely deficient. Accurate and precise automated peak picking would accelerate the structure calculation, and analysis of dynamics and interactions of macromolecules. Recent advancement in handling big data, together with an outburst of machine learning techniques, offer an opportunity to tackle the peak picking problem substantially faster than manual picking and on par with human accuracy...
March 14, 2018: Bioinformatics
Jiade Wang, Tian Zhang, Yu Mei, Bingjun Pan
Reverse osmosis concentrate (ROC) of printing and dyeing wastewater remains as a daunting environmental issue, which is characterized by high salinity, chemical oxygen demand (COD), chroma and low biodegradability. In this study electro-oxidation process (PbO2 /Ti electrode) coupled with oxidation-reduction potential (ORP) online monitor was applied to treat such a ROC effluent. The results show that with the increase of specific electrical charge (Qsp ), the removal efficiencies of COD, TN and chroma increased significantly at the incipience and then reached a gentle stage; the optimal total current efficiency (12...
March 8, 2018: Chemosphere
V Reggie Edgerton, Parag Gad
What are the implications of the vagus nerve being able to mediate the time-dependent plasticity of an array of sensorimotor networks?
March 16, 2018: ELife
Alexander H Tuttle, Mark J Molinaro, Jasmine F Jethwa, Susana G Sotocinal, Juan C Prieto, Martin A Styner, Jeffrey S Mogil, Mark J Zylka
Grimace scales quantify characteristic facial expressions associated with spontaneous pain in rodents and other mammals. However, these scales have not been widely adopted largely because of the time and effort required for highly trained humans to manually score the images. Convoluted neural networks were recently developed that distinguish individual humans and objects in images. Here, we trained one of these networks, the InceptionV3 convolutional neural net, with a large set of human-scored mouse images...
January 2018: Molecular Pain
Neta Blau, Eyal Klang, Nahum Kiryati, Marianne Amitai, Orith Portnoy, Arnaldo Mayer
PURPOSE: Simple renal cysts are a common benign finding in abdominal CT scans. However, since they may evolve in time, simple cysts need to be reported. With an ever-growing number of slices per CT scan, cysts are easily overlooked by the overloaded radiologist. In this paper, we address the detection of simple renal cysts as an incidental finding in a real clinical setting. METHODS: We propose a fully automatic framework for renal cyst detection, supported by a robust segmentation of the kidneys performed by a fully convolutional neural network...
March 15, 2018: International Journal of Computer Assisted Radiology and Surgery
Nikhil Krishnan, Daniel B Poll, Zachary P Kilpatrick
Working memory (WM) is limited in its temporal length and capacity. Classic conceptions of WM capacity assume the system possesses a finite number of slots, but recent evidence suggests WM may be a continuous resource. Resource models typically assume there is no hard upper bound on the number of items that can be stored, but WM fidelity decreases with the number of items. We analyze a neural field model of multi-item WM that associates each item with the location of a bump in a finite spatial domain, considering items that span a one-dimensional continuous feature space...
March 15, 2018: Journal of Computational Neuroscience
Li Zhang, Jiasheng Chen, Chunming Gao, Chuanmiao Liu, Kuihua Xu
Hepatocellular carcinoma (HCC) is a leading cause of cancer-related death worldwide. The early diagnosis of HCC is greatly helpful to achieve long-term disease-free survival. However, HCC is usually difficult to be diagnosed at an early stage. The aim of this study was to create the prediction model to diagnose HCC based on gene expression programming (GEP). GEP is an evolutionary algorithm and a domain-independent problem-solving technique. Clinical data show that six serum biomarkers, including gamma-glutamyl transferase, C-reaction protein, carcinoembryonic antigen, alpha-fetoprotein, carbohydrate antigen 153, and carbohydrate antigen 199, are related to HCC characteristics...
