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Publisher: Hindawi   (Total: 292 journals)

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Showing 1 - 200 of 293 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: 33, SJR: 0.259, h-index: 6)
Advances in Aerospace Engineering     Open Access   (Followers: 52)
Advances in Agriculture     Open Access   (Followers: 8)
Advances in Artificial Intelligence     Open Access   (Followers: 15)
Advances in Astronomy     Open Access   (Followers: 36, SJR: 0.351, h-index: 17)
Advances in Bioinformatics     Open Access   (Followers: 17, SJR: 0.421, h-index: 8)
Advances in Chemistry     Open Access   (Followers: 20)
Advances in Civil Engineering     Open Access   (Followers: 38, SJR: 0.338, h-index: 8)
Advances in Condensed Matter Physics     Open Access   (Followers: 10, SJR: 0.248, h-index: 10)
Advances in Decision Sciences     Open Access   (Followers: 3, SJR: 0.231, h-index: 6)
Advances in Electrical Engineering     Open Access   (Followers: 26)
Advances in Electronics     Open Access   (Followers: 63)
Advances in Emergency Medicine     Open Access   (Followers: 11)
Advances in Endocrinology     Open Access   (Followers: 5)
Advances in Fuzzy Systems     Open Access   (Followers: 5, SJR: 0.258, h-index: 7)
Advances in Hematology     Open Access   (Followers: 11, SJR: 0.892, h-index: 18)
Advances in High Energy Physics     Open Access   (Followers: 18, 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: 4, SJR: 0.332, h-index: 10)
Advances in Medicine     Open Access   (Followers: 2)
Advances in Meteorology     Open Access   (Followers: 19, SJR: 0.498, h-index: 10)
Advances in Multimedia     Open Access   (Followers: 1, SJR: 0.191, h-index: 10)
Advances in Nonlinear Optics     Open Access   (Followers: 6)
Advances in Numerical Analysis     Open Access   (Followers: 5)
Advances in Nursing     Open Access   (Followers: 26)
Advances in Operations Research     Open Access   (Followers: 12, 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 Orthopedics     Open Access   (Followers: 8)
Advances in Pharmacological Sciences     Open Access   (Followers: 7, SJR: 0.695, h-index: 13)
Advances in Physical Chemistry     Open Access   (Followers: 9, SJR: 0.297, h-index: 7)
Advances in Power Electronics     Open Access   (Followers: 29, SJR: 0.26, h-index: 6)
Advances in Preventive Medicine     Open Access   (Followers: 5)
Advances in Public Health     Open Access   (Followers: 23)
Advances in Software Engineering     Open Access   (Followers: 10)
Advances in Tribology     Open Access   (Followers: 12, SJR: 0.267, h-index: 6)
Advances in Urology     Open Access   (Followers: 9, 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: 2, SJR: 0.334, h-index: 12)
Anatomy Research Intl.     Open Access   (Followers: 2)
Anemia     Open Access   (Followers: 5, 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: 16, 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: 11)
Archaea     Open Access   (Followers: 3, SJR: 1.248, h-index: 27)
Arthritis     Open Access   (Followers: 5)
Autism Research and Treatment     Open Access   (Followers: 25)
Autoimmune Diseases     Open Access   (Followers: 3, SJR: 0.909, h-index: 17)
Behavioural Neurology     Open Access   (Followers: 9, 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: 4, SJR: 0.725, h-index: 59)
Biotechnology Research Intl.     Open Access   (Followers: 1)
Bone Marrow Research     Open Access   (Followers: 2)
Canadian J. of Gastroenterology & Hepatology     Open Access   (Followers: 5, SJR: 0.856, h-index: 53)
Canadian J. of Infectious Diseases and Medical Microbiology     Open Access   (Followers: 5, 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: 8, SJR: 0.941, h-index: 17)
Case Reports in Anesthesiology     Open Access   (Followers: 10)
Case Reports in Cardiology     Open Access   (Followers: 3)
Case Reports in Critical Care     Open Access   (Followers: 8)
Case Reports in Dentistry     Open Access   (Followers: 5)
Case Reports in Dermatological Medicine     Open Access   (Followers: 2)
Case Reports in Emergency Medicine     Open Access   (Followers: 14)
Case Reports in Endocrinology     Open Access   (Followers: 1, SJR: 0.326, h-index: 1)
Case Reports in Gastrointestinal Medicine     Open Access   (Followers: 2)
Case Reports in Genetics     Open Access   (Followers: 1)
Case Reports in Hematology     Open Access   (Followers: 5)
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: 2)
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: 3)
Case Reports in Orthopedics     Open Access   (Followers: 5)
Case Reports in Otolaryngology     Open Access   (Followers: 6)
Case Reports in Pathology     Open Access   (Followers: 5)
Case Reports in Pediatrics     Open Access   (Followers: 6)
Case Reports in Psychiatry     Open Access   (Followers: 12)
Case Reports in Pulmonology     Open Access   (Followers: 3)
Case Reports in Radiology     Open Access   (Followers: 8)
Case Reports in Rheumatology     Open Access   (Followers: 5)
Case Reports in Surgery     Open Access   (Followers: 11)
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: 6)
Child Development Research     Open Access   (Followers: 16)
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: 6)
Complexity     Hybrid Journal   (Followers: 6, SJR: 0.526, h-index: 27)
Computational and Mathematical Methods in Medicine     Open Access   (Followers: 2, SJR: 0.415, h-index: 22)
Computational Intelligence and Neuroscience     Open Access   (Followers: 10, SJR: 0.232, h-index: 30)
Contrast Media & Molecular Imaging     Open Access   (Followers: 3, SJR: 0.932, h-index: 34)
Critical Care Research and Practice     Open Access   (Followers: 10, SJR: 0.916, h-index: 14)
