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https://www.readbyqxmd.com/read/29457314/latent-source-mining-in-fmri-via-restricted-boltzmann-machine
#1
Xintao Hu, Heng Huang, Bo Peng, Junwei Han, Nian Liu, Jinglei Lv, Lei Guo, Christine Guo, Tianming Liu
Blind source separation (BSS) is commonly used in functional magnetic resonance imaging (fMRI) data analysis. Recently, BSS models based on restricted Boltzmann machine (RBM), one of the building blocks of deep learning models, have been shown to improve brain network identification compared to conventional single matrix factorization models such as independent component analysis (ICA). These models, however, trained RBM on fMRI volumes, and are hence challenged by model complexity and limited training set...
February 18, 2018: Human Brain Mapping
https://www.readbyqxmd.com/read/29457134/trophic-signatures-of-seabirds-suggest-shifts-in-oceanic-ecosystems
#2
Tyler O Gagne, K David Hyrenbach, Molly E Hagemann, Kyle S Van Houtan
Pelagic ecosystems are dynamic ocean regions whose immense natural capital is affected by climate change, pollution, and commercial fisheries. Trophic level-based indicators derived from fishery catch data may reveal the food web status of these systems, but the utility of these metrics has been debated because of targeting bias in fisheries catch. We analyze a unique, fishery-independent data set of North Pacific seabird tissues to inform ecosystem trends over 13 decades (1890s to 2010s). Trophic position declined broadly in five of eight species sampled, indicating a long-term shift from higher-trophic level to lower-trophic level prey...
February 2018: Science Advances
https://www.readbyqxmd.com/read/29456922/propensity-scores-in-pharmacoepidemiology-beyond-the-horizon
#3
John W Jackson, Ian Schmid, Elizabeth A Stuart
Purpose of review: Propensity score methods have become commonplace in pharmacoepidemiology over the past decade. Their adoption has confronted formidable obstacles that arise from pharmacoepidemiology's reliance on large healthcare databases of considerable heterogeneity and complexity. These include identifying clinically meaningful samples, defining treatment comparisons, and measuring covariates in ways that respect sound epidemiologic study design. Additional complexities involve correctly modeling treatment decisions in the face of variation in healthcare practice, and dealing with missing information and unmeasured confounding...
December 2017: Current Epidemiology Reports
https://www.readbyqxmd.com/read/29456256/clustering-the-orion-b-giant-molecular-cloud-based-on-its-molecular-emission
#4
Emeric Bron, Chloé Daudon, Jérôme Pety, François Levrier, Maryvonne Gerin, Pierre Gratier, Jan H Orkisz, Viviana Guzman, Sébastien Bardeau, Javier R Goicoechea, Harvey Liszt, Karin Öberg, Nicolas Peretto, Albrecht Sievers, Pascal Tremblin
Context: Previous attempts at segmenting molecular line maps of molecular clouds have focused on using position-position-velocity data cubes of a single molecular line to separate the spatial components of the cloud. In contrast, wide field spectral imaging over a large spectral bandwidth in the (sub)mm domain now allows one to combine multiple molecular tracers to understand the different physical and chemical phases that constitute giant molecular clouds (GMCs). Aims: We aim at using multiple tracers (sensitive to different physical processes and conditions) to segment a molecular cloud into physically/chemically similar regions (rather than spatially connected components), thus disentangling the different physical/chemical phases present in the cloud...
February 2018: Astronomy and Astrophysics
https://www.readbyqxmd.com/read/29456026/neural-population-dynamics-underlying-motor-learning-transfer
#5
Saurabh Vyas, Nir Even-Chen, Sergey D Stavisky, Stephen I Ryu, Paul Nuyujukian, Krishna V Shenoy
Covert motor learning can sometimes transfer to overt behavior. We investigated the neural mechanism underlying transfer by constructing a two-context paradigm. Subjects performed cursor movements either overtly using arm movements, or covertly via a brain-machine interface that moves the cursor based on motor cortical activity (in lieu of arm movement). These tasks helped evaluate whether and how cortical changes resulting from "covert rehearsal" affect overt performance. We found that covert learning indeed transfers to overt performance and is accompanied by systematic population-level changes in motor preparatory activity...
