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Ryan Eshleman, Rahul Singh
BACKGROUND: Adverse drug events (ADEs) constitute one of the leading causes of post-therapeutic death and their identification constitutes an important challenge of modern precision medicine. Unfortunately, the onset and effects of ADEs are often underreported complicating timely intervention. At over 500 million posts per day, Twitter is a commonly used social media platform. The ubiquity of day-to-day personal information exchange on Twitter makes it a promising target for data mining for ADE identification and intervention...
October 6, 2016: BMC Bioinformatics
Shashanka Ubaru, Abd-Krim Seghouane, Yousef Saad
This letter considers the problem of dictionary learning for sparse signal representation whose atoms have low mutual coherence. To learn such dictionaries, at each step, we first updated the dictionary using the method of optimal directions (MOD) and then applied a dictionary rank shrinkage step to decrease its mutual coherence. In the rank shrinkage step, we first compute a rank 1 decomposition of the column-normalized least squares estimate of the dictionary obtained from the MOD step. We then shrink the rank of this learned dictionary by transforming the problem of reducing the rank to a nonnegative garrotte estimation problem and solving it using a path-wise coordinate descent approach...
October 20, 2016: Neural Computation
Shuai Liu, Licheng Jiao, Shuyuan Yang
During the acquisition process hyperspectral images (HSI) are inevitably corrupted by various noises, which greatly influence their visual impression and subsequent applications. In this paper, a novel Bayesian approach integrating hierarchical sparse learning and spectral-spatial information is proposed for HSI denoising. Based on the structure correlations, spectral bands with similar and continuous features are segmented into the same band-subset. To exploit local similarity, each subset is then divided into overlapping cubic patches...
October 17, 2016: Sensors
Mandy Day-Calder
The Cambridge dictionary defines a role model as 'a person who someone admires and whose behaviour they try to copy'. When considering what professional traits you would like to portray to others, it might help to think of colleagues who inspire you.
October 12, 2016: Nursing Standard
Nurkhalida Kamal, Christina V Viegelmann, Carol J Clements, RuAngelie Edrada-Ebel
Fungal endophytes offer diverse and unique secondary metabolites, making these organisms potential sources of promising drug leads. The application of high-resolution-liquid chromatography mass spectrometry and nuclear magnetic resonance-based metabolomics to fungal endophytes is practical in terms of dereplication studies and the mining of bioactive compounds. In this paper, we report the application of metabolomics in parallel with anti-trypanosomal assays to determine the ideal conditions for the medium-scale fermentation of the endophyte Lasiodiplodia theobromae...
October 19, 2016: Planta Medica
Christoph Ahlgrim, Oliver Maenner, Manfred W Baumstark
BACKGROUND: Speech recognition software might increase productivity in clinical documentation. However, low user satisfaction with speech recognition software has been observed. In this case study, an approach for implementing a speech recognition software package at a university-based outpatient department is presented. METHODS: Methods to create a specific dictionary for the context "sports medicine" and a shared vocabulary learning function are demonstrated. The approach is evaluated for user satisfaction (using a questionnaire before and 10 weeks after software implementation) and its impact on the time until the final medical document was saved into the system...
October 18, 2016: BMC Medical Informatics and Decision Making
Shanshan Wang, Jianbo Liu, Xi Peng, Pei Dong, Qiegen Liu, Dong Liang
Compressed sensing magnetic resonance imaging (CSMRI) employs image sparsity to reconstruct MR images from incoherently undersampled K-space data. Existing CSMRI approaches have exploited analysis transform, synthesis dictionary, and their variants to trigger image sparsity. Nevertheless, the accuracy, efficiency, or acceleration rate of existing CSMRI methods can still be improved due to either lack of adaptability, high complexity of the training, or insufficient sparsity promotion. To properly balance the three factors, this paper proposes a two-layer tight frame sparsifying (TRIMS) model for CSMRI by sparsifying the image with a product of a fixed tight frame and an adaptively learned tight frame...
