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Deep learning

Jiangming Sun, Yang De Marinis, Peter Osmark, Pratibha Singh, Annika Bagge, Bérengère Valtat, Petter Vikman, Peter Spégel, Hindrik Mulder
RNA editing is a post-transcriptional alteration of RNA sequences that, via insertions, deletions or base substitutions, can affect protein structure as well as RNA and protein expression. Recently, it has been suggested that RNA editing may be more frequent than previously thought. A great impediment, however, to a deeper understanding of this process is the paramount sequencing effort that needs to be undertaken to identify RNA editing events. Here, we describe an in silico approach, based on machine learning, that ameliorates this problem...
2016: PloS One
(no author information available yet)
[This corrects the article on p. 38 in vol. 7, PMID: 27688929.].
2016: Journal of Pathology Informatics
Ali Yusuf Öner, Berrak Barutcu, Şükrü Aykol, Emin Turgut Tali
OBJECTIVES: There have been recent studies evaluating brain magnetic resonance imaging changes in patients with normal renal function, after intravenous administration of gadolinium-based contrast agents (GBCAs). Their findings were supported by histological evidence as well and brought a new vision concerning what needs to be learned to provide better patient care. In this report, we aim to present brain magnetic resonance imaging changes after intrathecal administration of a linear ionic agent (gadopentetate dimeglumine)...
October 13, 2016: Investigative Radiology
Yuan Liu, Benoit M Dawant
Deep brain stimulation, as a primary surgical treatment for various neurological disorders, involves implanting electrodes to stimulate target nuclei within millimeter accuracy. Accurate pre-operative target selection is challenging due to the poor contrast in its surrounding region in MR images. In this paper, we present a learning-based method to automatically and rapidly localize the target using multi-modal images. A learning-based technique is applied first to spatially normalize the images in a common coordinate space...
February 2016: ... IEEE-EMBS International Conference on Biomedical and Health Informatics. IEEE-EMBS International Conference on Biomedical and Health Informatics
Peng Jiang, Zhixin Hu, Jun Liu, Shanen Yu, Feng Wu
Big sensor data provide significant potential for chemical fault diagnosis, which involves the baseline values of security, stability and reliability in chemical processes. A deep neural network (DNN) with novel active learning for inducing chemical fault diagnosis is presented in this study. It is a method using large amount of chemical sensor data, which is a combination of deep learning and active learning criterion to target the difficulty of consecutive fault diagnosis. DNN with deep architectures, instead of shallow ones, could be developed through deep learning to learn a suitable feature representation from raw sensor data in an unsupervised manner using stacked denoising auto-encoder (SDAE) and work through a layer-by-layer successive learning process...
October 13, 2016: Sensors
Christine H Stortini, Denis Chabot, Nancy L Shackell
We have learned much about the impacts of warming on the productivity and distribution of marine organisms, but less about the impact of warming combined with other environmental stressors, including oxygen depletion. Also, the combined impact of multiple environmental stressors requires evaluation at the scales most relevant to resource managers. We use the Gulf of St. Lawrence, Canada, characterized by a large permanently hypoxic zone, as a case study. Species Distribution Models were used to predict the impact of multiple scenarios of warming and oxygen depletion on the local density of three commercially and ecologically important species...
October 18, 2016: Global Change Biology
Yafit Maza, Efrat Shechter, Neta Pur Eizenberg, Efrat Gortler Segev, Moshe Y Flugelman
BACKGROUND: The physician manager role in the health care system is invaluable as they serve as role models and quality setters. The requirements from physician managers have become more demanding and the role less prestigious; yet burnout and its prevention in this group have received little attention. Physician leadership development programmes have generally dealt directly with skill and knowledge acquisition. The aim of this research was to evaluate an intensive workshop designed to modify attitudes and improve skills of physician-managers of community clinics, through focus on personal well-being and empowerment...
