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https://www.readbyqxmd.com/read/28445774/voxresnet-deep-voxelwise-residual-networks-for-brain-segmentation-from-3d-mr-images
#1
REVIEW
Hao Chen, Qi Dou, Lequan Yu, Jing Qin, Pheng-Ann Heng
Segmentation of key brain tissues from 3D medical images is of great significance for brain disease diagnosis, progression assessment and monitoring of neurologic conditions. While manual segmentation is time-consuming, laborious, and subjective, automated segmentation is quite challenging due to the complicated anatomical environment of brain and the large variations of brain tissues. We propose a novel voxelwise residual network (VoxResNet) with a set of effective training schemes to cope with this challenging problem...
April 23, 2017: NeuroImage
https://www.readbyqxmd.com/read/28445748/directional-force-originating-from-atp-hydrolysis-drives-the-groel-conformational-change
#2
Jie Liu, Kannan Sankar, Yuan Wang, Kejue Jia, Robert L Jernigan
Protein functional mechanisms usually require conformational changes, and often there are known structures for the different conformational states. However, usually neither the origin of the driving force nor the underlying pathways for these conformational transitions is known. Exothermic chemical reactions may be an important source of forces that drive conformational changes. Here we investigate this type of force originating from ATP hydrolysis in the chaperonin GroEL, by applying forces originating from the chemical reaction...
April 25, 2017: Biophysical Journal
https://www.readbyqxmd.com/read/28444127/hla-class-i-binding-prediction-via-convolutional-neural-networks
#3
Yeeleng S Vang, Xiaohui Xie
Motivation: Many biological processes are governed by protein-ligand interactions. One such example is the recognition of self and nonself cells by the immune system. This immune response process is regulated by the major histocompatibility complex (MHC) protein which is encoded by the human leukocyte antigen (HLA) complex. Understanding the binding potential between MHC and peptides can lead to the design of more potent, peptide-based vaccines and immunotherapies for infectious autoimmune diseases...
April 21, 2017: Bioinformatics
https://www.readbyqxmd.com/read/28443814/neural-networks-subtract-and-conquer
#4
Guillaume Hennequin
Two theoretical studies reveal how networks of neurons may behave during reward-based learning.
April 26, 2017: ELife
https://www.readbyqxmd.com/read/28442279/avoiding-catastrophic-forgetting
#5
Michael E Hasselmo
Humans regularly perform new learning without losing memory for previous information, but neural network models suffer from the phenomenon of catastrophic forgetting in which new learning impairs prior function. A recent article presents an algorithm that spares learning at synapses important for previously learned function, reducing catastrophic forgetting.
April 22, 2017: Trends in Cognitive Sciences
https://www.readbyqxmd.com/read/28441880/a-three-dimensional-collagen-fiber-network-model-of-the-extracellular-matrix-for-the-simulation-of-the-mechanical-behaviors-and-micro-structures
#6
Shoubin Dong, Zetao Huang, Liqun Tang, Xiaoyang Zhang, Yongrou Zhang, Yi Jiang
The extracellular matrix (ECM) provides structural and biochemical support to cells and tissues, which is a critical factor for modulating cell dynamic behavior and intercellular communication. In order to further understand the mechanisms of the interactive relationship between cell and the ECM, we developed a three-dimensional (3D) collagen-fiber network model to simulate the micro structure and mechanical behaviors of the ECM and studied the stress-strain relationship as well as the deformation of the ECM under tension...
April 26, 2017: Computer Methods in Biomechanics and Biomedical Engineering
https://www.readbyqxmd.com/read/28441114/neural-circuitry-of-reward-prediction-error
#7
Mitsuko Watabe-Uchida, Neir Eshel, Naoshige Uchida
Dopamine neurons facilitate learning by calculating reward prediction error, or the difference between expected and actual reward. Despite two decades of research, it remains unclear how dopamine neurons make this calculation. Here we review studies that tackle this problem from a diverse set of approaches, from anatomy to electrophysiology to computational modeling and behavior. Several patterns emerge from this synthesis: that dopamine neurons themselves calculate reward prediction error, rather than inherit it passively from upstream regions; that they combine multiple separate and redundant inputs, which are themselves interconnected in a dense recurrent network; and that despite the complexity of inputs, the output from dopamine neurons is remarkably homogeneous and robust...
