The Experts below are selected from a list of 2069082 Experts worldwide ranked by ideXlab platform
Richard W Katz - One of the best experts on this subject based on the ideXlab platform.
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extremes 2 0 an extreme Value Analysis package in r
Journal of Statistical Software, 2016Co-Authors: Eric Gilleland, Richard W KatzAbstract:This article describes the extreme Value Analysis (EVA) R package extRemes version 2.0, which is completely redesigned from previous versions. The functions primarily provide utilities for implementing univariate EVA, with a focus on weather and climate applications, including the incorporation of covariates, as well as some functionality for assessing bivariate tail dependence.
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non stationary extreme Value Analysis in a changing climate
Climatic Change, 2014Co-Authors: Linyin Cheng, Amir Aghakouchak, Eric Gilleland, Richard W KatzAbstract:This paper introduces a framework for estimating stationary and non-stationary return levels, return periods, and risks of climatic extremes using Bayesian inference. This framework is implemented in the Non-stationary Extreme Value Analysis (NEVA) software package, explicitly designed to facilitate Analysis of extremes in the geosciences. In a Bayesian approach, NEVA estimates the extreme Value parameters with a Differential Evolution Markov Chain (DE-MC) approach for global optimization over the parameter space. NEVA includes posterior probability intervals (uncertainty bounds) of estimated return levels through Bayesian inference, with its inherent advantages in uncertainty quantification. The software presents the results of non-stationary extreme Value Analysis using various exceedance probability methods. We evaluate both stationary and non-stationary components of the package for a case study consisting of annual temperature maxima for a gridded global temperature dataset. The results show that NEVA can reliably describe extremes and their return levels.
Linyin Cheng - One of the best experts on this subject based on the ideXlab platform.
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a generalized framework for process informed nonstationary extreme Value Analysis
Advances in Water Resources, 2019Co-Authors: Elisa Ragno, Linyin Cheng, Amir Aghakouchak, Mojtaba SadeghAbstract:Abstract Evolving climate conditions and anthropogenic factors, such as CO2 emissions, urbanization and population growth, can cause changes in weather and climate extremes. Most current risk assessment models rely on the assumption of stationarity (i.e., no temporal change in statistics of extremes). Most nonstationary modeling studies focus primarily on changes in extremes over time. Here, we present Process-informed Nonstationary Extreme Value Analysis (ProNEVA) as a generalized tool for incorporating different types of physical drivers (i.e., underlying processes), stationary and nonstationary concepts, and extreme Value Analysis methods (i.e., annual maxima, peak-over-threshold). ProNEVA builds upon a newly-developed hybrid evolution Markov Chain Monte Carlo (MCMC) approach for numerical parameters estimation and uncertainty assessment. This offers more robust uncertainty estimates of return periods of climatic extremes under both stationary and nonstationary assumptions. ProNEVA is designed as a generalized tool allowing using different types of data and nonstationarity concepts physically-based or purely statistical) into account. In this paper, we show a wide range of applications describing changes in: annual maxima river discharge in response to urbanization, annual maxima sea levels over time, annual maxima temperatures in response to CO2 emissions in the atmosphere, and precipitation with a peak-over-threshold approach. ProNEVA is freely available to the public and includes a user-friendly Graphical User Interface (GUI) to enhance its implementation.
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non stationary extreme Value Analysis in a changing climate
Climatic Change, 2014Co-Authors: Linyin Cheng, Amir Aghakouchak, Eric Gilleland, Richard W KatzAbstract:This paper introduces a framework for estimating stationary and non-stationary return levels, return periods, and risks of climatic extremes using Bayesian inference. This framework is implemented in the Non-stationary Extreme Value Analysis (NEVA) software package, explicitly designed to facilitate Analysis of extremes in the geosciences. In a Bayesian approach, NEVA estimates the extreme Value parameters with a Differential Evolution Markov Chain (DE-MC) approach for global optimization over the parameter space. NEVA includes posterior probability intervals (uncertainty bounds) of estimated return levels through Bayesian inference, with its inherent advantages in uncertainty quantification. The software presents the results of non-stationary extreme Value Analysis using various exceedance probability methods. We evaluate both stationary and non-stationary components of the package for a case study consisting of annual temperature maxima for a gridded global temperature dataset. The results show that NEVA can reliably describe extremes and their return levels.
Per Jemth - One of the best experts on this subject based on the ideXlab platform.
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Conserved nucleation sites reinforce the significance of Phi Value Analysis in protein-folding studies.
IUBMB Life, 2014Co-Authors: Stefano Gianni, Per JemthAbstract:The only experimental strategy to address the structure of folding transition states, the so-called Φ Value Analysis, relies on the synergy between site directed mutagenesis and the measurement of reaction kinetics. Despite its importance, the Φ Value Analysis has been often criticized and its power to pinpoint structural information has been questioned. In this hypothesis, we demonstrate that comparing the Φ Values between proteins not only allows highlighting the robustness of folding pathways but also provides per se a strong validation of the method.
Peter R Waylen - One of the best experts on this subject based on the ideXlab platform.
