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12:21 EDT 25th April 2017 | BioPortfolio

The US National Library of Medicine and National Institutes of Health manage PubMed.gov which comprises of more than 21 million records, papers, reports for biomedical literature, including MEDLINE, life science and medical journals, articles, reviews, reports and  books.  BioPortfolio aims to publish relevant information on published papers, clinical trials and news associated with users selected topics.

For example view all recent relevant publications on Epigenetics and associated publications and clincial trials.

Showing PubMed Articles 1–25 of 1,700,000+

Predicting the Quality of Fused Long Wave Infrared and Visible Light Images.

The capability to automatically evaluate the quality of long wave infrared (LWIR) and visible light images has the potential to play an important role in determining and controlling the quality of a resulting fused LWIR-visible light image. Extensive work has been conducted on studying the statistics of natural LWIR and visible images. Nonetheless, there has been little work done on analyzing the statistics of fused LWIR and visible images and associated distortions. In this paper, we analyze five multi-res...

Track Everything: Limiting Prior Knowledge in Online Multi-Object Recognition.

This paper addresses the problem of online tracking and classification of multiple objects in an image sequence. Our proposed solution is to first track all objects in the scene without relying on object-specific prior knowledge, which in other systems can take the form of hand-crafted features or user-based track initialization. We then classify the tracked objects with a fastlearning image classifier that is based on a shallow convolutional neural network architecture and demonstrate that object recogniti...

Zero-shot Learning with Transferred Samples.

By transferring knowledge from the abundant labeled samples of known source classes, zero-shot learning (ZSL) makes it possible to train recognition models for novel target classes that have no labeled samples. Conventional ZSL approaches usually adopt a two-step recognition strategy, in which the test sample is projected into an intermediary space in the first step, and then the recognition is carried out by considering the similarity between the sample and target classes in the intermediary space. Due to ...

Deeply Learned View-Invariant Features for Cross-View Action Recognition.

Classifying human actions from varied views is challenging due to huge data variations in different views. The key to this problem is to learn discriminative view-invariant features robust to view variations. In this work, we address this problem by learning view-specific and view-shared features using novel deep models. View-specific features capture unique dynamics of each view while view-shared features encode common patterns across views. A novel sample-affinity matrix (SAM) is introduced in learning sh...

Packing Vertex Data into Hardware-Decompressible Textures.

Most graphics hardware features memory to store textures and vertex data for rendering. However, because of the irreversible trend of increasing complexity of scenes, rendering a scene can easily reach the limit of memory resources. Thus, vertex data are preferably compressed, with a requirement that they can be decompressed during rendering. In this paper, we present a novel method to exploit existing hardware texture compression circuits to facilitate the decompression of vertex data in graphics processin...

Biofeedback for gait retraining based on real-time estimation of tibiofemoral joint contact forces.

Biofeedback assisted rehabilitation and intervention technologies have the potential to modify clinically relevant biomechanics. Gait retraining has been used to reduce the knee adduction moment, a surrogate of medial tibiofemoral joint loading often used in knee osteoarthritis research. In this study we present an electromyogram-driven neuromusculoskeletal model of the lower-limb to estimate, in real-time, the tibiofemoral joint loads. The model included 34 musculotendon units spanning the hip, knee, and a...

Study on interaction between temporal and spatial information in classification of EMG signals in myoelectric prostheses.

Advanced forearm prosthetic devices employ classifiers to recognize different electromyography (EMG) signal patterns, in order to identify the user's intended motion gesture. The classification accuracy is one of the main determinants of real-time controllability of a prosthetic limb and hence the necessity to achieve as high an accuracy as possible. In this paper, we study the effects of the temporal and spatial information provided to the classifier on its offline performance and analyze their inter-depen...

A Human-Robot Co-Manipulation Approach Based on Human Sensorimotor Information.

