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Jiawei Wang, Bingjiao Yang, André Leier, Tatiana T Marquez-Lago, Morihiro Hayashida, Andrea Rocker, Zhang Yanju, Tatsuya Akutsu, Kuo-Chen Chou, Richard A Strugnell, Jiangning Song, Trevor Lithgow
Motivation: Many Gram-negative bacteria use type VI secretion systems (T6SS) to export effector proteins into adjacent target cells. These secreted effectors (T6SEs) play vital roles in the competitive survival in bacterial populations, as well as pathogenesis of bacteria. Although various computational analyses have been previously applied to identify effectors secreted by certain bacterial species, there is no universal method available to accurately predict T6SS effector proteins from the growing tide of bacterial genome sequence data...
March 14, 2018: Bioinformatics
Jyoti Singh Kirar, R K Agrawal
This paper presents a novel algorithm (CVSTSCSP) for determining discriminative features from an optimal combination of temporal, spectral and spatial information for motor imagery brain computer interfaces. The proposed method involves four phases. In the first phase, EEG signal is segmented into overlapping time segments and bandpass filtered through frequency filter bank of variable size subbands. In the next phase, features are extracted from the segmented and filtered data using stationary common spatial pattern technique (SCSP) that can handle the non- stationarity and artifacts of EEG signal...
March 16, 2018: Journal of Medical Systems
Jing Zhang, Siyue Li, Ruozhu Dong, Changsheng Jiang
The Three Gorges Reservoir (TGR) is one of the largest hydropower reservoirs in the world. However, changes of the important physical characteristics of the reservoir covering pre-, during-, and post- dam have not been well studied. This study analyzed the lengths and water surface areas of the TGR using advanced support vector machine method (SVM) combined Landsat images with the Shuttle Radar Topography Mission (SRTM) digital elevation model (DEM), which showed an increasing trend of lengths and surface areas with variable growth rates from pre-dam period to post-dam period...
March 15, 2018: Environmental Science and Pollution Research International
Irene Fondón, Auxiliadora Sarmiento, Ana Isabel García, María Silvestre, Catarina Eloy, António Polónia, Paulo Aguiar
Breast cancer is the second leading cause of cancer death among women. Its early diagnosis is extremely important to prevent avoidable deaths. However, malignancy assessment of tissue biopsies is complex and dependent on observer subjectivity. Moreover, hematoxylin and eosin (H&E)-stained histological images exhibit a highly variable appearance, even within the same malignancy level. In this paper, we propose a computer-aided diagnosis (CAD) tool for automated malignancy assessment of breast tissue samples based on the processing of histological images...
March 8, 2018: Computers in Biology and Medicine
Anisur Rahman, Mohammad Akbar Faqeerzada, Byoung-Kwan Cho
BACKGROUND: Allicin and soluble solid content (SSC) in garlic is the responsible for its pungent flavor and odor. However, current conventional methods such as high-pressure liquid chromatography (HPLC), refractometer has a critical drawbacks in that they are time-consuming, labor-intensive and destructive procedures. The present study aimed to predict the allicin and soluble solid content (SSC) in garlic using hyperspectral imaging in combination with variable selection algorithms and calibration models...
March 14, 2018: Journal of the Science of Food and Agriculture
David Haro Alonso, Miles N Wernick, Yongyi Yang, Guido Germano, Daniel S Berman, Piotr Slomka
BACKGROUND: We developed machine-learning (ML) models to estimate a patient's risk of cardiac death based on adenosine myocardial perfusion SPECT (MPS) and associated clinical data, and compared their performance to baseline logistic regression (LR). We demonstrated an approach to visually convey the reasoning behind a patient's risk to provide insight to clinicians beyond that of a "black box." METHODS: We trained multiple models using 122 potential clinical predictors (features) for 8321 patients, including 551 cases of subsequent cardiac death...
March 14, 2018: Journal of Nuclear Cardiology: Official Publication of the American Society of Nuclear Cardiology
Niloofar Yousefi Moteghaed, Keivan Maghooli, Masoud Garshasbi
Background: Gene expression data are characteristically high dimensional with a small sample size in contrast to the feature size and variability inherent in biological processes that contribute to difficulties in analysis. Selection of highly discriminative features decreases the computational cost and complexity of the classifier and improves its reliability for prediction of a new class of samples. Methods: The present study used hybrid particle swarm optimization and genetic algorithms for gene selection and a fuzzy support vector machine (SVM) as the classifier...
