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https://www.readbyqxmd.com/read/29782369/neuroimaging-in-epilepsy
#1
Meneka Kaur Sidhu, John S Duncan, Josemir W Sander
PURPOSE OF REVIEW: Epilepsy neuroimaging is important for detecting the seizure onset zone, predicting and preventing deficits from surgery and illuminating mechanisms of epileptogenesis. An aspiration is to integrate imaging and genetic biomarkers to enable personalized epilepsy treatments. RECENT FINDINGS: The ability to detect lesions, particularly focal cortical dysplasia and hippocampal sclerosis, is increased using ultra high-field imaging and postprocessing techniques such as automated volumetry, T2 relaxometry, voxel-based morphometry and surface-based techniques...
May 17, 2018: Current Opinion in Neurology
https://www.readbyqxmd.com/read/29781047/a-survey-on-coronary-atherosclerotic-plaque-tissue-characterization-in-intravascular-optical-coherence-tomography
#2
REVIEW
Alberto Boi, Ankush D Jamthikar, Luca Saba, Deep Gupta, Aditya Sharma, Bruno Loi, John R Laird, Narendra N Khanna, Jasjit S Suri
PURPOSE OF REVIEW: Atherosclerotic plaque deposition within the coronary vessel wall leads to arterial stenosis and severe catastrophic events over time. Identification of these atherosclerotic plaque components is essential to pre-estimate the risk of cardiovascular disease (CVD) and stratify them as a high or low risk. The characterization and quantification of coronary plaque components are not only vital but also a challenging task which can be possible using high-resolution imaging techniques...
May 21, 2018: Current Atherosclerosis Reports
https://www.readbyqxmd.com/read/29780972/a-general-method-for-predicting-amino-acid-residues-experiencing-hydrogen-exchange
#3
Boshen Wang, Alan Perez-Rathke, Renhao Li, Jie Liang
Information on protein hydrogen exchange can help delineate key regions involved in protein-protein interactions and provides important insight towards determining functional roles of genetic variants and their possible mechanisms in disease processes. Previous studies have shown that the degree of hydrogen exchange is affected by hydrogen bond formations, solvent accessibility, proximity to other residues, and experimental conditions. However, a general predictive method for identifying residues capable of hydrogen exchange transferable to a broad set of proteins is lacking...
March 2018: IEEE-EMBS International Conference on Biomedical and Health Informatics
https://www.readbyqxmd.com/read/29780658/the-selective-labels-problem-evaluating-algorithmic-predictions-in-the-presence-of-unobservables
#4
Himabindu Lakkaraju, Jon Kleinberg, Jure Leskovec, Jens Ludwig, Sendhil Mullainathan
Evaluating whether machines improve on human performance is one of the central questions of machine learning. However, there are many domains where the data is selectively labeled in the sense that the observed outcomes are themselves a consequence of the existing choices of the human decision-makers. For instance, in the context of judicial bail decisions, we observe the outcome of whether a defendant fails to return for their court appearance only if the human judge decides to release the defendant on bail...
August 2017: KDD: Proceedings
https://www.readbyqxmd.com/read/29780407/deep-learning-methods-for-underwater-target-feature-extraction-and-recognition
#5
Gang Hu, Kejun Wang, Yuan Peng, Mengran Qiu, Jianfei Shi, Liangliang Liu
The classification and recognition technology of underwater acoustic signal were always an important research content in the field of underwater acoustic signal processing. Currently, wavelet transform, Hilbert-Huang transform, and Mel frequency cepstral coefficients are used as a method of underwater acoustic signal feature extraction. In this paper, a method for feature extraction and identification of underwater noise data based on CNN and ELM is proposed. An automatic feature extraction method of underwater acoustic signals is proposed using depth convolution network...
2018: Computational Intelligence and Neuroscience
https://www.readbyqxmd.com/read/29778926/a-computational-approach-to-estimate-postmortem-interval-using-opacity-development-of-eye-for-human-subjects
#6
İsmail Cantürk, Lale Özyılmaz
This paper presents an approach to postmortem interval (PMI) estimation, which is a very debated and complicated area of forensic science. Most of the reported methods to determine PMI in the literature are not practical because of the need for skilled persons and significant amounts of time, and give unsatisfactory results. Additionally, the error margin of PMI estimation increases proportionally with elapsed time after death. It is crucial to develop practical PMI estimation methods for forensic science. In this study, a computational system is developed to determine the PMI of human subjects by investigating postmortem opacity development of the eye...