Current Gerontology and Geriatrics Research     Open Access   (Followers: 9, SJR: 0.8, h-index: 12)
Depression Research and Treatment     Open Access   (Followers: 13, SJR: 0.77, h-index: 11)
Dermatology Research and Practice     Open Access   (Followers: 3, 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)
Education Research Intl.     Open Access   (Followers: 19)
Emergency Medicine Intl.     Open Access   (Followers: 7)
Enzyme Research     Open Access   (Followers: 3, SJR: 0.457, h-index: 18)
Evidence-based Complementary and Alternative Medicine     Open Access   (Followers: 20, SJR: 0.615, h-index: 50)
Experimental Diabetes Research     Open Access   (Followers: 14, SJR: 1.591, h-index: 30)
Gastroenterology Research and Practice     Open Access   (Followers: 2, SJR: 0.664, h-index: 21)
Genetics Research Intl.     Open Access   (Followers: 1)
Geofluids     Open Access   (Followers: 4, SJR: 0.693, h-index: 38)
HPB Surgery     Open Access   (Followers: 4, SJR: 0.798, h-index: 22)
Infectious Diseases in Obstetrics and Gynecology     Open Access   (Followers: 5, SJR: 0.976, h-index: 34)
Interdisciplinary Perspectives on Infectious Diseases     Open Access   (Followers: 1, SJR: 0.763, h-index: 15)
Intl. J. of Aerospace Engineering     Open Access   (Followers: 72, SJR: 0.241, h-index: 6)
Intl. J. of Agronomy     Open Access   (Followers: 5, 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: 20, SJR: 0.157, h-index: 2)
Intl. J. of Antennas and Propagation     Open Access   (Followers: 11, SJR: 0.385, h-index: 15)
Intl. J. of Atmospheric Sciences     Open Access   (Followers: 21)
Intl. J. of Biodiversity     Open Access   (Followers: 4)
Intl. J. of Biomaterials     Open Access   (Followers: 4, SJR: 0.485, h-index: 10)
Intl. J. of Biomedical Imaging     Open Access   (Followers: 3, SJR: 0.581, h-index: 23)
Intl. J. of Breast Cancer     Open Access   (Followers: 13)
Intl. J. of Cell Biology     Open Access   (Followers: 3, 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 Computer Games Technology     Open Access   (Followers: 9, 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: 7, 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 Electrochemistry     Open Access   (Followers: 8)
Intl. J. of Endocrinology     Open Access   (Followers: 4, SJR: 0.961, h-index: 24)
Intl. J. of Engineering Mathematics     Open Access   (Followers: 5)
Intl. J. of Food Science     Open Access   (Followers: 3)
Intl. J. of Forestry Research     Open Access   (Followers: 3)
Intl. J. of Genomics     Open Access   (Followers: 2, SJR: 0.721, h-index: 7)
Intl. J. of Hepatology     Open Access   (Followers: 4)
Intl. J. of Hypertension     Open Access   (Followers: 6, 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: 3)
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: 5)
Intl. J. of Microbiology     Open Access   (Followers: 4, SJR: 1.006, h-index: 18)
Intl. J. of Navigation and Observation     Open Access   (Followers: 20, SJR: 0.411, h-index: 7)
Intl. J. of Nephrology     Open Access   (Followers: 1, SJR: 0.926, h-index: 14)
Intl. J. of Oceanography     Open Access   (Followers: 7)
Intl. J. of Optics     Open Access   (Followers: 7, SJR: 0.262, h-index: 7)
Intl. J. of Otolaryngology     Open Access   (Followers: 3)
Intl. J. of Partial Differential Equations     Open Access   (Followers: 2)
Intl. J. of Pediatrics     Open Access   (Followers: 6)
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: 24, SJR: 0.265, h-index: 11)
Intl. J. of Population Research     Open Access   (Followers: 2)
Intl. J. of Quality, Statistics, and Reliability     Open Access   (Followers: 15, 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: 4)
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: 6)
Intl. J. of Stochastic Analysis     Open Access   (Followers: 3, 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: 4, SJR: 0.757, h-index: 14)
Intl. J. of Vascular Medicine     Open Access   (SJR: 0.865, h-index: 16)
Intl. J. of Zoology     Open Access   (Followers: 1, SJR: 0.389, h-index: 8)
Intl. Scholarly Research Notices     Open Access   (Followers: 197)
ISRN Astronomy and Astrophysics     Open Access   (Followers: 6)
J. of Addiction     Open Access   (Followers: 12)
J. of Advanced Transportation     Hybrid Journal   (Followers: 13, SJR: 0.911, h-index: 24)
J. of Aerodynamics     Open Access   (Followers: 5)
J. of Aging Research     Open Access   (Followers: 6, SJR: 1.259, h-index: 23)
J. of Analytical Methods in Chemistry     Open Access   (Followers: 1, SJR: 0.296, h-index: 13)
J. of Anthropology     Open Access   (Followers: 19)
J. of Applied Chemistry     Open Access   (Followers: 4)
J. of Applied Mathematics     Open Access   (Followers: 2, SJR: 0.341, h-index: 22)
J. of Biomedical Education     Open Access   (Followers: 3)
J. of Blood Transfusion     Open Access   (Followers: 1)
J. of Botany     Open Access   (Followers: 3, SJR: 0.101, h-index: 2)
J. of Cancer Epidemiology     Open Access   (Followers: 5, SJR: 1.427, h-index: 12)
J. of Chemistry     Open Access   (Followers: 5, SJR: 0.225, h-index: 11)
J. of Combustion     Open Access   (Followers: 22, SJR: 0.27, h-index: 8)
J. of Complex Analysis     Open Access   (Followers: 3)
J. of Composites     Open Access   (Followers: 80)
J. of Computer Networks and Communications     Open Access   (Followers: 4, SJR: 0.257, h-index: 8)
J. of Construction Engineering     Open Access   (Followers: 8)
J. of Control Science and Engineering     Open Access   (Followers: 1, SJR: 0.299, h-index: 9)
J. of Criminology     Open Access   (Followers: 12)
J. of Diabetes Research     Open Access   (Followers: 12, SJR: 1.024, h-index: 13)