April 24, 2017: Annual Review of Neuroscience
https://www.readbyqxmd.com/read/28440122/effects-of-low-dose-ionizing-radiation-on-dna-damage-caused-pathways-by-reverse-phase-protein-array-and-bayesian-networks
#8
Dong-Chul Kim, Mingon Kang, Ashis Biswas, Chin-Rang Yang, Xiaoyu Wang, Jean X Gao
Ionizing radiation (IR) causing damages to Deoxyribonucleic acid (DNA) constitutes a broad range of base damage and double strand break, and thereby, it induces the operation of relevant signaling pathways such as DNA repair, cell cycle control, and cell apoptosis. The goal of this paper is to study how the exposure to low dose radiation affects the human body by observing the signaling pathway associated with Ataxia Telangiectasia mutated (ATM) using Reverse-Phase Protein Array (RPPA) and isogenic human Ataxia Telangiectasia (A-T) cells under different amounts and durations of IR exposure...
April 2017: Journal of Bioinformatics and Computational Biology
https://www.readbyqxmd.com/read/28439325/graph-frequency-analysis-of-brain-signals
#9
Weiyu Huang, Leah Goldsberry, Nicholas F Wymbs, Scott T Grafton, Danielle S Bassett, Alejandro Ribeiro
This paper presents methods to analyze functional brain networks and signals from graph spectral perspectives. The notion of frequency and filters traditionally defined for signals supported on regular domains such as discrete time and image grids has been recently generalized to irregular graph domains, and defines brain graph frequencies associated with different levels of spatial smoothness across the brain regions. Brain network frequency also enables the decomposition of brain signals into pieces corresponding to smooth or rapid variations...
October 2016: IEEE Journal of Selected Topics in Signal Processing
https://www.readbyqxmd.com/read/28439014/brain-networks-for-confidence-weighting-and-hierarchical-inference-during-probabilistic-learning
#10
Florent Meyniel, Stanislas Dehaene
Learning is difficult when the world fluctuates randomly and ceaselessly. Classical learning algorithms, such as the delta rule with constant learning rate, are not optimal. Mathematically, the optimal learning rule requires weighting prior knowledge and incoming evidence according to their respective reliabilities. This "confidence weighting" implies the maintenance of an accurate estimate of the reliability of what has been learned. Here, using fMRI and an ideal-observer analysis, we demonstrate that the brain's learning algorithm relies on confidence weighting...
April 24, 2017: Proceedings of the National Academy of Sciences of the United States of America
https://www.readbyqxmd.com/read/28438906/novel-screening-tool-for-stroke-using-artificial-neural-network
#11
Vida Abedi, Nitin Goyal, Georgios Tsivgoulis, Niyousha Hosseinichimeh, Raquel Hontecillas, Josep Bassaganya-Riera, Lucas Elijovich, Jeffrey E Metter, Anne W Alexandrov, David S Liebeskind, Andrei V Alexandrov, Ramin Zand
BACKGROUND AND PURPOSE: The timely diagnosis of stroke at the initial examination is extremely important given the disease morbidity and narrow time window for intervention. The goal of this study was to develop a supervised learning method to recognize acute cerebral ischemia (ACI) and differentiate that from stroke mimics in an emergency setting. METHODS: Consecutive patients presenting to the emergency department with stroke-like symptoms, within 4.5 hours of symptoms onset, in 2 tertiary care stroke centers were randomized for inclusion in the model...
April 24, 2017: Stroke; a Journal of Cerebral Circulation
https://www.readbyqxmd.com/read/28438243/factors-which-motivate-the-use-of-social-networks-by-students
#12
Mercedes González Sanmamed, Pablo C Muñoz Carril, Isabel Dans Álvarez de Sotomayor
BACKGROUND: The aim of this research was to identify those factors which motivate the use of social networks by 4th year students in Secondary Education between the ages of 15 and 18. METHOD: 1,144 students from 29 public and private schools took part. The data were analysed using Partial Least Squares Structural Equation Modelling technique. RESULTS: Versatility was confirmed to be the variable which most influences the motivation of students in their use of social networks...
May 2017: Psicothema
https://www.readbyqxmd.com/read/28437797/central-and-peripheral-vision-for-scene-recognition-a-neurocomputational-modeling-exploration
#13
Panqu Wang, Garrison W Cottrell
What are the roles of central and peripheral vision in human scene recognition? Larson and Loschky (2009) showed that peripheral vision contributes more than central vision in obtaining maximum scene recognition accuracy. However, central vision is more efficient for scene recognition than peripheral, based on the amount of visual area needed for accurate recognition. In this study, we model and explain the results of Larson and Loschky (2009) using a neurocomputational modeling approach. We show that the advantage of peripheral vision in scene recognition, as well as the efficiency advantage for central vision, can be replicated using state-of-the-art deep neural network models...
