Capturing spatial and temporal patterns of widespread, extreme flooding across Europe

NASA Astrophysics Data System (ADS)

Busby, Kathryn; Raven, Emma; Liu, Ye

2013-04-01

Statistical characterisation of physical hazards is an integral part of probabilistic catastrophe models used by the reinsurance industry to estimate losses from large scale events. Extreme flood events are not restricted by country boundaries which poses an issue for reinsurance companies as their exposures often extend beyond them. We discuss challenges and solutions that allow us to appropriately capture the spatial and temporal dependence of extreme hydrological events on a continental-scale, which in turn enables us to generate an industry-standard stochastic event set for estimating financial losses for widespread flooding. By presenting our event set methodology, we focus on explaining how extreme value theory (EVT) and dependence modelling are used to account for short, inconsistent hydrological data from different countries, and how to make appropriate statistical decisions that best characterise the nature of flooding across Europe. The consistency of input data is of vital importance when identifying historical flood patterns. Collating data from numerous sources inherently causes inconsistencies and we demonstrate our robust approach to assessing the data and refining it to compile a single consistent dataset. This dataset is then extrapolated using a parameterised EVT distribution to estimate extremes. Our method then captures the dependence of flood events across countries using an advanced multivariate extreme value model. Throughout, important statistical decisions are explored including: (1) distribution choice; (2) the threshold to apply for extracting extreme data points; (3) a regional analysis; (4) the definition of a flood event, which is often linked with reinsurance industry's hour's clause; and (5) handling of missing values. Finally, having modelled the historical patterns of flooding across Europe, we sample from this model to generate our stochastic event set comprising of thousands of events over thousands of years. We then briefly illustrate how this is applied within a probabilistic model to estimate catastrophic loss curves used by the reinsurance industry.

Understanding perception of active noise control system through multichannel EEG analysis.

PubMed

Bagha, Sangeeta; Tripathy, R K; Nanda, Pranati; Preetam, C; Das, Debi Prasad

2018-06-01

In this Letter, a method is proposed to investigate the effect of noise with and without active noise control (ANC) on multichannel electroencephalogram (EEG) signal. The multichannel EEG signal is recorded during different listening conditions such as silent, music, noise, ANC with background noise and ANC with both background noise and music. The multiscale analysis of EEG signal of each channel is performed using the discrete wavelet transform. The multivariate multiscale matrices are formulated based on the sub-band signals of each EEG channel. The singular value decomposition is applied to the multivariate matrices of multichannel EEG at significant scales. The singular value features at significant scales and the extreme learning machine classifier with three different activation functions are used for classification of multichannel EEG signal. The experimental results demonstrate that, for ANC with noise and ANC with noise and music classes, the proposed method has sensitivity values of 75.831% ( p < 0.001 ) and 99.31% ( p < 0.001 ), respectively. The method has an accuracy value of 83.22% for the classification of EEG signal with music and ANC with music as stimuli. The important finding of this study is that by the introduction of ANC, music can be better perceived by the human brain.

Adaptation potential of naturally ventilated barns to high temperature extremes: The OptiBarn project

NASA Astrophysics Data System (ADS)

Menz, Christoph

2016-04-01

Climate change interferes with various aspects of the socio-economic system. One important aspect is its influence on animal husbandry, especially dairy faming. Dairy cows are usually kept in naturally ventilated barns (NVBs) which are particular vulnerable to extreme events due to their low adaptation capabilities. An effective adaptation to high outdoor temperatures for example, is only possible under certain wind and humidity conditions. High temperature extremes are expected to increase in number and strength under climate change. To assess the impact of this change on NVBs and dairy cows also the changes in wind and humidity needs to be considered. Hence we need to consider the multivariate structure of future temperature extremes. The OptiBarn project aims to develop sustainable adaptation strategies for dairy housings under climate change for Europe, by considering the multivariate structure of high temperature extremes. In a first step we identify various multivariate high temperature extremes for three core regions in Europe. With respect to dairy cows in NVBs we will focus on the wind and humidity field during high temperature events. In a second step we will use the CORDEX-EUR-11 ensemble to evaluate the capability of the RCMs to model such events and assess their future change potential. By transferring the outdoor conditions to indoor climate and animal wellbeing the results of this assessment can be used to develop technical, architectural and animal specific adaptation strategies for high temperature extremes.

Salivary Telomere Length and Lung Function in Adolescents Born Very Preterm: A Prospective Multicenter Study.

PubMed

Hadchouel, Alice; Marchand-Martin, Laetitia; Franco-Montoya, Marie-Laure; Peaudecerf, Laetitia; Ancel, Pierre-Yves; Delacourt, Christophe

2015-01-01

Preterm birth is associated with abnormal respiratory functions throughout life. The mechanisms underlying these long-term consequences are still unclear. Shortening of telomeres was associated with many conditions, such as chronic obstructive pulmonary disease. We aimed to search for an association between telomere length and lung function in adolescents born preterm. Lung function and telomere length were measured in 236 adolescents born preterm and 38 born full-term from the longitudinal EPIPAGE cohort. Associations between telomere length and spirometric indices were tested in univariate and multivariate models accounting for confounding factors in the study population. Airflows were significantly lower in adolescents born preterm than controls; forced expiratory volume in one second was 12% lower in the extremely preterm born group than controls (p<0.001). Lower birth weight, bronchopulmonary dysplasia and postnatal sepsis were significantly associated with lower airflow values. Gender was the only factor that was significantly associated with telomere length. Telomere length correlated with forced expiratory flow 25-75 in the extremely preterm adolescent group in univariate and multivariate analyses (p = 0.01 and p = 0.02, respectively). We evidenced an association between telomere length and abnormal airflow in a population of adolescents born extremely preterm. There was no evident association with perinatal events. This suggests other involved factors, such as a continuing airway oxidative stress leading to persistent inflammation and altered lung function, ultimately increasing susceptibility to chronic obstructive pulmonary disease.

Studying Resist Stochastics with the Multivariate Poisson Propagation Model

DOE PAGES

Naulleau, Patrick; Anderson, Christopher; Chao, Weilun; ...

2014-01-01

Progress in the ultimate performance of extreme ultraviolet resist has arguably decelerated in recent years suggesting an approach to stochastic limits both in photon counts and material parameters. Here we report on the performance of a variety of leading extreme ultraviolet resist both with and without chemical amplification. The measured performance is compared to stochastic modeling results using the Multivariate Poisson Propagation Model. The results show that the best materials are indeed nearing modeled performance limits.

Physical Function in Older Men With Hyperkyphosis

PubMed Central

Harrison, Stephanie L.; Fink, Howard A.; Marshall, Lynn M.; Orwoll, Eric; Barrett-Connor, Elizabeth; Cawthon, Peggy M.; Kado, Deborah M.

2015-01-01

Background. Age-related hyperkyphosis has been associated with poor physical function and is a well-established predictor of adverse health outcomes in older women, but its impact on health in older men is less well understood. Methods. We conducted a cross-sectional study to evaluate the association of hyperkyphosis and physical function in 2,363 men, aged 71–98 (M = 79) from the Osteoporotic Fractures in Men Study. Kyphosis was measured using the Rancho Bernardo Study block method. Measurements of grip strength and lower extremity function, including gait speed over 6 m, narrow walk (measure of dynamic balance), repeated chair stands ability and time, and lower extremity power (Nottingham Power Rig) were included separately as primary outcomes. We investigated associations of kyphosis and each outcome in age-adjusted and multivariable linear or logistic regression models, controlling for age, clinic, education, race, bone mineral density, height, weight, diabetes, and physical activity. Results. In multivariate linear regression, we observed a dose-related response of worse scores on each lower extremity physical function test as number of blocks increased, p for trend ≤.001. Using a cutoff of ≥4 blocks, 20% (N = 469) of men were characterized with hyperkyphosis. In multivariate logistic regression, men with hyperkyphosis had increased odds (range 1.5–1.8) of being in the worst quartile of performing lower extremity physical function tasks (p < .001 for each outcome). Kyphosis was not associated with grip strength in any multivariate analysis. Conclusions. Hyperkyphosis is associated with impaired lower extremity physical function in older men. Further studies are needed to determine the direction of causality. PMID:25431353

Modeling the Pineapple Express phenomenon via Multivariate Extreme Value Theory

NASA Astrophysics Data System (ADS)

Weller, G.; Cooley, D. S.

2011-12-01

The pineapple express (PE) phenomenon is responsible for producing extreme winter precipitation events in the coastal and mountainous regions of the western United States. Because the PE phenomenon is also associated with warm temperatures, the heavy precipitation and associated snowmelt can cause destructive flooding. In order to study impacts, it is important that regional climate models from NARCCAP are able to reproduce extreme precipitation events produced by PE. We define a daily precipitation quantity which captures the spatial extent and intensity of precipitation events produced by the PE phenomenon. We then use statistical extreme value theory to model the tail dependence of this quantity as seen in an observational data set and each of the six NARCCAP regional models driven by NCEP reanalysis. We find that most NCEP-driven NARCCAP models do exhibit tail dependence between daily model output and observations. Furthermore, we find that not all extreme precipitation events are pineapple express events, as identified by Dettinger et al. (2011). The synoptic-scale atmospheric processes that drive extreme precipitation events produced by PE have only recently begun to be examined. Much of the current work has focused on pattern recognition, rather than quantitative analysis. We use daily mean sea-level pressure (MSLP) fields from NCEP to develop a "pineapple express index" for extreme precipitation, which exhibits tail dependence with our observed precipitation quantity for pineapple express events. We build a statistical model that connects daily precipitation output from the WRFG model, daily MSLP fields from NCEP, and daily observed precipitation in the western US. Finally, we use this model to simulate future observed precipitation based on WRFG output driven by the CCSM model, and our pineapple express index derived from future CCSM output. Our aim is to use this model to develop a better understanding of the frequency and intensity of extreme precipitation events produced by PE under climate change.

Genome Wide Association Study of Sepsis in Extremely Premature Infants

PubMed Central

Srinivasan, Lakshmi; Page, Grier; Kirpalani, Haresh; Murray, Jeffrey C.; Das, Abhik; Higgins, Rosemary D.; Carlo, Waldemar A.; Bell, Edward F.; Goldberg, Ronald N.; Schibler, Kurt; Sood, Beena G.; Stevenson, David K.; Stoll, Barbara J.; Van Meurs, Krisa P.; Johnson, Karen J.; Levy, Joshua; McDonald, Scott A.; Zaterka-Baxter, Kristin M.; Kennedy, Kathleen A.; Sánchez, Pablo J.; Duara, Shahnaz; Walsh, Michele C.; Shankaran, Seetha; Wynn, James L.; Cotten, C. Michael

2017-01-01

Objective To identify genetic variants associated with sepsis (early and late-onset) using a genome wide association (GWA) analysis in a cohort of extremely premature infants. Study Design Previously generated GWA data from the Neonatal Research Network’s anonymized genomic database biorepository of extremely premature infants were used for this study. Sepsis was defined as culture-positive early-onset or late-onset sepsis or culture-proven meningitis. Genomic and whole genome amplified DNA was genotyped for 1.2 million single nucleotide polymorphisms (SNPs); 91% of SNPs were successfully genotyped. We imputed 7.2 million additional SNPs. P values and false discovery rates were calculated from multivariate logistic regression analysis adjusting for gender, gestational age and ancestry. Target statistical value was p<10−5. Secondary analyses assessed associations of SNPs with pathogen type. Pathway analyses were also run on primary and secondary end points. Results Data from 757 extremely premature infants were included: 351 infants with sepsis and 406 infants without sepsis. No SNPs reached genome-wide significance levels (5×10−8); two SNPs in proximity to FOXC2 and FOXL1 genes achieved target levels of significance. In secondary analyses, SNPs for ELMO1, IRAK2 (Gram positive sepsis), RALA, IMMP2L (Gram negative sepsis) and PIEZO2 (fungal sepsis) met target significance levels. Pathways associated with sepsis and Gram negative sepsis included gap junctions, fibroblast growth factor receptors, regulators of cell division and Interleukin-1 associated receptor kinase 2 (p values<0.001 and FDR<20%). Conclusions No SNPs met genome-wide significance in this cohort of ELBW infants; however, areas of potential association and pathways meriting further study were identified. PMID:28283553

A multivariate copula-based framework for dealing with hazard scenarios and failure probabilities

NASA Astrophysics Data System (ADS)

Salvadori, G.; Durante, F.; De Michele, C.; Bernardi, M.; Petrella, L.

2016-05-01

This paper is of methodological nature, and deals with the foundations of Risk Assessment. Several international guidelines have recently recommended to select appropriate/relevant Hazard Scenarios in order to tame the consequences of (extreme) natural phenomena. In particular, the scenarios should be multivariate, i.e., they should take into account the fact that several variables, generally not independent, may be of interest. In this work, it is shown how a Hazard Scenario can be identified in terms of (i) a specific geometry and (ii) a suitable probability level. Several scenarios, as well as a Structural approach, are presented, and due comparisons are carried out. In addition, it is shown how the Hazard Scenario approach illustrated here is well suited to cope with the notion of Failure Probability, a tool traditionally used for design and risk assessment in engineering practice. All the results outlined throughout the work are based on the Copula Theory, which turns out to be a fundamental theoretical apparatus for doing multivariate risk assessment: formulas for the calculation of the probability of Hazard Scenarios in the general multidimensional case (d≥2) are derived, and worthy analytical relationships among the probabilities of occurrence of Hazard Scenarios are presented. In addition, the Extreme Value and Archimedean special cases are dealt with, relationships between dependence ordering and scenario levels are studied, and a counter-example concerning Tail Dependence is shown. Suitable indications for the practical application of the techniques outlined in the work are given, and two case studies illustrate the procedures discussed in the paper.

Physical function in older men with hyperkyphosis.

PubMed

Katzman, Wendy B; Harrison, Stephanie L; Fink, Howard A; Marshall, Lynn M; Orwoll, Eric; Barrett-Connor, Elizabeth; Cawthon, Peggy M; Kado, Deborah M

2015-05-01

Age-related hyperkyphosis has been associated with poor physical function and is a well-established predictor of adverse health outcomes in older women, but its impact on health in older men is less well understood. We conducted a cross-sectional study to evaluate the association of hyperkyphosis and physical function in 2,363 men, aged 71-98 (M = 79) from the Osteoporotic Fractures in Men Study. Kyphosis was measured using the Rancho Bernardo Study block method. Measurements of grip strength and lower extremity function, including gait speed over 6 m, narrow walk (measure of dynamic balance), repeated chair stands ability and time, and lower extremity power (Nottingham Power Rig) were included separately as primary outcomes. We investigated associations of kyphosis and each outcome in age-adjusted and multivariable linear or logistic regression models, controlling for age, clinic, education, race, bone mineral density, height, weight, diabetes, and physical activity. In multivariate linear regression, we observed a dose-related response of worse scores on each lower extremity physical function test as number of blocks increased, p for trend ≤.001. Using a cutoff of ≥4 blocks, 20% (N = 469) of men were characterized with hyperkyphosis. In multivariate logistic regression, men with hyperkyphosis had increased odds (range 1.5-1.8) of being in the worst quartile of performing lower extremity physical function tasks (p < .001 for each outcome). Kyphosis was not associated with grip strength in any multivariate analysis. Hyperkyphosis is associated with impaired lower extremity physical function in older men. Further studies are needed to determine the direction of causality. © The Author 2014. Published by Oxford University Press on behalf of The Gerontological Society of America. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Changes in Concurrent Risk of Warm and Dry Years under Impact of Climate Change

NASA Astrophysics Data System (ADS)

Sarhadi, A.; Wiper, M.; Touma, D. E.; Ausín, M. C.; Diffenbaugh, N. S.

2017-12-01

Anthropogenic global warming has changed the nature and the risk of extreme climate phenomena. The changing concurrence of multiple climatic extremes (warm and dry years) may result in intensification of undesirable consequences for water resources, human and ecosystem health, and environmental equity. The present study assesses how global warming influences the probability that warm and dry years co-occur in a global scale. In the first step of the study a designed multivariate Mann-Kendall trend analysis is used to detect the areas in which the concurrence of warm and dry years has increased in the historical climate records and also climate models in the global scale. The next step investigates the concurrent risk of the extremes under dynamic nonstationary conditions. A fully generalized multivariate risk framework is designed to evolve through time under dynamic nonstationary conditions. In this methodology, Bayesian, dynamic copulas are developed to model the time-varying dependence structure between the two different climate extremes (warm and dry years). The results reveal an increasing trend in the concurrence risk of warm and dry years, which are in agreement with the multivariate trend analysis from historical and climate models. In addition to providing a novel quantification of the changing probability of compound extreme events, the results of this study can help decision makers develop short- and long-term strategies to prepare for climate stresses now and in the future.

Two case studies on NARCCAP precipitation extremes

NASA Astrophysics Data System (ADS)

Weller, Grant B.; Cooley, Daniel; Sain, Stephan R.; Bukovsky, Melissa S.; Mearns, Linda O.

2013-09-01

We introduce novel methodology to examine the ability of six regional climate models (RCMs) in the North American Regional Climate Change Assessment Program (NARCCAP) ensemble to simulate past extreme precipitation events seen in the observational record over two different regions and seasons. Our primary objective is to examine the strength of daily correspondence of extreme precipitation events between observations and the output of both the RCMs and the driving reanalysis product. To explore this correspondence, we employ methods from multivariate extreme value theory. These methods require that we account for marginal behavior, and we first model and compare climatological quantities which describe tail behavior of daily precipitation for both the observations and model output before turning attention to quantifying the correspondence of the extreme events. Daily precipitation in a West Coast region of North America is analyzed in two seasons, and it is found that the simulated extreme events from the reanalysis-driven NARCCAP models exhibit strong daily correspondence to extreme events in the observational record. Precipitation over a central region of the United States is examined, and we find some daily correspondence between winter extremes simulated by reanalysis-driven NARCCAP models and those seen in observations, but no such correspondence is found for summer extremes. Furthermore, we find greater discrepancies among the NARCCAP models in the tail characteristics of the distribution of daily summer precipitation over this region than seen in precipitation over the West Coast region. We find that the models which employ spectral nudging exhibit stronger tail dependence to observations in the central region.

Effects of anthropogenic activity emerging as intensified extreme precipitation over China

NASA Astrophysics Data System (ADS)

Li, Huixin; Chen, Huopo; Wang, Huijun

2017-07-01

This study aims to provide an assessment of the effects of anthropogenic (ANT) forcings and other external factors on observed increases in extreme precipitation over China from 1961 to 2005. Extreme precipitation is represented by the annual maximum 1 day precipitation (RX1D) and the annual maximum 5 day consecutive precipitation (RX5D), and these variables are investigated using observations and simulations from the Coupled Model Intercomparison Project phase 5. The analyses mainly focus on the probability-based index (PI), which is derived from RX1D and RX5D by fitting generalized extreme value distributions. The results indicate that the simulations that include the ANT forcings provide the best representation of the spatial and temporal characteristics of extreme precipitation over China. We use the optimal fingerprint method to obtain the univariate and multivariate fingerprints of the responses to external forcings. The results show that only the ANT forcings are detectable at a 90% confidence level, both individually and when natural forcings are considered simultaneously. The impact of the forcing associated with greenhouse gases (GHGs) is also detectable in RX1D, but its effects cannot be separated from those of combinations of forcings that exclude the GHG forcings in the two-signal analyses. Besides, the estimated changes of PI, extreme precipitation, and events with a 20 year return period under nonstationary climate states are potentially attributable to ANT or GHG forcings, and the relationships between extreme precipitation and temperature from ANT forcings show agreement with observations.

Challenges estimating the return period of extreme floods for reinsurance applications

NASA Astrophysics Data System (ADS)

Raven, Emma; Busby, Kathryn; Liu, Ye

2013-04-01

Mapping and modelling extreme natural events is fundamental within the insurance and reinsurance industry for assessing risk. For example, insurers might use a 1 in 100-year flood hazard map to set the annual premium of a property, whilst a reinsurer might assess the national scale loss associated with the 1 in 200-year return period for capital and regulatory requirements. Using examples from a range of international flood projects, we focus on exploring how to define what the n-year flood looks like for predictive uses in re/insurance applications, whilst considering challenges posed by short historical flow records and the spatial and temporal complexities of flood. First, we shall explore the use of extreme value theory (EVT) statistics for extrapolating data beyond the range of observations in a marginal analysis. In particular, we discuss how to estimate the return period of historical flood events and explore the impact that a range of statistical decisions have on these estimates. Decisions include: (1) selecting which distribution type to apply (e.g. generalised Pareto distribution (GPD) vs. generalised extreme value distribution (GEV)); (2) if former, the choice of the threshold above which the GPD is fitted to the data; and (3) the necessity to perform a cluster analysis to group flow peaks to temporally represent individual flood events. Second, we summarise a specialised multivariate extreme value model, which combines the marginal analysis above with dependence modelling to generate industry standard event sets containing thousands of simulated, equi-probable floods across a region/country. These events represent the typical range of anticipated flooding across a region and can be used to estimate the largest or most widespread events that are expected to occur. Finally, we summarise how a reinsurance catastrophe model combines the event set with detailed flood hazard maps to estimate the financial cost of floods; both the full event set and also individual extreme events. Since the predicted loss estimates, typically in the form of a curve plotting return period against modelled loss, are used in the pricing of reinsurance, we demonstrate the importance of the estimated return period and understanding the uncertainties associated with it.

Multivariate hydrological frequency analysis for extreme events using Archimedean copula. Case study: Lower Tunjuelo River basin (Colombia)

NASA Astrophysics Data System (ADS)

Gómez, Wilmar

2017-04-01

By analyzing the spatial and temporal variability of extreme precipitation events we can prevent or reduce the threat and risk. Many water resources projects require joint probability distributions of random variables such as precipitation intensity and duration, which can not be independent with each other. The problem of defining a probability model for observations of several dependent variables is greatly simplified by the joint distribution in terms of their marginal by taking copulas. This document presents a general framework set frequency analysis bivariate and multivariate using Archimedean copulas for extreme events of hydroclimatological nature such as severe storms. This analysis was conducted in the lower Tunjuelo River basin in Colombia for precipitation events. The results obtained show that for a joint study of the intensity-duration-frequency, IDF curves can be obtained through copulas and thus establish more accurate and reliable information from design storms and associated risks. It shows how the use of copulas greatly simplifies the study of multivariate distributions that introduce the concept of joint return period used to represent the needs of hydrological designs properly in frequency analysis.

Neuroimaging and Neurodevelopmental Outcome in Extremely Preterm Infants

PubMed Central

Barnes, Patrick D.; Bulas, Dorothy; Slovis, Thomas L.; Finer, Neil N.; Wrage, Lisa A.; Das, Abhik; Tyson, Jon E.; Stevenson, David K.; Carlo, Waldemar A.; Walsh, Michele C.; Laptook, Abbot R.; Yoder, Bradley A.; Van Meurs, Krisa P.; Faix, Roger G.; Rich, Wade; Newman, Nancy S.; Cheng, Helen; Heyne, Roy J.; Vohr, Betty R.; Acarregui, Michael J.; Vaucher, Yvonne E.; Pappas, Athina; Peralta-Carcelen, Myriam; Wilson-Costello, Deanne E.; Evans, Patricia W.; Goldstein, Ricki F.; Myers, Gary J.; Poindexter, Brenda B.; McGowan, Elisabeth C.; Adams-Chapman, Ira; Fuller, Janell; Higgins, Rosemary D.

2015-01-01

BACKGROUND: Extremely preterm infants are at risk for neurodevelopmental impairment (NDI). Early cranial ultrasound (CUS) is usual practice, but near-term brain MRI has been reported to better predict outcomes. We prospectively evaluated MRI white matter abnormality (WMA) and cerebellar lesions, and serial CUS adverse findings as predictors of outcomes at 18 to 22 months’ corrected age. METHODS: Early and late CUS, and brain MRI were read by masked central readers, in a large cohort (n = 480) of infants <28 weeks’ gestation surviving to near term in the Neonatal Research Network. Outcomes included NDI or death after neuroimaging, and significant gross motor impairment or death, with NDI defined as cognitive composite score <70, significant gross motor impairment, and severe hearing or visual impairment. Multivariable models evaluated the relative predictive value of neuroimaging while controlling for other factors. RESULTS: Of 480 infants, 15 died and 20 were lost. Increasing severity of WMA and significant cerebellar lesions on MRI were associated with adverse outcomes. Cerebellar lesions were rarely identified by CUS. In full multivariable models, both late CUS and MRI, but not early CUS, remained independently associated with NDI or death (MRI cerebellar lesions: odds ratio, 3.0 [95% confidence interval: 1.3–6.8]; late CUS: odds ratio, 9.8 [95% confidence interval: 2.8–35]), and significant gross motor impairment or death. In models that did not include late CUS, MRI moderate-severe WMA was independently associated with adverse outcomes. CONCLUSIONS: Both late CUS and near-term MRI abnormalities were associated with outcomes, independent of early CUS and other factors, underscoring the relative prognostic value of near-term neuroimaging. PMID:25554820

A radiomics model from joint FDG-PET and MRI texture features for the prediction of lung metastases in soft-tissue sarcomas of the extremities

NASA Astrophysics Data System (ADS)

Vallières, M.; Freeman, C. R.; Skamene, S. R.; El Naqa, I.

2015-07-01

This study aims at developing a joint FDG-PET and MRI texture-based model for the early evaluation of lung metastasis risk in soft-tissue sarcomas (STSs). We investigate if the creation of new composite textures from the combination of FDG-PET and MR imaging information could better identify aggressive tumours. Towards this goal, a cohort of 51 patients with histologically proven STSs of the extremities was retrospectively evaluated. All patients had pre-treatment FDG-PET and MRI scans comprised of T1-weighted and T2-weighted fat-suppression sequences (T2FS). Nine non-texture features (SUV metrics and shape features) and forty-one texture features were extracted from the tumour region of separate (FDG-PET, T1 and T2FS) and fused (FDG-PET/T1 and FDG-PET/T2FS) scans. Volume fusion of the FDG-PET and MRI scans was implemented using the wavelet transform. The influence of six different extraction parameters on the predictive value of textures was investigated. The incorporation of features into multivariable models was performed using logistic regression. The multivariable modeling strategy involved imbalance-adjusted bootstrap resampling in the following four steps leading to final prediction model construction: (1) feature set reduction; (2) feature selection; (3) prediction performance estimation; and (4) computation of model coefficients. Univariate analysis showed that the isotropic voxel size at which texture features were extracted had the most impact on predictive value. In multivariable analysis, texture features extracted from fused scans significantly outperformed those from separate scans in terms of lung metastases prediction estimates. The best performance was obtained using a combination of four texture features extracted from FDG-PET/T1 and FDG-PET/T2FS scans. This model reached an area under the receiver-operating characteristic curve of 0.984 ± 0.002, a sensitivity of 0.955 ± 0.006, and a specificity of 0.926 ± 0.004 in bootstrapping evaluations. Ultimately, lung metastasis risk assessment at diagnosis of STSs could improve patient outcomes by allowing better treatment adaptation.

Dependence of drivers affects risks associated with compound events

NASA Astrophysics Data System (ADS)

Zscheischler, Jakob; Seneviratne, Sonia I.

2017-04-01

Compound climate extremes are receiving increasing attention because of their disproportionate impacts on humans and ecosystems. Risks assessments, however, generally focus on univariate statistics even when multiple stressors are considered. Concurrent extreme droughts and heatwaves have been observed to cause a suite of extreme impacts on natural and human systems alike. For example, they can substantially affect vegetation health, prompting tree mortality, and thereby facilitating insect outbreaks and fires. In addition, hot droughts have the potential to trigger and intensify fires and can cause severe economical damage. By promoting disease spread, extremely hot and dry conditions also strongly affect human health. We analyse the co-occurrence of dry and hot summers and show that these are strongly correlated for many regions, inducing a much higher frequency of concurrent hot and dry summers than what would be assumed from the independent combination of the univariate statistics. Our results demonstrate how the dependence structure between variables affects the occurrence frequency of multivariate extremes. Assessments based on univariate statistics can thus strongly underestimate risks associated with given extremes, if impacts depend on multiple (dependent) variables. We conclude that a multivariate perspective is necessary in order to appropriately assess changes in climate extremes and their impacts, and to design adaptation strategies.

Towards a Multi-scale Montecarlo Climate Emulator for Coastal Flooding and Long-Term Coastal Change Modeling: The Beautiful Problem

NASA Astrophysics Data System (ADS)

Rueda, A.; Alvarez Antolinez, J. A.; Hegermiller, C.; Serafin, K.; Anderson, D. L.; Ruggiero, P.; Barnard, P.; Erikson, L. H.; Vitousek, S.; Camus, P.; Tomas, A.; Gonzalez, M.; Mendez, F. J.

2016-02-01

Long-term coastal evolution and coastal flooding hazards are the result of the non-linear interaction of multiple oceanographic, hydrological, geological and meteorological forcings (e.g., astronomical tide, monthly mean sea level, large-scale storm surge, dynamic wave set-up, shoreline evolution, backshore erosion). Additionally, interannual variability and trends in storminess and sea level rise are climate drivers that must be considered. Moreover, the chronology of the hydraulic boundary conditions plays an important role since a collection of consecutive minor storm events can have more impact than the 100-yr return level event. Therefore, proper modeling of shoreline erosion, beach recovery and coastal flooding should consider the sequence of storms, the multivariate nature of the hydrodynamic forcings, and the different time scales of interest (seasonality, interannual and decadal variability). To address this `beautiful problem', we propose a hybrid approach that combines: (a) numerical hydrodynamic and morphodynamic models (SWAN for wave transformation, a shoreline change model, X-Beach for modeling infragravity waves and erosion of the backshore during extreme events and RFSM-EDA (Jamieson et al, 2012) for high resolution flooding of the coastal hinterland); (b) long-term data bases (observational and hindcast) of sea state parameters, astronomical tides and non-tidal residuals; and (c) statistical downscaling techniques, non-linear data mining, and extreme value models. The statistical downscaling approaches for multivariate variables are based on circulation patterns (Espejo et al., 2014), the chronology of the circulation patterns (Guanche et al, 2013) and the event hydrographs of multivariate extremes, resulting in a time-dependent climate emulator of hydraulic boundary conditions for coupled simulations of the coastal change and flooding models. ReferencesEspejo et al (2014) Spectral ocean wave climate variability based on circulation patterns, J Phys Oc, doi: 10.1175/JPO-D-13-0276.1 Guanche et al (2013) Autoregressive logistic regression applied to atmospheric circulation patterns, Clim Dyn, doi: 10.1007/s00382-013-1690-3 Jamieson et al (2012) A highly efficient 2D flood model with sub-element topography, Proc. Of the Inst Civil Eng., 165(10), 581-595

Factors affecting the 7Be surface concentration and its extremely high occurrences over the Scandinavian Peninsula during autumn and winter.

PubMed

Ajtić, J; Brattich, E; Sarvan, D; Djurdjevic, V; Hernández-Ceballos, M A

2018-05-01

Relationships between the beryllium-7 activity concentrations in surface air and meteorological parameters (temperature, atmospheric pressure, and precipitation), teleconnection indices (Arctic Oscillation, North Atlantic Oscillation, and Scandinavian pattern) and number of sunspots are investigated using two multivariate statistical techniques: hierarchical cluster and factor analysis. The beryllium-7 surface measurements over 1995-2011, at four sampling sites located in the Scandinavian Peninsula, are obtained from the Radioactivity Environmental Monitoring Database. In all sites, the statistical analyses show that the beryllium-7 concentrations are strongly linked to temperature. Although the beryllium-7 surface concentration exhibits the well-characterised spring/summer maximum, our study shows that extremely high beryllium-7 concentrations, defined as the values exceeding the 90 th percentile in the data records for each site, also occur over the October-March period. Two types of autumn/winter extremes are distinguished: type-1 when the number of extremes in a given month is less than three, and type-2 when at least three extremes occur in a month. Factor analysis performed for these autumn/winter events shows a weaker effect of temperature and a stronger impact of the transport and production signal on the beryllium-7 concentrations. Further, the majority of the type-2 extremes are associated with a very high monthly Scandinavian teleconnection index. The type-2 extremes that occurred in January, February and March are also linked to sudden stratospheric warmings of the Arctic vortex. Our results indicate that the Scandinavian teleconnection index might be a good indicator of the meteorological conditions facilitating extremely high beryllium-7 surface concentrations over Scandinavia during autumn and winter. Copyright © 2018 Elsevier Ltd. All rights reserved.

Measures of dependence for multivariate Lévy distributions

NASA Astrophysics Data System (ADS)

Boland, J.; Hurd, T. R.; Pivato, M.; Seco, L.

2001-02-01

Recent statistical analysis of a number of financial databases is summarized. Increasing agreement is found that logarithmic equity returns show a certain type of asymptotic behavior of the largest events, namely that the probability density functions have power law tails with an exponent α≈3.0. This behavior does not vary much over different stock exchanges or over time, despite large variations in trading environments. The present paper proposes a class of multivariate distributions which generalizes the observed qualities of univariate time series. A new consequence of the proposed class is the "spectral measure" which completely characterizes the multivariate dependences of the extreme tails of the distribution. This measure on the unit sphere in M-dimensions, in principle completely general, can be determined empirically by looking at extreme events. If it can be observed and determined, it will prove to be of importance for scenario generation in portfolio risk management.

Item usage in a multidimensional computerized adaptive test (MCAT) measuring health-related quality of life.

PubMed

Paap, Muirne C S; Kroeze, Karel A; Terwee, Caroline B; van der Palen, Job; Veldkamp, Bernard P

2017-11-01

Examining item usage is an important step in evaluating the performance of a computerized adaptive test (CAT). We study item usage for a newly developed multidimensional CAT which draws items from three PROMIS domains, as well as a disease-specific one. The multidimensional item bank used in the current study contained 194 items from four domains: the PROMIS domains fatigue, physical function, and ability to participate in social roles and activities, and a disease-specific domain (the COPD-SIB). The item bank was calibrated using the multidimensional graded response model and data of 795 patients with chronic obstructive pulmonary disease. To evaluate the item usage rates of all individual items in our item bank, CAT simulations were performed on responses generated based on a multivariate uniform distribution. The outcome variables included active bank size and item overuse (usage rate larger than the expected item usage rate). For average θ-values, the overall active bank size was 9-10%; this number quickly increased as θ-values became more extreme. For values of -2 and +2, the overall active bank size equaled 39-40%. There was 78% overlap between overused items and active bank size for average θ-values. For more extreme θ-values, the overused items made up a much smaller part of the active bank size: here the overlap was only 35%. Our results strengthen the claim that relatively short item banks may suffice when using polytomous items (and no content constraints/exposure control mechanisms), especially when using MCAT.

Dose-dependent effect of mammographic breast density on the risk of contralateral breast cancer.

PubMed

Chowdhury, Marzana; Euhus, David; O'Donnell, Maureen; Onega, Tracy; Choudhary, Pankaj K; Biswas, Swati

2018-07-01

Increased mammographic breast density is a significant risk factor for breast cancer. It is not clear if it is also a risk factor for the development of contralateral breast cancer. The data were obtained from Breast Cancer Surveillance Consortium and included women diagnosed with invasive breast cancer or ductal carcinoma in situ between ages 18 and 88 and years 1995 and 2009. Each case of contralateral breast cancer was matched with three controls based on year of first breast cancer diagnosis, race, and length of follow-up. A total of 847 cases and 2541 controls were included. The risk factors included in the study were mammographic breast density, age of first breast cancer diagnosis, family history of breast cancer, anti-estrogen treatment, hormone replacement therapy, menopausal status, and estrogen receptor status, all from the time of first breast cancer diagnosis. Both univariate analysis and multivariate conditional logistic regression analysis were performed. In the final multivariate model, breast density, family history of breast cancer, and anti-estrogen treatment remained significant with p values less than 0.01. Increasing breast density had a dose-dependent effect on the risk of contralateral breast cancer. Relative to 'almost entirely fat' category of breast density, the adjusted odds ratios (and p values) in the multivariate analysis for 'scattered density,' 'heterogeneously dense,' and 'extremely dense' categories were 1.65 (0.036), 2.10 (0.002), and 2.32 (0.001), respectively. Breast density is an independent and significant risk factor for development of contralateral breast cancer. This risk factor should contribute to clinical decision making.

Osteogenesis imperfecta in childhood: prognosis for walking.

PubMed

Engelbert, R H; Uiterwaal, C S; Gulmans, V A; Pruijs, H; Helders, P J

2000-09-01

We studied the predicted value of disease-related characteristics for the ability of children with osteogenesis imperfecta (OI) to walk. The severity of OI was classified according to Sillence. The parents were asked to report the age at which the child achieved motor milestones, the fracture incidence, and the age and localization of the first surgical intervention. The present main means of mobility was classified according to Bleck. There were 76 replies to the 98 questionnaires, of which 70 were included (type I, 41; type III, 11; type IV, 18). The type of OI was strongly associated with current walking ability, as was the presence of dentinogenesis imperfecta. Patients with type III and IV had a lower chance of ultimately walking compared with those with type I. Children with more than 2 intramedullary rods in the lower extremities had a reduced chance of walking than patients without rods. Rolling over before 8 months, unsupported sitting before 9 months, the ability to get in sitting position without support before 12 months, and the ability to get in a standing position without support before 12 months showed positive odds ratios. In Bleck > or = 4, multivariate analysis revealed that only the presence of rodding (yes/no) in the lower extremities had additional predictive value to the type of OI. The presence of dentinogenesis imperfecta and rodding (yes/no) had additional value in Bleck > or = 5. The type of OI is the single most important clinical indicator of the ultimate ability to walk. Information about motor development adds little. The early achievement of motor milestones contributes to the ability of independent walking when the type of OI is uncertain. Intramedullary rodding of the lower extremities is primarily related to the severity of the disease and in this way provides consequences for the ability to walk.

Rare events modeling with support vector machine: Application to forecasting large-amplitude geomagnetic substorms and extreme events in financial markets.

NASA Astrophysics Data System (ADS)

Gavrishchaka, V. V.; Ganguli, S. B.

2001-12-01

Reliable forecasting of rare events in a complex dynamical system is a challenging problem that is important for many practical applications. Due to the nature of rare events, data set available for construction of the statistical and/or machine learning model is often very limited and incomplete. Therefore many widely used approaches including such robust algorithms as neural networks can easily become inadequate for rare events prediction. Moreover in many practical cases models with high-dimensional inputs are required. This limits applications of the existing rare event modeling techniques (e.g., extreme value theory) that focus on univariate cases. These approaches are not easily extended to multivariate cases. Support vector machine (SVM) is a machine learning system that can provide an optimal generalization using very limited and incomplete training data sets and can efficiently handle high-dimensional data. These features may allow to use SVM to model rare events in some applications. We have applied SVM-based system to the problem of large-amplitude substorm prediction and extreme event forecasting in stock and currency exchange markets. Encouraging preliminary results will be presented and other possible applications of the system will be discussed.

Developing a Hierarchical Model for the Spatial Analysis of PM10 Pollution Extremes in the Mexico City Metropolitan Area.

PubMed

Aguirre-Salado, Alejandro Ivan; Vaquera-Huerta, Humberto; Aguirre-Salado, Carlos Arturo; Reyes-Mora, Silvia; Olvera-Cervantes, Ana Delia; Lancho-Romero, Guillermo Arturo; Soubervielle-Montalvo, Carlos

2017-07-06

We implemented a spatial model for analysing PM 10 maxima across the Mexico City metropolitan area during the period 1995-2016. We assumed that these maxima follow a non-identical generalized extreme value (GEV) distribution and modeled the trend by introducing multivariate smoothing spline functions into the probability GEV distribution. A flexible, three-stage hierarchical Bayesian approach was developed to analyse the distribution of the PM 10 maxima in space and time. We evaluated the statistical model's performance by using a simulation study. The results showed strong evidence of a positive correlation between the PM 10 maxima and the longitude and latitude. The relationship between time and the PM 10 maxima was negative, indicating a decreasing trend over time. Finally, a high risk of PM 10 maxima presenting levels above 1000 μ g/m 3 (return period: 25 yr) was observed in the northwestern region of the study area.

Developing a Hierarchical Model for the Spatial Analysis of PM10 Pollution Extremes in the Mexico City Metropolitan Area

PubMed Central

Aguirre-Salado, Alejandro Ivan; Vaquera-Huerta, Humberto; Aguirre-Salado, Carlos Arturo; Reyes-Mora, Silvia; Olvera-Cervantes, Ana Delia; Lancho-Romero, Guillermo Arturo; Soubervielle-Montalvo, Carlos

2017-01-01

We implemented a spatial model for analysing PM10 maxima across the Mexico City metropolitan area during the period 1995–2016. We assumed that these maxima follow a non-identical generalized extreme value (GEV) distribution and modeled the trend by introducing multivariate smoothing spline functions into the probability GEV distribution. A flexible, three-stage hierarchical Bayesian approach was developed to analyse the distribution of the PM10 maxima in space and time. We evaluated the statistical model’s performance by using a simulation study. The results showed strong evidence of a positive correlation between the PM10 maxima and the longitude and latitude. The relationship between time and the PM10 maxima was negative, indicating a decreasing trend over time. Finally, a high risk of PM10 maxima presenting levels above 1000 μg/m3 (return period: 25 yr) was observed in the northwestern region of the study area. PMID:28684720

Brain shaving: adaptive detection for brain PET data

NASA Astrophysics Data System (ADS)

Grecchi, Elisabetta; Doyle, Orla M.; Bertoldo, Alessandra; Pavese, Nicola; Turkheimer, Federico E.

2014-05-01

The intricacy of brain biology is such that the variation of imaging end-points in health and disease exhibits an unpredictable range of spatial distributions from the extremely localized to the very diffuse. This represents a challenge for the two standard approaches to analysis, the mass univariate and the multivariate that exhibit either strong specificity but not as good sensitivity (the former) or poor specificity and comparatively better sensitivity (the latter). In this work, we develop an analytical methodology for positron emission tomography that operates an extraction (‘shaving’) of coherent patterns of signal variation while maintaining control of the type I error. The methodology operates two rotations on the image data, one local using the wavelet transform and one global using the singular value decomposition. The control of specificity is obtained by using the gap statistic that selects, within each eigenvector, a subset of significantly coherent elements. Face-validity of the algorithm is demonstrated using a paradigmatic data-set with two radiotracers, [11C]-raclopride and [11C]-(R)-PK11195, measured on the same Huntington's disease patients, a disorder with a genetic based diagnosis. The algorithm is able to detect the two well-known separate but connected processes of dopamine neuronal loss (localized in the basal ganglia) and neuroinflammation (diffusive around the whole brain). These processes are at the two extremes of the distributional envelope, one being very sparse and the latter being perfectly Gaussian and they are not adequately detected by the univariate and the multivariate approaches.

Downscaling wind and wavefields for 21st century coastal flood hazard projections in a region of complex terrain

USGS Publications Warehouse

O'Neill, Andrea; Erikson, Li; Barnard, Patrick

2017-01-01

While global climate models (GCMs) provide useful projections of near-surface wind vectors into the 21st century, resolution is not sufficient enough for use in regional wave modeling. Statistically downscaled GCM projections from Multivariate Adaptive Constructed Analogues provide daily averaged near-surface winds at an appropriate spatial resolution for wave modeling within the orographically complex region of San Francisco Bay, but greater resolution in time is needed to capture the peak of storm events. Short-duration high wind speeds, on the order of hours, are usually excluded in statistically downscaled climate models and are of key importance in wave and subsequent coastal flood modeling. Here we present a temporal downscaling approach, similar to constructed analogues, for near-surface winds suitable for use in local wave models and evaluate changes in wind and wave conditions for the 21st century. Reconstructed hindcast winds (1975–2004) recreate important extreme wind values within San Francisco Bay. A computationally efficient method for simulating wave heights over long time periods was used to screen for extreme events. Wave hindcasts show resultant maximum wave heights of 2.2 m possible within the Bay. Changes in extreme over-water wind speeds suggest contrasting trends within the different regions of San Francisco Bay, but 21th century projections show little change in the overall magnitude of extreme winds and locally generated waves.

Modeling Compound Flood Hazards in Coastal Embayments

NASA Astrophysics Data System (ADS)

Moftakhari, H.; Schubert, J. E.; AghaKouchak, A.; Luke, A.; Matthew, R.; Sanders, B. F.

2017-12-01

Coastal cities around the world are built on lowland topography adjacent to coastal embayments and river estuaries, where multiple factors threaten increasing flood hazards (e.g. sea level rise and river flooding). Quantitative risk assessment is required for administration of flood insurance programs and the design of cost-effective flood risk reduction measures. This demands a characterization of extreme water levels such as 100 and 500 year return period events. Furthermore, hydrodynamic flood models are routinely used to characterize localized flood level intensities (i.e., local depth and velocity) based on boundary forcing sampled from extreme value distributions. For example, extreme flood discharges in the U.S. are estimated from measured flood peaks using the Log-Pearson Type III distribution. However, configuring hydrodynamic models for coastal embayments is challenging because of compound extreme flood events: events caused by a combination of extreme sea levels, extreme river discharges, and possibly other factors such as extreme waves and precipitation causing pluvial flooding in urban developments. Here, we present an approach for flood risk assessment that coordinates multivariate extreme analysis with hydrodynamic modeling of coastal embayments. First, we evaluate the significance of correlation structure between terrestrial freshwater inflow and oceanic variables; second, this correlation structure is described using copula functions in unit joint probability domain; and third, we choose a series of compound design scenarios for hydrodynamic modeling based on their occurrence likelihood. The design scenarios include the most likely compound event (with the highest joint probability density), preferred marginal scenario and reproduced time series of ensembles based on Monte Carlo sampling of bivariate hazard domain. The comparison between resulting extreme water dynamics under the compound hazard scenarios explained above provides an insight to the strengths/weaknesses of each approach and helps modelers choose the appropriate scenario that best fit to the needs of their project. The proposed risk assessment approach can help flood hazard modeling practitioners achieve a more reliable estimate of risk, by cautiously reducing the dimensionality of the hazard analysis.

The extremely narrow-caliber esophagus is a treatment-resistant subphenotype of eosinophilic esophagitis.

PubMed

Eluri, Swathi; Runge, Thomas M; Cotton, Cary C; Burk, Caitlin M; Wolf, W Asher; Woosley, John T; Shaheen, Nicholas J; Dellon, Evan S

2016-06-01

Some patients with eosinophilic esophagitis (EoE) have an extremely narrow esophagus, but the characteristics of this group have not been extensively described. We aimed to characterize the narrow-caliber phenotype of EoE, determine associated risk factors, and identify differences in treatment response in this subgroup of patients. This retrospective cohort study from 2001 to 2014 included subjects with a new diagnosis of EoE per consensus guidelines. Demographic, endoscopic, histologic, and treatment response data were extracted from medical records. An extremely narrow-caliber esophagus was defined when a neonatal endoscope was required to traverse the esophagus due to the inability to pass an adult endoscope. Patients with and without an extremely narrow-caliber esophagus were compared. Multivariable logistical regression was performed to assess treatment outcomes. Of 513 patients with EoE, 46 (9%) had an extremely narrow-caliber esophagus. These patients were older (33 vs 22 years; P < .01), had longer symptom duration (11 vs 3 years; P < .01), more dysphagia (98% vs 66%; P < .01), and food impactions (53% vs 31%; P < .01). Dilation was more common with extreme narrowing (69% vs 17%; P < .01). Patients with a narrow-caliber esophagus were more refractory to steroid treatment, with lower symptom (56% vs 85%), endoscopic (52% vs 76%), and histologic (33% vs 63%) responses (P < .01 for all), and these differences persisted after multivariate analysis. The extremely narrow-caliber esophagus is a more treatment-resistant subphenotype of EoE and is characterized by longer symptom duration and the need for multiple dilations. Recognition of an extremely narrow-caliber esophagus at diagnosis of EoE can provide important prognostic information. Copyright © 2016 American Society for Gastrointestinal Endoscopy. Published by Elsevier Inc. All rights reserved.

Multiscale climate emulator of multimodal wave spectra: MUSCLE-spectra

NASA Astrophysics Data System (ADS)

Rueda, Ana; Hegermiller, Christie A.; Antolinez, Jose A. A.; Camus, Paula; Vitousek, Sean; Ruggiero, Peter; Barnard, Patrick L.; Erikson, Li H.; Tomás, Antonio; Mendez, Fernando J.

2017-02-01

Characterization of multimodal directional wave spectra is important for many offshore and coastal applications, such as marine forecasting, coastal hazard assessment, and design of offshore wave energy farms and coastal structures. However, the multivariate and multiscale nature of wave climate variability makes this complex problem tractable using computationally expensive numerical models. So far, the skill of statistical-downscaling model-based parametric (unimodal) wave conditions is limited in large ocean basins such as the Pacific. The recent availability of long-term directional spectral data from buoys and wave hindcast models allows for development of stochastic models that include multimodal sea-state parameters. This work introduces a statistical downscaling framework based on weather types to predict multimodal wave spectra (e.g., significant wave height, mean wave period, and mean wave direction from different storm systems, including sea and swells) from large-scale atmospheric pressure fields. For each weather type, variables of interest are modeled using the categorical distribution for the sea-state type, the Generalized Extreme Value (GEV) distribution for wave height and wave period, a multivariate Gaussian copula for the interdependence between variables, and a Markov chain model for the chronology of daily weather types. We apply the model to the southern California coast, where local seas and swells from both the Northern and Southern Hemispheres contribute to the multimodal wave spectrum. This work allows attribution of particular extreme multimodal wave events to specific atmospheric conditions, expanding knowledge of time-dependent, climate-driven offshore and coastal sea-state conditions that have a significant influence on local nearshore processes, coastal morphology, and flood hazards.

Multiscale Climate Emulator of Multimodal Wave Spectra: MUSCLE-spectra

NASA Astrophysics Data System (ADS)

Rueda, A.; Hegermiller, C.; Alvarez Antolinez, J. A.; Camus, P.; Vitousek, S.; Ruggiero, P.; Barnard, P.; Erikson, L. H.; Tomas, A.; Mendez, F. J.

2016-12-01

Characterization of multimodal directional wave spectra is important for many offshore and coastal applications, such as marine forecasting, coastal hazard assessment, and design of offshore wave energy farms and coastal structures. However, the multivariate and multiscale nature of wave climate variability makes this problem complex yet tractable using computationally-expensive numerical models. So far, the skill of statistical-downscaling models based parametric (unimodal) wave conditions is limited in large ocean basins such as the Pacific. The recent availability of long-term directional spectral data from buoys and wave hindcast models allows for development of stochastic models that include multimodal sea-state parameters. This work introduces a statistical-downscaling framework based on weather types to predict multimodal wave spectra (e.g., significant wave height, mean wave period, and mean wave direction from different storm systems, including sea and swells) from large-scale atmospheric pressure fields. For each weather type, variables of interest are modeled using the categorical distribution for the sea-state type, the Generalized Extreme Value (GEV) distribution for wave height and wave period, a multivariate Gaussian copula for the interdependence between variables, and a Markov chain model for the chronology of daily weather types. We apply the model to the Southern California coast, where local seas and swells from both the Northern and Southern Hemispheres contribute to the multimodal wave spectrum. This work allows attribution of particular extreme multimodal wave events to specific atmospheric conditions, expanding knowledge of time-dependent, climate-driven offshore and coastal sea-state conditions that have a significant influence on local nearshore processes, coastal morphology, and flood hazards.

