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Publisher: Hindawi   (Total: 333 journals)

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Showing 1 - 200 of 333 Journals sorted alphabetically
Abstract and Applied Analysis     Open Access   (Followers: 3, SJR: 0.512, h-index: 32)
Active and Passive Electronic Components     Open Access   (Followers: 7, SJR: 0.157, h-index: 15)
Advances in Acoustics and Vibration     Open Access   (Followers: 24, SJR: 0.259, h-index: 6)
Advances in Agriculture     Open Access   (Followers: 7)
Advances in Artificial Intelligence     Open Access   (Followers: 14)
Advances in Artificial Neural Systems     Open Access   (Followers: 3)
Advances in Astronomy     Open Access   (Followers: 34, SJR: 0.351, h-index: 17)
Advances in Bioinformatics     Open Access   (Followers: 18, SJR: 0.421, h-index: 8)
Advances in Biology     Open Access   (Followers: 8)
Advances in Chemistry     Open Access   (Followers: 12)
Advances in Civil Engineering     Open Access   (Followers: 33, SJR: 0.338, h-index: 8)
Advances in Condensed Matter Physics     Open Access   (Followers: 8, SJR: 0.248, h-index: 10)
Advances in Decision Sciences     Open Access   (Followers: 4, SJR: 0.231, h-index: 6)
Advances in Ecology     Open Access   (Followers: 13)
Advances in Electrical Engineering     Open Access   (Followers: 18)
Advances in Endocrinology     Open Access   (Followers: 1)
Advances in Fuzzy Systems     Open Access   (Followers: 5, SJR: 0.258, h-index: 7)
Advances in Hematology     Open Access   (Followers: 9, SJR: 0.892, h-index: 18)
Advances in High Energy Physics     Open Access   (Followers: 21, SJR: 0.892, h-index: 19)
Advances in Human-Computer Interaction     Open Access   (Followers: 19, SJR: 0.439, h-index: 9)
Advances in Materials Science and Engineering     Open Access   (Followers: 30, SJR: 0.263, h-index: 11)
Advances in Mathematical Physics     Open Access   (Followers: 6, SJR: 0.332, h-index: 10)
Advances in Medicine     Open Access   (Followers: 3)
Advances in Meteorology     Open Access   (Followers: 18, SJR: 0.498, h-index: 10)
Advances in Multimedia     Open Access   (Followers: 2, SJR: 0.191, h-index: 10)
Advances in Neuroscience     Open Access   (Followers: 8)
Advances in Nonlinear Optics     Open Access   (Followers: 5)
Advances in Numerical Analysis     Open Access   (Followers: 3)
Advances in Nursing     Open Access   (Followers: 21)
Advances in Operations Research     Open Access   (Followers: 11, SJR: 0.343, h-index: 7)
Advances in Optical Technologies     Open Access   (Followers: 3, SJR: 0.283, h-index: 16)
Advances in OptoElectronics     Open Access   (Followers: 5, SJR: 0.973, h-index: 16)
Advances in Orthopedic Surgery     Open Access   (Followers: 9)
Advances in Orthopedics     Open Access   (Followers: 9)
Advances in Pharmacological Sciences     Open Access   (Followers: 5, SJR: 0.695, h-index: 13)
Advances in Physical Chemistry     Open Access   (Followers: 11, SJR: 0.297, h-index: 7)
Advances in Power Electronics     Open Access   (Followers: 20, SJR: 0.26, h-index: 6)
Advances in Preventive Medicine     Open Access   (Followers: 6)
Advances in Public Health     Open Access   (Followers: 19)
Advances in Software Engineering     Open Access   (Followers: 10)
Advances in Tribology     Open Access   (Followers: 10, SJR: 0.267, h-index: 6)
Advances in Urology     Open Access   (Followers: 10, SJR: 0.629, h-index: 16)
Advances in Virology     Open Access   (Followers: 7, SJR: 1.04, h-index: 12)
AIDS Research and Treatment     Open Access   (Followers: 3, SJR: 1.125, h-index: 14)
Analytical Cellular Pathology     Open Access   (Followers: 1, SJR: 0.334, h-index: 12)
Anatomy Research Intl.     Open Access   (Followers: 1)
Anemia     Open Access   (Followers: 4, SJR: 0.991, h-index: 11)
Anesthesiology Research and Practice     Open Access   (Followers: 13, SJR: 0.513, h-index: 12)
Applied and Environmental Soil Science     Open Access   (Followers: 15, SJR: 0.53, h-index: 9)
Applied Bionics and Biomechanics     Open Access   (Followers: 8, SJR: 0.23, h-index: 13)
Applied Computational Intelligence and Soft Computing     Open Access   (Followers: 12)
Archaea     Open Access   (Followers: 3, SJR: 1.248, h-index: 27)
Arthritis     Open Access   (Followers: 4)
Autism Research and Treatment     Open Access   (Followers: 29)
Autoimmune Diseases     Open Access   (Followers: 3, SJR: 0.909, h-index: 17)
Behavioural Neurology     Open Access   (Followers: 7, SJR: 0.696, h-index: 34)
Biochemistry Research Intl.     Open Access   (Followers: 6, SJR: 1.085, h-index: 17)
Bioinorganic Chemistry and Applications     Open Access   (Followers: 9, SJR: 0.286, h-index: 19)
BioMed Research Intl.     Open Access   (Followers: 6, SJR: 0.725, h-index: 59)
Biotechnology Research Intl.     Open Access   (Followers: 2)
Bone Marrow Research     Open Access   (Followers: 2)
Canadian J. of Gastroenterology & Hepatology     Open Access   (Followers: 3, SJR: 0.856, h-index: 53)
Canadian J. of Infectious Diseases and Medical Microbiology     Open Access   (Followers: 3, SJR: 0.409, h-index: 25)
Canadian Respiratory J.     Open Access   (Followers: 1, SJR: 0.503, h-index: 42)
Cardiology Research and Practice     Open Access   (Followers: 7, SJR: 0.941, h-index: 17)
Cardiovascular Psychiatry and Neurology     Open Access   (Followers: 4, SJR: 1.091, h-index: 14)
Case Reports in Anesthesiology     Open Access   (Followers: 10)
Case Reports in Cardiology     Open Access   (Followers: 2)
Case Reports in Critical Care     Open Access   (Followers: 8)
Case Reports in Dentistry     Open Access   (Followers: 3)
Case Reports in Dermatological Medicine     Open Access   (Followers: 2)
Case Reports in Emergency Medicine     Open Access   (Followers: 13)
Case Reports in Endocrinology     Open Access   (SJR: 0.326, h-index: 1)
Case Reports in Gastrointestinal Medicine     Open Access   (Followers: 3)
Case Reports in Genetics     Open Access   (Followers: 1)
Case Reports in Hematology     Open Access   (Followers: 2)
Case Reports in Hepatology     Open Access   (Followers: 1)
Case Reports in Immunology     Open Access   (Followers: 4)
Case Reports in Infectious Diseases     Open Access   (Followers: 5)
Case Reports in Medicine     Open Access   (Followers: 3)
Case Reports in Nephrology     Open Access   (Followers: 4)
Case Reports in Neurological Medicine     Open Access   (Followers: 1)
Case Reports in Obstetrics and Gynecology     Open Access   (Followers: 10)
Case Reports in Oncological Medicine     Open Access   (Followers: 2)
Case Reports in Ophthalmological Medicine     Open Access   (Followers: 2)
Case Reports in Orthopedics     Open Access   (Followers: 7)
Case Reports in Otolaryngology     Open Access   (Followers: 5)
Case Reports in Pathology     Open Access   (Followers: 3)
Case Reports in Pediatrics     Open Access   (Followers: 5)
Case Reports in Psychiatry     Open Access   (Followers: 10)
Case Reports in Pulmonology     Open Access   (Followers: 2)
Case Reports in Radiology     Open Access   (Followers: 8)
Case Reports in Rheumatology     Open Access   (Followers: 4)
Case Reports in Surgery     Open Access   (Followers: 7)
Case Reports in Transplantation     Open Access  
Case Reports in Urology     Open Access   (Followers: 8)
Case Reports in Vascular Medicine     Open Access  
Case Reports in Veterinary Medicine     Open Access   (Followers: 5)
Chemotherapy Research and Practice     Open Access   (Followers: 1)
Child Development Research     Open Access   (Followers: 13)
Chinese J. of Engineering     Open Access   (Followers: 2)
