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https://www.readbyqxmd.com/read/28538622/towards-precision-medicine-accurate-predictive-modeling-of-infectious-complications-in-combat-casualties
#1
Christopher J Dente, Matthew Bradley, Seth Schobel, Beverly Gaucher, Timothy Buchman, Allan D Kirk, Eric Elster
BACKGROUND: The biomarker profile of trauma patients may allow for the creation of models to assist bedside decision making & prediction of complications. We sought to determine the utility of modeling in the prediction of bacteremia & pneumonia in combat casualties. METHODS: This is a prospective, observational trial of patients with complex wounds treated at Walter Reed National Military Medical Center (2007-2012). Tissue, serum and wound effluent samples were collected during operative interventions until wound closure...
May 22, 2017: Journal of Trauma and Acute Care Surgery
https://www.readbyqxmd.com/read/28538142/an-introduction-to-infinite-hmms-for-single-molecule-data-analysis
#2
REVIEW
Ioannis Sgouralis, Steve Pressé
The hidden Markov model (HMM) has been a workhorse of single-molecule data analysis and is now commonly used as a stand-alone tool in time series analysis or in conjunction with other analysis methods such as tracking. Here, we provide a conceptual introduction to an important generalization of the HMM, which is poised to have a deep impact across the field of biophysics: the infinite HMM (iHMM). As a modeling tool, iHMMs can analyze sequential data without a priori setting a specific number of states as required for the traditional (finite) HMM...
May 23, 2017: Biophysical Journal
https://www.readbyqxmd.com/read/28535421/differentiation-of-crataegus-spp-guided-by-nuclear-magnetic-resonance-spectrometry-with-chemometric-analyses
#3
Jensen A Lund, Paula N Brown, Paul R Shipley
For compliance with US Current Good Manufacturing Practice regulations for dietary supplements, manufacturers must provide identity of source plant material. Despite the popularity of hawthorn as a dietary supplement, relatively little is known about the comparative phytochemistry of different hawthorn species, and in particular North American hawthorns. The combination of NMR spectrometry with chemometric analyses offers an innovative approach to differentiating hawthorn species and exploring the phytochemistry...
May 20, 2017: Phytochemistry
https://www.readbyqxmd.com/read/28534802/organ-location-determination-and-contour-sparse-representation-for-multi-organ-segmentation
#4
Siqi Li, Huiyan Jiang, Yu-Dong Yao, Benqiang Yang
Organ segmentation on computed tomography (CT) images is of great importance in medical diagnoses and treatment. This paper proposes organ location determination and contour sparse representation methods (OLD-CSR) for multiorgan segmentation (liver, kidney, and spleen) on abdomen CT images using an extreme learning machine (ELM) classifier. First, a location determination method is designed to obtain location information of each organ, which is used for coarse segmentation. Second, for coarse-to-fine segmentation, a contour gradient and rate change based feature point extraction method is proposed...
May 17, 2017: IEEE Journal of Biomedical and Health Informatics
https://www.readbyqxmd.com/read/28534790/neighborhood-based-stopping-criterion-for-contrastive-divergence
#5
Enrique Romero Merino, Ferran Mazzanti Castrillejo, Jordi Delgado Pin
Restricted Boltzmann Machines (RBMs) are general unsupervised learning devices to ascertain generative models of data distributions. RBMs are often trained using the Contrastive Divergence (CD) learning algorithm, an approximation to the gradient of the data log-likelihood (logL). A simple reconstruction error is often used as a stopping criterion for CD, although several authors have raised doubts concerning the feasibility of this procedure. In many cases, the evolution curve of the reconstruction error is monotonic, while the logL is not, thus indicating that the former is not a good estimator of the optimal stopping point for learning...