April 1, 2017: Journal of Vision
https://www.readbyqxmd.com/read/28437616/neural-network-and-nearest-neighbour-algorithms-for-enhancing-sampling-of-molecular-dynamics
#14
Raimondas Galvelis, Yuji Sugita
The free energy calculations of complex chemical and biological systems with molecular dynamics (MD) are inefficient due to multiple local minima separated by high-energy barriers. The minima can be escaped using an enhanced sampling method such as metadynamics, which apply bias (i.e. importance sampling) along a set of collective variables (CV), but the maximum number of CVs (or dimensions) is severely limited. We propose a high-dimensional bias potential method (NN2B) based on two machine learning algorithms: the nearest neighbour density estimator (NNDE) and the artificial neural network (ANN) for the bias potential approximation...
April 24, 2017: Journal of Chemical Theory and Computation
https://www.readbyqxmd.com/read/28437615/experimental-demonstration-of-feature-extraction-and-dimensionality-reduction-using-memristor-networks
#15
Shinhyun Choi, Jong Hoon Shin, Jihang Lee, Patrick Sheridan, Wei D Lu
Memristors have been considered as a leading candidate for a number of critical applications ranging from non-volatile memory to non-Von Neumann computing systems. Feature extraction, which aims to transform input data from a high dimensional space to a space with fewer dimensions, is an important technique widely used in machine learning and pattern recognition applications. Here, we experimentally demonstrate that memristor arrays can be used to perform principal component analysis (PCA), one of the most commonly-used feature extraction techniques, through online, unsupervised learning...
April 24, 2017: Nano Letters
https://www.readbyqxmd.com/read/28437486/prediction-of-crime-occurrence-from-multi-modal-data-using-deep-learning
#16
Hyeon-Woo Kang, Hang-Bong Kang
In recent years, various studies have been conducted on the prediction of crime occurrences. This predictive capability is intended to assist in crime prevention by facilitating effective implementation of police patrols. Previous studies have used data from multiple domains such as demographics, economics, and education. Their prediction models treat data from different domains equally. These methods have problems in crime occurrence prediction, such as difficulty in discovering highly nonlinear relationships, redundancies, and dependencies between multiple datasets...
2017: PloS One
https://www.readbyqxmd.com/read/28436914/a-time-variant-log-linear-learning-approach-to-the-set-k-cover-problem-in-wireless-sensor-networks
#17
Changhao Sun
Toward the global optimality of the SET K-COVER problem in wireless sensor networks, we view each sensor node as a rational player and propose a time variant log-linear learning algorithm (TVLLA) that relies on local information only. By defining the local utility as the normalized area covered by one node alone, we formulate the problem as a spatial potential game. The resulting optimal Nash equilibria correspond to the optimal partition. Such equilibria are obtained by designing a time varying parameter β that approaches infinity with time...
April 19, 2017: IEEE Transactions on Cybernetics
https://www.readbyqxmd.com/read/28436904/user-preference-based-dual-memory-neural-model-with-memory-consolidation-approach
#18
Jauwairia Nasir, Yong-Ho Yoo, Deok-Hwa Kim, Jong-Hwan Kim
Memory modeling has been a popular topic of research for improving the performance of autonomous agents in cognition related problems. Apart from learning distinct experiences correctly, significant or recurring experiences are expected to be learned better and be retrieved easier. In order to achieve this objective, this paper proposes a user preference-based dual-memory adaptive resonance theory network model, which makes use of a user preference to encode memories with various strengths and to learn and forget at various rates...
April 24, 2017: IEEE Transactions on Neural Networks and Learning Systems
https://www.readbyqxmd.com/read/28436902/learning-to-predict-consequences-as-a-method-of-knowledge-transfer-in-reinforcement-learning
#19
Eric Chalmers, Edgar Bermudez Contreras, Brandon Robertson, Artur Luczak, Aaron Gruber
The reinforcement learning (RL) paradigm allows agents to solve tasks through trial-and-error learning. To be capable of efficient, long-term learning, RL agents should be able to apply knowledge gained in the past to new tasks they may encounter in the future. The ability to predict actions' consequences may facilitate such knowledge transfer. We consider here domains where an RL agent has access to two kinds of information: agent-centric information with constant semantics across tasks, and environment-centric information, which is necessary to solve the task, but with semantics that differ between tasks...
April 17, 2017: IEEE Transactions on Neural Networks and Learning Systems
https://www.readbyqxmd.com/read/28436897/tensor-factorized-neural-networks
#20
Jen-Tzung Chien, Yi-Ting Bao
The growing interests in multiway data analysis and deep learning have drawn tensor factorization (TF) and neural network (NN) as the crucial topics. Conventionally, the NN model is estimated from a set of one-way observations. Such a vectorized NN is not generalized for learning the representation from multiway observations. The classification performance using vectorized NN is constrained, because the temporal or spatial information in neighboring ways is disregarded. More parameters are required to learn the complicated data structure...
April 17, 2017: IEEE Transactions on Neural Networks and Learning Systems
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