2016: BioMed Research International
Xiaodong Zhang, Shasha Jing, Peiyi Gao, Jing Xue, Lu Su, Weiping Li, Lijie Ren, Qingmao Hu
Segmentation of infarcts at hyperacute stage is challenging as they exhibit substantial variability which may even be hard for experts to delineate manually. In this paper, a sparse representation based classification method is explored. For each patient, four volumetric data items including three volumes of diffusion weighted imaging and a computed asymmetry map are employed to extract patch features which are then fed to dictionary learning and classification based on sparse representation. Elastic net is adopted to replace the traditional L0-norm/L1-norm constraints on sparse representation to stabilize sparse code...
2016: Computational and Mathematical Methods in Medicine
Christina Backes, Tim Kehl, Daniel Stöckel, Tobias Fehlmann, Lara Schneider, Eckart Meese, Hans-Peter Lenhof, Andreas Keller
In the last decade, miRNAs and their regulatory mechanisms have been intensively studied and many tools for the analysis of miRNAs and their targets have been developed. We previously presented a dictionary on single miRNAs and their putative target pathways. Since then, the number of miRNAs has tripled and the knowledge on miRNAs and targets has grown substantially. This, along with changes in pathway resources such as KEGG, leads to an improved understanding of miRNAs, their target genes and related pathways...
October 13, 2016: Nucleic Acids Research
Xi Peng, Canyi Lu, Zhang Yi, Huajin Tang
A lot of works have shown that frobenius-norm-based representation (FNR) is competitive to sparse representation and nuclear-norm-based representation (NNR) in numerous tasks such as subspace clustering. Despite the success of FNR in experimental studies, less theoretical analysis is provided to understand its working mechanism. In this brief, we fill this gap by building the theoretical connections between FNR and NNR. More specially, we prove that: 1) when the dictionary can provide enough representative capacity, FNR is exactly NNR even though the data set contains the Gaussian noise, Laplacian noise, or sample-specified corruption and 2) otherwise, FNR and NNR are two solutions on the column space of the dictionary...
October 6, 2016: IEEE Transactions on Neural Networks and Learning Systems
Bo Ma, Lianghua Huang, Jianbing Shen, Ling Shao, Ming-Hsuan Yang, Fatih Porikli
Most existing tracking algorithms do not explicitly consider the motion blur contained in video sequences, which degrades their performance in real world applications where motion blur often occurs. In this paper, we propose to solve the motion blur problem in visual tracking in a unified framework. Specifically, a joint blur state estimation and multi-task reverse sparse learning framework is presented, where the closed-form solution of blur kernel and sparse code matrix are obtained simultaneously. The reverse process considers the blurry candidates as dictionary elements, and sparsely represents blurred templates with the candidates...
October 6, 2016: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
Moisés A Calderón, Carmen Vidal, Pablo Rodríguez Del Río, Jocelyne Just, Oliver Pfaar, Ana I Tabar, Inmaculada Sánchez-Machín, Petra Bubel, Jesus Borja, Peter Eberle, Rainer Reiber, Michel Bouvier, Alain Lepelliez, Ludger Klimek, Pascal Demoly
BACKGROUND: Outside clinical trials, data on systemic reactions (SRs) due to allergen immunotherapy (AIT) are scarce. METHODS: A prospective, longitudinal, web-based survey of "real-life" respiratory allergen immunotherapy (AIT) clinical practice was conducted in France, Germany and Spain. SRs were recorded and coded according to the Medical Dictionary for Regulatory Activities (MedDRA) and risk factors associated with SRs were identified. RESULTS: A total of 4,316 patients (corresponding to 4,363 ongoing courses of AIT) were included...