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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  [292 journals]
  • Fast and Accurate Genome-Wide Association Test of Multiple Quantitative
           Traits

    • Abstract: Multiple correlated traits are often collected in genetic studies. By jointly analyzing multiple traits, we can increase power by aggregating multiple weak effects and reveal additional insights into the genetic architecture of complex human diseases. In this article, we propose a multivariate linear regression-based method to test the joint association of multiple quantitative traits. It is flexible to accommodate any covariates, has very accurate control of type I errors, and offers very competitive performance. We also discuss fast and accurate significance value computation especially for genome-wide association studies with small-to-medium sample sizes. We demonstrate through extensive numerical studies that the proposed method has competitive performance. Its usefulness is further illustrated with application to genome-wide association analysis of diabetes-related traits in the Atherosclerosis Risk in Communities (ARIC) study. We found some very interesting associations with diabetes traits which have not been reported before. We implemented the proposed methods in a publicly available R package.
      PubDate: Sun, 18 Mar 2018 00:00:00 +000
       
  • Transfer Kernel Common Spatial Patterns for Motor Imagery Brain-Computer
           Interface Classification

    • Abstract: Motor-imagery-based brain-computer interfaces (BCIs) commonly use the common spatial pattern (CSP) as preprocessing step before classification. The CSP method is a supervised algorithm. Therefore a lot of time-consuming training data is needed to build the model. To address this issue, one promising approach is transfer learning, which generalizes a learning model can extract discriminative information from other subjects for target classification task. To this end, we propose a transfer kernel CSP (TKCSP) approach to learn a domain-invariant kernel by directly matching distributions of source subjects and target subjects. The dataset IVa of BCI Competition III is used to demonstrate the validity by our proposed methods. In the experiment, we compare the classification performance of the TKCSP against CSP, CSP for subject-to-subject transfer (CSP SJ-to-SJ), regularizing CSP (RCSP), stationary subspace CSP (ssCSP), multitask CSP (mtCSP), and the combined mtCSP and ssCSP (ss + mtCSP) method. The results indicate that the superior mean classification performance of TKCSP can achieve 81.14%, especially in case of source subjects with fewer number of training samples. Comprehensive experimental evidence on the dataset verifies the effectiveness and efficiency of the proposed TKCSP approach over several state-of-the-art methods.
      PubDate: Sun, 18 Mar 2018 00:00:00 +000
       
  • Automated Segmentation Methods of Drusen to Diagnose Age-Related Macular
           Degeneration Screening in Retinal Images

    • Abstract: Existing drusen measurement is difficult to use in clinic because it requires a lot of time and effort for visual inspection. In order to resolve this problem, we propose an automatic drusen detection method to help clinical diagnosis of age-related macular degeneration. First, we changed the fundus image to a green channel and extracted the ROI of the macular area based on the optic disk. Next, we detected the candidate group using the difference image of the median filter within the ROI. We also segmented vessels and removed them from the image. Finally, we detected the drusen through Renyi’s entropy threshold algorithm. We performed comparisons and statistical analysis between the manual detection results and automatic detection results for 30 cases in order to verify validity. As a result, the average sensitivity was 93.37% (80.95%~100%) and the average DSC was 0.73 (0.3~0.98). In addition, the value of the ICC was 0.984 (CI: 0.967~0.993, ), showing the high reliability of the proposed automatic method. We expect that the automatic drusen detection helps clinicians to improve the diagnostic performance in the detection of drusen on fundus image.
      PubDate: Mon, 12 Mar 2018 00:00:00 +000
       
  • Calculating the Mean Amplitude of Glycemic Excursions from Continuous
           Glucose Data Using an Open-Code Programmable Algorithm Based on the
           Integer Nonlinear Method

    • Abstract: The mean amplitude of glycemic excursions (MAGE) is an essential index for glycemic variability assessment, which is treated as a key reference for blood glucose controlling at clinic. However, the traditional “ruler and pencil” manual method for the calculation of MAGE is time-consuming and prone to error due to the huge data size, making the development of robust computer-aided program an urgent requirement. Although several software products are available instead of manual calculation, poor agreement among them is reported. Therefore, more studies are required in this field. In this paper, we developed a mathematical algorithm based on integer nonlinear programming. Following the proposed mathematical method, an open-code computer program named MAGECAA v1.0 was developed and validated. The results of the statistical analysis indicated that the developed program was robust compared to the manual method. The agreement among the developed program and currently available popular software is satisfied, indicating that the worry about the disagreement among different software products is not necessary. The open-code programmable algorithm is an extra resource for those peers who are interested in the related study on methodology in the future.
      PubDate: Thu, 08 Mar 2018 00:00:00 +000
       
  • Multiclass Classification of Cardiac Arrhythmia Using Improved Feature
           Selection and SVM Invariants