On set-valued functionals: Multivariate risk measures and Aumann integrals

NASA Astrophysics Data System (ADS)

Ararat, Cagin

In this dissertation, multivariate risk measures for random vectors and Aumann integrals of set-valued functions are studied. Both are set-valued functionals with values in a complete lattice of subsets of Rm. Multivariate risk measures are considered in a general d-asset financial market with trading opportunities in discrete time. Specifically, the following features of the market are incorporated in the evaluation of multivariate risk: convex transaction costs modeled by solvency regions, intermediate trading constraints modeled by convex random sets, and the requirement of liquidation into the first m ≤ d of the assets. It is assumed that the investor has a "pure" multivariate risk measure R on the space of m-dimensional random vectors which represents her risk attitude towards the assets but does not take into account the frictions of the market. Then, the investor with a d-dimensional position minimizes the set-valued functional R over all m-dimensional positions that she can reach by trading in the market subject to the frictions described above. The resulting functional Rmar on the space of d-dimensional random vectors is another multivariate risk measure, called the market-extension of R. A dual representation for R mar that decomposes the effects of R and the frictions of the market is proved. Next, multivariate risk measures are studied in a utility-based framework. It is assumed that the investor has a complete risk preference towards each individual asset, which can be represented by a von Neumann-Morgenstern utility function. Then, an incomplete preference is considered for multivariate positions which is represented by the vector of the individual utility functions. Under this structure, multivariate shortfall and divergence risk measures are defined as the optimal values of set minimization problems. The dual relationship between the two classes of multivariate risk measures is constructed via a recent Lagrange duality for set optimization. In particular, it is shown that a shortfall risk measure can be written as an intersection over a family of divergence risk measures indexed by a scalarization parameter. Examples include the multivariate versions of the entropic risk measure and the average value at risk. In the second part, Aumann integrals of set-valued functions on a measurable space are viewed as set-valued functionals and a Daniell-Stone type characterization theorem is proved for such functionals. More precisely, it is shown that a functional that maps measurable set-valued functions into a certain complete lattice of subsets of Rm can be written as the Aumann integral with respect to a measure if and only if the functional is (1) additive and (2) positively homogeneous, (3) it preserves decreasing limits, (4) it maps halfspace-valued functions to halfspaces, and (5) it maps shifted cone-valued functions to shifted cones. While the first three properties already exist in the classical Daniell-Stone theorem for the Lebesgue integral, the last two properties are peculiar to the set-valued framework and they suffice to complement the first three properties to identify a set-valued functional as the Aumann integral with respect to a measure.

Forecasting peak asthma admissions in London: an application of quantile regression models.

PubMed

Soyiri, Ireneous N; Reidpath, Daniel D; Sarran, Christophe

2013-07-01

Asthma is a chronic condition of great public health concern globally. The associated morbidity, mortality and healthcare utilisation place an enormous burden on healthcare infrastructure and services. This study demonstrates a multistage quantile regression approach to predicting excess demand for health care services in the form of asthma daily admissions in London, using retrospective data from the Hospital Episode Statistics, weather and air quality. Trivariate quantile regression models (QRM) of asthma daily admissions were fitted to a 14-day range of lags of environmental factors, accounting for seasonality in a hold-in sample of the data. Representative lags were pooled to form multivariate predictive models, selected through a systematic backward stepwise reduction approach. Models were cross-validated using a hold-out sample of the data, and their respective root mean square error measures, sensitivity, specificity and predictive values compared. Two of the predictive models were able to detect extreme number of daily asthma admissions at sensitivity levels of 76 % and 62 %, as well as specificities of 66 % and 76 %. Their positive predictive values were slightly higher for the hold-out sample (29 % and 28 %) than for the hold-in model development sample (16 % and 18 %). QRMs can be used in multistage to select suitable variables to forecast extreme asthma events. The associations between asthma and environmental factors, including temperature, ozone and carbon monoxide can be exploited in predicting future events using QRMs.

Forecasting peak asthma admissions in London: an application of quantile regression models

NASA Astrophysics Data System (ADS)

Soyiri, Ireneous N.; Reidpath, Daniel D.; Sarran, Christophe

2013-07-01

Asthma is a chronic condition of great public health concern globally. The associated morbidity, mortality and healthcare utilisation place an enormous burden on healthcare infrastructure and services. This study demonstrates a multistage quantile regression approach to predicting excess demand for health care services in the form of asthma daily admissions in London, using retrospective data from the Hospital Episode Statistics, weather and air quality. Trivariate quantile regression models (QRM) of asthma daily admissions were fitted to a 14-day range of lags of environmental factors, accounting for seasonality in a hold-in sample of the data. Representative lags were pooled to form multivariate predictive models, selected through a systematic backward stepwise reduction approach. Models were cross-validated using a hold-out sample of the data, and their respective root mean square error measures, sensitivity, specificity and predictive values compared. Two of the predictive models were able to detect extreme number of daily asthma admissions at sensitivity levels of 76 % and 62 %, as well as specificities of 66 % and 76 %. Their positive predictive values were slightly higher for the hold-out sample (29 % and 28 %) than for the hold-in model development sample (16 % and 18 %). QRMs can be used in multistage to select suitable variables to forecast extreme asthma events. The associations between asthma and environmental factors, including temperature, ozone and carbon monoxide can be exploited in predicting future events using QRMs.

Racial, Socioeconomic, and Gender Disparities in the Presentation, Treatment, and Outcomes of Adult Chiari I Malformations.

PubMed

Krucoff, Max O; Cook, Steven; Adogwa, Owoicho; Moreno, Jessica; Yang, Siyun; Xie, Jichun; Firempong, Alexander O; Lad, Nandan; Bagley, Carlos A

2017-01-01

To examine the influence of race, gender, and socioeconomic factors on presentations and outcomes of adult Chiari I malformations. The charts of 638 adult patients with Chiari I malformations were reviewed, and 287 patients were included in the study. Race, gender, insurance status, symptoms, depth of cerebellar tonsillar herniation, and presence of syringomyelia were examined as covariates in multivariate logistic regression models to identify independent predictors of presentation and outcome. Patients with public insurance had a longer stay in the hospital (P = 0.01). A higher proportion of male patients presented with upper extremity weakness (P = 0.01), lower extremity weakness (P = 0.040), and cranial nerve findings (P = 0.02). Men had shorter onset to diagnosis times (P = 0.02), worse tonsillar herniation (P = 0.03), and more severe symptoms (P = 0.05). White patients more frequently presented with back pain (P = 0.03), and African American patients more frequently presented with lower extremity weakness (P = 0.01). African Americans had worse tonsillar herniation (P < 0.01) and were more likely to present with syringomyelia (P = 0.01). Multivariate regression analysis revealed that back pain (P < 0.01), upper extremity weakness (P ≤ 0.01), upper extremity paresthesias (P < 0.01), and upper with lower extremity paresthesias (P = 0.04) were significant predictors of syringomyelia. The only independent predictor of outcome was size of tonsillar herniation (P = 0.03). Significant differences in presentation of Chiari I malformation resulting from gender, race, and insurance status were quantified for the first time. Copyright © 2016 Elsevier Inc. All rights reserved.

NONPARAMETRIC MANOVA APPROACHES FOR NON-NORMAL MULTIVARIATE OUTCOMES WITH MISSING VALUES

PubMed Central

He, Fanyin; Mazumdar, Sati; Tang, Gong; Bhatia, Triptish; Anderson, Stewart J.; Dew, Mary Amanda; Krafty, Robert; Nimgaonkar, Vishwajit; Deshpande, Smita; Hall, Martica; Reynolds, Charles F.

2017-01-01

Between-group comparisons often entail many correlated response variables. The multivariate linear model, with its assumption of multivariate normality, is the accepted standard tool for these tests. When this assumption is violated, the nonparametric multivariate Kruskal-Wallis (MKW) test is frequently used. However, this test requires complete cases with no missing values in response variables. Deletion of cases with missing values likely leads to inefficient statistical inference. Here we extend the MKW test to retain information from partially-observed cases. Results of simulated studies and analysis of real data show that the proposed method provides adequate coverage and superior power to complete-case analyses. PMID:29416225

Indulging our gendered selves? Sex segregation by field of study in 44 countries.

PubMed

Charles, Maria; Bradley, Karen

2009-01-01

Data from 44 societies are used to explore sex segregation by field of study. Contrary to accounts linking socioeconomic modernization to a "degendering" of public-sphere institutions, sex typing of curricular fields is stronger in more economically developed contexts. The authors argue that two cultural forces combine in advanced industrial societies to create a new sort of sex segregation regime. The first is gender-essentialist ideology, which has proven to be extremely resilient even in the most liberal-egalitarian of contexts; the second is self-expressive value systems, which create opportunities and incentives for the expression of "gendered selves." Multivariate analyses suggest that structural features of postindustrial labor markets and modern educational systems support the cultivation, realization, and display of gender-specific curricular affinities.

Min and Max Exponential Extreme Interval Values and Statistics

ERIC Educational Resources Information Center

Jance, Marsha; Thomopoulos, Nick

2009-01-01

The extreme interval values and statistics (expected value, median, mode, standard deviation, and coefficient of variation) for the smallest (min) and largest (max) values of exponentially distributed variables with parameter ? = 1 are examined for different observation (sample) sizes. An extreme interval value g[subscript a] is defined as a…

Solving chromatographic challenges in comprehensive two-dimensional gas chromatography-time-of-flight mass spectrometry using multivariate curve resolution-alternating least squares.

PubMed

Parastar, Hadi; Radović, Jagoš R; Bayona, Josep M; Tauler, Roma

2013-07-01

Multivariate curve resolution-alternating least squares (MCR-ALS) analysis is proposed to solve chromatographic challenges during two-dimensional gas chromatography-time-of-flight mass spectrometry (GC × GC-TOFMS) analysis of complex samples, such as crude oil extract. In view of the fact that the MCR-ALS method is based on the fulfillment of the bilinear model assumption, three-way and four-way GC × GC-TOFMS data are preferably arranged in a column-wise superaugmented data matrix in which mass-to-charge ratios (m/z) are in its columns and the elution times in the second and first chromatographic columns are in its rows. Since m/z values are common for all measured spectra in all second-column modulations, unavoidable chromatographic challenges such as retention time shifts within and between GC × GC-TOFMS experiments are properly handled. In addition, baseline/background contributions can be modeled by adding extra components to the MCR-ALS model. Another outstanding aspect of MCR-ALS analysis is its extreme flexibility to consider all samples (standards, unknowns, and replicates) in a single superaugmented data matrix, allowing joint analysis. In this way, resolution, identification, and quantification results can be simultaneously obtained in a very fast and reliable way. The potential of MCR-ALS analysis is demonstrated in GC × GC-TOFMS analysis of a North Sea crude oil extract sample with relative errors in estimated concentrations of target compounds below 6.0 % and relative standard deviations lower than 7.0 %. The results obtained, along with reasonable values for the lack of fit of the MCR-ALS model and high values of the reversed match factor in mass spectra similarity searches, confirm the reliability of the proposed strategy for GC × GC-TOFMS data analysis.

Novel high-resolution computed tomography-based radiomic classifier for screen-identified pulmonary nodules in the National Lung Screening Trial.

PubMed

Peikert, Tobias; Duan, Fenghai; Rajagopalan, Srinivasan; Karwoski, Ronald A; Clay, Ryan; Robb, Richard A; Qin, Ziling; Sicks, JoRean; Bartholmai, Brian J; Maldonado, Fabien

2018-01-01

Optimization of the clinical management of screen-detected lung nodules is needed to avoid unnecessary diagnostic interventions. Herein we demonstrate the potential value of a novel radiomics-based approach for the classification of screen-detected indeterminate nodules. Independent quantitative variables assessing various radiologic nodule features such as sphericity, flatness, elongation, spiculation, lobulation and curvature were developed from the NLST dataset using 726 indeterminate nodules (all ≥ 7 mm, benign, n = 318 and malignant, n = 408). Multivariate analysis was performed using least absolute shrinkage and selection operator (LASSO) method for variable selection and regularization in order to enhance the prediction accuracy and interpretability of the multivariate model. The bootstrapping method was then applied for the internal validation and the optimism-corrected AUC was reported for the final model. Eight of the originally considered 57 quantitative radiologic features were selected by LASSO multivariate modeling. These 8 features include variables capturing Location: vertical location (Offset carina centroid z), Size: volume estimate (Minimum enclosing brick), Shape: flatness, Density: texture analysis (Score Indicative of Lesion/Lung Aggression/Abnormality (SILA) texture), and surface characteristics: surface complexity (Maximum shape index and Average shape index), and estimates of surface curvature (Average positive mean curvature and Minimum mean curvature), all with P<0.01. The optimism-corrected AUC for these 8 features is 0.939. Our novel radiomic LDCT-based approach for indeterminate screen-detected nodule characterization appears extremely promising however independent external validation is needed.

Leisure-time sport and overuse injuries of extremities in children age 6-13, a 2.5 years prospective cohort study: the CHAMPS-study DK.

PubMed

Chéron, Charlène; Leboeuf-Yde, Charlotte; Le Scanff, Christine; Jespersen, Eva; Rexen, Christina Trifonov; Franz, Claudia; Wedderkopp, Niels

2017-01-13

It is not known which sports are most likely to cause overuse injuries of the extremities in children. In this study, we report on the incidence of overuse injuries of the upper and lower extremities in children who participate in various leisure-time sports and relate this to the frequency of sport sessions. Natural experiment including a prospective cohort study. 10 state schools in 1 Danish municipality: Svendborg. 1270 children aged 6-13 years participating in the Childhood Health, Activity, and Motor Performance School Study Denmark. Over 2.5 years, parents answered weekly SMS-track messages (a) on type and frequency of leisure-time sports undertaken by their child, and (b) reporting if their child had experienced any musculoskeletal pain. Children with reported pain were examined by a clinician and diagnosed as having an overuse injury of an extremity or not. The incidence of diagnosed overuse injury was calculated for each of the 9 most common sports in relation to 5-week periods. Incidence by frequency of sessions was calculated, and multivariable analysis was performed taking into account age, sex and frequency of physical education classes at school. Incidence of overuse injuries of the lower extremity ranged from 0.2 to 3.3 for the 9 sports, but was near 0 for overuse injuries of the upper extremities. There was no obvious dose-response. The multivariate analysis showed soccer and handball to be the sports most likely to result in an overuse injury. Among a general population of schoolchildren, overuse injuries of the lower extremities were not common and overuse injuries of the upper extremities were rare. Organised leisure-time sport, as practised in Denmark, can be considered a safe activity for children. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/.

Leisure-time sport and overuse injuries of extremities in children age 6–13, a 2.5 years prospective cohort study: the CHAMPS-study DK

PubMed Central

Chéron, Charlène; Leboeuf-Yde, Charlotte; Le Scanff, Christine; Jespersen, Eva; Rexen, Christina Trifonov; Franz, Claudia; Wedderkopp, Niels

2017-01-01

Objectives It is not known which sports are most likely to cause overuse injuries of the extremities in children. In this study, we report on the incidence of overuse injuries of the upper and lower extremities in children who participate in various leisure-time sports and relate this to the frequency of sport sessions. Design Natural experiment including a prospective cohort study. Setting 10 state schools in 1 Danish municipality: Svendborg. Participants 1270 children aged 6–13 years participating in the Childhood Health, Activity, and Motor Performance School Study Denmark. Outcomes measures Over 2.5 years, parents answered weekly SMS-track messages (a) on type and frequency of leisure-time sports undertaken by their child, and (b) reporting if their child had experienced any musculoskeletal pain. Children with reported pain were examined by a clinician and diagnosed as having an overuse injury of an extremity or not. The incidence of diagnosed overuse injury was calculated for each of the 9 most common sports in relation to 5-week periods. Incidence by frequency of sessions was calculated, and multivariable analysis was performed taking into account age, sex and frequency of physical education classes at school. Results Incidence of overuse injuries of the lower extremity ranged from 0.2 to 3.3 for the 9 sports, but was near 0 for overuse injuries of the upper extremities. There was no obvious dose–response. The multivariate analysis showed soccer and handball to be the sports most likely to result in an overuse injury. Conclusions Among a general population of schoolchildren, overuse injuries of the lower extremities were not common and overuse injuries of the upper extremities were rare. Organised leisure-time sport, as practised in Denmark, can be considered a safe activity for children. PMID:28087543

Upper extremity deep venous thrombosis after port insertion: What are the risk factors?

PubMed

Tabatabaie, Omidreza; Kasumova, Gyulnara G; Kent, Tara S; Eskander, Mariam F; Fadayomi, Ayotunde B; Ng, Sing Chau; Critchlow, Jonathan F; Tawa, Nicholas E; Tseng, Jennifer F

2017-08-01

Totally implantable venous access devices (ports) are widely used, especially for cancer chemotherapy. Although their use has been associated with upper extremity deep venous thrombosis, the risk factors of upper extremity deep venous thrombosis in patients with a port are not studied adequately. The Healthcare Cost and Utilization Project's Florida State Ambulatory Surgery and Services Database was queried between 2007 and 2011 for patients who underwent outpatient port insertion, identified by Current Procedural Terminology code. Patients were followed in the State Ambulatory Surgery and Services Database, State Inpatient Database, and State Emergency Department Database for upper extremity deep venous thrombosis occurrence. The cohort was divided into a test cohort and a validation cohort based on the year of port placement. A multivariable logistic regression model was developed to identify risk factors for upper extremity deep venous thrombosis in patients with a port. The model then was tested on the validation cohort. Of the 51,049 patients in the derivation cohort, 926 (1.81%) developed an upper extremity deep venous thrombosis. On multivariate analysis, independently significant predictors of upper extremity deep venous thrombosis included age <65 years (odds ratio = 1.22), Elixhauser score of 1 to 2 compared with zero (odds ratio = 1.17), end-stage renal disease (versus no kidney disease; odds ratio = 2.63), history of any deep venous thrombosis (odds ratio = 1.77), all-cause 30-day revisit (odds ratio = 2.36), African American race (versus white; odds ratio = 1.86), and other nonwhite races (odds ratio = 1.35). Additionally, compared with genitourinary malignancies, patients with gastrointestinal (odds ratio = 1.55), metastatic (odds ratio = 1.76), and lung cancers (odds ratio = 1.68) had greater risks of developing an upper extremity deep venous thrombosis. This study identified major risk factors of upper extremity deep venous thrombosis. Further studies are needed to evaluate the appropriateness of thromboprophylaxis in patients at greater risk of upper extremity deep venous thrombosis. Copyright © 2017 Elsevier Inc. All rights reserved.

A user-targeted synthesis of the VALUE perfect predictor experiment

NASA Astrophysics Data System (ADS)

Maraun, Douglas; Widmann, Martin; Gutierrez, Jose; Kotlarski, Sven; Hertig, Elke; Wibig, Joanna; Rössler, Ole; Huth, Radan

2016-04-01

VALUE is an open European network to validate and compare downscaling methods for climate change research. A key deliverable of VALUE is the development of a systematic validation framework to enable the assessment and comparison of both dynamical and statistical downscaling methods. VALUE's main approach to validation is user-focused: starting from a specific user problem, a validation tree guides the selection of relevant validation indices and performance measures. We consider different aspects: (1) marginal aspects such as mean, variance and extremes; (2) temporal aspects such as spell length characteristics; (3) spatial aspects such as the de-correlation length of precipitation extremes; and multi-variate aspects such as the interplay of temperature and precipitation or scale-interactions. Several experiments have been designed to isolate specific points in the downscaling procedure where problems may occur. Experiment 1 (perfect predictors): what is the isolated downscaling skill? How do statistical and dynamical methods compare? How do methods perform at different spatial scales? Experiment 2 (Global climate model predictors): how is the overall representation of regional climate, including errors inherited from global climate models? Experiment 3 (pseudo reality): do methods fail in representing regional climate change? Here, we present a user-targeted synthesis of the results of the first VALUE experiment. In this experiment, downscaling methods are driven with ERA-Interim reanalysis data to eliminate global climate model errors, over the period 1979-2008. As reference data we use, depending on the question addressed, (1) observations from 86 meteorological stations distributed across Europe; (2) gridded observations at the corresponding 86 locations or (3) gridded spatially extended observations for selected European regions. With more than 40 contributing methods, this study is the most comprehensive downscaling inter-comparison project so far. The results clearly indicate that for several aspects, the downscaling skill varies considerably between different methods. For specific purposes, some methods can therefore clearly be excluded.

Hypofractionated Whole-Brain Radiotherapy for Multiple Brain Metastases From Transitional Cell Carcinoma of the Bladder

DOE Office of Scientific and Technical Information (OSTI.GOV)

Rades, Dirk, E-mail: Rades.Dirk@gmx.ne; Department of Radiation Oncology, University of Hamburg; Meyners, Thekla

2010-10-01

Purpose: Brain metastases in bladder cancer patients are extremely rare. Most patients with multiple lesions receive longer-course whole-brain radiotherapy (WBRT) with 10 x 3 Gy/2 weeks or 20 x 2 Gy/4 weeks. Because its radiosensitivity is relatively low, metastases from bladder cancer may be treated better with hypofractionated radiotherapy. This study compared short-course hypofractionated WBRT (5 x 4 Gy/1 week) to longer-course WBRT. Methods and Materials: Data for 33 patients receiving WBRT alone for multiple brain metastases from transitional cell bladder carcinoma were retrospectively analyzed. Short-course WBRT with 5 x 4 Gy (n = 12 patients) was compared to longer-coursemore » WBRT with 10 x 3 Gy/20 x 2 Gy (n = 21 patients) for overall survival (OS) and local (intracerebral) control (LC). Five additional potential prognostic factors were investigated: age, gender, Karnofsky performance score (KPS), number of brain metastases, and extracranial metastases. The Bonferroni correction for multiple tests was used to adjust the p values derived from the multivariate analysis. p values of <0.025 were considered significant. Results: At 6 months, OS was 42% after 5 x 4 Gy and 24% after 10 x 3/20 x 2 Gy (p = 0.31). On univariate analysis, improved OS was associated with less than four brain metastases (p = 0.021) and almost associated with a lack of extracranial metastases (p = 0.057). On multivariate analysis, both factors were not significant. At 6 months, LC was 83% after 5 x 4 Gy and 27% after 10 x 3/20 x 2 Gy (p = 0.035). Improved LC was almost associated with a KPS of {>=}70 (p = 0.051). On multivariate analysis, WBRT regimen was almost significant (p = 0.036). KPS showed a trend (p = 0.07). Conclusions: Short-course WBRT with 5 x 4 Gy should be seriously considered for most patients with multiple brain metastases from bladder cancer, as it resulted in improved LC.« less

FREQ: A computational package for multivariable system loop-shaping procedures

NASA Technical Reports Server (NTRS)

Giesy, Daniel P.; Armstrong, Ernest S.

1989-01-01

Many approaches in the field of linear, multivariable time-invariant systems analysis and controller synthesis employ loop-sharing procedures wherein design parameters are chosen to shape frequency-response singular value plots of selected transfer matrices. A software package, FREQ, is documented for computing within on unified framework many of the most used multivariable transfer matrices for both continuous and discrete systems. The matrices are evaluated at user-selected frequency-response values, and singular values against frequency. Example computations are presented to demonstrate the use of the FREQ code.

Development of disability in chronic obstructive pulmonary disease: beyond lung function.

PubMed

Eisner, Mark D; Iribarren, Carlos; Blanc, Paul D; Yelin, Edward H; Ackerson, Lynn; Byl, Nancy; Omachi, Theodore A; Sidney, Stephen; Katz, Patricia P

2011-02-01

COPD is a major cause of disability, but little is known about how disability develops in this condition. The authors analysed data from the Function, Living, Outcomes and Work (FLOW) Study which enrolled 1202 Kaiser Permanente Northern California members with COPD at baseline and re-evaluated 1051 subjects at 2-year follow-up. The authors tested the specific hypothesis that the development of specific non-respiratory impairments (abnormal body composition and muscle strength) and functional limitations (decreased lower extremity function, poor balance, mobility-related dyspnoea, reduced exercise performance and decreased cognitive function) will determine the risk of disability in COPD, after controlling for respiratory impairment (FEV(1) and oxygen saturation). The Valued Life Activities Scale was used to assess disability in terms of a broad range of daily activities. The primary disability outcome measure was defined as an increase in the proportion of activities that cannot be performed of 3.3% or greater from baseline to 2-year follow-up (the estimated minimal important difference). Multivariable logistic regression was used for analysis. Respiratory impairment measures were related to an increased prospective risk of disability (multivariate OR 1.75; 95% CI 1.26 to 2.44 for 1 litre decrement of FEV(1) and OR 1.57 per 5% decrement in oxygen saturation; 95% CI 1.13 to 2.18). Non-respiratory impairment (body composition and lower extremity muscle strength) and functional limitations (lower extremity function, exercise performance, and mobility-related dyspnoea) were all associated with an increased longitudinal risk of disability after controlling for respiratory impairment (p<0.05 in all cases). Non-respiratory impairment and functional limitations were predictive of prospective disability, above-and-beyond sociodemographic characteristics, smoking status and respiratory impairment (area under the receiver operating characteristic curve increased from 0.65 to 0.75; p<0.001). Development of non-respiratory impairment and functional limitations, which reflect the systemic nature of COPD, appear to be critical determinants of disablement. Prevention and treatment of disability require a comprehensive approach to the COPD patient.

Assessing the features of extreme smog in China and the differentiated treatment strategy

NASA Astrophysics Data System (ADS)

Deng, Lu; Zhang, Zhengjun

2018-01-01

Extreme smog can have potentially harmful effects on human health, the economy and daily life. However, the average (mean) values do not provide strategically useful information on the hazard analysis and control of extreme smog. This article investigates China's smog extremes by applying extreme value analysis to hourly PM2.5 data from 2014 to 2016 obtained from monitoring stations across China. By fitting a generalized extreme value (GEV) distribution to exceedances over a station-specific extreme smog level at each monitoring location, all study stations are grouped into eight different categories based on the estimated mean and shape parameter values of fitted GEV distributions. The extreme features characterized by the mean of the fitted extreme value distribution, the maximum frequency and the tail index of extreme smog at each location are analysed. These features can provide useful information for central/local government to conduct differentiated treatments in cities within different categories and conduct similar prevention goals and control strategies among those cities belonging to the same category in a range of areas. Furthermore, hazardous hours, breaking probability and the 1-year return level of each station are demonstrated by category, based on which the future control and reduction targets of extreme smog are proposed for the cities of Beijing, Tianjin and Hebei as an example.

Regional downscaling of temporal resolution in near-surface wind from statistically downscaled Global Climate Models (GCMs) for use in San Francisco Bay coastal flood modeling

NASA Astrophysics Data System (ADS)

O'Neill, A.; Erikson, L. H.; Barnard, P.

2013-12-01

While Global Climate Models (GCMs) provide useful projections of near-surface wind vectors into the 21st century, resolution is not sufficient enough for use in regional wave modeling. Statistically downscaled GCM projections from Multivariate Adaptive Constructed Analogues (MACA) provide daily near-surface winds at an appropriate spatial resolution for wave modeling within San Francisco Bay. Using 30 years (1975-2004) of climatological data from four representative stations around San Francisco Bay, a library of example daily wind conditions for four corresponding over-water sub-regions is constructed. Empirical cumulative distribution functions (ECDFs) of station conditions are compared to MACA GFDL hindcasts to create correction factors, which are then applied to 21st century MACA wind projections. For each projection day, a best match example is identified via least squares error among all stations from the library. The best match's daily variation in velocity components (u/v) is used as an analogue of representative wind variation and is applied at 3-hour increments about the corresponding sub-region's projected u/v values. High temporal resolution reconstructions using this methodology on hindcast MACA fields from 1975-2004 accurately recreate extreme wind values within the San Francisco Bay, and because these extremes in wind forcing are of key importance in wave and subsequent coastal flood modeling, this represents a valuable method of generating near-surface wind vectors for use in coastal flood modeling.

EMMPRIN co-expressed with matrix metalloproteinases predicts poor prognosis in patients with osteosarcoma.

PubMed

Futamura, Naohisa; Nishida, Yoshihiro; Urakawa, Hiroshi; Kozawa, Eiji; Ikuta, Kunihiro; Hamada, Shunsuke; Ishiguro, Naoki

2014-06-01

Several studies have focused on the relationships between the expression of extracellular matrix metalloproteinase inducer (EMMPRIN) and the prognosis of patients with malignant tumors. However, few of these have investigated the expression of EMMPRIN in osteosarcoma. We examined expression levels of EMMPRIN immunohistochemically in 53 cases of high-grade osteosarcoma of the extremities and analyzed the correlation of its expression with patient prognosis. The correlation between matrix metalloproteinases (MMPs) and EMMPRIN expression and the prognostic value of co-expression were also analyzed. Staining positivity for EMMPRIN was negative in 7 cases, low in 17, moderate in 19, and strong in 10. The overall and disease-free survivals (OS and DFS) in patients with higher EMMPRIN expression (strong-moderate) were significantly lower than those in the lower (weak-negative) group (0.037 and 0.024, respectively). In multivariate analysis, age (P=0.004), location (P=0.046), and EMMPRIN expression (P=0.038) were significant prognostic factors for overall survival. EMMPRIN expression (P=0.024) was also a significant prognostic factor for disease-free survival. Co-expression analyses of EMMPRIN and MMPs revealed that strong co-expression of EMMPRIN and membrane-type 1 (MT1)-MMP had a poor prognostic value (P=0.056 for DFS, P=0.006 for OS). EMMPRIN expression and co-expression with MMPs well predict the prognosis of patients with extremity osteosarcoma, making EMMPRIN a possible therapeutic target in these patients.

Prematurity and fetal lung response after tracheal occlusion in fetuses with severe congenital diaphragmatic hernia.

PubMed

Sananes, Nicolas; Rodo, Carlota; Peiro, Jose Luis; Britto, Ingrid Schwach Werneck; Sangi-Haghpeykar, Haleh; Favre, Romain; Joal, Arnaud; Gaudineau, Adrien; Silva, Marcos Marques da; Tannuri, Uenis; Zugaib, Marcelo; Carreras, Elena; Ruano, Rodrigo

2016-09-01

To evaluate the independent association of fetal pulmonary response and prematurity to postnatal outcomes after fetal tracheal occlusion for congenital diaphragmatic hernia. Fetal pulmonary response, prematurity (<37 weeks at delivery) and extreme prematurity (<32 weeks at delivery) were evaluated and compared between survivors and non-survivors at 6 months of life. Multivariable analysis was conducted with generalized linear mixed models for variables significantly associated with survival in univariate analysis. Eighty-four infants were included, of whom 40 survived (47.6%) and 44 died (52.4%). Univariate analysis demonstrated that survival was associated with greater lung response (p=0.006), and the absence of extreme preterm delivery (p=0.044). In multivariable analysis, greater pulmonary response after FETO was an independent predictor of survival (aOR 1.87, 95% CI 1.08-3.33, p=0.023), whereas the presence of extreme prematurity was not statistically associated with mortality after controlling for fetal pulmonary response (aOR 0.52, 95% CI 0.12-2.30, p=0.367). Fetal pulmonary response after FETO is the most important factor associated with survival, independently from the gestational age at delivery.

Comparison of the Effects of Walking with and without Nordic Pole on Upper Extremity and Lower Extremity Muscle Activation.

PubMed

Shim, Je-Myung; Kwon, Hae-Yeon; Kim, Ha-Roo; Kim, Bo-In; Jung, Ju-Hyeon

2013-12-01

[Purpose] The aim of this study was to assess the effect of Nordic pole walking on the electromyographic activities of upper extremity and lower extremity muscles. [Subjects and Methods] The subjects were randomly divided into two groups as follows: without Nordic pole walking group (n=13) and with Nordic pole walking group (n=13). The EMG data were collected by measurement while the subjects walking on a treadmill for 30 minutes by measuring from one heel strike to the next. [Results] Both the average values and maximum values of the muscle activity of the upper extremity increased in both the group that used Nordic poles and the group that did not use Nordic poles, and the values showed statistically significant differences. There was an increase in the average value for muscle activity of the latissimus dorsi, but the difference was not statistically significant, although there was a statistically significant increase in its maximum value. The average and maximum values for muscle activity of the lower extremity did not show large differences in either group, and the values did not show any statistically significant differences. [Conclusion] The use of Nordic poles by increased muscle activity of the upper extremity compared with regular walking but did not affect the lower extremity.

Comparison of the Effects of Walking with and without Nordic Pole on Upper Extremity and Lower Extremity Muscle Activation

PubMed Central

Shim, Je-myung; Kwon, Hae-yeon; Kim, Ha-roo; Kim, Bo-in; Jung, Ju-hyeon

2014-01-01

[Purpose] The aim of this study was to assess the effect of Nordic pole walking on the electromyographic activities of upper extremity and lower extremity muscles. [Subjects and Methods] The subjects were randomly divided into two groups as follows: without Nordic pole walking group (n=13) and with Nordic pole walking group (n=13). The EMG data were collected by measurement while the subjects walking on a treadmill for 30 minutes by measuring from one heel strike to the next. [Results] Both the average values and maximum values of the muscle activity of the upper extremity increased in both the group that used Nordic poles and the group that did not use Nordic poles, and the values showed statistically significant differences. There was an increase in the average value for muscle activity of the latissimus dorsi, but the difference was not statistically significant, although there was a statistically significant increase in its maximum value. The average and maximum values for muscle activity of the lower extremity did not show large differences in either group, and the values did not show any statistically significant differences. [Conclusion] The use of Nordic poles by increased muscle activity of the upper extremity compared with regular walking but did not affect the lower extremity. PMID:24409018

An R2 statistic for fixed effects in the linear mixed model.

PubMed

Edwards, Lloyd J; Muller, Keith E; Wolfinger, Russell D; Qaqish, Bahjat F; Schabenberger, Oliver

2008-12-20

Statisticians most often use the linear mixed model to analyze Gaussian longitudinal data. The value and familiarity of the R(2) statistic in the linear univariate model naturally creates great interest in extending it to the linear mixed model. We define and describe how to compute a model R(2) statistic for the linear mixed model by using only a single model. The proposed R(2) statistic measures multivariate association between the repeated outcomes and the fixed effects in the linear mixed model. The R(2) statistic arises as a 1-1 function of an appropriate F statistic for testing all fixed effects (except typically the intercept) in a full model. The statistic compares the full model with a null model with all fixed effects deleted (except typically the intercept) while retaining exactly the same covariance structure. Furthermore, the R(2) statistic leads immediately to a natural definition of a partial R(2) statistic. A mixed model in which ethnicity gives a very small p-value as a longitudinal predictor of blood pressure (BP) compellingly illustrates the value of the statistic. In sharp contrast to the extreme p-value, a very small R(2) , a measure of statistical and scientific importance, indicates that ethnicity has an almost negligible association with the repeated BP outcomes for the study.

[Predictive factors of mortality in extremely preterm infants].

PubMed

Lin, L; Fang, M C; Jiang, H; Zhu, M L; Chen, S Q; Lin, Z L

2018-04-02

Objective: To investigate the predictive factors of mortality in extremely preterm infants. Methods: The retrospective case-control study was accomplished in the Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University. A total of 268 extremely preterm infants seen from January 1, 1999 to December 31, 2015 were divided into survival group (192 cases) and death group (76 cases). The potential predictive factors of mortality were identified by univariate analysis, and then analyzed by multivariate unconditional Logistic regression analysis. The mortality and predictive factors were also compared between two time periods, which were January 1, 1999 to December 31, 2007 (65 cases) and January 1, 2008 to December 31, 2015 (203 cases). Results: The median gestational age (GA) of extremely preterm infants was 27 weeks (23 +3 -27 +6 weeks). The mortality was higher in infants with GA of 25-<26 weeks ( OR= 2.659, 95% CI: 1.211-5.840) and<25 weeks ( OR= 10.029, 95% CI: 3.266-30.792) compared to that in infants with GA> 26 weeks. From January 1, 2008 to December 31, 2015, the number of extremely preterm infants was increased significantly compared to the previous 9 years, while the mortality decreased significantly ( OR= 0.490, 95% CI: 0.272-0.884). Multivariate unconditional Logistic regression analysis showed that GA below 25 weeks ( OR= 6.033, 95% CI: 1.393-26.133), lower birth weight ( OR= 0.997, 95% CI: 0.995-1.000), stage Ⅲ necrotizing enterocolitis (NEC) ( OR= 15.907, 95% CI: 3.613-70.033), grade Ⅰ and Ⅱ intraventricular hemorrhage (IVH) ( OR= 0.260, 95% CI: 0.117-0.575) and dependence on invasive mechanical ventilation ( OR= 3.630, 95% CI: 1.111-11.867) were predictive factors of mortality in extremely preterm infants. Conclusions: GA below 25 weeks, lower birth weight, stage Ⅲ NEC and dependence on invasive mechanical ventilation are risk factors of mortality in extremely preterm infants. But grade ⅠandⅡ IVH is protective factor.

Extreme values in the Chinese and American stock markets based on detrended fluctuation analysis

NASA Astrophysics Data System (ADS)

Cao, Guangxi; Zhang, Minjia

2015-10-01

This paper focuses on the comparative analysis of extreme values in the Chinese and American stock markets based on the detrended fluctuation analysis (DFA) algorithm using the daily data of Shanghai composite index and Dow Jones Industrial Average. The empirical results indicate that the multifractal detrended fluctuation analysis (MF-DFA) method is more objective than the traditional percentile method. The range of extreme value of Dow Jones Industrial Average is smaller than that of Shanghai composite index, and the extreme value of Dow Jones Industrial Average is more time clustering. The extreme value of the Chinese or American stock markets is concentrated in 2008, which is consistent with the financial crisis in 2008. Moreover, we investigate whether extreme events affect the cross-correlation between the Chinese and American stock markets using multifractal detrended cross-correlation analysis algorithm. The results show that extreme events have nothing to do with the cross-correlation between the Chinese and American stock markets.

Valuing happiness is associated with bipolar disorder.

PubMed

Ford, Brett Q; Mauss, Iris B; Gruber, June

2015-04-01

Although people who experience happiness tend to have better psychological health, people who value happiness to an extreme tend to have worse psychological health, including more depression. We propose that the extreme valuing of happiness may be a general risk factor for mood disturbances, both depressive and manic. To test this hypothesis, we examined the relationship between the extreme valuing of happiness and risk for, diagnosis of, and illness course for bipolar disorder (BD). Supporting our hypothesis, the extreme valuing of happiness was associated with a measure of increased risk for developing BD (Studies 1 and 2), increased likelihood of past diagnosis of BD (Studies 2 and 3), and worse prospective illness course in BD (Study 3), even when controlling for current mood symptoms (Studies 1-3). These findings indicate that the extreme valuing of happiness is associated with and even predicts BD. Taken together with previous evidence, these findings suggest that the extreme valuing of happiness is a general risk factor for mood disturbances. More broadly, what emotions people strive to feel may play a critical role in psychological health. (c) 2015 APA, all rights reserved).

Valuing happiness is associated with bipolar disorder

PubMed Central

Ford, Brett Q.; Mauss, Iris B.; Gruber, June

2015-01-01

While people who experience happiness tend to have better psychological health, people who value happiness to an extreme tend to have worse psychological health, including more depression. We propose that the extreme valuing of happiness may be a general risk factor for mood disturbances, both depressive and manic. To test this hypothesis, we examined the relationship between the extreme valuing of happiness and risk for, diagnosis of, and illness course for Bipolar Disorder (BD). Supporting our hypothesis, the extreme valuing of happiness was associated with a measure of increased risk for developing BD (Studies 1–2), increased likelihood of past diagnosis of BD (Studies 2–3), and worse prospective illness course in BD (Study 3), even when controlling for current mood symptoms (Studies 1–3). These findings indicate that the extreme valuing of happiness is associated with and even predicts BD. Taken together with previous evidence, these findings suggest that the extreme valuing of happiness is a general risk factor for mood disturbances. More broadly, what emotions people strive to feel may play a critical role in psychological health. PMID:25603134

Tobacco Withdrawal Components and Their Relations with Cessation Success

PubMed Central

Piper, Megan E.; Schlam, Tanya R.; Cook, Jessica W.; Sheffer, Megan A.; Smith, Stevens S.; Loh, Wei-Yin; Bolt, Daniel M.; Kim, Su-Young; Kaye, Jesse T.; Hefner, Kathryn R.; Baker, Timothy B.

2011-01-01

Rationale Tobacco withdrawal is a key factor in smoking relapse, but important questions about the withdrawal phenomenon remain. Objectives This research was intended to provide information about two core components of withdrawal (negative affect and craving): 1) how various withdrawal symptom profile dimensions (e.g., mean level, volatility, extreme values) differ between negative affect and craving; and 2) how these dimensions relate to cessation outcome. Methods Adult smokers (N=1504) in a double-blind randomized placebo-controlled smoking cessation trial provided real-time withdrawal symptom data four times per day for 4 weeks (2 weeks pre-quit and 2 weeks post-quit) via palmtop computers. Cessation outcome was biochemically confirmed 8-week point-prevalence abstinence. Results Examination of craving and negative affect dimensions following a cessation attempt revealed that craving symptoms differed from negative affect symptoms, with higher means, greater variability, and a greater incidence of extreme peaks. Regression analyses revealed that abstinence was associated with lower mean levels of both craving and negative affect and fewer incidences of extreme craving peaks. In a multivariate model, the increase in mean craving and negative affect scores each uniquely predicted relapse. Conclusions Real-time reports revealed different patterns of abstinence-related negative affect and craving and that dimensions of both predict cessation outcome, suggesting that negative affect and craving dimensions each has motivational significance. This underscores the complexity of withdrawal as a determinant of relapse and the need to measure its distinct components and dimensions. PMID:21416234

Prognosis Relevance of Serum Cytokines in Pancreatic Cancer

PubMed Central

Alejandre, Maria José; Palomino-Morales, Rogelio J.; Prados, Jose; Aránega, Antonia; Delgado, Juan R.; Irigoyen, Antonio; Martínez-Galán, Joaquina; Ortuño, Francisco M.

2015-01-01

The overall survival of patients with pancreatic ductal adenocarcinoma is extremely low. Although gemcitabine is the standard used chemotherapy for this disease, clinical outcomes do not reflect significant improvements, not even when combined with adjuvant treatments. There is an urgent need for prognosis markers to be found. The aim of this study was to analyze the potential value of serum cytokines to find a profile that can predict the clinical outcome in patients with pancreatic cancer and to establish a practical prognosis index that significantly predicts patients' outcomes. We have conducted an extensive analysis of serum prognosis biomarkers using an antibody array comprising 507 human cytokines. Overall survival was estimated using the Kaplan-Meier method. Univariate and multivariate Cox's proportional hazard models were used to analyze prognosis factors. To determine the extent that survival could be predicted based on this index, we used the leave-one-out cross-validation model. The multivariate model showed a better performance and it could represent a novel panel of serum cytokines that correlates to poor prognosis in pancreatic cancer. B7-1/CD80, EG-VEGF/PK1, IL-29, NRG1-beta1/HRG1-beta1, and PD-ECGF expressions portend a poor prognosis for patients with pancreatic cancer and these cytokines could represent novel therapeutic targets for this disease. PMID:26346854

Using phenotypic manipulations to study multivariate selection of floral trait associations.

PubMed

Campbell, Diane R

2009-06-01

A basic theme in the study of plant-pollinator interactions is that pollinators select not just for single floral traits, but for associations of traits. Responses of pollinators to sets of traits are inherent in the idea of pollinator syndromes. In its most extreme form, selection on a suite of traits can take the form of correlational selection, in which a response to one trait depends on the value of another, thereby favouring floral integration. Despite the importance of selection for combinations of traits in the evolution of flowers, evidence is relatively sparse and relies mostly on observational approaches. Here, methods for measuring selection on multivariate suites of floral traits are presented, and the studies to date are reviewed. It is argued that phenotypic manipulations present a powerful, but rarely used, approach to teasing apart the separate and combined effects of particular traits. The approach is illustrated with data from studies of alpine plants in Colorado and New Zealand, and recommendations are made about several features of the design of such experiments. Phenotypic manipulations of two or more traits in combination provide a direct way of testing for selection of floral trait associations. Such experiments will be particularly valuable if rooted in hypotheses about differences between types of pollinators and tied to a proposed evolutionary history.

Semi-nonparametric VaR forecasts for hedge funds during the recent crisis

NASA Astrophysics Data System (ADS)

Del Brio, Esther B.; Mora-Valencia, Andrés; Perote, Javier

2014-05-01

The need to provide accurate value-at-risk (VaR) forecasting measures has triggered an important literature in econophysics. Although these accurate VaR models and methodologies are particularly demanded for hedge fund managers, there exist few articles specifically devoted to implement new techniques in hedge fund returns VaR forecasting. This article advances in these issues by comparing the performance of risk measures based on parametric distributions (the normal, Student’s t and skewed-t), semi-nonparametric (SNP) methodologies based on Gram-Charlier (GC) series and the extreme value theory (EVT) approach. Our results show that normal-, Student’s t- and Skewed t- based methodologies fail to forecast hedge fund VaR, whilst SNP and EVT approaches accurately success on it. We extend these results to the multivariate framework by providing an explicit formula for the GC copula and its density that encompasses the Gaussian copula and accounts for non-linear dependences. We show that the VaR obtained by the meta GC accurately captures portfolio risk and outperforms regulatory VaR estimates obtained through the meta Gaussian and Student’s t distributions.

Extreme-value dependence: An application to exchange rate markets

NASA Astrophysics Data System (ADS)

Fernandez, Viviana

2007-04-01

Extreme value theory (EVT) focuses on modeling the tail behavior of a loss distribution using only extreme values rather than the whole data set. For a sample of 10 countries with dirty/free float regimes, we investigate whether paired currencies exhibit a pattern of asymptotic dependence. That is, whether an extremely large appreciation or depreciation in the nominal exchange rate of one country might transmit to another. In general, after controlling for volatility clustering and inertia in returns, we do not find evidence of extreme-value dependence between paired exchange rates. However, for asymptotic-independent paired returns, we find that tail dependency of exchange rates is stronger under large appreciations than under large depreciations.

Exchangeability, extreme returns and Value-at-Risk forecasts

NASA Astrophysics Data System (ADS)

Huang, Chun-Kai; North, Delia; Zewotir, Temesgen

2017-07-01

In this paper, we propose a new approach to extreme value modelling for the forecasting of Value-at-Risk (VaR). In particular, the block maxima and the peaks-over-threshold methods are generalised to exchangeable random sequences. This caters for the dependencies, such as serial autocorrelation, of financial returns observed empirically. In addition, this approach allows for parameter variations within each VaR estimation window. Empirical prior distributions of the extreme value parameters are attained by using resampling procedures. We compare the results of our VaR forecasts to that of the unconditional extreme value theory (EVT) approach and the conditional GARCH-EVT model for robust conclusions.

Bayesian Methods for Scalable Multivariate Value-Added Assessment

ERIC Educational Resources Information Center

Lockwood, J. R.; McCaffrey, Daniel F.; Mariano, Louis T.; Setodji, Claude

2007-01-01

There is increased interest in value-added models relying on longitudinal student-level test score data to isolate teachers' contributions to student achievement. The complex linkage of students to teachers as students progress through grades poses both substantive and computational challenges. This article introduces a multivariate Bayesian…

Statistic analysis of annual total ozone extremes for the period 1964-1988

NASA Technical Reports Server (NTRS)

Krzyscin, Janusz W.

1994-01-01

Annual extremes of total column amount of ozone (in the period 1964-1988) from a network of 29 Dobson stations have been examined using the extreme value analysis. The extremes have been calculated as the highest deviation of daily mean total ozone from its long-term monthly mean, normalized by the monthly standard deviations. The extremes have been selected from the direct-Sun total ozone observations only. The extremes resulting from abrupt changes in ozone (day to day changes greater than 20 percent) have not been considered. The ordered extremes (maxima in ascending way, minima in descending way) have been fitted to one of three forms of the Fisher-Tippet extreme value distribution by the nonlinear least square method (Levenberg-Marguard method). We have found that the ordered extremes from a majority of Dobson stations lie close to Fisher-Tippet type III. The extreme value analysis of the composite annual extremes (combined from averages of the annual extremes selected at individual stations) has shown that the composite maxima are fitted by the Fisher-Tippet type III and the composite minima by the Fisher-Tippet type I. The difference between the Fisher-Tippet types of the composite extremes seems to be related to the ozone downward trend. Extreme value prognoses for the period 1964-2014 (derived from the data taken at: all analyzed stations, the North American, and the European stations) have revealed that the prognostic extremes are close to the largest annual extremes in the period 1964-1988 and there are only small regional differences in the prognoses.

Household food insecurity and symptoms of neurologic disorder in Ethiopia: an observational analysis.

PubMed

El-Sayed, Abdulrahman M; Hadley, Craig; Tessema, Fasil; Tegegn, Ayelew; Cowan, John A; Galea, Sandro

2010-12-31

Food insecurity (FI) has been shown to be associated with poor health both in developing and developed countries. Little is known about the relation between FI and neurological disorder. We assessed the relation between FI and risk for neurologic symptoms in southwest Ethiopia. Data about food security, gender, age, household assets, and self-reported neurologic symptoms were collected from a representative, community-based sample of adults (N = 900) in Jimma Zone, Ethiopia. We calculated univariate statistics and used bivariate chi-square tests and multivariate logistic regression models to assess the relation between FI and risk of neurologic symptoms including seizures, extremity weakness, extremity numbness, tremors/ataxia, aphasia, carpal tunnel syndrome, vision dysfunction, and spinal pain. In separate multivariate models by outcome and gender, adjusting for age and household socioeconomic status, severe FI was associated with higher odds of seizures, movement abnormalities, carpal tunnel, vision dysfunction, spinal pain, and comorbid disorders among women. Severe FI was associated with higher odds of seizures, extremity numbness, movement abnormalities, difficulty speaking, carpal tunnel, vision dysfunction, and comorbid disorders among men. We found that FI was associated with symptoms of neurologic disorder. Given the cross-sectional nature of our study, the directionality of these associations is unclear. Future research should assess causal mechanisms relating FI to neurologic symptoms in sub-Saharan Africa.

Extreme events in total ozone over Arosa - Part 2: Fingerprints of atmospheric dynamics and chemistry and effects on mean values and long-term changes

NASA Astrophysics Data System (ADS)

Rieder, H. E.; Staehelin, J.; Maeder, J. A.; Peter, T.; Ribatet, M.; Davison, A. C.; Stübi, R.; Weihs, P.; Holawe, F.

2010-10-01

In this study the frequency of days with extreme low (termed ELOs) and extreme high (termed EHOs) total ozone values and their influence on mean values and trends are analyzed for the world's longest total ozone record (Arosa, Switzerland). The results show (i) an increase in ELOs and (ii) a decrease in EHOs during the last decades and (iii) that the overall trend during the 1970s and 1980s in total ozone is strongly dominated by changes in these extreme events. After removing the extremes, the time series shows a strongly reduced trend (reduction by a factor of 2.5 for trend in annual mean). Excursions in the frequency of extreme events reveal "fingerprints" of dynamical factors such as ENSO or NAO, and chemical factors, such as cold Arctic vortex ozone losses, as well as major volcanic eruptions of the 20th century (Gunung Agung, El Chichón, Mt. Pinatubo). Furthermore, atmospheric loading of ozone depleting substances leads to a continuous modification of column ozone in the Northern Hemisphere also with respect to extreme values (partly again in connection with polar vortex contributions). Application of extreme value theory allows the identification of many more such "fingerprints" than conventional time series analysis of annual and seasonal mean values. The analysis shows in particular the strong influence of dynamics, revealing that even moderate ENSO and NAO events have a discernible effect on total ozone. Overall the approach to extremal modelling provides new information on time series properties, variability, trends and the influence of dynamics and chemistry, complementing earlier analyses focusing only on monthly (or annual) mean values.