October 14, 2016: BMC Medical Education
Janet Landeen, Donna Carr, Kirsten Culver, Lynn Martin, Nancy Matthew-Maich, Charlotte Noesgaard, Larissa Beney-Gadsby
Ongoing curricular renewal is a necessary phenomenon in nursing education to align learning with ever-changing professional practice demands. The McMaster Mohawk Conestoga BScN Program in Hamilton, Ontario, Canada recently engaged in a comprehensive curriculum renewal. The purpose of this study was to evaluate the impact of curricular changes on students' deep learning. Faculty perceptions about student learning outcomes during final year clinical placements were gathered through a combination of individual interviews and focus groups using Interpretive Descriptive qualitative research methodology...
October 4, 2016: Nurse Education in Practice
Xiaoyang Wang, Qiang Ji
Current video event recognition research remains largely target-centered. For real-world surveillance videos, targetcentered event recognition faces great challenges due to large intra-class target variation, limited image resolution, and poor detection and tracking results. To mitigate these challenges, we introduced a context-augmented video event recognition approach. Specifically, we explicitly capture different types of contexts from three levels including image level, semantic level, and prior level. At the image level, we introduce two types of contextual features including the appearance context features and interaction context features to capture the appearance of context objects and their interactions with the target objects...
October 11, 2016: IEEE Transactions on Pattern Analysis and Machine Intelligence
Mehmet Ş Bademci, Serkan Yazman, Tevfik Güneş, Gokhan Ocakoglu, Kaptanderya Tayfur, Orhan Gokalp
BACKGROUND: No work has been reported on the use of video websites to learn about deep vein thrombosis and the value of education using them. We examined the characteristics and scientific accuracy of videos related to deep vein thrombosis on YouTube. METHODS: YouTube was surveyed using no filter and the key words 'deep vein thrombosis' and 'leg vein clot' in June 2016. The videos evaluated were divided into three groups in terms of their scientific content, accuracy, and currency: useful, partly useful, and useless...
October 12, 2016: Phlebology
Xiang Li, Ling Peng, Yuan Hu, Jing Shao, Tianhe Chi
With the rapid development of urbanization and industrialization, many developing countries are suffering from heavy air pollution. Governments and citizens have expressed increasing concern regarding air pollution because it affects human health and sustainable development worldwide. Current air quality prediction methods mainly use shallow models; however, these methods produce unsatisfactory results, which inspired us to investigate methods of predicting air quality based on deep architecture models. In this paper, a novel spatiotemporal deep learning (STDL)-based air quality prediction method that inherently considers spatial and temporal correlations is proposed...
October 13, 2016: Environmental Science and Pollution Research International
Alvin Rajkomar, Sneha Lingam, Andrew G Taylor, Michael Blum, John Mongan
The study aimed to determine if computer vision techniques rooted in deep learning can use a small set of radiographs to perform clinically relevant image classification with high fidelity. One thousand eight hundred eighty-five chest radiographs on 909 patients obtained between January 2013 and July 2015 at our institution were retrieved and anonymized. The source images were manually annotated as frontal or lateral and randomly divided into training, validation, and test sets. Training and validation sets were augmented to over 150,000 images using standard image manipulations...
October 11, 2016: Journal of Digital Imaging: the Official Journal of the Society for Computer Applications in Radiology
Lin-Peng Jin, Jun Dong
Ensemble learning has been proved to improve the generalization ability effectively in both theory and practice. In this paper, we briefly outline the current status of research on it first. Then, a new deep neural network-based ensemble method that integrates filtering views, local views, distorted views, explicit training, implicit training, subview prediction, and Simple Average is proposed for biomedical time series classification. Finally, we validate its effectiveness on the Chinese Cardiovascular Disease Database containing a large number of electrocardiogram recordings...
2016: Computational Intelligence and Neuroscience
Nikola K Kasabov, Maryam Gholami Doborjeh, Zohreh Gholami Doborjeh
This paper introduces a new methodology for dynamic learning, visualization, and classification of functional magnetic resonance imaging (fMRI) as spatiotemporal brain data. The method is based on an evolving spatiotemporal data machine of evolving spiking neural networks (SNNs) exemplified by the NeuCube architecture [1]. The method consists of several steps: mapping spatial coordinates of fMRI data into a 3-D SNN cube (SNNc) that represents a brain template; input data transformation into trains of spikes; deep, unsupervised learning in the 3-D SNNc of spatiotemporal patterns from data; supervised learning in an evolving SNN classifier; parameter optimization; and 3-D visualization and model interpretation...
