A NEW METHOD FOR NONHOMOGENEOUS TIME COURSE EXPRESSION ANALYSIS

2012 ◽  
Vol 10 (04) ◽  
pp. 1250007 ◽  
Author(s):  
JIN XU
Keyword(s):  
Gaussian Process ◽  
Expression Analysis ◽  
Process Model ◽  
Time Course ◽  
Temporal Changes ◽  
New Method ◽  
Test Statistic ◽  
Directional Information ◽  
Time Points ◽  
Multiple Groups

Time course expression analysis constitutes a large portion of applications of microarray experiments. One primary goal of such experiments is to detect genes with the temporal changes over a period of time or at some interested time points. Difficulties arising from data with small number of replicates over only a few unaligned time points in multiple groups pose challenges for efficient statistical analysis. Some known methods are limited by the unverifiable assumptions or by the scope of applications for only two groups. We present a new method for detecting differentially expressed genes under nonhomogeneous time course experiments in multiple groups. The new method first models the time course curve of one gene by a Gaussian process to align the nonhomogeneous time course data and to compute the gradient of the time course curve as well, the latter of which is used as directional information to enhance the sensitivity of detection for temporal changes. Second, we adopt a nonparametric method to test a surrogate hypothesis based on the augmented data from the Gaussian process model. The proposed method is robust in terms of model fitting and testing. It does not require any distributional assumption for the observations or the test statistic and the method works for the case with as few as triplicate samples over four or five time points under multiple groups. We show the effectiveness and superiority of the new method in comparison with some existing methods using simulated models and two real data sets.

Time Course of Simulator Sickness in Asian Military Pilots

2020 ◽  
Vol 91 (11) ◽  
pp. 892-896
Author(s):  
Janine En Qi Loi ◽  
Magdalene Li Ling Lee ◽  
Benjamin Boon Chuan Tan ◽  
Brian See
Keyword(s):  
Air Force ◽  
Time Course ◽  
Simulator Training ◽  
Simulator Sickness ◽  
Time Points ◽  
True Prevalence ◽  
Subjective Scale ◽  
Significant Difference ◽  
Simulator Sickness Questionnaire ◽  
Military Pilots

INTRODUCTION: This study sought to determine the incidence, severity, and time-course of simulator sickness (SS) among Asian military pilots following flight simulator training.METHODS: A survey was conducted on Republic of Singapore Air Force pilots undergoing simulator training. Each subject completed a questionnaire immediately after (0H), and at the 3-h (3H) and 6-h (6H) marks. The questionnaire included the simulator sickness questionnaire (SSQ) and a subjective scale to rate their confidence to fly.RESULTS: In this study, 258 pilots with a median age of 31.50 yr (range, 2155 yr) and mean age of 32.61 6.56 yr participated. The prevalence of SS was 48.1% at 0H, 30.8% at 3H, and 16.4% at 6H. Based on a threshold of an SSQ score >10, the prevalence of operationally significant SS was 33.3% at 0H, 13.2% at 3H, and 8.1% at 6H. The most frequent symptoms were fatigue (38.1%), eye strain (29.0%), and fullness of head (19.9%). There was no significant difference in mean scores between rotary and fixed wing pilots. Older, more experienced pilots had greater scores at 0H, but this association did not persist. A correlation was found between SSQ score and self-reported confidence.DISCUSSION: To our knowledge, this study is the first to report the prevalence of operationally significant SS in Asian military pilots over serial time points. Most pilots with SS are able to subjectively judge their fitness to fly. Sensitivity analysis suggests the true prevalence of SS symptoms at 3H and 6H to be closer to 23.8% and 12.0%, respectively.Loi JEQ, Lee MLL, Tan BBC, See B. Time course of simulator sickness in Asian military pilots. Aerosp Med Hum Perform. 2020; 91(11):892896.