Extreme events in total ozone over Arosa - Part 2: Fingerprints of atmospheric dynamics and chemistry and effects on mean values and long-term changes

NASA Astrophysics Data System (ADS)

Rieder, H. E.; Staehelin, J.; Maeder, J. A.; Peter, T.; Ribatet, M.; Davison, A. C.; Stübi, R.; Weihs, P.; Holawe, F.

2010-05-01

In this study the frequency of days with extreme low (termed ELOs) and extreme high (termed EHOs) total ozone values and their influence on mean values and trends are analyzed for the world's longest total ozone record (Arosa, Switzerland). The results show (a) an increase in ELOs and (b) a decrease in EHOs during the last decades and (c) that the overall trend during the 1970s and 1980s in total ozone is strongly dominated by changes in these extreme events. After removing the extremes, the time series shows a strongly reduced trend (reduction by a factor of 2.5 for trend in annual mean). Excursions in the frequency of extreme events reveal "fingerprints" of dynamical factors such as ENSO or NAO, and chemical factors, such as cold Arctic vortex ozone losses, as well as major volcanic eruptions of the 20th century (Gunung Agung, El Chichón, Mt. Pinatubo). Furthermore, atmospheric loading of ozone depleting substances leads to a continuous modification of column ozone in the Northern Hemisphere also with respect to extreme values (partly again in connection with polar vortex contributions). Application of extreme value theory allows the identification of many more such "fingerprints" than conventional time series analysis of annual and seasonal mean values. The analysis shows in particular the strong influence of dynamics, revealing that even moderate ENSO and NAO events have a discernible effect on total ozone. Overall the approach to extremal modelling provides new information on time series properties, variability, trends and the influence of dynamics and chemistry, complementing earlier analyses focusing only on monthly (or annual) mean values.

Risk factors associated with PICC-related upper extremity venous thrombosis in cancer patients.

PubMed

Yi, Xiao-lei; Chen, Jie; Li, Jia; Feng, Liang; Wang, Yan; Zhu, Jia-An; Shen, E; Hu, Bing

2014-03-01

To investigate the incidence and risk factors for peripherally inserted central venous catheters-related upper extremity venous thrombosis in patients with cancer. With the widespread use of peripherally inserted central venous catheters, peripherally inserted central venous catheters-related upper extremity venous thrombosis in patients with cancer leads to increasing morbidity and mortality. It is very important to further explore the incidence and risk factors for peripherally inserted central venous catheters-related venous thrombosis. Consecutive patients with cancer who were scheduled to receive peripherally inserted central venous catheters, between September 2009 and May 2012, were prospectively studied in our centre. They were investigated for venous thrombosis by Doppler sonography three times a day within 30 days after catheter insertion. Univariable and multivariable logistic regressions' analyses were performed to identify the risk factors for peripherally inserted central venous catheters-related thrombosis. A total of 89 patients with cancer were studied in our research. Of these, 81 patients were followed up within one month. The mean interval between catheter insertion and the onset of thrombosis was 12.45 ± 6.17 days. The multivariable analyses showed that chemotherapy history, less activities and diabetes were the key risk factors for thrombosis. Peripherally inserted central venous catheters-related upper extremity venous thrombosis had high incidence rate, and most cases had no significant symptoms. The history of chemotherapy, less activities and diabetes were found to be the key risk factors. It should be routinely scanned in high-risk patients every 3-5 days after catheter insertion, which would then find blood clots in time and reduce the incidence of pulmonary embolism. Risk factors associated with peripherally inserted central venous catheters-related upper extremity venous thrombosis are of critical importance in improving the quality of patients' life. It is very important to grasp the indications to reduce the incidence rate of peripherally inserted central venous catheters-related upper extremity venous thrombosis. © 2013 John Wiley & Sons Ltd.

Rainfall extremes from TRMM data and the Metastatistical Extreme Value Distribution

NASA Astrophysics Data System (ADS)

Zorzetto, Enrico; Marani, Marco

2017-04-01

A reliable quantification of the probability of weather extremes occurrence is essential for designing resilient water infrastructures and hazard mitigation measures. However, it is increasingly clear that the presence of inter-annual climatic fluctuations determines a substantial long-term variability in the frequency of occurrence of extreme events. This circumstance questions the foundation of the traditional extreme value theory, hinged on stationary Poisson processes or on asymptotic assumptions to derive the Generalized Extreme Value (GEV) distribution. We illustrate here, with application to daily rainfall, a new approach to extreme value analysis, the Metastatistical Extreme Value Distribution (MEVD). The MEVD relaxes the above assumptions and is based on the whole distribution of daily rainfall events, thus allowing optimal use of all available observations. Using a global dataset of rain gauge observations, we show that the MEVD significantly outperforms the Generalized Extreme Value distribution, particularly for long average recurrence intervals and when small samples are available. The latter property suggests MEVD to be particularly suited for applications to satellite rainfall estimates, which only cover two decades, thus making extreme value estimation extremely challenging. Here we apply MEVD to the TRMM TMPA 3B42 product, an 18-year dataset of remotely-sensed daily rainfall providing a quasi-global coverage. Our analyses yield a global scale mapping of daily rainfall extremes and of their distributional tail properties, bridging the existing large gaps in ground-based networks. Finally, we illustrate how our global-scale analysis can provide insight into how properties of local rainfall regimes affect tail estimation uncertainty when using the GEV or MEVD approach. We find a dependence of the estimation uncertainty, for both the GEV- and MEV-based approaches, on the average annual number and on the inter-annual variability of rainy days. In particular, estimation uncertainty decreases 1) as the mean annual number of wet days increases, and 2) as the variability in the number of rainy days, expressed by its coefficient of variation, decreases. We tentatively explain this behavior in terms of the assumptions underlying the two approaches.

Nonstationary Extreme Value Analysis in a Changing Climate: A Software Package

NASA Astrophysics Data System (ADS)

Cheng, L.; AghaKouchak, A.; Gilleland, E.

2013-12-01

Numerous studies show that climatic extremes have increased substantially in the second half of the 20th century. For this reason, analysis of extremes under a nonstationary assumption has received a great deal of attention. This paper presents a software package developed for estimation of return levels, return periods, and risks of climatic extremes in a changing climate. This MATLAB software package offers tools for analysis of climate extremes under both stationary and non-stationary assumptions. The Nonstationary Extreme Value Analysis (hereafter, NEVA) provides an efficient and generalized framework for analyzing extremes using Bayesian inference. NEVA estimates the extreme value parameters using a Differential Evolution Markov Chain (DE-MC) which utilizes the genetic algorithm Differential Evolution (DE) for global optimization over the real parameter space with the Markov Chain Monte Carlo (MCMC) approach and has the advantage of simplicity, speed of calculation and convergence over conventional MCMC. NEVA also offers the confidence interval and uncertainty bounds of estimated return levels based on the sampled parameters. NEVA integrates extreme value design concepts, data analysis tools, optimization and visualization, explicitly designed to facilitate analysis extremes in geosciences. The generalized input and output files of this software package make it attractive for users from across different fields. Both stationary and nonstationary components of the package are validated for a number of case studies using empirical return levels. The results show that NEVA reliably describes extremes and their return levels.

Applied extreme-value statistics

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kinnison, R.R.

1983-05-01

The statistical theory of extreme values is a well established part of theoretical statistics. Unfortunately, it is seldom part of applied statistics and is infrequently a part of statistical curricula except in advanced studies programs. This has resulted in the impression that it is difficult to understand and not of practical value. In recent environmental and pollution literature, several short articles have appeared with the purpose of documenting all that is necessary for the practical application of extreme value theory to field problems (for example, Roberts, 1979). These articles are so concise that only a statistician can recognise all themore » subtleties and assumptions necessary for the correct use of the material presented. The intent of this text is to expand upon several recent articles, and to provide the necessary statistical background so that the non-statistician scientist can recognize and extreme value problem when it occurs in his work, be confident in handling simple extreme value problems himself, and know when the problem is statistically beyond his capabilities and requires consultation.« less

Bivariate extreme value distributions

NASA Technical Reports Server (NTRS)

Elshamy, M.

1992-01-01

In certain engineering applications, such as those occurring in the analyses of ascent structural loads for the Space Transportation System (STS), some of the load variables have a lower bound of zero. Thus, the need for practical models of bivariate extreme value probability distribution functions with lower limits was identified. We discuss the Gumbel models and present practical forms of bivariate extreme probability distributions of Weibull and Frechet types with two parameters. Bivariate extreme value probability distribution functions can be expressed in terms of the marginal extremel distributions and a 'dependence' function subject to certain analytical conditions. Properties of such bivariate extreme distributions, sums and differences of paired extremals, as well as the corresponding forms of conditional distributions, are discussed. Practical estimation techniques are also given.

Future Projection of Summer Extreme Precipitation from High Resolution Multi-RCMs over East Asia

NASA Astrophysics Data System (ADS)

Kim, Gayoung; Park, Changyong; Cha, Dong-Hyun; Lee, Dong-Kyou; Suh, Myoung-Seok; Ahn, Joong-Bae; Min, Seung-Ki; Hong, Song-You; Kang, Hyun-Suk

2017-04-01

Recently, the frequency and intensity of natural hazards have been increasing due to human-induced climate change. Because most damages of natural hazards over East Asia have been related to extreme precipitation events, it is important to estimate future change in extreme precipitation characteristics caused by climate change. We investigate future changes in extremal values of summer precipitation simulated by five regional climate models participating in the CORDEX-East Asia project (i.e., HadGEM3-RA, RegCM4, MM5, WRF, and GRIMs) over East Asia. 100-year return value calculated from the generalized extreme value (GEV) parameters is analysed as an indicator of extreme intensity. In the future climate, the mean values as well as the extreme values of daily precipitation tend to increase over land region. The increase of 100-year return value can be significantly associated with the changes in the location (intensity) and scale (variability) GEV parameters for extreme precipitation. It is expected that the results of this study can be used as fruitful references when making the policy of disaster management. Acknowledgements The research was supported by the Ministry of Public Safety and Security of Korean government and Development program under grant MPSS-NH-2013-63 and the National Research Foundation of Korea Grant funded by the Ministry of Science, ICT and Future Planning of Korea (NRF-2016M3C4A7952637) for its support and assistant in completion of the study.

Extremely cold events and sudden air temperature drops during winter season in the Czech Republic

NASA Astrophysics Data System (ADS)

Crhová, Lenka; Valeriánová, Anna; Holtanová, Eva; Müller, Miloslav; Kašpar, Marek; Stříž, Martin

2014-05-01

Today a great attention is turned to analysis of extreme weather events and frequency of their occurrence under changing climate. In most cases, these studies are focused on extremely warm events in summer season. However, extremely low values of air temperature during winter can have serious impacts on many sectors as well (e.g. power engineering, transportation, industry, agriculture, human health). Therefore, in present contribution we focus on extremely and abnormally cold air temperature events in winter season in the Czech Republic. Besides the seasonal extremes of minimum air temperature determined from station data, the standardized data with removed annual cycle are used as well. Distribution of extremely cold events over the season and the temporal evolution of frequency of occurrence during the period 1961-2010 are analyzed. Furthermore, the connection of cold events with extreme sudden temperature drops is studied. The extreme air temperature events and events of extreme sudden temperature drop are assessed using the Weather Extremity Index, which evaluates the extremity (based on return periods) and spatial extent of the meteorological extreme event of interest. The generalized extreme value distribution parameters are used to estimate return periods of daily temperature values. The work has been supported by the grant P209/11/1990 funded by the Czech Science Foundation.

Spatiotemporal variability of extreme temperature frequency and amplitude in China

NASA Astrophysics Data System (ADS)

Zhang, Yuanjie; Gao, Zhiqiu; Pan, Zaitao; Li, Dan; Huang, Xinhui

2017-03-01

Temperature extremes in China are examined based on daily maximum and minimum temperatures from station observations and multiple global climate models. The magnitude and frequency of extremes are expressed in terms of return values and periods, respectively, estimated by the fitted Generalized Extreme Value (GEV) distribution of annual extreme temperatures. The observations suggest that changes in temperature extremes considerably exceed changes in the respective climatological means during the past five decades, with greater amplitude of increases in cold extremes than in warm extremes. The frequency of warm (cold) extremes increases (decreases) over most areas, with an increasingly faster rate as the extremity level rises. Changes in warm extremes are more dependent on the varying shape of GEV distribution than the location shift, whereas changes in cold extremes are more closely associated with the location shift. The models simulate the overall pattern of temperature extremes during 1961-1981 reasonably well in China, but they show a smaller asymmetry between changes in warm and cold extremes primarily due to their underestimation of increases in cold extremes especially over southern China. Projections from a high emission scenario show the multi-model median change in warm and cold extremes by 2040 relative to 1971 will be 2.6 °C and 2.8 °C, respectively, with the strongest changes in cold extremes shifting southward. By 2040, warm extremes at the 1971 20-year return values would occur about every three years, while the 1971 cold extremes would occur once in > 500 years.

Identifying and Clarifying Organizational Values.

ERIC Educational Resources Information Center

Seevers, Brenda S.

2000-01-01

Of the 14 organizational values ranked by a majority of 146 New Mexico Cooperative Extension educators as extremely valued, 9 were extremely evident in organizational policies and procedures. A values audit such as this forms an important initial step in strategic planning. (SK)

A general framework for multivariate multi-index drought prediction based on Multivariate Ensemble Streamflow Prediction (MESP)

NASA Astrophysics Data System (ADS)

Hao, Zengchao; Hao, Fanghua; Singh, Vijay P.

2016-08-01

Drought is among the costliest natural hazards worldwide and extreme drought events in recent years have caused huge losses to various sectors. Drought prediction is therefore critically important for providing early warning information to aid decision making to cope with drought. Due to the complicated nature of drought, it has been recognized that the univariate drought indicator may not be sufficient for drought characterization and hence multivariate drought indices have been developed for drought monitoring. Alongside the substantial effort in drought monitoring with multivariate drought indices, it is of equal importance to develop a drought prediction method with multivariate drought indices to integrate drought information from various sources. This study proposes a general framework for multivariate multi-index drought prediction that is capable of integrating complementary prediction skills from multiple drought indices. The Multivariate Ensemble Streamflow Prediction (MESP) is employed to sample from historical records for obtaining statistical prediction of multiple variables, which is then used as inputs to achieve multivariate prediction. The framework is illustrated with a linearly combined drought index (LDI), which is a commonly used multivariate drought index, based on climate division data in California and New York in the United States with different seasonality of precipitation. The predictive skill of LDI (represented with persistence) is assessed by comparison with the univariate drought index and results show that the LDI prediction skill is less affected by seasonality than the meteorological drought prediction based on SPI. Prediction results from the case study show that the proposed multivariate drought prediction outperforms the persistence prediction, implying a satisfactory performance of multivariate drought prediction. The proposed method would be useful for drought prediction to integrate drought information from various sources for early drought warning.

Risk factors for lower extremity injuries in elite female soccer players.

PubMed

Nilstad, Agnethe; Andersen, Thor Einar; Bahr, Roald; Holme, Ingar; Steffen, Kathrin

2014-04-01

The incidence of lower extremity injuries in female soccer players is high, but the risk factors for injuries are unknown. To investigate risk factors for lower extremity injuries in elite female soccer players. Cohort study; Level of evidence, 3. Players in the Norwegian elite female soccer league (N = 12 teams) participated in baseline screening tests before the 2009 competitive soccer season. The screening included tests assessing maximal lower extremity strength, dynamic balance, knee valgus angles in a drop-jump landing, knee joint laxity, generalized joint laxity, and foot pronation. Also included was a questionnaire to collect information on demographic data, elite-level experience, and injury history. Time-loss injuries and exposure in training and matches were recorded prospectively in the subsequent soccer season using weekly text messaging. Players reporting an injury were contacted to collect data regarding injury circumstances. Univariate and multivariate regression analyses were used to calculate odds ratios (ORs) and 95% confidence intervals (CIs) for ±1 standard deviation of change. In total, 173 players underwent complete screening tests and registration of injuries and exposure throughout the season. A total of 171 injuries in 107 players (62%) were recorded; ligament and muscle injuries were the most frequent. Multivariate analyses showed that a greater body mass index (BMI) (OR, 1.51; 95% CI, 1.21-1.90; P = .001) was the only factor significantly associated with new lower extremity injuries. A greater BMI was associated with new thigh injuries (OR, 1.51; 95% CI, 1.08-2.11; P = .01), a lower knee valgus angle in a drop-jump landing was associated with new ankle injuries (OR, 0.64; 95% CI, 0.41-1.00; P = .04), and a previous knee injury was associated with new lower leg and foot injuries (OR, 3.57; 95% CI, 1.27-9.99; P = .02), whereas none of the factors investigated influenced the risk of new knee injuries. A greater BMI was associated with lower extremity injuries in elite female soccer players. Increased knowledge on risk factors for lower extremity injuries enables more targeted prevention strategies with the aim of reducing injury rates in female soccer players.

Applications of Extreme Value Theory in Public Health.

PubMed

Thomas, Maud; Lemaitre, Magali; Wilson, Mark L; Viboud, Cécile; Yordanov, Youri; Wackernagel, Hans; Carrat, Fabrice

2016-01-01

We present how Extreme Value Theory (EVT) can be used in public health to predict future extreme events. We applied EVT to weekly rates of Pneumonia and Influenza (P&I) deaths over 1979-2011. We further explored the daily number of emergency department visits in a network of 37 hospitals over 2004-2014. Maxima of grouped consecutive observations were fitted to a generalized extreme value distribution. The distribution was used to estimate the probability of extreme values in specified time periods. An annual P&I death rate of 12 per 100,000 (the highest maximum observed) should be exceeded once over the next 30 years and each year, there should be a 3% risk that the P&I death rate will exceed this value. Over the past 10 years, the observed maximum increase in the daily number of visits from the same weekday between two consecutive weeks was 1133. We estimated at 0.37% the probability of exceeding a daily increase of 1000 on each month. The EVT method can be applied to various topics in epidemiology thus contributing to public health planning for extreme events.

Variation in vulnerability to extreme-temperature-related mortality in Japan: A 40-year time-series analysis.

PubMed

Onozuka, Daisuke; Hagihara, Akihito

2015-07-01

Although the impact of extreme heat and cold on mortality has been documented in recent years, few studies have investigated whether variation in susceptibility to extreme temperatures has changed in Japan. We used data on daily total mortality and mean temperatures in Fukuoka, Japan, for 1973-2012. We used time-series analysis to assess the effects of extreme hot and low temperatures on all-cause mortality, stratified by decade, gender, and age, adjusting for time trends. We used a multivariate meta-analysis with a distributed lag non-linear model to estimate pooled non-linear lag-response relationships associated with extreme temperatures on mortality. The relative risk of mortality increased during heat extremes in all decades, with a declining trend over time. The mortality risk was higher during cold extremes for the entire study period, with a dispersed pattern across decades. Meta-analysis showed that both heat and cold extremes increased the risk of mortality. Cold effects were delayed and lasted for several days, whereas heat effects appeared quickly and did not last long. Our study provides quantitative evidence that extreme heat and low temperatures were significantly and non-linearly associated with the increased risk of mortality with substantial variation. Our results suggest that timely preventative measures are important for extreme high temperatures, whereas several days' protection should be provided for extreme low temperatures. Copyright © 2015 Elsevier Inc. All rights reserved.

Developing predictive models for return to work using the Military Power, Performance and Prevention (MP3) musculoskeletal injury risk algorithm: a study protocol for an injury risk assessment programme.

PubMed

Rhon, Daniel I; Teyhen, Deydre S; Shaffer, Scott W; Goffar, Stephen L; Kiesel, Kyle; Plisky, Phil P

2018-02-01

Musculoskeletal injuries are a primary source of disability in the US Military, and low back pain and lower extremity injuries account for over 44% of limited work days annually. History of prior musculoskeletal injury increases the risk for future injury. This study aims to determine the risk of injury after returning to work from a previous injury. The objective is to identify criteria that can help predict likelihood for future injury or re-injury. There will be 480 active duty soldiers recruited from across four medical centres. These will be patients who have sustained a musculoskeletal injury in the lower extremity or lumbar/thoracic spine, and have now been cleared to return back to work without any limitations. Subjects will undergo a battery of physical performance tests and fill out sociodemographic surveys. They will be followed for a year to identify any musculoskeletal injuries that occur. Prediction algorithms will be derived using regression analysis from performance and sociodemographic variables found to be significantly different between injured and non-injured subjects. Due to the high rates of injuries, injury prevention and prediction initiatives are growing. This is the first study looking at predicting re-injury rates after an initial musculoskeletal injury. In addition, multivariate prediction models appear to have move value than models based on only one variable. This approach aims to validate a multivariate model used in healthy non-injured individuals to help improve variables that best predict the ability to return to work with lower risk of injury, after a recent musculoskeletal injury. NCT02776930. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/.

Examining personal values in extreme environment contexts: Revisiting the question of generalizability

NASA Astrophysics Data System (ADS)

Smith, N.; Sandal, G. M.; Leon, G. R.; Kjærgaard, A.

2017-08-01

Land-based extreme environments (e.g. polar expeditions, Antarctic research stations, confinement chambers) have often been used as analog settings for spaceflight. These settings share similarities with the conditions experienced during space missions, including confinement, isolation and limited possibilities for evacuation. To determine the utility of analog settings for understanding human spaceflight, researchers have examined the extent to which the individual characteristics (e.g., personality) of people operating in extreme environments can be generalized across contexts (Sandal, 2000) [1]. Building on previous work, and utilising new and pre-existing data, the present study examined the extent to which personal value motives could be generalized across extreme environments. Four populations were assessed; mountaineers (N =59), military personnel (N = 25), Antarctic over-winterers (N = 21) and Mars simulation participants (N = 12). All participants completed the Portrait Values Questionnaire (PVQ; Schwartz; 2) capturing information on 10 personal values. Rank scores suggest that all groups identified Self-direction, Stimulation, Universalism and Benevolence as important values and acknowledged Power and Tradition as being low priorities. Results from difference testing suggest the extreme environment groups were most comparable on Self-direction, Stimulation, Benevolence, Tradition and Security. There were significant between-group differences on five of the ten values. Overall, findings pinpointed specific values that may be important for functioning in challenging environments. However, the differences that emerged on certain values highlight the importance of considering the specific population when comparing results across extreme settings. We recommend that further research examine the impact of personal value motives on indicators of adjustment, group working, and performance. Information from such studies could then be used to aid selection and training processes for personnel operating in extreme settings, and in space.

Extreme event statistics in a drifting Markov chain

NASA Astrophysics Data System (ADS)

Kindermann, Farina; Hohmann, Michael; Lausch, Tobias; Mayer, Daniel; Schmidt, Felix; Widera, Artur

2017-07-01

We analyze extreme event statistics of experimentally realized Markov chains with various drifts. Our Markov chains are individual trajectories of a single atom diffusing in a one-dimensional periodic potential. Based on more than 500 individual atomic traces we verify the applicability of the Sparre Andersen theorem to our system despite the presence of a drift. We present detailed analysis of four different rare-event statistics for our system: the distributions of extreme values, of record values, of extreme value occurrence in the chain, and of the number of records in the chain. We observe that, for our data, the shape of the extreme event distributions is dominated by the underlying exponential distance distribution extracted from the atomic traces. Furthermore, we find that even small drifts influence the statistics of extreme events and record values, which is supported by numerical simulations, and we identify cases in which the drift can be determined without information about the underlying random variable distributions. Our results facilitate the use of extreme event statistics as a signal for small drifts in correlated trajectories.

Risk assessment of precipitation extremes in northern Xinjiang, China

NASA Astrophysics Data System (ADS)

Yang, Jun; Pei, Ying; Zhang, Yanwei; Ge, Quansheng

2018-05-01

This study was conducted using daily precipitation records gathered at 37 meteorological stations in northern Xinjiang, China, from 1961 to 2010. We used the extreme value theory model, generalized extreme value (GEV) and generalized Pareto distribution (GPD), statistical distribution function to fit outputs of precipitation extremes with different return periods to estimate risks of precipitation extremes and diagnose aridity-humidity environmental variation and corresponding spatial patterns in northern Xinjiang. Spatiotemporal patterns of daily maximum precipitation showed that aridity-humidity conditions of northern Xinjiang could be well represented by the return periods of the precipitation data. Indices of daily maximum precipitation were effective in the prediction of floods in the study area. By analyzing future projections of daily maximum precipitation (2, 5, 10, 30, 50, and 100 years), we conclude that the flood risk will gradually increase in northern Xinjiang. GEV extreme value modeling yielded the best results, proving to be extremely valuable. Through example analysis for extreme precipitation models, the GEV statistical model was superior in terms of favorable analog extreme precipitation. The GPD model calculation results reflect annual precipitation. For most of the estimated sites' 2 and 5-year T for precipitation levels, GPD results were slightly greater than GEV results. The study found that extreme precipitation reaching a certain limit value level will cause a flood disaster. Therefore, predicting future extreme precipitation may aid warnings of flood disaster. A suitable policy concerning effective water resource management is thus urgently required.

Missing Data and Multiple Imputation in the Context of Multivariate Analysis of Variance

ERIC Educational Resources Information Center

Finch, W. Holmes

2016-01-01

Multivariate analysis of variance (MANOVA) is widely used in educational research to compare means on multiple dependent variables across groups. Researchers faced with the problem of missing data often use multiple imputation of values in place of the missing observations. This study compares the performance of 2 methods for combining p values in…

Alcohol Intake and Risk of Incident Melanoma: A Pooled Analysis of Three Prospective Studies in the U.S

PubMed Central

Rivera, Andrew; Nan, Hongmei; Li, Tricia; Qureshi, Abrar; Cho, Eunyoung

2016-01-01

Background Alcohol consumption is associated with increased risk of numerous cancers, but existing evidence for an association with melanoma is equivocal. No study has evaluated the association with different anatomic locations of melanoma. Methods We used data from three large prospective cohort studies to investigate whether alcohol intake was associated with risk of melanoma. Alcohol intake was assessed repeatedly by food-frequency questionnaires. A Cox proportional hazards model was used to calculate multivariate-adjusted hazard ratios (HRs). Results A total of 1,374 cases of invasive melanoma were documented during 3,855,706 person-years of follow-up. There was an association between higher alcohol intake and incidence of invasive melanoma (pooled multivariate HR 1.14; 95% confidence interval [CI]: 1.00–1.29] per drink/d, p trend = 0.04). Among alcoholic beverages, white wine consumption was associated with an increased risk of melanoma (pooled multivariate HR 1.13 [95% CI: 1.04–1.24] per drink/d, p trend <0.01) after adjusting for other alcoholic beverages. The association between alcohol consumption and melanoma risk was stronger for melanoma in relatively UV-spared sites (trunk) versus more UV-exposed sites (head, neck, or extremities). Compared to non-drinkers, the pooled multivariate-adjusted HRs for ≥20g/d of alcohol were 1.02 (95% CI: 0.64–1.62; P trend =0.25) for melanomas of the head, neck, and extremities and 1.73 (95% CI: 1.25–2.38; P trend =0.02) for melanomas of the trunk. Conclusions Alcohol intake was associated with a modest increase in the risk of melanoma, particularly in UV-protected sites. Impact These findings further support American Cancer Society Guidelines for Cancer Prevention to limit alcohol intake. PMID:27909090

The application of the statistical theory of extreme values to gust-load problems

NASA Technical Reports Server (NTRS)

Press, Harry

1950-01-01

An analysis is presented which indicates that the statistical theory of extreme values is applicable to the problems of predicting the frequency of encountering the larger gust loads and gust velocities for both specific test conditions as well as commercial transport operations. The extreme-value theory provides an analytic form for the distributions of maximum values of gust load and velocity. Methods of fitting the distribution are given along with a method of estimating the reliability of the predictions. The theory of extreme values is applied to available load data from commercial transport operations. The results indicate that the estimates of the frequency of encountering the larger loads are more consistent with the data and more reliable than those obtained in previous analyses. (author)

[The role of preoperative (18)F-FDG PET-CT in lymphatic metastasis diagnosis of cutaneous malignant melanoma on extremities and trunk].

PubMed

Zhang, X X; Fang, Y; Xu, L B; Xu, S F; Zhao, Z G; Sun, C; Ma, P Q; Liu, T; Yu, S J; Zhang, W J

2018-05-23

Objective: To evaluate the clinical value of preoperative (18)F-Fludeoxyglucose ((18)F-FDG PET-CT) in lymphatic metastasis diagnosis of cutaneous melanoma on extremities and trunk. Methods: 112 patients with cutaneous melanoma pathologically of extremities and trunk from January 2006 to December 2016, who received (18)F-FDG PET-CT examination preoperatively, were retrospectively reviewed. The correlations between the maximal diameters of lymph nodes, the maximal standard uptake value (SUV) and the diagnostic impression grades of PET-CT examination, and the final pathological diagnosis were analyzed. The correlations between Breslow thickness of primary lesions and the diagnostic impression of PET-CT examination were also analyzed. All the above were analyzed with Receiver Operating Characteristic (ROC) curve to get the cut-off value. Based on the final results of pathological diagnosis of lymph nodes as the golden standard, the statistically significant indicators of ROC curve analysis were used to evaluate the diagnostic effect, as well as to calculate the sensitivity, specificity and accuracy. With gender, age, maximal diameter of lymph nodes, maximal SUV, diagnosis impressions, and Breslow thickness as the independent variables and pathological diagnosis results of lymph nodes as the dependent variable, two-class stepwise Logistic regression analysis was used to determine the independence of diagnostic indicators. ROC curve analysis and log rank test were used to analyze the relationship between Breslow thickness and patient survival. Results: To evaluate melanoma patients' lymph node status, the results of ROC curve analysis showed that the area under the curve of lymph node maximal diameter, maximal SUV, diagnosis impression of PET-CT examinations were 0.789, 0.786 and 0.816, respectively (all P <0.05). The cut-off values were 0.85 cm, 1.45 and 2.5, respectively. The sensitivity of the cut-off values to determine the status of lymph nodes in melanoma patients were 71.4%, 64.9% and 72.1% respectively, and the specificities were 85.2%, 88.7% and 87.0% respectively. Multivariate Logistic regression analysis showed that PET-CT diagnosis impressions had independent diagnostic significance for the lymph node status of melanoma patients ( OR =11.296, 95% CI : 2.550~50.033). The area under the curve of Breslow thickness evaluating PET-CT diagnostic impression is 0.664 ( P =0.042) and the cut-off value was 4.25 mm. The survival rate of the patients with Breslow thickness ≥ 4.25 mm was lower than that in the group <4.25 mm ( P =0.006). Conclusions: (18)F-FDG PET-CT can help to evaluate metastases and make treatment decisions for cutaneous melanoma of extremities and trunk, especially for patients whose primary lesion's Breslow thickness has reached more than 4.25 mm. For the patients whose maximal SUV of regional lymph node is higher than 1.45 and short diameter of the largest lymph node is larger than 0.85cm, the possibility of metastases should be considered.

Remote-sensing data processing with the multivariate regression analysis method for iron mineral resource potential mapping: a case study in the Sarvian area, central Iran

NASA Astrophysics Data System (ADS)

Mansouri, Edris; Feizi, Faranak; Jafari Rad, Alireza; Arian, Mehran

2018-03-01

This paper uses multivariate regression to create a mathematical model for iron skarn exploration in the Sarvian area, central Iran, using multivariate regression for mineral prospectivity mapping (MPM). The main target of this paper is to apply multivariate regression analysis (as an MPM method) to map iron outcrops in the northeastern part of the study area in order to discover new iron deposits in other parts of the study area. Two types of multivariate regression models using two linear equations were employed to discover new mineral deposits. This method is one of the reliable methods for processing satellite images. ASTER satellite images (14 bands) were used as unique independent variables (UIVs), and iron outcrops were mapped as dependent variables for MPM. According to the results of the probability value (p value), coefficient of determination value (R2) and adjusted determination coefficient (Radj2), the second regression model (which consistent of multiple UIVs) fitted better than other models. The accuracy of the model was confirmed by iron outcrops map and geological observation. Based on field observation, iron mineralization occurs at the contact of limestone and intrusive rocks (skarn type).

Extreme events in total ozone over Arosa: Application of extreme value theory and fingerprints of atmospheric dynamics and chemistry and their effects on mean values and long-term changes

NASA Astrophysics Data System (ADS)

Rieder, Harald E.; Staehelin, Johannes; Maeder, Jörg A.; Peter, Thomas; Ribatet, Mathieu; Davison, Anthony C.; Stübi, Rene; Weihs, Philipp; Holawe, Franz

2010-05-01

In this study tools from extreme value theory (e.g. Coles, 2001; Ribatet, 2007) are applied for the first time in the field of stratospheric ozone research, as statistical analysis showed that previously used concepts assuming a Gaussian distribution (e.g. fixed deviations from mean values) of total ozone data do not address the internal data structure concerning extremes adequately. The study illustrates that tools based on extreme value theory are appropriate to identify ozone extremes and to describe the tails of the world's longest total ozone record (Arosa, Switzerland - for details see Staehelin et al., 1998a,b) (Rieder et al., 2010a). A daily moving threshold was implemented for consideration of the seasonal cycle in total ozone. The frequency of days with extreme low (termed ELOs) and extreme high (termed EHOs) total ozone and the influence of those on mean values and trends is analyzed for Arosa total ozone time series. The results show (a) an increase in ELOs and (b) a decrease in EHOs during the last decades and (c) that the overall trend during the 1970s and 1980s in total ozone is strongly dominated by changes in these extreme events. After removing the extremes, the time series shows a strongly reduced trend (reduction by a factor of 2.5 for trend in annual mean). Furthermore, it is shown that the fitted model represents the tails of the total ozone data set with very high accuracy over the entire range (including absolute monthly minima and maxima). Also the frequency distribution of ozone mini-holes (using constant thresholds) can be calculated with high accuracy. Analyzing the tails instead of a small fraction of days below constant thresholds provides deeper insight in time series properties. Excursions in the frequency of extreme events reveal "fingerprints" of dynamical factors such as ENSO or NAO, and chemical factors, such as cold Arctic vortex ozone losses, as well as major volcanic eruptions of the 20th century (e.g. Gunung Agung, El Chichón, Mt. Pinatubo). Furthermore, atmospheric loading in ozone depleting substances lead to a continuous modification of column ozone in the northern hemisphere also with respect to extreme values (partly again in connection with polar vortex contributions). It is shown that application of extreme value theory allows the identification of many more such fingerprints than conventional time series analysis of annual and seasonal mean values. Especially, the analysis shows the strong influence of dynamics, revealing that even moderate ENSO and NAO events have a discernible effect on total ozone (Rieder et al., 2010b). Overall the presented new extremes concept provides new information on time series properties, variability, trends and the influence of dynamics and chemistry, complementing earlier analyses focusing only on monthly (or annual) mean values. References: Coles, S.: An Introduction to Statistical Modeling of Extreme Values, Springer Series in Statistics, ISBN:1852334592, Springer, Berlin, 2001. Ribatet, M.: POT: Modelling peaks over a threshold, R News, 7, 34-36, 2007. Rieder ,H.E., Staehelin, J., Maeder, J.A., Ribatet, M., Stübi, R., Weihs, P., Holawe, F., Peter, T., and A.D., Davison (2010): Extreme events in total ozone over Arosa - Part I: Application of extreme value theory, to be submitted to ACPD. Rieder, H.E., Staehelin, J., Maeder, J.A., Ribatet, M., Stübi, R., Weihs, P., Holawe, F., Peter, T., and A.D., Davison (2010): Extreme events in total ozone over Arosa - Part II: Fingerprints of atmospheric dynamics and chemistry and effects on mean values and long-term changes, to be submitted to ACPD. Staehelin, J., Renaud, A., Bader, J., McPeters, R., Viatte, P., Hoegger, B., Bugnion, V., Giroud, M., and Schill, H.: Total ozone series at Arosa (Switzerland): Homogenization and data comparison, J. Geophys. Res., 103(D5), 5827-5842, doi:10.1029/97JD02402, 1998a. Staehelin, J., Kegel, R., and Harris, N. R.: Trend analysis of the homogenized total ozone series of Arosa (Switzerland), 1929-1996, J. Geophys. Res., 103(D7), 8389-8400, doi:10.1029/97JD03650, 1998b.

Application of Fluorescence Spectrometry With Multivariate Calibration to the Enantiomeric Recognition of Fluoxetine in Pharmaceutical Preparations.

PubMed

Poláček, Roman; Májek, Pavel; Hroboňová, Katarína; Sádecká, Jana

2016-04-01

Fluoxetine is the most prescribed antidepressant chiral drug worldwide. Its enantiomers have a different duration of serotonin inhibition. A novel simple and rapid method for determination of the enantiomeric composition of fluoxetine in pharmaceutical pills is presented. Specifically, emission, excitation, and synchronous fluorescence techniques were employed to obtain the spectral data, which with multivariate calibration methods, namely, principal component regression (PCR) and partial least square (PLS), were investigated. The chiral recognition of fluoxetine enantiomers in the presence of β-cyclodextrin was based on diastereomeric complexes. The results of the multivariate calibration modeling indicated good prediction abilities. The obtained results for tablets were compared with those from chiral HPLC and no significant differences are shown by Fisher's (F) test and Student's t-test. The smallest residuals between reference or nominal values and predicted values were achieved by multivariate calibration of synchronous fluorescence spectral data. This conclusion is supported by calculated values of the figure of merit.

Extreme value statistics analysis of fracture strengths of a sintered silicon nitride failing from pores

NASA Technical Reports Server (NTRS)

Chao, Luen-Yuan; Shetty, Dinesh K.

1992-01-01

Statistical analysis and correlation between pore-size distribution and fracture strength distribution using the theory of extreme-value statistics is presented for a sintered silicon nitride. The pore-size distribution on a polished surface of this material was characterized, using an automatic optical image analyzer. The distribution measured on the two-dimensional plane surface was transformed to a population (volume) distribution, using the Schwartz-Saltykov diameter method. The population pore-size distribution and the distribution of the pore size at the fracture origin were correllated by extreme-value statistics. Fracture strength distribution was then predicted from the extreme-value pore-size distribution, usin a linear elastic fracture mechanics model of annular crack around pore and the fracture toughness of the ceramic. The predicted strength distribution was in good agreement with strength measurements in bending. In particular, the extreme-value statistics analysis explained the nonlinear trend in the linearized Weibull plot of measured strengths without postulating a lower-bound strength.

On alternative q-Weibull and q-extreme value distributions: Properties and applications

NASA Astrophysics Data System (ADS)

Zhang, Fode; Ng, Hon Keung Tony; Shi, Yimin

2018-01-01

Tsallis statistics and Tsallis distributions have been attracting a significant amount of research work in recent years. Importantly, the Tsallis statistics, q-distributions have been applied in different disciplines. Yet, a relationship between some existing q-Weibull distributions and q-extreme value distributions that is parallel to the well-established relationship between the conventional Weibull and extreme value distributions through a logarithmic transformation has not be established. In this paper, we proposed an alternative q-Weibull distribution that leads to a q-extreme value distribution via the q-logarithm transformation. Some important properties of the proposed q-Weibull and q-extreme value distributions are studied. Maximum likelihood and least squares estimation methods are used to estimate the parameters of q-Weibull distribution and their performances are investigated through a Monte Carlo simulation study. The methodologies and the usefulness of the proposed distributions are illustrated by fitting the 2014 traffic fatalities data from The National Highway Traffic Safety Administration.

Using phenotypic manipulations to study multivariate selection of floral trait associations

PubMed Central

Campbell, Diane R.

2009-01-01

Background A basic theme in the study of plant–pollinator interactions is that pollinators select not just for single floral traits, but for associations of traits. Responses of pollinators to sets of traits are inherent in the idea of pollinator syndromes. In its most extreme form, selection on a suite of traits can take the form of correlational selection, in which a response to one trait depends on the value of another, thereby favouring floral integration. Despite the importance of selection for combinations of traits in the evolution of flowers, evidence is relatively sparse and relies mostly on observational approaches. Scope Here, methods for measuring selection on multivariate suites of floral traits are presented, and the studies to date are reviewed. It is argued that phenotypic manipulations present a powerful, but rarely used, approach to teasing apart the separate and combined effects of particular traits. The approach is illustrated with data from studies of alpine plants in Colorado and New Zealand, and recommendations are made about several features of the design of such experiments. Conclusions Phenotypic manipulations of two or more traits in combination provide a direct way of testing for selection of floral trait associations. Such experiments will be particularly valuable if rooted in hypotheses about differences between types of pollinators and tied to a proposed evolutionary history. PMID:19218579

Regional trends in short-duration precipitation extremes: a flexible multivariate monotone quantile regression approach

NASA Astrophysics Data System (ADS)

Cannon, Alex

2017-04-01

Estimating historical trends in short-duration rainfall extremes at regional and local scales is challenging due to low signal-to-noise ratios and the limited availability of homogenized observational data. In addition to being of scientific interest, trends in rainfall extremes are of practical importance, as their presence calls into question the stationarity assumptions that underpin traditional engineering and infrastructure design practice. Even with these fundamental challenges, increasingly complex questions are being asked about time series of extremes. For instance, users may not only want to know whether or not rainfall extremes have changed over time, they may also want information on the modulation of trends by large-scale climate modes or on the nonstationarity of trends (e.g., identifying hiatus periods or periods of accelerating positive trends). Efforts have thus been devoted to the development and application of more robust and powerful statistical estimators for regional and local scale trends. While a standard nonparametric method like the regional Mann-Kendall test, which tests for the presence of monotonic trends (i.e., strictly non-decreasing or non-increasing changes), makes fewer assumptions than parametric methods and pools information from stations within a region, it is not designed to visualize detected trends, include information from covariates, or answer questions about the rate of change in trends. As a remedy, monotone quantile regression (MQR) has been developed as a nonparametric alternative that can be used to estimate a common monotonic trend in extremes at multiple stations. Quantile regression makes efficient use of data by directly estimating conditional quantiles based on information from all rainfall data in a region, i.e., without having to precompute the sample quantiles. The MQR method is also flexible and can be used to visualize and analyze the nonlinearity of the detected trend. However, it is fundamentally a univariate technique, and cannot incorporate information from additional covariates, for example ENSO state or physiographic controls on extreme rainfall within a region. Here, the univariate MQR model is extended to allow the use of multiple covariates. Multivariate monotone quantile regression (MMQR) is based on a single hidden-layer feedforward network with the quantile regression error function and partial monotonicity constraints. The MMQR model is demonstrated via Monte Carlo simulations and the estimation and visualization of regional trends in moderate rainfall extremes based on homogenized sub-daily precipitation data at stations in Canada.

Extreme values and the level-crossing problem: An application to the Feller process

NASA Astrophysics Data System (ADS)

Masoliver, Jaume

2014-04-01

We review the question of the extreme values attained by a random process. We relate it to level crossings to one boundary (first-passage problems) as well as to two boundaries (escape problems). The extremes studied are the maximum, the minimum, the maximum absolute value, and the range or span. We specialize in diffusion processes and present detailed results for the Wiener and Feller processes.

Toward high value sensing: monolayer-protected metal nanoparticles in multivariable gas and vapor sensors.

PubMed

Potyrailo, Radislav A

2017-08-29

For detection of gases and vapors in complex backgrounds, "classic" analytical instruments are an unavoidable alternative to existing sensors. Recently a new generation of sensors, known as multivariable sensors, emerged with a fundamentally different perspective for sensing to eliminate limitations of existing sensors. In multivariable sensors, a sensing material is designed to have diverse responses to different gases and vapors and is coupled to a multivariable transducer that provides independent outputs to recognize these diverse responses. Data analytics tools provide rejection of interferences and multi-analyte quantitation. This review critically analyses advances of multivariable sensors based on ligand-functionalized metal nanoparticles also known as monolayer-protected nanoparticles (MPNs). These MPN sensing materials distinctively stand out from other sensing materials for multivariable sensors due to their diversity of gas- and vapor-response mechanisms as provided by organic and biological ligands, applicability of these sensing materials for broad classes of gas-phase compounds such as condensable vapors and non-condensable gases, and for several principles of signal transduction in multivariable sensors that result in non-resonant and resonant electrical sensors as well as material- and structure-based photonic sensors. Such features should allow MPN multivariable sensors to be an attractive high value addition to existing analytical instrumentation.

Default options and neonatal resuscitation decisions.

PubMed

Haward, Marlyse Frieda; Murphy, Ryan O; Lorenz, John M

2012-12-01

To determine whether presenting delivery room management options as defaults influences decisions to resuscitate extremely premature infants. Adult volunteers recruited from the world wide web were randomised to receive either resuscitation or comfort care as the delivery room management default option for a hypothetical delivery of a 23-week gestation infant. Participants were required to check a box to opt out of the default. The primary outcome measure was the proportion of respondents electing resuscitation. Data were analysed using χ(2) tests and multivariate logistic regression. Participants who were told the delivery room management default option was resuscitation were more likely to opt for resuscitation (OR 6.54 95% CI 3.85 to 11.11, p<0.001). This effect persisted on multivariate regression analysis (OR 7.00, 95% CI 3.97 to 12.36, p<0.001). Female gender, being married or in a committed relationship, being highly religious, experiences with prematurity, and favouring sanctity of life were significantly associated with decisions to resuscitate. Presenting delivery room options for extremely premature infants as defaults exert a significant effect on decision makers. The information structure of the choice task may act as a subtle form of manipulation. Further, this effect may operate in ways that a decision maker is not aware of and this raises questions of patient autonomy. Presenting delivery room options for extremely premature infants as defaults may compromise autonomous decision-making.

A review of statistical methods to analyze extreme precipitation and temperature events in the Mediterranean region

NASA Astrophysics Data System (ADS)

Lazoglou, Georgia; Anagnostopoulou, Christina; Tolika, Konstantia; Kolyva-Machera, Fotini

2018-04-01

The increasing trend of the intensity and frequency of temperature and precipitation extremes during the past decades has substantial environmental and socioeconomic impacts. Thus, the objective of the present study is the comparison of several statistical methods of the extreme value theory (EVT) in order to identify which is the most appropriate to analyze the behavior of the extreme precipitation, and high and low temperature events, in the Mediterranean region. The extremes choice was made using both the block maxima and the peaks over threshold (POT) technique and as a consequence both the generalized extreme value (GEV) and generalized Pareto distributions (GPDs) were used to fit them. The results were compared, in order to select the most appropriate distribution for extremes characterization. Moreover, this study evaluates the maximum likelihood estimation, the L-moments and the Bayesian method, based on both graphical and statistical goodness-of-fit tests. It was revealed that the GPD can characterize accurately both precipitation and temperature extreme events. Additionally, GEV distribution with the Bayesian method is proven to be appropriate especially for the greatest values of extremes. Another important objective of this investigation was the estimation of the precipitation and temperature return levels for three return periods (50, 100, and 150 years) classifying the data into groups with similar characteristics. Finally, the return level values were estimated with both GEV and GPD and with the three different estimation methods, revealing that the selected method can affect the return level values for both the parameter of precipitation and temperature.

Extreme sexual behavior in dementia as a specific manifestation of disinhibition.

PubMed

Bartelet, Marjukka; Waterink, Wim; van Hooren, Susan

2014-01-01

In nursing homes, extreme sexual behavior is one of the most challenging behaviors in dementia. Despite this, however, there is no conformity in the literature regarding how to label and define this type of behavior. Examples of labels used include inappropriate sexual behavior, improper sexual behavior, sexually disinhibited behavior, or hyper sexuality. According to recent theoretical perspectives, extreme sexual behavior may be regarded as a part of disinhibited behavior or could be considered as an independent neuropsychiatric symptom. In this multicenter study, it was investigated whether there is a relationship between extreme sexual behavior and the typical neuropsychiatric symptoms seen in dementia. In 179 residents diagnosed with dementia, extreme sexual behavior was measured using an observation scale. Twelve neuropsychiatric symptoms were measured by the Neuropsychiatric Inventory. Multivariate analysis of covariance with gender showed that residents with observed extreme sexual behavior (n = 43) only showed a higher score on neuropsychiatric symptom 'disinhibition', as compared to residents with non-observed sexual behavior (n = 136). In addition, the effect size was large. These findings indicate that among residents with dementia, extreme sexual behaviors should not be considered as an independent neuropsychiatric symptom. Instead, disinhibition may be an important underlying mechanism for extreme sexual behavior and thus validates the label 'sexually disinhibited behavior'.

A Fiducial Approach to Extremes and Multiple Comparisons

ERIC Educational Resources Information Center

Wandler, Damian V.

2010-01-01

Generalized fiducial inference is a powerful tool for many difficult problems. Based on an extension of R. A. Fisher's work, we used generalized fiducial inference for two extreme value problems and a multiple comparison procedure. The first extreme value problem is dealing with the generalized Pareto distribution. The generalized Pareto…

Modified Brown-Forsythe Procedure for Testing Interaction Effects in Split-Plot Designs

ERIC Educational Resources Information Center

Vallejo, Guillermo; Ato, Manuel

2006-01-01

The standard univariate and multivariate methods are conventionally used to analyze continuous data from groups by trials repeated measures designs, in spite of being extremely sensitive to departures from the multisample sphericity assumption when group sizes are unequal. However, in the last 10 years several authors have offered alternative…

Associations between Smoking and Extreme Dieting among Adolescents

ERIC Educational Resources Information Center

Seo, Dong-Chul; Jiang, Nan

2009-01-01

This study examined the association between cigarette smoking and dieting behaviors and trends in that association among US adolescents in grades 9-12 between 1999 and 2007. Youth Risk Behavior Survey datasets were analyzed using the multivariable logistic regression method. The sample size of each survey year ranged from 13,554 to 15,273 with…

Application of multivariable search techniques to structural design optimization

NASA Technical Reports Server (NTRS)

Jones, R. T.; Hague, D. S.

1972-01-01

Multivariable optimization techniques are applied to a particular class of minimum weight structural design problems: the design of an axially loaded, pressurized, stiffened cylinder. Minimum weight designs are obtained by a variety of search algorithms: first- and second-order, elemental perturbation, and randomized techniques. An exterior penalty function approach to constrained minimization is employed. Some comparisons are made with solutions obtained by an interior penalty function procedure. In general, it would appear that an interior penalty function approach may not be as well suited to the class of design problems considered as the exterior penalty function approach. It is also shown that a combination of search algorithms will tend to arrive at an extremal design in a more reliable manner than a single algorithm. The effect of incorporating realistic geometrical constraints on stiffener cross-sections is investigated. A limited comparison is made between minimum weight cylinders designed on the basis of a linear stability analysis and cylinders designed on the basis of empirical buckling data. Finally, a technique for locating more than one extremal is demonstrated.

Comparative multivariate analyses of transient otoacoustic emissions and distorsion products in normal and impaired hearing.