    • Abstract: Arrhythmia is considered a life-threatening disease causing serious health issues in patients, when left untreated. An early diagnosis of arrhythmias would be helpful in saving lives. This study is conducted to classify patients into one of the sixteen subclasses, among which one class represents absence of disease and the other fifteen classes represent electrocardiogram records of various subtypes of arrhythmias. The research is carried out on the dataset taken from the University of California at Irvine Machine Learning Data Repository. The dataset contains a large volume of feature dimensions which are reduced using wrapper based feature selection technique. For multiclass classification, support vector machine (SVM) based approaches including one-against-one (OAO), one-against-all (OAA), and error-correction code (ECC) are employed to detect the presence and absence of arrhythmias. The SVM method results are compared with other standard machine learning classifiers using varying parameters and the performance of the classifiers is evaluated using accuracy, kappa statistics, and root mean square error. The results show that OAO method of SVM outperforms all other classifiers by achieving an accuracy rate of 81.11% when used with 80/20 data split and 92.07% using 90/10 data split option.
      PubDate: Mon, 05 Mar 2018 00:00:00 +000
       
  • Feasibility of an AI-Based Measure of the Hand Motions of Expert and
           Novice Surgeons

    • Abstract: This study investigated whether parameters derived from hand motions of expert and novice surgeons accurately and objectively reflect laparoscopic surgical skill levels using an artificial intelligence system consisting of a three-layer chaos neural network. Sixty-seven surgeons (23 experts and 44 novices) performed a laparoscopic skill assessment task while their hand motions were recorded using a magnetic tracking sensor. Eight parameters evaluated as measures of skill in a previous study were used as inputs to the neural network. Optimization of the neural network was achieved after seven trials with a training dataset of 38 surgeons, with a correct judgment ratio of 0.99. The neural network that prospectively worked with the remaining 29 surgeons had a correct judgment rate of 79% for distinguishing between expert and novice surgeons. In conclusion, our artificial intelligence system distinguished between expert and novice surgeons among surgeons with unknown skill levels.
      PubDate: Sun, 04 Mar 2018 00:00:00 +000
       
  • Computer-Aided Diagnosis of Acute Lymphoblastic Leukaemia

    • Abstract: Leukaemia is a form of blood cancer which affects the white blood cells and damages the bone marrow. Usually complete blood count (CBC) and bone marrow aspiration are used to diagnose the acute lymphoblastic leukaemia. It can be a fatal disease if not diagnosed at the earlier stage. In practice, manual microscopic evaluation of stained sample slide is used for diagnosis of leukaemia. But manual diagnostic methods are time-consuming, less accurate, and prone to errors due to various human factors like stress, fatigue, and so forth. Therefore, different automated systems have been proposed to wrestle the glitches in the manual diagnostic methods. In recent past, some computer-aided leukaemia diagnosis methods are presented. These automated systems are fast, reliable, and accurate as compared to manual diagnosis methods. This paper presents review of computer-aided diagnosis systems regarding their methodologies that include enhancement, segmentation, feature extraction, classification, and accuracy.
      PubDate: Wed, 28 Feb 2018 00:00:00 +000
       
  • An Adaptive Calibration Framework for mVEP-Based Brain-Computer Interface

    • Abstract: Electroencephalogram signals and the states of subjects are nonstationary. To track changing states effectively, an adaptive calibration framework is proposed for the brain-computer interface (BCI) with the motion-onset visual evoked potential (mVEP) as the control signal. The core of this framework is to update the training set adaptively for classifier training. The updating procedure consists of two operations, that is, adding new samples to the training set and removing old samples from the training set. In the proposed framework, a support vector machine (SVM) and fuzzy C-mean clustering (fCM) are combined to select the reliable samples for the training set from the blocks close to the current blocks to be classified. Because of the complementary information provided by SVM and fCM, they can guarantee the reliability of information fed into classifier training. The removing procedure will aim to remove those old samples recorded a relatively long time before current new blocks. These two operations could yield a new training set, which could be used to calibrate the classifier to track the changing state of the subjects. Experimental results demonstrate that the adaptive calibration framework is effective and efficient and it could improve the performance of online BCI systems.
      PubDate: Mon, 26 Feb 2018 00:00:00 +000
       
  • Bayesian Correction for Misclassification in Multilevel Count Data Models

    • Abstract: Covariate misclassification is well known to yield biased estimates in single level regression models. The impact on hierarchical count models has been less studied. A fully Bayesian approach to modeling both the misclassified covariate and the hierarchical response is proposed. Models with a single diagnostic test and with multiple diagnostic tests are considered. Simulation studies show the ability of the proposed model to appropriately account for the misclassification by reducing bias and improving performance of interval estimators. A real data example further demonstrated the consequences of ignoring the misclassification. Ignoring misclassification yielded a model that indicated there was a significant, positive impact on the number of children of females who observed spousal abuse between their parents. When the misclassification was accounted for, the relationship switched to negative, but not significant. Ignoring misclassification in standard linear and generalized linear models is well known to lead to biased results. We provide an approach to extend misclassification modeling to the important area of hierarchical generalized linear models.
      PubDate: Sun, 25 Feb 2018 00:00:00 +000
       
  • Simultaneous Segmentation of Leukocyte and Erythrocyte in Microscopic
           Images Using a Marker-Controlled Watershed Algorithm