March 16, 2018: Medical & Biological Engineering & Computing
Mariana Fortunata Donadon, Rocio Martin-Santos, Flávia de Lima Osório
Studies have shown that traumatic experiences may affect hormonal systems mediated by the hypothalamic-pituitary-adrenal (HPA) axis and the oxytocinergic system. This effect is the result of long-term impairments in hypothalamic structures and negative feedback mechanisms within the HPA axis, structures that mediate the response to stress. This deregulation reduces the production and release of cortisol and oxytocin (OXT), which may alter stress responses and lead to increased vulnerability to impairments from stressful experiences...
2018: Frontiers in Pharmacology
Rupesh K Chikara, Erik C Chang, Yi-Chen Lu, Dar-Shong Lin, Chin-Teng Lin, Li-Wei Ko
A reward or punishment can modulate motivation and emotions, which in turn affect cognitive processing. The present simultaneous functional magnetic resonance imaging-electroencephalography study examines neural mechanisms of response inhibition under the influence of a monetary reward or punishment by implementing a modified stop-signal task in a virtual battlefield scenario. The participants were instructed to play as snipers who open fire at a terrorist target but withhold shooting in the presence of a hostage...
2018: Frontiers in Human Neuroscience
Luqing Wei, Hong Chen, Guo-Rong Wu
The neurovisceral integration model has shown a key role of the amygdala in neural circuits underlying heart rate variability (HRV) modulation, and suggested that reciprocal connections from amygdala to brain regions centered on the central autonomic network (CAN) are associated with HRV. To provide neuroanatomical evidence for these theoretical perspectives, the current study used covariance analysis of MRI-based gray matter volume (GMV) to map structural covariance network of the amygdala, and then determined whether the interregional structural correlations related to individual differences in HRV...
2018: Frontiers in Human Neuroscience
Zhenyu Zhu, Rubin Wang, Fengyun Zhu
Based on the Hodgkin-Huxley model, the present study established a fully connected structural neural network to simulate the neural activity and energy consumption of the network by neural energy coding theory. The numerical simulation result showed that the periodicity of the network energy distribution was positively correlated to the number of neurons and coupling strength, but negatively correlated to signal transmitting delay. Moreover, a relationship was established between the energy distribution feature and the synchronous oscillation of the neural network, which showed that when the proportion of negative energy in power consumption curve was high, the synchronous oscillation of the neural network was apparent...
2018: Frontiers in Neuroscience
Luis R Peraza, Ruth Cromarty, Xenia Kobeleva, Michael J Firbank, Alison Killen, Sara Graziadio, Alan J Thomas, John T O'Brien, John-Paul Taylor
Dementia with Lewy bodies (DLB) and Alzheimer's disease (AD) require differential management despite presenting with symptomatic overlap. Currently, there is a need of inexpensive DLB biomarkers which can be fulfilled by electroencephalography (EEG). In this regard, an established electrophysiological difference in DLB is a decrease of dominant frequency (DF)-the frequency with the highest signal power between 4 and 15 Hz. Here, we investigated network connectivity in EEG signals acquired from DLB patients, and whether these networks were able to differentiate DLB from healthy controls (HCs) and associated dementias...
March 15, 2018: Scientific Reports
Simone Franceschini, Emanuele Gandola, Marco Martinoli, Lorenzo Tancioni, Michele Scardi
Species distribution is the result of complex interactions that involve environmental parameters as well as biotic factors. However, methodological approaches that consider the use of biotic variables during the prediction process are still largely lacking. Here, a cascaded Artificial Neural Networks (ANN) approach is proposed in order to increase the accuracy of fish species occurrence estimates and a case study for Leucos aula in NE Italy is presented as a demonstration case. Potentially useful biotic information (i...
March 15, 2018: Scientific Reports
Dezső Ribli, Anna Horváth, Zsuzsa Unger, Péter Pollner, István Csabai
In the last two decades, Computer Aided Detection (CAD) systems were developed to help radiologists analyse screening mammograms, however benefits of current CAD technologies appear to be contradictory, therefore they should be improved to be ultimately considered useful. Since 2012, deep convolutional neural networks (CNN) have been a tremendous success in image recognition, reaching human performance. These methods have greatly surpassed the traditional approaches, which are similar to currently used CAD solutions...