May 17, 2017: IEEE Transactions on Neural Networks and Learning Systems
https://www.readbyqxmd.com/read/28534788/rankmap-a-framework-for-distributed-learning-from-dense-data-sets
#6
Azalia Mirhoseini, Eva L Dyer, Ebrahim M Songhori, Richard Baraniuk, Farinaz Koushanfar
This paper introduces RankMap, a platform-aware end-to-end framework for efficient execution of a broad class of iterative learning algorithms for massive and dense data sets. Our framework exploits data structure to scalably factorize it into an ensemble of lower rank subspaces. The factorization creates sparse low-dimensional representations of the data, a property which is leveraged to devise effective mapping and scheduling of iterative learning algorithms on the distributed computing machines. We provide two APIs, one matrix-based and one graph-based, which facilitate automated adoption of the framework for performing several contemporary learning applications...
May 17, 2017: IEEE Transactions on Neural Networks and Learning Systems
https://www.readbyqxmd.com/read/28534781/reviving-the-two-state-markov-chain-approach
#7
Andrzej Mizera, Jun Pang, Qixia Yuan
Probabilistic Boolean networks (PBNs) is a well-established computational framework for modelling biological systems. The steady-state dynamics of PBNs is of crucial importance in the study of such systems. However, for large PBNs, which often arise in systems biology, obtaining the steady-state distribution poses a significant challenge. In this paper, we revive the two-state Markov chain approach to solve this problem. This paper contributes in three aspects. First, we identify a problem of generating biased results with the approach and we propose a few heuristics to avoid such a pitfall...
May 16, 2017: IEEE/ACM Transactions on Computational Biology and Bioinformatics
https://www.readbyqxmd.com/read/28534767/domain-generalization-and-adaptation-using-low-rank-exemplar-svms
#8
Wen Li, Zheng Xu, Dong Xu, Dengxin Dai, Luc Van Gool
Domain adaptation between diverse source and target domains is a challenging research problem, especially in the real-world visual recognition tasks where the images and videos consist of significant variations in viewpoints, illuminations, qualities, etc. In this paper, we propose a new approach for domain generalization and domain adaptation based on exemplar SVMs. Specifically, we decompose the source domain into many subdomains, each of which contains only one positive training sample and all negative samples...
May 16, 2017: IEEE Transactions on Pattern Analysis and Machine Intelligence
https://www.readbyqxmd.com/read/28534414/estimating-pm2-5-concentrations-in-the-conterminous-united-states-using-the-random-forest-approach
#9
Xuefei Hu, Jessica Hartmann Belle, Xia Meng, Avani Wildani, Lance Waller, Matthew Strickland, Yang Liu
To estimate PM2.5 concentrations, many parametric regression models have been developed, while non-parametric machine learning algorithms are used less often and national-scale models are rare. In this paper, we develop a random forest model incorporating Aerosol Optical Depth (AOD) data, meteorological fields, and land use variables to estimate daily 24-hour averaged ground level PM2.5 concentrations over the conterminous United States in 2011. Random forests are an ensemble learning method that provides predictions with high accuracy and interpretability...
May 23, 2017: Environmental Science & Technology
https://www.readbyqxmd.com/read/28533819/the-optimal-crowd-learning-machine
#10
Bilguunzaya Battogtokh, Majid Mojirsheibani, James Malley
BACKGROUND: Any family of learning machines can be combined into a single learning machine using various methods with myriad degrees of usefulness. RESULTS: For making predictions on an outcome, it is provably at least as good as the best machine in the family, given sufficient data. And if one machine in the family minimizes the probability of misclassification, in the limit of large data, then Optimal Crowd does also. That is, the Optimal Crowd is asymptotically Bayes optimal if any machine in the crowd is such...
2017: BioData Mining
https://www.readbyqxmd.com/read/28533766/predicting-the-hma-lma-status-in-marine-sponges-by-machine-learning
#11
Lucas Moitinho-Silva, Georg Steinert, Shaun Nielsen, Cristiane C P Hardoim, Yu-Chen Wu, Grace P McCormack, Susanna López-Legentil, Roman Marchant, Nicole Webster, Torsten Thomas, Ute Hentschel
The dichotomy between high microbial abundance (HMA) and low microbial abundance (LMA) sponges has been observed in sponge-microbe symbiosis, although the extent of this pattern remains poorly unknown. We characterized the differences between the microbiomes of HMA (n = 19) and LMA (n = 17) sponges (575 specimens) present in the Sponge Microbiome Project. HMA sponges were associated with richer and more diverse microbiomes than LMA sponges, as indicated by the comparison of alpha diversity metrics. Microbial community structures differed between HMA and LMA sponges considering Operational Taxonomic Units (OTU) abundances and across microbial taxonomic levels, from phylum to species...