This paper aims to improve the interaction and coordination between the human and the robot in cooperative execution of complex, powerful and dynamic tasks. We propose a novel approach that integrates online information about the human motor function and manipulability properties into the hybrid controller of the assistive robot. Through this human-inthe- loop framework, the robot can adapt to the human motor behaviour and provide the appropriate assistive response in different phases of the cooperative tas...

Modulating Lateral Geniculate Nucleus Neuronal Firing for Visual Prostheses: A Kalman Filter-based Strategy.

Developing visual prostheses that target inner brain structures along the visual pathway represent a new hope for patients with completely damaged early visual pathway sites. One of the major challenges in the development of subcortical and cortical visual prostheses is tuning electrical stimulation that could optimally induce desired visual percepts. In this paper, we propose a Kalman filter-based strategy that could be used to identify electrical stimulation patterns that mimic a specific visual input for...

Home-based Therapy after Stroke Using the Hand Spring Operated Movement Enhancer (HandSOME).

In previous work, we developed a lightweight wearable hand exoskeleton (HandSOME) that improves range of motion and function in laboratory testing. In this pilot study, we added the ability to log movement data for extended periods and recruited 10 chronic stroke subjects to use the device during reach and grasp task practice at home for 1.5 hours/day, 5 days per week, for 4 weeks. Seven subjects completed the study, performing 448 ± 651 hand movements per training day. After training, impairment was reduc...

A Bipartite Network and Resource Transfer-based Approach to Infer LncRNA-environmental Factor Associations.

Phenotypes and diseases are often determined by the complex interactions between genetic factors and environmental factors (EFs). However, compared with protein-coding genes and microRNAs, there is a paucity of computational methods for understanding the associations between long non-coding RNAs (lncRNAs) and EFs. In this study, we focused on the associations between lncRNA and EFs. By using the common miRNA partners of any pair of lncRNA and EF, based on the competing endogenous RNA (ceRNA) hypothesis and ...

Novelty Indicator for Enhanced Prioritization of Predicted Gene Ontology Annotations.

Biomolecular controlled annotations have become pivotal in computational biology, because they allow scientists to analyze large amounts of biological data to better understand test results, and to infer new knowledge. Yet, biomolecular annotation databases are incomplete by definition, like our knowledge of biology, and might contain errors and inconsistent information. In this context, machine-learning algorithms able to predict and prioritize new annotations are both effective and efficient, especially i...

Investigating Correlation between Protein Sequence Similarity and Semantic Similarity Using Gene Ontology Annotations.

Sequence similarity is a commonly used measure to compare proteins. With the increasing use of ontologies, semantic (function) similarity is getting importance. The correlation between these measures has been applied in the evaluation of new semantic similarity methods, and in protein function prediction. In this research, we investigate the relationship between the two similarity methods. The results suggest absence of a strong correlation between sequence and semantic similarities. There is a large number...

DNA Assembly with De Bruijn Graphs Using an FPGA Platform.

This paper presents an FPGA implementation of a DNA assembly algorithm, called Ray, initially developed to run on parallel CPUs. The OpenCL language is used and the focus is placed on modifying and optimizing the original algorithm to better suit the new parallelization tool and the radically different hardware architecture. The results show that the execution time is roughly one fourth that of the CPU and factoring energy consumption yields a tenfold savings.

Optimal Block-Based Trimming for Next Generation Sequencing.

Read trimming is a fundamental first step of the analysis of next generation sequencing (NGS) data. Traditionally, it is performed heuristically, and algorithmic work in this area has been neglected. Here, we address this topic and formulate three optimization problems for block-based trimming (truncating the same low-quality positions at both ends for all reads and removing low-quality truncated reads). We find that all problems are NP-hard. Hence, we investigate the approximability of the problems. Two of...

Neuromorphic Hardware Architecture Using the Neural Engineering Framework for Pattern Recognition.

We present a hardware architecture that uses the neural engineering framework (NEF) to implement large-scale neural networks on field programmable gate arrays (FPGAs) for performing massively parallel real-time pattern recognition. NEF is a framework that is capable of synthesising large-scale cognitive systems from subnetworks and we have previously presented an FPGA implementation of the NEF that successfully performs nonlinear mathematical computations. That work was developed based on a compact digital ...

