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https://www.readbyqxmd.com/read/28325033/user-intent-prediction-with-a-scaled-conjugate-gradient-trained-artificial-neural-network-for-lower-limb-amputees-using-a-powered-prosthesis
#1
Richard B Woodward, John A Spanias, Levi J Hargrove
Powered lower limb prostheses have the ability to provide greater mobility for amputee patients. Such prostheses often have pre-programmed modes which can allow activities such as climbing stairs and descending ramps, something which many amputees struggle with when using non-powered limbs. Previous literature has shown how pattern classification can allow seamless transitions between modes with a high accuracy and without any user interaction. Although accurate, training and testing each subject with their own dependent data is time consuming...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28324951/mentor-s-brain-functional-connectivity-network-during-robotic-assisted-surgery-mentorship
#2
Somayeh B Shafiei, Scott T Doyle, Khurshid A Guru
In many complicated cognitive-motor tasks mentoring is inevitable during the learning process. Although mentors are expert in doing the task, trainee's operation might be new for a mentor. This makes mentoring a very difficult task which demands not only the knowledge and experience of a mentor, but also his/her ability to follow trainee's movements and patiently advise him/her during the operation. We hypothesize that information binding throughout the mentor's brain areas, contributed to the task, changes as the expertise level of the trainee improves from novice to intermediate and expert...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28324924/automatic-tissue-characterization-of-air-trapping-in-chest-radiographs-using-deep-neural-networks
#3
Awais Mansoor, Geovanny Perez, Gustavo Nino, Marius George Linguraru
Significant progress has been made in recent years for computer-aided diagnosis of abnormal pulmonary textures from computed tomography (CT) images. Similar initiatives in chest radiographs (CXR), the common modality for pulmonary diagnosis, are much less developed. CXR are fast, cost effective and low-radiation solution to diagnosis over CT. However, the subtlety of textures in CXR makes them hard to discern even by trained eye. We explore the performance of deep learning abnormal tissue characterization from CXR...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28324695/imagining-the-future-the-core-episodic-simulation-network-dissociates-as-a-function-of-timecourse-and-the-amount-of-simulated-information
#4
Preston P Thakral, Roland G Benoit, Daniel L Schacter
Neuroimaging data indicate that episodic memory (i.e., remembering specific past experiences) and episodic simulation (i.e., imagining specific future experiences) are associated with enhanced activity in a common set of neural regions, often referred to as the core network. This network comprises the hippocampus, parahippocampal cortex, lateral and medial parietal cortex, lateral temporal cortex, and medial prefrontal cortex. Evidence for a core network has been taken as support for the idea that episodic memory and episodic simulation are supported by common processes...
February 24, 2017: Cortex; a Journal Devoted to the Study of the Nervous System and Behavior
https://www.readbyqxmd.com/read/28324301/neuroimaging-in-epilepsy
#5
REVIEW
Erik H Middlebrooks, Lawrence Ver Hoef, Jerzy P Szaflarski
In recent years, the field of neuroimaging has undergone dramatic development. Specifically, of importance for clinicians and researchers managing patients with epilepsies, new methods of brain imaging in search of the seizure-producing abnormalities have been implemented, and older methods have undergone additional refinement. Methodology to predict seizure freedom and cognitive outcome has also rapidly progressed. In general, the image data processing methods are very different and more complicated than even a decade ago...
April 2017: Current Neurology and Neuroscience Reports
https://www.readbyqxmd.com/read/28319275/mrf-ann-a-machine-learning-approach-for-automated-er-scoring-of-breast-cancer-immunohistochemical-images
#6
T Mungle, S Tewary, D K DAS, I Arun, B Basak, S Agarwal, R Ahmed, S Chatterjee, C Chakraborty
Molecular pathology, especially immunohistochemistry, plays an important role in evaluating hormone receptor status along with diagnosis of breast cancer. Time-consumption and inter-/intraobserver variability are major hindrances for evaluating the receptor score. In view of this, the paper proposes an automated Allred Scoring methodology for estrogen receptor (ER). White balancing is used to normalize the colour image taking into consideration colour variation during staining in different labs. Markov random field model with expectation-maximization optimization is employed to segment the ER cells...