May 17, 2018: Computers in Biology and Medicine
https://www.readbyqxmd.com/read/29778925/deep-learning-strategy-for-accurate-carotid-intima-media-thickness-measurement-an-ultrasound-study-on-japanese-diabetic-cohort
#7
Mainak Biswas, Venkatanareshbabu Kuppili, Tadashi Araki, Damodar Reddy Edla, Elisa Cuadrado Godia, Luca Saba, Harman S Suri, Tomaž Omerzu, John R Laird, Narendra N Khanna, Andrew Nicolaides, Jasjit S Suri
MOTIVATION: The carotid intima-media thickness (cIMT) is an important biomarker for cardiovascular diseases and stroke monitoring. This study presents an intelligence-based, novel, robust, and clinically-strong strategy that uses a combination of deep-learning (DL) and machine-learning (ML) paradigms. METHODOLOGY: A two-stage DL-based system (a class of AtheroEdge™ systems) was proposed for cIMT measurements. Stage I consisted of a convolution layer-based encoder for feature extraction and a fully convolutional network-based decoder for image segmentation...
May 12, 2018: Computers in Biology and Medicine
https://www.readbyqxmd.com/read/29777650/modeling-dengue-vector-population-using-remotely-sensed-data-and-machine-learning
#8
Juan M Scavuzzo, Francisco Trucco, Manuel Espinosa, Carolina B Tauro, Marcelo Abril, Carlos M Scavuzzo, Alejandro C Frery
Mosquitoes are vectors of many human diseases. In particular, Aedes ægypti (Linnaeus) is the main vector for Chikungunya, Dengue, and Zika viruses in Latin America and it represents a global threat. Public health policies that aim at combating this vector require dependable and timely information, which is usually expensive to obtain with field campaigns. For this reason, several efforts have been done to use remote sensing due to its reduced cost. The present work includes the temporal modeling of the oviposition activity (measured weekly on 50 ovitraps in a north Argentinean city) of Aedes ægypti (Linnaeus), based on time series of data extracted from operational earth observation satellite images...
May 16, 2018: Acta Tropica
https://www.readbyqxmd.com/read/29777580/context-encoding-enables-machine-learning-based-quantitative-photoacoustics
#9
Thomas Kirchner, Janek Gröhl, Lena Maier-Hein
Real-time monitoring of functional tissue parameters, such as local blood oxygenation, based on optical imaging could provide groundbreaking advances in the diagnosis and interventional therapy of various diseases. Although photoacoustic (PA) imaging is a modality with great potential to measure optical absorption deep inside tissue, quantification of the measurements remains a major challenge. We introduce the first machine learning-based approach to quantitative PA imaging (qPAI), which relies on learning the fluence in a voxel to deduce the corresponding optical absorption...
May 2018: Journal of Biomedical Optics
https://www.readbyqxmd.com/read/29777110/using-network-analysis-for-the-prediction-of-treatment-dropout-in-patients-with-mood-and-anxiety-disorders-a-methodological-proof-of-concept-study
#10
Wolfgang Lutz, Brian Schwartz, Stefan G Hofmann, Aaron J Fisher, Kristin Husen, Julian A Rubel
There are large health, societal, and economic costs associated with attrition from psychological services. The recently emerged, innovative statistical tool of complex network analysis was used in the present proof-of-concept study to improve the prediction of attrition. Fifty-eight patients undergoing psychological treatment for mood or anxiety disorders were assessed using Ecological Momentary Assessments four times a day for two weeks before treatment (3,248 measurements). Multilevel vector autoregressive models were employed to compute dynamic symptom networks...