PubMed

Stamate, Mirela Cristina; Todor, Nicolae; Cosgarea, Marcel

2015-01-01

The clinical utility of otoacoustic emissions as a noninvasive objective test of cochlear function has been long studied. Both transient otoacoustic emissions and distorsion products can be used to identify hearing loss, but to what extent they can be used as predictors for hearing loss is still debated. Most studies agree that multivariate analyses have better test performances than univariate analyses. The aim of the study was to determine transient otoacoustic emissions and distorsion products performance in identifying normal and impaired hearing loss, using the pure tone audiogram as a gold standard procedure and different multivariate statistical approaches. The study included 105 adult subjects with normal hearing and hearing loss who underwent the same test battery: pure-tone audiometry, tympanometry, otoacoustic emission tests. We chose to use the logistic regression as a multivariate statistical technique. Three logistic regression models were developed to characterize the relations between different risk factors (age, sex, tinnitus, demographic features, cochlear status defined by otoacoustic emissions) and hearing status defined by pure-tone audiometry. The multivariate analyses allow the calculation of the logistic score, which is a combination of the inputs, weighted by coefficients, calculated within the analyses. The accuracy of each model was assessed using receiver operating characteristics curve analysis. We used the logistic score to generate receivers operating curves and to estimate the areas under the curves in order to compare different multivariate analyses. We compared the performance of each otoacoustic emission (transient, distorsion product) using three different multivariate analyses for each ear, when multi-frequency gold standards were used. We demonstrated that all multivariate analyses provided high values of the area under the curve proving the performance of the otoacoustic emissions. Each otoacoustic emission test presented high values of area under the curve, suggesting that implementing a multivariate approach to evaluate the performances of each otoacoustic emission test would serve to increase the accuracy in identifying the normal and impaired ears. We encountered the highest area under the curve value for the combined multivariate analysis suggesting that both otoacoustic emission tests should be used in assessing hearing status. Our multivariate analyses revealed that age is a constant predictor factor of the auditory status for both ears, but the presence of tinnitus was the most important predictor for the hearing level, only for the left ear. Age presented similar coefficients, but tinnitus coefficients, by their high value, produced the highest variations of the logistic scores, only for the left ear group, thus increasing the risk of hearing loss. We did not find gender differences between ears for any otoacoustic emission tests, but studies still debate this question as the results are contradictory. Neither gender, nor environment origin had any predictive value for the hearing status, according to the results of our study. Like any other audiological test, using otoacoustic emissions to identify hearing loss is not without error. Even when applying multivariate analysis, perfect test performance is never achieved. Although most studies demonstrated the benefit of using the multivariate analysis, it has not been incorporated into clinical decisions maybe because of the idiosyncratic nature of multivariate solutions or because of the lack of the validation studies.

Comparative multivariate analyses of transient otoacoustic emissions and distorsion products in normal and impaired hearing

PubMed Central

STAMATE, MIRELA CRISTINA; TODOR, NICOLAE; COSGAREA, MARCEL

2015-01-01

Background and aim The clinical utility of otoacoustic emissions as a noninvasive objective test of cochlear function has been long studied. Both transient otoacoustic emissions and distorsion products can be used to identify hearing loss, but to what extent they can be used as predictors for hearing loss is still debated. Most studies agree that multivariate analyses have better test performances than univariate analyses. The aim of the study was to determine transient otoacoustic emissions and distorsion products performance in identifying normal and impaired hearing loss, using the pure tone audiogram as a gold standard procedure and different multivariate statistical approaches. Methods The study included 105 adult subjects with normal hearing and hearing loss who underwent the same test battery: pure-tone audiometry, tympanometry, otoacoustic emission tests. We chose to use the logistic regression as a multivariate statistical technique. Three logistic regression models were developed to characterize the relations between different risk factors (age, sex, tinnitus, demographic features, cochlear status defined by otoacoustic emissions) and hearing status defined by pure-tone audiometry. The multivariate analyses allow the calculation of the logistic score, which is a combination of the inputs, weighted by coefficients, calculated within the analyses. The accuracy of each model was assessed using receiver operating characteristics curve analysis. We used the logistic score to generate receivers operating curves and to estimate the areas under the curves in order to compare different multivariate analyses. Results We compared the performance of each otoacoustic emission (transient, distorsion product) using three different multivariate analyses for each ear, when multi-frequency gold standards were used. We demonstrated that all multivariate analyses provided high values of the area under the curve proving the performance of the otoacoustic emissions. Each otoacoustic emission test presented high values of area under the curve, suggesting that implementing a multivariate approach to evaluate the performances of each otoacoustic emission test would serve to increase the accuracy in identifying the normal and impaired ears. We encountered the highest area under the curve value for the combined multivariate analysis suggesting that both otoacoustic emission tests should be used in assessing hearing status. Our multivariate analyses revealed that age is a constant predictor factor of the auditory status for both ears, but the presence of tinnitus was the most important predictor for the hearing level, only for the left ear. Age presented similar coefficients, but tinnitus coefficients, by their high value, produced the highest variations of the logistic scores, only for the left ear group, thus increasing the risk of hearing loss. We did not find gender differences between ears for any otoacoustic emission tests, but studies still debate this question as the results are contradictory. Neither gender, nor environment origin had any predictive value for the hearing status, according to the results of our study. Conclusion Like any other audiological test, using otoacoustic emissions to identify hearing loss is not without error. Even when applying multivariate analysis, perfect test performance is never achieved. Although most studies demonstrated the benefit of using the multivariate analysis, it has not been incorporated into clinical decisions maybe because of the idiosyncratic nature of multivariate solutions or because of the lack of the validation studies. PMID:26733749

Effects of Climate Change on Flood Frequency in the Pacific Northwest

NASA Astrophysics Data System (ADS)

Gergel, D. R.; Stumbaugh, M. R.; Lee, S. Y.; Nijssen, B.; Lettenmaier, D. P.

2014-12-01

A key concern about climate change as related to water resources is the potential for changes in hydrologic extremes, including flooding. We explore changes in flood frequency in the Pacific Northwest using downscaled output from ten Global Climate Models (GCMs) from the Coupled Model Inter-Comparison Project 5 (CMIP5) for historical forcings (1950-2005) and future Representative Concentration Pathways (RCPs) 4.5 and 8.5 (2006-2100). We use archived output from the Integrated Scenarios Project (ISP) (http://maca.northwestknowledge.net/), which uses the Multivariate Adaptive Constructed Analogs (MACA) method for statistical downscaling. The MACA-downscaled GCM output was then used to force the Variable Infiltration Capacity (VIC) hydrology model with a 1/16th degree spatial resolution and a daily time step. For each of the 238 HUC-08 areas within the Pacific Northwest (USGS Hydrologic Region 15), we computed, from the ISP archive, the series of maximum daily runoff values (surrogate for the annual maximum flood), and then the mean annual flood. Finally, we computed the ratios of the RCP4.5 and RCP8.5 mean annual floods to their corresponding values for the historical period. We evaluate spatial patterns in the results. For snow-dominated watersheds, the changes are dominated by reductions in flood frequency in basins that currently have spring-dominant floods, and increases in snow affected basins with fall-dominant floods. In low elevation basins west of the Cascades, changes in flooding are more directly related to changes in precipitation extremes. We further explore the nature of these effects by evaluating the mean Julian day of the annual maximum flood for each HUC-08 and how this changes between the historical and RCP4.5 and RCP8.5 scenarios.

Stochastic Generation of Spatiotemporal Rainfall Events for Flood Risk Assessment

NASA Astrophysics Data System (ADS)

Diederen, D.; Liu, Y.; Gouldby, B.; Diermanse, F.

2017-12-01

Current flood risk analyses that only consider peaks of hydrometeorological forcing variables have limitations regarding their representation of reality. Simplistic assumptions regarding antecedent conditions are required, often different sources of flooding are considered in isolation, and the complex temporal and spatial evolution of the events is not considered. Mid-latitude storms, governed by large scale climatic conditions, often exhibit a high degree of temporal dependency, for example. For sustainable flood risk management, that accounts appropriately for climate change, it is desirable for flood risk analyses to reflect reality more appropriately. Analysis of risk mitigation measures and comparison of their relative performance is therefore likely to be more robust and lead to improved solutions. We provide a new framework for the provision of boundary conditions to flood risk analyses that more appropriately reflects reality. The boundary conditions capture the temporal dependencies of complex storms whilst preserving the extreme values and associated spatial dependencies. We demonstrate the application of this framework to generate a synthetic rainfall events time series boundary condition set from reanalysis rainfall data (CFSR) on the continental scale. We define spatiotemporal clusters of rainfall as events, extract hydrological parameters for each event, generate synthetic parameter sets with a multivariate distribution with a focus on the joint tail probability [Heffernan and Tawn, 2004], and finally create synthetic events from the generated synthetic parameters. We highlight the stochastic integration of (a) spatiotemporal features, e.g. event occurrence intensity over space-time, or time to previous event, which we use for the spatial placement and sequencing of the synthetic events, and (b) value-specific parameters, e.g. peak intensity and event extent. We contrast this to more traditional approaches to highlight the significant improvements in terms of representing the reality of extreme flood events.

Same Abbreviated Injury Scale Values May Be Associated with Different Risks to Mortality in Trauma Patients: A Cross-Sectional Retrospective Study Based on the Trauma Registry System in a Level I Trauma Center.

PubMed

Rau, Cheng-Shyuan; Wu, Shao-Chun; Kuo, Pao-Jen; Chen, Yi-Chun; Chien, Peng-Chen; Hsieh, Hsiao-Yun; Hsieh, Ching-Hua

2017-12-11

The Abbreviated Injury Scale (AIS) measures injury severity of a trauma patient with a numeric method for ranking anatomy-based specific injuries. The summation of the squares of the three most severe injuries in the AIS of six predefined body regions comprises the Injury Severity Score (ISS). It assumes that the mortality of a given AIS value is similar across all body regions. However, such an assumption is less explored in the literature. In this study, we aimed to compare the mortality rates of the patients with the same AIS value in different injured body regions in a level I trauma center. Hospitalized adult trauma patients with isolated serious to critical injury (AIS of 3 to 5) between 1 January 2009, and 31 December 2016, from the Trauma Registry System in a level I trauma center were grouped according to the injured body regions (including, the head/neck, thorax, abdomen, or extremities) and were exclusively compared according to their AIS stratum. Categorical data were compared using the two-sided Fisher exact or Pearson chi-square tests. ANOVA with Games-Howell post hoc test was performed to assess the differences in continuous data of the patients with injury in different body regions. The primary outcome of the study was in-hospital mortality. The adjusted odds ratios (AORs) were estimated using a stepwise selection of a multivariable regression model adjusted by controlling the confounding variables such as sex, age, comorbidities, and ISS. Survival curves were estimated with the Kaplan-Meier approach with a corresponding log-rank test. The patients with AIS of 5 for abdomen injury and those with AIS of 3 for extremity injury had a significantly lower odds of adjusted mortality (adjusted odds ratio (AOR) 0.1, 95% confidence interval (CI) 0.01-0.39, p = 0.004 and AOR 0.3, 95% CI 0.15-0.51, p < 0.001, respectively) than that of the patients with head/neck injury. However, the patients with AIS of 4 for extremity injury demonstrated significantly higher odds of adjusted mortality (AOR 8.4, 95% CI 2.84-25.07, p < 0.001) than the patients with head/neck injury. This study found that the risks to mortality in the patients with a given AIS value of serious to critical injury in different injured body regions were not the same, even after controlling for confounding variables such as sex, age, comorbidities, and ISS.

Same Abbreviated Injury Scale Values May Be Associated with Different Risks to Mortality in Trauma Patients: A Cross-Sectional Retrospective Study Based on the Trauma Registry System in a Level I Trauma Center

PubMed Central

Rau, Cheng-Shyuan; Wu, Shao-Chun; Kuo, Pao-Jen; Chen, Yi-Chun; Chien, Peng-Chen; Hsieh, Hsiao-Yun

2017-01-01

The Abbreviated Injury Scale (AIS) measures injury severity of a trauma patient with a numeric method for ranking anatomy-based specific injuries. The summation of the squares of the three most severe injuries in the AIS of six predefined body regions comprises the Injury Severity Score (ISS). It assumes that the mortality of a given AIS value is similar across all body regions. However, such an assumption is less explored in the literature. In this study, we aimed to compare the mortality rates of the patients with the same AIS value in different injured body regions in a level I trauma center. Hospitalized adult trauma patients with isolated serious to critical injury (AIS of 3 to 5) between 1 January 2009, and 31 December 2016, from the Trauma Registry System in a level I trauma center were grouped according to the injured body regions (including, the head/neck, thorax, abdomen, or extremities) and were exclusively compared according to their AIS stratum. Categorical data were compared using the two-sided Fisher exact or Pearson chi-square tests. ANOVA with Games-Howell post hoc test was performed to assess the differences in continuous data of the patients with injury in different body regions. The primary outcome of the study was in-hospital mortality. The adjusted odds ratios (AORs) were estimated using a stepwise selection of a multivariable regression model adjusted by controlling the confounding variables such as sex, age, comorbidities, and ISS. Survival curves were estimated with the Kaplan–Meier approach with a corresponding log-rank test. The patients with AIS of 5 for abdomen injury and those with AIS of 3 for extremity injury had a significantly lower odds of adjusted mortality (adjusted odds ratio (AOR) 0.1, 95% confidence interval (CI) 0.01–0.39, p = 0.004 and AOR 0.3, 95% CI 0.15–0.51, p < 0.001, respectively) than that of the patients with head/neck injury. However, the patients with AIS of 4 for extremity injury demonstrated significantly higher odds of adjusted mortality (AOR 8.4, 95% CI 2.84–25.07, p < 0.001) than the patients with head/neck injury. This study found that the risks to mortality in the patients with a given AIS value of serious to critical injury in different injured body regions were not the same, even after controlling for confounding variables such as sex, age, comorbidities, and ISS. PMID:29232883

Seasonal and Non-Seasonal Generalized Pareto Distribution to Estimate Extreme Significant Wave Height in The Banda Sea

NASA Astrophysics Data System (ADS)

Nursamsiah; Nugroho Sugianto, Denny; Suprijanto, Jusup; Munasik; Yulianto, Bambang

2018-02-01

The information of extreme wave height return level was required for maritime planning and management. The recommendation methods in analyzing extreme wave were better distributed by Generalized Pareto Distribution (GPD). Seasonal variation was often considered in the extreme wave model. This research aims to identify the best model of GPD by considering a seasonal variation of the extreme wave. By using percentile 95 % as the threshold of extreme significant wave height, the seasonal GPD and non-seasonal GPD fitted. The Kolmogorov-Smirnov test was applied to identify the goodness of fit of the GPD model. The return value from seasonal and non-seasonal GPD was compared with the definition of return value as criteria. The Kolmogorov-Smirnov test result shows that GPD fits data very well both seasonal and non-seasonal model. The seasonal return value gives better information about the wave height characteristics.

Mental health and social competencies of 10- to 12-year-old children born at 23 to 25 weeks of gestation in the 1990s: a Swedish national prospective follow-up study.

PubMed

Farooqi, Aijaz; Hägglöf, Bruno; Sedin, Gunnar; Gothefors, Leif; Serenius, Fredrik

2007-07-01

We investigated a national cohort of extremely immature children with respect to behavioral and emotional problems and social competencies, from the perspectives of parents, teachers, and children themselves. We examined 11-year-old children who were born before 26 completed weeks of gestation in Sweden between 1990 and 1992. All had been evaluated at a corrected age of 36 months. At 11 years of age, 86 of 89 survivors were studied and compared with an equal number of control subjects, matched with respect to age and gender. Behavioral and emotional problems, social competencies, and adaptive functioning at school were evaluated with standardized, well-validated instruments, including parent and teacher report questionnaires and a child self-report, administered by mail. Compared with control subjects, parents of extremely immature children reported significantly more problems with internalizing behaviors (anxiety/depression, withdrawn, and somatic problems) and attention, thought, and social problems. Teachers reported a similar pattern. Reports from children showed a trend toward increased depression symptoms compared with control subjects. Multivariate analysis of covariance of parent-reported behavioral problems revealed no interactions, but significant main effects emerged for group status (extremely immature versus control), family function, social risk, and presence of a chronic medical condition, with all effect sizes being medium and accounting for 8% to 12% of the variance. Multivariate analysis of covariance of teacher-reported behavioral problems showed significant effects for group status and gender but not for the covariates mentioned above. According to the teachers' ratings, extremely immature children were less well adjusted to the school environment than were control subjects. However, a majority of extremely immature children (85%) were functioning in mainstream schools without major adjustment problems. Despite favorable outcomes for many children born at the limit of viability, these children are at risk for mental health problems, with poorer school results.

Postoperative complications after lower extremity arterial bypass increase the risk of new deep venous thrombosis.

PubMed

Aziz, Faisal; Lehman, Erik; Blebea, John; Lurie, Fedor

2017-01-01

Background Deep venous thrombosis after any surgical operations is considered a preventable complication. Lower extremity bypass surgery is a commonly performed operation to improve blood flow to lower extremities in patients with severe peripheral arterial disease. Despite advances in endovascular surgery, lower extremity arterial bypass remains the gold standard treatment for severe, symptomatic peripheral arterial disease. The purpose of this study is to identify the clinical risk factors associated with development of deep venous thrombosis after lower extremity bypass surgery. Methods The American College of Surgeons' NSQIP database was utilized and all lower extremity bypass procedures performed in 2013 were examined. Patient and procedural characteristics were evaluated. Univariate and multivariate logistic regression analysis was used to determine independent risk factors for the development of postoperative deep venous thrombosis. Results A total of 2646 patients (65% males and 35% females) underwent lower extremity open revascularization during the year 2013. The following factors were found to be significantly associated with postoperative deep venous thrombosis: transfusion >4 units of packed red blood cells (odds ratio (OR) = 5.21, confidence interval (CI) = 1.29-22.81, p = 0.03), postoperative urinary tract infection (OR = 12.59, CI = 4.12-38.48, p < 0.01), length of hospital stay >28 days (OR = 9.30, CI = 2.79-30.92, p < 0.01), bleeding (OR = 2.93, CI = 1.27-6.73, p = 0.01), deep wound infection (OR = 3.21, CI = 1.37-7.56, p < 0.01), and unplanned reoperation (OR = 4.57, CI = 2.03-10.26, p < 0.01). Of these, multivariable analysis identified the factors independently associated with development of deep venous thrombosis after lower extremity bypass surgery to be unplanned reoperation (OR = 3.57, CI = 1.54-8.30, p < 0.01), reintubation (OR = 8.93, CI = 2.66-29.97, p < 0.01), and urinary tract infection (OR = 7.64, CI = 2.27-25.73, p < 0.01). Presence of all three factors was associated with a 54% incidence of deep venous thrombosis. Conclusions Development of deep venous thrombosis after lower extremity bypass is a serious but infrequent complication. Patients who require unplanned return to the operating room, reintubation, or develop a postoperative urinary tract are at high risk for developing postoperative deep venous thrombosis. Increased monitoring of these patients and ensuring adequate deep venous thrombosis prophylaxis for such patients is suggested.

Glaucomatous Optic Neuropathy Associated with Nocturnal Dip in Blood Pressure: Findings from the Maracaibo Aging Study.

PubMed

Melgarejo, Jesús D; Lee, Joseph H; Petitto, Michele; Yépez, Juan B; Murati, Felipe A; Jin, Zhezhen; Chávez, Carlos A; Pirela, Rosa V; Calmón, Gustavo E; Lee, Winston; Johnson, Matthew P; Mena, Luis J; Al-Aswad, Lama A; Terwilliger, Joseph D; Allikmets, Rando; Maestre, Gladys E; De Moraes, C Gustavo

2018-06-01

To determine which nocturnal blood pressure (BP) parameters (low levels or extreme dipper status) are associated with an increased risk of glaucomatous damage in Hispanics. Observational cross-sectional study. A subset (n = 93) of the participants from the Maracaibo Aging Study (MAS) who met the study eligibility criteria were included. These participants, who were at least 40 years of age, had measurements for optical tomography coherence, visual field (VF) tests, 24-hour BP, office BP, and intraocular pressure <22 mmHg. Univariate and multivariate logistic regression analyses under the generalized estimating equations (GEE) framework were used to examine the relationships between glaucomatous damage and BP parameters, with particular attention to decreases in nocturnal BP. Glaucomatous optic neuropathy (GON) based on the presence of optic nerve damage and VF defects. The mean age was 61.9 years, and 87.1% were women. Of 185 eyes evaluated, 19 (26.5%) had signs of GON. Individuals with GON had significantly lower 24-hour and nighttime diastolic BP levels than those without. However, results of the multivariate GEE models indicated that the glaucomatous damage was not related to the average systolic or diastolic BP levels measured over 24 hours, daytime, or nighttime. In contrast, extreme decreases in nighttime systolic and diastolic BP (>20% compared with daytime BP) were significant risk factors for glaucomatous damage (odds ratio, 19.78 and 5.55, respectively). In this population, the link between nocturnal BP and GON is determined by extreme dipping effects rather than low nocturnal BP levels alone. Further studies considering extreme decreases in nocturnal BP in individuals at high risk of glaucoma are warranted. Copyright © 2018 American Academy of Ophthalmology. Published by Elsevier Inc. All rights reserved.

The effect of equalizing landing task demands on sex differences in lower extremity energy absorption.

PubMed

Montgomery, Melissa M; Shultz, Sandra J; Schmitz, Randy J

2014-08-01

Less lean mass and strength may result in greater relative task demands on females compared to males when landing from a standardized height and could explain sex differences in energy absorption strategies. We compared the magnitude of sex differences in energy absorption when task demands were equalized relative to the amount of lower extremity lean mass available to dissipate kinetic energy upon landing. Male-female pairs (n=35) were assessed for lower extremity lean mass with dual-energy X-ray absorptiometry. Relative task demands were calculated when landing from a standardized height. Based on the difference in lower extremity lean mass within each pair, task demands were equalized by increasing the drop height for males. Joint energetics were measured while landing from the two heights. Multivariate repeated measures ANOVAs compared the magnitude of sex differences in joint energetics between conditions. The multivariate test for absolute energy absorption was significant (P<0.01). The magnitude of sex difference in energy absorption was greater at the hip and knee (both P<0.01), but not the ankle (P=0.43) during the equalized condition compared to the standardized and exaggerated conditions (all P<0.01). There was no difference in the magnitude of sex differences between equalized, standardized and exaggerated conditions for relative energy absorption (P=0.18). Equalizing task demands increased the difference in absolute hip and knee energy absorption between sexes, but had no effect on relative joint contributions to total energy absorption. Sex differences in energy absorption are likely influenced by factors other than differences in relative task demands. Copyright © 2014 Elsevier Ltd. All rights reserved.

Nonparametric functional data estimation applied to ozone data: prediction and extreme value analysis.

PubMed

Quintela-del-Río, Alejandro; Francisco-Fernández, Mario

2011-02-01

The study of extreme values and prediction of ozone data is an important topic of research when dealing with environmental problems. Classical extreme value theory is usually used in air-pollution studies. It consists in fitting a parametric generalised extreme value (GEV) distribution to a data set of extreme values, and using the estimated distribution to compute return levels and other quantities of interest. Here, we propose to estimate these values using nonparametric functional data methods. Functional data analysis is a relatively new statistical methodology that generally deals with data consisting of curves or multi-dimensional variables. In this paper, we use this technique, jointly with nonparametric curve estimation, to provide alternatives to the usual parametric statistical tools. The nonparametric estimators are applied to real samples of maximum ozone values obtained from several monitoring stations belonging to the Automatic Urban and Rural Network (AURN) in the UK. The results show that nonparametric estimators work satisfactorily, outperforming the behaviour of classical parametric estimators. Functional data analysis is also used to predict stratospheric ozone concentrations. We show an application, using the data set of mean monthly ozone concentrations in Arosa, Switzerland, and the results are compared with those obtained by classical time series (ARIMA) analysis. Copyright © 2010 Elsevier Ltd. All rights reserved.

Investigating NARCCAP Precipitation Extremes via Bivariate Extreme Value Theory (Invited)

NASA Astrophysics Data System (ADS)

Weller, G. B.; Cooley, D. S.; Sain, S. R.; Bukovsky, M. S.; Mearns, L. O.

2013-12-01

We introduce methodology from statistical extreme value theory to examine the ability of reanalysis-drive regional climate models to simulate past daily precipitation extremes. Going beyond a comparison of summary statistics such as 20-year return values, we study whether the most extreme precipitation events produced by climate model simulations exhibit correspondence to the most extreme events seen in observational records. The extent of this correspondence is formulated via the statistical concept of tail dependence. We examine several case studies of extreme precipitation events simulated by the six models of the North American Regional Climate Change Assessment Program (NARCCAP) driven by NCEP reanalysis. It is found that the NARCCAP models generally reproduce daily winter precipitation extremes along the Pacific coast quite well; in contrast, simulation of past daily summer precipitation extremes in a central US region is poor. Some differences in the strength of extremal correspondence are seen in the central region between models which employ spectral nudging and those which do not. We demonstrate how these techniques may be used to draw a link between extreme precipitation events and large-scale atmospheric drivers, as well as to downscale extreme precipitation simulated by a future run of a regional climate model. Specifically, we examine potential future changes in the nature of extreme precipitation along the Pacific coast produced by the pineapple express (PE) phenomenon. A link between extreme precipitation events and a "PE Index" derived from North Pacific sea-surface pressure fields is found. This link is used to study PE-influenced extreme precipitation produced by a future-scenario climate model run.

Extreme Unconditional Dependence Vs. Multivariate GARCH Effect in the Analysis of Dependence Between High Losses on Polish and German Stock Indexes

NASA Astrophysics Data System (ADS)

Rokita, Pawel

Classical portfolio diversification methods do not take account of any dependence between extreme returns (losses). Many researchers provide, however, some empirical evidence for various assets that extreme-losses co-occur. If the co-occurrence is frequent enough to be statistically significant, it may seriously influence portfolio risk. Such effects may result from a few different properties of financial time series, like for instance: (1) extreme dependence in a (long-term) unconditional distribution, (2) extreme dependence in subsequent conditional distributions, (3) time-varying conditional covariance, (4) time-varying (long-term) unconditional covariance, (5) market contagion. Moreover, a mix of these properties may be present in return time series. Modeling each of them requires different approaches. It seams reasonable to investigate whether distinguishing between the properties is highly significant for portfolio risk measurement. If it is, identifying the effect responsible for high loss co-occurrence would be of a great importance. If it is not, the best solution would be selecting the easiest-to-apply model. This article concentrates on two of the aforementioned properties: extreme dependence (in a long-term unconditional distribution) and time-varying conditional covariance.

Climate and its change over the Tibetan Plateau and its Surroundings in 1963-2015

NASA Astrophysics Data System (ADS)

Ding, J.; Cuo, L.

2017-12-01

Tibetan Plateau and its surroundings (TPS, 23°-43°N, 73°-106°E) lies in the southwest of China and includes Tibet Autonomous Region, Qinghai Province, southern Xinjiang Uygur Autonomous Region, part of Gansu Province, western Sichuan Province, and northern Yunnan Province. The region is of strategic importance in water resources because it is the headwater of ten large rivers that support more than 16 billion population. In this study, we use daily temperature maximum and minimum, precipitation and wind speed in 1963-2015 obtained from Climate Data Center of China Meteorological Administration and Qinghai Meteorological Bureau to investigate extreme climate conditions and their changes over the TPS. The extreme events are selected based on annual extreme values and percentiles. Annual extreme value approach produces one value each year for all variables, which enables us to examine the magnitude of extreme events; whereas percentile approach selects extreme values by setting 95th percentile as thresholds for maximum temperature, precipitation and wind speed, and 5th percentile for minimum temperature. Percentile approach not only enables us to investigate the magnitude but also frequency of the extreme events. Also, Mann-Kendall trend and mutation analysis were applied to analyze the changes in mean and extreme conditions. The results will help us understand more about the extreme events during the past five decades on the TPS and will provide valuable information for the upcoming IPCC reports on climate change.

Accurate and fast multiple-testing correction in eQTL studies.

PubMed

Sul, Jae Hoon; Raj, Towfique; de Jong, Simone; de Bakker, Paul I W; Raychaudhuri, Soumya; Ophoff, Roel A; Stranger, Barbara E; Eskin, Eleazar; Han, Buhm

2015-06-04

In studies of expression quantitative trait loci (eQTLs), it is of increasing interest to identify eGenes, the genes whose expression levels are associated with variation at a particular genetic variant. Detecting eGenes is important for follow-up analyses and prioritization because genes are the main entities in biological processes. To detect eGenes, one typically focuses on the genetic variant with the minimum p value among all variants in cis with a gene and corrects for multiple testing to obtain a gene-level p value. For performing multiple-testing correction, a permutation test is widely used. Because of growing sample sizes of eQTL studies, however, the permutation test has become a computational bottleneck in eQTL studies. In this paper, we propose an efficient approach for correcting for multiple testing and assess eGene p values by utilizing a multivariate normal distribution. Our approach properly takes into account the linkage-disequilibrium structure among variants, and its time complexity is independent of sample size. By applying our small-sample correction techniques, our method achieves high accuracy in both small and large studies. We have shown that our method consistently produces extremely accurate p values (accuracy > 98%) for three human eQTL datasets with different sample sizes and SNP densities: the Genotype-Tissue Expression pilot dataset, the multi-region brain dataset, and the HapMap 3 dataset. Copyright © 2015 The American Society of Human Genetics. Published by Elsevier Inc. All rights reserved.

Spatiotemporal variation and statistical characteristic of extreme precipitation in the middle reaches of the Yellow River Basin during 1960-2013

NASA Astrophysics Data System (ADS)

Zhang, Yin; Xia, Jun; She, Dunxian

2018-01-01

In recent decades, extreme precipitation events have been a research hotspot worldwide. Based on 12 extreme precipitation indices, the spatiotemporal variation and statistical characteristic of precipitation extremes in the middle reaches of the Yellow River Basin (MRYRB) during 1960-2013 were investigated. The results showed that the values of most extreme precipitation indices (except consecutive dry days (CDD)) increased from the northwest to the southeast of the MRYRB, reflecting that the southeast was the wettest region in the study area. Temporally, the precipitation extremes presented a drying trend with less frequent precipitation events. Generalized extreme value (GEV) distribution was selected to fit the time series of all indices, and the quantiles values under the 50-year return period showed a similar spatial extent with the corresponding precipitation extreme indices during 1960-2013, indicating a higher risk of extreme precipitation in the southeast of the MRYRB. Furthermore, the changes in probability distribution functions of indices for the period of 1960-1986 and 1987-2013 revealed a drying tendency in our study area. Both El Niño-Southern Oscillation (ENSO) and Pacific Decadal Oscillation (PDO) were proved to have a strong influence on precipitation extremes in the MRYRB. The results of this study are useful to master the change rule of local precipitation extremes, which will help to prevent natural hazards caused.

Extreme value analysis in biometrics.

PubMed

Hüsler, Jürg

2009-04-01

We review some approaches of extreme value analysis in the context of biometrical applications. The classical extreme value analysis is based on iid random variables. Two different general methods are applied, which will be discussed together with biometrical examples. Different estimation, testing, goodness-of-fit procedures for applications are discussed. Furthermore, some non-classical situations are considered where the data are possibly dependent, where a non-stationary behavior is observed in the data or where the observations are not univariate. A few open problems are also stated.

Effect of elevation on extreme precipitation of short durations: evidences of orographic signature on the parameters of Depth-Duration-Frequency curves

NASA Astrophysics Data System (ADS)

Avanzi, Francesco; De Michele, Carlo; Gabriele, Salvatore; Ghezzi, Antonio; Rosso, Renzo

2015-04-01

Here, we show how atmospheric circulation and topography rule the variability of depth-duration-frequency (DDF) curves parameters, and we discuss how this variability has physical implications on the formation of extreme precipitations at high elevations. A DDF is a curve ruling the value of the maximum annual precipitation H as a function of duration D and the level of probability F. We consider around 1500 stations over the Italian territory, with at least 20 years of data of maximum annual precipitation depth at different durations. We estimated the DDF parameters at each location by using the asymptotic distribution of extreme values, i.e. the so-called Generalized Extreme Value (GEV) distribution, and considering a statistical simple scale invariance hypothesis. Consequently, a DDF curve depends on five different parameters. A first set relates H with the duration (namely, the mean value of annual maximum precipitation depth for unit duration and the scaling exponent), while a second set links H to F (namely, a scale, position and shape parameter). The value of the shape parameter has consequences on the type of random variable (unbounded, upper or lower bounded). This extensive analysis shows that the variability of the mean value of annual maximum precipitation depth for unit duration obeys to the coupled effect of topography and modal direction of moisture flux during extreme events. Median values of this parameter decrease with elevation. We called this phenomenon "reverse orographic effect" on extreme precipitation of short durations, since it is in contrast with general knowledge about the orographic effect on mean precipitation. Moreover, the scaling exponent is mainly driven by topography alone (with increasing values of this parameter at increasing elevations). Therefore, the quantiles of H(D,F) at durations greater than unit turn to be more variable at high elevations than at low elevations. Additionally, the analysis of the variability of the shape parameter with elevation shows that extreme events at high elevations appear to be distributed according to an upper bounded probability distribution. These evidences could be a characteristic sign of the formation of extreme precipitation events at high elevations.

Extreme events in total ozone over Arosa - Part 1: Application of extreme value theory

NASA Astrophysics Data System (ADS)

Rieder, H. E.; Staehelin, J.; Maeder, J. A.; Peter, T.; Ribatet, M.; Davison, A. C.; Stübi, R.; Weihs, P.; Holawe, F.

2010-10-01

In this study ideas from extreme value theory are for the first time applied in the field of stratospheric ozone research, because statistical analysis showed that previously used concepts assuming a Gaussian distribution (e.g. fixed deviations from mean values) of total ozone data do not adequately address the structure of the extremes. We show that statistical extreme value methods are appropriate to identify ozone extremes and to describe the tails of the Arosa (Switzerland) total ozone time series. In order to accommodate the seasonal cycle in total ozone, a daily moving threshold was determined and used, with tools from extreme value theory, to analyse the frequency of days with extreme low (termed ELOs) and high (termed EHOs) total ozone at Arosa. The analysis shows that the Generalized Pareto Distribution (GPD) provides an appropriate model for the frequency distribution of total ozone above or below a mathematically well-defined threshold, thus providing a statistical description of ELOs and EHOs. The results show an increase in ELOs and a decrease in EHOs during the last decades. The fitted model represents the tails of the total ozone data set with high accuracy over the entire range (including absolute monthly minima and maxima), and enables a precise computation of the frequency distribution of ozone mini-holes (using constant thresholds). Analyzing the tails instead of a small fraction of days below constant thresholds provides deeper insight into the time series properties. Fingerprints of dynamical (e.g. ENSO, NAO) and chemical features (e.g. strong polar vortex ozone loss), and major volcanic eruptions, can be identified in the observed frequency of extreme events throughout the time series. Overall the new approach to analysis of extremes provides more information on time series properties and variability than previous approaches that use only monthly averages and/or mini-holes and mini-highs.

Extreme events in total ozone over Arosa - Part 1: Application of extreme value theory

NASA Astrophysics Data System (ADS)

Rieder, H. E.; Staehelin, J.; Maeder, J. A.; Peter, T.; Ribatet, M.; Davison, A. C.; Stübi, R.; Weihs, P.; Holawe, F.

2010-05-01

In this study ideas from extreme value theory are for the first time applied in the field of stratospheric ozone research, because statistical analysis showed that previously used concepts assuming a Gaussian distribution (e.g. fixed deviations from mean values) of total ozone data do not adequately address the structure of the extremes. We show that statistical extreme value methods are appropriate to identify ozone extremes and to describe the tails of the Arosa (Switzerland) total ozone time series. In order to accommodate the seasonal cycle in total ozone, a daily moving threshold was determined and used, with tools from extreme value theory, to analyse the frequency of days with extreme low (termed ELOs) and high (termed EHOs) total ozone at Arosa. The analysis shows that the Generalized Pareto Distribution (GPD) provides an appropriate model for the frequency distribution of total ozone above or below a mathematically well-defined threshold, thus providing a statistical description of ELOs and EHOs. The results show an increase in ELOs and a decrease in EHOs during the last decades. The fitted model represents the tails of the total ozone data set with high accuracy over the entire range (including absolute monthly minima and maxima), and enables a precise computation of the frequency distribution of ozone mini-holes (using constant thresholds). Analyzing the tails instead of a small fraction of days below constant thresholds provides deeper insight into the time series properties. Fingerprints of dynamical (e.g. ENSO, NAO) and chemical features (e.g. strong polar vortex ozone loss), and major volcanic eruptions, can be identified in the observed frequency of extreme events throughout the time series. Overall the new approach to analysis of extremes provides more information on time series properties and variability than previous approaches that use only monthly averages and/or mini-holes and mini-highs.

Can rotational thromboelastometry predict thrombotic complications in reconstructive microsurgery?

PubMed

Kolbenschlag, Jonas; Daigeler, Adrien; Lauer, Sarah; Wittenberg, Gerhard; Fischer, Sebastian; Kapalschinski, Nicolai; Lehnhardt, Marcus; Goertz, Ole

2014-05-01

Thrombotic occlusion of the microvascular pedicle is the major reason for flap loss. Thus, identifying patients who are at risk for such events is paramount. Rotational thromboelastometry (RTE) is widely used to detect coagulopathy and hypercoagulable states. The aim of our study was to assess its diagnostic value in reconstructive microsurgery. In all 181 patients undergoing free tissue transfer at our department between February 2010 and November 2011 preoperative RTE was performed. In addition, coagulation values as well as patient's demographic data, cause and localization of defect, type of flap and surgical revisions were recorded. The majority of patients was male (59.6%) with traumatic (59.7%) defects located on the lower extremity (60.3%). ALT was the most often used flap (35.9%). Preoperatively, 36.5% of patients had a hypercoagulable RTE (higher than physiological RTE values; intrinsic (ICPT) or extrinsic (ECPT) mean clot firmness (MCF) >72mm or functional fibrinogen (ICF) MCF >25mm). A total of 28 primary thrombosis of the microvascular pedicle occurred, 11 of those in-patients with a hypercoagulable state. Total flap loss rate because ofthrombosis was 7.7% (n = 14). Both a hypercoagulable RTE assay and a functional fibrinogen to platelet ratio (FPR) of >43 (MCF value of ICF divided by the MCF value of ICPT) were significant predictors of thrombotic flap loss when performing multivariate binary logistic regression, co-factoring for age, sex, and comorbidities (p = 0.036 and 0.003, respectively). RTE seems to be able to identify patients that are prone to thrombotic complications and might be used as a screening tool. Copyright © 2013 Wiley Periodicals, Inc.

Linear, multivariable robust control with a mu perspective

NASA Technical Reports Server (NTRS)

Packard, Andy; Doyle, John; Balas, Gary

1993-01-01

The structured singular value is a linear algebra tool developed to study a particular class of matrix perturbation problems arising in robust feedback control of multivariable systems. These perturbations are called linear fractional, and are a natural way to model many types of uncertainty in linear systems, including state-space parameter uncertainty, multiplicative and additive unmodeled dynamics uncertainty, and coprime factor and gap metric uncertainty. The structured singular value theory provides a natural extension of classical SISO robustness measures and concepts to MIMO systems. The structured singular value analysis, coupled with approximate synthesis methods, make it possible to study the tradeoff between performance and uncertainty that occurs in all feedback systems. In MIMO systems, the complexity of the spatial interactions in the loop gains make it difficult to heuristically quantify the tradeoffs that must occur. This paper examines the role played by the structured singular value (and its computable bounds) in answering these questions, as well as its role in the general robust, multivariable control analysis and design problem.

A Review of Multivariate Distributions for Count Data Derived from the Poisson Distribution.

PubMed

Inouye, David; Yang, Eunho; Allen, Genevera; Ravikumar, Pradeep

2017-01-01

The Poisson distribution has been widely studied and used for modeling univariate count-valued data. Multivariate generalizations of the Poisson distribution that permit dependencies, however, have been far less popular. Yet, real-world high-dimensional count-valued data found in word counts, genomics, and crime statistics, for example, exhibit rich dependencies, and motivate the need for multivariate distributions that can appropriately model this data. We review multivariate distributions derived from the univariate Poisson, categorizing these models into three main classes: 1) where the marginal distributions are Poisson, 2) where the joint distribution is a mixture of independent multivariate Poisson distributions, and 3) where the node-conditional distributions are derived from the Poisson. We discuss the development of multiple instances of these classes and compare the models in terms of interpretability and theory. Then, we empirically compare multiple models from each class on three real-world datasets that have varying data characteristics from different domains, namely traffic accident data, biological next generation sequencing data, and text data. These empirical experiments develop intuition about the comparative advantages and disadvantages of each class of multivariate distribution that was derived from the Poisson. Finally, we suggest new research directions as explored in the subsequent discussion section.

Associations between workplace factors and carpal tunnel syndrome: A multi-site cross sectional study.

PubMed

Fan, Z Joyce; Harris-Adamson, Carisa; Gerr, Fred; Eisen, Ellen A; Hegmann, Kurt T; Bao, Stephen; Silverstein, Barbara; Evanoff, Bradley; Dale, Ann Marie; Thiese, Matthew S; Garg, Arun; Kapellusch, Jay; Burt, Susan; Merlino, Linda; Rempel, David

2015-05-01

Few large epidemiologic studies have used rigorous case criteria, individual-level exposure measurements, and appropriate control for confounders to examine associations between workplace psychosocial and biomechanical factors and carpal tunnel syndrome (CTS). Pooling data from five independent research studies, we assessed associations between prevalent CTS and personal, work psychosocial, and biomechanical factors while adjusting for confounders using multivariable logistic regression. Prevalent CTS was associated with personal factors of older age, obesity, female sex, medical conditions, previous distal upper extremity disorders, workplace measures of peak forceful hand activity, a composite measure of force and repetition (ACGIH Threshold Limit Value for Hand Activity Level), and hand vibration. In this cross-sectional analysis of production and service workers, CTS prevalence was associated with workplace and biomechanical factors. The findings were similar to those from a prospective analysis of the same cohort with differences that may be due to recall bias and other factors. © 2015 Wiley Periodicals, Inc.

Environmental monitoring of water resources around a municipal landfill of the Rio Grande do Sul state, Brazil.

PubMed

de Medeiros Engelmann, Pâmela; Dos Santos, Victor Hugo Jacks Mendes; Moser, Letícia Isabela; do Canto Bruzza, Eduardo; Barbieri, Cristina Barazzetti; Barela, Pâmela Susin; de Moraes, Diogo Pompéu; Augustin, Adolpho Herbert; Goudinho, Flávio Soares; Melo, Clarissa Lovato; Ketzer, João Marcelo Medina; Rodrigues, Luiz Frederico

2017-09-01

In Brazil, landfills are commonly used as a method for the final disposal of waste that is compliant with the legislation. This technique, however, presents a risk to surface water and groundwater resources, owing to the leakage of metals, anions, and organic compounds. The geochemical monitoring of water resources is therefore extremely important, since the leachate can compromise the quality and use of surface water and groundwater close to landfills. In this paper, the results of analyses of metals, anions, ammonia, and physicochemical parameters were used to identify possible contamination of surface water and groundwater in a landfill area. A statistical multivariate approach was used. The values found for alkali metals, nitrate, and chloride indicate contamination in the regional groundwater and, moreover, surface waters also show variation when compared to the other background points, mainly for ammonia. Thus, the results of this study evidence the landfill leachate influence on the quality of groundwater and surface water in the study area.

A Statistical Learning Framework for Materials Science: Application to Elastic Moduli of k-nary Inorganic Polycrystalline Compounds.

PubMed

de Jong, Maarten; Chen, Wei; Notestine, Randy; Persson, Kristin; Ceder, Gerbrand; Jain, Anubhav; Asta, Mark; Gamst, Anthony

2016-10-03

Materials scientists increasingly employ machine or statistical learning (SL) techniques to accelerate materials discovery and design. Such pursuits benefit from pooling training data across, and thus being able to generalize predictions over, k-nary compounds of diverse chemistries and structures. This work presents a SL framework that addresses challenges in materials science applications, where datasets are diverse but of modest size, and extreme values are often of interest. Our advances include the application of power or Hölder means to construct descriptors that generalize over chemistry and crystal structure, and the incorporation of multivariate local regression within a gradient boosting framework. The approach is demonstrated by developing SL models to predict bulk and shear moduli (K and G, respectively) for polycrystalline inorganic compounds, using 1,940 compounds from a growing database of calculated elastic moduli for metals, semiconductors and insulators. The usefulness of the models is illustrated by screening for superhard materials.

A Statistical Learning Framework for Materials Science: Application to Elastic Moduli of k-nary Inorganic Polycrystalline Compounds

PubMed Central

de Jong, Maarten; Chen, Wei; Notestine, Randy; Persson, Kristin; Ceder, Gerbrand; Jain, Anubhav; Asta, Mark; Gamst, Anthony

2016-01-01

Materials scientists increasingly employ machine or statistical learning (SL) techniques to accelerate materials discovery and design. Such pursuits benefit from pooling training data across, and thus being able to generalize predictions over, k-nary compounds of diverse chemistries and structures. This work presents a SL framework that addresses challenges in materials science applications, where datasets are diverse but of modest size, and extreme values are often of interest. Our advances include the application of power or Hölder means to construct descriptors that generalize over chemistry and crystal structure, and the incorporation of multivariate local regression within a gradient boosting framework. The approach is demonstrated by developing SL models to predict bulk and shear moduli (K and G, respectively) for polycrystalline inorganic compounds, using 1,940 compounds from a growing database of calculated elastic moduli for metals, semiconductors and insulators. The usefulness of the models is illustrated by screening for superhard materials. PMID:27694824

A Statistical Learning Framework for Materials Science: Application to Elastic Moduli of k-nary Inorganic Polycrystalline Compounds

DOE PAGES

de Jong, Maarten; Chen, Wei; Notestine, Randy; ...

2016-10-03

Materials scientists increasingly employ machine or statistical learning (SL) techniques to accelerate materials discovery and design. Such pursuits benefit from pooling training data across, and thus being able to generalize predictions over, k-nary compounds of diverse chemistries and structures. This work presents a SL framework that addresses challenges in materials science applications, where datasets are diverse but of modest size, and extreme values are often of interest. Our advances include the application of power or Hölder means to construct descriptors that generalize over chemistry and crystal structure, and the incorporation of multivariate local regression within a gradient boosting framework. Themore » approach is demonstrated by developing SL models to predict bulk and shear moduli (K and G, respectively) for polycrystalline inorganic compounds, using 1,940 compounds from a growing database of calculated elastic moduli for metals, semiconductors and insulators. The usefulness of the models is illustrated by screening for superhard materials.« less

Estimation of muscle torque in various combat sports.

PubMed

Pędzich, Wioletta; Mastalerz, Andrzej; Sadowski, Jerzy

2012-01-01

The purpose of the research was to compare muscle torque of elite combat groups. Twelve taekwondo WTF athletes, twelve taekwondo ITF athletes and nine boxers participated in the study. Measurements of muscle torques were done under static conditions on a special stand which belonged to the Department of Biomechanics. The sum of muscle torque of lower right and left extremities of relative values was significantly higher for taekwondo WTF athletes than for boxers (16%, p < 0.001 for right and 10%, p < 0.05 for left extremities) and taekwondo ITF (10%, p < 0.05 for right and 8% for left extremities). Taekwondo ITF athletes attained significantly higher absolute muscle torque values than boxers for elbow flexors (20%, p < 0.05 for right and 11% for left extremities) and extensors (14% for right and 18%, p < 0.05 for left extremities) and shoulder flexors (10% for right and 12%, p < 0.05 for left extremities) and extensors (11% for right and 1% for left extremities). Taekwondo WTF and taekwondo ITF athletes obtained significantly different relative values of muscle torque of the hip flexors (16%, p < 0.05) and extensors (11%, p < 0.05) of the right extremities.

Risk Factors for Lower-Extremity Injuries Among Contemporary Dance Students.

PubMed

van Seters, Christine; van Rijn, Rogier M; van Middelkoop, Marienke; Stubbe, Janine H

2017-10-10

To determine whether student characteristics, lower-extremity kinematics, and strength are risk factors for sustaining lower-extremity injuries in preprofessional contemporary dancers. Prospective cohort study. Codarts University of the Arts. Forty-five first-year students of Bachelor Dance and Bachelor Dance Teacher. At the beginning of the academic year, the injury history (only lower-extremity) and student characteristics (age, sex, educational program) were assessed using a questionnaire. Besides, lower-extremity kinematics [single-leg squat (SLS)], strength (countermovement jump) and height and weight (body mass index) were measured during a physical performance test. Substantial lower-extremity injuries during the academic year were defined as any problems leading to moderate or severe reductions in training volume or in performance, or complete inability to participate in dance at least once during follow-up as measured with the Oslo Sports Trauma Research Center (OSTRC) Questionnaire on Health Problems. Injuries were recorded on a monthly basis using a questionnaire. Analyses on leg-level were performed using generalized estimating equations to test the associations between substantial lower-extremity injuries and potential risk factors. The 1-year incidence of lower-extremity injuries was 82.2%. Of these, 51.4% was a substantial lower-extremity injury. Multivariate analyses identified that ankle dorsiflexion during the SLS (OR 1.25; 95% confidence interval, 1.03-1.52) was a risk factor for a substantial lower-extremity injury. The findings indicate that contemporary dance students are at high risk for lower-extremity injuries. Therefore, the identified risk factor (ankle dorsiflexion) should be considered for prevention purposes.

Simulation of climate characteristics and extremes of the Volta Basin using CCLM and RCA regional climate models

NASA Astrophysics Data System (ADS)

Darko, Deborah; Adjei, Kwaku A.; Appiah-Adjei, Emmanuel K.; Odai, Samuel N.; Obuobie, Emmanuel; Asmah, Ruby

2018-06-01

The extent to which statistical bias-adjusted outputs of two regional climate models alter the projected change signals for the mean (and extreme) rainfall and temperature over the Volta Basin is evaluated. The outputs from two regional climate models in the Coordinated Regional Climate Downscaling Experiment for Africa (CORDEX-Africa) are bias adjusted using the quantile mapping technique. Annual maxima rainfall and temperature with their 10- and 20-year return values for the present (1981-2010) and future (2051-2080) climates are estimated using extreme value analyses. Moderate extremes are evaluated using extreme indices (viz. percentile-based, duration-based, and intensity-based). Bias adjustment of the original (bias-unadjusted) models improves the reproduction of mean rainfall and temperature for the present climate. However, the bias-adjusted models poorly reproduce the 10- and 20-year return values for rainfall and maximum temperature whereas the extreme indices are reproduced satisfactorily for the present climate. Consequently, projected changes in rainfall and temperature extremes were weak. The bias adjustment results in the reduction of the change signals for the mean rainfall while the mean temperature signals are rather magnified. The projected changes for the original mean climate and extremes are not conserved after bias adjustment with the exception of duration-based extreme indices.

Estimating multivariate similarity between neuroimaging datasets with sparse canonical correlation analysis: an application to perfusion imaging.