October 8, 2016: Allergy
Yudan Ren, Jun Fang, Jinglei Lv, Xintao Hu, Cong Christine Guo, Lei Guo, Jiansong Xu, Marc N Potenza, Tianming Liu
Assessing functional brain activation patterns in neuropsychiatric disorders such as cocaine dependence (CD) or pathological gambling (PG) under naturalistic stimuli has received rising interest in recent years. In this paper, we propose and apply a novel group-wise sparse representation framework to assess differences in neural responses to naturalistic stimuli across multiple groups of participants (healthy control, cocaine dependence, pathological gambling). Specifically, natural stimulus fMRI (N-fMRI) signals from all three groups of subjects are aggregated into a big data matrix, which is then decomposed into a common signal basis dictionary and associated weight coefficient matrices via an effective online dictionary learning and sparse coding method...
October 4, 2016: Brain Imaging and Behavior
Marc Hebrant
No abstract text is available yet for this article.
October 1, 2016: Acta Crystallographica. Section C, Structural Chemistry
Holly C Wilcox, Hadi Kharrazi, Renee F Wilson, Rashelle J Musci, Ryoko Susukida, Fardad Gharghabi, Allen Zhang, Lawrence Wissow, Karen A Robinson
Background: Linking national, state, and community data systems to data from prevention programs could allow for longer-term assessment of outcomes and evaluation of interventions to prevent suicide. Purpose: To identify and describe data systems that can be linked to data from prevention studies to advance youth suicide prevention research. Data Sources: A systematic review, an environmental scan, and a targeted search were conducted to identify prevention studies and potentially linkable external data systems with suicide outcomes from January 1990 through December 2015...
October 4, 2016: Annals of Internal Medicine
Julien P Fortineau, François Vander Meulen, Jérôme Fortineau, Guy Feuillard
We propose a method to identify the different echoes of an overlapped ultrasonic signal. This method is based on an iterative algorithm that compares the experimental signal to a realistic dictionary of trial functions and allows identification of one overlapped echo at each iteration. Adding physical parameters to the dictionary such as sample attenuation and ultrasound beam diffraction allows the method to be applied to various materials and sample geometries. Measurements at 500kHz and 5MHz on a ABS material and a copper plate are reported...
September 12, 2016: Ultrasonics
Hadyl Asfari, Julien Souvignet, Agnès Lillo-Le Louët, Béatrice Trombert, Marie-Christine Jaulent, Cédric Bousquet
AIM: To propose an alternative approach for building custom groupings of terms that complements the usual approach based on both hierarchical method (selection of reference groupings in medical dictionary for regulatory activities [MedDRA]) and/or textual method (string search), for case reports extraction from a pharmacovigilance database in response to a safety problem. Here we take cardiac valve fibrosis as an example. METHODS: The list of terms obtained by an automated approach, based on querying ontology of adverse drug reactions (OntoADR), a knowledge base defining MedDRA terms through relationships with systematized nomenclature of medicine-clinical terms (SNOMED CT) concepts, was compared with the reference list consisting of 53 preferred terms obtained by hierarchical and textual method...
July 21, 2016: Thérapie
Fan Zhang, Yang Song, Weidong Cai, Alexander G Hauptmann, Sidong Liu, Sonia Pujol, Ron Kikinis, Michael J Fulham, David Dagan Feng, Mei Chen
Content-based medical image retrieval (CBMIR) is an active research area for disease diagnosis and treatment but it can be problematic given the small visual variations between anatomical structures. We propose a retrieval method based on a bag-of-visual-words (BoVW) to identify discriminative characteristics between different medical images with Pruned Dictionary based on Latent Semantic Topic description. We refer to this as the PD-LST retrieval. Our method has two main components. First, we calculate a topic-word significance value for each visual word given a certain latent topic to evaluate how the word is connected to this latent topic...
February 12, 2016: Neurocomputing
Donghao Wang, Jiangwen Wan, Junying Chen, Qiang Zhang
To adapt to sense signals of enormous diversities and dynamics, and to decrease the reconstruction errors caused by ambient noise, a novel online dictionary learning method-based compressive data gathering (ODL-CDG) algorithm is proposed. The proposed dictionary is learned from a two-stage iterative procedure, alternately changing between a sparse coding step and a dictionary update step. The self-coherence of the learned dictionary is introduced as a penalty term during the dictionary update procedure. The dictionary is also constrained with sparse structure...
2016: Sensors
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