March 15, 2018: Scientific Reports
James P Roach, Aleksandra Pidde, Eitan Katz, Jiaxing Wu, Nicolette Ognjanovski, Sara J Aton, Michal R Zochowski
Network oscillations across and within brain areas are critical for learning and performance of memory tasks. While a large amount of work has focused on the generation of neural oscillations, their effect on neuronal populations' spiking activity and information encoding is less known. Here, we use computational modeling to demonstrate that a shift in resonance responses can interact with oscillating input to ensure that networks of neurons properly encode new information represented in external inputs to the weights of recurrent synaptic connections...
March 15, 2018: Proceedings of the National Academy of Sciences of the United States of America
Laura B Tucker, Alexander G Velosky, Joseph T McCabe
Acquired traumatic brain injury (TBI) is frequently accompanied by persistent cognitive symptoms, including executive function disruptions and memory deficits. The Morris Water Maze (MWM) is the most widely-employed laboratory behavioral test for assessing cognitive deficits in rodents after experimental TBI. Numerous protocols exist for performing the test, which has shown great robustness in detecting learning and memory deficits in rodents after infliction of TBI. We review applications of the MWM for the study of cognitive deficits following TBI in pre-clinical studies, describing multiple ways in which the test can be employed to examine specific aspects of learning and memory...
March 12, 2018: Neuroscience and Biobehavioral Reviews
Ellen V S Hessel, Yvonne C M Staal, Aldert H Piersma
Developmental neurotoxicity entails one of the most complex areas in toxicology. Animal studies provide only limited information as to human relevance. A multitude of alternative models have been developed over the years, providing insights into mechanisms of action. We give an overview of fundamental processes in neural tube formation, brain development and neural specification, aiming at illustrating complexity rather than comprehensiveness. We also give a flavor of the wealth of alternative methods in this area...
March 12, 2018: Toxicology and Applied Pharmacology
Qihong Zou, Shuqin Zhou, Jing Xu, Zihui Su, Yuezhen Li, Yundong Ma, Hongqiang Sun, Changwei W Wu, Jia-Hong Gao
Rapid eye movement (REM) sleep has been frequently associated with dreaming. However, mounting evidence obtained from behavioral, pharmacological, and brain imaging studies suggests that REM sleep is not indicative of the dream report and may originate from diverse neural substrates in brain functionality. The aim of the current study was to investigate the functional systems associated with inter-individual differences in dream recall and REM sleep through assessments of the resting-state functional connectivity...
March 12, 2018: NeuroImage
Shuchao Pang, Mehmet A Orgun, Zhezhou Yu
BACKGROUND AND OBJECTIVES: The traditional biomedical image retrieval methods as well as content-based image retrieval (CBIR) methods originally designed for non-biomedical images either only consider using pixel and low-level features to describe an image or use deep features to describe images but still leave a lot of room for improving both accuracy and efficiency. In this work, we propose a new approach, which exploits deep learning technology to extract the high-level and compact features from biomedical images...
May 2018: Computer Methods and Programs in Biomedicine
Piotr Chudzik, Somshubra Majumdar, Francesco Calivá, Bashir Al-Diri, Andrew Hunter
BACKROUND AND OBJECTIVES: Diabetic retinopathy is a microvascular complication of diabetes that can lead to sight loss if treated not early enough. Microaneurysms are the earliest clinical signs of diabetic retinopathy. This paper presents an automatic method for detecting microaneurysms in fundus photographies. METHODS: A novel patch-based fully convolutional neural network with batch normalization layers and Dice loss function is proposed. Compared to other methods that require up to five processing stages, it requires only three...
May 2018: Computer Methods and Programs in Biomedicine
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