PubMed

Rosa, Maria J; Mehta, Mitul A; Pich, Emilio M; Risterucci, Celine; Zelaya, Fernando; Reinders, Antje A T S; Williams, Steve C R; Dazzan, Paola; Doyle, Orla M; Marquand, Andre F

2015-01-01

An increasing number of neuroimaging studies are based on either combining more than one data modality (inter-modal) or combining more than one measurement from the same modality (intra-modal). To date, most intra-modal studies using multivariate statistics have focused on differences between datasets, for instance relying on classifiers to differentiate between effects in the data. However, to fully characterize these effects, multivariate methods able to measure similarities between datasets are needed. One classical technique for estimating the relationship between two datasets is canonical correlation analysis (CCA). However, in the context of high-dimensional data the application of CCA is extremely challenging. A recent extension of CCA, sparse CCA (SCCA), overcomes this limitation, by regularizing the model parameters while yielding a sparse solution. In this work, we modify SCCA with the aim of facilitating its application to high-dimensional neuroimaging data and finding meaningful multivariate image-to-image correspondences in intra-modal studies. In particular, we show how the optimal subset of variables can be estimated independently and we look at the information encoded in more than one set of SCCA transformations. We illustrate our framework using Arterial Spin Labeling data to investigate multivariate similarities between the effects of two antipsychotic drugs on cerebral blood flow.

Risk of false decision on conformity of a multicomponent material when test results of the components' content are correlated.

PubMed

Kuselman, Ilya; Pennecchi, Francesca R; da Silva, Ricardo J N B; Hibbert, D Brynn

2017-11-01

The probability of a false decision on conformity of a multicomponent material due to measurement uncertainty is discussed when test results are correlated. Specification limits of the components' content of such a material generate a multivariate specification interval/domain. When true values of components' content and corresponding test results are modelled by multivariate distributions (e.g. by multivariate normal distributions), a total global risk of a false decision on the material conformity can be evaluated based on calculation of integrals of their joint probability density function. No transformation of the raw data is required for that. A total specific risk can be evaluated as the joint posterior cumulative function of true values of a specific batch or lot lying outside the multivariate specification domain, when the vector of test results, obtained for the lot, is inside this domain. It was shown, using a case study of four components under control in a drug, that the correlation influence on the risk value is not easily predictable. To assess this influence, the evaluated total risk values were compared with those calculated for independent test results and also with those assuming much stronger correlation than that observed. While the observed statistically significant correlation did not lead to a visible difference in the total risk values in comparison to the independent test results, the stronger correlation among the variables caused either the total risk decreasing or its increasing, depending on the actual values of the test results. Copyright © 2017 Elsevier B.V. All rights reserved.

Causes of Glacier Melt Extremes in the Alps Since 1949

NASA Astrophysics Data System (ADS)

Thibert, E.; Dkengne Sielenou, P.; Vionnet, V.; Eckert, N.; Vincent, C.

2018-01-01

Recent record-breaking glacier melt values are attributable to peculiar extreme events and long-term warming trends that shift averages upward. Analyzing one of the world's longest mass balance series with extreme value statistics, we show that detrending melt anomalies makes it possible to disentangle these effects, leading to a fairer evaluation of the return period of melt extreme values such as 2003, and to characterize them by a more realistic bounded behavior. Using surface energy balance simulations, we show that three independent drivers control melt: global radiation, latent heat, and the amount of snow at the beginning of the melting season. Extremes are governed by large deviations in global radiation combined with sensible heat. Long-term trends are driven by the lengthening of melt duration due to earlier and longer-lasting melting of ice along with melt intensification caused by trends in long-wave irradiance and latent heat due to higher air moisture.

Correlation dimension and phase space contraction via extreme value theory

NASA Astrophysics Data System (ADS)

Faranda, Davide; Vaienti, Sandro

2018-04-01

We show how to obtain theoretical and numerical estimates of correlation dimension and phase space contraction by using the extreme value theory. The maxima of suitable observables sampled along the trajectory of a chaotic dynamical system converge asymptotically to classical extreme value laws where: (i) the inverse of the scale parameter gives the correlation dimension and (ii) the extremal index is associated with the rate of phase space contraction for backward iteration, which in dimension 1 and 2, is closely related to the positive Lyapunov exponent and in higher dimensions is related to the metric entropy. We call it the Dynamical Extremal Index. Numerical estimates are straightforward to obtain as they imply just a simple fit to a univariate distribution. Numerical tests range from low dimensional maps, to generalized Henon maps and climate data. The estimates of the indicators are particularly robust even with relatively short time series.

Extreme events in total ozone: Spatio-temporal analysis from local to global scale

NASA Astrophysics Data System (ADS)

Rieder, Harald E.; Staehelin, Johannes; Maeder, Jörg A.; Ribatet, Mathieu; di Rocco, Stefania; Jancso, Leonhardt M.; Peter, Thomas; Davison, Anthony C.

2010-05-01

Recently tools from extreme value theory (e.g. Coles, 2001; Ribatet, 2007) have been applied for the first time in the field of stratospheric ozone research, as statistical analysis showed that previously used concepts assuming a Gaussian distribution (e.g. fixed deviations from mean values) of total ozone data do not address the internal data structure concerning extremes adequately (Rieder et al., 2010a,b). A case study the world's longest total ozone record (Arosa, Switzerland - for details see Staehelin et al., 1998a,b) illustrates that tools based on extreme value theory are appropriate to identify ozone extremes and to describe the tails of the total ozone record. Excursions in the frequency of extreme events reveal "fingerprints" of dynamical factors such as ENSO or NAO, and chemical factors, such as cold Arctic vortex ozone losses, as well as major volcanic eruptions of the 20th century (e.g. Gunung Agung, El Chichón, Mt. Pinatubo). Furthermore, atmospheric loading in ozone depleting substances led to a continuous modification of column ozone in the northern hemisphere also with respect to extreme values (partly again in connection with polar vortex contributions). It is shown that application of extreme value theory allows the identification of many more such fingerprints than conventional time series analysis of annual and seasonal mean values. Especially, the extremal analysis shows the strong influence of dynamics, revealing that even moderate ENSO and NAO events have a discernible effect on total ozone (Rieder et al., 2010b). Overall the extremes concept provides new information on time series properties, variability, trends and the influence of dynamics and chemistry, complementing earlier analyses focusing only on monthly (or annual) mean values. Findings described above could be proven also for the total ozone records of 5 other long-term series (Belsk, Hohenpeissenberg, Hradec Kralove, Potsdam, Uccle) showing that strong influence of atmospheric dynamics (NAO, ENSO) on total ozone is a global feature in the northern mid-latitudes (Rieder et al., 2010c). In a next step frequency distributions of extreme events are analyzed on global scale (northern and southern mid-latitudes). A specific focus here is whether findings gained through analysis of long-term European ground based stations can be clearly identified as a global phenomenon. By showing results from these three types of studies an overview of extreme events in total ozone (and the dynamical and chemical features leading to those) will be presented from local to global scales. References: Coles, S.: An Introduction to Statistical Modeling of Extreme Values, Springer Series in Statistics, ISBN:1852334592, Springer, Berlin, 2001. Ribatet, M.: POT: Modelling peaks over a threshold, R News, 7, 34-36, 2007. Rieder, H.E., Staehelin, J., Maeder, J.A., Ribatet, M., Stübi, R., Weihs, P., Holawe, F., Peter, T., and A.D., Davison (2010): Extreme events in total ozone over Arosa - Part I: Application of extreme value theory, to be submitted to ACPD. Rieder, H.E., Staehelin, J., Maeder, J.A., Ribatet, M., Stübi, R., Weihs, P., Holawe, F., Peter, T., and A.D., Davison (2010): Extreme events in total ozone over Arosa - Part II: Fingerprints of atmospheric dynamics and chemistry and effects on mean values and long-term changes, to be submitted to ACPD. Rieder, H.E., Jancso, L., Staehelin, J., Maeder, J.A., Ribatet, Peter, T., and A.D., Davison (2010): Extreme events in total ozone over the northern mid-latitudes: A case study based on long-term data sets from 5 ground-based stations, in preparation. Staehelin, J., Renaud, A., Bader, J., McPeters, R., Viatte, P., Hoegger, B., Bugnion, V., Giroud, M., and Schill, H.: Total ozone series at Arosa (Switzerland): Homogenization and data comparison, J. Geophys. Res., 103(D5), 5827-5842, doi:10.1029/97JD02402, 1998a. Staehelin, J., Kegel, R., and Harris, N. R.: Trend analysis of the homogenized total ozone series of Arosa (Switzerland), 1929-1996, J. Geophys. Res., 103(D7), 8389-8400, doi:10.1029/97JD03650, 1998b.

Mathematical aspects of assessing extreme events for the safety of nuclear plants

NASA Astrophysics Data System (ADS)

Potempski, Slawomir; Borysiewicz, Mieczyslaw

2015-04-01

In the paper the review of mathematical methodologies applied for assessing low frequencies of rare natural events like earthquakes, tsunamis, hurricanes or tornadoes, floods (in particular flash floods and surge storms), lightning, solar flares, etc., will be given in the perspective of the safety assessment of nuclear plants. The statistical methods are usually based on the extreme value theory, which deals with the analysis of extreme deviation from the median (or the mean). In this respect application of various mathematical tools can be useful, like: the extreme value theorem of Fisher-Tippett-Gnedenko leading to possible choices of general extreme value distributions, or the Pickands-Balkema-de Haan theorem for tail fitting, or the methods related to large deviation theory. In the paper the most important stochastic distributions relevant for performing rare events statistical analysis will be presented. This concerns, for example, the analysis of the data with the annual extreme values (maxima - "Annual Maxima Series" or minima), or the peak values, exceeding given thresholds at some periods of interest ("Peak Over Threshold"), or the estimation of the size of exceedance. Despite of the fact that there is a lack of sufficient statistical data directly containing rare events, in some cases it is still possible to extract useful information from existing larger data sets. As an example one can consider some data sets available from the web sites for floods, earthquakes or generally natural hazards. Some aspects of such data sets will be also presented taking into account their usefulness for the practical assessment of risk for nuclear power plants coming from extreme weather conditions.

Simulating Univariate and Multivariate Burr Type IIII and Type XII Distributions through the Method of L-Moments

ERIC Educational Resources Information Center

Pant, Mohan Dev

2011-01-01

The Burr families (Type III and Type XII) of distributions are traditionally used in the context of statistical modeling and for simulating non-normal distributions with moment-based parameters (e.g., Skew and Kurtosis). In educational and psychological studies, the Burr families of distributions can be used to simulate extremely asymmetrical and…

Estimating areal means and variances of forest attributes using the k-Nearest Neighbors technique and satellite imagery

Treesearch

Ronald E. McRoberts; Erkki O. Tomppo; Andrew O. Finley; Heikkinen Juha

2007-01-01

The k-Nearest Neighbor (k-NN) technique has become extremely popular for a variety of forest inventory mapping and estimation applications. Much of this popularity may be attributed to the non-parametric, multivariate features of the technique, its intuitiveness, and its ease of use. When used with satellite imagery and forest...

Predicting seasonal influenza transmission using functional regression models with temporal dependence.

PubMed

Oviedo de la Fuente, Manuel; Febrero-Bande, Manuel; Muñoz, María Pilar; Domínguez, Àngela

2018-01-01

This paper proposes a novel approach that uses meteorological information to predict the incidence of influenza in Galicia (Spain). It extends the Generalized Least Squares (GLS) methods in the multivariate framework to functional regression models with dependent errors. These kinds of models are useful when the recent history of the incidence of influenza are readily unavailable (for instance, by delays on the communication with health informants) and the prediction must be constructed by correcting the temporal dependence of the residuals and using more accessible variables. A simulation study shows that the GLS estimators render better estimations of the parameters associated with the regression model than they do with the classical models. They obtain extremely good results from the predictive point of view and are competitive with the classical time series approach for the incidence of influenza. An iterative version of the GLS estimator (called iGLS) was also proposed that can help to model complicated dependence structures. For constructing the model, the distance correlation measure [Formula: see text] was employed to select relevant information to predict influenza rate mixing multivariate and functional variables. These kinds of models are extremely useful to health managers in allocating resources in advance to manage influenza epidemics.

A comparative assessment of statistical methods for extreme weather analysis

NASA Astrophysics Data System (ADS)

Schlögl, Matthias; Laaha, Gregor

2017-04-01

Extreme weather exposure assessment is of major importance for scientists and practitioners alike. We compare different extreme value approaches and fitting methods with respect to their value for assessing extreme precipitation and temperature impacts. Based on an Austrian data set from 25 meteorological stations representing diverse meteorological conditions, we assess the added value of partial duration series over the standardly used annual maxima series in order to give recommendations for performing extreme value statistics of meteorological hazards. Results show the merits of the robust L-moment estimation, which yielded better results than maximum likelihood estimation in 62 % of all cases. At the same time, results question the general assumption of the threshold excess approach (employing partial duration series, PDS) being superior to the block maxima approach (employing annual maxima series, AMS) due to information gain. For low return periods (non-extreme events) the PDS approach tends to overestimate return levels as compared to the AMS approach, whereas an opposite behavior was found for high return levels (extreme events). In extreme cases, an inappropriate threshold was shown to lead to considerable biases that may outperform the possible gain of information from including additional extreme events by far. This effect was neither visible from the square-root criterion, nor from standardly used graphical diagnosis (mean residual life plot), but from a direct comparison of AMS and PDS in synoptic quantile plots. We therefore recommend performing AMS and PDS approaches simultaneously in order to select the best suited approach. This will make the analyses more robust, in cases where threshold selection and dependency introduces biases to the PDS approach, but also in cases where the AMS contains non-extreme events that may introduce similar biases. For assessing the performance of extreme events we recommend conditional performance measures that focus on rare events only in addition to standardly used unconditional indicators. The findings of this study are of relevance for a broad range of environmental variables, including meteorological and hydrological quantities.

Parameter estimation of multivariate multiple regression model using bayesian with non-informative Jeffreys’ prior distribution

NASA Astrophysics Data System (ADS)

Saputro, D. R. S.; Amalia, F.; Widyaningsih, P.; Affan, R. C.

2018-05-01

Bayesian method is a method that can be used to estimate the parameters of multivariate multiple regression model. Bayesian method has two distributions, there are prior and posterior distributions. Posterior distribution is influenced by the selection of prior distribution. Jeffreys’ prior distribution is a kind of Non-informative prior distribution. This prior is used when the information about parameter not available. Non-informative Jeffreys’ prior distribution is combined with the sample information resulting the posterior distribution. Posterior distribution is used to estimate the parameter. The purposes of this research is to estimate the parameters of multivariate regression model using Bayesian method with Non-informative Jeffreys’ prior distribution. Based on the results and discussion, parameter estimation of β and Σ which were obtained from expected value of random variable of marginal posterior distribution function. The marginal posterior distributions for β and Σ are multivariate normal and inverse Wishart. However, in calculation of the expected value involving integral of a function which difficult to determine the value. Therefore, approach is needed by generating of random samples according to the posterior distribution characteristics of each parameter using Markov chain Monte Carlo (MCMC) Gibbs sampling algorithm.

A Review of Multivariate Distributions for Count Data Derived from the Poisson Distribution

PubMed Central

Inouye, David; Yang, Eunho; Allen, Genevera; Ravikumar, Pradeep

2017-01-01

The Poisson distribution has been widely studied and used for modeling univariate count-valued data. Multivariate generalizations of the Poisson distribution that permit dependencies, however, have been far less popular. Yet, real-world high-dimensional count-valued data found in word counts, genomics, and crime statistics, for example, exhibit rich dependencies, and motivate the need for multivariate distributions that can appropriately model this data. We review multivariate distributions derived from the univariate Poisson, categorizing these models into three main classes: 1) where the marginal distributions are Poisson, 2) where the joint distribution is a mixture of independent multivariate Poisson distributions, and 3) where the node-conditional distributions are derived from the Poisson. We discuss the development of multiple instances of these classes and compare the models in terms of interpretability and theory. Then, we empirically compare multiple models from each class on three real-world datasets that have varying data characteristics from different domains, namely traffic accident data, biological next generation sequencing data, and text data. These empirical experiments develop intuition about the comparative advantages and disadvantages of each class of multivariate distribution that was derived from the Poisson. Finally, we suggest new research directions as explored in the subsequent discussion section. PMID:28983398

Multivariate two-part statistics for analysis of correlated mass spectrometry data from multiple biological specimens.

PubMed

Taylor, Sandra L; Ruhaak, L Renee; Weiss, Robert H; Kelly, Karen; Kim, Kyoungmi

2017-01-01

High through-put mass spectrometry (MS) is now being used to profile small molecular compounds across multiple biological sample types from the same subjects with the goal of leveraging information across biospecimens. Multivariate statistical methods that combine information from all biospecimens could be more powerful than the usual univariate analyses. However, missing values are common in MS data and imputation can impact between-biospecimen correlation and multivariate analysis results. We propose two multivariate two-part statistics that accommodate missing values and combine data from all biospecimens to identify differentially regulated compounds. Statistical significance is determined using a multivariate permutation null distribution. Relative to univariate tests, the multivariate procedures detected more significant compounds in three biological datasets. In a simulation study, we showed that multi-biospecimen testing procedures were more powerful than single-biospecimen methods when compounds are differentially regulated in multiple biospecimens but univariate methods can be more powerful if compounds are differentially regulated in only one biospecimen. We provide R functions to implement and illustrate our method as supplementary information CONTACT: sltaylor@ucdavis.eduSupplementary information: Supplementary data are available at Bioinformatics online. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Proton radius from electron scattering data

NASA Astrophysics Data System (ADS)

Higinbotham, Douglas W.; Kabir, Al Amin; Lin, Vincent; Meekins, David; Norum, Blaine; Sawatzky, Brad

2016-05-01

Background: The proton charge radius extracted from recent muonic hydrogen Lamb shift measurements is significantly smaller than that extracted from atomic hydrogen and electron scattering measurements. The discrepancy has become known as the proton radius puzzle. Purpose: In an attempt to understand the discrepancy, we review high-precision electron scattering results from Mainz, Jefferson Lab, Saskatoon, and Stanford. Methods: We make use of stepwise regression techniques using the F test as well as the Akaike information criterion to systematically determine the predictive variables to use for a given set and range of electron scattering data as well as to provide multivariate error estimates. Results: Starting with the precision, low four-momentum transfer (Q2) data from Mainz (1980) and Saskatoon (1974), we find that a stepwise regression of the Maclaurin series using the F test as well as the Akaike information criterion justify using a linear extrapolation which yields a value for the proton radius that is consistent with the result obtained from muonic hydrogen measurements. Applying the same Maclaurin series and statistical criteria to the 2014 Rosenbluth results on GE from Mainz, we again find that the stepwise regression tends to favor a radius consistent with the muonic hydrogen radius but produces results that are extremely sensitive to the range of data included in the fit. Making use of the high-Q2 data on GE to select functions which extrapolate to high Q2, we find that a Padé (N =M =1 ) statistical model works remarkably well, as does a dipole function with a 0.84 fm radius, GE(Q2) =(1+Q2/0.66 GeV2) -2 . Conclusions: Rigorous applications of stepwise regression techniques and multivariate error estimates result in the extraction of a proton charge radius that is consistent with the muonic hydrogen result of 0.84 fm; either from linear extrapolation of the extremely-low-Q2 data or by use of the Padé approximant for extrapolation using a larger range of data. Thus, based on a purely statistical analysis of electron scattering data, we conclude that the electron scattering results and the muonic hydrogen results are consistent. It is the atomic hydrogen results that are the outliers.

Generalized extreme gust wind speeds distributions

USGS Publications Warehouse

Cheng, E.; Yeung, C.

2002-01-01

Since summer 1996, the US wind engineers are using the extreme gust (or 3-s gust) as the basic wind speed to quantify the destruction of extreme winds. In order to better understand these destructive wind forces, it is important to know the appropriate representations of these extreme gust wind speeds. Therefore, the purpose of this study is to determine the most suitable extreme value distributions for the annual extreme gust wind speeds recorded in large selected areas. To achieve this objective, we are using the generalized Pareto distribution as the diagnostic tool for determining the types of extreme gust wind speed distributions. The three-parameter generalized extreme value distribution function is, thus, reduced to either Type I Gumbel, Type II Frechet or Type III reverse Weibull distribution function for the annual extreme gust wind speeds recorded at a specific site.With the considerations of the quality and homogeneity of gust wind data collected at more than 750 weather stations throughout the United States, annual extreme gust wind speeds at selected 143 stations in the contiguous United States were used in the study. ?? 2002 Elsevier Science Ltd. All rights reserved.

Atomic-scale phase composition through multivariate statistical analysis of atom probe tomography data.

PubMed

Keenan, Michael R; Smentkowski, Vincent S; Ulfig, Robert M; Oltman, Edward; Larson, David J; Kelly, Thomas F

2011-06-01

We demonstrate for the first time that multivariate statistical analysis techniques can be applied to atom probe tomography data to estimate the chemical composition of a sample at the full spatial resolution of the atom probe in three dimensions. Whereas the raw atom probe data provide the specific identity of an atom at a precise location, the multivariate results can be interpreted in terms of the probabilities that an atom representing a particular chemical phase is situated there. When aggregated to the size scale of a single atom (∼0.2 nm), atom probe spectral-image datasets are huge and extremely sparse. In fact, the average spectrum will have somewhat less than one total count per spectrum due to imperfect detection efficiency. These conditions, under which the variance in the data is completely dominated by counting noise, test the limits of multivariate analysis, and an extensive discussion of how to extract the chemical information is presented. Efficient numerical approaches to performing principal component analysis (PCA) on these datasets, which may number hundreds of millions of individual spectra, are put forward, and it is shown that PCA can be computed in a few seconds on a typical laptop computer.

Examining global extreme sea level variations on the coast from in-situ and remote observations

NASA Astrophysics Data System (ADS)

Menendez, Melisa; Benkler, Anna S.

2017-04-01

The estimation of extreme water level values on the coast is a requirement for a wide range of engineering and coastal management applications. In addition, climate variations of extreme sea levels on the coastal area result from a complex interacting of oceanic, atmospheric and terrestrial processes across a wide range of spatial and temporal scales. In this study, variations of extreme sea level return values are investigated from two available sources of information: in-situ tide-gauge records and satellite altimetry data. Long time series of sea level from tide-gauge records are the most valuable observations since they directly measure water level in a specific coastal location. They have however a number of sources of in-homogeneities that may affect the climate description of extremes when this data source is used. Among others, the presence of gaps, historical time in-homogeneities and jumps in the mean sea level signal are factors that can provide uncertainty in the characterization of the extreme sea level behaviour. Moreover, long records from tide-gauges are sparse and there are many coastal areas worldwide without in-situ available information. On the other hand, with the accumulating altimeter records of several satellite missions from the 1990s, approaching 25 recorded years at the time of writing, it is becoming possible the analysis of extreme sea level events from this data source. Aside the well-known issue of altimeter measurements very close to the coast (mainly due to corruption by land, wet troposphere path delay errors and local tide effects on the coastal area), there are other aspects that have to be considered when sea surface height values estimated from satellite are going to be used in a statistical extreme model, such as the use of a multi-mission product to get long observed periods and the selection of the maxima sample, since altimeter observations do not provide values uniform in time and space. Here, we have compared the extreme values of 'still water level' and 'non-tidal-residual' of in-situ records from the GESLA2 dataset (Woodworth et al. 2016) against the novel coastal altimetry datasets (Cipollini et al. 2016). Seasonal patterns, inter-annual variability and long-term trends are analyzed. Then, a time-dependent extreme model (Menendez et al. 2009) is applied to characterize extreme sea level return values and their variability on the coastal area around the world.

Association Between Brain-Derived Neurotrophic Factor Genotype and Upper Extremity Motor Outcome After Stroke.

PubMed

Chang, Won Hyuk; Park, Eunhee; Lee, Jungsoo; Lee, Ahee; Kim, Yun-Hee

2017-06-01

The identification of intrinsic factors for predicting upper extremity motor outcome could aid the design of individualized treatment plans in stroke rehabilitation. The aim of this study was to identify prognostic factors, including intrinsic genetic factors, for upper extremity motor outcome in patients with subacute stroke. A total of 97 patients with subacute stroke were enrolled. Upper limb motor impairment was scored according to the upper limb of Fugl-Meyer assessment score at 3 months after stroke. The prediction of upper extremity motor outcome at 3 months was modeled using various factors that could potentially influence this impairment, including patient characteristics, baseline upper extremity motor impairment, functional and structural integrity of the corticospinal tract, and brain-derived neurotrophic factor genotype. Multivariate ordinal logistic regression models were used to identify the significance of each factor. The independent predictors of motor outcome at 3 months were baseline upper extremity motor impairment, age, stroke type, and corticospinal tract functional integrity in all stroke patients. However, in the group with severe motor impairment at baseline (upper limb score of Fugl-Meyer assessment <25), the number of Met alleles in the brain-derived neurotrophic factor genotype was also an independent predictor of upper extremity motor outcome 3 months after stroke. Brain-derived neurotrophic factor genotype may be a potentially useful predictor of upper extremity motor outcome in patients with subacute stroke with severe baseline motor involvement. © 2017 American Heart Association, Inc.

Estimating the Effect of Climate Change on Crop Yields and Farmland Values: The Importance of Extreme Temperatures

EPA Pesticide Factsheets

This is a presentation titled Estimating the Effect of Climate Change on Crop Yields and Farmland Values: The Importance of Extreme Temperatures that was given for the National Center for Environmental Economics

DUALITY IN MULTIVARIATE RECEPTOR MODEL. (R831078)

EPA Science Inventory

Multivariate receptor models are used for source apportionment of multiple observations of compositional data of air pollutants that obey mass conservation. Singular value decomposition of the data leads to two sets of eigenvectors. One set of eigenvectors spans a space in whi...

Fast Genome-Wide QTL Association Mapping on Pedigree and Population Data.

PubMed

Zhou, Hua; Blangero, John; Dyer, Thomas D; Chan, Kei-Hang K; Lange, Kenneth; Sobel, Eric M

2017-04-01

Since most analysis software for genome-wide association studies (GWAS) currently exploit only unrelated individuals, there is a need for efficient applications that can handle general pedigree data or mixtures of both population and pedigree data. Even datasets thought to consist of only unrelated individuals may include cryptic relationships that can lead to false positives if not discovered and controlled for. In addition, family designs possess compelling advantages. They are better equipped to detect rare variants, control for population stratification, and facilitate the study of parent-of-origin effects. Pedigrees selected for extreme trait values often segregate a single gene with strong effect. Finally, many pedigrees are available as an important legacy from the era of linkage analysis. Unfortunately, pedigree likelihoods are notoriously hard to compute. In this paper, we reexamine the computational bottlenecks and implement ultra-fast pedigree-based GWAS analysis. Kinship coefficients can either be based on explicitly provided pedigrees or automatically estimated from dense markers. Our strategy (a) works for random sample data, pedigree data, or a mix of both; (b) entails no loss of power; (c) allows for any number of covariate adjustments, including correction for population stratification; (d) allows for testing SNPs under additive, dominant, and recessive models; and (e) accommodates both univariate and multivariate quantitative traits. On a typical personal computer (six CPU cores at 2.67 GHz), analyzing a univariate HDL (high-density lipoprotein) trait from the San Antonio Family Heart Study (935,392 SNPs on 1,388 individuals in 124 pedigrees) takes less than 2 min and 1.5 GB of memory. Complete multivariate QTL analysis of the three time-points of the longitudinal HDL multivariate trait takes less than 5 min and 1.5 GB of memory. The algorithm is implemented as the Ped-GWAS Analysis (Option 29) in the Mendel statistical genetics package, which is freely available for Macintosh, Linux, and Windows platforms from http://genetics.ucla.edu/software/mendel. © 2016 WILEY PERIODICALS, INC.

Extreme Statistics of Storm Surges in the Baltic Sea

NASA Astrophysics Data System (ADS)

Kulikov, E. A.; Medvedev, I. P.

2017-11-01

Statistical analysis of the extreme values of the Baltic Sea level has been performed for a series of observations for 15-125 years at 13 tide gauge stations. It is shown that the empirical relation between value of extreme sea level rises or ebbs (caused by storm events) and its return period in the Baltic Sea can be well approximated by the Gumbel probability distribution. The maximum values of extreme floods/ebbs of the 100-year recurrence were observed in the Gulf of Finland and the Gulf of Riga. The two longest data series, observed in Stockholm and Vyborg over 125 years, have shown a significant deviation from the Gumbel distribution for the rarest events. Statistical analysis of the hourly sea level data series reveals some asymmetry in the variability of the Baltic Sea level. The probability of rises proved higher than that of ebbs. As for the magnitude of the 100-year recurrence surge, it considerably exceeded the magnitude of ebbs almost everywhere. This asymmetry effect can be attributed to the influence of low atmospheric pressure during storms. A statistical study of extreme values has also been applied to sea level series for Narva over the period of 1994-2000, which were simulated by the ROMS numerical model. Comparisons of the "simulated" and "observed" extreme sea level distributions show that the model reproduces quite satisfactorily extreme floods of "moderate" magnitude; however, it underestimates sea level changes for the most powerful storm surges.

Extreme events in total ozone over the northern mid-latitudes: A case study based on long-term data sets from 5 ground-based stations

NASA Astrophysics Data System (ADS)

Rieder, Harald E.; Jancso, Leonhardt M.; Staehelin, Johannes; Maeder, Jörg A.; Ribatet, Mathieu; Peter, Thomas; Davison, Anthony C.

2010-05-01

In this study we analyze the frequency distribution of extreme events in low and high total ozone (termed ELOs and EHOs) for 5 long-term stations in the northern mid-latitudes in Europe (Belsk, Poland; Hradec Kralove, Czech Republic; Hohenpeissenberg and Potsdam, Germany; and Uccle, Belgium). Further, the influence of these extreme events on annual and seasonal mean values and trends is analysed. The applied method follows the new "ozone extreme concept", which is based on tools from extreme value theory [Coles, 2001; Ribatet, 2007], recently developed by Rieder et al. [2010a, b]. Mathematically seen the decisive feature within the extreme concept is the Generalized Pareto Distribution (GPD). In this analysis, the long-term trends needed to be removed first, differently to the treatment of Rieder et al. [2010a, b], in which the time series of Arosa was analysed, covering many decades of measurements in the anthropogenically undisturbed stratosphere. In contrast to previous studies only focusing on so called ozone mini-holes and mini-highs the "ozone extreme concept" provides a statistical description of the tails in total ozone distributions (i.e. extreme low and high values). It is shown that this concept is not only an appropriate method to describe the frequency and distribution of extreme events, it also provides new information on time series properties and internal variability. Furthermore it allows detection of fingerprints of physical (e.g. El Niño, NAO) and chemical (e.g. polar vortex ozone loss) features in the Earth's atmosphere as well as major volcanic eruptions (e.g. El Chichón, Mt. Pinatubo). It is shown that mean values and trends in total ozone are strongly influenced by extreme events. Trend calculations (for the period 1970-1990) are performed for the entire as well as the extremes-removed time series. The results after excluding extremes show that annual trends are most reduced at Hradec Kralove (about a factor of 3), followed by Potsdam (factor of 2.5), and Hohenpeissenberg and Belsk (both about a factor of 2). In general the reduction of trend is strongest during winter and spring. Throughout all stations the influence of ELOs on observed trends is larger than those of EHOs. Especially from the 1990s on ELOs dominate the picture as only a relatively small fraction of EHOs can be observed in the records (due to strong influence of Mt. Pinatubo eruption and polar vortex ozone loss contributions). Additionally it is evidenced that the number of observed mini-holes can be estimated highly accurate by the GPD-model. Overall the results of this thesis show that extreme events play a major role in total ozone and the "ozone extremes concept" provides deeper insight in the influence of chemical and physical features on column ozone. References: Coles, S.: An Introduction to Statistical Modeling of Extreme Values, Springer Series in Statistics, ISBN:1852334592, Springer, Berlin, 2001. Ribatet, M.: POT: Modelling peaks over a threshold, R News, 7, 34-36, 2007. Rieder ,H.E., Staehelin, J., Maeder, J.A., Ribatet, M., Stübi, R., Weihs, P., Holawe, F., Peter, T., and A.D., Davison (2010): Extreme events in total ozone over Arosa - Part I: Application of extreme value theory, to be submitted to ACPD. Rieder, H.E., Staehelin, J., Maeder, J.A., Ribatet, M., Stübi, R., Weihs, P., Holawe, F., Peter, T., and A.D., Davison (2010): Extreme events in total ozone over Arosa - Part II: Fingerprints of atmospheric dynamics and chemistry and effects on mean values and long-term changes, to be submitted to ACPD.

Linking the Weather Generator with Regional Climate Model

NASA Astrophysics Data System (ADS)

Dubrovsky, Martin; Farda, Ales; Skalak, Petr; Huth, Radan

2013-04-01

One of the downscaling approaches, which transform the raw outputs from the climate models (GCMs or RCMs) into data with more realistic structure, is based on linking the stochastic weather generator with the climate model output. The present contribution, in which the parametric daily surface weather generator (WG) M&Rfi is linked to the RCM output, follows two aims: (1) Validation of the new simulations of the present climate (1961-1990) made by the ALADIN-Climate Regional Climate Model at 25 km resolution. The WG parameters are derived from the RCM-simulated surface weather series and compared to those derived from weather series observed in 125 Czech meteorological stations. The set of WG parameters will include statistics of the surface temperature and precipitation series (including probability of wet day occurrence). (2) Presenting a methodology for linking the WG with RCM output. This methodology, which is based on merging information from observations and RCM, may be interpreted as a downscaling procedure, whose product is a gridded WG capable of producing realistic synthetic multivariate weather series for weather-ungauged locations. In this procedure, WG is calibrated with RCM-simulated multi-variate weather series in the first step, and the grid specific WG parameters are then de-biased by spatially interpolated correction factors based on comparison of WG parameters calibrated with gridded RCM weather series and spatially scarcer observations. The quality of the weather series produced by the resultant gridded WG will be assessed in terms of selected climatic characteristics (focusing on characteristics related to variability and extremes of surface temperature and precipitation). Acknowledgements: The present experiment is made within the frame of projects ALARO-Climate (project P209/11/2405 sponsored by the Czech Science Foundation), WG4VALUE (project LD12029 sponsored by the Ministry of Education, Youth and Sports of CR) and VALUE (COST ES 1102 action).

The Association of Vitamin D Status in Lower Extremity Muscle Strains and Core Muscle Injuries at the National Football League Combine.

PubMed

Rebolledo, Brian J; Bernard, Johnathan A; Werner, Brian C; Finlay, Andrea K; Nwachukwu, Benedict U; Dare, David M; Warren, Russell F; Rodeo, Scott A

2018-04-01

To evaluate the association between serum vitamin D level and the prevalence of lower extremity muscle strains and core muscle injuries in elite level athletes at the National Football League (NFL) combine. During the 2015 NFL combine, all athletes with available serum vitamin D levels were included for study. Baseline data were collected, including age, race, body mass index, position, injury history specific to lower extremity muscle strain or core muscle injury, and Functional Movement Screen scores. Serum 25-hydroxyvitamin D was collected and defined as normal (≥32 ng/mL), insufficient (20-31 ng/mL), and deficient (<20 ng/mL). Univariate regression analysis was used to examine the association of vitamin D level and injury history. Subsequent multivariate regression analysis was used to examine this relation with adjustment for collected baseline data variables. The study population included 214 athletes, including 78% African American athletes and 51% skilled position players. Inadequate vitamin D was present in 59%, including 10% with deficient levels. Lower extremity muscle strain or core muscle injury was present in 50% of athletes, which was associated with lower vitamin D levels (P = .03). Athletes with a positive injury history also showed significantly lower vitamin D levels as compared with uninjured athletes (P = .03). African American/black race (P < .001) and injury history (P < .001) was associated with lower vitamin D. Vitamin D groups showed no differences in age (P = .9), body mass index (P = .9), or Functional Movement Screen testing (P = .2). Univariate analysis of inadequate vitamin D levels showed a 1.86 higher odds of lower extremity strain or core muscle injury (P = .03), and 3.61 higher odds of hamstring injury (P < .001). Multivariate analysis did not reach an independent association of low vitamin D with injury history (P = .07). Inadequate vitamin D levels are a widespread finding in athletes at the NFL combine. Players with a history of lower extremity muscle strain and core muscle injury had a higher prevalence of inadequate vitamin D. Level IV, retrospective study-case series. Copyright © 2017 Arthroscopy Association of North America. Published by Elsevier Inc. All rights reserved.

The PROMIS physical function correlates with the QuickDASH in patients with upper extremity illness.

PubMed

Overbeek, Celeste L; Nota, Sjoerd P F T; Jayakumar, Prakash; Hageman, Michiel G; Ring, David

2015-01-01

To assess disability more efficiently with less burden on the patient, the National Institutes of Health has developed the Patient Reported Outcomes Measurement Information System (PROMIS) Physical Function-an instrument based on item response theory and using computer adaptive testing (CAT). Initially, upper and lower extremity disabilities were not separated and we were curious if the PROMIS Physical Function CAT could measure upper extremity disability and the Quick Disability of Arm, Shoulder and Hand (QuickDASH). We aimed to find correlation between the PROMIS Physical Function and the QuickDASH questionnaires in patients with upper extremity illness. Secondarily, we addressed whether the PROMIS Physical Function and QuickDASH correlate with the PROMIS Depression CAT and PROMIS Pain Interference CAT instruments. Finally, we assessed factors associated with QuickDASH and PROMIS Physical Function in multivariable analysis. A cohort of 93 outpatients with upper extremity illnesses completed the QuickDASH and three PROMIS CAT questionnaires: Physical Function, Pain Interference, and Depression. Pain intensity was measured with an 11-point ordinal measure (0-10 numeric rating scale). Correlation between PROMIS Physical Function and the QuickDASH was assessed. Factors that correlated with the PROMIS Physical Function and QuickDASH were assessed in multivariable regression analysis after initial bivariate analysis. There was a moderate correlation between the PROMIS Physical Function and the QuickDASH questionnaire (r=-0.55, p<0.001). Greater disability as measured with the PROMIS and QuickDASH correlated most strongly with PROMIS Depression (r=-0.35, p<0.001 and r=0.34, p<0.001 respectively) and Pain Interference (r=-0.51, p<0.001 and r=0.74, p<0.001 respectively). The factors accounting for the variability in PROMIS scores are comparable to those for the QuickDASH except that the PROMIS Physical Function is influenced by other pain conditions while the QuickDASH is not. The PROMIS Physical Function instrument may be used as an upper extremity disability measure, as it correlates with the QuickDASH questionnaire, and both instruments are influenced most strongly by the degree to which pain interferes with achieving goals. Level III, diagnostic study. See the Instructions for Authors for a complete description of levels of evidence.

Disentangling the Effects of Violent Victimization, Violent Behavior, and Gun Carrying for Minority Inner-City Youth Living in Extreme Poverty

ERIC Educational Resources Information Center

Spano, Richard; Bolland, John

2013-01-01

Two waves of longitudinal data were used to examine the sequencing between violent victimization, violent behavior, and gun carrying in a high-poverty sample of African American youth. Multivariate logistic regression results indicated that violent victimization T1 and violent behavior T1 increased the likelihood of initiation of gun carrying T2…

A stochastic storm surge generator for the German North Sea and the multivariate statistical assessment of the simulation results

NASA Astrophysics Data System (ADS)

Wahl, Thomas; Jensen, Jürgen; Mudersbach, Christoph

2010-05-01

Storm surges along the German North Sea coastline led to major damages in the past and the risk of inundation is expected to increase in the course of an ongoing climate change. The knowledge of the characteristics of possible storm surges is essential for the performance of integrated risk analyses, e.g. based on the source-pathway-receptor concept. The latter includes the storm surge simulation/analyses (source), modelling of dike/dune breach scenarios (pathway) and the quantification of potential losses (receptor). In subproject 1b of the German joint research project XtremRisK (www.xtremrisk.de), a stochastic storm surge generator for the south-eastern North Sea area is developed. The input data for the multivariate model are high resolution sea level observations from tide gauges during extreme events. Based on 25 parameters (19 sea level parameters and 6 time parameters) observed storm surge hydrographs consisting of three tides are parameterised. Followed by the adaption of common parametric probability distributions and a large number of Monte-Carlo-Simulations, the final reconstruction leads to a set of 100.000 (default) synthetic storm surge events with a one-minute resolution. Such a data set can potentially serve as the basis for a large number of applications. For risk analyses, storm surges with peak water levels exceeding the design water levels are of special interest. The occurrence probabilities of the simulated extreme events are estimated based on multivariate statistics, considering the parameters "peak water level" and "fullness/intensity". In the past, most studies considered only the peak water levels during extreme events, which might not be the most important parameter in any cases. Here, a 2D-Archimedian copula model is used for the estimation of the joint probabilities of the selected parameters, accounting for the structures of dependence overlooking the margins. In coordination with subproject 1a, the results will be used as the input for the XtremRisK subprojects 2 to 4. The project is funded by the German Federal Ministry of Education and Research (BMBF) (Project No. 03 F 0483 B).

Flap reconstruction does not increase complication rates following surgical resection of extremity soft tissue sarcoma.

PubMed

Slump, Jelena; Hofer, Stefan O P; Ferguson, Peter C; Wunder, Jay S; Griffin, Anthony M; Hoekstra, Harald J; Bastiaannet, Esther; O'Neill, Anne C

2018-02-01

Flap reconstruction plays an essential role in the surgical management of extremity soft tissue sarcoma (ESTS) for many patients. But flaps increase the duration and complexity of the surgery and their contribution to overall morbidity is unclear. This study directly compares the complication rates in patients with ESTS undergoing either flap reconstruction or primary wound closure and explores contributing factors. Eight hundred and ninety-seven patients who underwent ESTS resection followed by primary closure (631) or flap reconstruction (266) were included in this study. Data on patient, tumour and treatment variables and post-operative medical and surgical complications were collected. Univariate and multivariate regression analyses were performed to identify independent predictors of complications. Post-operative complications occurred in 33% of patients. Flap patients were significantly older, had more advanced disease and were more likely to require neoadjuvant chemo- and radiotherapy. There was no significant difference in complication rates following flap reconstruction compared to primary closure on multivariate analysis (38 vs 30.9% OR 1.12, CI 0.77-1.64, p = 0.53). Pre-operative radiation and distal lower extremity tumour location were significant risk factors in patients who underwent primary wound closure but not in those who had flap reconstruction. Patients with comorbidities, increased BMI and systemic disease were at increased risk of complications following flap reconstruction. Flap reconstruction is not associated with increased post-operative complications following ESTS resection. Flaps may mitigate the effects of some risk factors in selected patients. Copyright © 2017. Published by Elsevier Ltd.

Extreme value analysis of the time derivative of the horizontal magnetic field and computed electric field

NASA Astrophysics Data System (ADS)

Wintoft, Peter; Viljanen, Ari; Wik, Magnus

2016-05-01

High-frequency ( ≈ minutes) variability of ground magnetic fields is caused by ionospheric and magnetospheric processes driven by the changing solar wind. The varying magnetic fields induce electrical fields that cause currents to flow in man-made conductors like power grids and pipelines. Under extreme conditions the geomagnetically induced currents (GIC) may be harmful to the power grids. Increasing our understanding of the extreme events is thus important for solar-terrestrial science and space weather. In this work 1-min resolution of the time derivative of measured local magnetic fields (|dBh/dt|) and computed electrical fields (Eh), for locations in Europe, have been analysed with extreme value analysis (EVA). The EVA results in an estimate of the generalized extreme value probability distribution that is described by three parameters: location, width, and shape. The shape parameter controls the extreme behaviour. The stations cover geomagnetic latitudes from 40 to 70° N. All stations included in the study have contiguous coverage of 18 years or more with 1-min resolution data. As expected, the EVA shows that the higher latitude stations have higher probability of large |dBh/dt| and |Eh| compared to stations further south. However, the EVA also shows that the shape of the distribution changes with magnetic latitude. The high latitudes have distributions that fall off faster to zero than the low latitudes, and upward bounded distributions can not be ruled out. The transition occurs around 59-61° N magnetic latitudes. Thus, the EVA shows that the observed series north of ≈ 60° N have already measured values that are close to the expected maxima values, while stations south of ≈ ° N will measure larger values in the future.

Multivariable polynomial fitting of controlled single-phase nonlinear load of input current total harmonic distortion

NASA Astrophysics Data System (ADS)

Sikora, Roman; Markiewicz, Przemysław; Pabjańczyk, Wiesława

2018-04-01

The power systems usually include a number of nonlinear receivers. Nonlinear receivers are the source of disturbances generated to the power system in the form of higher harmonics. The level of these disturbances describes the total harmonic distortion coefficient THD. Its value depends on many factors. One of them are the deformation and change in RMS value of supply voltage. A modern LED luminaire is a nonlinear receiver as well. The paper presents the results of the analysis of the influence of change in RMS value of supply voltage and the level of dimming of the tested luminaire on the value of the current THD. The analysis was made using a mathematical model based on multivariable polynomial fitting.

The Multivariate Largest Lyapunov Exponent as an Age-Related Metric of Quiet Standing Balance

PubMed Central

Liu, Kun; Wang, Hongrui; Xiao, Jinzhuang

2015-01-01

The largest Lyapunov exponent has been researched as a metric of the balance ability during human quiet standing. However, the sensitivity and accuracy of this measurement method are not good enough for clinical use. The present research proposes a metric of the human body's standing balance ability based on the multivariate largest Lyapunov exponent which can quantify the human standing balance. The dynamic multivariate time series of ankle, knee, and hip were measured by multiple electrical goniometers. Thirty-six normal people of different ages participated in the test. With acquired data, the multivariate largest Lyapunov exponent was calculated. Finally, the results of the proposed approach were analysed and compared with the traditional method, for which the largest Lyapunov exponent and power spectral density from the centre of pressure were also calculated. The following conclusions can be obtained. The multivariate largest Lyapunov exponent has a higher degree of differentiation in differentiating balance in eyes-closed conditions. The MLLE value reflects the overall coordination between multisegment movements. Individuals of different ages can be distinguished by their MLLE values. The standing stability of human is reduced with the increment of age. PMID:26064182

Regional estimation of extreme suspended sediment concentrations using watershed characteristics

NASA Astrophysics Data System (ADS)

Tramblay, Yves; Ouarda, Taha B. M. J.; St-Hilaire, André; Poulin, Jimmy

2010-01-01

SummaryThe number of stations monitoring daily suspended sediment concentration (SSC) has been decreasing since the 1980s in North America while suspended sediment is considered as a key variable for water quality. The objective of this study is to test the feasibility of regionalising extreme SSC, i.e. estimating SSC extremes values for ungauged basins. Annual maximum SSC for 72 rivers in Canada and USA were modelled with probability distributions in order to estimate quantiles corresponding to different return periods. Regionalisation techniques, originally developed for flood prediction in ungauged basins, were tested using the climatic, topographic, land cover and soils attributes of the watersheds. Two approaches were compared, using either physiographic characteristics or seasonality of extreme SSC to delineate the regions. Multiple regression models to estimate SSC quantiles as a function of watershed characteristics were built in each region, and compared to a global model including all sites. Regional estimates of SSC quantiles were compared with the local values. Results show that regional estimation of extreme SSC is more efficient than a global regression model including all sites. Groups/regions of stations have been identified, using either the watershed characteristics or the seasonality of occurrence for extreme SSC values providing a method to better describe the extreme events of SSC. The most important variables for predicting extreme SSC are the percentage of clay in the soils, precipitation intensity and forest cover.

Multivariate analysis for source identification of pollution in sediment of Linggi River, Malaysia.

PubMed

Elias, Md Suhaimi; Ibrahim, Shariff; Samuding, Kamarudin; Rahman, Shamsiah Ab; Wo, Yii Mei; Daung, Jeremy Andy Dominic

2018-03-29

Rapid socioeconomic development in the Linggi River Basin has contributed to the significant increase of pollution discharge into the Linggi River and its adjacent coastal areas. The toxic element contents and distributions in the sediment samples collected along the Linggi River were determined using neutron activation analysis (NAA) and inductively coupled plasma-mass spectrometry (ICP-MS) techniques. The measured mean concentration of As, Cd, Pb, Sb, U, Th and Zn is relatively higher compared to the continental crust value of the respective element. Most of the elements (As, Cr, Fe, Pb, Sb and Zn) exceeded the freshwater sediment quality guideline-threshold effect concentration (FSQG-TEC) value. Downstream stations of the Linggi River showed that As concentrations in sediment exceeded the freshwater sediment quality guideline-probable effect concentration (FSQG-PEC) value. This indicates that the concentration of As will give an adverse effect to the growth of sediment-dwelling organisms. Generally, the Linggi River sediment can be categorised as unpolluted to strongly polluted and unpolluted to strongly to extremely polluted. The correlation matrix of metal-metal relationship, principle component analysis (PCA) and cluster analysis (CA) indicates that the pollution sources of Cu, Ni, Zn, Cd and Pb in sediments of the Linggi River originated from the industry of electronics and electroplating. Elements of As, Cr, Sb and Fe mainly originated from motor-vehicle workshops and metal work, whilst U and Th originated from natural processes such as terrestrial runoff and land erosion.

Stationary and non-stationary extreme value modeling of extreme temperature in Malaysia

NASA Astrophysics Data System (ADS)

Hasan, Husna; Salleh, Nur Hanim Mohd; Kassim, Suraiya

2014-09-01

Extreme annual temperature of eighteen stations in Malaysia is fitted to the Generalized Extreme Value distribution. Stationary and non-stationary models with trend are considered for each station and the Likelihood Ratio test is used to determine the best-fitting model. Results show that three out of eighteen stations i.e. Bayan Lepas, Labuan and Subang favor a model which is linear in the location parameter. A hierarchical cluster analysis is employed to investigate the existence of similar behavior among the stations. Three distinct clusters are found in which one of them consists of the stations that favor the non-stationary model. T-year estimated return levels of the extreme temperature are provided based on the chosen models.

The Effect of Social Integration on Outcomes after Major Lower Extremity Amputation

PubMed Central

Hawkins, Alexander T.; Pallangyo, Anthony J.; Herman, Ayesiga M.; Schaumeier, Maria J.; Smith, Ann D.; Hevelone, Nathanael D.; Crandell, David M.; Nguyen, Louis

2016-01-01

Objective Major lower extremity amputation is a common procedure that results in a profound change in a patient's life. We sought to determine the association between social support and outcomes after amputation. We hypothesized that patients with greater social support will have better post amputation outcomes. Methods From November 2011 to May 2013, we conducted a cross-sectional, observational, multicenter study. Social integration was measured by the social integration subset of the Short Form (Craig Handicap Assessment and Reporting Technique (CHART). Systemic social support was assessed by comparing a US and Tanzanian population. Walking function was measured using the 6MWT and quality of life (QoL) was measured using the EQ-5D. Results 102 major lower extremity amputees were recruited. 63 patients were enrolled in the US with a mean age of 58.0. Forty-two (67%) were male. Patients with low social integration were more likely to be unable to ambulate (no walk 39% vs. slow walk 23% vs. fast walk 10%; P=.01) and those with high social integration were more likely to be fast walkers (no walk 10% vs. slow walk 59% vs. fast walk 74%; P=.01). This relationship persisted in a multivariable analysis. Increasing social integration scores were also positively associated with increasing quality of life scores in a multivariable analysis (β .002; SE .0008; P = .02). In comparing the US population with the Tanzanian cohort (39 subjects), there were no significant differences between functional or quality of life outcomes in the systemic social support analysis. Conclusions In the US population, increased social integration is associated with both improved function and quality of life outcomes among major lower extremity amputees. Systemic social support, as measured by comparing the US population with a Tanzanian population, was not associated with improved function or quality of life outcomes. In the US, steps should be taken to identify and aid amputees with poor social integration. PMID:26474508

Effect of a Lower Extremity Preventive Training Program on Physical Performance Scores in Military Recruits.