    • Abstract: The density or quantity of leukocytes and erythrocytes in a unit volume of blood, which can be automatically measured through a computer-based microscopic image analysis system, is frequently considered an indicator of diseases. The segmentation of blood cells, as a basis of quantitative statistics, plays an important role in the system. However, many conventional methods must firstly distinguish blood cells into two types (i.e., leukocyte and erythrocyte) and segment them in independent procedures. In this paper, we present a marker-controlled watershed algorithm for simultaneously extracting the two types of blood cells to simplify operations and reduce computing time. The method consists of two steps, that is, cell nucleus segmentation and blood cell segmentation. An image enhancement technique is used to obtain the leukocyte marker. Two marker-controlled watershed algorithms are based on distance transformation and edge gradient information to acquire blood cell contour. The segmented leukocytes and erythrocytes are obtained simultaneously by classification. Experimental results demonstrate that the proposed method is fast, robust, and efficient.
      PubDate: Thu, 22 Feb 2018 00:00:00 +000
       
  • Global Dynamics of an Avian Influenza A(H7N9) Epidemic Model with Latent
           Period and Nonlinear Recovery Rate

    • Abstract: An SEIR type of compartmental model with nonlinear incidence and recovery rates was formulated to study the combined impacts of psychological effect and available resources of public health system especially the number of hospital beds on the transmission and control of A(H7N9) virus. Global stability of the disease-free and endemic equilibria is determined by the basic reproduction number as a threshold parameter and is obtained by constructing Lyapunov function and second additive compound matrix. The results obtained reveal that psychological effect and available resources do not change the stability of the steady states but can indeed diminish the peak and the final sizes of the infected. Our studies have practical implications for the transmission and control of A(H7N9) virus.
      PubDate: Thu, 22 Feb 2018 00:00:00 +000
       
  • Direct-Current Electric Field Distribution in the Brain for Tumor Treating
           Field Applications: A Simulation Study

    • Abstract: Tumor Treating Fields (TTFields) in combination with chemotherapy and/or radiotherapy have been clinically reported to provide prolonged overall survival in glioblastoma patients. Alternating electric fields with frequencies of 100~300 kHz and magnitudes of 1~3 V/cm are shown to suppress the growth of cancer cells via interactions with polar molecules within dividing cells. Since it is difficult to directly measure the electric fields inside the brain, simulation models of the human head provide a useful tool for predicting the electric field distribution. In the present study, a three-dimensional finite element head model consisting of the scalp, the skull, the dura, the cerebrospinal fluid, and the brain was built to study the electric field distribution under various applied potentials and electrode configurations. For simplicity, a direct-current electric field was used in the simulation. The total power dissipation and temperature elevation due to Joule heating in different head tissues were also evaluated. Based on the results, some guidelines are obtained in designing the electrode configuration for personalized glioblastoma electrotherapy.
      PubDate: Thu, 22 Feb 2018 00:00:00 +000
       
  • Numerical Simulations of the Motion and Deformation of Three RBCs during
           Poiseuille Flow through a Constricted Vessel Using IB-LBM

    • Abstract: The immersed boundary-lattice Boltzmann method (IB-LBM) was used to examine the motion and deformation of three elastic red blood cells (RBCs) during Poiseuille flow through constricted microchannels. The objective was to determine the effects of the degree of constriction and the Reynolds (Re) number of the flow on the physical characteristics of the RBCs. It was found that, with decreasing constriction ratio, the RBCs experienced greater forced deformation as they squeezed through the constriction area compared to at other parts of the microchannel. It was also observed that a longer time was required for the RBCs to squeeze through a narrower constriction. The RBCs subsequently regained a stable shape and gradually migrated toward the centerline of the flow beyond the constriction area. However, a sick RBC was observed to be incapable of passing through a constricted vessel with a constriction ratio ≤1/3 for Re numbers below 0.40.
      PubDate: Wed, 21 Feb 2018 00:00:00 +000
       
  • Analysis and Numerical Simulations of a Stochastic SEIQR Epidemic System
           with Quarantine-Adjusted Incidence and Imperfect Vaccination

    • Abstract: This paper considers a high-dimensional stochastic SEIQR (susceptible-exposed-infected-quarantined-recovered) epidemic model with quarantine-adjusted incidence and the imperfect vaccination. The main aim of this study is to investigate stochastic effects on the SEIQR epidemic model and obtain its thresholds. We first obtain the sufficient condition for extinction of the disease of the stochastic system. Then, by using the theory of Hasminskii and the Lyapunov analysis methods, we show there is a unique stationary distribution of the stochastic system and it has an ergodic property, which means the infectious disease is prevalent. This implies that the stochastic disturbance is conducive to epidemic diseases control. At last, computer numerical simulations are carried out to illustrate our theoretical results.
      PubDate: Tue, 20 Feb 2018 00:00:00 +000
       
  • Corrigendum to “Association of Stressful Life Events with Psychological
           Problems: A Large-Scale Community-Based Study Using Grouped Outcomes
           Latent Factor Regression with Latent Predictors”

    • PubDate: Tue, 20 Feb 2018 00:00:00 +000
       
  • Novel Model for Cascading Failure Based on Degree Strength and Its
           Application in Directed Gene Logic Networks

    • Abstract: A novel model for cascading failures in a directed logic network based on the degree strength at a node was proposed. The definitions of in-degree and out-degree strength of a node were initially reconsidered, and the load at a nonisolated node was proposed as the ratio of in-degree strength to out-degree strength of the node. The cascading failure model based on degree strength was applied to the logic network for three types of cancer including adenocarcinoma of lung, prostate cancer, and colon cancer based on their gene expression profiles. In order to highlight the differences between the three networks by the cascading failure mechanism, we used the largest-scale cascades and the cumulative cascade probability to depict the damage. It was found that the cascading failures caused by hubs are usually larger. Furthermore, the result shows that propagations against the networks were correlated with the structures motifs of connected logical doublets. Finally, some genes were selected based on cascading failure mechanism. We believe that these genes may be involved in the occurrence and development of three types of cancer.
      PubDate: Mon, 19 Feb 2018 00:00:00 +000
       