March 20, 2017: Journal of Microscopy
https://www.readbyqxmd.com/read/28319187/the-impact-of-high-grade-glial-neoplasms-on-human-cortical-electrophysiology
#7
S Kathleen Bandt, Jarod L Roland, Mrinal Pahwa, Carl D Hacker, David T Bundy, Jonathan D Breshears, Mohit Sharma, Joshua S Shimony, Eric C Leuthardt
OBJECTIVE: The brain's functional architecture of interconnected network-related oscillatory patterns in discrete cortical regions has been well established with functional magnetic resonance imaging (fMRI) studies or direct cortical electrophysiology from electrodes placed on the surface of the brain, or electrocorticography (ECoG). These resting state networks exhibit a robust functional architecture that persists through all stages of sleep and under anesthesia. While the stability of these networks provides a fundamental understanding of the organization of the brain, understanding how these regions can be perturbed is also critical in defining the brain's ability to adapt while learning and recovering from injury...
2017: PloS One
https://www.readbyqxmd.com/read/28318904/dual-memory-neural-networks-for-modeling-cognitive-activities-of-humans-via-wearable-sensors
#8
Sang-Woo Lee, Chung-Yeon Lee, Dong-Hyun Kwak, Jung-Woo Ha, Jeonghee Kim, Byoung-Tak Zhang
Wearable devices, such as smart glasses and watches, allow for continuous recording of everyday life in a real world over an extended period of time or lifelong. This possibility helps better understand the cognitive behavior of humans in real life as well as build human-aware intelligent agents for practical purposes. However, modeling the human cognitive activity from wearable-sensor data stream is challenging because learning new information often results in loss of previously acquired information, causing a problem known as catastrophic forgetting...
February 20, 2017: Neural Networks: the Official Journal of the International Neural Network Society
https://www.readbyqxmd.com/read/28318903/understanding-human-intention-by-connecting-perception-and-action-learning-in-artificial-agents
#9
Sangwook Kim, Zhibin Yu, Minho Lee
To develop an advanced human-robot interaction system, it is important to first understand how human beings learn to perceive, think, and act in an ever-changing world. In this paper, we propose an intention understanding system that uses an Object Augmented-Supervised Multiple Timescale Recurrent Neural Network (OA-SMTRNN) and demonstrate the effects of perception-action connected learning in an artificial agent, which is inspired by psychological and neurological phenomena in humans. We believe that action and perception are not isolated processes in human mental development, and argue that these psychological and neurological interactions can be replicated in a human-machine scenario...
February 11, 2017: Neural Networks: the Official Journal of the International Neural Network Society
https://www.readbyqxmd.com/read/28315750/fun-cube-based-brain-gym-cognitive-function-assessment-system
#10
Tao Zhang, Chung-Chih Lin, Tsang-Chu Yu, Jing Sun, Wen-Chuin Hsu, Alice May-Kuen Wong
The aim of this study is to design and develop a fun cube (FC) based brain gym (BG) cognitive function assessment system using the wireless sensor network and multimedia technologies. The system comprised (1) interaction devices, FCs and a workstation used as interactive tools for collecting and transferring data to the server, (2) a BG information management system responsible for managing the cognitive games and storing test results, and (3) a feedback system used for conducting the analysis of cognitive functions to assist caregivers in screening high risk groups with mild cognitive impairment...