PubMed

Peck, Karen Y; DiStefano, Lindsay J; Marshall, Stephen W; Padua, Darin A; Beutler, Anthony I; de la Motte, Sarah J; Frank, Barnett S; Martinez, Jessica C; Cameron, Kenneth L

2017-11-01

Peck, KY, DiStefano, LJ, Marshall, SW, Padua, DA, Beutler, AI, de la Motte, SJ, Frank, BS, Martinez, JC, and Cameron, KL. Effect of a lower extremity preventive training program on physical performance scores in military recruits. J Strength Cond Res 31(11): 3146-3157, 2017-Exercise-based preventive training programs are designed to improve movement patterns associated with lower extremity injury risk; however, the impact of these programs on general physical fitness has not been evaluated. The purpose of this study was to compare fitness scores between participants in a preventive training program and a control group. One thousand sixty-eight freshmen from a U.S. Service Academy were cluster-randomized into either the intervention or control group during 6 weeks of summer training. The intervention group performed a preventive training program, specifically the Dynamic Integrated Movement Enhancement (DIME), which is designed to improve lower extremity movement patterns. The control group performed the Army Preparation Drill (PD), a warm-up designed to prepare soldiers for training. Main outcome measures were the Army Physical Fitness Test (APFT) raw and scaled (for age and sex) scores. Independent t tests were used to assess between-group differences. Multivariable logistic regression models were used to control for the influence of confounding variables. Dynamic Integrated Movement Enhancement group participants completed the APFT 2-mile run 20 seconds faster compared with the PD group (p < 0.001), which corresponded with significantly higher scaled scores (p < 0.001). Army Physical Fitness Test push-up scores were significantly higher in the DIME group (p = 0.041), but there were no significant differences in APFT sit-up scores. The DIME group had significantly higher total APFT scores compared with the PD group (p < 0.001). Similar results were observed in multivariable models after controlling for sex and body mass index (BMI). Committing time to the implementation of a preventive training program does not appear to negatively affect fitness test scores.

On the identification of Dragon Kings among extreme-valued outliers

NASA Astrophysics Data System (ADS)

Riva, M.; Neuman, S. P.; Guadagnini, A.

2013-07-01

Extreme values of earth, environmental, ecological, physical, biological, financial and other variables often form outliers to heavy tails of empirical frequency distributions. Quite commonly such tails are approximated by stretched exponential, log-normal or power functions. Recently there has been an interest in distinguishing between extreme-valued outliers that belong to the parent population of most data in a sample and those that do not. The first type, called Gray Swans by Nassim Nicholas Taleb (often confused in the literature with Taleb's totally unknowable Black Swans), is drawn from a known distribution of the tails which can thus be extrapolated beyond the range of sampled values. However, the magnitudes and/or space-time locations of unsampled Gray Swans cannot be foretold. The second type of extreme-valued outliers, termed Dragon Kings by Didier Sornette, may in his view be sometimes predicted based on how other data in the sample behave. This intriguing prospect has recently motivated some authors to propose statistical tests capable of identifying Dragon Kings in a given random sample. Here we apply three such tests to log air permeability data measured on the faces of a Berea sandstone block and to synthetic data generated in a manner statistically consistent with these measurements. We interpret the measurements to be, and generate synthetic data that are, samples from α-stable sub-Gaussian random fields subordinated to truncated fractional Gaussian noise (tfGn). All these data have frequency distributions characterized by power-law tails with extreme-valued outliers about the tail edges.

Modelling hydrological extremes under non-stationary conditions using climate covariates

NASA Astrophysics Data System (ADS)

Vasiliades, Lampros; Galiatsatou, Panagiota; Loukas, Athanasios

2013-04-01

Extreme value theory is a probabilistic theory that can interpret the future probabilities of occurrence of extreme events (e.g. extreme precipitation and streamflow) using past observed records. Traditionally, extreme value theory requires the assumption of temporal stationarity. This assumption implies that the historical patterns of recurrence of extreme events are static over time. However, the hydroclimatic system is nonstationary on time scales that are relevant to extreme value analysis, due to human-mediated and natural environmental change. In this study the generalized extreme value (GEV) distribution is used to assess nonstationarity in annual maximum daily rainfall and streamflow timeseries at selected meteorological and hydrometric stations in Greece and Cyprus. The GEV distribution parameters (location, scale, and shape) are specified as functions of time-varying covariates and estimated using the conditional density network (CDN) as proposed by Cannon (2010). The CDN is a probabilistic extension of the multilayer perceptron neural network. Model parameters are estimated via the generalized maximum likelihood (GML) approach using the quasi-Newton BFGS optimization algorithm, and the appropriate GEV-CDN model architecture for the selected meteorological and hydrometric stations is selected by fitting increasingly complicated models and choosing the one that minimizes the Akaike information criterion with small sample size correction. For all case studies in Greece and Cyprus different formulations are tested with combinational cases of stationary and nonstationary parameters of the GEV distribution, linear and non-linear architecture of the CDN and combinations of the input climatic covariates. Climatic indices such as the Southern Oscillation Index (SOI), which describes atmospheric circulation in the eastern tropical pacific related to El Niño Southern Oscillation (ENSO), the Pacific Decadal Oscillation (PDO) index that varies on an interdecadal rather than interannual time scale and the atmospheric circulation patterns as expressed by the North Atlantic Oscillation (NAO) index are used to express the GEV parameters as functions of the covariates. Results show that the nonstationary GEV model can be an efficient tool to take into account the dependencies between extreme value random variables and the temporal evolution of the climate.

Extreme between-study homogeneity in meta-analyses could offer useful insights.

PubMed

Ioannidis, John P A; Trikalinos, Thomas A; Zintzaras, Elias

2006-10-01

Meta-analyses are routinely evaluated for the presence of large between-study heterogeneity. We examined whether it is also important to probe whether there is extreme between-study homogeneity. We used heterogeneity tests with left-sided statistical significance for inference and developed a Monte Carlo simulation test for testing extreme homogeneity in risk ratios across studies, using the empiric distribution of the summary risk ratio and heterogeneity statistic. A left-sided P=0.01 threshold was set for claiming extreme homogeneity to minimize type I error. Among 11,803 meta-analyses with binary contrasts from the Cochrane Library, 143 (1.21%) had left-sided P-value <0.01 for the asymptotic Q statistic and 1,004 (8.50%) had left-sided P-value <0.10. The frequency of extreme between-study homogeneity did not depend on the number of studies in the meta-analyses. We identified examples where extreme between-study homogeneity (left-sided P-value <0.01) could result from various possibilities beyond chance. These included inappropriate statistical inference (asymptotic vs. Monte Carlo), use of a specific effect metric, correlated data or stratification using strong predictors of outcome, and biases and potential fraud. Extreme between-study homogeneity may provide useful insights about a meta-analysis and its constituent studies.

Extreme events in total ozone over the Northern mid-latitudes: an analysis based on long-term data sets from five European ground-based stations

NASA Astrophysics Data System (ADS)

Rieder, Harald E.; Jancso, Leonhardt M.; Rocco, Stefania Di; Staehelin, Johannes; Maeder, Joerg A.; Peter, Thomas; Ribatet, Mathieu; Davison, Anthony C.; de Backer, Hugo; Koehler, Ulf; Krzyścin, Janusz; Vaníček, Karel

2011-11-01

We apply methods from extreme value theory to identify extreme events in high (termed EHOs) and low (termed ELOs) total ozone and to describe the distribution tails (i.e. very high and very low values) of five long-term European ground-based total ozone time series. The influence of these extreme events on observed mean values, long-term trends and changes is analysed. The results show a decrease in EHOs and an increase in ELOs during the last decades, and establish that the observed downward trend in column ozone during the 1970-1990s is strongly dominated by changes in the frequency of extreme events. Furthermore, it is shown that clear ‘fingerprints’ of atmospheric dynamics (NAO, ENSO) and chemistry [ozone depleting substances (ODSs), polar vortex ozone loss] can be found in the frequency distribution of ozone extremes, even if no attribution is possible from standard metrics (e.g. annual mean values). The analysis complements earlier analysis for the world's longest total ozone record at Arosa, Switzerland, confirming and revealing the strong influence of atmospheric dynamics on observed ozone changes. The results provide clear evidence that in addition to ODS, volcanic eruptions and strong/moderate ENSO and NAO events had significant influence on column ozone in the European sector.

Linkage Analysis of a Model Quantitative Trait in Humans: Finger Ridge Count Shows Significant Multivariate Linkage to 5q14.1

PubMed Central

Medland, Sarah E; Loesch, Danuta Z; Mdzewski, Bogdan; Zhu, Gu; Montgomery, Grant W; Martin, Nicholas G

2007-01-01

The finger ridge count (a measure of pattern size) is one of the most heritable complex traits studied in humans and has been considered a model human polygenic trait in quantitative genetic analysis. Here, we report the results of the first genome-wide linkage scan for finger ridge count in a sample of 2,114 offspring from 922 nuclear families. Both univariate linkage to the absolute ridge count (a sum of all the ridge counts on all ten fingers), and multivariate linkage analyses of the counts on individual fingers, were conducted. The multivariate analyses yielded significant linkage to 5q14.1 (Logarithm of odds [LOD] = 3.34, pointwise-empirical p-value = 0.00025) that was predominantly driven by linkage to the ring, index, and middle fingers. The strongest univariate linkage was to 1q42.2 (LOD = 2.04, point-wise p-value = 0.002, genome-wide p-value = 0.29). In summary, the combination of univariate and multivariate results was more informative than simple univariate analyses alone. Patterns of quantitative trait loci factor loadings consistent with developmental fields were observed, and the simple pleiotropic model underlying the absolute ridge count was not sufficient to characterize the interrelationships between the ridge counts of individual fingers. PMID:17907812

Extreme weather exposure identification for road networks - a comparative assessment of statistical methods

NASA Astrophysics Data System (ADS)

Schlögl, Matthias; Laaha, Gregor

2017-04-01

The assessment of road infrastructure exposure to extreme weather events is of major importance for scientists and practitioners alike. In this study, we compare the different extreme value approaches and fitting methods with respect to their value for assessing the exposure of transport networks to extreme precipitation and temperature impacts. Based on an Austrian data set from 25 meteorological stations representing diverse meteorological conditions, we assess the added value of partial duration series (PDS) over the standardly used annual maxima series (AMS) in order to give recommendations for performing extreme value statistics of meteorological hazards. Results show the merits of the robust L-moment estimation, which yielded better results than maximum likelihood estimation in 62 % of all cases. At the same time, results question the general assumption of the threshold excess approach (employing PDS) being superior to the block maxima approach (employing AMS) due to information gain. For low return periods (non-extreme events) the PDS approach tends to overestimate return levels as compared to the AMS approach, whereas an opposite behavior was found for high return levels (extreme events). In extreme cases, an inappropriate threshold was shown to lead to considerable biases that may outperform the possible gain of information from including additional extreme events by far. This effect was visible from neither the square-root criterion nor standardly used graphical diagnosis (mean residual life plot) but rather from a direct comparison of AMS and PDS in combined quantile plots. We therefore recommend performing AMS and PDS approaches simultaneously in order to select the best-suited approach. This will make the analyses more robust, not only in cases where threshold selection and dependency introduces biases to the PDS approach but also in cases where the AMS contains non-extreme events that may introduce similar biases. For assessing the performance of extreme events we recommend the use of conditional performance measures that focus on rare events only in addition to standardly used unconditional indicators. The findings of the study directly address road and traffic management but can be transferred to a range of other environmental variables including meteorological and hydrological quantities.

Validation of two (parametric vs non-parametric) daily weather generators

NASA Astrophysics Data System (ADS)

Dubrovsky, M.; Skalak, P.

2015-12-01

As the climate models (GCMs and RCMs) fail to satisfactorily reproduce the real-world surface weather regime, various statistical methods are applied to downscale GCM/RCM outputs into site-specific weather series. The stochastic weather generators are among the most favourite downscaling methods capable to produce realistic (observed-like) meteorological inputs for agrological, hydrological and other impact models used in assessing sensitivity of various ecosystems to climate change/variability. To name their advantages, the generators may (i) produce arbitrarily long multi-variate synthetic weather series representing both present and changed climates (in the latter case, the generators are commonly modified by GCM/RCM-based climate change scenarios), (ii) be run in various time steps and for multiple weather variables (the generators reproduce the correlations among variables), (iii) be interpolated (and run also for sites where no weather data are available to calibrate the generator). This contribution will compare two stochastic daily weather generators in terms of their ability to reproduce various features of the daily weather series. M&Rfi is a parametric generator: Markov chain model is used to model precipitation occurrence, precipitation amount is modelled by the Gamma distribution, and the 1st order autoregressive model is used to generate non-precipitation surface weather variables. The non-parametric GoMeZ generator is based on the nearest neighbours resampling technique making no assumption on the distribution of the variables being generated. Various settings of both weather generators will be assumed in the present validation tests. The generators will be validated in terms of (a) extreme temperature and precipitation characteristics (annual and 30-years extremes and maxima of duration of hot/cold/dry/wet spells); (b) selected validation statistics developed within the frame of VALUE project. The tests will be based on observational weather series from several European stations available from the ECA&D database. Acknowledgements: The weather generator is developed and validated within the frame of projects WG4VALUE (sponsored by the Ministry of Education, Youth and Sports of CR), and VALUE (COST ES 1102 action).

Multi-Fault Diagnosis of Rolling Bearings via Adaptive Projection Intrinsically Transformed Multivariate Empirical Mode Decomposition and High Order Singular Value Decomposition

PubMed Central

Lv, Yong; Song, Gangbing

2018-01-01

Rolling bearings are important components in rotary machinery systems. In the field of multi-fault diagnosis of rolling bearings, the vibration signal collected from single channels tends to miss some fault characteristic information. Using multiple sensors to collect signals at different locations on the machine to obtain multivariate signal can remedy this problem. The adverse effect of a power imbalance between the various channels is inevitable, and unfavorable for multivariate signal processing. As a useful, multivariate signal processing method, Adaptive-projection has intrinsically transformed multivariate empirical mode decomposition (APIT-MEMD), and exhibits better performance than MEMD by adopting adaptive projection strategy in order to alleviate power imbalances. The filter bank properties of APIT-MEMD are also adopted to enable more accurate and stable intrinsic mode functions (IMFs), and to ease mode mixing problems in multi-fault frequency extractions. By aligning IMF sets into a third order tensor, high order singular value decomposition (HOSVD) can be employed to estimate the fault number. The fault correlation factor (FCF) analysis is used to conduct correlation analysis, in order to determine effective IMFs; the characteristic frequencies of multi-faults can then be extracted. Numerical simulations and the application of multi-fault situation can demonstrate that the proposed method is promising in multi-fault diagnoses of multivariate rolling bearing signal. PMID:29659510

Multi-Fault Diagnosis of Rolling Bearings via Adaptive Projection Intrinsically Transformed Multivariate Empirical Mode Decomposition and High Order Singular Value Decomposition.

PubMed

Yuan, Rui; Lv, Yong; Song, Gangbing

2018-04-16

Rolling bearings are important components in rotary machinery systems. In the field of multi-fault diagnosis of rolling bearings, the vibration signal collected from single channels tends to miss some fault characteristic information. Using multiple sensors to collect signals at different locations on the machine to obtain multivariate signal can remedy this problem. The adverse effect of a power imbalance between the various channels is inevitable, and unfavorable for multivariate signal processing. As a useful, multivariate signal processing method, Adaptive-projection has intrinsically transformed multivariate empirical mode decomposition (APIT-MEMD), and exhibits better performance than MEMD by adopting adaptive projection strategy in order to alleviate power imbalances. The filter bank properties of APIT-MEMD are also adopted to enable more accurate and stable intrinsic mode functions (IMFs), and to ease mode mixing problems in multi-fault frequency extractions. By aligning IMF sets into a third order tensor, high order singular value decomposition (HOSVD) can be employed to estimate the fault number. The fault correlation factor (FCF) analysis is used to conduct correlation analysis, in order to determine effective IMFs; the characteristic frequencies of multi-faults can then be extracted. Numerical simulations and the application of multi-fault situation can demonstrate that the proposed method is promising in multi-fault diagnoses of multivariate rolling bearing signal.

Personal Exposure to Mixtures of Volatile Organic Compounds: Modeling and Further Analysis of the RIOPA Data

PubMed Central

Batterman, Stuart; Su, Feng-Chiao; Li, Shi; Mukherjee, Bhramar; Jia, Chunrong

2015-01-01

INTRODUCTION Emission sources of volatile organic compounds (VOCs) are numerous and widespread in both indoor and outdoor environments. Concentrations of VOCs indoors typically exceed outdoor levels, and most people spend nearly 90% of their time indoors. Thus, indoor sources generally contribute the majority of VOC exposures for most people. VOC exposure has been associated with a wide range of acute and chronic health effects; for example, asthma, respiratory diseases, liver and kidney dysfunction, neurologic impairment, and cancer. Although exposures to most VOCs for most persons fall below health-based guidelines, and long-term trends show decreases in ambient emissions and concentrations, a subset of individuals experience much higher exposures that exceed guidelines. Thus, exposure to VOCs remains an important environmental health concern. The present understanding of VOC exposures is incomplete. With the exception of a few compounds, concentration and especially exposure data are limited; and like other environmental data, VOC exposure data can show multiple modes, low and high extreme values, and sometimes a large portion of data below method detection limits (MDLs). Field data also show considerable spatial or interpersonal variability, and although evidence is limited, temporal variability seems high. These characteristics can complicate modeling and other analyses aimed at risk assessment, policy actions, and exposure management. In addition to these analytic and statistical issues, exposure typically occurs as a mixture, and mixture components may interact or jointly contribute to adverse effects. However most pollutant regulations, guidelines, and studies remain focused on single compounds, and thus may underestimate cumulative exposures and risks arising from coexposures. In addition, the composition of VOC mixtures has not been thoroughly investigated, and mixture components show varying and complex dependencies. Finally, although many factors are known to affect VOC exposures, many personal, environmental, and socioeconomic determinants remain to be identified, and the significance and applicability of the determinants reported in the literature are uncertain. To help answer these unresolved questions and overcome limitations of previous analyses, this project used several novel and powerful statistical modeling and analysis techniques and two large data sets. The overall objectives of this project were (1) to identify and characterize exposure distributions (including extreme values), (2) evaluate mixtures (including dependencies), and (3) identify determinants of VOC exposure. METHODS VOC data were drawn from two large data sets: the Relationships of Indoor, Outdoor, and Personal Air (RIOPA) study (1999–2001) and the National Health and Nutrition Examination Survey (NHANES; 1999–2000). The RIOPA study used a convenience sample to collect outdoor, indoor, and personal exposure measurements in three cities (Elizabeth, NJ; Houston, TX; Los Angeles, CA). In each city, approximately 100 households with adults and children who did not smoke were sampled twice for 18 VOCs. In addition, information about 500 variables associated with exposure was collected. The NHANES used a nationally representative sample and included personal VOC measurements for 851 participants. NHANES sampled 10 VOCs in common with RIOPA. Both studies used similar sampling methods and study periods. Specific Aim 1 To estimate and model extreme value exposures, extreme value distribution models were fitted to the top 10% and 5% of VOC exposures. Health risks were estimated for individual VOCs and for three VOC mixtures. Simulated extreme value data sets, generated for each VOC and for fitted extreme value and lognormal distributions, were compared with measured concentrations (RIOPA observations) to evaluate each model’s goodness of fit. Mixture distributions were fitted with the conventional finite mixture of normal distributions and the semi-parametric Dirichlet process mixture (DPM) of normal distributions for three individual VOCs (chloroform, 1,4-DCB, and styrene). Goodness of fit for these full distribution models was also evaluated using simulated data. Specific Aim 2 Mixtures in the RIOPA VOC data set were identified using positive matrix factorization (PMF) and by toxicologic mode of action. Dependency structures of a mixture’s components were examined using mixture fractions and were modeled using copulas, which address correlations of multiple components across their entire distributions. Five candidate copulas (Gaussian, t, Gumbel, Clayton, and Frank) were evaluated, and the performance of fitted models was evaluated using simulation and mixture fractions. Cumulative cancer risks were calculated for mixtures, and results from copulas and multivariate lognormal models were compared with risks based on RIOPA observations. Specific Aim 3 Exposure determinants were identified using stepwise regressions and linear mixed-effects models (LMMs). RESULTS Specific Aim 1 Extreme value exposures in RIOPA typically were best fitted by three-parameter generalized extreme value (GEV) distributions, and sometimes by the two-parameter Gumbel distribution. In contrast, lognormal distributions significantly underestimated both the level and likelihood of extreme values. Among the VOCs measured in RIOPA, 1,4-dichlorobenzene (1,4-DCB) was associated with the greatest cancer risks; for example, for the highest 10% of measurements of 1,4-DCB, all individuals had risk levels above 10−4, and 13% of all participants had risk levels above 10−2. Of the full-distribution models, the finite mixture of normal distributions with two to four clusters and the DPM of normal distributions had superior performance in comparison with the lognormal models. DPM distributions provided slightly better fit than the finite mixture distributions; the advantages of the DPM model were avoiding certain convergence issues associated with the finite mixture distributions, adaptively selecting the number of needed clusters, and providing uncertainty estimates. Although the results apply to the RIOPA data set, GEV distributions and mixture models appear more broadly applicable. These models can be used to simulate VOC distributions, which are neither normally nor lognormally distributed, and they accurately represent the highest exposures, which may have the greatest health significance. Specific Aim 2 Four VOC mixtures were identified and apportioned by PMF; they represented gasoline vapor, vehicle exhaust, chlorinated solvents and disinfection byproducts, and cleaning products and odorants. The last mixture (cleaning products and odorants) accounted for the largest fraction of an individual’s total exposure (average of 42% across RIOPA participants). Often, a single compound dominated a mixture but the mixture fractions were heterogeneous; that is, the fractions of the compounds changed with the concentration of the mixture. Three VOC mixtures were identified by toxicologic mode of action and represented VOCs associated with hematopoietic, liver, and renal tumors. Estimated lifetime cumulative cancer risks exceeded 10−3 for about 10% of RIOPA participants. The dependency structures of the VOC mixtures in the RIOPA data set fitted Gumbel (two mixtures) and t copulas (four mixtures). These copula types emphasize dependencies found in the upper and lower tails of a distribution. The copulas reproduced both risk predictions and exposure fractions with a high degree of accuracy and performed better than multivariate lognormal distributions. Specific Aim 3 In an analysis focused on the home environment and the outdoor (close to home) environment, home VOC concentrations dominated personal exposures (66% to 78% of the total exposure, depending on VOC); this was largely the result of the amount of time participants spent at home and the fact that indoor concentrations were much higher than outdoor concentrations for most VOCs. In a different analysis focused on the sources inside the home and outside (but close to the home), it was assumed that 100% of VOCs from outside sources would penetrate the home. Outdoor VOC sources accounted for 5% (d-limonene) to 81% (carbon tetrachloride [CTC]) of the total exposure. Personal exposure and indoor measurements had similar determinants depending on the VOC. Gasoline-related VOCs (e.g., benzene and methyl tert-butyl ether [MTBE]) were associated with city, residences with attached garages, pumping gas, wind speed, and home air exchange rate (AER). Odorant and cleaning-related VOCs (e.g., 1,4-DCB and chloroform) also were associated with city, and a residence’s AER, size, and family members showering. Dry-cleaning and industry-related VOCs (e.g., tetrachloroethylene [or perchloroethylene, PERC] and trichloroethylene [TCE]) were associated with city, type of water supply to the home, and visits to the dry cleaner. These and other relationships were significant, they explained from 10% to 40% of the variance in the measurements, and are consistent with known emission sources and those reported in the literature. Outdoor concentrations of VOCs had only two determinants in common: city and wind speed. Overall, personal exposure was dominated by the home setting, although a large fraction of indoor VOC concentrations were due to outdoor sources. City of residence, personal activities, household characteristics, and meteorology were significant determinants. Concentrations in RIOPA were considerably lower than levels in the nationally representative NHANES for all VOCs except MTBE and 1,4-DCB. Differences between RIOPA and NHANES results can be explained by contrasts between the sampling designs and staging in the two studies, and by differences in the demographics, smoking, employment, occupations, and home locations. A portion of these differences are due to the nature of the convenience (RIOPA) and representative (NHANES) sampling strategies used in the two studies. CONCLUSIONS Accurate models for exposure data, which can feature extreme values, multiple modes, data below the MDL, heterogeneous interpollutant dependency structures, and other complex characteristics, are needed to estimate exposures and risks and to develop control and management guidelines and policies. Conventional and novel statistical methods were applied to data drawn from two large studies to understand the nature and significance of VOC exposures. Both extreme value distributions and mixture models were found to provide excellent fit to single VOC compounds (univariate distributions), and copulas may be the method of choice for VOC mixtures (multivariate distributions), especially for the highest exposures, which fit parametric models poorly and which may represent the greatest health risk. The identification of exposure determinants, including the influence of both certain activities (e.g., pumping gas) and environments (e.g., residences), provides information that can be used to manage and reduce exposures. The results obtained using the RIOPA data set add to our understanding of VOC exposures and further investigations using a more representative population and a wider suite of VOCs are suggested to extend and generalize results. PMID:25145040

Analysis of extreme precipitation characteristics in low mountain areas based on three-dimensional copulas—taking Kuandian County as an example

NASA Astrophysics Data System (ADS)

Wang, Cailin; Ren, Xuehui; Li, Ying

2017-04-01

We defined the threshold of extreme precipitation using detrended fluctuation analysis based on daily precipitation during 1955-2013 in Kuandian County, Liaoning Province. Three-dimensional copulas were introduced to analyze the characteristics of four extreme precipitation factors: the annual extreme precipitation day, extreme precipitation amount, annual average extreme precipitation intensity, and extreme precipitation rate of contribution. The results show that (1) the threshold is 95.0 mm, extreme precipitation events generally occur 1-2 times a year, the average extreme precipitation intensity is 100-150 mm, and the extreme precipitation amount is 100-270 mm accounting for 10 to 37 % of annual precipitation. (2) The generalized extreme value distribution, extreme value distribution, and generalized Pareto distribution are suitable for fitting the distribution function for each element of extreme precipitation. The Ali-Mikhail-Haq (AMH) copula function reflects the joint characteristics of extreme precipitation factors. (3) The return period of the three types has significant synchronicity, and the joint return period and co-occurrence return period have long delay when the return period of the single factor is long. This reflects the inalienability of extreme precipitation factors. The co-occurrence return period is longer than that of the single factor and joint return period. (4) The single factor fitting only reflects single factor information of extreme precipitation but is unrelated to the relationship between factors. Three-dimensional copulas represent the internal information of extreme precipitation factors and are closer to the actual. The copula function is potentially widely applicable for the multiple factors of extreme precipitation.

Extreme Value Theory and the New Sunspot Number Series

NASA Astrophysics Data System (ADS)

Acero, F. J.; Carrasco, V. M. S.; Gallego, M. C.; García, J. A.; Vaquero, J. M.

2017-04-01

Extreme value theory was employed to study solar activity using the new sunspot number index. The block maxima approach was used at yearly (1700-2015), monthly (1749-2016), and daily (1818-2016) scales, selecting the maximum sunspot number value for each solar cycle, and the peaks-over-threshold (POT) technique was used after a declustering process only for the daily data. Both techniques led to negative values for the shape parameters. This implies that the extreme sunspot number value distribution has an upper bound. The return level (RL) values obtained from the POT approach were greater than when using the block maxima technique. Regarding the POT approach, the 110 year (550 and 1100 year) RLs were lower (higher) than the daily maximum observed sunspot number value of 528. Furthermore, according to the block maxima approach, the 10-cycle RL lay within the block maxima daily sunspot number range, as expected, but it was striking that the 50- and 100-cycle RLs were also within that range. Thus, it would seem that the RL is reaching a plateau, and, although one must be cautious, it would be difficult to attain sunspot number values greater than 550. The extreme value trends from the four series (yearly, monthly, and daily maxima per solar cycle, and POT after declustering the daily data) were analyzed with the Mann-Kendall test and Sen’s method. Only the negative trend of the daily data with the POT technique was statistically significant.

GPS FOM Chimney Analysis using Generalized Extreme Value Distribution

NASA Technical Reports Server (NTRS)

Ott, Rick; Frisbee, Joe; Saha, Kanan

2004-01-01

Many a time an objective of a statistical analysis is to estimate a limit value like 3-sigma 95% confidence upper limit from a data sample. The generalized Extreme Value Distribution method can be profitably employed in many situations for such an estimate. . .. It is well known that according to the Central Limit theorem the mean value of a large data set is normally distributed irrespective of the distribution of the data from which the mean value is derived. In a somewhat similar fashion it is observed that many times the extreme value of a data set has a distribution that can be formulated with a Generalized Distribution. In space shuttle entry with 3-string GPS navigation the Figure Of Merit (FOM) value gives a measure of GPS navigated state accuracy. A GPS navigated state with FOM of 6 or higher is deemed unacceptable and is said to form a FOM 6 or higher chimney. A FOM chimney is a period of time during which the FOM value stays higher than 5. A longer period of FOM of value 6 or higher causes navigated state to accumulate more error for a lack of state update. For an acceptable landing it is imperative that the state error remains low and hence at low altitude during entry GPS data of FOM greater than 5 must not last more than 138 seconds. I To test the GPS performAnce many entry test cases were simulated at the Avionics Development Laboratory. Only high value FoM chimneys are consequential. The extreme value statistical technique is applied to analyze high value FOM chimneys. The Maximum likelihood method is used to determine parameters that characterize the GEV distribution, and then the limit value statistics are estimated.

Nonparametric Regression Subject to a Given Number of Local Extreme Value

DTIC Science & Technology

2001-07-01

compilation report: ADP013708 thru ADP013761 UNCLASSIFIED Nonparametric regression subject to a given number of local extreme value Ali Majidi and Laurie...locations of the local extremes for the smoothing algorithm. 280 A. Majidi and L. Davies 3 The smoothing problem We make the smoothing problem precise...is the solution of QP3. k--oo 282 A. Majidi and L. Davies FiG. 2. The captions top-left, top-right, bottom-left, bottom-right show the result of the

Persistence Mapping Using EUV Solar Imager Data

NASA Technical Reports Server (NTRS)

Thompson, B. J.; Young, C. A.

2016-01-01

We describe a simple image processing technique that is useful for the visualization and depiction of gradually evolving or intermittent structures in solar physics extreme-ultraviolet imagery. The technique is an application of image segmentation, which we call "Persistence Mapping," to isolate extreme values in a data set, and is particularly useful for the problem of capturing phenomena that are evolving in both space and time. While integration or "time-lapse" imaging uses the full sample (of size N ), Persistence Mapping rejects (N - 1)/N of the data set and identifies the most relevant 1/N values using the following rule: if a pixel reaches an extreme value, it retains that value until that value is exceeded. The simplest examples isolate minima and maxima, but any quantile or statistic can be used. This paper demonstrates how the technique has been used to extract the dynamics in long-term evolution of comet tails, erupting material, and EUV dimming regions.

A New Multivariate Approach for Prognostics Based on Extreme Learning Machine and Fuzzy Clustering.

PubMed

Javed, Kamran; Gouriveau, Rafael; Zerhouni, Noureddine

2015-12-01

Prognostics is a core process of prognostics and health management (PHM) discipline, that estimates the remaining useful life (RUL) of a degrading machinery to optimize its service delivery potential. However, machinery operates in a dynamic environment and the acquired condition monitoring data are usually noisy and subject to a high level of uncertainty/unpredictability, which complicates prognostics. The complexity further increases, when there is absence of prior knowledge about ground truth (or failure definition). For such issues, data-driven prognostics can be a valuable solution without deep understanding of system physics. This paper contributes a new data-driven prognostics approach namely, an "enhanced multivariate degradation modeling," which enables modeling degrading states of machinery without assuming a homogeneous pattern. In brief, a predictability scheme is introduced to reduce the dimensionality of the data. Following that, the proposed prognostics model is achieved by integrating two new algorithms namely, the summation wavelet-extreme learning machine and subtractive-maximum entropy fuzzy clustering to show evolution of machine degradation by simultaneous predictions and discrete state estimation. The prognostics model is equipped with a dynamic failure threshold assignment procedure to estimate RUL in a realistic manner. To validate the proposition, a case study is performed on turbofan engines data from PHM challenge 2008 (NASA), and results are compared with recent publications.

Research in Stochastic Processes.

DTIC Science & Technology

1982-12-01

constant high level boundary. References 1. Jurg Husler , Extremie values of non-stationary sequ-ences ard the extr-rmal index, Center for Stochastic...A. Weron, Oct. 82. 20. "Extreme values of non-stationary sequences and the extremal index." Jurg Husler , Oct. 82. 21. "A finitely additive white noise...string model, Y. Miyahara, Carleton University and Nagoya University. Sept. 22 On extremfe values of non-stationary sequences, J. Husler , University of

More tornadoes in the most extreme U.S. tornado outbreaks

NASA Astrophysics Data System (ADS)

Tippett, Michael K.; Lepore, Chiara; Cohen, Joel E.

2016-12-01

Tornadoes and severe thunderstorms kill people and damage property every year. Estimated U.S. insured losses due to severe thunderstorms in the first half of 2016 were $8.5 billion (US). The largest U.S. effects of tornadoes result from tornado outbreaks, which are sequences of tornadoes that occur in close succession. Here, using extreme value analysis, we find that the frequency of U.S. outbreaks with many tornadoes is increasing and that it is increasing faster for more extreme outbreaks. We model this behavior by extreme value distributions with parameters that are linear functions of time or of some indicators of multidecadal climatic variability. Extreme meteorological environments associated with severe thunderstorms show consistent upward trends, but the trends do not resemble those currently expected to result from global warming.

Outcomes of lower extremity bypass performed for acute limb ischemia

PubMed Central

Baril, Donald T.; Patel, Virendra I.; Judelson, Dejah R.; Goodney, Philip P.; McPhee, James T.; Hevelone, Nathanael D.; Cronenwett, Jack L.; Schanzer, Andres

2013-01-01

Objective Acute limb ischemia remains one of the most challenging emergencies in vascular surgery. Historically, outcomes following interventions for acute limb ischemia have been associated with high rates of morbidity and mortality. The purpose of this study was to determine contemporary outcomes following lower extremity bypass performed for acute limb ischemia. Methods All patients undergoing infrainguinal lower extremity bypass between 2003 and 2011 within hospitals comprising the Vascular Study Group of New England were identified. Patients were stratified according to whether or not the indication for lower extremity bypass was acute limb ischemia. Primary end points included bypass graft occlusion, major amputation, and mortality at 1 year postoperatively as determined by Kaplan-Meier life table analysis. Multivariable Cox proportional hazards models were constructed to evaluate independent predictors of mortality and major amputation at 1 year. Results Of 5712 lower extremity bypass procedures, 323 (5.7%) were performed for acute limb ischemia. Patients undergoing lower extremity bypass for acute limb ischemia were similar in age (66 vs 67; P = .084) and sex (68% male vs 69% male; P = .617) compared with chronic ischemia patients, but were less likely to be on aspirin (63% vs 75%; P < .0001) or a statin (55% vs 68%; P < .0001). Patients with acute limb ischemia were more likely to be current smokers (49% vs 39%; P < .0001), to have had a prior ipsilateral bypass (33% vs 24%; P = .004) or a prior ipsilateral percutaneous intervention (41% vs 29%; P = .001). Bypasses performed for acute limb ischemia were longer in duration (270 vs 244 minutes; P = .007), had greater blood loss (363 vs 272 mL; P < .0001), and more commonly utilized prosthetic conduits (41% vs 33%; P = .003). Acute limb ischemia patients experienced increased in-hospital major adverse events (20% vs 12%; P < .0001) including myocardial infarction, congestive heart failure exacerbation, deterioration in renal function, and respiratory complications. Patients who underwent lower extremity bypass for acute limb ischemia had no difference in rates of graft occlusion (18.1% vs 18.5%; P = .77), but did have significantly higher rates of limb loss (22.4% vs 9.7%; P < .0001) and mortality (20.9% vs 13.1%; P < .0001) at 1 year. On multivariable analysis, acute limb ischemia was an independent predictor of both major amputation (hazard ratio, 2.16; confidence interval, 1.38–3.40; P = .001) and mortality (hazard ratio, 1.41; confidence interval, 1.09–1.83; P = .009) at 1 year. Conclusions Patients who present with acute limb ischemia represent a less medically optimized subgroup within the population of patients undergoing lower extremity bypass. These patients may be expected to have more complex operations followed by increased rates of perioperative adverse events. Additionally, despite equivalent graft patency rates, patients undergoing lower extremity bypass for acute ischemia have significantly higher rates of major amputation and mortality at 1 year. PMID:23714364

Outcomes of lower extremity bypass performed for acute limb ischemia.

PubMed

Baril, Donald T; Patel, Virendra I; Judelson, Dejah R; Goodney, Philip P; McPhee, James T; Hevelone, Nathanael D; Cronenwett, Jack L; Schanzer, Andres

2013-10-01

Acute limb ischemia remains one of the most challenging emergencies in vascular surgery. Historically, outcomes following interventions for acute limb ischemia have been associated with high rates of morbidity and mortality. The purpose of this study was to determine contemporary outcomes following lower extremity bypass performed for acute limb ischemia. All patients undergoing infrainguinal lower extremity bypass between 2003 and 2011 within hospitals comprising the Vascular Study Group of New England were identified. Patients were stratified according to whether or not the indication for lower extremity bypass was acute limb ischemia. Primary end points included bypass graft occlusion, major amputation, and mortality at 1 year postoperatively as determined by Kaplan-Meier life table analysis. Multivariable Cox proportional hazards models were constructed to evaluate independent predictors of mortality and major amputation at 1 year. Of 5712 lower extremity bypass procedures, 323 (5.7%) were performed for acute limb ischemia. Patients undergoing lower extremity bypass for acute limb ischemia were similar in age (66 vs 67; P = .084) and sex (68% male vs 69% male; P = .617) compared with chronic ischemia patients, but were less likely to be on aspirin (63% vs 75%; P < .0001) or a statin (55% vs 68%; P < .0001). Patients with acute limb ischemia were more likely to be current smokers (49% vs 39%; P < .0001), to have had a prior ipsilateral bypass (33% vs 24%; P = .004) or a prior ipsilateral percutaneous intervention (41% vs 29%; P = .001). Bypasses performed for acute limb ischemia were longer in duration (270 vs 244 minutes; P = .007), had greater blood loss (363 vs 272 mL; P < .0001), and more commonly utilized prosthetic conduits (41% vs 33%; P = .003). Acute limb ischemia patients experienced increased in-hospital major adverse events (20% vs 12%; P < .0001) including myocardial infarction, congestive heart failure exacerbation, deterioration in renal function, and respiratory complications. Patients who underwent lower extremity bypass for acute limb ischemia had no difference in rates of graft occlusion (18.1% vs 18.5%; P = .77), but did have significantly higher rates of limb loss (22.4% vs 9.7%; P < .0001) and mortality (20.9% vs 13.1%; P < .0001) at 1 year. On multivariable analysis, acute limb ischemia was an independent predictor of both major amputation (hazard ratio, 2.16; confidence interval, 1.38-3.40; P = .001) and mortality (hazard ratio, 1.41; confidence interval, 1.09-1.83; P = .009) at 1 year. Patients who present with acute limb ischemia represent a less medically optimized subgroup within the population of patients undergoing lower extremity bypass. These patients may be expected to have more complex operations followed by increased rates of perioperative adverse events. Additionally, despite equivalent graft patency rates, patients undergoing lower extremity bypass for acute ischemia have significantly higher rates of major amputation and mortality at 1 year. Copyright © 2013 Society for Vascular Surgery. Published by Mosby, Inc. All rights reserved.

Transforming RNA-Seq data to improve the performance of prognostic gene signatures.

PubMed

Zwiener, Isabella; Frisch, Barbara; Binder, Harald

2014-01-01

Gene expression measurements have successfully been used for building prognostic signatures, i.e for identifying a short list of important genes that can predict patient outcome. Mostly microarray measurements have been considered, and there is little advice available for building multivariable risk prediction models from RNA-Seq data. We specifically consider penalized regression techniques, such as the lasso and componentwise boosting, which can simultaneously consider all measurements and provide both, multivariable regression models for prediction and automated variable selection. However, they might be affected by the typical skewness, mean-variance-dependency or extreme values of RNA-Seq covariates and therefore could benefit from transformations of the latter. In an analytical part, we highlight preferential selection of covariates with large variances, which is problematic due to the mean-variance dependency of RNA-Seq data. In a simulation study, we compare different transformations of RNA-Seq data for potentially improving detection of important genes. Specifically, we consider standardization, the log transformation, a variance-stabilizing transformation, the Box-Cox transformation, and rank-based transformations. In addition, the prediction performance for real data from patients with kidney cancer and acute myeloid leukemia is considered. We show that signature size, identification performance, and prediction performance critically depend on the choice of a suitable transformation. Rank-based transformations perform well in all scenarios and can even outperform complex variance-stabilizing approaches. Generally, the results illustrate that the distribution and potential transformations of RNA-Seq data need to be considered as a critical step when building risk prediction models by penalized regression techniques.

Transforming RNA-Seq Data to Improve the Performance of Prognostic Gene Signatures

PubMed Central

Zwiener, Isabella; Frisch, Barbara; Binder, Harald

2014-01-01

Gene expression measurements have successfully been used for building prognostic signatures, i.e for identifying a short list of important genes that can predict patient outcome. Mostly microarray measurements have been considered, and there is little advice available for building multivariable risk prediction models from RNA-Seq data. We specifically consider penalized regression techniques, such as the lasso and componentwise boosting, which can simultaneously consider all measurements and provide both, multivariable regression models for prediction and automated variable selection. However, they might be affected by the typical skewness, mean-variance-dependency or extreme values of RNA-Seq covariates and therefore could benefit from transformations of the latter. In an analytical part, we highlight preferential selection of covariates with large variances, which is problematic due to the mean-variance dependency of RNA-Seq data. In a simulation study, we compare different transformations of RNA-Seq data for potentially improving detection of important genes. Specifically, we consider standardization, the log transformation, a variance-stabilizing transformation, the Box-Cox transformation, and rank-based transformations. In addition, the prediction performance for real data from patients with kidney cancer and acute myeloid leukemia is considered. We show that signature size, identification performance, and prediction performance critically depend on the choice of a suitable transformation. Rank-based transformations perform well in all scenarios and can even outperform complex variance-stabilizing approaches. Generally, the results illustrate that the distribution and potential transformations of RNA-Seq data need to be considered as a critical step when building risk prediction models by penalized regression techniques. PMID:24416353

Nocturnal 6-hydroxymelatonin sulfate excretion in female workers exposed to magnetic fields

DOE Office of Scientific and Technical Information (OSTI.GOV)

Juutilainen, J; Stevens, Richard G.; Anderson, Larry E.

The objective of this study was to determine whether daytime occupational exposure to extremely low frequency magnetic fields (MFs) suppresses nocturnal melatonin production. Sixty female volunteers were recruited. Thirty-nine worked in a garment factory, and 21 office workers served as a reference group. Exposure assessment was based on the type of sewing machine used and MF measurements around each type of machine. Eye-level MF flux density was used to classify the operators to higher (> 1 microT) and lower (0.3-1 microT) exposure categories. A third group of factory workers had diverse MF exposures from other sources. The reference group hadmore » average exposure of about 0.15 microT. Urine samples were collected on Friday and Monday for three consecutive weeks. Melatonin production was assessed as urinary 6-hydroxymelatonin sulfate (6-OHMS) excretion. The ratio of Friday morning/Monday morning 6-OHMS was used to test the hypothesis that melatonin production is suppressed after 4 days of occupational MF exposure with significant recovery during the weekend. Possible chronic suppression of melatonin production was evaluated by studying exposure-related differences in the Friday values by multivariate regression analysis. The Monday/Friday ratios were close to 1.0, suggesting that there is no increase in melatonin production over the weekend. The average 6-OHMS excretion on Friday was lower among the factory workers than in the reference group, but no monotonous dose-response was observed. Multivariate regression analysis identified MF exposure, smoking, and age as significant explanatory variables associated with decreased 6-OHMS excretion.« less

Climate extremes in Malaysia and the equatorial South China Sea

NASA Astrophysics Data System (ADS)

Salahuddin, Ahmed; Curtis, Scott

2011-08-01

The southern extent of the South China Sea (SCS) is an important natural resource epicenter for Malaysia which experiences climate extremes. This paper documents the variability of extremes in the equatorial SCS through selected ground-based observations of precipitation in Malaysia and ship-based observations of wind data in the Maritime Continent region, to elucidate the interrelationship between precipitation variability over Malaysia and wind variability over the ocean. The data have been carefully inspected and analyzed, and related to the real-time multivariate Madden-Julian Oscillation (MJO) time series. The analysis suggests that the northeast or boreal winter monsoon dominates extreme rainfall in eastern Malaysian cities. Further, the west coast of Peninsular Malaysia and Borneo Malaysia are affected by the MJO differently than the east coast of Peninsular Malaysia. From the wind analysis we found that average zonal wind is westerly from May to September and easterly from November to April. When the active (convective) phase of the MJO is centered over the Maritime Continent, the strong westerly wind bursts are more frequent in the South China Sea. While more investigation is needed, these results suggest that the status of the Madden-Julian Oscillation can be used to help forecast climate extremes in areas of Malaysia.

Effects of BMI on the risk and frequency of AIS 3+ injuries in motor-vehicle crashes.

PubMed

Rupp, Jonathan D; Flannagan, Carol A C; Leslie, Andrew J; Hoff, Carrie N; Reed, Matthew P; Cunningham, Rebecca M

2013-01-01

Determine the effects of BMI on the risk of serious-to-fatal injury (Abbreviated Injury Scale ≥ 3 or AIS 3+) to different body regions for adults in frontal, nearside, farside, and rollover crashes. Multivariate logistic regression analysis was applied to a probability sample of adult occupants involved in crashes generated by combining the National Automotive Sampling System (NASS-CDS) with a pseudoweighted version of the Crash Injury Research and Engineering Network database. Logistic regression models were applied to weighted data to estimate the change in the number of occupants with AIS 3+ injuries if no occupants were obese. Increasing BMI increased risk of lower-extremity injury in frontal crashes, decreased risk of lower-extremity injury in nearside impacts, increased risk of upper-extremity injury in frontal and nearside crashes, and increased risk of spine injury in frontal crashes. Several of these findings were affected by interactions with gender and vehicle type. If no occupants in frontal crashes were obese, 7% fewer occupants would sustain AIS 3+ upper-extremity injuries, 8% fewer occupants would sustain AIS 3+ lower-extremity injuries, and 28% fewer occupants would sustain AIS 3+ spine injuries. Results of this study have implications on the design and evaluation of vehicle safety systems. Copyright © 2013 The Obesity Society.

Prognosis of patients presenting extreme acidosis (pH <7) on admission to intensive care unit.

PubMed

Allyn, Jérôme; Vandroux, David; Jabot, Julien; Brulliard, Caroline; Galliot, Richard; Tabatchnik, Xavier; Combe, Patrice; Martinet, Olivier; Allou, Nicolas

2016-02-01

The purpose was to determine prognosis of patients presenting extreme acidosis (pH <7) on admission to the intensive care unit (ICU) and to identify mortality risk factors. We retrospectively analyzed all patients who presented with extreme acidosis within 24 hours of admission to a polyvalent ICU in a university hospital between January 2011 and July 2013. Multivariate analysis and survival analysis were used. Among the 2156 patients admitted, 77 patients (3.6%) presented extreme acidosis. Thirty (39%) patients suffered cardiac arrest before admission. Although the mortality rate predicted by severity score was 93.6%, death occurred in 52 cases (67.5%) in a median delay of 13 (5-27) hours. Mortality rate depended on reason for admission, varying between 22% for cases linked to diabetes mellitus and 100% for cases of mesenteric infarction (P = .002), cardiac arrest before admission (P < .001), type of lactic acidosis (P = .007), high Simplified Acute Physiology Score II (P = .008), and low serum creatinine (P = .012). Patients with extreme acidosis on admission to ICU have a less severe than expected prognosis. Whereas mortality is almost 100% in cases of cardiac arrest before admission, mortality is much lower in the absence of cardiac arrest before admission, which justifies aggressive ICU therapies. Copyright © 2015 Elsevier Inc. All rights reserved.

Studying a Rare Disease Using Multi-Institutional Research Collaborations vs Big Data: Where Lies the Truth?

PubMed

Johnson, Aileen C; Ethun, Cecilia G; Liu, Yuan; Lopez-Aguiar, Alexandra G; Tran, Thuy B; Poultsides, George; Grignol, Valerie; Howard, J Harrison; Bedi, Meena; Gamblin, T Clark; Tseng, Jennifer; Roggin, Kevin K; Chouliaras, Konstantinos; Votanopoulos, Konstantinos; Cullinan, Darren; Fields, Ryan C; Delman, Keith A; Wood, William C; Cardona, Kenneth; Maithel, Shishir K

2018-06-12

Multi-institutional collaborations provide granularity lacking in epidemiologic datasets to enable in-depth study of rare diseases. For pts with superficial, high-grade soft tissue sarcomas (STS) of the trunk/extremity, the value of radiation therapy (RT) is not clear. We aimed to utilize the 7-institution US-Sarcoma-Collaborative (USSC) and the National Cancer Database (NCDB) to investigate this issue. All adult pts with superficial truncal/extremity high-grade STS who underwent primary curative-intent resection from 2000-2016 at USSC institutions or were included in the NCDB from 2004-2013 were analyzed. Propensity-score matching was performed. Endpoints were locoregional recurrence-free survival(LRFS), overall-survival(OS), and disease-specific survival(DSS). Of 4,153pts in the USSC, 169pts with superficial high-grade tumors underwent primary curative-intent resection, of whom 38% received RT. On multivariable Cox-regression analysis, RT was not associated with improved LRFS(p=0.56), OS(p=0.31), or DSS(p=0.20). On analysis of 51 propensity-score matched-pairs, RT was still not associated with increased LRFS, OS, or DSS. Analysis of 631 propensity-score matched-pairs in the NCDB demonstrated improved 5-yr OS associated with RT (80%vs70%;p=0.02). LRFS and DSS were not evaluable. Granular data afforded by collaborative research enables in-depth analysis of patient outcomes. The NCDB, although powered with large numbers, cannot assess many relevant outcomes (recurrence, DSS, or complications). In this study, the approaches yielded conflicting results. USSC data suggested no value of radiation while the NCDB demonstrated improved overall survival, contradicting all randomized-controlled trials in sarcoma. The pros/cons of either approach must be considered when applying results to clinical practice, and underscore the importance of randomized-controlled trials. Copyright © 2018. Published by Elsevier Inc.

Methodology to assess clinical liver safety data.

PubMed

Merz, Michael; Lee, Kwan R; Kullak-Ublick, Gerd A; Brueckner, Andreas; Watkins, Paul B

2014-11-01

Analysis of liver safety data has to be multivariate by nature and needs to take into account time dependency of observations. Current standard tools for liver safety assessment such as summary tables, individual data listings, and narratives address these requirements to a limited extent only. Using graphics in the context of a systematic workflow including predefined graph templates is a valuable addition to standard instruments, helping to ensure completeness of evaluation, and supporting both hypothesis generation and testing. Employing graphical workflows interactively allows analysis in a team-based setting and facilitates identification of the most suitable graphics for publishing and regulatory reporting. Another important tool is statistical outlier detection, accounting for the fact that for assessment of Drug-Induced Liver Injury, identification and thorough evaluation of extreme values has much more relevance than measures of central tendency in the data. Taken together, systematical graphical data exploration and statistical outlier detection may have the potential to significantly improve assessment and interpretation of clinical liver safety data. A workshop was convened to discuss best practices for the assessment of drug-induced liver injury (DILI) in clinical trials.