SAMPL6 Challenge Results from pKa Predictions Based on a General Gaussian Process Model

2018 ◽  
Author(s):  
Caitlin C. Bannan ◽  
David Mobley ◽  
A. Geoff Skillman
Keyword(s):  
Gaussian Process ◽  
Process Model ◽  
Molecular Graph ◽  
Gaussian Process Regression ◽  
Ionization State ◽  
Training Set ◽  
Physiochemical Properties ◽  
Quantile Plots ◽  
Physical And Chemical ◽  
Good Agreement

<div>A variety of fields would benefit from accurate pK<sub>a</sub> predictions, especially drug design due to the affect a change in ionization state can have on a molecules physiochemical properties.</div><div>Participants in the recent SAMPL6 blind challenge were asked to submit predictions for microscopic and macroscopic pK<sub>a</sub>s of 24 drug like small molecules.</div><div>We recently built a general model for predicting pK<sub>a</sub>s using a Gaussian process regression trained using physical and chemical features of each ionizable group.</div><div>Our pipeline takes a molecular graph and uses the OpenEye Toolkits to calculate features describing the removal of a proton.</div><div>These features are fed into a Scikit-learn Gaussian process to predict microscopic pK<sub>a</sub>s which are then used to analytically determine macroscopic pK<sub>a</sub>s.</div><div>Our Gaussian process is trained on a set of 2,700 macroscopic pK<sub>a</sub>s from monoprotic and select diprotic molecules.</div><div>Here, we share our results for microscopic and macroscopic predictions in the SAMPL6 challenge.</div><div>Overall, we ranked in the middle of the pack compared to other participants, but our fairly good agreement with experiment is still promising considering the challenge molecules are chemically diverse and often polyprotic while our training set is predominately monoprotic.</div><div>Of particular importance to us when building this model was to include an uncertainty estimate based on the chemistry of the molecule that would reflect the likely accuracy of our prediction. </div><div>Our model reports large uncertainties for the molecules that appear to have chemistry outside our domain of applicability, along with good agreement in quantile-quantile plots, indicating it can predict its own accuracy.</div><div>The challenge highlighted a variety of means to improve our model, including adding more polyprotic molecules to our training set and more carefully considering what functional groups we do or do not identify as ionizable. </div>

scholarly journals Serial computed tomography findings of Coronavirus disease 2019 (COVID-19) pneumonia treated with favipiravir and steroid therapy: report of 11 cases

2021 ◽  
Vol 45 (1) ◽  
Author(s):  
Naoki Irizato ◽  
Hiroshi Matsuura ◽  
Atsuya Okada ◽  
Ken Ueda ◽  
Hitoshi Yamamura
Keyword(s):  
Computed Tomography ◽  
Mechanical Ventilation ◽  
Steroid Therapy ◽  
Time Course ◽  
Ground Glass ◽  
Time Points ◽  
Ct Findings ◽  
Mixed Pattern ◽  
Bronchial Dilatation

Abstract Background This study evaluated the time course of computed tomography (CT) findings of patients with COVID-19 pneumonia who required mechanical ventilation and were treated with favipiravir and steroid therapy. Results Eleven patients with severe COVID-19 pneumonia were included. CT findings assessed at the three time points showed that all patients had ground-glass opacities (GGO) and consolidation and mixed pattern at intubation. Consolidation and mixed pattern disappeared in most of the patients whereas GGO persisted in all patients at 1-month follow-up. In addition to GGO, a subpleural line and bronchus distortion and bronchial dilatation were frequent findings. The degree of resolution of GGO varied depending on each patient. The GGO score correlated significantly with the time from symptoms onset to initiation of steroid therapy (ρ = 0.707, p = 0.015). Conclusions At 1-month follow-up after discharge, non-GGO lesions were absorbed almost completely, and GGO were a predominant CT manifestation. Starting steroid therapy earlier after onset of symptoms in severe COVID-19 pneumonia may reduce the extent of GGO at 1-month follow-up.

scholarly journals Bayesian approach for predicting responses to therapy from high-dimensional time-course gene expression profiles

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Arika Fukushima ◽  
Masahiro Sugimoto ◽  
Satoru Hiwa ◽  
Tomoyuki Hiroyasu
Keyword(s):  
Gene Expression ◽  
Time Course ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Response To Therapy ◽  
Hcv Infection ◽  
Entire Treatment Period ◽  
Time Points ◽  
Time Course Data ◽  
Conventional Methods