Chinese J. of Mathematics     Open Access  
Cholesterol     Open Access   (Followers: 1, SJR: 0.906, h-index: 12)
Chromatography Research Intl.     Open Access   (Followers: 7)
Computational and Mathematical Methods in Medicine     Open Access   (Followers: 2, SJR: 0.415, h-index: 22)
Computational Intelligence and Neuroscience     Open Access   (Followers: 9, SJR: 0.232, h-index: 30)
Critical Care Research and Practice     Open Access   (Followers: 9, SJR: 0.916, h-index: 14)
Current Gerontology and Geriatrics Research     Open Access   (Followers: 9, SJR: 0.8, h-index: 12)
Dataset Papers in Science     Open Access  
Depression Research and Treatment     Open Access   (Followers: 13, SJR: 0.77, h-index: 11)
Dermatology Research and Practice     Open Access   (Followers: 2, SJR: 0.576, h-index: 15)
Diagnostic and Therapeutic Endoscopy     Open Access   (SJR: 0.651, h-index: 18)
Discrete Dynamics in Nature and Society     Open Access   (Followers: 5, SJR: 0.323, h-index: 24)
Disease Markers     Open Access   (Followers: 1, SJR: 0.774, h-index: 49)
Economics Research Intl.     Open Access   (Followers: 2)
Education Research Intl.     Open Access   (Followers: 18)
Emergency Medicine Intl.     Open Access   (Followers: 6)
Enzyme Research     Open Access   (Followers: 4, SJR: 0.457, h-index: 18)
Epidemiology Research Intl.     Open Access   (Followers: 10)
Epilepsy Research and Treatment     Open Access   (Followers: 3)
Evidence-based Complementary and Alternative Medicine     Open Access   (Followers: 18, SJR: 0.615, h-index: 50)
Experimental Diabetes Research     Open Access   (Followers: 10, SJR: 1.591, h-index: 30)
Gastroenterology Research and Practice     Open Access   (Followers: 3, SJR: 0.664, h-index: 21)
Genetics Research Intl.     Open Access   (Followers: 1)
Hepatitis Research and Treatment     Open Access   (Followers: 6)
HPB Surgery     Open Access   (Followers: 5, SJR: 0.798, h-index: 22)
Indian J. of Materials Science     Open Access  
Infectious Diseases in Obstetrics and Gynecology     Open Access   (Followers: 7, SJR: 0.976, h-index: 34)
Influenza Research and Treatment     Open Access   (Followers: 2)
Interdisciplinary Perspectives on Infectious Diseases     Open Access   (Followers: 2, SJR: 0.763, h-index: 15)
Intl. J. of Aerospace Engineering     Open Access   (Followers: 65, SJR: 0.241, h-index: 6)
Intl. J. of Agronomy     Open Access   (Followers: 8, SJR: 0.223, h-index: 2)
Intl. J. of Alzheimer's Disease     Open Access   (Followers: 11, SJR: 1.193, h-index: 25)
Intl. J. of Analysis     Open Access  
Intl. J. of Analytical Chemistry     Open Access   (Followers: 21, SJR: 0.157, h-index: 2)
Intl. J. of Antennas and Propagation     Open Access   (Followers: 9, SJR: 0.385, h-index: 15)
Intl. J. of Atmospheric Sciences     Open Access   (Followers: 23)
Intl. J. of Bacteriology     Open Access  
Intl. J. of Biodiversity     Open Access   (Followers: 4)
Intl. J. of Biomaterials     Open Access   (Followers: 5, SJR: 0.485, h-index: 10)
Intl. J. of Biomedical Imaging     Open Access   (Followers: 5, SJR: 0.581, h-index: 23)
Intl. J. of Breast Cancer     Open Access   (Followers: 12)
Intl. J. of Carbohydrate Chemistry     Open Access   (Followers: 7)
Intl. J. of Cell Biology     Open Access   (Followers: 4, SJR: 2.658, h-index: 25)
Intl. J. of Chemical Engineering     Open Access   (Followers: 7, SJR: 0.361, h-index: 10)
Intl. J. of Chronic Diseases     Open Access   (Followers: 1)
Intl. J. of Combinatorics     Open Access   (Followers: 1)
Intl. J. of Computer Games Technology     Open Access   (Followers: 11, SJR: 0.213, h-index: 12)
Intl. J. of Corrosion     Open Access   (Followers: 10, SJR: 0.19, h-index: 7)
Intl. J. of Dentistry     Open Access   (Followers: 6, SJR: 0.558, h-index: 11)
Intl. J. of Differential Equations     Open Access   (Followers: 6, SJR: 0.363, h-index: 11)
Intl. J. of Digital Multimedia Broadcasting     Open Access   (Followers: 5, SJR: 0.144, h-index: 10)
Intl. J. of Ecology     Open Access   (Followers: 7, SJR: 0.8, h-index: 11)
Intl. J. of Electrochemistry     Open Access   (Followers: 6)
Intl. J. of Endocrinology     Open Access   (Followers: 3, SJR: 0.961, h-index: 24)
Intl. J. of Engineering Mathematics     Open Access   (Followers: 3)
Intl. J. of Evolutionary Biology     Open Access   (Followers: 9)
Intl. J. of Family Medicine     Open Access   (Followers: 2)
Intl. J. of Food Science     Open Access   (Followers: 3)
Intl. J. of Forestry Research     Open Access   (Followers: 4)
Intl. J. of Genomics     Open Access   (Followers: 2, SJR: 0.721, h-index: 7)
Intl. J. of Geophysics     Open Access   (Followers: 5, SJR: 0.416, h-index: 8)
Intl. J. of Hepatology     Open Access   (Followers: 3)
Intl. J. of Hypertension     Open Access   (Followers: 5, SJR: 0.823, h-index: 20)
Intl. J. of Inflammation     Open Access   (SJR: 0.876, h-index: 14)
Intl. J. of Inorganic Chemistry     Open Access   (Followers: 2)
Intl. J. of Manufacturing Engineering     Open Access   (Followers: 2)
Intl. J. of Mathematics and Mathematical Sciences     Open Access   (Followers: 3, SJR: 0.346, h-index: 27)
Intl. J. of Medicinal Chemistry     Open Access   (Followers: 6)
Intl. J. of Metals     Open Access   (Followers: 4)
Intl. J. of Microbiology     Open Access   (Followers: 5, SJR: 1.006, h-index: 18)
Intl. J. of Microwave Science and Technology     Open Access   (Followers: 4, SJR: 0.167, h-index: 5)
Intl. J. of Molecular Imaging     Open Access  
Intl. J. of Navigation and Observation     Open Access   (Followers: 19, SJR: 0.411, h-index: 7)
Intl. J. of Nephrology     Open Access   (Followers: 2, SJR: 0.926, h-index: 14)
Intl. J. of Oceanography     Open Access   (Followers: 8)
Intl. J. of Optics     Open Access   (Followers: 6, SJR: 0.262, h-index: 7)
Intl. J. of Otolaryngology     Open Access   (Followers: 1)
Intl. J. of Pediatrics     Open Access   (Followers: 4)
Intl. J. of Peptides     Open Access   (Followers: 4, SJR: 0.73, h-index: 16)
Intl. J. of Photoenergy     Open Access   (Followers: 2, SJR: 0.348, h-index: 28)
Intl. J. of Plant Genomics     Open Access   (Followers: 4, SJR: 1.578, h-index: 20)
Intl. J. of Polymer Science     Open Access   (Followers: 23, SJR: 0.265, h-index: 11)
Intl. J. of Population Research     Open Access   (Followers: 2)
Intl. J. of Proteomics     Open Access   (Followers: 1)
Intl. J. of Quality, Statistics, and Reliability     Open Access   (Followers: 13, SJR: 0.345, h-index: 4)
Intl. J. of Reconfigurable Computing     Open Access   (SJR: 0.182, h-index: 8)
Intl. J. of Reproductive Medicine     Open Access   (Followers: 5)
Intl. J. of Rheumatology     Open Access   (Followers: 4, SJR: 1.015, h-index: 18)
Intl. J. of Rotating Machinery     Open Access   (Followers: 2, SJR: 0.402, h-index: 19)
Intl. J. of Spectroscopy     Open Access   (Followers: 8)
Intl. J. of Stochastic Analysis     Open Access   (Followers: 4, SJR: 0.234, h-index: 19)
Intl. J. of Surgical Oncology     Open Access   (Followers: 1, SJR: 0.753, h-index: 11)
Intl. J. of Telemedicine and Applications     Open Access   (Followers: 3, SJR: 0.757, h-index: 14)
Intl. J. of Vascular Medicine     Open Access   (SJR: 0.865, h-index: 16)
Intl. J. of Vehicular Technology     Open Access   (Followers: 4, SJR: 0.169, h-index: 6)
Intl. J. of Zoology     Open Access   (Followers: 2, SJR: 0.389, h-index: 8)
Intl. Scholarly Research Notices     Open Access   (Followers: 197)
ISRN Astronomy and Astrophysics     Open Access   (Followers: 7)
J. of Addiction     Open Access   (Followers: 10)