The Greatest Challenge Ever for Mankind, Requiring Policies of Accelerating Hardship and Implementation Difficulty

NASA Astrophysics Data System (ADS)

Wilson, John

2015-04-01

Providing energy for the contemporary world has resulted in a multi-variable problem in which a confluence of historical anomalies and economic, psychological, political, and demographic factors thwart efforts to prevent significant harm from increasing atmospheric CO2. This unlikely combination has created the perfect storm in which the warnings by scientists are ineffective. Global warming is occurring simultaneously with increased population, some dysfunctional political institutions, ascendency of oversimplified economic theory, campaigns to discredit scientists, misinterpretation of the meaning of noise in the Milankovitch climate cycles, and substantially improved hydrocarbon extraction methods. These factors are compounded by traits of human nature, such as greed and resistance to changing the familiar and discontinuing profitable endeavors. The idea that future people are equal with us may not be widely supported, yet this value is the foundation of climate change action. History shows that most people and nations will not take appropriate measures until forced, yet the cost increases as action is delayed. This makes appropriate policies even more extreme and difficult to accomplish as more wealth is consumed in treating global warming symptoms.

Multivariate Meta-Analysis of Preference-Based Quality of Life Values in Coronary Heart Disease.

PubMed

Stevanović, Jelena; Pechlivanoglou, Petros; Kampinga, Marthe A; Krabbe, Paul F M; Postma, Maarten J

2016-01-01

There are numerous health-related quality of life (HRQol) measurements used in coronary heart disease (CHD) in the literature. However, only values assessed with preference-based instruments can be directly applied in a cost-utility analysis (CUA). To summarize and synthesize instrument-specific preference-based values in CHD and the underlying disease-subgroups, stable angina and post-acute coronary syndrome (post-ACS), for developed countries, while accounting for study-level characteristics, and within- and between-study correlation. A systematic review was conducted to identify studies reporting preference-based values in CHD. A multivariate meta-analysis was applied to synthesize the HRQoL values. Meta-regression analyses examined the effect of study level covariates age, publication year, prevalence of diabetes and gender. A total of 40 studies providing preference-based values were detected. Synthesized estimates of HRQoL in post-ACS ranged from 0.64 (Quality of Well-Being) to 0.92 (EuroQol European"tariff"), while in stable angina they ranged from 0.64 (Short form 6D) to 0.89 (Standard Gamble). Similar findings were observed in estimates applying to general CHD. No significant improvement in model fit was found after adjusting for study-level covariates. Large between-study heterogeneity was observed in all the models investigated. The main finding of our study is the presence of large heterogeneity both within and between instrument-specific HRQoL values. Current economic models in CHD ignore this between-study heterogeneity. Multivariate meta-analysis can quantify this heterogeneity and offers the means for uncertainty around HRQoL values to be translated to uncertainty in CUAs.

Extreme value modelling of Ghana stock exchange index.

PubMed

Nortey, Ezekiel N N; Asare, Kwabena; Mettle, Felix Okoe

2015-01-01

Modelling of extreme events has always been of interest in fields such as hydrology and meteorology. However, after the recent global financial crises, appropriate models for modelling of such rare events leading to these crises have become quite essential in the finance and risk management fields. This paper models the extreme values of the Ghana stock exchange all-shares index (2000-2010) by applying the extreme value theory (EVT) to fit a model to the tails of the daily stock returns data. A conditional approach of the EVT was preferred and hence an ARMA-GARCH model was fitted to the data to correct for the effects of autocorrelation and conditional heteroscedastic terms present in the returns series, before the EVT method was applied. The Peak Over Threshold approach of the EVT, which fits a Generalized Pareto Distribution (GPD) model to excesses above a certain selected threshold, was employed. Maximum likelihood estimates of the model parameters were obtained and the model's goodness of fit was assessed graphically using Q-Q, P-P and density plots. The findings indicate that the GPD provides an adequate fit to the data of excesses. The size of the extreme daily Ghanaian stock market movements were then computed using the value at risk and expected shortfall risk measures at some high quantiles, based on the fitted GPD model.

Long-term trend of satellite-observed significant wave height and impact on ecosystem in the East/Japan Sea

NASA Astrophysics Data System (ADS)

Woo, Hye-Jin; Park, Kyung-Ae

2017-09-01

Significant wave height (SWH) data of nine satellite altimeters were validated with in-situ SWH measurements from buoy stations in the East/Japan Sea (EJS) and the Northwest Pacific Ocean. The spatial and temporal variability of extreme SWHs was investigated by defining the 90th, 95th, and 99th percentiles based on percentile analysis. The annual mean of extreme SWHs was dramatically increased by 3.45 m in the EJS, which is significantly higher than the normal mean of about 1.44 m. The spatial distributions of SWHs showed significantly higher values in the eastern region of the EJS than those in the western part. Characteristic seasonality was found from the time-series SWHs with high SWHs (>2.5 m) in winter but low values (<1 m) in summer. The trends of the normal and extreme (99th percentile) SWHs in the EJS had a positive value of 0.0056 m year-1 and 0.0125 m year-1, respectively. The long-term trend demonstrated that higher SWH values were more extreme with time during the past decades. The predominant spatial distinctions between the coastal regions in the marginal seas of the Northwest Pacific Ocean and open ocean regions were presented. In spring, both normal and extreme SWHs showed substantially increasing trends in the EJS. Finally, we first presented the impact of the long-term trend of extreme SWHs on the marine ecosystem through vertical mixing enhancement in the upper ocean of the EJS.

The end of trend-estimation for extreme floods under climate change?

NASA Astrophysics Data System (ADS)

Schulz, Karsten; Bernhardt, Matthias

2016-04-01

An increased risk of flood events is one of the major threats under future climate change conditions. Therefore, many recent studies have investigated trends in flood extreme occurences using historic long-term river discharge data as well as simulations from combined global/regional climate and hydrological models. Severe floods are relatively rare events and the robust estimation of their probability of occurrence requires long time series of data (6). Following a method outlined by the IPCC research community, trends in extreme floods are calculated based on the difference of discharge values exceeding e.g. a 100-year level (Q100) between two 30-year windows, which represents prevailing conditions in a reference and a future time period, respectively. Following this approach, we analysed multiple, synthetically derived 2,000-year trend-free, yearly maximum runoff data generated using three different extreme value distributions (EDV). The parameters were estimated from long term runoff data of four large European watersheds (Danube, Elbe, Rhine, Thames). Both, Q100-values estimated from 30-year moving windows, as well as the subsequently derived trends showed enormous variations with time: for example, estimating the Extreme Value (Gumbel) - distribution for the Danube data, trends of Q100 in the synthetic time-series range from -4,480 to 4,028 m³/s per 100 years (Q100 =10,071m³/s, for reference). Similar results were found when applying other extreme value distributions (Weibull, and log-Normal) to all of the watersheds considered. This variability or "background noise" of estimating trends in flood extremes makes it almost impossible to significantly distinguish any real trend in observed as well as modelled data when such an approach is applied. These uncertainties, even though known in principle are hardly addressed and discussed by the climate change impact community. Any decision making and flood risk management, including the dimensioning of flood protection measures, that is based on such studies might therefore be fundamentally flawed.

Beyond Traditional Extreme Value Theory Through a Metastatistical Approach: Lessons Learned from Precipitation, Hurricanes, and Storm Surges

NASA Astrophysics Data System (ADS)

Marani, M.; Zorzetto, E.; Hosseini, S. R.; Miniussi, A.; Scaioni, M.

2017-12-01

The Generalized Extreme Value (GEV) distribution is widely adopted irrespective of the properties of the stochastic process generating the extreme events. However, GEV presents several limitations, both theoretical (asymptotic validity for a large number of events/year or hypothesis of Poisson occurrences of Generalized Pareto events), and practical (fitting uses just yearly maxima or a few values above a high threshold). Here we describe the Metastatistical Extreme Value Distribution (MEVD, Marani & Ignaccolo, 2015), which relaxes asymptotic or Poisson/GPD assumptions and makes use of all available observations. We then illustrate the flexibility of the MEVD by applying it to daily precipitation, hurricane intensity, and storm surge magnitude. Application to daily rainfall from a global raingauge network shows that MEVD estimates are 50% more accurate than those from GEV when the recurrence interval of interest is much greater than the observational period. This makes MEVD suited for application to satellite rainfall observations ( 20 yrs length). Use of MEVD on TRMM data yields extreme event patterns that are in better agreement with surface observations than corresponding GEV estimates.Applied to the HURDAT2 Atlantic hurricane intensity dataset, MEVD significantly outperforms GEV estimates of extreme hurricanes. Interestingly, the Generalized Pareto distribution used for "ordinary" hurricane intensity points to the existence of a maximum limit wind speed that is significantly smaller than corresponding physically-based estimates. Finally, we applied the MEVD approach to water levels generated by tidal fluctuations and storm surges at a set of coastal sites spanning different storm-surge regimes. MEVD yields accurate estimates of large quantiles and inferences on tail thickness (fat vs. thin) of the underlying distribution of "ordinary" surges. In summary, the MEVD approach presents a number of theoretical and practical advantages, and outperforms traditional approaches in several applications. We conclude that the MEVD is a significant contribution to further generalize extreme value theory, with implications for a broad range of Earth Sciences.

Using remotely sensed data and stochastic models to simulate realistic flood hazard footprints across the continental US

NASA Astrophysics Data System (ADS)

Bates, P. D.; Quinn, N.; Sampson, C. C.; Smith, A.; Wing, O.; Neal, J. C.

2017-12-01

Remotely sensed data has transformed the field of large scale hydraulic modelling. New digital elevation, hydrography and river width data has allowed such models to be created for the first time, and remotely sensed observations of water height, slope and water extent has allowed them to be calibrated and tested. As a result, we are now able to conduct flood risk analyses at national, continental or even global scales. However, continental scale analyses have significant additional complexity compared to typical flood risk modelling approaches. Traditional flood risk assessment uses frequency curves to define the magnitude of extreme flows at gauging stations. The flow values for given design events, such as the 1 in 100 year return period flow, are then used to drive hydraulic models in order to produce maps of flood hazard. Such an approach works well for single gauge locations and local models because over relatively short river reaches (say 10-60km) one can assume that the return period of an event does not vary. At regional to national scales and across multiple river catchments this assumption breaks down, and for a given flood event the return period will be different at different gauging stations, a pattern known as the event `footprint'. Despite this, many national scale risk analyses still use `constant in space' return period hazard layers (e.g. the FEMA Special Flood Hazard Areas) in their calculations. Such an approach can estimate potential exposure, but will over-estimate risk and cannot determine likely flood losses over a whole region or country. We address this problem by using a stochastic model to simulate many realistic extreme event footprints based on observed gauged flows and the statistics of gauge to gauge correlations. We take the entire USGS gauge data catalogue for sites with > 45 years of record and use a conditional approach for multivariate extreme values to generate sets of flood events with realistic return period variation in space. We undertake a number of quality checks of the stochastic model and compare real and simulated footprints to show that the method is able to re-create realistic patterns even at continental scales where there is large variation in flood generating mechanisms. We then show how these patterns can be used to drive a large scale 2D hydraulic to predict regional scale flooding.

Choosing the Mean versus an Extreme Resolution for Intrapersonal Values Conflicts: Is the Mean Usually More Golden?

ERIC Educational Resources Information Center

Kinnier, Richard T.

1984-01-01

Examined the resolution of value conflicts in 60 adults who wrote a solution to their conflicts. Compared extreme resolutions with those representing compromise. Compromisers and extremists did not differ in how rationally resolved they were about their solutions but compromisers felt better about their solutions. (JAC)

Modeling extreme PM10 concentration in Malaysia using generalized extreme value distribution

NASA Astrophysics Data System (ADS)

Hasan, Husna; Mansor, Nadiah; Salleh, Nur Hanim Mohd

2015-05-01

Extreme PM10 concentration from the Air Pollutant Index (API) at thirteen monitoring stations in Malaysia is modeled using the Generalized Extreme Value (GEV) distribution. The data is blocked into monthly selection period. The Mann-Kendall (MK) test suggests a non-stationary model so two models are considered for the stations with trend. The likelihood ratio test is used to determine the best fitted model and the result shows that only two stations favor the non-stationary model (Model 2) while the other eleven stations favor stationary model (Model 1). The return level of PM10 concentration that is expected to exceed the maximum once within a selected period is obtained.

Multivariable control of a twin lift helicopter system using the LQG/LTR design methodology

NASA Technical Reports Server (NTRS)

Rodriguez, A. A.; Athans, M.

1986-01-01

Guidelines for developing a multivariable centralized automatic flight control system (AFCS) for a twin lift helicopter system (TLHS) are presented. Singular value ideas are used to formulate performance and stability robustness specifications. A linear Quadratic Gaussian with Loop Transfer Recovery (LQG/LTR) design is obtained and evaluated.

Synonymous codon choices in the extremely GC-poor genome of Plasmodium falciparum: compositional constraints and translational selection.

PubMed

Musto, H; Romero, H; Zavala, A; Jabbari, K; Bernardi, G

1999-07-01

We have analyzed the patterns of synonymous codon preferences of the nuclear genes of Plasmodium falciparum, a unicellular parasite characterized by an extremely GC-poor genome. When all genes are considered, codon usage is strongly biased toward A and T in third codon positions, as expected, but multivariate statistical analysis detects a major trend among genes. At one end genes display codon choices determined mainly by the extreme genome composition of this parasite, and very probably their expression level is low. At the other end a few genes exhibit an increased relative usage of a particular subset of codons, many of which are C-ending. Since the majority of these few genes is putatively highly expressed, we postulate that the increased C-ending codons are translationally optimal. In conclusion, while codon usage of the majority of P. falciparum genes is determined mainly by compositional constraints, a small number of genes exhibit translational selection.

Multiple burn fuel-optimal orbit transfers: Numerical trajectory computation and neighboring optimal feedback guidance

NASA Technical Reports Server (NTRS)

Chuang, C.-H.; Goodson, Troy D.; Ledsinger, Laura A.

1995-01-01

This report describes current work in the numerical computation of multiple burn, fuel-optimal orbit transfers and presents an analysis of the second variation for extremal multiple burn orbital transfers as well as a discussion of a guidance scheme which may be implemented for such transfers. The discussion of numerical computation focuses on the use of multivariate interpolation to aid the computation in the numerical optimization. The second variation analysis includes the development of the conditions for the examination of both fixed and free final time transfers. Evaluations for fixed final time are presented for extremal one, two, and three burn solutions of the first variation. The free final time problem is considered for an extremal two burn solution. In addition, corresponding changes of the second variation formulation over thrust arcs and coast arcs are included. The guidance scheme discussed is an implicit scheme which implements a neighboring optimal feedback guidance strategy to calculate both thrust direction and thrust on-off times.

MToS: A Tree of Shapes for Multivariate Images.

PubMed

Carlinet, Edwin; Géraud, Thierry

2015-12-01

The topographic map of a gray-level image, also called tree of shapes, provides a high-level hierarchical representation of the image contents. This representation, invariant to contrast changes and to contrast inversion, has been proved very useful to achieve many image processing and pattern recognition tasks. Its definition relies on the total ordering of pixel values, so this representation does not exist for color images, or more generally, multivariate images. Common workarounds, such as marginal processing, or imposing a total order on data, are not satisfactory and yield many problems. This paper presents a method to build a tree-based representation of multivariate images, which features marginally the same properties of the gray-level tree of shapes. Briefly put, we do not impose an arbitrary ordering on values, but we only rely on the inclusion relationship between shapes in the image definition domain. The interest of having a contrast invariant and self-dual representation of multivariate image is illustrated through several applications (filtering, segmentation, and object recognition) on different types of data: color natural images, document images, satellite hyperspectral imaging, multimodal medical imaging, and videos.

[Upper extremities, neck and back symptoms in office employees working at computer stations].

PubMed

Zejda, Jan E; Bugajska, Joanna; Kowalska, Małgorzata; Krzych, Lukasz; Mieszkowska, Marzena; Brozek, Grzegorz; Braczkowska, Bogumiła

2009-01-01

To obtain current data on the occurrence ofwork-related symptoms of office computer users in Poland we implemented a questionnaire survey. Its goal was to assess the prevalence and intensity of symptoms of upper extremities, neck and back in office workers who use computers on a regular basis, and to find out if the occurrence of symptoms depends on the duration of computer use and other work-related factors. Office workers in two towns (Warszawa and Katowice), employed in large social services companies, were invited to fill in the Polish version of Nordic Questionnaire. The questions included work history and history of last-week symptoms of pain of hand/wrist, elbow, arm, neck and upper and lower back (occurrence and intensity measured by visual scale). Altogether 477 men and women returned the completed questionnaires. Between-group symptom differences (chi-square test) were verified by multivariate analysis (GLM). The prevalence of symptoms in individual body parts was as follows: neck, 55.6%; arm, 26.9%; elbow, 13.3%; wrist/hand, 29.9%; upper back, 49.6%; and lower back, 50.1%. Multivariate analysis confirmed the effect of gender, age and years of computer use on the occurrence of symptoms. Among other determinants, forearm support explained pain of wrist/hand, wrist support of elbow pain, and chair adjustment of arm pain. Association was also found between low back pain and chair adjustment and keyboard position. The findings revealed frequent occurrence of symptoms of pain in upper extremities and neck in office workers who use computers on a regular basis. Seating position could also contribute to the frequent occurrence of back pain in the examined population.

Validation of the association between a branched chain amino acid metabolite profile and extremes of coronary artery disease in patients referred for cardiac catheterization.

PubMed

Bhattacharya, Sayanti; Granger, Christopher B; Craig, Damian; Haynes, Carol; Bain, James; Stevens, Robert D; Hauser, Elizabeth R; Newgard, Christopher B; Kraus, William E; Newby, L Kristin; Shah, Svati H

2014-01-01

To validate independent associations between branched-chain amino acids (BCAA) and other metabolites with coronary artery disease (CAD). We conducted mass-spectrometry-based profiling of 63 metabolites in fasting plasma from 1983 sequential patients undergoing cardiac catheterization. Significant CAD was defined as CADindex ≥ 32 (at least one vessel with ≥ 95% stenosis; N = 995) and no CAD as CADindex ≤ 23 and no previous cardiac events (N = 610). Individuals (N = 378) with CAD severity between these extremes were excluded. Principal components analysis (PCA) reduced large numbers of correlated metabolites into uncorrelated factors. Association between metabolite factors and significant CAD vs. no CAD was tested using logistic regression; and between metabolite factors and severity of CAD was tested using linear regression. Of twelve PCA-derived metabolite factors, two were associated with CAD in multivariable models: factor 10, composed of BCAA (adjusted odds ratio, OR, 1.20; 95% CI 1.05-1.35, p = 0.005) and factor 7, composed of short-chain acylcarnitines, which include byproducts of BCAA metabolism (adjusted OR 1.30; 95% CI 1.14-1.48, p = 0.001). After adjustment for glycated albumin (marker of insulin resistance [IR]) both factors 7 (p = 0.0001) and 10 (p = 0.004) remained associated with CAD. Severity of CAD as a continuous variable (including patients with non-obstructive disease) was associated with metabolite factors 2, 3, 6, 7, 8 and 9; only factors 7 and 10 were associated in multivariable models. We validated the independent association of metabolites involved in BCAA metabolism with CAD extremes. These metabolites may be reporting on novel mechanisms of CAD pathogenesis that are independent of IR and diabetes. Copyright © 2013. Published by Elsevier Ireland Ltd.

Multivariate non-normally distributed random variables in climate research - introduction to the copula approach

NASA Astrophysics Data System (ADS)

Schölzel, C.; Friederichs, P.

2008-10-01

Probability distributions of multivariate random variables are generally more complex compared to their univariate counterparts which is due to a possible nonlinear dependence between the random variables. One approach to this problem is the use of copulas, which have become popular over recent years, especially in fields like econometrics, finance, risk management, or insurance. Since this newly emerging field includes various practices, a controversial discussion, and vast field of literature, it is difficult to get an overview. The aim of this paper is therefore to provide an brief overview of copulas for application in meteorology and climate research. We examine the advantages and disadvantages compared to alternative approaches like e.g. mixture models, summarize the current problem of goodness-of-fit (GOF) tests for copulas, and discuss the connection with multivariate extremes. An application to station data shows the simplicity and the capabilities as well as the limitations of this approach. Observations of daily precipitation and temperature are fitted to a bivariate model and demonstrate, that copulas are valuable complement to the commonly used methods.

Wind Turbine Load Mitigation based on Multivariable Robust Control and Blade Root Sensors

NASA Astrophysics Data System (ADS)

Díaz de Corcuera, A.; Pujana-Arrese, A.; Ezquerra, J. M.; Segurola, E.; Landaluze, J.

2014-12-01

This paper presents two H∞ multivariable robust controllers based on blade root sensors' information for individual pitch angle control. The wind turbine of 5 MW defined in the Upwind European project is the reference non-linear model used in this research work, which has been modelled in the GH Bladed 4.0 software package. The main objective of these controllers is load mitigation in different components of wind turbines during power production in the above rated control zone. The first proposed multi-input multi-output (MIMO) individual pitch H" controller mitigates the wind effect on the tower side-to-side acceleration and reduces the asymmetrical loads which appear in the rotor due to its misalignment. The second individual pitch H" multivariable controller mitigates the loads on the three blades reducing the wind effect on the bending flapwise and edgewise momentums in the blades. The designed H" controllers have been validated in GH Bladed and an exhaustive analysis has been carried out to calculate fatigue load reduction on wind turbine components, as well as to analyze load mitigation in some extreme cases.

More tornadoes in the most extreme U.S. tornado outbreaks.

PubMed

Tippett, Michael K; Lepore, Chiara; Cohen, Joel E

2016-12-16

Tornadoes and severe thunderstorms kill people and damage property every year. Estimated U.S. insured losses due to severe thunderstorms in the first half of 2016 were $8.5 billion (US). The largest U.S. effects of tornadoes result from tornado outbreaks, which are sequences of tornadoes that occur in close succession. Here, using extreme value analysis, we find that the frequency of U.S. outbreaks with many tornadoes is increasing and that it is increasing faster for more extreme outbreaks. We model this behavior by extreme value distributions with parameters that are linear functions of time or of some indicators of multidecadal climatic variability. Extreme meteorological environments associated with severe thunderstorms show consistent upward trends, but the trends do not resemble those currently expected to result from global warming. Copyright © 2016, American Association for the Advancement of Science.

Effect of sexual steroids on boar kinematic sperm subpopulations.

PubMed

Ayala, E M E; Aragón, M A

2017-11-01

Here, we show the effects of sexual steroids, progesterone, testosterone, or estradiol on motility parameters of boar sperm. Sixteen commercial seminal doses, four each of four adult boars, were analyzed using computer assisted sperm analysis (CASA). Mean values of motility parameters were analyzed by bivariate and multivariate statistics. Principal component analysis (PCA), followed by hierarchical clustering, was applied on data of motility parameters, provided automatically as intervals by the CASA system. Effects of sexual steroids were described in the kinematic subpopulations identified from multivariate statistics. Mean values of motility parameters were not significantly changed after addition of sexual steroids. Multivariate graphics showed that sperm subpopulations were not sensitive to the addition of either testosterone or estradiol, but sperm subpopulations responsive to progesterone were found. Distribution of motility parameters were wide in controls but sharpened at distinct concentrations of progesterone. We conclude that kinematic sperm subpopulations responsive to progesterone are present in boar semen, and these subpopulations are masked in evaluations of mean values of motility parameters. © 2017 International Society for Advancement of Cytometry. © 2017 International Society for Advancement of Cytometry.

Quantitative assessment of metal elements using moss species as biomonitors in downwind area of lead-zinc mine.

PubMed

Balabanova, Biljana; Stafilov, Trajče; Šajn, Robert; Andonovska, Katerina Bačeva

2017-02-23

Distributions of a total of 21 elements were monitored in significantly lead-zinc polluted area using moss species (Hypnum cupressiforme and Camptothecium lutescens) used interchangeably, covering a denser sampling network. Interspecies comparison was conducted using Box-Cox transformed values, due to their skewed distribution. The median concentrations of trace elements in the both mosses examined decreased in the following order: Fe>Mn>Zn>Pb>Cu>Ni∼Cr∼As>Co>Cd>Hg. For almost all analyzed elements, H. cupressiforme revealed higher bio-accumulative abilities. For arsenic contents was obtained ER-value in favor of C. lutescens. The ER for the element contents according to the distance from the pollution source in selected areas was significantly enriched for the anthropogenic introduced elements As, Cd, Cu, Pb and Zn. After Box-Cox transformation of the content values, T B was significantly different for As (4.82), Cd (3.84), Cu (2.95), Pb (4.38), and Zn (4.23). Multivariate factor analysis singled out four elemental associations: F1 (Al-Co-Cr-Fe-Li-Ni-V), F2 (Cd-Pb-Zn), F3 (Ca-Mg-Na-P) and F4 (Cu) with a total variance of 89%. Spatial distribution visualized the hazardously higher contents of "hot spots" of Cd > 1.30 mg/kg, Cu > 22 mg/kg, Pb > 130 mg/kg and Zn > 160 mg/kg. Therefore, main approach in moss biomonitoring should be based on data management of the element distribution by reducing the effect of extreme values (considering Box-Cox data transformation); the interspecies variation in sampling media does not deviate in relation to H. cupressiforme vs. C. lutescens.

Specifying the Role of Exposure to Violence and Violent Behavior on Initiation of Gun Carrying: A Longitudinal Test of Three Models of Youth Gun Carrying

ERIC Educational Resources Information Center

Spano, Richard; Pridemore, William Alex; Bolland, John

2012-01-01

Two waves of longitudinal data from 1,049 African American youth living in extreme poverty are used to examine the impact of exposure to violence (Time 1) and violent behavior (Time 1) on first time gun carrying (Time 2). Multivariate logistic regression results indicate that (a) violent behavior (Time 1) increased the likelihood of initiation of…

A Generalized Framework for Non-Stationary Extreme Value Analysis

NASA Astrophysics Data System (ADS)

Ragno, E.; Cheng, L.; Sadegh, M.; AghaKouchak, A.

2017-12-01

Empirical trends in climate variables including precipitation, temperature, snow-water equivalent at regional to continental scales are evidence of changes in climate over time. The evolving climate conditions and human activity-related factors such as urbanization and population growth can exert further changes in weather and climate extremes. As a result, the scientific community faces an increasing demand for updated appraisal of the time-varying climate extremes. The purpose of this study is to offer a robust and flexible statistical tool for non-stationary extreme value analysis which can better characterize the severity and likelihood of extreme climatic variables. This is critical to ensure a more resilient environment in a changing climate. Following the positive feedback on the first version of Non-Stationary Extreme Value Analysis (NEVA) Toolbox by Cheng at al. 2014, we present an improved version, i.e. NEVA2.0. The upgraded version herein builds upon a newly-developed hybrid evolution Markov Chain Monte Carlo (MCMC) approach for numerical parameters estimation and uncertainty assessment. This addition leads to a more robust uncertainty estimates of return levels, return periods, and risks of climatic extremes under both stationary and non-stationary assumptions. Moreover, NEVA2.0 is flexible in incorporating any user-specified covariate other than the default time-covariate (e.g., CO2 emissions, large scale climatic oscillation patterns). The new feature will allow users to examine non-stationarity of extremes induced by physical conditions that underlie the extreme events (e.g. antecedent soil moisture deficit, large-scale climatic teleconnections, urbanization). In addition, the new version offers an option to generate stationary and/or non-stationary rainfall Intensity - Duration - Frequency (IDF) curves that are widely used for risk assessment and infrastructure design. Finally, a Graphical User Interface (GUI) of the package is provided, making NEVA accessible to a broader audience.

Extreme Value Analysis of hydro meteorological extremes in the ClimEx Large-Ensemble

NASA Astrophysics Data System (ADS)

Wood, R. R.; Martel, J. L.; Willkofer, F.; von Trentini, F.; Schmid, F. J.; Leduc, M.; Frigon, A.; Ludwig, R.

2017-12-01

Many studies show an increase in the magnitude and frequency of hydrological extreme events in the course of climate change. However the contribution of natural variability to the magnitude and frequency of hydrological extreme events is not yet settled. A reliable estimate of extreme events is from great interest for water management and public safety. In the course of the ClimEx Project (www.climex-project.org) a new single-model large-ensemble was created by dynamically downscaling the CanESM2 large-ensemble with the Canadian Regional Climate Model version 5 (CRCM5) for an European Domain and a Northeastern North-American domain. By utilizing the ClimEx 50-Member Large-Ensemble (CRCM5 driven by CanESM2 Large-Ensemble) a thorough analysis of natural variability in extreme events is possible. Are the current extreme value statistical methods able to account for natural variability? How large is the natural variability for e.g. a 1/100 year return period derived from a 50-Member Large-Ensemble for Europe and Northeastern North-America? These questions should be answered by applying various generalized extreme value distributions (GEV) to the ClimEx Large-Ensemble. Hereby various return levels (5-, 10-, 20-, 30-, 60- and 100-years) based on various lengths of time series (20-, 30-, 50-, 100- and 1500-years) should be analyzed for the maximum one day precipitation (RX1d), the maximum three hourly precipitation (RX3h) and the streamflow for selected catchments in Europe. The long time series of the ClimEx Ensemble (7500 years) allows us to give a first reliable estimate of the magnitude and frequency of certain extreme events.

A cross-sectional, randomized cluster sample survey of household vulnerability to extreme heat among slum dwellers in ahmedabad, india.

PubMed

Tran, Kathy V; Azhar, Gulrez S; Nair, Rajesh; Knowlton, Kim; Jaiswal, Anjali; Sheffield, Perry; Mavalankar, Dileep; Hess, Jeremy

2013-06-18

Extreme heat is a significant public health concern in India; extreme heat hazards are projected to increase in frequency and severity with climate change. Few of the factors driving population heat vulnerability are documented, though poverty is a presumed risk factor. To facilitate public health preparedness, an assessment of factors affecting vulnerability among slum dwellers was conducted in summer 2011 in Ahmedabad, Gujarat, India. Indicators of heat exposure, susceptibility to heat illness, and adaptive capacity, all of which feed into heat vulnerability, was assessed through a cross-sectional household survey using randomized multistage cluster sampling. Associations between heat-related morbidity and vulnerability factors were identified using multivariate logistic regression with generalized estimating equations to account for clustering effects. Age, preexisting medical conditions, work location, and access to health information and resources were associated with self-reported heat illness. Several of these variables were unique to this study. As sociodemographics, occupational heat exposure, and access to resources were shown to increase vulnerability, future interventions (e.g., health education) might target specific populations among Ahmedabad urban slum dwellers to reduce vulnerability to extreme heat. Surveillance and evaluations of future interventions may also be worthwhile.

Large scale variability, long-term trends and extreme events in total ozone over the northern mid-latitudes based on satellite time series

NASA Astrophysics Data System (ADS)

Rieder, H. E.; Staehelin, J.; Maeder, J. A.; Ribatet, M.; Davison, A. C.

2009-04-01

Various generations of satellites (e.g. TOMS, GOME, OMI) made spatial datasets of column ozone available to the scientific community. This study has a special focus on column ozone over the northern mid-latitudes. Tools from geostatistics and extreme value theory are applied to analyze variability, long-term trends and frequency distributions of extreme events in total ozone. In a recent case study (Rieder et al., 2009) new tools from extreme value theory (Coles, 2001; Ribatet, 2007) have been applied to the world's longest total ozone record from Arosa, Switzerland (e.g. Staehelin 1998a,b), in order to describe extreme events in low and high total ozone. Within the current study this analysis is extended to satellite datasets for the northern mid-latitudes. Further special emphasis is given on patterns and spatial correlations and the influence of changes in atmospheric dynamics (e.g. tropospheric and lower stratospheric pressure systems) on column ozone. References: Coles, S.: An Introduction to Statistical Modeling of Extreme Values, Springer Series in Statistics, ISBN:1852334592, Springer, Berlin, 2001. Ribatet, M.: POT: Modelling peaks over a threshold, R News, 7, 34-36, 2007. Rieder, H.E., Staehelin, J., Maeder, J.A., Ribatet, M., Stübi, R., Weihs, P., Holawe, F., Peter, T., and Davison, A.C.: From ozone mini holes and mini highs towards extreme value theory: New insights from extreme events and non stationarity, submitted to J. Geophys. Res., 2009. Staehelin, J., Kegel, R., and Harris, N. R.: Trend analysis of the homogenized total ozone series of Arosa (Switzerland), 1929-1996, J. Geophys. Res., 103(D7), 8389-8400, doi:10.1029/97JD03650, 1998a. Staehelin, J., Renaud, A., Bader, J., McPeters, R., Viatte, P., Hoegger, B., Bugnion, V., Giroud, M., and Schill, H.: Total ozone series at Arosa (Switzerland): Homogenization and data comparison, J. Geophys. Res., 103(D5), 5827-5842, doi:10.1029/97JD02402, 1998b.

Probabilistic forecasting of extreme weather events based on extreme value theory

NASA Astrophysics Data System (ADS)

Van De Vyver, Hans; Van Schaeybroeck, Bert

2016-04-01

Extreme events in weather and climate such as high wind gusts, heavy precipitation or extreme temperatures are commonly associated with high impacts on both environment and society. Forecasting extreme weather events is difficult, and very high-resolution models are needed to describe explicitly extreme weather phenomena. A prediction system for such events should therefore preferably be probabilistic in nature. Probabilistic forecasts and state estimations are nowadays common in the numerical weather prediction community. In this work, we develop a new probabilistic framework based on extreme value theory that aims to provide early warnings up to several days in advance. We consider the combined events when an observation variable Y (for instance wind speed) exceeds a high threshold y and its corresponding deterministic forecasts X also exceeds a high forecast threshold y. More specifically two problems are addressed:} We consider pairs (X,Y) of extreme events where X represents a deterministic forecast, and Y the observation variable (for instance wind speed). More specifically two problems are addressed: Given a high forecast X=x_0, what is the probability that Y>y? In other words: provide inference on the conditional probability: [ Pr{Y>y|X=x_0}. ] Given a probabilistic model for Problem 1, what is the impact on the verification analysis of extreme events. These problems can be solved with bivariate extremes (Coles, 2001), and the verification analysis in (Ferro, 2007). We apply the Ramos and Ledford (2009) parametric model for bivariate tail estimation of the pair (X,Y). The model accommodates different types of extremal dependence and asymmetry within a parsimonious representation. Results are presented using the ensemble reforecast system of the European Centre of Weather Forecasts (Hagedorn, 2008). Coles, S. (2001) An Introduction to Statistical modelling of Extreme Values. Springer-Verlag.Ferro, C.A.T. (2007) A probability model for verifying deterministic forecasts of extreme events. Wea. Forecasting {22}, 1089-1100.Hagedorn, R. (2008) Using the ECMWF reforecast dataset to calibrate EPS forecasts. ECMWF Newsletter, {117}, 8-13.Ramos, A., Ledford, A. (2009) A new class of models for bivariate joint tails. J.R. Statist. Soc. B {71}, 219-241.

New Insights into the Estimation of Extreme Geomagnetic Storm Occurrences

NASA Astrophysics Data System (ADS)

Ruffenach, Alexis; Winter, Hugo; Lavraud, Benoit; Bernardara, Pietro

2017-04-01

Space weather events such as intense geomagnetic storms are major disturbances of the near-Earth environment that may lead to serious impacts on our modern society. As such, it is of great importance to estimate their probability, and in particular that of extreme events. One approach largely used in statistical sciences for extreme events probability estimates is Extreme Value Analysis (EVA). Using this rigorous statistical framework, estimations of the occurrence of extreme geomagnetic storms are performed here based on the most relevant global parameters related to geomagnetic storms, such as ground parameters (e.g. geomagnetic Dst and aa indexes), and space parameters related to the characteristics of Coronal Mass Ejections (CME) (velocity, southward magnetic field component, electric field). Using our fitted model, we estimate the annual probability of a Carrington-type event (Dst = -850nT) to be on the order of 10-3, with a lower limit of the uncertainties on the return period of ˜500 years. Our estimate is significantly higher than that of most past studies, which typically had a return period of a few 100 years at maximum. Thus precautions are required when extrapolating intense values. Currently, the complexity of the processes and the length of available data inevitably leads to significant uncertainties in return period estimates for the occurrence of extreme geomagnetic storms. However, our application of extreme value models for extrapolating into the tail of the distribution provides a mathematically justified framework for the estimation of extreme return periods, thereby enabling the determination of more accurate estimates and reduced associated uncertainties.

TATES: Efficient Multivariate Genotype-Phenotype Analysis for Genome-Wide Association Studies

PubMed Central

van der Sluis, Sophie; Posthuma, Danielle; Dolan, Conor V.

2013-01-01

To date, the genome-wide association study (GWAS) is the primary tool to identify genetic variants that cause phenotypic variation. As GWAS analyses are generally univariate in nature, multivariate phenotypic information is usually reduced to a single composite score. This practice often results in loss of statistical power to detect causal variants. Multivariate genotype–phenotype methods do exist but attain maximal power only in special circumstances. Here, we present a new multivariate method that we refer to as TATES (Trait-based Association Test that uses Extended Simes procedure), inspired by the GATES procedure proposed by Li et al (2011). For each component of a multivariate trait, TATES combines p-values obtained in standard univariate GWAS to acquire one trait-based p-value, while correcting for correlations between components. Extensive simulations, probing a wide variety of genotype–phenotype models, show that TATES's false positive rate is correct, and that TATES's statistical power to detect causal variants explaining 0.5% of the variance can be 2.5–9 times higher than the power of univariate tests based on composite scores and 1.5–2 times higher than the power of the standard MANOVA. Unlike other multivariate methods, TATES detects both genetic variants that are common to multiple phenotypes and genetic variants that are specific to a single phenotype, i.e. TATES provides a more complete view of the genetic architecture of complex traits. As the actual causal genotype–phenotype model is usually unknown and probably phenotypically and genetically complex, TATES, available as an open source program, constitutes a powerful new multivariate strategy that allows researchers to identify novel causal variants, while the complexity of traits is no longer a limiting factor. PMID:23359524

Childhood Obesity is Associated With Osteochondritis Dissecans of the Knee, Ankle, and Elbow in Children and Adolescents.

PubMed

Kessler, Jeffrey I; Jacobs, John C; Cannamela, Peter C; Shea, Kevin G; Weiss, Jennifer M

Osteochondritis dissecans (OCD) is a joint disorder of the subchondral bone and articular cartilage whose association with obesity in children is not clearly known. The purpose of this study was to assess the magnitude of the association between childhood obesity and the occurrence of OCD of the knee, ankle, and elbow in children. A retrospective chart review of an integrated health system was performed on OCD patients aged 2 to 19 from 2007 to 2011, with over 1 million patients in this cohort. Lesion location, laterality, and all patient demographics were recorded. The body mass index (BMI) for each patient in the cohort was used to stratify patients into 5 weight classes (underweight, normal weight, overweight, moderately obese, and extremely obese) based on BMI-for-age. The associations between the 5 weight classes and OCD of the ankle, knee, and elbow were assessed using multiple logistic regression models to estimate odds ratios (OR) and 95% confidence intervals using multivariate analysis to adjust for patient demographic variables. In total, 269 patients fit the inclusion criteria. Mean BMI, both absolute and percentile, was significantly higher for patients with OCD of the knee, elbow, and ankle than patients without OCD. In the multivariate analysis, extremely obese patients were found to have an increased OR of OCD for all patients, with an 86% increased risk of any OCD compared with normal weight patients. In addition, assessment by different types of OCD revealed that extremely obese patients had an increased OR of OCD of the elbow and ankle individually, with a 3.1 times increased OCD elbow risk and 3.0 times increased risk of ankle OCD in extremely obese patients. Although extremely obese patients did not have a statistically significant increased risk of knee OCD, moderately obese patients did have a 1.8 times increased risk of knee OCD as compared with normal weight children. There were no significantly different risks of any type of OCD seen in overweight or underweight patients as compared with normal weight patients. In this population-based cohort study, extreme obesity is strongly associated with an increased risk of OCD overall and OCD of the elbow and ankle specifically. In addition, moderate obesity is associated with an increased risk of knee OCD. All types of OCD were also found to have a significantly greater average BMI when compared with patients without OCD. Level IV-descriptive epidemiology study.

Visual Analysis among Novices: Training and Trend Lines as Graphic Aids

ERIC Educational Resources Information Center

Nelson, Peter M.; Van Norman, Ethan R.; Christ, Theodore J.

2017-01-01

The current study evaluated the degree to which novice visual analysts could discern trends in simulated time-series data across differing levels of variability and extreme values. Forty-five novice visual analysts were trained in general principles of visual analysis. One group received brief training on how to identify and omit extreme values.…

Implementing Extreme Value Analysis in a Geospatial Workflow for Storm Surge Hazard Assessment

NASA Astrophysics Data System (ADS)

Catelli, J.; Nong, S.

2014-12-01

Gridded data of 100-yr (1%) and 500-yr (0.2%) storm surge flood elevations for the United States, Gulf of Mexico, and East Coast are critical to understanding this natural hazard. Storm surge heights were calculated across the study area utilizing SLOSH (Sea, Lake, and Overland Surges from Hurricanes) model data for thousands of synthetic US landfalling hurricanes. Based on the results derived from SLOSH, a series of interpolations were performed using spatial analysis in a geographic information system (GIS) at both the SLOSH basin and the synthetic event levels. The result was a single grid of maximum flood elevations for each synthetic event. This project addresses the need to utilize extreme value theory in a geospatial environment to analyze coincident cells across multiple synthetic events. The results are 100-yr (1%) and 500-yr (0.2%) values for each grid cell in the study area. This talk details a geospatial approach to move raster data to SciPy's NumPy Array structure using the Python programming language. The data are then connected through a Python library to an outside statistical package like R to fit cell values to extreme value theory distributions and return values for specified recurrence intervals. While this is not a new process, the value behind this work is the ability to keep this process in a single geospatial environment and be able to easily replicate this process for other natural hazard applications and extreme event modeling.

Extreme Gleason Upgrading From Biopsy to Radical Prostatectomy: A Population-based Analysis.

PubMed

Winters, Brian R; Wright, Jonathan L; Holt, Sarah K; Lin, Daniel W; Ellis, William J; Dalkin, Bruce L; Schade, George R

2016-10-01

To examine the risk factors associated with the odds of extreme Gleason upgrading at radical prostatectomy (RP) (defined as a Gleason prognostic group score increase of ≥2), we utilized a large, population-based cancer registry. The Surveillance, Epidemiologic, and End Results database was queried (2010-2011) for all patients diagnosed with Gleason 3 + 3 or 3 + 4 on prostate needle biopsy. Available clinicopathologic factors and the odds of upgrading and extreme upgrading at RP were evaluated using multivariate logistic regression. A total of 12,459 patients were identified, with a median age of 61 (interquartile range: 56-65) and a diagnostic prostate-specific antigen (PSA) of 5.5 ng/mL (interquartile range: 4.3-7.5). Upgrading was observed in 34% of men, including 44% of 7402 patients with Gleason 3 + 3 and 19% of 5057 patients with Gleason 3 + 4 disease. Age, clinical stage, diagnostic PSA, and % prostate needle biopsy cores positive were independently associated with odds of any upgrading at RP. In baseline Gleason 3 + 3 disease, extreme upgrading was observed in 6%, with increasing age, diagnostic PSA, and >50% core positivity associated with increased odds. In baseline Gleason 3 + 4 disease, extreme upgrading was observed in 4%, with diagnostic PSA and palpable disease remaining predictive. Positive surgical margins were significantly higher in patients with extreme upgrading at RP (P < .001). Gleason upgrading at RP is common in this large population-based cohort, including extreme upgrading in a clinically significant portion. Copyright © 2016 Elsevier Inc. All rights reserved.

Quantitative monitoring of sucrose, reducing sugar and total sugar dynamics for phenotyping of water-deficit stress tolerance in rice through spectroscopy and chemometrics

NASA Astrophysics Data System (ADS)

Das, Bappa; Sahoo, Rabi N.; Pargal, Sourabh; Krishna, Gopal; Verma, Rakesh; Chinnusamy, Viswanathan; Sehgal, Vinay K.; Gupta, Vinod K.; Dash, Sushanta K.; Swain, Padmini

2018-03-01

In the present investigation, the changes in sucrose, reducing and total sugar content due to water-deficit stress in rice leaves were modeled using visible, near infrared (VNIR) and shortwave infrared (SWIR) spectroscopy. The objectives of the study were to identify the best vegetation indices and suitable multivariate technique based on precise analysis of hyperspectral data (350 to 2500 nm) and sucrose, reducing sugar and total sugar content measured at different stress levels from 16 different rice genotypes. Spectral data analysis was done to identify suitable spectral indices and models for sucrose estimation. Novel spectral indices in near infrared (NIR) range viz. ratio spectral index (RSI) and normalised difference spectral indices (NDSI) sensitive to sucrose, reducing sugar and total sugar content were identified which were subsequently calibrated and validated. The RSI and NDSI models had R2 values of 0.65, 0.71 and 0.67; RPD values of 1.68, 1.95 and 1.66 for sucrose, reducing sugar and total sugar, respectively for validation dataset. Different multivariate spectral models such as artificial neural network (ANN), multivariate adaptive regression splines (MARS), multiple linear regression (MLR), partial least square regression (PLSR), random forest regression (RFR) and support vector machine regression (SVMR) were also evaluated. The best performing multivariate models for sucrose, reducing sugars and total sugars were found to be, MARS, ANN and MARS, respectively with respect to RPD values of 2.08, 2.44, and 1.93. Results indicated that VNIR and SWIR spectroscopy combined with multivariate calibration can be used as a reliable alternative to conventional methods for measurement of sucrose, reducing sugars and total sugars of rice under water-deficit stress as this technique is fast, economic, and noninvasive.

Measures of precision for dissimilarity-based multivariate analysis of ecological communities

PubMed Central

Anderson, Marti J; Santana-Garcon, Julia

2015-01-01

Ecological studies require key decisions regarding the appropriate size and number of sampling units. No methods currently exist to measure precision for multivariate assemblage data when dissimilarity-based analyses are intended to follow. Here, we propose a pseudo multivariate dissimilarity-based standard error (MultSE) as a useful quantity for assessing sample-size adequacy in studies of ecological communities. Based on sums of squared dissimilarities, MultSE measures variability in the position of the centroid in the space of a chosen dissimilarity measure under repeated sampling for a given sample size. We describe a novel double resampling method to quantify uncertainty in MultSE values with increasing sample size. For more complex designs, values of MultSE can be calculated from the pseudo residual mean square of a permanova model, with the double resampling done within appropriate cells in the design. R code functions for implementing these techniques, along with ecological examples, are provided. PMID:25438826

Value-at-risk estimation with wavelet-based extreme value theory: Evidence from emerging markets

NASA Astrophysics Data System (ADS)

Cifter, Atilla

2011-06-01

This paper introduces wavelet-based extreme value theory (EVT) for univariate value-at-risk estimation. Wavelets and EVT are combined for volatility forecasting to estimate a hybrid model. In the first stage, wavelets are used as a threshold in generalized Pareto distribution, and in the second stage, EVT is applied with a wavelet-based threshold. This new model is applied to two major emerging stock markets: the Istanbul Stock Exchange (ISE) and the Budapest Stock Exchange (BUX). The relative performance of wavelet-based EVT is benchmarked against the Riskmetrics-EWMA, ARMA-GARCH, generalized Pareto distribution, and conditional generalized Pareto distribution models. The empirical results show that the wavelet-based extreme value theory increases predictive performance of financial forecasting according to number of violations and tail-loss tests. The superior forecasting performance of the wavelet-based EVT model is also consistent with Basel II requirements, and this new model can be used by financial institutions as well.

PERSISTENCE MAPPING USING EUV SOLAR IMAGER DATA

DOE Office of Scientific and Technical Information (OSTI.GOV)

Thompson, B. J.; Young, C. A., E-mail: barbara.j.thompson@nasa.gov

We describe a simple image processing technique that is useful for the visualization and depiction of gradually evolving or intermittent structures in solar physics extreme-ultraviolet imagery. The technique is an application of image segmentation, which we call “Persistence Mapping,” to isolate extreme values in a data set, and is particularly useful for the problem of capturing phenomena that are evolving in both space and time. While integration or “time-lapse” imaging uses the full sample (of size N ), Persistence Mapping rejects ( N − 1)/ N of the data set and identifies the most relevant 1/ N values using themore » following rule: if a pixel reaches an extreme value, it retains that value until that value is exceeded. The simplest examples isolate minima and maxima, but any quantile or statistic can be used. This paper demonstrates how the technique has been used to extract the dynamics in long-term evolution of comet tails, erupting material, and EUV dimming regions.« less

Reliability of the mangled extremity severity score in combat-related upper and lower extremity injuries.

PubMed

Ege, Tolga; Unlu, Aytekin; Tas, Huseyin; Bek, Dogan; Turkan, Selim; Cetinkaya, Aytac

2015-01-01

Decision of limb salvage or amputation is generally aided with several trauma scoring systems such as the mangled extremity severity score (MESS). However, the reliability of the injury scores in the settling of open fractures due to explosives and missiles is challenging. Mortality and morbidity of the extremity trauma due to firearms are generally associated with time delay in revascularization, injury mechanism, anatomy of the injured site, associated injuries, age and the environmental circumstance. The purpose of the retrospective study was to evaluate the extent of extremity injuries due to ballistic missiles and to detect the reliability of mangled extremity severity score (MESS) in both upper and lower extremities. Between 2004 and 2014, 139 Gustillo Anderson Type III open fractures of both the upper and lower extremities were enrolled in the study. Data for patient age, fire arm type, transporting time from the field to the hospital (and the method), injury severity scores, MESS scores, fracture types, amputation levels, bone fixation methods and postoperative infections and complications retrieved from the two level-2 trauma center's data base. Sensitivity, specificity, positive and negative predictive values of the MESS were calculated to detect the ability in deciding amputation in the mangled limb. Amputation was performed in 39 extremities and limb salvage attempted in 100 extremities. The mean followup time was 14.6 months (range 6-32 months). In the amputated group, the mean MESS scores for upper and lower extremity were 8.8 (range 6-11) and 9.24 (range 6-11), respectively. In the limb salvage group, the mean MESS scores for upper and lower extremities were 5.29 (range 4-7) and 5.19 (range 3-8), respectively. Sensitivity of MESS in upper and lower extremities were calculated as 80% and 79.4% and positive predictive values detected as 55.55% and 83.3%, respectively. Specificity of MESS score for upper and lower extremities was 84% and 86.6%; negative predictive values were calculated as 95.45% and 90.2%, respectively. MESS is not predictive in combat related extremity injuries especially if between a score of 6-8. Limb ischemia and presence or absence of shock can be used in initial decision-making for amputation.