  • Scenario Screen: A Dynamic and Context Dependent P300 Stimulator Screen
           Aimed at Wheelchair Navigation Control

    • Abstract: P300 spellers have been widely modified to implement nonspelling tasks. In this work, we propose a “scenario” stimulation screen that is a P300 speller variation to command a wheelchair. Our approach utilized a stimulation screen with an image background (scenario snapshot for a wheelchair) and stimulation markers arranged asymmetrically over relevant landmarks, such as suitable paths, doors, windows, and wall signs. Other scenario stimulation screen features were green/blue stimulation marker color scheme, variable Interstimulus Interval, single marker stimulus mode, and optimized stimulus sequence generator. Eighteen able-bodied subjects participated in the experiment; 78% had no experience in BCI usage. A waveform feature analysis and a Mann–Whitney test performed over the pairs of target and nontarget coherent averages confirmed that 94% of the subjects elicit P300 () on this modified stimulator. Least Absolute Shrinkage and Selection Operator optimization and Linear Discriminant Analysis were utilized for the automatic detection of P300. For evaluation with unseen data, target detection was computed (median sensitivity = 1.00 (0.78–1.00)), together with nontarget discrimination (median specificity = 1.00 (0.98–1.00)). The scenario screen adequately elicits P300 and seems suitable for commanding a wheelchair even when users have no previous experience on the BCI spelling task.
      PubDate: Wed, 14 Feb 2018 00:00:00 +000
       
  • Electrocardiogram Delineation in a Wistar Rat Experimental Model

    • Abstract: Background and Objectives. The extensive use of electrocardiogram (ECG) recordings during experimental protocols using small rodents requires an automatic delineation technique in the ECG with high performance. It has been shown that the wavelet transform (WT) based ECG delineator is a suitable tool to delineate electrocardiographic waveforms. The aim of this work is to implement and evaluate the ECG waves delineation in Wistar rats applying WT. We also describe the ECG signal of the Wistar rats giving the characteristics of its spectrum among other useful information. Methods. We evaluated a delineator based on WT in a Wistar rat electrocardiograms database which was annotated manually by experienced observers. Results. The delineation showed an “overall performance” such as sensitivity and a positive predictive value of 99.2% and 83.9% for P-wave, 100% and 99.9% for QRS complex, and 100% and 99.8% for T-wave, respectively. We also compared temporal analysis based ECG delineator with the WT based ECG delineator in RR interval, QRS duration, QT interval, and T-wave peak-to-end duration. The results showed that WT outperforms the temporal delineation technique in all parameters analyzed. Conclusions. Finally, we propose a WT based ECG delineator as a methodology to implement in a wide diversity of experimental ECG analyses using Wistar rats.
      PubDate: Thu, 08 Feb 2018 00:00:00 +000
       
  • Feature Genes Selection Using Supervised Locally Linear Embedding and
           Correlation Coefficient for Microarray Classification

    • Abstract: The selection of feature genes with high recognition ability from the gene expression profiles has gained great significance in biology. However, most of the existing methods have a high time complexity and poor classification performance. Motivated by this, an effective feature selection method, called supervised locally linear embedding and Spearman’s rank correlation coefficient (SLLE-SC2), is proposed which is based on the concept of locally linear embedding and correlation coefficient algorithms. Supervised locally linear embedding takes into account class label information and improves the classification performance. Furthermore, Spearman’s rank correlation coefficient is used to remove the coexpression genes. The experiment results obtained on four public tumor microarray datasets illustrate that our method is valid and feasible.
      PubDate: Wed, 31 Jan 2018 11:52:20 +000
       
  • Prediction of GPCR-Ligand Binding Using Machine Learning Algorithms

    • Abstract: We propose a novel method that predicts binding of G-protein coupled receptors (GPCRs) and ligands. The proposed method uses hub and cycle structures of ligands and amino acid motif sequences of GPCRs, rather than the 3D structure of a receptor or similarity of receptors or ligands. The experimental results show that these new features can be effective in predicting GPCR-ligand binding (average area under the curve [AUC] of 0.944), because they are thought to include hidden properties of good ligand-receptor binding. Using the proposed method, we were able to identify novel ligand-GPCR bindings, some of which are supported by several studies.
      PubDate: Tue, 30 Jan 2018 10:44:30 +000
       
  • Novel Signal Noise Reduction Method through Cluster Analysis, Applied to
           Photoplethysmography

    • Abstract: Physiological signals can often become contaminated by noise from a variety of origins. In this paper, an algorithm is described for the reduction of sporadic noise from a continuous periodic signal. The design can be used where a sample of a periodic signal is required, for example, when an average pulse is needed for pulse wave analysis and characterization. The algorithm is based on cluster analysis for selecting similar repetitions or pulses from a periodic single. This method selects individual pulses without noise, returns a clean pulse signal, and terminates when a sufficiently clean and representative signal is received. The algorithm is designed to be sufficiently compact to be implemented on a microcontroller embedded within a medical device. It has been validated through the removal of noise from an exemplar photoplethysmography (PPG) signal, showing increasing benefit as the noise contamination of the signal increases. The algorithm design is generalised to be applicable for a wide range of physiological (physical) signals.
      PubDate: Mon, 29 Jan 2018 09:31:58 +000
       