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Journal Cover Computational and Mathematical Methods in Medicine
  [SJR: 0.415]   [H-I: 22]   [2 followers]  Follow
    
  This is an Open Access Journal Open Access journal
   ISSN (Print) 1748-670X - ISSN (Online) 1748-6718
   Published by Hindawi Homepage  [333 journals]
  • A Web-Based Tool for Automatic Data Collection, Curation, and
           Visualization of Complex Healthcare Survey Studies including Social
           Network Analysis

    • Abstract: There is a great concern nowadays regarding alcohol consumption and drug abuse, especially in young people. Analyzing the social environment where these adolescents are immersed, as well as a series of measures determining the alcohol abuse risk or personal situation and perception using a number of questionnaires like AUDIT, FAS, KIDSCREEN, and others, it is possible to gain insight into the current situation of a given individual regarding his/her consumption behavior. But this analysis, in order to be achieved, requires the use of tools that can ease the process of questionnaire creation, data gathering, curation and representation, and later analysis and visualization to the user. This research presents the design and construction of a web-based platform able to facilitate each of the mentioned processes by integrating the different phases into an intuitive system with a graphical user interface that hides the complexity underlying each of the questionnaires and techniques used and presenting the results in a flexible and visual way, avoiding any manual handling of data during the process. Advantages of this approach are shown and compared to the previous situation where some of the tasks were accomplished by time consuming and error prone manipulations of data.
      PubDate: Wed, 26 Apr 2017 06:51:11 +000
       