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investigating teleconnection drivers of bivariate heat waves in florida using extreme Value Analysis
Climate Dynamics, 2015Co-Authors: David Keellings, Peter R WaylenAbstract:Maximum and minimum daily temperatures from the second half of the twentieth century are examined using a high resolution dataset of 833 grid cells across the state of Florida. A bivariate extreme Value Analysis point process approach is used to model characteristics including the frequency, magnitude, duration, and timing of periods or heat waves during which both daily maximum and minimum temperatures exceed their respective 90th percentile thresholds. The temperature dataset is combined with indices of the El Nino-Southern Oscillation (ENSO) and the Atlantic multi-decadal oscillation (AMO) to explore the influence of these oscillations on heat wave characteristics in Florida. In order to investigate the influence of a time varying signal (ENSO and AMO) on heat waves the signals are introduced into non-stationary models as covariates in the location and log-transformed scale parameters. The improvements to the model obtained by introducing covariates are examined using the deviance statistic whereby the difference in negative log-likelihood Values between two models is tested for significance using a Chi squared distribution. Significant improvements in the non-stationary models with ENSO and AMO covariates indicate spatially varying impacts in the frequency, magnitude, and duration of heat waves. In particular, the warm phase of the AMO brings heat waves earlier in the summertime while also increasing their magnitude, frequency, and duration.
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increased risk of heat waves in florida characterizing changes in bivariate heat wave risk using extreme Value Analysis
Applied Geography, 2014Co-Authors: David Keellings, Peter R WaylenAbstract:Maximum and minimum daily temperatures from the second half of the 20th century are examined using a high resolution dataset of 833 grid cells across the state of Florida. A bivariate Extreme Value Analysis Point Process approach is used to model characteristics including the frequency, magnitude, duration, and timing of periods or heat waves during which both daily maximum and minimum temperatures exceed their respective 90th percentile thresholds. Variability in heat wave characteristics is examined across the state to give an indication of those areas where heat waves with certain characteristics may be more likely to occur. Changes in heat wave characteristics through time are examined by halving the temperature record and determining changes to heat wave characteristics between the two periods. This exploration of changes in heat wave risk through time gives a possible suggestion of trends in future heat wave risk. Findings indicate that there is considerable spatial variability in heat wave characteristics although heat waves have become increasingly frequent and intense throughout much of the state.
Alan R Fersht - One of the best experts on this subject based on the ideXlab platform.
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φ Value Analysis and the nature of protein folding transition states
Proceedings of the National Academy of Sciences of the United States of America, 2004Co-Authors: Alan R Fersht, Satoshi SatoAbstract:Abstract Φ Values are used to map structures of protein-folding transition states from changes in free energies of denaturation (ΔΔG D-N) and activation on mutation. A recent reappraisal proposed that Φ Values for ΔΔG D-N 1.7 kcal/mol are often found for large side chains that make dispersed tertiary interactions, especially in hydrophobic cores that are in the process of being formed in the transition state. Conversely, specific local interactions that probe secondary structure tend to have ΔΔG D-N ≈ 0.5–2 kcal/mol. Discarding Φ Values from lower-energy changes discards the crucial information about local interactions and makes transition states appear uniformly diffuse by overemphasizing the dispersed tertiary interactions. The evidence for the 1.7 kcal/mol cutoff was based on mutations that had been deliberately designed to be unsuitable for Φ-Value Analysis because they are structurally disruptive. We confirm that reliable Φ Values can be derived from the recommended mutations in suitable proteins with 0.6 < ΔΔG D-N < 1.7 kcal/mol, and there are many reliable high Φ Values. Transition states vary from being rather diffuse to being well formed with islands of near-complete secondary structure. We also confirm that the structures of transition-state ensembles can be perturbed by mutations with ΔΔG D-N » 2 kcal/mol and that protein-folding transition states do move on the energy surface on mutation. barnase protein A nucleation–condensation framework Hammond
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combined molecular dynamics and φ Value Analysis of structure reactivity relationships in the transition state and unfolding pathway of barnase structural basis of hammond and anti hammond effects
Journal of the American Chemical Society, 1998Co-Authors: Valerie Daggett, And Aijun Li, Alan R FershtAbstract:The folding/unfolding pathway of barnase has been analyzed using a method similar to the classical Bronsted-β approach: Φ-Value Analysis. Kinetic and equilibrium measurements on the folding/unfolding of over 100 designed mutants have led to a residue-by-residue description of the transition state. The transition state responds to mutation and changes in solvent in a manner analogous to both classical Hammond and anti-Hammond behavior as the energy landscape is perturbed. Here, we compare the Φ-Value Analysis with an explicit structural Analysis of the transition state by molecular dynamics simulations of thermal denaturation of wild-type and two mutant forms of barnase. We look for similarities in the results of experiment and simulation to provide a detailed and reliable description of the folding reaction and for differences that could point to deficiencies in the methods. In general, there is excellent agreement between simulation and experiment, with a correlation coefficient of 0.93 between observed...
