1,721,131 research outputs found
Replication Data for: Bird’s Decision to Shift the Direction of Migration Path Depends on the Position of Sun as well as Moon: A Directional Statistical Inference
No full textDataset for: Bird’s Decision to Shift the Direction of Migration
Path Depends on the Position of the Sun as well as Moon:
A Directional Statistical Inference
(Author: Prithwish Ghosh, Debashis Chatterjee, Amlan Banerjee
STRUCTURAL BASIS OF THE FUNCTIONAL ROLES OF HUMAN AROMATASE
Full text linkHumanaromatase(AROM) catalyzes theconversion of androgens to estrogensand is a major breast cancer drug target. Structural investigation has provided insights intothe active siteandaromatization mechanism.Utilization of the structural data has permitted rational design of a series of novel steroidal inhibitors. Investigation ofthe roles of key amino acids is facilitated by a recombinant AROM identical in crystal structureto the placental AROM.We use mutagenesis, chromatography, ultracentrifugation, spectrophotometry, enzyme kinetics, and X-ray crystallography to probe the roles of critical residues and the molecular basis of oligomerization. Furthermore, weevaluate the potencies of novel inhibitors and determine the structural basis of inhibition andselectivity. A critical active site residue D309 withan elevated pKa remainsprotonated at neutral pHand facilitatessubstrate binding and catalysis.The “gatekeeper” R192, linked to D309 via a watermolecule, is postulated to have a role in proton relay and substrate selectivity. D309N and R192Q mutants are virtually inactive supporting thehypothesis that both play keyrolesin aromatization.AROM oligomerization is driven bytheD-E loop of one moleculeand heme-proximal region of another via hydrogen bonding, electrostatic interactions between E181 and K440, and shape complementarity.Del7, generated by deletionof 7 residues in the D-E loop, experiences 65% reductionin activitydue to the loss of oligomer formation. Mutants Del4, E181A, and E181K exhibit normal enzymatic activity,and maintain some oligomeric interactions. The heme-proximal interface is also the putative coupling site of the reductasethatsupplieselectronsfor aromatization. The siteis larger than the active site, and at least twice aslarge asother P450s.MutantsK440Qand Y361Fof this region are virtuallyinactive.Collectivelythe results suggestfunctional significanceof oligomerization. Several newly designedAIs are superiortoexemestane, the steroidal AI currently used as a drug, in inhibition and anti-proliferation assays. The C6β-(pent-2-yn-1-yloxy) side chains ofthe most potent compoundspenetrate the access channelunique to AROM and havethe sameconformation asin the enzyme-free state.Astructural-based approachcan improve drug efficacy by improving specificity and selectivity, and reducing sideeffects.NAUpstate Medical UniversityPharmacologyPh
Combining multiple biomarker models in logistic regression and survival analysis: Model combining and instability measure.
No full textIn medical research, there is great interest in developing methods for combining multiple biomarkers to predict clinical outcomes. We argue that the selection of markers should be considered. A major concern of selection methods is that uncer tainty in variable selection process is ignored, which could lead to poor prediction. An alternative approach is to combine predictions from multiple models, called model combining. The primary interest of this dissertation is to combine the information from multiple biomarkers using model combining methods. In Chapter 2, we consider binary outcomes. A model combining method, Adaptive Regression by Mixing with Screening (ARMS), is proposed for the classification of binary outcomes in logistic regression. It works by considering weighted combinations of logistic regression models; five different weighting schemes are developed. The weights and method are justified using decision theory and risk bound results. Chapter 3 is concerned with censored survival outcomes. We propose a model combining method, Adaptive Regression by Mixing with Adaptive Screening (ARMAS), for survival data. It works by considering weighted combinations of Cox survival models. To reduce the set of models for combining in high-dimensional data, we propose an adaptive screening procedure in ARMAS based on the idea of adaptive model selection. In addition, we propose a modified version of ARMAS based on the imputed data from multiple imputations, called ARMAS-impute, which does not reply on the proportional hazards assumption. Chapter 4 is devoted to the instability measure and shrinkage estimation for model combining in logistic regression. We propose an instability measure to capture the uncertainty of model selection procedures, called GDF Instability (GDFI), based on the concept of generalized degrees of freedom (GDF). A rule of thumb for the use of the GDFI is suggested. We also propose a shrinkage version of ARMS, ARMS-shrink, applying shrinkage estimation into the ARMS algorithm. All methods are assessed using simulation studies and illustrated with an application to a dataset in prostate cancer.PhDBiological SciencesBiostatisticsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/126601/2/3253441.pd