May 18, 2018: Scientific Reports
https://www.readbyqxmd.com/read/29775912/an-accurate-sleep-stages-classification-system-using-a-new-class-of-optimally-time-frequency-localized-three-band-wavelet-filter-bank
#11
Manish Sharma, Deepanshu Goyal, P V Achuth, U Rajendra Acharya
Sleep related disorder causes diminished quality of lives in human beings. Sleep scoring or sleep staging is the process of classifying various sleep stages which helps to detect the quality of sleep. The identification of sleep-stages using electroencephalogram (EEG) signals is an arduous task. Just by looking at an EEG signal, one cannot determine the sleep stages precisely. Sleep specialists may make errors in identifying sleep stages by visual inspection. To mitigate the erroneous identification and to reduce the burden on doctors, a computer-aided EEG based system can be deployed in the hospitals, which can help identify the sleep stages, correctly...
May 10, 2018: Computers in Biology and Medicine
https://www.readbyqxmd.com/read/29775322/prediction-of-human-cytochrome-p450-inhibition-using-a-multi-task-deep-autoencoder-neural-network
#12
Xiang Li, Youjun Xu, Luhua Lai, Jianfeng Pei
Adverse side effects of drug-drug interactions induced by human cytochrome P450 (CYP450) inhibition is an important consideration in drug discovery. It is highly desirable to develop computational models that can predict the inhibitive effect of a compound against a specific CYP450 isoform. In this study, we developed a multi-task model for concurrent inhibition prediction of five major CYP450 isoforms, namely 1A2, 2C9, 2C19, 2D6, and 3A4. The model was built by training multi-task autoencoder deep neural network (DNN) on a large dataset containing more than 13000 compounds, extracted from PubChem BioAssay Database...
May 18, 2018: Molecular Pharmaceutics
https://www.readbyqxmd.com/read/29775077/evaluating-the-influence-of-road-lighting-on-traffic-safety-at-accesses-using-an-artificial-neural-network
#13
Yueru Xu, Zhirui Ye, Yuan Wang, Chao Wang, Cuicui Sun
OBJECTIVES: This paper focuses on the effect of road lighting on road safety at accesses and tries to quantitatively analyze the relationship between road lighting and road safety. METHODS: An Artificial Neural Network (ANN) was applied in this study. This method is one of the most popular machine-learning methods in recent years and does not require any pre-defined assumptions. This method was applied using field data collected from ten road segments in Nanjing, Jiangsu Province, China...
May 18, 2018: Traffic Injury Prevention
https://www.readbyqxmd.com/read/29774657/cheminformatics-in-drug-discovery-an-industrial-perspective
#14
REVIEW
Hongming Chen, Thierry Kogej, Ola Engkvist
Cheminformatics has established itself as a core discipline within large scale drug discovery operations. It would be impossible to handle the amount of data generated today in a small molecule drug discovery project without persons skilled in cheminformatics. In addition, due to increased emphasis on "Big Data", machine learning and artificial intelligence, not only in the society in general, but also in drug discovery, it is expected that the cheminformatics field will be even more important in the future...
May 18, 2018: Molecular Informatics
https://www.readbyqxmd.com/read/29774244/classification-and-characterisation-of-brain-network-changes-in-chronic-back-pain-a-multicenter-study
#15
Hiroaki Mano, Gopal Kotecha, Kenji Leibnitz, Takashi Matsubara, Aya Nakae, Nicholas Shenker, Masahiko Shibata, Valerie Voon, Wako Yoshida, Michael Lee, Toshio Yanagida, Mitsuo Kawato, Maria Joao Rosa, Ben Seymour
Background. Chronic pain is a common, often disabling condition thought to involve a combination of peripheral and central neurobiological factors. However, the extent and nature of changes in the brain is poorly understood. Methods. We investigated brain network architecture using resting-state fMRI data in chronic back pain patients in the UK and Japan (41 patients, 56 controls), as well as open data from USA. We applied machine learning and deep learning (conditional variational autoencoder architecture) methods to explore classification of patients/controls based on network connectivity...