February 13, 2018: Neuron
https://www.readbyqxmd.com/read/29455350/analysing-the-accuracy-of-machine-learning-techniques-to-develop-an-integrated-influent-time-series-model-case-study-of-a-sewage-treatment-plant-malaysia
#6
Mozafar Ansari, Faridah Othman, Taher Abunama, Ahmed El-Shafie
The function of a sewage treatment plant is to treat the sewage to acceptable standards before being discharged into the receiving waters. To design and operate such plants, it is necessary to measure and predict the influent flow rate. In this research, the influent flow rate of a sewage treatment plant (STP) was modelled and predicted by autoregressive integrated moving average (ARIMA), nonlinear autoregressive network (NAR) and support vector machine (SVM) regression time series algorithms. To evaluate the models' accuracy, the root mean square error (RMSE) and coefficient of determination (R2 ) were calculated as initial assessment measures, while relative error (RE), peak flow criterion (PFC) and low flow criterion (LFC) were calculated as final evaluation measures to demonstrate the detailed accuracy of the selected models...
February 17, 2018: Environmental Science and Pollution Research International
https://www.readbyqxmd.com/read/29454659/predicting-visual-acuity-by-using-machine-learning-in-patients-treated-for-neovascular-age-related-macular-degeneration
#7
Markus Rohm, Volker Tresp, Michael Müller, Christoph Kern, Ilja Manakov, Maximilian Weiss, Dawn A Sim, Siegfried Priglinger, Pearse A Keane, Karsten Kortuem
PURPOSE: To predict, by using machine learning, visual acuity (VA) at 3 and 12 months in patients with neovascular age-related macular degeneration (AMD) after initial upload of 3 anti-vascular endothelial growth factor (VEGF) injections. DESIGN: Database study. PARTICIPANTS: For the 3-month VA forecast, 653 patients (379 female) with 738 eyes and an average age of 74.1 years were included. The baseline VA before the first injection was 0...
February 14, 2018: Ophthalmology
https://www.readbyqxmd.com/read/29454222/obesity-dyslipidemia-and-brain-age-in-first-episode-psychosis
#8
Marian Kolenic, Katja Franke, Jaroslav Hlinka, Martin Matejka, Jana Capkova, Zdenka Pausova, Rudolf Uher, Martin Alda, Filip Spaniel, Tomas Hajek
INTRODUCTION: Obesity and dyslipidemia may negatively affect brain health and are frequent medical comorbidities of schizophrenia and related disorders. Despite the high burden of metabolic disorders, little is known about their effects on brain structure in psychosis. We investigated, whether obesity or dyslipidemia contributed to brain alterations in first-episode psychosis (FEP). METHODS: 120 participants with FEP, who were undergoing their first psychiatric hospitalization, had <24 months of untreated psychosis and were 18-35 years old and 114 controls within the same age range participated in the study...
February 10, 2018: Journal of Psychiatric Research
https://www.readbyqxmd.com/read/29453709/prediction-of-autism-at-3-years-from-behavioural-and-developmental-measures-in-high-risk-infants-a-longitudinal-cross-domain-classifier-analysis
#9
G Bussu, E J H Jones, T Charman, M H Johnson, J K Buitelaar
We integrated multiple behavioural and developmental measures from multiple time-points using machine learning to improve early prediction of individual Autism Spectrum Disorder (ASD) outcome. We examined Mullen Scales of Early Learning, Vineland Adaptive Behavior Scales, and early ASD symptoms between 8 and 36 months in high-risk siblings (HR; n = 161) and low-risk controls (LR; n = 71). Longitudinally, LR and HR-Typical showed higher developmental level and functioning, and fewer ASD symptoms than HR-Atypical and HR-ASD...
February 16, 2018: Journal of Autism and Developmental Disorders
https://www.readbyqxmd.com/read/29452923/statistical-and-machine-learning-approaches-to-predicting-protein-ligand-interactions
#10
REVIEW
Lucy J Colwell
Data driven computational approaches to predicting protein-ligand binding are currently achieving unprecedented levels of accuracy on held-out test datasets. Up until now, however, this has not led to corresponding breakthroughs in our ability to design novel ligands for protein targets of interest. This review summarizes the current state of the art in this field, emphasizing the recent development of deep neural networks for predicting protein-ligand binding. We explain the major technical challenges that have caused difficulty with predicting novel ligands, including the problems of sampling noise and the challenge of using benchmark datasets that are sufficiently unbiased that they allow the model to extrapolate to new regimes...