March 3, 2017: Computers in Biology and Medicine
https://www.readbyqxmd.com/read/28315708/2016-international-meeting-of-the-global-virus-network
#11
REVIEW
Ramesh Akkina, Heinz Ellerbrok, William Hall, Hideki Hasagawa, Yasushi Kawaguchi, Harold Kleanthous, Edward McSweegan, Natalia Mercer, Victor Romanowski, Hirofumi Sawa, Anders Vahlne
The Global Virus Network (GVN) was established in 2011 in order to strengthen research and responses to current viral causes of human disease and to prepare against new viral pandemic threats. There are now 38 GVN Centers of Excellence and 6 Affiliate laboratories in 24 countries. GVN scientists meet annually to learn about each other's current research, address collaborative priorities and plan future programs. The 2016 meeting was held from October 23-25 in Hokkaido, Japan, in partnership with the Japanese Society for Virology, the National Institute of Infectious Diseases of Japan and the Research Center for Zoonosis Control of Hokkaido University...
March 15, 2017: Antiviral Research
https://www.readbyqxmd.com/read/28315459/dynamic-reorganization-of-intrinsic-functional-networks-in-the-mouse-brain
#12
Joanes Grandjean, Maria Giulia Preti, Thomas Aw Bolton, Michaela Buerge, Erich Seifritz, Christopher R Pryce, Dimitri Van De Ville, Markus Rudin
Functional connectivity (FC) derived from resting-state functional magnetic resonance imaging (rs-fMRI) allows for the integrative study of neuronal processes at a macroscopic level. The majority of studies to date have assumed stationary interactions between brain regions, without considering the dynamic aspects of network organization. Only recently has the latter received increased attention, predominantly in human studies. Applying dynamic FC (dFC) analysis to mice is attractive given the relative simplicity of the mouse brain and the possibility to explore mechanisms underlying network dynamics using pharmacological, environmental or genetic interventions...
March 14, 2017: NeuroImage
https://www.readbyqxmd.com/read/28315260/introduction-to-sigma-receptors-their-role-in-disease-and-as-therapeutic-targets
#13
Sylvia B Smith
This book highlights contributions from leaders in the field of sigma receptor research. Sigma receptors represent a promising, novel target for the treatment of neurodegenerative diseases, retinal degenerations, pain and substance abuse. Information is presented about tracers for molecular imaging these receptors, the newly determined crystal structure of human sigma 1 receptor and information about sigma 2 receptor. New discoveries about the role of sigma 1 receptors in cancer, pain, neuropsychiatric disorders, learning and memory, neuronal networks and depression are described...
2017: Advances in Experimental Medicine and Biology
https://www.readbyqxmd.com/read/28315069/toolkits-and-libraries-for-deep-learning
#14
REVIEW
Bradley J Erickson, Panagiotis Korfiatis, Zeynettin Akkus, Timothy Kline, Kenneth Philbrick
Deep learning is an important new area of machine learning which encompasses a wide range of neural network architectures designed to complete various tasks. In the medical imaging domain, example tasks include organ segmentation, lesion detection, and tumor classification. The most popular network architecture for deep learning for images is the convolutional neural network (CNN). Whereas traditional machine learning requires determination and calculation of features from which the algorithm learns, deep learning approaches learn the important features as well as the proper weighting of those features to make predictions for new data...
March 17, 2017: Journal of Digital Imaging: the Official Journal of the Society for Computer Applications in Radiology
https://www.readbyqxmd.com/read/28314196/governance-and-management-dynamics-of-landscape-restoration-at-multiple-scales-learning-from-successful-environmental-managers-in-sweden
#15
Lucas Dawson, Marine Elbakidze, Per Angelstam, Johanna Gordon
Due to a long history of intensive land and water use, habitat networks for biodiversity conservation are generally degraded in Sweden. Landscape restoration (LR) is an important strategy for achieving representative and functional green infrastructures. However, outcomes of LR efforts are poorly studied, particularly the dynamics of LR governance and management. We apply systems thinking methods to a series of LR case studies to analyse the causal structures underlying LR governance and management in Sweden...