  • Cuffless Blood Pressure Estimation Based on Data-Oriented Continuous
           Health Monitoring System

    • Abstract: Measuring blood pressure continuously helps monitor health and also prevent lifestyle related diseases to extend the expectancy of healthy life. Blood pressure, which is nowadays used for monitoring patient, is one of the most useful indexes for prevention of lifestyle related diseases such as hypertension. However, continuously monitoring the blood pressure is unrealistic because of discomfort caused by the tightening of a cuff belt. We have earlier researched the data-oriented blood pressure estimation without using a cuff. Remarkably, our blood pressure estimation method only uses a photoplethysmograph sensor. Therefore, the application is flexible for sensor locations and measuring situations. In this paper, we describe the implementation of our estimation method, the launch of a cloud system which can collect and manage blood pressure data measured by a wristwatch-type photoplethysmograph sensor, and the construction of our applications to visualize life-log data including the time-series data of blood pressure.
      PubDate: Mon, 24 Apr 2017 00:00:00 +000
       
  • Automated Detection of Red Lesions Using Superpixel Multichannel
           Multifeature

    • Abstract: Red lesions can be regarded as one of the earliest lesions in diabetic retinopathy (DR) and automatic detection of red lesions plays a critical role in diabetic retinopathy diagnosis. In this paper, a novel superpixel Multichannel Multifeature (MCMF) classification approach is proposed for red lesion detection. In this paper, firstly, a new candidate extraction method based on superpixel is proposed. Then, these candidates are characterized by multichannel features, as well as the contextual feature. Next, FDA classifier is introduced to classify the red lesions among the candidates. Finally, a postprocessing technique based on multiscale blood vessels detection is modified for removing nonlesions appearing as red. Experiments on publicly available DiaretDB1 database are conducted to verify the effectiveness of our proposed method.
      PubDate: Sun, 23 Apr 2017 08:08:50 +000
       
  • Machine Learning Applications in Medical Image Analysis

    • PubDate: Thu, 13 Apr 2017 00:00:00 +000
       
  • Depression Disorder Classification of fMRI Data Using Sparse Low-Rank
           Functional Brain Network and Graph-Based Features

    • Abstract: Study of functional brain network (FBN) based on functional magnetic resonance imaging (fMRI) has proved successful in depression disorder classification. One popular approach to construct FBN is Pearson correlation. However, it only captures pairwise relationship between brain regions, while it ignores the influence of other brain regions. Another common issue existing in many depression disorder classification methods is applying only single local feature extracted from constructed FBN. To address these issues, we develop a new method to classify fMRI data of patients with depression and healthy controls. First, we construct the FBN using a sparse low-rank model, which considers the relationship between two brain regions given all the other brain regions. Moreover, it can automatically remove weak relationship and retain the modular structure of FBN. Secondly, FBN are effectively measured by eight graph-based features from different aspects. Tested on fMRI data of 31 patients with depression and 29 healthy controls, our method achieves 95% accuracy, 96.77% sensitivity, and 93.10% specificity, which outperforms the Pearson correlation FBN and sparse FBN. In addition, the combination of graph-based features in our method further improves classification performance. Moreover, we explore the discriminative brain regions that contribute to depression disorder classification, which can help understand the pathogenesis of depression disorder.
      PubDate: Wed, 12 Apr 2017 00:00:00 +000
       
  • Node-Structured Integrative Gaussian Graphical Model Guided by Pathway
           Information

    • Abstract: Up to date, many biological pathways related to cancer have been extensively applied thanks to outputs of burgeoning biomedical research. This leads to a new technical challenge of exploring and validating biological pathways that can characterize transcriptomic mechanisms across different disease subtypes. In pursuit of accommodating multiple studies, the joint Gaussian graphical model was previously proposed to incorporate nonzero edge effects. However, this model is inevitably dependent on post hoc analysis in order to confirm biological significance. To circumvent this drawback, we attempt not only to combine transcriptomic data but also to embed pathway information, well-ascertained biological evidence as such, into the model. To this end, we propose a novel statistical framework for fitting joint Gaussian graphical model simultaneously with informative pathways consistently expressed across multiple studies. In theory, structured nodes can be prespecified with multiple genes. The optimization rule employs the structured input-output lasso model, in order to estimate a sparse precision matrix constructed by simultaneous effects of multiple studies and structured nodes. With an application to breast cancer data sets, we found that the proposed model is superior in efficiently capturing structures of biological evidence (e.g., pathways). An R software package nsiGGM is publicly available at author’s webpage.
      PubDate: Wed, 12 Apr 2017 00:00:00 +000
       
  • A Predictive Model for Guillain-Barré Syndrome Based on Single
           Learning Algorithms

    • Abstract: Background. Guillain-Barré Syndrome (GBS) is a potentially fatal autoimmune neurological disorder. The severity varies among the four main subtypes, named as Acute Inflammatory Demyelinating Polyneuropathy (AIDP), Acute Motor Axonal Neuropathy (AMAN), Acute Motor Sensory Axonal Neuropathy (AMSAN), and Miller-Fisher Syndrome (MF). A proper subtype identification may help to promptly carry out adequate treatment in patients. Method. We perform experiments with 15 single classifiers in two scenarios: four subtypes’ classification and One versus All (OvA) classification. We used a dataset with the 16 relevant features identified in a previous phase. Performance evaluation is made by 10-fold cross validation (10-FCV). Typical classification performance measures are used. A statistical test is conducted in order to identify the top five classifiers for each case. Results. In four GBS subtypes’ classification, half of the classifiers investigated in this study obtained an average accuracy above 0.90. In OvA classification, the two subtypes with the largest number of instances resulted in the best classification results. Conclusions. This study represents a comprehensive effort on creating a predictive model for Guillain-Barré Syndrome subtypes. Also, the analysis performed in this work provides insight about the best single classifiers for each classification case.
      PubDate: Tue, 11 Apr 2017 00:00:00 +000
       