February 13, 2018: Current Opinion in Structural Biology
https://www.readbyqxmd.com/read/29452755/machine-learning-algorithms-based-on-signals-from-a-single-wearable-inertial-sensor-can-detect-surface-and-age-related-differences-in-walking
#11
B Hu, P C Dixon, J V Jacobs, J T Dennerlein, J M Schiffman
The aim of this study was to investigate if a machine learning algorithm utilizing triaxial accelerometer, gyroscope, and magnetometer data from an inertial motion unit (IMU) could detect surface- and age-related differences in walking. Seventeen older (71.5 ± 4.2 years) and eighteen young (27.0 ± 4.7 years) healthy adults walked over flat and uneven brick surfaces wearing an inertial measurement unit (IMU) over the L5 vertebra. IMU data were binned into smaller data segments using 4-s sliding windows with 1-s step lengths...
January 12, 2018: Journal of Biomechanics
https://www.readbyqxmd.com/read/29452575/a-machine-learning-approach-to-detect-changes-in-gait-parameters-following-a-fatiguing-occupational-task
#12
Amir Baghdadi, Fadel M Megahed, Ehsan T Esfahani, Lora A Cavuoto
The purpose of this study is to provide a method for classifying non-fatigued versus fatigued states following manual material handling. A method of template matching pattern recognition for feature extraction (1$ Recognizer) along with the support vector machine (SVM) model for classification were applied on the kinematics of gait cycles segmented by our stepwise search-based segmentation algorithm. A single inertial measurement unit (IMU) on the ankle was used, providing a minimally intrusive and inexpensive tool for monitoring...
February 16, 2018: Ergonomics
https://www.readbyqxmd.com/read/29451898/integrating-linear-optimization-with-structural-modeling-to-increase-hiv-neutralization-breadth
#13
Alexander M Sevy, Swetasudha Panda, James E Crowe, Jens Meiler, Yevgeniy Vorobeychik
Computational protein design has been successful in modeling fixed backbone proteins in a single conformation. However, when modeling large ensembles of flexible proteins, current methods in protein design have been insufficient. Large barriers in the energy landscape are difficult to traverse while redesigning a protein sequence, and as a result current design methods only sample a fraction of available sequence space. We propose a new computational approach that combines traditional structure-based modeling using the Rosetta software suite with machine learning and integer linear programming to overcome limitations in the Rosetta sampling methods...
February 16, 2018: PLoS Computational Biology
https://www.readbyqxmd.com/read/29451874/a-stochastic-and-dynamical-view-of-pluripotency-in-mouse-embryonic-stem-cells
#14
Yen Ting Lin, Peter G Hufton, Esther J Lee, Davit A Potoyan
Pluripotent embryonic stem cells are of paramount importance for biomedical sciences because of their innate ability for self-renewal and differentiation into all major cell lines. The fateful decision to exit or remain in the pluripotent state is regulated by complex genetic regulatory networks. The rapid growth of single-cell sequencing data has greatly stimulated applications of statistical and machine learning methods for inferring topologies of pluripotency regulating genetic networks. The inferred network topologies, however, often only encode Boolean information while remaining silent about the roles of dynamics and molecular stochasticity inherent in gene expression...
February 16, 2018: PLoS Computational Biology
https://www.readbyqxmd.com/read/29450843/detection-of-lung-contour-with-closed-principal-curve-and-machine-learning
#15
Tao Peng, Yihuai Wang, Thomas Canhao Xu, Lianmin Shi, Jianwu Jiang, Shilang Zhu
Radiation therapy plays an essential role in the treatment of cancer. In radiation therapy, the ideal radiation doses are delivered to the observed tumor while not affecting neighboring normal tissues. In three-dimensional computed tomography (3D-CT) scans, the contours of tumors and organs-at-risk (OARs) are often manually delineated by radiologists. The task is complicated and time-consuming, and the manually delineated results will be variable from different radiologists. We propose a semi-supervised contour detection algorithm, which firstly uses a few points of region of interest (ROI) as an approximate initialization...
