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Search Results: 1 - 10 of 211083 matches for " William N. Rust "
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Parallel Bayesian Additive Regression Trees
Matthew T. Pratola,Hugh A. Chipman,James R. Gattiker,David M. Higdon,Robert McCulloch,William N. Rust
Statistics , 2013,
Abstract: Bayesian Additive Regression Trees (BART) is a Bayesian approach to flexible non-linear regression which has been shown to be competitive with the best modern predictive methods such as those based on bagging and boosting. BART offers some advantages. For example, the stochastic search Markov Chain Monte Carlo (MCMC) algorithm can provide a more complete search of the model space and variation across MCMC draws can capture the level of uncertainty in the usual Bayesian way. The BART prior is robust in that reasonable results are typically obtained with a default prior specification. However, the publicly available implementation of the BART algorithm in the R package BayesTree is not fast enough to be considered interactive with over a thousand observations, and is unlikely to even run with 50,000 to 100,000 observations. In this paper we show how the BART algorithm may be modified and then computed using single program, multiple data (SPMD) parallel computation implemented using the Message Passing Interface (MPI) library. The approach scales nearly linearly in the number of processor cores, enabling the practitioner to perform statistical inference on massive datasets. Our approach can also handle datasets too massive to fit on any single data repository.
Spatiotemporal Modeling of Node Temperatures in Supercomputers
Curtis B Storlie,Brian J Reich,William N Rust,Lawrence O Ticknor,Amanda M Bonnie,Andrew J Montoya,Sarah E Michalak
Statistics , 2015,
Abstract: Los Alamos National Laboratory (LANL) is home to many large supercomputing clusters. These clusters require an enormous amount of power ($\sim$500-2000 kW each), and most of this energy is converted into heat. Thus, cooling the components of the supercomputer becomes a critical and expensive endeavor. Recently a project was initiated to optimize the cooling system used to cool one of the rooms housing three of these large clusters and develop a general good-practice procedure for reducing cooling costs and monitoring other machine rooms. This work focuses on the statistical approach used to quantify the effect that several cooling changes to the room had on the temperatures of the individual nodes of the computers. The largest cluster in the room has 1600 nodes that run a variety of jobs during general use. Since extremes temperatures are important, a Normal distribution plus generalized Pareto distribution for the upper tail is used to model the marginal distribution, along with a Gaussian process copula to account for spatio-temporal dependence. A Gaussian Markov random field (GMRF) model is used to model the spatial and/or temporal effects on the node temperatures as the cooling changes take place. This model is then used to assess the condition of the node temperatures after each change to the room. The analysis approach was used to uncover the cause of a problematic episode of overheating nodes on one of the supercomputing clusters. The next step is to also use the model to estimate the trend in node temperatures due to an increase in supply air temperature and ultimately decide when any further temperature increases would become unsafe. This same process can be applied to reduce the cooling expenses for other data centers as well.
Analyzing neural responses to natural signals: Maximally informative dimensions
Tatyana Sharpee,Nicole C. Rust,William Bialek
Physics , 2002,
Abstract: We propose a method that allows for a rigorous statistical analysis of neural responses to natural stimuli which are non-Gaussian and exhibit strong correlations. We have in mind a model in which neurons are selective for a small number of stimulus dimensions out of a high dimensional stimulus space, but within this subspace the responses can be arbitrarily nonlinear. Existing analysis methods are based on correlation functions between stimuli and responses, but these methods are guaranteed to work only in the case of Gaussian stimulus ensembles. As an alternative to correlation functions, we maximize the mutual information between the neural responses and projections of the stimulus onto low dimensional subspaces. The procedure can be done iteratively by increasing the dimensionality of this subspace. Those dimensions that allow the recovery of all of the information between spikes and the full unprojected stimuli describe the relevant subspace. If the dimensionality of the relevant subspace indeed is small, it becomes feasible to map the neuron's input-output function even under fully natural stimulus conditions. These ideas are illustrated in simulations on model visual and auditory neurons responding to natural scenes and sounds, respectively.