  • Erratum to “A Model for Spheroid versus Monolayer Response of SK-N-SH
           Neuroblastoma Cells to Treatment with 15-Deoxy-PGJ2”

    • PubDate: Wed, 05 Apr 2017 00:00:00 +000
       
  • Curvature-Induced Spatial Ordering of Composition in Lipid Membranes

    • Abstract: Phase segregation of membranal components, such as proteins, lipids, and cholesterols, leads to the formation of aggregates or domains that are rich in specific constituents. This process is important in the interaction of the cell with its surroundings and in determining the cell’s behavior and fate. Motivated by published experiments on curvature-modulated phase separation in lipid membranes, we formulate a mathematical model aiming at studying the spatial ordering of composition in a two-component biomembrane that is subjected to a prescribed (imposed) geometry. Based on this model, we identified key nondimensional quantities that govern the biomembrane response and performed numerical simulations to quantitatively explore their influence. We reproduce published experimental observations and extend them to surfaces with geometric features (imposed geometry) and lipid phases beyond those used in the experiments. In addition, we demonstrate the possibility for curvature-modulated phase separation above the critical temperature and propose a systematic procedure to determine which mechanism, the difference in bending stiffness or difference in spontaneous curvatures of the two phases, dominates the coupling between shape and composition.
      PubDate: Tue, 04 Apr 2017 07:29:30 +000
       
  • Artificial Neural Networks in Image Processing for Early Detection of
           Breast Cancer

    • Abstract: Medical imaging techniques have widely been in use in the diagnosis and detection of breast cancer. The drawback of applying these techniques is the large time consumption in the manual diagnosis of each image pattern by a professional radiologist. Automated classifiers could substantially upgrade the diagnosis process, in terms of both accuracy and time requirement by distinguishing benign and malignant patterns automatically. Neural network (NN) plays an important role in this respect, especially in the application of breast cancer detection. Despite the large number of publications that describe the utilization of NN in various medical techniques, only a few reviews are available that guide the development of these algorithms to enhance the detection techniques with respect to specificity and sensitivity. The purpose of this review is to analyze the contents of recently published literature with special attention to techniques and states of the art of NN in medical imaging. We discuss the usage of NN in four different medical imaging applications to show that NN is not restricted to few areas of medicine. Types of NN used, along with the various types of feeding data, have been reviewed. We also address hybrid NN adaptation in breast cancer detection.
      PubDate: Mon, 03 Apr 2017 09:31:52 +000
       
  • A Novel Fusion Framework Based on Adaptive PCNN in NSCT Domain for
           Whole-Body PET and CT Images

    • Abstract: The PET and CT fusion images, combining the anatomical and functional information, have important clinical meaning. This paper proposes a novel fusion framework based on adaptive pulse-coupled neural networks (PCNNs) in nonsubsampled contourlet transform (NSCT) domain for fusing whole-body PET and CT images. Firstly, the gradient average of each pixel is chosen as the linking strength of PCNN model to implement self-adaptability. Secondly, to improve the fusion performance, the novel sum-modified Laplacian (NSML) and energy of edge (EOE) are extracted as the external inputs of the PCNN models for low- and high-pass subbands, respectively. Lastly, the rule of max region energy is adopted as the fusion rule and different energy templates are employed in the low- and high-pass subbands. The experimental results on whole-body PET and CT data (239 slices contained by each modality) show that the proposed framework outperforms the other six methods in terms of the seven commonly used fusion performance metrics.
      PubDate: Mon, 03 Apr 2017 08:21:53 +000
       
  • Increase of Short-Term Heart Rate Variability Induced by Blood Pressure
           Measurements during Ambulatory Blood Pressure Monitoring

    • Abstract: Objective. The possible effect of blood pressure measurements per se on heart rate variability (HRV) was studied in the setting of concomitant ambulatory blood pressure monitoring (ABPM) and Holter ECG monitoring (HM). Methods. In 25 hypertensive patients (14 women and 11 men, mean age: 58.1 years), 24-hour combined ABPM and HM were performed. For every blood pressure measurement, 2-minute ECG segments (before, during, and after measurement) were analyzed to obtain time domain parameters of HRV: SDNN and rMSSD. Mean of normal RR intervals (MNN), SDNN/MNN, and rMSSD/MNN were calculated, too. Parameter variations related to blood pressure measurements were analyzed using one-way ANOVA with multiple comparisons. Results. 2281 measurements (1518 during the day and 763 during the night) were included in the analysis. Both SDNN and SDNN/MNN had a constant (the same for 24-hour, daytime, and nighttime values) and significant change related to blood pressure measurements: an increase during measurements and a decrease after them ( for any variation). Conclusion. In the setting of combined ABPM and HM, the blood pressure measurement itself produces an increase in short-term heart rate variability. Clarifying the physiological basis and the possible clinical value of this phenomenon needs further studies.
      PubDate: Mon, 03 Apr 2017 06:05:34 +000
       
  • Second-Order Regression-Based MR Image Upsampling

    • Abstract: The spatial resolution of magnetic resonance imaging (MRI) is often limited due to several reasons, including a short data acquisition time. Several advanced interpolation-based image upsampling algorithms have been developed to increase the resolution of MR images. These methods estimate the voxel intensity in a high-resolution (HR) image by a weighted combination of voxels in the original low-resolution (LR) MR image. As these methods fall into the zero-order point estimation framework, they only include a local constant approximation of the image voxel and hence cannot fully represent the underlying image structure(s). To this end, we extend the existing zero-order point estimation to higher orders of regression, allowing us to approximate a mapping function between local LR-HR image patches by a polynomial function. Extensive experiments on open-access MR image datasets and actual clinical MR images demonstrate that our algorithm can maintain sharp edges and preserve fine details, while the current state-of-the-art algorithms remain prone to some visual artifacts such as blurring and staircasing artifacts.
      PubDate: Thu, 30 Mar 2017 06:51:28 +000
       
  • Research on Techniques of Multifeatures Extraction for Tongue Image and
           Its Application in Retrieval