2017: Frontiers in Microbiology
https://www.readbyqxmd.com/read/28531339/limtox-a-web-tool-for-applied-text-mining-of-adverse-event-and-toxicity-associations-of-compounds-drugs-and-genes
#12
Andres Cañada, Salvador Capella-Gutierrez, Obdulia Rabal, Julen Oyarzabal, Alfonso Valencia, Martin Krallinger
A considerable effort has been devoted to retrieve systematically information for genes and proteins as well as relationships between them. Despite the importance of chemical compounds and drugs as a central bio-entity in pharmacological and biological research, only a limited number of freely available chemical text-mining/search engine technologies are currently accessible. Here we present LimTox (Literature Mining for Toxicology), a web-based online biomedical search tool with special focus on adverse hepatobiliary reactions...
May 22, 2017: Nucleic Acids Research
https://www.readbyqxmd.com/read/28531125/characterizing-dynamic-walking-patterns-and-detecting-falls-with-wearable-sensors-using-gaussian-process-methods
#13
Taehwan Kim, Jeongho Park, Seongman Heo, Keehoon Sung, Jooyoung Park
By incorporating a growing number of sensors and adopting machine learning technologies, wearable devices have recently become a prominent health care application domain. Among the related research topics in this field, one of the most important issues is detecting falls while walking. Since such falls may lead to serious injuries, automatically and promptly detecting them during daily use of smartphones and/or smart watches is a particular need. In this paper, we investigate the use of Gaussian process (GP) methods for characterizing dynamic walking patterns and detecting falls while walking with built-in wearable sensors in smartphones and/or smartwatches...
May 20, 2017: Sensors
https://www.readbyqxmd.com/read/28530547/application-of-machine-learning-approaches-for-protein-protein-interactions-prediction
#14
Mengying Zhang, Qiang Su, Yi Lu, Manman Zhao, Bing Niu
BACKGROUND: Proteomics endeavors to study the structures, functions and interactions of proteins. Information of the protein-protein interactions (PPIs) helps to improve our knowledge of the functions and the 3D structures of proteins. Thus determining the PPIs is essential for the study of the proteomics. OBJECTIVE: In this review, in order to study the application of machine learning in predicting PPI, some machine learning approaches such as support vector machine (SVM), artificial neural networks (ANNs) and random forest (RF) were selected, and the examples of its applications in PPIs were listed...
May 22, 2017: Medicinal Chemistry
https://www.readbyqxmd.com/read/28529536/mid-infrared-spectroscopy-combined-with-chemometrics-to-detect-sclerotinia-stem-rot-on-oilseed-rape-brassica-napus-l-leaves
#15
Chu Zhang, Xuping Feng, Jian Wang, Fei Liu, Yong He, Weijun Zhou
BACKGROUND: Detection of plant diseases in a fast and simple way is crucial for timely disease control. Conventionally, plant diseases are accurately identified by DNA, RNA or serology based methods which are time consuming, complex and expensive. Mid-infrared spectroscopy is a promising technique that simplifies the detection procedure for the disease. Mid-infrared spectroscopy was used to identify the spectral differences between healthy and infected oilseed rape leaves. Two different sample sets from two experiments were used to explore and validate the feasibility of using mid-infrared spectroscopy in detecting Sclerotinia stem rot (SSR) on oilseed rape leaves...