Abstract Background Historical and updated information provided by time-course data collected during an entire treatment period proves to be more useful than information provided by single-point data. Accurate predictions made using time-course data on multiple biomarkers that indicate a patient’s response to therapy contribute positively to the decision-making process associated with designing effective treatment programs for various diseases. Therefore, the development of prediction methods incorporating time-course data on multiple markers is necessary. Results We proposed new methods that may be used for prediction and gene selection via time-course gene expression profiles. Our prediction method consolidated multiple probabilities calculated using gene expression profiles collected over a series of time points to predict therapy response. Using two data sets collected from patients with hepatitis C virus (HCV) infection and multiple sclerosis (MS), we performed numerical experiments that predicted response to therapy and evaluated their accuracies. Our methods were more accurate than conventional methods and successfully selected genes, the functions of which were associated with the pathology of HCV infection and MS. Conclusions The proposed method accurately predicted response to therapy using data at multiple time points. It showed higher accuracies at early time points compared to those of conventional methods. Furthermore, this method successfully selected genes that were directly associated with diseases.

scholarly journals A Hybrid Energy System Workflow for Energy Portfolio Optimization

Energies ◽  
2021 ◽  
Vol 14 (15) ◽  
pp. 4392
Author(s):  
Jia Zhou ◽  
Hany Abdel-Khalik ◽  
Paul Talbot ◽  
Cristian Rabiti
Keyword(s):  
Time Series ◽  
Gaussian Process ◽  
Electricity Market ◽  
Process Model ◽  
Net Present Value ◽  
Historical Data ◽  
Energy System ◽  
Initial Guess ◽  
Reduced Order ◽  
The Impact

This manuscript develops a workflow, driven by data analytics algorithms, to support the optimization of the economic performance of an Integrated Energy System. The goal is to determine the optimum mix of capacities from a set of different energy producers (e.g., nuclear, gas, wind and solar). A stochastic-based optimizer is employed, based on Gaussian Process Modeling, which requires numerous samples for its training. Each sample represents a time series describing the demand, load, or other operational and economic profiles for various types of energy producers. These samples are synthetically generated using a reduced order modeling algorithm that reads a limited set of historical data, such as demand and load data from past years. Numerous data analysis methods are employed to construct the reduced order models, including, for example, the Auto Regressive Moving Average, Fourier series decomposition, and the peak detection algorithm. All these algorithms are designed to detrend the data and extract features that can be employed to generate synthetic time histories that preserve the statistical properties of the original limited historical data. The optimization cost function is based on an economic model that assesses the effective cost of energy based on two figures of merit: the specific cash flow stream for each energy producer and the total Net Present Value. An initial guess for the optimal capacities is obtained using the screening curve method. The results of the Gaussian Process model-based optimization are assessed using an exhaustive Monte Carlo search, with the results indicating reasonable optimization results. The workflow has been implemented inside the Idaho National Laboratory’s Risk Analysis and Virtual Environment (RAVEN) framework. The main contribution of this study addresses several challenges in the current optimization methods of the energy portfolios in IES: First, the feasibility of generating the synthetic time series of the periodic peak data; Second, the computational burden of the conventional stochastic optimization of the energy portfolio, associated with the need for repeated executions of system models; Third, the inadequacies of previous studies in terms of the comparisons of the impact of the economic parameters. The proposed workflow can provide a scientifically defendable strategy to support decision-making in the electricity market and to help energy distributors develop a better understanding of the performance of integrated energy systems.

Two-dimensional Bayesian inversion of magnetotelluric data using trans-dimensional Gaussian processes

2021 ◽  
Author(s):  
Daniel Blatter ◽  
Anandaroop Ray ◽  
Kerry Key
Keyword(s):  
Gaussian Process ◽  
Process Model ◽  
Field Data ◽  
Probability Distributions ◽  
Bulk Composition ◽  
Computational Cost ◽  
Model Space ◽  
Bayesian Inversion ◽  
Adaptive Finite Element ◽  
Magnetotelluric Data