Design and Implementation of a Tactile Stimulation Device to Increase Auditory Discrimination.

Reading is a complex process that requires various simultaneous brain processes. One of the most common types of reading disorders is developmental dyslexia, and one of the objectives of speech therapy sessions for children with developmental dyslexia is to increase their auditory discrimination. One of the most commonly used Auditory Discrimination Tests (ADTs) is Wepman's Auditory Discrimination Test (WADT). It includes minimal pair words categorized by characteristics of vowels and consonants. The goal o...

In-Situ Force Augmentation Improves Surface Contact and Force Control.

Surgeons routinely perform surgery with noisy, sub-threshold, or obscured visual and haptic feedback, either due to the necessary surgical approach, or because the systems on which they are operating are exceedingly delicate. Technological solutions incorporating haptic feedback augmentation have been proposed to address these difficulties, but the consequences for motor control have not been directly investigated and quantified. In this paper, we present two isometric force generation tasks performed with ...

Off-Policy Reinforcement Learning for Synchronization in Multiagent Graphical Games.

This paper develops an off-policy reinforcement learning (RL) algorithm to solve optimal synchronization of multiagent systems. This is accomplished by using the framework of graphical games. In contrast to traditional control protocols, which require complete knowledge of agent dynamics, the proposed off-policy RL algorithm is a model-free approach, in that it solves the optimal synchronization problem without knowing any knowledge of the agent dynamics. A prescribed control policy, called behavior policy,...

GoDec+: Fast and Robust Low-Rank Matrix Decomposition Based on Maximum Correntropy.

GoDec is an efficient low-rank matrix decomposition algorithm. However, optimal performance depends on sparse errors and Gaussian noise. This paper aims to address the problem that a matrix is composed of a low-rank component and unknown corruptions. We introduce a robust local similarity measure called correntropy to describe the corruptions and, in doing so, obtain a more robust and faster low-rank decomposition algorithm: GoDec+. Based on half-quadratic optimization and greedy bilateral paradigm, we deli...

A Parallel Multiclassification Algorithm for Big Data Using an Extreme Learning Machine.

As data sets become larger and more complicated, an extreme learning machine (ELM) that runs in a traditional serial environment cannot realize its ability to be fast and effective. Although a parallel ELM (PELM) based on MapReduce to process large-scale data shows more efficient learning speed than identical ELM algorithms in a serial environment, some operations, such as intermediate results stored on disks and multiple copies for each task, are indispensable, and these operations create a large amount of...

Online Learning Algorithms Can Converge Comparably Fast as Batch Learning.

Online learning algorithms in a reproducing kernel Hilbert space associated with convex loss functions are studied. We show that in terms of the expected excess generalization error, they can converge comparably fast as corresponding kernel-based batch learning algorithms. Under mild conditions on loss functions and approximation errors, fast learning rates and finite sample upper bounds are established using polynomially decreasing step-size sequences. For some commonly used loss functions for classificati...

Online Learning Algorithm Based on Adaptive Control Theory.

This paper proposes a new online learning algorithm which is based on adaptive control (AC) theory, thus, we call this proposed algorithm as AC algorithm. Comparing to the gradient descent (GD) and exponential gradient (EG) algorithm which have been applied to online prediction problems, we find a new form of AC theory for online prediction problems and investigate two key questions: how to get a new update law which has a tighter upper bound on the error than the square loss? How to compare the upper bound...

Online Hashing.

Although hash function learning algorithms have achieved great success in recent years, most existing hash models are off-line, which are not suitable for processing sequential or online data. To address this problem, this paper proposes an online hash model to accommodate data coming in stream for online learning. Specifically, a new loss function is proposed to measure the similarity loss between a pair of data samples in hamming space. Then, a structured hash model is derived and optimized in a passive-a...

Tensor-Factorized Neural Networks.

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...


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