  • Prediction of Pathological Subjects Using Genetic Algorithms

    • Abstract: This paper aims at estimating pathological subjects from a population through various physical information using genetic algorithm (GA). For comparison purposes, -Means (KM) clustering algorithm has also been used for the estimation. Dataset consisting of some physical factors (age, weight, and height) and tibial rotation values was provided from the literature. Tibial rotation types are four groups as RTER, RTIR, LTER, and LTIR. Each tibial rotation group is divided into three types. Narrow (Type 1) and wide (Type 3) angular values were called pathological and normal (Type 2) angular values were called nonpathological. Physical information was used to examine if the tibial rotations of the subjects were pathological. Since the GA starts randomly and walks all solution space, the GA is seen to produce far better results than the KM for clustering and optimizing the tibial rotation data assessments with large number of subjects even though the KM algorithm has similar effect with the GA in clustering with a small number of subjects. These findings are discovered to be very useful for all health workers such as physiotherapists and orthopedists, in which this consequence is expected to help clinicians in organizing proper treatment programs for patients.
      PubDate: Mon, 29 Jan 2018 00:00:00 +000
       
  • Lung Cancer Mortality in Tuscany from 1971 to 2010 and Its Connections
           with Silicosis: A Space-Cohort Analysis Based on Shared Models

    • Abstract: Lung cancer mortality in Tuscany (Italy) for males, from 1971 and 2010, is investigated. A hierarchical Bayesian model for space-time disease mapping is introduced. Such a model belongs to the class of shared random effect models and exploits the birth-cohort as the relevant time dimension. It allows for highlighting common and specific patterns of risk for each birth-cohort. The results show that different birth-cohorts exhibit quite different spatial patterns, even if the socioeconomic status is taken into account. In fact, there were different occupational exposures before and after the Second World War. The birth-cohort 1930–35 exhibits high relative risks related to particular areas. This fact could be connected with occupational exposure to risk factors for silicosis, perhaps a prognostic status for lung cancer.
      PubDate: Sun, 28 Jan 2018 00:00:00 +000
       
  • Linearized and Kernelized Sparse Multitask Learning for Predicting
           Cognitive Outcomes in Alzheimer’s Disease

    • Abstract: Alzheimer’s disease (AD) has been not only the substantial financial burden to the health care system but also the emotional burden to patients and their families. Predicting cognitive performance of subjects from their magnetic resonance imaging (MRI) measures and identifying relevant imaging biomarkers are important research topics in the study of Alzheimer’s disease. Recently, the multitask learning (MTL) methods with sparsity-inducing norm (e.g., -norm) have been widely studied to select the discriminative feature subset from MRI features by incorporating inherent correlations among multiple clinical cognitive measures. However, these previous works formulate the prediction tasks as a linear regression problem. The major limitation is that they assumed a linear relationship between the MRI features and the cognitive outcomes. Some multikernel-based MTL methods have been proposed and shown better generalization ability due to the nonlinear advantage. We quantify the power of existing linear and nonlinear MTL methods by evaluating their performance on cognitive score prediction of Alzheimer’s disease. Moreover, we extend the traditional -norm to a more general -norm (). Experiments on the Alzheimer’s Disease Neuroimaging Initiative database showed that the nonlinear -MKMTL method not only achieved better prediction performance than the state-of-the-art competitive methods but also effectively fused the multimodality data.
      PubDate: Wed, 24 Jan 2018 00:00:00 +000
       
  • A Biologically Inspired Approach to Frequency Domain Feature Extraction
           for EEG Classification

    • Abstract: Classification of electroencephalogram (EEG) signal is important in mental decoding for brain-computer interfaces (BCI). We introduced a feature extraction approach based on frequency domain analysis to improve the classification performance on different mental tasks using single-channel EEG. This biologically inspired method extracts the most discriminative spectral features from power spectral densities (PSDs) of the EEG signals. We applied our method on a dataset of six subjects who performed five different imagination tasks: (i) resting state, (ii) mental arithmetic, (iii) imagination of left hand movement, (iv) imagination of right hand movement, and (v) imagination of letter “A.” Pairwise and multiclass classifications were performed in single EEG channel using Linear Discriminant Analysis and Support Vector Machines. Our method produced results (mean classification accuracy of 83.06% for binary classification and 91.85% for multiclassification) that are on par with the state-of-the-art methods, using single-channel EEG with low computational cost. Among all task pairs, mental arithmetic versus letter imagination yielded the best result (mean classification accuracy of 90.29%), indicating that this task pair could be the most suitable pair for a binary class BCI. This study contributes to the development of single-channel BCI, as well as finding the best task pair for user defined applications.
      PubDate: Tue, 23 Jan 2018 00:00:00 +000
       
  • A PSO-Powell Hybrid Method to Extract Fiber Orientations from ODF

    • Abstract: High angular resolution diffusion imaging (HARDI) has opened up new perspectives for the delineation of crossing and branching fiber pathways by orientation distribution function (ODF). The fiber orientations contained in an imaging voxel are the key factor in tractography. To extract real fiber orientations from ODF, a hybrid method is proposed for computing the principal directions of ODF by combining the variation of Particle Swarm Optimization (PSO) algorithm with the modified Powell algorithm. This method is comprised of the global searching ability of PSO and the powerful local optimizing of Powell search. This combination can guarantee finding all the diffusion directions without applying sliding windows and improve the accuracy and efficiency. The proposed approach was evaluated on simulated crossing-fiber datasets, Tractometer, and in vivo datasets. The results show that this method could correctly identify fiber directions under a range of noise levels. This method was compared with the state-of-the-art methods, such as modified Powell, ball-stick model, and diffusion decomposition, showing that it outperformed them. As to the multimodal voxels where different fiber populations exist, the proposed approach allows us to improve the estimation accuracy of fiber orientations from ODF. It can play a significant role in the nerve fiber tracking.
      PubDate: Sun, 21 Jan 2018 00:00:00 +000
       