Summary Bayesian inversion of electromagnetic data produces crucial uncertainty information on inferred subsurface resistivity. Due to their high computational cost, however, Bayesian inverse methods have largely been restricted to computationally expedient 1D resistivity models. In this study, we successfully demonstrate, for the first time, a fully 2D, trans-dimensional Bayesian inversion of magnetotelluric data. We render this problem tractable from a computational standpoint by using a stochastic interpolation algorithm known as a Gaussian process to achieve a parsimonious parametrization of the model vis-a-vis the dense parameter grids used in numerical forward modeling codes. The Gaussian process links a trans-dimensional, parallel tempered Markov chain Monte Carlo sampler, which explores the parsimonious model space, to MARE2DEM, an adaptive finite element forward solver. MARE2DEM computes the model response using a dense parameter mesh with resistivity assigned via the Gaussian process model. We demonstrate the new trans-dimensional Gaussian process sampler by inverting both synthetic and field magnetotelluric data for 2D models of electrical resistivity, with the field data example converging within 10 days on 148 cores, a non-negligible but tractable computational cost. For a field data inversion, our algorithm achieves a parameter reduction of over 32x compared to the fixed parameter grid used for the MARE2DEM regularized inversion. Resistivity probability distributions computed from the ensemble of models produced by the inversion yield credible intervals and interquartile plots that quantitatively show the non-linear 2D uncertainty in model structure. This uncertainty could then be propagated to other physical properties that impact resistivity including bulk composition, porosity and pore-fluid content.

A Novel K-Harmonic Means Clustering Based on Multiple Initial Centers

2013 ◽  
Vol 321-324 ◽  
pp. 1947-1950
Author(s):  
Lei Gu ◽  
Xian Ling Lu
Keyword(s):  
New Method ◽  
Final Solution ◽  
Data Set ◽  
Number Of Clusters ◽  
Multiple Groups ◽  
Harmonic Means

In the initialization of the traditional k-harmonic means clustering, the initial centers are generated randomly and its number is equal to the number of clusters. Although the k-harmonic means clustering is insensitive to the initial centers, this initialization method cannot improve clustering performance. In this paper, a novel k-harmonic means clustering based on multiple initial centers is proposed. The number of the initial centers is more than the number of clusters in this new method. The new method with multiple initial centers can divide the whole data set into multiple groups and combine these groups into the final solution. Experiments show that the presented algorithm can increase the better clustering accuracies than the traditional k-means and k-harmonic methods.

Cutaneous wound healing is impaired in hemophilia B

Blood ◽  
2006 ◽  
Vol 108 (9) ◽  
pp. 3053-3060 ◽  
Author(s):  
Maureane Hoffman ◽  
Anna Harger ◽  
Angela Lenkowski ◽  
Ulla Hedner ◽  
Harold R. Roberts ◽  
...  
Keyword(s):  
Wound Healing ◽  
Time Course ◽  
Ferric Iron ◽  
Hemophilia B ◽  
Cutaneous Wound Healing ◽  
Delayed Wound Healing ◽  
Cutaneous Wound ◽  
Time Points ◽  
Hemophilic Arthropathy ◽  
Healing Wounds

Abstract We used a mouse model to test the hypothesis that the time course and histology of wound healing is altered in hemophilia B. Punch biopsies (3 mm) were placed in the skin of normal mice and mice with hemophilia. The size of the wounds was measured daily until the epidermal defect closed. All wounds closed in mice with hemophilia by 12 days, compared with 10 days in normal animals. Skin from the area of the wound was harvested at different time points and examined histologically. Hemophilic animals developed subcutaneous hematomas; normal animals did not. Macrophage infiltration was significantly delayed in hemophilia B. Unexpectedly, hemophilic mice developed twice as many blood vessels in the healing wounds as controls, and the increased vascularity persisted for at least 2 weeks. The deposition and persistence of ferric iron was also greater in hemophilic mice. We hypothesize that iron plays a role in promoting excess angiogenesis after wounding as it had been proposed to do in hemophilic arthropathy. We have demonstrated that impaired coagulation leads to delayed wound healing with abnormal histology. Our findings have significant implications for treatment of patients with hemophilia, and also highlight the importance of rapidly establishing hemostasis following trauma or surgery.

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