Corrigendum to 'Pilot study on arsenic removal from groundwater using a small-scale reverse osmosis system—Towards sustainable drinking water production' [J. Hazard. Mater. 318 (2016) 671–678]
No full textRefers to Stefan-Andre Schmidt, Ephraim Gukelberger, Mario Hermann, Florian Fiedler, Benjamin Grossmann, Jan Hoinkis, Ashok Ghosh, Debashis Chatterjee, Jochen Bundschuh
Pilot study on arsenic removal from groundwater using a small-scale reverse osmosis system—Towards sustainable drinking water production, Journal of Hazardous Materials, Volume 318, 15 November 2016, Pages 671-678
Semiparametric regression for multi-dimensional data: Kernel machines and mixed -effects models.
No full textThis dissertation focuses on the kernel machine semiparametric regression of multidimensional data. It consists of three related papers. Chapter II is concerned with continuous outcomes. We propose a semiparametric regression model to relate the outcome to clinical covariates and gene expression profiles, where the clinical effects are modelled parametrically while the genetic effects are modelled nonparametrically using least square kernel machines (LSKMs). With the equivalent relationship between the primal and dual problems of a constrained optimization problem, we show that the dual problem derived from the primal problem of the LSKMs can be formulated using a linear mixed model. The estimates hence can proceed within the linear mixed model framework. The regression coefficients and nonparametric function can be obtained using the Best Linear Unbiased Predictor (BLUP), and the regularization and kernel parameters can be estimated as variance components using REML. A score test is developed to test for the nonparametric gene expression effect. The methods are illustrated using a prostate cancer data set and evaluated using simulation studies. In Chapter III we consider binary outcomes. A semiparametric logistic regression model is proposed to relate the outcome to clinical covariates and gene expression profiles, where the clinical effects are modelled parametrically while the genetic effects are modelled nonparametrically using the kernel machines. We show that the model parameters and nonparametric functions can be estimated using penalized quasi-likelihood (PQL) under a generalized linear mixed model framework. The methods are demonstrated using a prostate cancer data set, and their performance is evaluated through simulation studies. Chapter IV is devoted to the equivalent kernel derivation of the Gaussian LSKM. We first show that this LSKM is equivalent to radial basis function neural network. Then we obtain the equivalent kernel under the neural network formulation. The equivalent kernel result makes clear the roles played by the regularization and kernel parameters. The performance of the equivalent kernel approximation is evaluated using simulated data set. This dissertation ends with Chapter V, which gives concluding remarks and future research extensions.PhDBiological SciencesBiostatisticsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/125427/2/3192708.pd
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
A Bayesian method for finding interactions.
No full textIn genomic studies, datasets with a small sample size and a large number of potential predictors are common. Recently, gene-gene interactions (epistasis) and gene-environment interactions have been drawing increasing attention due to the etiology of complex diseases. If all possible pair wise interactions are to be explored, then this leads to a high dimensional model space. There is very little work to handle this common problem. The emphasis of my research is on selecting interactions and controlling the number of falsely discovered predictors with a limited sample size. The method I propose simultaneously satisfies the two properties for inclusion of interactions: interpretability and discovery. In addition, I develop a novel equivalence between variable selection procedures and the false discovery rate. One application of my research is the development of a model to aid the therapeutic decision by identifying prognostic factors or interactions among abundant variables from the clinical and molecular profiles of patients. Given a patient's profile, an optimal treatment involves a trade-off between efficacy and toxicity. My research also proposes a novel way to compare treatments with multiple endpoints.PhDBiological SciencesBiostatisticsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/126120/2/3237929.pd
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
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