Maximally informative dimensions: Analyzing neural responses to natural signals
Tatyana Sharpee,Nicole C. Rust,William Bialek
Physics , 2002,
Abstract: We propose a method that would allow for a rigorous statistical analysis of neural responses to natural stimuli, which are non-Gaussian and exhibit strong correlations. We have in mind a model in which neurons are selective for a small number of stimulus dimensions out of the high dimensional stimulus space, but within this subspace the responses can be arbitrarily nonlinear. Existing analysis methods are based on correlation functions between stimuli and responses, but these methods are guaranteed to work only in the case of Gaussian stimulus ensembles. As an alternative to correlation functions, we maximize the mutual information between the neural responses and projections of the stimulus onto low dimensional subspaces. The procedure can be done iteratively by increasing the dimensionality of this subspace. Those dimensions that allow the recovery of all of the information between spikes and the full unprojected stimuli describe the relevant subspace. If the dimensionality of the relevant subspace indeed is small, it becomes feasible to map out the neuron's input-output function even under fully natural stimulus conditions. These ideas are illustrated in simulations on model visual neurons responding to natural scenes.
Helicity transport in a simulated coronal mass ejection
B. Kliem,S. Rust,N. Seehafer
Physics , 2010, DOI: 10.1017/S1743921311006715
Abstract: It has been suggested that coronal mass ejections (CMEs) remove the magnetic helicity of their coronal source region from the Sun. Such removal is often regarded to be necessary due to the hemispheric sign preference of the helicity, which inhibits a simple annihilation by reconnection between volumes of opposite chirality. Here we monitor the relative magnetic helicity contained in the coronal volume of a simulated flux rope CME, as well as the upward flux of relative helicity through horizontal planes in the simulation box. The unstable and erupting flux rope carries away only a minor part of the initial relative helicity; the major part remains in the volume. This is a consequence of the requirement that the current through an expanding loop must decrease if the magnetic energy of the configuration is to decrease as the loop rises, to provide the kinetic energy of the CME.
The Promise of Integrins as Effective Targets for Anticancer Agents
William L. Rust,Stephen W. Carper,George E. Plopper
Journal of Biomedicine and Biotechnology , 2002, DOI: 10.1155/s1110724302204015
Abstract: This review will briefly describe integrin function, address why integrins are attractive targets for chemotherapeutic drug design, and discuss some ongoing studies aimed at inhibiting integrin activity. Integrins are cell surface heterodimeric receptors. They modulate many cellular processes including: growth, death (apoptosis), adhesion, migration, and invasion by activating several signaling pathways. Many potential chemotherapeutic agents target integrins directly (eg, polypeptides, monoclonal antibodies, adenovirus vectors). These agents may be clinically useful in controlling the metastatic spread of cancer.
The Promise of Integrins as Effective Targets for Anticancer Agents
Rust William L.,Carper Stephen W.,Plopper George E.
Journal of Biomedicine and Biotechnology , 2002,
Abstract: This review will briefly describe integrin function, address why integrins are attractive targets for chemotherapeutic drug design, and discuss some ongoing studies aimed at inhibiting integrin activity. Integrins are cell surface heterodimeric receptors. They modulate many cellular processes including: growth, death (apoptosis), adhesion, migration, and invasion by activating several signaling pathways. Many potential chemotherapeutic agents target integrins directly (eg, polypeptides, monoclonal antibodies, adenovirus vectors). These agents may be clinically useful in controlling the metastatic spread of cancer.
The molecular mechanism for DDT detoxification in Anopheles gambiae: A molecular docking study  [PDF]
William N. Setzer
Journal of Biophysical Chemistry (JBPC) , 2011, DOI: 10.4236/jbpc.2011.22016
Abstract: The epsilon class glutathione-S-transferase of Anopheles gambiae, agGSTe2, is capable of metabolizing DDT. A molecular docking analysis of DDT with agGSTe2 support an E2 elimination mechanism wherein the glutathione sulfur serves as the base to convert DDT to DDE.