    • Abstract: Tongue diagnosis is one of the important methods in the Chinese traditional medicine. Doctors can judge the disease’s situation by observing patient’s tongue color and texture. This paper presents a novel approach to extract color and texture features of tongue images. First, we use improved GLA (Generalized Lloyd Algorithm) to extract the main color of tongue image. Considering that the color feature cannot fully express tongue image information, the paper analyzes tongue edge’s texture features and proposes an algorithm to extract them. Then, we integrate the two features in retrieval by different weight. Experimental results show that the proposed method can improve the detection rate of lesion in tongue image relative to single feature retrieval.
      PubDate: Thu, 30 Mar 2017 00:00:00 +000
       
  • Femoral Neck Strain during Maximal Contraction of Isolated Hip-Spanning
           Muscle Groups

    • Abstract: The aim of the study was to investigate femoral neck strain during maximal isometric contraction of the hip-spanning muscles. The musculoskeletal and the femur finite-element models from an elderly white woman were taken from earlier studies. The hip-spanning muscles were grouped by function in six hip-spanning muscle groups. The peak hip and knee moments in the model were matched to corresponding published measurements of the hip and knee moments during maximal isometric exercises about the hip and the knee in elderly participants. The femoral neck strain was calculated using full activation of the agonist muscles at fourteen physiological joint angles. The of the femoral neck volume exceeded the 90th percentile of the strain distribution across the 84 studied scenarios. Hip extensors, flexors, and abductors generated the highest tension in the proximal neck (2727 με), tension (986 με) and compression (−2818 με) in the anterior and posterior neck, and compression (−2069 με) in the distal neck, respectively. Hip extensors and flexors generated the highest neck strain per unit of joint moment (63–67 με·m·N−1) at extreme hip angles. Therefore, femoral neck strain is heterogeneous and muscle contraction and posture dependent.
      PubDate: Wed, 22 Mar 2017 07:29:34 +000
       
  • Automatic Microaneurysms Detection Based on Multifeature Fusion Dictionary
           Learning

    • Abstract: Recently, microaneurysm (MA) detection has attracted a lot of attention in the medical image processing community. Since MAs can be seen as the earliest lesions in diabetic retinopathy, their detection plays a critical role in diabetic retinopathy diagnosis. In this paper, we propose a novel MA detection approach named multifeature fusion dictionary learning (MFFDL). The proposed method consists of four steps: preprocessing, candidate extraction, multifeature dictionary learning, and classification. The novelty of our proposed approach lies in incorporating the semantic relationships among multifeatures and dictionary learning into a unified framework for automatic detection of MAs. We evaluate the proposed algorithm by comparing it with the state-of-the-art approaches and the experimental results validate the effectiveness of our algorithm.
      PubDate: Tue, 21 Mar 2017 06:28:06 +000
       
  • Using Agent-Based Models to Develop Public Policy about Food Behaviours:
           Future Directions and Recommendations

    • Abstract: Most adults are overweight or obese in many western countries. Several population-level interventions on the physical, economical, political, or sociocultural environment have thus attempted to achieve a healthier weight. These interventions have involved different weight-related behaviours, such as food behaviours. Agent-based models (ABMs) have the potential to help policymakers evaluate food behaviour interventions from a systems perspective. However, fully realizing this potential involves a complex procedure starting with obtaining and analyzing data to populate the model and eventually identifying more efficient cross-sectoral policies. Current procedures for ABMs of food behaviours are mostly rooted in one technique, often ignore the food environment beyond home and work, and underutilize rich datasets. In this paper, we address some of these limitations to better support policymakers through two contributions. First, via a scoping review, we highlight readily available datasets and techniques to deal with these limitations independently. Second, we propose a three steps’ process to tackle all limitations together and discuss its use to develop future models for food behaviours. We acknowledge that this integrated process is a leap forward in ABMs. However, this long-term objective is well-worth addressing as it can generate robust findings to effectively inform the design of food behaviour interventions.
      PubDate: Tue, 21 Mar 2017 00:00:00 +000
       
  • Computational and Mathematical Methods in Cardiovascular Diseases

    • PubDate: Sun, 19 Mar 2017 00:00:00 +000
       
  • Comparison of Baseline Wander Removal Techniques considering the
           Preservation of ST Changes in the Ischemic ECG: A Simulation Study

    • Abstract: The most important ECG marker for the diagnosis of ischemia or infarction is a change in the ST segment. Baseline wander is a typical artifact that corrupts the recorded ECG and can hinder the correct diagnosis of such diseases. For the purpose of finding the best suited filter for the removal of baseline wander, the ground truth about the ST change prior to the corrupting artifact and the subsequent filtering process is needed. In order to create the desired reference, we used a large simulation study that allowed us to represent the ischemic heart at a multiscale level from the cardiac myocyte to the surface ECG. We also created a realistic model of baseline wander to evaluate five filtering techniques commonly used in literature. In the simulation study, we included a total of 5.5 million signals coming from 765 electrophysiological setups. We found that the best performing method was the wavelet-based baseline cancellation. However, for medical applications, the Butterworth high-pass filter is the better choice because it is computationally cheap and almost as accurate. Even though all methods modify the ST segment up to some extent, they were all proved to be better than leaving baseline wander unfiltered.
      PubDate: Wed, 08 Mar 2017 06:49:36 +000
       
  • Topological Measurements of DWI Tractography for Alzheimer’s
           Disease Detection

    • Abstract: Neurodegenerative diseases affect brain morphology and connectivity, making complex networks a suitable tool to investigate and model their effects. Because of its stereotyped pattern Alzheimer’s disease (AD) is a natural benchmark for the study of novel methodologies. Several studies have investigated the network centrality and segregation changes induced by AD, especially with a single subject approach. In this work, a holistic perspective based on the application of multiplex network concepts is introduced. We define and assess a diagnostic score to characterize the brain topology and measure the disease effects on a mixed cohort of 52 normal controls (NC) and 47 AD patients, from Alzheimer’s Disease Neuroimaging Initiative (ADNI). The proposed topological score allows an accurate NC-AD classification: the average area under the curve (AUC) is 95% and the 95% confidence interval is 92%–99%. Besides, the combination of topological information and structural measures, such as the hippocampal volumes, was also investigated. Topology is able to capture the disease signature of AD and, as the methodology is general, it can find interesting applications to enhance our insight into disease with more heterogeneous patterns.
      PubDate: Thu, 02 Mar 2017 07:28:18 +000
       