2018: Wellcome Open Research
https://www.readbyqxmd.com/read/29773825/consistent-prediction-of-go-protein-localization
#16
Flavio E Spetale, Debora Arce, Flavia Krsticevic, Pilar Bulacio, Elizabeth Tapia
The GO-Cellular Component (GO-CC) ontology provides a controlled vocabulary for the consistent description of the subcellular compartments or macromolecular complexes where proteins may act. Current machine learning-based methods used for the automated GO-CC annotation of proteins suffer from the inconsistency of individual GO-CC term predictions. Here, we present FGGA-CC+ , a class of hierarchical graph-based classifiers for the consistent GO-CC annotation of protein coding genes at the subcellular compartment or macromolecular complex levels...
May 17, 2018: Scientific Reports
https://www.readbyqxmd.com/read/29773078/optimizing-taxonomic-classification-of-marker-gene-amplicon-sequences-with-qiime-2-s-q2-feature-classifier-plugin
#17
Nicholas A Bokulich, Benjamin D Kaehler, Jai Ram Rideout, Matthew Dillon, Evan Bolyen, Rob Knight, Gavin A Huttley, J Gregory Caporaso
BACKGROUND: Taxonomic classification of marker-gene sequences is an important step in microbiome analysis. RESULTS: We present q2-feature-classifier ( https://github.com/qiime2/q2-feature-classifier ), a QIIME 2 plugin containing several novel machine-learning and alignment-based methods for taxonomy classification. We evaluated and optimized several commonly used classification methods implemented in QIIME 1 (RDP, BLAST, UCLUST, and SortMeRNA) and several new methods implemented in QIIME 2 (a scikit-learn naive Bayes machine-learning classifier, and alignment-based taxonomy consensus methods based on VSEARCH, and BLAST+) for classification of bacterial 16S rRNA and fungal ITS marker-gene amplicon sequence data...
May 17, 2018: Microbiome
https://www.readbyqxmd.com/read/29772957/brain-machine-interfaces-powerful-tools-for-clinical-treatment-and-neuroscientific-investigations
#18
Marc W Slutzky
Brain-machine interfaces (BMIs) have exploded in popularity in the past decade. BMIs, also called brain-computer interfaces, provide a direct link between the brain and a computer, usually to control an external device. BMIs have a wide array of potential clinical applications, ranging from restoring communication to people unable to speak due to amyotrophic lateral sclerosis or a stroke, to restoring movement to people with paralysis from spinal cord injury or motor neuron disease, to restoring memory to people with cognitive impairment...
May 1, 2018: Neuroscientist: a Review Journal Bringing Neurobiology, Neurology and Psychiatry
https://www.readbyqxmd.com/read/29772901/the-catch-22-of-predicting-herg-blockade-using-publicly-accessible-bioactivity-data
#19
Vishal Babu Siramshetty, Qiaofeng Chen, Prashanth Devarakonda, Robert Preissner
Drug-induced inhibition of the human Ether-à-go-go-Related Gene (hERG) encoded potassium ion (K+) channels can lead to fatal cardiotoxicity. Several marketed drugs and promising drug candidates were recalled due to this concern. Diverse modeling methods ranging from molecular similarity assessment to quantitative structure activity relationship analysis employing machine learning techniques have been applied to datasets of varying size and composition (number of blockers and non-blockers). In this study, we highlight the challenges involved in development of a robust classifier for predicting the hERG endpoint using bioactivity data extracted from the public domain...
May 17, 2018: Journal of Chemical Information and Modeling
https://www.readbyqxmd.com/read/29772818/integrated-method-for-personal-thermal-comfort-assessment-and-optimization-through-users-feedback-iot-and-machine-learning-a-case-study-%C3%A2
#20
Francesco Salamone, Lorenzo Belussi, Cristian Currò, Ludovico Danza, Matteo Ghellere, Giulia Guazzi, Bruno Lenzi, Valentino Megale, Italo Meroni
Thermal comfort has become a topic issue in building performance assessment as well as energy efficiency. Three methods are mainly recognized for its assessment. Two of them based on standardized methodologies, face the problem by considering the indoor environment in steady-state conditions (PMV and PPD) and users as active subjects whose thermal perception is influenced by outdoor climatic conditions (adaptive approach). The latter method is the starting point to investigate thermal comfort from an overall perspective by considering endogenous variables besides the traditional physical and environmental ones...
May 17, 2018: Sensors
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