2017: Plant Methods
https://www.readbyqxmd.com/read/28528295/a-machine-learning-graph-based-approach-for-3d-segmentation-of-bruch-s-membrane-opening-from-glaucomatous-sd-oct-volumes
#16
Mohammad Saleh Miri, Michael D Abràmoff, Young H Kwon, Milan Sonka, Mona K Garvin
Bruch's membrane opening-minimum rim width (BMO-MRW) is a recently proposed structural parameter which estimates the remaining nerve fiber bundles in the retina and is superior to other conventional structural parameters for diagnosing glaucoma. Measuring this structural parameter requires identification of BMO locations within spectral domain-optical coherence tomography (SD-OCT) volumes. While most automated approaches for segmentation of the BMO either segment the 2D projection of BMO points or identify BMO points in individual B-scans, in this work, we propose a machine-learning graph-based approach for true 3D segmentation of BMO from glaucomatous SD-OCT volumes...
May 6, 2017: Medical Image Analysis
https://www.readbyqxmd.com/read/28528170/disparities-in-digital-reporting-of-illness-a-demographic-and-socioeconomic-assessment
#17
Samuel Henly, Gaurav Tuli, Sheryl Kluberg, Jared B Hawkins, Quynh Nguyen, Aranka Anema, Adyasha Maharana, John S Brownstein, Elaine O Nsoesie
Although digital reports of disease are currently used by public health officials for disease surveillance and decision making, little is known about environmental factors and compositional characteristics that may influence reporting patterns. The objective of this study is to quantify the association between climate, demographic and socio-economic factors on digital reporting of disease at the US county level. We reference approximately 1.5 million foodservice business reviews between 2004 and 2014, and use census data, machine learning methods and regression models to assess whether digital reporting of disease is associated with climate, socio-economic and demographic factors...
May 17, 2017: Preventive Medicine
https://www.readbyqxmd.com/read/28526878/a-data-mining-approach-using-cortical-thickness-for-diagnosis-and-characterization-of-essential-tremor
#18
J Ignacio Serrano, Juan P Romero, Ma Dolores Del Castillo, Eduardo Rocon, Elan D Louis, Julián Benito-León
Essential tremor (ET) is one of the most prevalent movement disorders. Being that it is a common disorder, its diagnosis is considered routine. However, misdiagnoses may occur regularly. Over the past decade, several studies have identified brain morphometric changes in ET, but these changes remain poorly understood. Here, we tested the informativeness of measuring cortical thickness for the purposes of ET diagnosis, applying feature selection and machine learning methods to a study sample of 18 patients with ET and 18 age- and sex-matched healthy control subjects...
May 19, 2017: Scientific Reports
https://www.readbyqxmd.com/read/28525590/sdm-a-server-for-predicting-effects-of-mutations-on-protein-stability
#19
Arun Prasad Pandurangan, Bernardo Ochoa-Montaño, David B Ascher, Tom L Blundell
Here, we report a webserver for the improved SDM, used for predicting the effects of mutations on protein stability. As a pioneering knowledge-based approach, SDM has been highlighted as the most appropriate method to use in combination with many other approaches. We have updated the environment-specific amino-acid substitution tables based on the current expanded PDB (a 5-fold increase in information), and introduced new residue-conformation and interaction parameters, including packing density and residue depth...
May 19, 2017: Nucleic Acids Research
https://www.readbyqxmd.com/read/28525568/scenery-a-web-application-for-causal-network-reconstruction-from-cytometry-data
#20
Georgios Papoutsoglou, Giorgos Athineou, Vincenzo Lagani, Iordanis Xanthopoulos, Angelika Schmidt, Szabolcs Éliás, Jesper Tegnér, Ioannis Tsamardinos
Flow and mass cytometry technologies can probe proteins as biological markers in thousands of individual cells simultaneously, providing unprecedented opportunities for reconstructing networks of protein interactions through machine learning algorithms. The network reconstruction (NR) problem has been well-studied by the machine learning community. However, the potentials of available methods remain largely unknown to the cytometry community, mainly due to their intrinsic complexity and the lack of comprehensive, powerful and easy-to-use NR software implementations specific for cytometry data...
May 19, 2017: Nucleic Acids Research
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