Perillyl Alcohol Inhibits Breast Cell Migration without Affecting Cell Adhesion
Johanna E. Wagner,Janice L. Huff,William L. Rust,Karl Kingsley,George E. Plopper
Journal of Biomedicine and Biotechnology , 2002, DOI: 10.1155/s1110724302207020
Abstract: The monoterpene d-limonene exhibits chemotherapeutic and chemopreventive potential in breast cancer patients. D-limonene and its related compounds, perillyl alcohol and perillyl aldehyde, were chosen as candidate drugs for application in a screen for nontoxic inhibitors of cell migration. Using the nontumorigenic human breast cell line MCF-10A, we delineated the toxicity as greatest for the perillyl aldehyde, intermediate for perillyl alcohol, and least for limonene. A noncytotoxic concentration of 0.5 mmol/L perillyl alcohol inhibited the migration, while the same concentration of limonene failed to do so. Adhesion of the MCF-10A cell line and the human breast cancer cell line MDA-MB 435 to fibronectin was unaffected by 1.5 mmol/L perillyl alcohol. 0.4 mmol/L perillyl alcohol inhibited the growth of MDA-MB 435 cells. All migration-inhibiting concentrations of perillyl alcohol for MDA-MB 435 cells proved to be toxic. These results suggest that subtoxic doses of perillyl alcohol may have prophylactic potential in the treatment of breast cancer.
Comparison of ELF, FibroTest and FibroScan for the non-invasive assessment of liver fibrosis
Mireen Friedrich-Rust, William Rosenberg, Julie Parkes, Eva Herrmann, Stefan Zeuzem, Christoph Sarrazin
BMC Gastroenterology , 2010, DOI: 10.1186/1471-230x-10-103
Abstract: In the present study, the ELF-test was analyzed retrospectively in patients with chronic liver disease, who received a liver biopsy, transient elastography (TE) and the FibroTest using histology as the reference method. Histology was classified according to METAVIR and the Ludwig's classification (F0-F4) for patients with chronic hepatitis C and B virus (HCV, HBV) infection and primary biliary cirrhosis (PBC), respectively.Seventy-four patients were analysed: 36 with HCV, 10 with HBV, and 28 with PBC. The accuracy (AUROC) for the diagnosis of significant fibrosis (F≥2) for ELF and FibroTest was 0.78 (95%CI:0.67-0.89) and 0.69 (95%-CI:0.57-0.82), respectively (difference not statistically significant, n.s.). The AUROC for the diagnosis of liver cirrhosis was 0.92 (95%CI:0.83-1,00), and 0.91 (95%CI:0.83-0.99), respectively (n.s.). For 66 patients with reliable TE measurements the AUROC for the diagnosis of significant fibrosis (cirrhosis) for TE, ELF and FT were 0.80 (0.94), 0.76 (0.92), and 0.67 (0.91), respectively (n.s.).FibroTest and ELF can be performed with comparable diagnostic accuracy for the non-invasive staging of liver fibrosis. Serum tests are informative in a higher proportion of patients than transient elastography.For different causes of chronic liver disease assessment of liver fibrosis is important to estimate the prognosis and to determine surveillance strategies for liver cancer. In addition, for chronic viral hepatitis the degree of liver fibrosis is one important parameter for decision on antiviral therapy [1]. At present, liver biopsy is still most commonly used as reference standard for the assessment of liver fibrosis. However, it is an invasive method associated with patient discomfort and in rare cases with serious complications [2]. In addition, the accuracy of liver biopsy is limited due to sampling error and significant intra- and inter-observer variability in histological staging [3,4]. Therefore, research has focused on the evaluation o
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