January 2018: Journal of Medical Signals and Sensors
Lina Xu, Giles Tetteh, Jana Lipkova, Yu Zhao, Hongwei Li, Patrick Christ, Marie Piraud, Andreas Buck, Kuangyu Shi, Bjoern H Menze
The identification of bone lesions is crucial in the diagnostic assessment of multiple myeloma (MM).68 Ga-Pentixafor PET/CT can capture the abnormal molecular expression of CXCR-4 in addition to anatomical changes. However, whole-body detection of dozens of lesions on hybrid imaging is tedious and error prone. It is even more difficult to identify lesions with a large heterogeneity. This study employed deep learning methods to automatically combine characteristics of PET and CT for whole-body MM bone lesion detection in a 3D manner...
2018: Contrast Media & Molecular Imaging
Ali Abdollahi Gharbali, Shirin Najdi, José Manuel Fonseca
OBJECTIVE: In this paper, the contribution of distance-based features to automatic sleep stage classification is investigated. The potency of these features is analyzed individually and in combination with 48 conventionally used features. METHODS: The distance-based set consists of 32 features extracted by calculating Itakura, Itakura-Saito and COSH distances of autoregressive and spectral coefficients of Electrocardiography (EEG) (C3 -A2 ), Left EOG, Chin EMG and ECG signals...
March 7, 2018: Computers in Biology and Medicine
Gaoyang Le, Huizhong Yang, Xiaodong Yu
We improved the ultraviolet (UV)/O3 -based method for measuring chemical oxygen demand (COD) in water. An on-line COD monitoring device was developed and the UV/O3 method was used to oxidize sample solutions. A model was established by using support vector machines (SVM) algorithm to estimate dissolved oxygen and CO2 in solutions. Based on the measured data by each sensor during the oxidation process and the estimated dissolved oxygen and CO2 , the UV/O3 -based COD test accuracy was improved. This approach overcomes many problems associated with the conventional COD determination techniques such as long analysis time, consumption of expensive and toxic reagents, and production of secondary toxic waste...
March 2018: Water Science and Technology: a Journal of the International Association on Water Pollution Research
Buranee Kanchanatawan, Sira Sriswasdi, Supaksorn Thika, Sunee Sirivichayakul, André F Carvalho, Michel Geffard, Marta Kubera, Michael Maes
Deficit schizophrenia is characterized by neurocognitive impairments and changes in the patterning of IgA/IgM responses to plasma tryptophan catabolites (TRYCATs). In the current study, supervised pattern recognition methods, including logistic regression analysis (LRA), Support Vector Machine (SVM), and Soft Independent Modeling of Class Analogy (SIMCA), were used to examine whether deficit schizophrenia is a discrete diagnostic class with respect to Consortium To Establish a Registry for Alzheimer's disease (CERAD) and Cambridge Neuropsychological Test Automated Battery (CANTAB) tests and IgA/IgM responses to noxious (NOX) and generally more protective (PRO) TRYCATs...
March 11, 2018: Metabolic Brain Disease
Peng Zhao, Yusen Wu, Chuying Feng, Lili Wang, Yun Ding, Aiguo Hu
A fluorescence sensing array (or fluorescent electronic nose) is designed on disposable paper card using 36 sets of soluble conjugated polymeric nanoparticles (SCPNs) as sensors to easily identify wide ranges of volatile analytes, including explosives and toxic industrial chemicals (amines and pungent acids). A 108-dimensional vector obtained from the fluorescent color change in the sensing array is defined and directly treated as an index in standard chemical library (30 kinds of volatile analytes and a control group)...
March 10, 2018: Analytical Chemistry
Battuya Bayarmagnai, Louisiane Perrin, Kamyar Esmaeili Pourfarhangi, Bojana Gligorijevic
Cancer cell motility and invasion are key features of metastatic tumors. Both are highly linked to tumor microenvironmental parameters, such as collagen architecture or macrophage density. However, due to the genetic, epigenetic and microenvironmental heterogeneities, only a small portion of tumor cells in the primary tumor are motile and furthermore, only a small portion of those will metastasize. This creates a challenge in predicting metastatic fate of single cells based on the phenotype they exhibit in the primary tumor...
2018: Methods in Molecular Biology
Yasmeen George, Mohammad Aldeen, Rahil Garnavi
Psoriasis is a chronic skin disease which can be life-threatening. Accurate severity scoring helps dermatologists to decide on the treatment. In this paper, we present a semi-supervised computer-aided system for automatic erythema severity scoring in psoriasis images. Firstly, the unsupervised stage includes a novel image representation method. We construct a dictionary, which is then used in the sparse representation for local feature extraction. To acquire the final image representation vector, an aggregation method is exploited over the local features...