March 14, 2017: Journal of Environmental Management
https://www.readbyqxmd.com/read/28306716/localization-and-diagnosis-framework-for-pediatric-cataracts-based-on-slit-lamp-images-using-deep-features-of-a-convolutional-neural-network
#16
Xiyang Liu, Jiewei Jiang, Kai Zhang, Erping Long, Jiangtao Cui, Mingmin Zhu, Yingying An, Jia Zhang, Zhenzhen Liu, Zhuoling Lin, Xiaoyan Li, Jingjing Chen, Qianzhong Cao, Jing Li, Xiaohang Wu, Dongni Wang, Haotian Lin
Slit-lamp images play an essential role for diagnosis of pediatric cataracts. We present a computer vision-based framework for the automatic localization and diagnosis of slit-lamp images by identifying the lens region of interest (ROI) and employing a deep learning convolutional neural network (CNN). First, three grading degrees for slit-lamp images are proposed in conjunction with three leading ophthalmologists. The lens ROI is located in an automated manner in the original image using two successive applications of Candy detection and the Hough transform, which are cropped, resized to a fixed size and used to form pediatric cataract datasets...
2017: PloS One
https://www.readbyqxmd.com/read/28304359/social-networking-sites-and-addiction-ten-lessons-learned
#17
REVIEW
Daria J Kuss, Mark D Griffiths
Online social networking sites (SNSs) have gained increasing popularity in the last decade, with individuals engaging in SNSs to connect with others who share similar interests. The perceived need to be online may result in compulsive use of SNSs, which in extreme cases may result in symptoms and consequences traditionally associated with substance-related addictions. In order to present new insights into online social networking and addiction, in this paper, 10 lessons learned concerning online social networking sites and addiction based on the insights derived from recent empirical research will be presented...
March 17, 2017: International Journal of Environmental Research and Public Health
https://www.readbyqxmd.com/read/28303334/an-insect-inspired-model-for-visual-binding-ii-functional-analysis-and-visual-attention
#18
Brandon D Northcutt, Charles M Higgins
We have developed a neural network model capable of performing visual binding inspired by neuronal circuitry in the optic glomeruli of flies: a brain area that lies just downstream of the optic lobes where early visual processing is performed. This visual binding model is able to detect objects in dynamic image sequences and bind together their respective characteristic visual features-such as color, motion, and orientation-by taking advantage of their common temporal fluctuations. Visual binding is represented in the form of an inhibitory weight matrix which learns over time which features originate from a given visual object...
March 16, 2017: Biological Cybernetics
https://www.readbyqxmd.com/read/28303333/an-insect-inspired-model-for-visual-binding-i-learning-objects-and-their-characteristics
#19
Brandon D Northcutt, Jonathan P Dyhr, Charles M Higgins
Visual binding is the process of associating the responses of visual interneurons in different visual submodalities all of which are responding to the same object in the visual field. Recently identified neuropils in the insect brain termed optic glomeruli reside just downstream of the optic lobes and have an internal organization that could support visual binding. Working from anatomical similarities between optic and olfactory glomeruli, we have developed a model of visual binding based on common temporal fluctuations among signals of independent visual submodalities...
March 16, 2017: Biological Cybernetics
https://www.readbyqxmd.com/read/28303256/recurring-functional-interactions-predict-network-architecture-of-interictal-and-ictal-states-in-neocortical-epilepsy
#20
Ankit N Khambhati, Danielle S Bassett, Brian S Oommen, Stephanie H Chen, Timothy H Lucas, Kathryn A Davis, Brian Litt
Human epilepsy patients suffer from spontaneous seizures, which originate in brain regions that also subserve normal function. Prior studies demonstrate focal, neocortical epilepsy is associated with dysfunction, several hours before seizures. How does the epileptic network perpetuate dysfunction during baseline periods? To address this question, we developed an unsupervised machine learning technique to disentangle patterns of functional interactions between brain regions, or subgraphs, from dynamic functional networks constructed from approximately 100 h of intracranial recordings in each of 22 neocortical epilepsy patients...
January 2017: ENeuro
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