  • Modelling of Soft Connective Tissues to Investigate Female Pelvic Floor
           Dysfunctions

    • Abstract: After menopause, decreased levels of estrogen and progesterone remodel the collagen of the soft tissues thereby reducing their stiffness. Stress urinary incontinence is associated with involuntary urine leakage due to pathological movement of the pelvic organs resulting from lax suspension system, fasciae, and ligaments. This study compares the changes in the orientation and position of the female pelvic organs due to weakened fasciae, ligaments, and their combined laxity. A mixture theory weighted by respective volume fraction of elastin-collagen fibre compound (5%), adipose tissue (85%), and smooth muscle (5%) is adopted to characterize the mechanical behaviour of the fascia. The load carrying response (other than the functional response to the pelvic organs) of each fascia component, pelvic organs, muscles, and ligaments are assumed to be isotropic, hyperelastic, and incompressible. Finite element simulations are conducted during Valsalva manoeuvre with weakened tissues modelled by reduced tissue stiffness. A significant dislocation of the urethrovesical junction is observed due to weakness of the fascia (13.89 mm) compared to the ligaments (5.47 mm). The dynamics of the pelvic floor observed in this study during Valsalva manoeuvre is associated with urethral-bladder hypermobility, greater levator plate angulation, and positive Q-tip test which are observed in incontinent females.
      PubDate: Mon, 15 Jan 2018 00:00:00 +000
       
  • Evaluation of Treatment Effect with Paired Failure Times in a Single-Arm
           Phase II Trial in Oncology

    • Abstract: In early phase clinical trials of cytotoxic drugs in oncology, the efficacy is typically evaluated based on the tumor shrinkage. However, this criterion is not always appropriate for more recent cytostatic agents, and alternative endpoints have been proposed. The growth modulation index (GMI), defined as the ratio between the times to progression in two successive treatment lines, has been proposed for a single-arm phase II trials. The treatment effect is evaluated by estimating the rate of patients having a GMI superior to a given threshold. To estimate this rate, we investigated a parametric method based on the distribution of the times to progression and a nonparametric one based on a midrank estimator. Through simulations, we studied their operating characteristics and the impact of different design parameters (censoring, dependence, and distribution) on them. In these simulations, the nonparametric estimator slightly underestimated the rate and had slightly overconservative confidence intervals in some cases. Conversely, the parametric estimator overestimated the rate and had anticonservative confidence intervals in some cases. The nonparametric method appeared to be more robust to censoring than the parametric one. In conclusion, we recommend the nonparametric method, but the parametric method can be used as a supplementary tool.
      PubDate: Thu, 11 Jan 2018 08:04:04 +000
       
  • Juxta-Vascular Pulmonary Nodule Segmentation in PET-CT Imaging Based on an
           LBF Active Contour Model with Information Entropy and Joint Vector

    • Abstract: The accurate segmentation of pulmonary nodules is an important preprocessing step in computer-aided diagnoses of lung cancers. However, the existing segmentation methods may cause the problem of edge leakage and cannot segment juxta-vascular pulmonary nodules accurately. To address this problem, a novel automatic segmentation method based on an LBF active contour model with information entropy and joint vector is proposed in this paper. Our method extracts the interest area of pulmonary nodules by a standard uptake value (SUV) in Positron Emission Tomography (PET) images, and automatic threshold iteration is used to construct an initial contour roughly. The SUV information entropy and the gray-value joint vector of Positron Emission Tomography–Computed Tomography (PET-CT) images are calculated to drive the evolution of contour curve. At the edge of pulmonary nodules, evolution will be stopped and accurate results of pulmonary nodule segmentation can be obtained. Experimental results show that our method can achieve 92.35% average dice similarity coefficient, 2.19 mm Hausdorff distance, and 3.33% false positive with the manual segmentation results. Compared with the existing methods, our proposed method that segments juxta-vascular pulmonary nodules in PET-CT images is more accurate and efficient.
      PubDate: Mon, 08 Jan 2018 08:34:19 +000
       
  • Quantitative Assessment of the Physiological Parameters Influencing QT
           Interval Response to Medication: Application of Computational Intelligence
           Tools

    • Abstract: Human heart electrophysiology is complex biological phenomenon, which is indirectly assessed by the measured ECG signal. ECG trace is further analyzed to derive interpretable surrogates including QT interval, QRS complex, PR interval, and T wave morphology. QT interval and its modification are the most commonly used surrogates of the drug triggered arrhythmia, but it is known that the QT interval itself is determined by other nondrug related parameters, physiological and pathological. In the current study, we used the computational intelligence algorithms to analyze correlations between various simulated physiological parameters and QT interval. Terfenadine given concomitantly with 8 enzymatic inhibitors was used as an example. The equation developed with the use of genetic programming technique leads to general reasoning about the changes in the prolonged QT. For small changes of the QT interval, the drug-related IKr and ICa currents inhibition potentials have major impact. The physiological parameters such as body surface area, potassium, sodium, and calcium ions concentrations are negligible. The influence of the physiological variables increases gradually with the more pronounced changes in QT. As the significant QT prolongation is associated with the drugs triggered arrhythmia risk, analysis of the role of physiological parameters influencing ECG seems to be advisable.
      PubDate: Thu, 04 Jan 2018 00:00:00 +000
       
 
 
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