October 6, 2016: IEEE Transactions on Neural Networks and Learning Systems
Nicolas Courty, Remi Flamary, Devis Tuia, Alain Rakotomamonjy
Domain adaptation is one of the most challenging tasks of modern data analytics. If the adaptation is done correctly, models built on a specific data representation become more robust when confronted to data depicting the same classes, but described by another observation system. Among the many strategies proposed, finding domain-invariant representations has shown excellent properties, in particular since it allows to train a unique classifier effective in all domains. In this paper, we propose a regularized unsupervised optimal transportation model to perform the alignment of the representations in the source and target domains...
October 7, 2016: IEEE Transactions on Pattern Analysis and Machine Intelligence
Sergio Santos, Chia-Yun Lai, Carlo A Amadei, Karim R Gadelrab, Tzu-Chieh Tang, Albert Verdaguer, Victor Barcons, Josep Font, Jaime Colchero, Matteo Chiesa
Here we present the Mendeleev-Meyer Force Project which aims at tabulating all materials and substances in a fashion similar to the periodic table. The goal is to group and tabulate substances using nanoscale force footprints rather than atomic number or electronic configuration as in the periodic table. The process is divided into: (1) acquiring nanoscale force data from materials, (2) parameterizing the raw data into standardized input features to generate a library, (3) feeding the standardized library into an algorithm to generate, enhance or exploit a model to identify a material or property...
October 14, 2016: Nanoscale
Sharada P Mohanty, David P Hughes, Marcel Salathé
Crop diseases are a major threat to food security, but their rapid identification remains difficult in many parts of the world due to the lack of the necessary infrastructure. The combination of increasing global smartphone penetration and recent advances in computer vision made possible by deep learning has paved the way for smartphone-assisted disease diagnosis. Using a public dataset of 54,306 images of diseased and healthy plant leaves collected under controlled conditions, we train a deep convolutional neural network to identify 14 crop species and 26 diseases (or absence thereof)...
2016: Frontiers in Plant Science
Dan Valsky, Odeya Marmor-Levin, Marc Deffains, Renana Eitan, Kim T Blackwell, Hagai Bergman, Zvi Israel
BACKGROUND: Microelectrode recordings along preplanned trajectories are often used for accurate definition of the subthalamic nucleus (STN) borders during deep brain stimulation (DBS) surgery for Parkinson's disease. Usually, the demarcation of the STN borders is performed manually by a neurophysiologist. The exact detection of the borders is difficult, especially detecting the transition between the STN and the substantia nigra pars reticulata. Consequently, demarcation may be inaccurate, leading to suboptimal location of the DBS lead and inadequate clinical outcomes...
October 6, 2016: Movement Disorders: Official Journal of the Movement Disorder Society
Michael David Abràmoff, Yiyue Lou, Ali Erginay, Warren Clarida, Ryan Amelon, James C Folk, Meindert Niemeijer
Purpose: To compare performance of a deep-learning enhanced algorithm for automated detection of diabetic retinopathy (DR), to the previously published performance of that algorithm, the Iowa Detection Program (IDP)-without deep learning components-on the same publicly available set of fundus images and previously reported consensus reference standard set, by three US Board certified retinal specialists. Methods: We used the previously reported consensus reference standard of referable DR (rDR), defined as International Clinical Classification of Diabetic Retinopathy moderate, severe nonproliferative (NPDR), proliferative DR, and/or macular edema (ME)...
October 1, 2016: Investigative Ophthalmology & Visual Science
Jesper Tegnér, Hector Zenil, Narsis A Kiani, Gordon Ball, David Gomez-Cabrero
Systems in nature capable of collective behaviour are nonlinear, operating across several scales. Yet our ability to account for their collective dynamics differs in physics, chemistry and biology. Here, we briefly review the similarities and differences between mathematical modelling of adaptive living systems versus physico-chemical systems. We find that physics-based chemistry modelling and computational neuroscience have a shared interest in developing techniques for model reductions aiming at the identification of a reduced subsystem or slow manifold, capturing the effective dynamics...
November 13, 2016: Philosophical Transactions. Series A, Mathematical, Physical, and Engineering Sciences
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