February 23, 2018: Computerized Medical Imaging and Graphics: the Official Journal of the Computerized Medical Imaging Society
Muhammad Febrian Rachmadi, Maria Del C Valdés-Hernández, Maria Leonora Fatimah Agan, Carol Di Perri, Taku Komura
We propose an adaptation of a convolutional neural network (CNN) scheme proposed for segmenting brain lesions with considerable mass-effect, to segment white matter hyperintensities (WMH) characteristic of brains with none or mild vascular pathology in routine clinical brain magnetic resonance images (MRI). This is a rather difficult segmentation problem because of the small area (i.e., volume) of the WMH and their similarity to non-pathological brain tissue. We investigate the effectiveness of the 2D CNN scheme by comparing its performance against those obtained from another deep learning approach: Deep Boltzmann Machine (DBM), two conventional machine learning approaches: Support Vector Machine (SVM) and Random Forest (RF), and a public toolbox: Lesion Segmentation Tool (LST), all reported to be useful for segmenting WMH in MRI...
February 17, 2018: Computerized Medical Imaging and Graphics: the Official Journal of the Computerized Medical Imaging Society
Minwoo Cho, Jee Hyun Kim, Hyoun Joong Kong, Kyoung Sup Hong, Sungwan Kim
PURPOSE: The colonoscopy adenoma detection rate depends largely on physician experience and skill, and overlooked colorectal adenomas could develop into cancer. This study assessed a system that detects polyps and summarizes meaningful information from colonoscopy videos. METHODS: One hundred thirteen consecutive patients had colonoscopy videos prospectively recorded at the Seoul National University Hospital. Informative video frames were extracted using a MATLAB support vector machine (SVM) model and classified as bleeding, polypectomy, tool, residue, thin wrinkle, folded wrinkle, or common...
March 8, 2018: International Journal of Colorectal Disease
Tingting Zhang, Wensong Wei, Bin Zhao, Ranran Wang, Mingliu Li, Liming Yang, Jianhua Wang, Qun Sun
This study investigated the possibility of using visible and near-infrared (VIS/NIR) hyperspectral imaging techniques to discriminate viable and non-viable wheat seeds. Both sides of individual seeds were subjected to hyperspectral imaging (400-1000 nm) to acquire reflectance spectral data. Four spectral datasets, including the ventral groove side, reverse side, mean (the mean of two sides' spectra of every seed), and mixture datasets (two sides' spectra of every seed), were used to construct the models. Classification models, partial least squares discriminant analysis (PLS-DA), and support vector machines (SVM), coupled with some pre-processing methods and successive projections algorithm (SPA), were built for the identification of viable and non-viable seeds...
March 8, 2018: Sensors
Xiangfei Geng, Junhai Xu, Baolin Liu, Yonggang Shi
Major depressive disorder (MDD) is a mental disorder characterized by at least 2 weeks of low mood, which is present across most situations. Diagnosis of MDD using rest-state functional magnetic resonance imaging (fMRI) data faces many challenges due to the high dimensionality, small samples, noisy and individual variability. To our best knowledge, no studies aim at classification with effective connectivity and functional connectivity measures between MDD patients and healthy controls. In this study, we performed a data-driving classification analysis using the whole brain connectivity measures which included the functional connectivity from two brain templates and effective connectivity measures created by the default mode network (DMN), dorsal attention network (DAN), frontal-parietal network (FPN), and silence network (SN)...
2018: Frontiers in Neuroscience
Zhenhu Liang, Cheng Huang, Yongwang Li, Darren F Hight, Logan J Voss, Jamie W Sleigh, Xiaoli Li, Yang Bai

 Objective. Significant spectral characteristics of electroencephalogram (EEG) patterns exist in individual patients during re-establishing consciousness after general anesthesia. However, these EEG patterns cannot be quantitatively identified using commercially available depth of anesthesia (DoA) monitors. This study proposed an effective classification method and indices to classify these patterns among patients.
 Approach. Four types of emergence EEG patterns were identified based on EEG data set from 52patients undergoing sevoflurane general anesthesia from two hospitals...
March 7, 2018: Physiological Measurement
Maria Paraskevaidi, Camilo L M Morais, Olivia Raglan, Kássio M G Lima, Evangelos Paraskevaidis, Pierre L Martin-Hirsch, Maria Kyrgiou, Francis L Martin
Biospectroscopy has the potential to investigate and characterise biological samples and could, therefore, be utilised to diagnose various diseases in a clinical environment. An important consideration in spectrochemical studies is the cost-effectiveness of the substrate used to support the sample, as high expense would limit their translation into clinic. In this paper, the performance of low-cost aluminium (Al) foil substrates was compared with the commonly used low-emissivity (low-E) slides. Attenuated total reflection-Fourier transform infrared (ATR-FTIR) spectroscopy was used to analyse blood plasma and serum samples from women with endometrial cancer and healthy controls...
March 7, 2018: Journal of Biophotonics
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