  • Cardioprotection Effects of Sevoflurane by Regulating the Pathway of
           Neuroactive Ligand-Receptor Interaction in Patients Undergoing Coronary
           Artery Bypass Graft Surgery

    • Abstract: This study was designed to identify attractor modules and further reveal the potential biological processes involving in sevoflurane-induced anesthesia in patients treated with coronary artery bypass graft (CABG) surgery. Microarray profile data (ID: E-GEOD-4386) on atrial samples obtained from patients receiving anesthetic gas sevoflurane prior to and following CABG procedure were downloaded from EMBL-EBI database for further analysis. Protein-protein interaction (PPI) networks of baseline and sevoflurane groups were inferred and reweighted according to Spearman correlation coefficient (SCC), followed by systematic modules inference using clique-merging approach. Subsequently, attract method was utilized to explore attractor modules. Finally, pathway enrichment analyses for genes in the attractor modules were implemented to illuminate the biological processes in sevoflurane group. Using clique-merging approach, 27 and 36 modules were obtained from the PPI networks of baseline and sevoflurane-treated samples, respectively. By comparing with the baseline condition, 5 module pairs with the same gene composition were identified. Subsequently, 1 out of 5 modules was identified as an attractor based on attract method. Additionally, pathway analysis indicated that genes in the attractor module were associated with neuroactive ligand-receptor interaction. Accordingly, sevoflurane might exert important functions in cardioprotection in patients following CABG, partially through regulating the pathway of neuroactive ligand-receptor interaction.
      PubDate: Tue, 28 Feb 2017 14:22:05 +000
       
  • Fast Parabola Detection Using Estimation of Distribution Algorithms

    • Abstract: This paper presents a new method based on Estimation of Distribution Algorithms (EDAs) to detect parabolic shapes in synthetic and medical images. The method computes a virtual parabola using three random boundary pixels to calculate the constant values of the generic parabola equation. The resulting parabola is evaluated by matching it with the parabolic shape in the input image by using the Hadamard product as fitness function. This proposed method is evaluated in terms of computational time and compared with two implementations of the generalized Hough transform and RANSAC method for parabola detection. Experimental results show that the proposed method outperforms the comparative methods in terms of execution time about on synthetic images and on retinal fundus and human plantar arch images. In addition, experimental results have also shown that the proposed method can be highly suitable for different medical applications.
      PubDate: Tue, 21 Feb 2017 00:00:00 +000
       
  • Feature Extraction and Classification of EHG between Pregnancy and Labour
           Group Using Hilbert-Huang Transform and Extreme Learning Machine

    • Abstract: Preterm birth (PTB) is the leading cause of perinatal mortality and long-term morbidity, which results in significant health and economic problems. The early detection of PTB has great significance for its prevention. The electrohysterogram (EHG) related to uterine contraction is a noninvasive, real-time, and automatic novel technology which can be used to detect, diagnose, or predict PTB. This paper presents a method for feature extraction and classification of EHG between pregnancy and labour group, based on Hilbert-Huang transform (HHT) and extreme learning machine (ELM). For each sample, each channel was decomposed into a set of intrinsic mode functions (IMFs) using empirical mode decomposition (EMD). Then, the Hilbert transform was applied to IMF to obtain analytic function. The maximum amplitude of analytic function was extracted as feature. The identification model was constructed based on ELM. Experimental results reveal that the best classification performance of the proposed method can reach an accuracy of 88.00%, a sensitivity of 91.30%, and a specificity of 85.19%. The area under receiver operating characteristic (ROC) curve is 0.88. Finally, experimental results indicate that the method developed in this work could be effective in the classification of EHG between pregnancy and labour group.
      PubDate: Sun, 19 Feb 2017 13:53:23 +000
       
  • Automated Classification of Severity in Cardiac Dyssynchrony Merging
           Clinical Data and Mechanical Descriptors

    • Abstract: Cardiac resynchronization therapy (CRT) improves functional classification among patients with left ventricle malfunction and ventricular electric conduction disorders. However, a high percentage of subjects under CRT (20%–30%) do not show any improvement. Nonetheless the presence of mechanical contraction dyssynchrony in ventricles has been proposed as an indicator of CRT response. This work proposes an automated classification model of severity in ventricular contraction dyssynchrony. The model includes clinical data such as left ventricular ejection fraction (LVEF), QRS and P-R intervals, and the 3 most significant factors extracted from the factor analysis of dynamic structures applied to a set of equilibrium radionuclide angiography images representing the mechanical behavior of cardiac contraction. A control group of 33 normal volunteers ( years, LVEF of ) and a HF group of 42 subjects ( years, LVEF < 35%) were studied. The proposed classifiers had hit rates of 90%, 50%, and 80% to distinguish between absent, mild, and moderate-severe interventricular dyssynchrony, respectively. For intraventricular dyssynchrony, hit rates of 100%, 50%, and 90% were observed distinguishing between absent, mild, and moderate-severe, respectively. These results seem promising in using this automated method for clinical follow-up of patients undergoing CRT.
      PubDate: Sun, 19 Feb 2017 07:15:47 +000
       
  • Noise Attenuation Estimation for Maximum Length Sequences in Deconvolution
           Process of Auditory Evoked Potentials

    • Abstract: The use of maximum length sequence (m-sequence) has been found beneficial for recovering both linear and nonlinear components at rapid stimulation. Since m-sequence is fully characterized by a primitive polynomial of different orders, the selection of polynomial order can be problematic in practice. Usually, the m-sequence is repetitively delivered in a looped fashion. Ensemble averaging is carried out as the first step and followed by the cross-correlation analysis to deconvolve linear/nonlinear responses. According to the classical noise reduction property based on additive noise model, theoretical equations have been derived in measuring noise attenuation ratios (NARs) after the averaging and correlation processes in the present study. A computer simulation experiment was conducted to test the derived equations, and a nonlinear deconvolution experiment was also conducted using order 7 and 9 m-sequences to address this issue with real data. Both theoretical and experimental results show that the NAR is essentially independent of the m-sequence order and is decided by the total length of valid data, as well as stimulation rate. The present study offers a guideline for m-sequence selections, which can be used to estimate required recording time and signal-to-noise ratio in designing m-sequence experiments.
      PubDate: Sun, 19 Feb 2017 00:00:00 +000
       