February 15, 2018: Journal of Digital Imaging: the Official Journal of the Society for Computer Applications in Radiology
https://www.readbyqxmd.com/read/29450781/measuring-use-of-evidence-based-psychotherapy-for-posttraumatic-stress-disorder-in-a-large-national-healthcare-system
#16
Shira Maguen, Erin Madden, Olga V Patterson, Scott L DuVall, Lizabeth A Goldstein, Kristine Burkman, Brian Shiner
To derive a method of identifying use of evidence-based psychotherapy (EBP) for post-traumatic stress disorder (PTSD), we used clinical note text from national Veterans Health Administration (VHA) medical records. Using natural language processing, we developed machine-learning algorithms to classify note text on a large scale in an observational study of Iraq and Afghanistan veterans with PTSD and one post-deployment psychotherapy visit by 8/5/15 (N = 255,968). PTSD visits were linked to 8.1 million psychotherapy notes...
February 15, 2018: Administration and Policy in Mental Health
https://www.readbyqxmd.com/read/29450713/radiomic-signature-as-a-diagnostic-factor-for-histologic-subtype-classification-of-non-small-cell-lung-cancer
#17
Xinzhong Zhu, Di Dong, Zhendong Chen, Mengjie Fang, Liwen Zhang, Jiangdian Song, Dongdong Yu, Yali Zang, Zhenyu Liu, Jingyun Shi, Jie Tian
OBJECTIVES: To distinguish squamous cell carcinoma (SCC) from lung adenocarcinoma (ADC) based on a radiomic signature METHODS: This study involved 129 patients with non-small cell lung cancer (NSCLC) (81 in the training cohort and 48 in the independent validation cohort). Approximately 485 features were extracted from a manually outlined tumor region. The LASSO logistic regression model selected the key features of a radiomic signature. Receiver operating characteristic curve and area under the curve (AUC) were used to evaluate the performance of the radiomic signature in the training and validation cohorts...
February 15, 2018: European Radiology
https://www.readbyqxmd.com/read/29450538/clinical-age-specific-seasonal-conjunctivitis-patterns-and-their-online-detection-in-twitter-blog-forum-and-comment-social-media-posts
#18
Michael S Deiner, Stephen D McLeod, James Chodosh, Catherine E Oldenburg, Cherie A Fathy, Thomas M Lietman, Travis C Porco
Purpose: We sought to determine whether big data from social media might reveal seasonal trends of conjunctivitis, most forms of which are nonreportable. Methods: Social media posts (from Twitter, and from online forums and blogs) were classified by age and by conjunctivitis type (allergic or infectious) using Boolean and machine learning methods. Based on spline smoothing, we estimated the circular mean occurrence time (a measure of central tendency for occurrence) and the circular variance (a measure of uniformity of occurrence throughout the year, providing an index of seasonality)...
February 1, 2018: Investigative Ophthalmology & Visual Science
https://www.readbyqxmd.com/read/29449872/citizen-crowds-and-experts-observer-variability-in-image-based-plant-phenotyping
#19
M Valerio Giuffrida, Feng Chen, Hanno Scharr, Sotirios A Tsaftaris
Background: Image-based plant phenotyping has become a powerful tool in unravelling genotype-environment interactions. The utilization of image analysis and machine learning have become paramount in extracting data stemming from phenotyping experiments. Yet we rely on observer (a human expert) input to perform the phenotyping process. We assume such input to be a 'gold-standard' and use it to evaluate software and algorithms and to train learning-based algorithms. However, we should consider whether any variability among experienced and non-experienced (including plain citizens) observers exists...
2018: Plant Methods
https://www.readbyqxmd.com/read/29449509/predicting-reaction-performance-in-c-n-cross-coupling-using-machine-learning
#20
Derek T Ahneman, Jesús G Estrada, Shishi Lin, Spencer D Dreher, Abigail G Doyle
Machine learning methods are becoming integral to scientific inquiry in numerous disciplines. Here we demonstrate that machine learning can be used to predict the performance of a synthetic reaction in multidimensional chemical space using data obtained via high-throughput experimentation. We created scripts to compute and extract atomic, molecular, and vibrational descriptors for the components of a palladium-catalyzed Buchwald-Hartwig cross-coupling of aryl halides with 4-methylaniline in the presence of various potentially inhibitory additives...
February 15, 2018: Science
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