  • Sentiment Analysis on Tweets about Diabetes: An Aspect-Level Approach

    • Abstract: In recent years, some methods of sentiment analysis have been developed for the health domain; however, the diabetes domain has not been explored yet. In addition, there is a lack of approaches that analyze the positive or negative orientation of each aspect contained in a document (a review, a piece of news, and a tweet, among others). Based on this understanding, we propose an aspect-level sentiment analysis method based on ontologies in the diabetes domain. The sentiment of the aspects is calculated by considering the words around the aspect which are obtained through N-gram methods (N-gram after, N-gram before, and N-gram around). To evaluate the effectiveness of our method, we obtained a corpus from Twitter, which has been manually labelled at aspect level as positive, negative, or neutral. The experimental results show that the best result was obtained through the N-gram around method with a precision of 81.93%, a recall of 81.13%, and an -measure of 81.24%.
      PubDate: Sun, 19 Feb 2017 00:00:00 +000
       
  • Mathematical Modelling of Immune Parameters in the Evolution of Severe
           Dengue

    • Abstract: Aims. Predicting the risk of severity at an early stage in an individual patient will be invaluable in preventing morbidity and mortality caused by dengue. We hypothesized that such predictions are possible by analyzing multiple parameters using mathematical modeling. Methodology. Data from 11 adult patients with dengue fever (DF) and 25 patients with dengue hemorrhagic fever (DHF) were analyzed. Multivariate statistical analysis was performed to study the characteristics and interactions of parameters using dengue NS1 antigen levels, dengue IgG antibody levels, platelet counts, and lymphocyte counts. Fuzzy logic fundamentals were used to map the risk of developing severe forms of dengue. The cumulative effects of the parameters were incorporated using the Hamacher and the OWA operators. Results. The operator classified the patients according to the severity level during the time period of 96 hours to 120 hours after the onset of fever. The accuracy ranged from 53% to 89%. Conclusion. The results show a robust mathematical model that explains the evolution from dengue to its serious forms in individual patients. The model allows prediction of severe cases of dengue which could be useful for optimal management of patients during a dengue outbreak. Further analysis of the model may also deepen our understanding of the pathways towards severe illness.
      PubDate: Wed, 15 Feb 2017 00:00:00 +000
       
  • Optimally Repeatable Kinetic Model Variant for Myocardial Blood Flow
           Measurements with 82Rb PET

    • Abstract: Purpose. Myocardial blood flow (MBF) quantification with positron emission tomography (PET) is gaining clinical adoption, but improvements in precision are desired. This study aims to identify analysis variants producing the most repeatable MBF measures. Methods. 12 volunteers underwent same-day test-retest rest and dipyridamole stress imaging with dynamic PET, from which MBF was quantified using 1-tissue-compartment kinetic model variants: () blood-pool versus uptake region sampled input function (Blood/Uptake-ROI), () dual spillover correction (SOC-On/Off), () right blood correction (RBC-On/Off), () arterial blood transit delay (Delay-On/Off), and () distribution volume (DV) constraint (Global/Regional-DV). Repeatability of MBF, stress/rest myocardial flow reserve (MFR), and stress/rest MBF difference (ΔMBF) was assessed using nonparametric reproducibility coefficients ( = 1.45 × interquartile range). Results. MBF using SOC-On, RVBC-Off, Blood-ROI, Global-DV, and Delay-Off was most repeatable for combined rest and stress: = 0.21 mL/min/g (15.8%). Corresponding MFR and ΔMBF were 0.42 (20.2%) and 0.24 mL/min/g (23.5%). MBF repeatability improved with SOC-On at stress () and tended to improve with RBC-Off at both rest and stress (). DV and ROI did not significantly influence repeatability. The Delay-On model was overdetermined and did not reliably converge. Conclusion. MBF and MFR test-retest repeatability were the best with dual spillover correction, left atrium blood input function, and global DV.
      PubDate: Mon, 13 Feb 2017 06:39:52 +000
       
  • A Psychometric Tool for a Virtual Reality Rehabilitation Approach for
           Dyslexia

    • Abstract: Dyslexia is a chronic problem that affects the life of subjects and often influences their life choices. The standard rehabilitation methods all use a classic paper and pencil training format but these exercises are boring and demanding for children who may have difficulty in completing the treatments. It is important to develop a new rehabilitation program that would help children in a funny and engaging way. A Wii-based game was developed to demonstrate that a short treatment with an action video game, rather than phonological or orthographic training, may improve the reading abilities in dyslexic children. According to the results, an approach using cues in the context of a virtual environment may represent a promising tool to improve attentional skills. On the other hand, our results do not demonstrate an immediate effect on reading performance, suggesting that a more prolonged protocol may be a future direction.
      PubDate: Mon, 13 Feb 2017 00:00:00 +000
       
  • Mixed Total Variation and Regularization Method for Optical Tomography
           Based on Radiative Transfer Equation

    • Abstract: Optical tomography is an emerging and important molecular imaging modality. The aim of optical tomography is to reconstruct optical properties of human tissues. In this paper, we focus on reconstructing the absorption coefficient based on the radiative transfer equation (RTE). It is an ill-posed parameter identification problem. Regularization methods have been broadly applied to reconstruct the optical coefficients, such as the total variation (TV) regularization and the regularization. In order to better reconstruct the piecewise constant and sparse coefficient distributions, TV and norms are combined as the regularization. The forward problem is discretized with the discontinuous Galerkin method on the spatial space and the finite element method on the angular space. The minimization problem is solved by a Jacobian-based Levenberg-Marquardt type method which is equipped with a split Bregman algorithms for the regularization. We use the adjoint method to compute the Jacobian matrix which dramatically improves the computation efficiency. By comparing with the other imaging reconstruction methods based on TV and regularizations, the simulation results show the validity and efficiency of the proposed method.
      PubDate: Thu, 09 Feb 2017 14:15:16 +000
       
 
 
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