Reliability of the mangled extremity severity score in combat-related upper and lower extremity injuries

PubMed Central

Ege, Tolga; Unlu, Aytekin; Tas, Huseyin; Bek, Dogan; Turkan, Selim; Cetinkaya, Aytac

2015-01-01

Background: Decision of limb salvage or amputation is generally aided with several trauma scoring systems such as the mangled extremity severity score (MESS). However, the reliability of the injury scores in the settling of open fractures due to explosives and missiles is challenging. Mortality and morbidity of the extremity trauma due to firearms are generally associated with time delay in revascularization, injury mechanism, anatomy of the injured site, associated injuries, age and the environmental circumstance. The purpose of the retrospective study was to evaluate the extent of extremity injuries due to ballistic missiles and to detect the reliability of mangled extremity severity score (MESS) in both upper and lower extremities. Materials and Methods: Between 2004 and 2014, 139 Gustillo Anderson Type III open fractures of both the upper and lower extremities were enrolled in the study. Data for patient age, fire arm type, transporting time from the field to the hospital (and the method), injury severity scores, MESS scores, fracture types, amputation levels, bone fixation methods and postoperative infections and complications retrieved from the two level-2 trauma center's data base. Sensitivity, specificity, positive and negative predictive values of the MESS were calculated to detect the ability in deciding amputation in the mangled limb. Results: Amputation was performed in 39 extremities and limb salvage attempted in 100 extremities. The mean followup time was 14.6 months (range 6–32 months). In the amputated group, the mean MESS scores for upper and lower extremity were 8.8 (range 6–11) and 9.24 (range 6–11), respectively. In the limb salvage group, the mean MESS scores for upper and lower extremities were 5.29 (range 4–7) and 5.19 (range 3–8), respectively. Sensitivity of MESS in upper and lower extremities were calculated as 80% and 79.4% and positive predictive values detected as 55.55% and 83.3%, respectively. Specificity of MESS score for upper and lower extremities was 84% and 86.6%; negative predictive values were calculated as 95.45% and 90.2%, respectively. Conclusion: MESS is not predictive in combat related extremity injuries especially if between a score of 6–8. Limb ischemia and presence or absence of shock can be used in initial decision-making for amputation. PMID:26806974

Statistical Modeling of Extreme Values and Evidence of Presence of Dragon King (DK) in Solar Wind

NASA Astrophysics Data System (ADS)

Gomes, T.; Ramos, F.; Rempel, E. L.; Silva, S.; C-L Chian, A.

2017-12-01

The solar wind constitutes a nonlinear dynamical system, presenting intermittent turbulence, multifractality and chaotic dynamics. One characteristic shared by many such complex systems is the presence of extreme events, that play an important role in several Geophysical phenomena and their statistical characterization is a problem of great practical relevance. This work investigates the presence of extreme events in time series of the modulus of the interplanetary magnetic field measured by Cluster spacecraft on February 2, 2002. One of the main results is that the solar wind near the Earth's bow shock can be modeled by the Generalized Pareto (GP) and Generalized Extreme Values (GEV) distributions. Both models present a statistically significant positive shape parameter which implyies a heavy tail in the probability distribution functions and an unbounded growth in return values as return periods become too long. There is evidence that current sheets are the main responsible for positive values of the shape parameter. It is also shown that magnetic reconnection at the interface between two interplanetary magnetic flux ropes in the solar wind can be considered as Dragon Kings (DK), a class of extreme events whose formation mechanisms are fundamentally different from others. As long as magnetic reconnection can be classified as a Dragon King, there is the possibility of its identification and even its prediction. Dragon kings had previously been identified in time series of financial crashes, nuclear power generation accidents, stock market and so on. It is believed that they are associated with the occurrence of extreme events in dynamical systems at phase transition, bifurcation, crises or tipping points.

The Total Ozone Series of Arosa: History, Homogenization and new results using statistical extreme value theory

NASA Astrophysics Data System (ADS)

Staehelin, J.; Rieder, H. E.; Maeder, J. A.; Ribatet, M.; Davison, A. C.; Stübi, R.

2009-04-01

Atmospheric ozone protects the biota living at the Earth's surface from harmful solar UV-B and UV-C radiation. The global ozone shield is expected to gradually recover from the anthropogenic disturbance of ozone depleting substances (ODS) in the coming decades. The stratospheric ozone layer at extratropics might significantly increase above the thickness of the chemically undisturbed atmosphere which might enhance ozone concentrations at the tropopause altitude where ozone is an important greenhouse gas. At Arosa, a resort village in the Swiss Alps, total ozone measurements started in 1926 leading to the longest total ozone series of the world. One Fery spectrograph and seven Dobson spectrophotometers were operated at Arosa and the method used to homogenize the series will be presented. Due to its unique length the series allows studying total ozone in the chemically undisturbed as well as in the ODS loaded stratosphere. The series is particularly valuable to study natural variability in the period prior to 1970, when ODS started to affect stratospheric ozone. Concepts developed by extreme value statistics allow objective definitions of "ozone extreme high" and "ozone extreme low" values by fitting the (daily mean) time series using the Generalized Pareto Distribution (GPD). Extreme high ozone events can be attributed to effects of ElNino and/or NAO, whereas in the chemically disturbed stratosphere high frequencies of extreme low total ozone values simultaneously occur with periods of strong polar ozone depletion (identified by statistical modeling with Equivalent Stratospheric Chlorine times Volume of Stratospheric Polar Clouds) and volcanic eruptions (such as El Chichon and Pinatubo).

In-Hospital Outcomes of Atherectomy During Endovascular Lower Extremity Revascularization.

PubMed

Panaich, Sidakpal S; Arora, Shilpkumar; Patel, Nilay; Patel, Nileshkumar J; Patel, Samir V; Savani, Chirag; Singh, Vikas; Jhamnani, Sunny; Sonani, Rajesh; Lahewala, Sopan; Thakkar, Badal; Patel, Achint; Dave, Abhishek; Shah, Harshil; Bhatt, Parth; Jaiswal, Radhika; Ghatak, Abhijit; Gupta, Vishal; Deshmukh, Abhishek; Kondur, Ashok; Schreiber, Theodore; Grines, Cindy; Badheka, Apurva O

2016-02-15

Contemporary data on clinical outcomes after utilization of atherectomy in lower extremity endovascular revascularization are sparse. The study cohort was derived from Healthcare Cost and Utilization Project nationwide inpatient sample database from the year 2012. Peripheral endovascular interventions including atherectomy were identified using appropriate International Classification of Diseases, Ninth Revision, Clinical Modification diagnostic and procedural codes. The subjects were divided and compared in 2 groups: atherectomy versus no atherectomy. Two-level hierarchical multivariate mixed models were created. The coprimary outcomes were in-hospital mortality and amputation; secondary outcome was a composite of in-hospital mortality and periprocedural complications. Hospitalization costs were also assessed. Atherectomy utilization (odds ratio, 95% CI, p value) was independently predictive of lower in-hospital mortality (0.46, 0.28 to 0.75, 0.002) and lower amputation rates (0.83, 0.71 to 0.97, 0.020). Atherectomy use was also predictive of significantly lower secondary composite outcome of in-hospital mortality and complications (0.79, 0.69 to 0.90, 0.001). In the propensity-matched cohort, atherectomy utilization was again associated with a lower rate of amputation (11.18% vs 12.92%, p = 0.029), in-hospital mortality (0.71% vs 1.53%, p 0.001), and any complication (13.24% vs 16.09%, p 0.001). However, atherectomy use was also associated with higher costs ($24,790 ± 397 vs $22635 ± 251, p <0.001). Atherectomy use in conjunction with angioplasty (with or without stenting) was associated with improved in-hospital outcomes in terms of lower amputation rates, mortality, and postprocedural complications. Copyright © 2016 Elsevier Inc. All rights reserved.

Variability in winter climate and winter extremes reduces population growth of an alpine butterfly.

PubMed

Roland, Jens; Matter, Stephen F

2013-01-01

We examined the long-term, 15-year pattern of population change in a network of 21 Rocky Mountain populations of Parnassius smintheus butterflies in response to climatic variation. We found that winter values of the broadscale climate variable, the Pacific Decadal Oscillation (PDO) index, were a strong predictor of annual population growth, much more so than were endogenous biotic factors related to population density. The relationship between PDO and population growth was nonlinear. Populations declined in years with extreme winter PDO values, when there were either extremely warm or extremely cold sea surface temperatures in the eastern Pacific relative to that in the western Pacific. Results suggest that more variable winters, and more frequent extremely cold or warm winters, will result in more frequent decline of these populations, a pattern exacerbated by the trend for increasingly variable winters seen over the past century.

Validation of extremes within the Perfect-Predictor Experiment of the COST Action VALUE

NASA Astrophysics Data System (ADS)

Hertig, Elke; Maraun, Douglas; Wibig, Joanna; Vrac, Mathieu; Soares, Pedro; Bartholy, Judith; Pongracz, Rita; Mares, Ileana; Gutierrez, Jose Manuel; Casanueva, Ana; Alzbutas, Robertas

2016-04-01

Extreme events are of widespread concern due to their damaging consequences on natural and anthropogenic systems. From science to applications the statistical attributes of rare and infrequent occurrence and low probability become connected with the socio-economic aspect of strong impact. Specific end-user needs regarding information about extreme events depend on the type of application, but as a joining element there is always the request for easily accessible climate change information with a clear description of their uncertainties and limitations. Within the Perfect-Predictor Experiment of the COST Action VALUE extreme indices modelled from a wide range of downscaling methods are compared to reference indices calculated from observational data. The experiment uses reference data from a selection of 86 weather stations representative of the different climates in Europe. Results are presented for temperature and precipitation extremes and include aspects of the marginal distribution as well as spell-length related aspects.

Extremes in ecology: Avoiding the misleading effects of sampling variation in summary analyses

USGS Publications Warehouse

Link, W.A.; Sauer, J.R.

1996-01-01

Surveys such as the North American Breeding Bird Survey (BBS) produce large collections of parameter estimates. One's natural inclination when confronted with lists of parameter estimates is to look for the extreme values: in the BBS, these correspond to the species that appear to have the greatest changes in population size through time. Unfortunately, extreme estimates are liable to correspond to the most poorly estimated parameters. Consequently, the most extreme parameters may not match up with the most extreme parameter estimates. The ranking of parameter values on the basis of their estimates are a difficult statistical problem. We use data from the BBS and simulations to illustrate the potential misleading effects of sampling variation in rankings of parameters. We describe empirical Bayes and constrained empirical Bayes procedures which provide partial solutions to the problem of ranking in the presence of sampling variation.

Power laws and extreme values in antibody repertoires

NASA Astrophysics Data System (ADS)

Boyer, Sebastien; Biswas, Dipanwita; Scaramozzino, Natale; Kumar, Ananda Soshee; Nizak, Clément; Rivoire, Olivier

2015-03-01

Evolution by natural selection involves the succession of three steps: mutations, selection and proliferation. We are interested in describing and characterizing the result of selection over a population of many variants. After selection, this population will be dominated by the few best variants, with highest propensity to be selected, or highest ``selectivity.'' We ask the following question: how is the selectivity of the best variants distributed in the population? Extreme value theory, which characterizes the extreme tail of probability distributions in terms of a few universality class, has been proposed to describe it. To test this proposition and identify the relevant universality class, we performed quantitative in vitro experimental selections of libraries of >105 antibodies using the technique of phage display. Data obtained by high-throughput sequencing allows us to fit the selectivity distribution over more than two decades. In most experiments, the results show a striking power law for the selectivity distribution of the top antibodies, consistent with extreme value theory.

Evaluation of the best fit distribution for partial duration series of daily rainfall in Madinah, western Saudi Arabia

NASA Astrophysics Data System (ADS)

Alahmadi, F.; Rahman, N. A.; Abdulrazzak, M.

2014-09-01

Rainfall frequency analysis is an essential tool for the design of water related infrastructure. It can be used to predict future flood magnitudes for a given magnitude and frequency of extreme rainfall events. This study analyses the application of rainfall partial duration series (PDS) in the vast growing urban Madinah city located in the western part of Saudi Arabia. Different statistical distributions were applied (i.e. Normal, Log Normal, Extreme Value type I, Generalized Extreme Value, Pearson Type III, Log Pearson Type III) and their distribution parameters were estimated using L-moments methods. Also, different selection criteria models are applied, e.g. Akaike Information Criterion (AIC), Corrected Akaike Information Criterion (AICc), Bayesian Information Criterion (BIC) and Anderson-Darling Criterion (ADC). The analysis indicated the advantage of Generalized Extreme Value as the best fit statistical distribution for Madinah partial duration daily rainfall series. The outcome of such an evaluation can contribute toward better design criteria for flood management, especially flood protection measures.

Variance analysis of forecasted streamflow maxima in a wet temperate climate

NASA Astrophysics Data System (ADS)

Al Aamery, Nabil; Fox, James F.; Snyder, Mark; Chandramouli, Chandra V.

2018-05-01

Coupling global climate models, hydrologic models and extreme value analysis provides a method to forecast streamflow maxima, however the elusive variance structure of the results hinders confidence in application. Directly correcting the bias of forecasts using the relative change between forecast and control simulations has been shown to marginalize hydrologic uncertainty, reduce model bias, and remove systematic variance when predicting mean monthly and mean annual streamflow, prompting our investigation for maxima streamflow. We assess the variance structure of streamflow maxima using realizations of emission scenario, global climate model type and project phase, downscaling methods, bias correction, extreme value methods, and hydrologic model inputs and parameterization. Results show that the relative change of streamflow maxima was not dependent on systematic variance from the annual maxima versus peak over threshold method applied, albeit we stress that researchers strictly adhere to rules from extreme value theory when applying the peak over threshold method. Regardless of which method is applied, extreme value model fitting does add variance to the projection, and the variance is an increasing function of the return period. Unlike the relative change of mean streamflow, results show that the variance of the maxima's relative change was dependent on all climate model factors tested as well as hydrologic model inputs and calibration. Ensemble projections forecast an increase of streamflow maxima for 2050 with pronounced forecast standard error, including an increase of +30(±21), +38(±34) and +51(±85)% for 2, 20 and 100 year streamflow events for the wet temperate region studied. The variance of maxima projections was dominated by climate model factors and extreme value analyses.

Relationship between diffusion tensor fractional anisotropy and motor outcome in patients with hemiparesis after corona radiata infarct.

PubMed

Koyama, Tetsuo; Marumoto, Kohei; Miyake, Hiroji; Domen, Kazuhisa

2013-11-01

This study examined the relationship between fractional anisotropy (FA) values of magnetic resonance-diffusion tensor imaging (DTI) and motor outcome (1 month after onset) in 15 patients with hemiparesis after ischemic stroke of corona radiata lesions. DTI data were obtained on days 14-18. FA values within the cerebral peduncle were analyzed using a computer-automated method. Motor outcome of hemiparesis was evaluated according to Brunnstrom stage (BRS; 6-point scale: severe to normal) for separate shoulder/elbow/forearm, wrist/hand, and lower extremity functions. The ratio of FA values in the affected hemisphere to those in the unaffected hemisphere (rFA) was assessed in relation to the BRS data (Spearman rank correlation test, P<.05). rFA values ranged from .715 to 1.002 (median=.924). BRS ranged from 1 to 6 (median=4) for shoulder/elbow/forearm, from 1 to 6 (median=5) for wrist/hand, and from 2 to 6 (median=4) for the lower extremities. Analysis revealed statistically significant relationships between rFA and upper extremity functions (correlation coefficient=.679 for shoulder/elbow/forearm and .706 for wrist/hand). Although slightly less evident, the relationship between rFA and lower extremity function was also statistically significant (correlation coefficient=.641). FA values within the cerebral peduncle are moderately associated with the outcome of both upper and lower extremity functions, suggesting that DTI may be applicable for outcome prediction in stroke patients with corona radiata infarct. Copyright © 2013 National Stroke Association. Published by Elsevier Inc. All rights reserved.

Nearly extremal apparent horizons in simulations of merging black holes

NASA Astrophysics Data System (ADS)

Lovelace, Geoffrey; Scheel, Mark A.; Owen, Robert; Giesler, Matthew; Katebi, Reza; Szilágyi, Béla; Chu, Tony; Demos, Nicholas; Hemberger, Daniel A.; Kidder, Lawrence E.; Pfeiffer, Harald P.; Afshari, Nousha

2015-03-01

The spin angular momentum S of an isolated Kerr black hole is bounded by the surface area A of its apparent horizon: 8π S≤slant A, with equality for extremal black holes. In this paper, we explore the extremality of individual and common apparent horizons for merging, rapidly spinning binary black holes. We consider simulations of merging black holes with equal masses M and initial spin angular momenta aligned with the orbital angular momentum, including new simulations with spin magnitudes up to S/{{M}2}=0.994. We measure the area and (using approximate Killing vectors) the spin on the individual and common apparent horizons, finding that the inequality 8π S\\lt A is satisfied in all cases but is very close to equality on the common apparent horizon at the instant it first appears. We also evaluate the Booth-Fairhurst extremality, whose value for a given apparent horizon depends on the scaling of the horizon’s null normal vectors. In particular, we introduce a gauge-invariant lower bound on the extremality by computing the smallest value that Booth and Fairhurst’s extremality parameter can take for any scaling. Using this lower bound, we conclude that the common horizons are at least moderately close to extremal just after they appear. Finally, following Lovelace et al (2008 Phys. Rev. D 78 084017), we construct quasiequilibrium binary-black hole initial data with ‘overspun’ marginally trapped surfaces with 8π S\\gt A. We show that the overspun surfaces are indeed superextremal: our lower bound on their Booth-Fairhurst extremality exceeds unity. However, we confirm that these superextremal surfaces are always surrounded by marginally outer trapped surfaces (i.e., by apparent horizons) with 8π S\\lt A. The extremality lower bound on the enclosing apparent horizon is always less than unity but can exceed the value for an extremal Kerr black hole.

An Application of Extreme Value Theory to Learning Analytics: Predicting Collaboration Outcome from Eye-Tracking Data

ERIC Educational Resources Information Center

Sharma, Kshitij; Chavez-Demoulin, Valérie; Dillenbourg, Pierre

2017-01-01

The statistics used in education research are based on central trends such as the mean or standard deviation, discarding outliers. This paper adopts another viewpoint that has emerged in statistics, called extreme value theory (EVT). EVT claims that the bulk of normal distribution is comprised mainly of uninteresting variations while the most…

Factors affecting the thermal environment of Agassiz’s Desert Tortoise (Gopherus agassizii) cover sites in the Central Mojave Desert during periods of temperature extremes

USGS Publications Warehouse

Mack, Jeremy S.; Berry, Kristin H.; Miller, David; Carlson, Andrea S.

2015-01-01

Agassiz's Desert Tortoises (Gopherus agassizii) spend >95% of their lives underground in cover sites that serve as thermal buffers from temperatures, which can fluctuate >40°C on a daily and seasonal basis. We monitored temperatures at 30 active tortoise cover sites within the Soda Mountains, San Bernardino County, California, from February 2004 to September 2006. Cover sites varied in type and structural characteristics, including opening height and width, soil cover depth over the opening, aspect, tunnel length, and surficial geology. We focused our analyses on periods of extreme temperature: in summer, between July 1 and September 1, and winter, between November 1 and February 15. With the use of multivariate regression tree analyses, we found cover-site temperatures were influenced largely by tunnel length and subsequently opening width and soil cover. Linear regression models further showed that increasing tunnel length increased temperature stability and dampened seasonal temperature extremes. Climate change models predict increased warming for southwestern North America. Cover sites that buffer temperature extremes and fluctuations will become increasingly important for survival of tortoises. In planning future translocation projects and conservation efforts, decision makers should consider habitats with terrain and underlying substrate that sustain cover sites with long tunnels and expanded openings for tortoises living under temperature extremes similar to those described here or as projected in the future.

A comparison of observed extreme water levels at the German Bight elaborated through an extreme value analysis (EVA) with extremes derived from a regionally coupled ocean-atmospheric climate model (MPI-OM)

NASA Astrophysics Data System (ADS)

Möller, Jens; Heinrich, Hartmut

2017-04-01

As a consequence of climate change atmospheric and oceanographic extremes and their potential impacts on coastal regions are of growing concern for governmental authorities responsible for the transportation infrastructure. Highest risks for shipping as well as for rail and road traffic originate from combined effects of extremes of storm surges and heavy rainfall which sometimes lead to insufficient dewatering of inland waterways. The German Ministry of Transport and digital Infrastructure therefore has tasked its Network of Experts to investigate the possible evolutions of extreme threats for low lands and especially for Kiel Canal, which is an important shortcut for shipping between the North and Baltic Seas. In this study we present results of a comparison of an Extreme Value Analysis (EVA) carried out on gauge observations and values derived from a coupled Regional Ocean-Atmosphere Climate Model (MPI-OM). High water levels at the coasts of the North and Baltic Seas are one of the most important hazards which increase the risk of flooding of the low-lying land and prevents such areas from an adequate dewatering. In this study changes in the intensity (magnitude of the extremes) and duration of extreme water levels (above a selected threshold) are investigated for several gauge stations with data partly reaching back to 1843. Different methods are used for the extreme value statistics, (1) a stationary general Pareto distribution (GPD) model as well as (2) an instationary statistical model for better reproduction of the impact of climate change. Most gauge stations show an increase of the mean water level of about 1-2 mm/year, with a stronger increase of the highest water levels and a decrease (or lower increase) of the lowest water levels. Also, the duration of possible dewatering time intervals for the Kiel-Canal was analysed. The results for the historical gauge station observations are compared to the statistics of modelled water levels from the coupled atmosphere-ocean climate model MPI-OM for the time interval from 1951 to 2000. We demonstrate that for high water levels the observations and MPI-OM results are in good agreement, and we provide an estimate on the decreasing dewatering potential for Kiel Canal until the end of the 21st century.

Proton radius from electron scattering data

DOE Office of Scientific and Technical Information (OSTI.GOV)

Higinbotham, Douglas W.; Kabir, Al Amin; Lin, Vincent

Background: The proton charge radius extracted from recent muonic hydrogen Lamb shift measurements is significantly smaller than that extracted from atomic hydrogen and electron scattering measurements. The discrepancy has become known as the proton radius puzzle. Purpose: In an attempt to understand the discrepancy, we review high-precision electron scattering results from Mainz, Jefferson Lab, Saskatoon and Stanford. Methods: We make use of stepwise regression techniques using the F-test as well as the Akaike information criterion to systematically determine the predictive variables to use for a given set and range of electron scattering data as well as to provide multivariate errormore » estimates. Results: Starting with the precision, low four-momentum transfer (Q 2) data from Mainz (1980) and Saskatoon (1974), we find that a stepwise regression of the Maclaurin series using the F-test as well as the Akaike information criterion justify using a linear extrapolation which yields a value for the proton radius that is consistent with the result obtained from muonic hydrogen measurements. Applying the same Maclaurin series and statistical criteria to the 2014 Rosenbluth results on GE from Mainz, we again find that the stepwise regression tends to favor a radius consistent with the muonic hydrogen radius but produces results that are extremely sensitive to the range of data included in the fit. Making use of the high-Q 2 data on G E to select functions which extrapolate to high Q 2, we find that a Pad´e (N = M = 1) statistical model works remarkably well, as does a dipole function with a 0.84 fm radius, G E(Q 2) = (1 + Q 2/0.66 GeV 2) -2. Conclusions: Rigorous applications of stepwise regression techniques and multivariate error estimates result in the extraction of a proton charge radius that is consistent with the muonic hydrogen result of 0.84 fm; either from linear extrapolation of the extreme low-Q 2 data or by use of the Pad´e approximant for extrapolation using a larger range of data. Thus, based on a purely statistical analysis of electron scattering data, we conclude that the electron scattering result and the muonic hydrogen result are consistent. Lastly, it is the atomic hydrogen results that are the outliers.« less

Framing 100-year overflowing and overtopping marine submersion hazard resulting from the propagation of 100-year joint hydrodynamic conditions

NASA Astrophysics Data System (ADS)

Nicolae Lerma, A.; Bulteau, T.; Elineau, S.; Paris, F.; Pedreros, R.

2016-12-01

Marine submersion is an increasing concern for coastal cities as urban development reinforces their vulnerabilities while climate change is likely to foster the frequency and magnitude of submersions. Characterising the coastal flooding hazard is therefore of paramount importance to ensure the security of people living in such places and for coastal planning. A hazard is commonly defined as an adverse phenomenon, often represented by a magnitude of a variable of interest (e.g. flooded area), hereafter called response variable, associated with a probability of exceedance or, alternatively, a return period. Characterising the coastal flooding hazard consists in finding the correspondence between the magnitude and the return period. The difficulty lies in the fact that the assessment is usually performed using physical numerical models taking as inputs scenarios composed by multiple forcing conditions that are most of the time interdependent. Indeed, a time series of the response variable is usually not available so we have to deal instead with time series of forcing variables (e.g. water level, waves). Thus, the problem is twofold: on the one hand, the definition of scenarios is a multivariate matter; on the other hand, it is tricky and approximate to associate the resulting response, being the output of the physical numerical model, to the return period defined for the scenarios. In this study, we illustrate the problem on the district of Leucate, located in the French Mediterranean coast. A multivariate extreme value analysis of waves and water levels is performed offshore using a conditional extreme model, then two different methods are used to define and select 100-year scenarios of forcing variables: one based on joint exceedance probability contours, a method classically used in coastal risks studies, the other based on environmental contours, which are commonly used in the field of structure design engineering. We show that these two methods enable one to frame the true 100-year response variable. The selected scenarios are propagated to the shore through a high resolution flood modelling coupling overflowing and overtopping processes. Results in terms of inundated areas and inland water volumes are finally compared for the two methods, giving upper and lower bounds for the true response variables.

Proton radius from electron scattering data

DOE PAGES

Higinbotham, Douglas W.; Kabir, Al Amin; Lin, Vincent; ...

2016-05-31

Background: The proton charge radius extracted from recent muonic hydrogen Lamb shift measurements is significantly smaller than that extracted from atomic hydrogen and electron scattering measurements. The discrepancy has become known as the proton radius puzzle. Purpose: In an attempt to understand the discrepancy, we review high-precision electron scattering results from Mainz, Jefferson Lab, Saskatoon and Stanford. Methods: We make use of stepwise regression techniques using the F-test as well as the Akaike information criterion to systematically determine the predictive variables to use for a given set and range of electron scattering data as well as to provide multivariate errormore » estimates. Results: Starting with the precision, low four-momentum transfer (Q 2) data from Mainz (1980) and Saskatoon (1974), we find that a stepwise regression of the Maclaurin series using the F-test as well as the Akaike information criterion justify using a linear extrapolation which yields a value for the proton radius that is consistent with the result obtained from muonic hydrogen measurements. Applying the same Maclaurin series and statistical criteria to the 2014 Rosenbluth results on GE from Mainz, we again find that the stepwise regression tends to favor a radius consistent with the muonic hydrogen radius but produces results that are extremely sensitive to the range of data included in the fit. Making use of the high-Q 2 data on G E to select functions which extrapolate to high Q 2, we find that a Pad´e (N = M = 1) statistical model works remarkably well, as does a dipole function with a 0.84 fm radius, G E(Q 2) = (1 + Q 2/0.66 GeV 2) -2. Conclusions: Rigorous applications of stepwise regression techniques and multivariate error estimates result in the extraction of a proton charge radius that is consistent with the muonic hydrogen result of 0.84 fm; either from linear extrapolation of the extreme low-Q 2 data or by use of the Pad´e approximant for extrapolation using a larger range of data. Thus, based on a purely statistical analysis of electron scattering data, we conclude that the electron scattering result and the muonic hydrogen result are consistent. Lastly, it is the atomic hydrogen results that are the outliers.« less

Body proportions of circumpolar peoples as evidenced from skeletal data: Ipiutak and Tigara (Point Hope) versus Kodiak Island Inuit.

PubMed

Holliday, Trenton W; Hilton, Charles E

2010-06-01

Given the well-documented fact that human body proportions covary with climate (presumably due to the action of selection), one would expect that the Ipiutak and Tigara Inuit samples from Point Hope, Alaska, would be characterized by an extremely cold-adapted body shape. Comparison of the Point Hope Inuit samples to a large (n > 900) sample of European and European-derived, African and African-derived, and Native American skeletons (including Koniag Inuit from Kodiak Island, Alaska) confirms that the Point Hope Inuit evince a cold-adapted body form, but analyses also reveal some unexpected results. For example, one might suspect that the Point Hope samples would show a more cold-adapted body form than the Koniag, given their more extreme environment, but this is not the case. Additionally, univariate analyses seldom show the Inuit samples to be more cold-adapted in body shape than Europeans, and multivariate cluster analyses that include a myriad of body shape variables such as femoral head diameter, bi-iliac breadth, and limb segment lengths fail to effectively separate the Inuit samples from Europeans. In fact, in terms of body shape, the European and the Inuit samples tend to be cold-adapted and tend to be separated in multivariate space from the more tropically adapted Africans, especially those groups from south of the Sahara. Copyright 2009 Wiley-Liss, Inc.

Obesity in trauma patients: correlations of body mass index with outcomes, injury patterns, and complications.

PubMed

Evans, David C; Stawicki, Stanislaw P A; Davido, H Tracy; Eiferman, Daniel

2011-08-01

Current understanding of the effects of obesity on trauma patients is incomplete. We hypothesized that among older trauma patients, obese patients differ from nonobese patients in injury patterns, complications, and mortality. Patients older than 45 years old presenting to a Level I trauma center were included in this retrospective database analysis (n = 461). Body mass index (BMI) groups were defined as underweight less than 18.5 kg/m(2), normal 18.5 to 24.9 kg/m(2), overweight 25.0 to 29.9 kg/m(2), or obese greater than 30 kg/m(2). Injury patterns, complications, and outcomes were analyzed using univariate analyses, multivariate logistic regression, and Kaplan-Meier survival analysis. Higher BMI is associated with a higher incidence of torso injury and proximal upper extremity injuries in blunt trauma (n = 410). All other injury patterns and complications (except anemia) were similar between BMI groups. The underweight (BMI less than 18.5 kg/m(2)) group had significantly lower 90-day survival than other groups (P < 0.05). BMI is not a predictor of morbidity or mortality in multivariate analysis. Among older blunt trauma patients, increasing BMI is associated with higher rates of torso and proximal upper extremity injuries. Our study suggests that obesity is not an independent risk factor for complications or mortality after trauma in older patients. Conversely, underweight trauma patients had a lower 90-day survival.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Sole, Claudio V., E-mail: cvsole@uc.cl; School of Medicine, Complutense University, Madrid; Service of Radiation Oncology, Instituto de Radiomedicina, Santiago

Purpose: To perform a joint analysis of data from 3 contributing centers within the intraoperative electron-beam radiation therapy (IOERT)-Spanish program, to determine the potential of IOERT as an anticipated boost before external beam radiation therapy in the multidisciplinary treatment of pediatric extremity soft-tissue sarcomas. Methods and Materials: From June 1993 to May 2013, 62 patients (aged <21 years) with a histologic diagnosis of primary extremity soft-tissue sarcoma with absence of distant metastases, undergoing limb-sparing grossly resected surgery, external beam radiation therapy (median dose 40 Gy) and IOERT (median dose 10 Gy) were considered eligible for this analysis. Results: After a median follow-up ofmore » 66 months (range, 4-235 months), 10-year local control, disease-free survival, and overall survival was 85%, 76%, and 81%, respectively. In multivariate analysis after adjustment for other covariates, tumor size >5 cm (P=.04) and R1 margin status (P=.04) remained significantly associated with local relapse. In regard to overall survival only margin status (P=.04) retained association on multivariate analysis. Ten patients (16%) reported severe chronic toxicity events (all grade 3). Conclusions: An anticipated IOERT boost allowed for external beam radiation therapy dose reduction, with high local control and acceptably low toxicity rates. The combined radiosurgical approach needs to be tested in a prospective trial to confirm these results.« less

Compound extremes of summer temperature and precipitation leading to intensified departures from natural variability.

NASA Astrophysics Data System (ADS)

Mahony, C. R.; Cannon, A. J.

2017-12-01

Climate change can drive local climates outside the range of their historical year-to-year variability, straining the adaptive capacity of ecological and human communities. We demonstrate that interactions between climate variables can produce larger and earlier departures from natural variability than is detectable in individual variables. For example, summer temperature (Tx) and precipitation (Pr) are negatively correlated in most terrestrial regions, such that interannual variability lies along an axis from warm-and-dry to cool-and-wet conditions. A climate change trend perpendicular to this axis, towards warmer-wetter conditions, can depart more quickly from the range of natural variability than a warmer-drier trend. This multivariate "departure intensification" effect is evident in all six CMIP5 models that we examined: 23% (9-34%) of the land area of each model exhibits a pronounced increase in 2σ extremesin the Tx-Pr regime relative to Tx or Pr alone. Observational data suggest that Tx-Pr correlations are sufficient to produce departure intensification in distinct regions on all continents. Departures from the historical Tx-Pr regime may produce ecological disruptions, such as in plant-pathogen interactions and human diseases, that could offset the drought mitigation benefits of increased precipitation. Our study alerts researchers and adaptation practitioners to the presence of multivariate climate change signals and compound extremes that are not detectable in individual climate variables.

Spatial variation of statistical properties of extreme water levels along the eastern Baltic Sea

NASA Astrophysics Data System (ADS)

Pindsoo, Katri; Soomere, Tarmo; Rocha, Eugénio

2016-04-01

Most of existing projections of future extreme water levels rely on the use of classic generalised extreme value distributions. The choice to use a particular distribution is often made based on the absolute value of the shape parameter of the Generalise Extreme Value distribution. If this parameter is small, the Gumbel distribution is most appropriate while in the opposite case the Weibull or Frechet distribution could be used. We demonstrate that the alongshore variation in the statistical properties of numerically simulated high water levels along the eastern coast of the Baltic Sea is so large that the use of a single distribution for projections of extreme water levels is highly questionable. The analysis is based on two simulated data sets produced in the Swedish Meteorological and Hydrological Institute. The output of the Rossby Centre Ocean model is sampled with a resolution of 6 h and the output of the circulation model NEMO with a resolution of 1 h. As the maxima of water levels of subsequent years may be correlated in the Baltic Sea, we also employ maxima for stormy seasons. We provide a detailed analysis of spatial variation of the parameters of the family of extreme value distributions along an approximately 600 km long coastal section from the north-western shore of Latvia in the Baltic Proper until the eastern Gulf of Finland. The parameters are evaluated using maximum likelihood method and method of moments. The analysis also covers the entire Gulf of Riga. The core parameter of this family of distributions, the shape parameter of the Generalised Extreme Value distribution, exhibits extensive variation in the study area. Its values evaluated using the Hydrognomon software and maximum likelihood method, vary from about -0.1 near the north-western coast of Latvia in the Baltic Proper up to about 0.05 in the eastern Gulf of Finland. This parameter is very close to zero near Tallinn in the western Gulf of Finland. Thus, it is natural that the Gumbel distribution gives adequate projections of extreme water levels for the vicinity of Tallinn. More importantly, this feature indicates that the use of a single distribution for the projections of extreme water levels and their return periods for the entire Baltic Sea coast is inappropriate. The physical reason is the interplay of the complex shape of large subbasins (such as the Gulf of Riga and Gulf of Finland) of the sea and highly anisotropic wind regime. The 'impact' of this anisotropy on the statistics of water level is amplified by the overall anisotropy of the distributions of the frequency of occurrence of high and low water levels. The most important conjecture is that long-term behaviour of water level extremes in different coastal sections of the Baltic Sea may be fundamentally different.

Trends in 1970-2010 southern California surface maximum temperatures: extremes and heat waves

NASA Astrophysics Data System (ADS)

Ghebreegziabher, Amanuel T.

Daily maximum temperatures from 1970-2010 were obtained from the National Climatic Data Center (NCDC) for 28 South Coast Air Basin (SoCAB) Cooperative Network (COOP) sites. Analyses were carried out on the entire data set, as well as on the 1970-1974 and 2006-2010 sub-periods, including construction of spatial distributions and time-series trends of both summer-average and annual-maximum values and of the frequency of two and four consecutive "daytime" heat wave events. Spatial patterns of average and extreme values showed three areas consistent with climatological SoCAB flow patterns: cold coastal, warm inland low-elevation, and cool further-inland mountain top. Difference (2006-2010 minus 1970-1974) distributions of both average and extreme-value trends were consistent with the shorter period (1970-2005) study of previous study, as they showed the expected inland regional warming and a "reverse-reaction" cooling in low elevation coastal and inland areas open to increasing sea breeze flows. Annual-extreme trends generally showed cooling at sites below 600 m and warming at higher elevations. As the warming trends of the extremes were larger than those of the averages, regional warming thus impacts extremes more than averages. Spatial distributions of hot-day frequencies showed expected maximum at inland low-elevation sites. Regional warming again thus induced increases at both elevated-coastal areas, but low-elevation areas showed reverse-reaction decreases.

Spatiotemporal changes in precipitation extremes over Yangtze River basin, China, considering the rainfall shift in the late 1970s

NASA Astrophysics Data System (ADS)

Gao, Tao; Xie, Lian

2016-12-01

Precipitation extremes are the dominated causes for the formation of severe flood disasters at regional and local scales under the background of global climate change. In the present study, five annual extreme precipitation events, including 1, 7 and 30 day annual maximum rainfall and 95th and 97.5th percentile threshold levels, are analyzed relating to the reference period 1960-2011 from 140 meteorological stations over Yangtze River basin (YRB). A generalized extreme value (GEV) distribution is applied to fit annual and percentile extreme precipitation events at each station with return periods up to 200 years. The entire time period is divided into preclimatic (preceding climatic) period 1960-1980 and aftclimatic (after climatic) period 1981-2011 by considering distinctly abrupt shift of precipitation regime in the late 1970s across YRB. And the Mann-Kendall trend test is adopted to conduct trend analysis during pre- and aftclimatic periods, respectively, for the purpose of exploring possible increasing/decreasing patterns in precipitation extremes. The results indicate that the increasing trends for return values during aftclimatic period change significantly in time and space in terms of different magnitudes of extreme precipitation, while the stations with significantly positive trends are mainly distributed in the vicinity of the mainstream and major tributaries as well as large lakes, this would result in more tremendous flood disasters in the mid-lower reaches of YRB, especially in southeast coastal regions. The increasing/decreasing linear trends based on annual maximum precipitation are also investigated in pre- and aftclimatic periods, respectively, whereas those changes are not significantly similar to the variations of return values during both subperiods. Moreover, spatiotemporal patterns of precipitation extremes become more uneven and unstable in the second half period over YRB.

Multiple imputation for multivariate data with missing and below-threshold measurements: time-series concentrations of pollutants in the Arctic.

PubMed

Hopke, P K; Liu, C; Rubin, D B

2001-03-01

Many chemical and environmental data sets are complicated by the existence of fully missing values or censored values known to lie below detection thresholds. For example, week-long samples of airborne particulate matter were obtained at Alert, NWT, Canada, between 1980 and 1991, where some of the concentrations of 24 particulate constituents were coarsened in the sense of being either fully missing or below detection limits. To facilitate scientific analysis, it is appealing to create complete data by filling in missing values so that standard complete-data methods can be applied. We briefly review commonly used strategies for handling missing values and focus on the multiple-imputation approach, which generally leads to valid inferences when faced with missing data. Three statistical models are developed for multiply imputing the missing values of airborne particulate matter. We expect that these models are useful for creating multiple imputations in a variety of incomplete multivariate time series data sets.

Risk Factors for Thirty-Day Readmissions After Lower Extremity Amputation in Patients With Vascular Disease.

PubMed

Vogel, Todd R; Smith, Jamie B; Kruse, Robin L

2018-05-29

Understanding risk factors associated with readmission after lower extremity amputation may indicate targets for reducing readmission. This study evaluated factors associated with all-cause 30-day readmission after lower extremity amputation procedures. Retrospective cohort study. Inpatient. A total of 2480 patients who had lower extremity amputations between 2008 and 2014 were selected from national electronic medical record database, Cerner Health Facts. Univariate analysis of demographics, diagnoses, postoperative medications, and laboratory results were examined. Multivariate logistic regression models were used to identify characteristics independently associated with readmission overall and by amputation location-above the knee (AKA) or below the knee (BKA). Readmission within 30 days of discharge. More than one half of patients (1403, 57%) underwent BKA and 1077 (43%) underwent AKA. Readmission within 30 days was 22% (24.1% BKA versus 19.4% AKA, P = .005). In multivariable logistic regression, factors associated with 30-day readmission after any amputation included BKA (odds ratio [OR] 1.41, 95% confidence interval [CI] 1.15-1.74, P = .001), hypertension (OR 1.70, 95% CI 1.33-2.16), surgical-site infections (OR 1.44, 95% CI 1.02-2.04), heart failure (OR 1.39, 95% CI 1.10-1.75), discharge to a skilled nursing facility (OR 1.88, 95% CI 1.41-2.51), and emergency/urgent procedures (OR 1.32, 95% CI 1.04-1.67). At readmission, 13.3% of patients with a BKA required an AKA revision, and 21.3% had a diagnosis of surgical-site infection. Risk factors for readmission after any amputation included cardiac comorbidities, associated postoperative medications, and discharge to a skilled nursing facility. The finding that acute arterial embolism or thrombosis and a BKA during the index admission was highly associated with readmission, combined with the high rates of 30-day conversion to an AKA when readmitted, suggests these patients more often develop stump complications or may be undertreated during the initial hospitalization. III. Copyright © 2018 American Academy of Physical Medicine and Rehabilitation. Published by Elsevier Inc. All rights reserved.

Morbidity and mortality after emergency lower extremity embolectomy.

PubMed

Casillas-Berumen, Sergio; Sadri, Lili; Farber, Alik; Eslami, Mohammad H; Kalish, Jeffrey A; Rybin, Denis; Doros, Gheorghe; Siracuse, Jeffrey J

2017-03-01

Emergency lower extremity embolectomy is a common vascular surgical procedure that has poorly defined outcomes. Our goal was to define the perioperative morbidity for emergency embolectomy and develop a risk prediction model for perioperative mortality. The American College of Surgeons National Surgical Quality Improvement database was queried to identify patients undergoing emergency unilateral and lower extremity embolectomy. Patients with previous critical limb ischemia, bilateral embolectomy, nonemergency indication, and those undergoing concurrent bypass were excluded. Patient characteristics and postoperative morbidity and mortality were analyzed. Multivariate analysis for predictors of mortality was performed, and from this, a risk prediction model was developed to identify preoperative predictors of mortality. There were 1749 patients (47.9% male) who met the inclusion criteria. The average age was 68.2 ± 14.8 years. Iliofemoral-popliteal embolectomy was performed in 1231 patients (70.4%), popliteal-tibioperoneal embolectomy in 303 (17.3%), and at both levels in 215 (12.3%). Fasciotomies were performed concurrently with embolectomy in 308 patients (17.6%). The 30-day postoperative mortality was 13.9%. Postoperative complications included myocardial infarction or cardiac arrest (4.7%), pulmonary complications (16.0%), and wound complications (8.2%). The rate of return to the operating room ≤30 days was 25.7%. Hospital length of stay was 9.8 ± 11.5 days, and the 30-day readmission rate was 16.3%. A perioperative mortality risk prediction model based on factors identified in multivariate analysis included age >70 years, male gender, functional dependence, history of chronic obstructive pulmonary disease, congestive heart failure, recent myocardial infarction/angina, chronic renal insufficiency, and steroid use. The model showed good discrimination (C = 0.769; 95% confidence interval, 0733-0.806) and calibrated well. Emergency lower extremity embolectomy has high morbidity, mortality, and resource utilization. These data provide a benchmark for this complex patient population and may assist in risk stratifying patients, allowing for improved informed consent and goals of care at the time of presentation. Copyright © 2016 Society for Vascular Surgery. Published by Elsevier Inc. All rights reserved.

The transformed-stationary approach: a generic and simplified methodology for non-stationary extreme value analysis

NASA Astrophysics Data System (ADS)

Mentaschi, Lorenzo; Vousdoukas, Michalis; Voukouvalas, Evangelos; Sartini, Ludovica; Feyen, Luc; Besio, Giovanni; Alfieri, Lorenzo

2016-09-01

Statistical approaches to study extreme events require, by definition, long time series of data. In many scientific disciplines, these series are often subject to variations at different temporal scales that affect the frequency and intensity of their extremes. Therefore, the assumption of stationarity is violated and alternative methods to conventional stationary extreme value analysis (EVA) must be adopted. Using the example of environmental variables subject to climate change, in this study we introduce the transformed-stationary (TS) methodology for non-stationary EVA. This approach consists of (i) transforming a non-stationary time series into a stationary one, to which the stationary EVA theory can be applied, and (ii) reverse transforming the result into a non-stationary extreme value distribution. As a transformation, we propose and discuss a simple time-varying normalization of the signal and show that it enables a comprehensive formulation of non-stationary generalized extreme value (GEV) and generalized Pareto distribution (GPD) models with a constant shape parameter. A validation of the methodology is carried out on time series of significant wave height, residual water level, and river discharge, which show varying degrees of long-term and seasonal variability. The results from the proposed approach are comparable with the results from (a) a stationary EVA on quasi-stationary slices of non-stationary series and (b) the established method for non-stationary EVA. However, the proposed technique comes with advantages in both cases. For example, in contrast to (a), the proposed technique uses the whole time horizon of the series for the estimation of the extremes, allowing for a more accurate estimation of large return levels. Furthermore, with respect to (b), it decouples the detection of non-stationary patterns from the fitting of the extreme value distribution. As a result, the steps of the analysis are simplified and intermediate diagnostics are possible. In particular, the transformation can be carried out by means of simple statistical techniques such as low-pass filters based on the running mean and the standard deviation, and the fitting procedure is a stationary one with a few degrees of freedom and is easy to implement and control. An open-source MATLAB toolbox has been developed to cover this methodology, which is available at https://github.com/menta78/tsEva/ (Mentaschi et al., 2016).

Predictive model for falling in Parkinson disease patients.

PubMed

Custodio, Nilton; Lira, David; Herrera-Perez, Eder; Montesinos, Rosa; Castro-Suarez, Sheila; Cuenca-Alfaro, Jose; Cortijo, Patricia

2016-12-01

Falls are a common complication of advancing Parkinson's disease (PD). Although numerous risk factors are known, reliable predictors of future falls are still lacking. The aim of this study was to develop a multivariate model to predict falling in PD patients. Prospective cohort with forty-nine PD patients. The area under the receiver-operating characteristic curve (AUC) was calculated to evaluate predictive performance of the purposed multivariate model. The median of PD duration and UPDRS-III score in the cohort was 6 years and 24 points, respectively. Falls occurred in 18 PD patients (30%). Predictive factors for falling identified by univariate analysis were age, PD duration, physical activity, and scores of UPDRS motor, FOG, ACE, IFS, PFAQ and GDS ( p -value < 0.001), as well as fear of falling score ( p -value = 0.04). The final multivariate model (PD duration, FOG, ACE, and physical activity) showed an AUC = 0.9282 (correctly classified = 89.83%; sensitivity = 92.68%; specificity = 83.33%). This study showed that our multivariate model have a high performance to predict falling in a sample of PD patients.

Analyzing phenological extreme events over the past five decades in Germany

NASA Astrophysics Data System (ADS)

Schleip, Christoph; Menzel, Annette; Estrella, Nicole; Graeser, Philipp

2010-05-01

As climate change may alter the frequency and intensity of extreme temperatures, we analysed whether warming of the last 5 decades has already changed the statistics of phenological extreme events. In this context, two extreme value statistical concepts are discussed and applied to existing phenological datasets of German Weather Service (DWD) in order to derive probabilities of occurrence for extreme early or late phenological events. We analyse four phenological groups; "begin of flowering, "leaf foliation", "fruit ripening" and "leaf colouring" as well as DWD indicator phases of the "phenological year". Additionally we put an emphasis on a between-species analysis; a comparison of differences in extreme onsets between three common northern conifers. Furthermore we conducted a within-species analysis with different phases of horse chestnut throughout a year. The first statistical approach fits data to a Gaussian model using traditional statistical techniques, and then analyses the extreme quantile. The key point of this approach is the adoption of an appropriate probability density function (PDF) to the observed data and the assessment of the PDF parameters change in time. The full analytical description in terms of the estimated PDF for defined time steps of the observation period allows probability assessments of extreme values for e.g. annual or decadal time steps. Related with this approach is the possibility of counting out the onsets which fall in our defined extreme percentiles. The estimation of the probability of extreme events on the basis of the whole data set is in contrast to analyses with the generalized extreme value distribution (GEV). The second approach deals with the extreme PDFs itself and fits the GEV distribution to annual minima of phenological series to provide useful estimates about return levels. For flowering and leaf unfolding phases exceptionally early extremes are seen since the mid 1980s and especially for the single years 1961, 1990 and 2007 whereas exceptionally extreme late events are seen in the year 1970. Summer phases such as fruit ripening exhibit stronger shifts to early extremes than spring phases. Leaf colouring phases reveal increasing probability for late extremes. The with GEV estimated 100-year event of Picea, Pinus and Larix amount to extreme early events of about -27, -31.48 and -32.79 days, respectively. If we assume non-stationary minimum data we get a more extreme 100-year event of about -35.40 for Picea but associated with wider confidence intervals. The GEV is simply another probability distribution but for purposes of extreme analysis in phenology it should be considered as equally important as (if not more important than) the Gaussian PDF approach.

The joint return period analysis of natural disasters based on monitoring and statistical modeling of multidimensional hazard factors.

PubMed

Liu, Xueqin; Li, Ning; Yuan, Shuai; Xu, Ning; Shi, Wenqin; Chen, Weibin

2015-12-15

As a random event, a natural disaster has the complex occurrence mechanism. The comprehensive analysis of multiple hazard factors is important in disaster risk assessment. In order to improve the accuracy of risk analysis and forecasting, the formation mechanism of a disaster should be considered in the analysis and calculation of multi-factors. Based on the consideration of the importance and deficiencies of multivariate analysis of dust storm disasters, 91 severe dust storm disasters in Inner Mongolia from 1990 to 2013 were selected as study cases in the paper. Main hazard factors from 500-hPa atmospheric circulation system, near-surface meteorological system, and underlying surface conditions were selected to simulate and calculate the multidimensional joint return periods. After comparing the simulation results with actual dust storm events in 54years, we found that the two-dimensional Frank Copula function showed the better fitting results at the lower tail of hazard factors and that three-dimensional Frank Copula function displayed the better fitting results at the middle and upper tails of hazard factors. However, for dust storm disasters with the short return period, three-dimensional joint return period simulation shows no obvious advantage. If the return period is longer than 10years, it shows significant advantages in extreme value fitting. Therefore, we suggest the multivariate analysis method may be adopted in forecasting and risk analysis of serious disasters with the longer return period, such as earthquake and tsunami. Furthermore, the exploration of this method laid the foundation for the prediction and warning of other nature disasters. Copyright © 2015 Elsevier B.V. All rights reserved.

Prognostic Value of External Beam Radiation Therapy in Patients Treated With Surgical Resection and Intraoperative Electron Beam Radiation Therapy for Locally Recurrent Soft Tissue Sarcoma: A Multicentric Long-Term Outcome Analysis

DOE Office of Scientific and Technical Information (OSTI.GOV)

Calvo, Felipe A.; School of Medicine, Complutense University, Madrid; Sole, Claudio V., E-mail: cvsole@uc.cl

Background: A joint analysis of data from centers involved in the Spanish Cooperative Initiative for Intraoperative Electron Radiotherapy was performed to investigate long-term outcomes of locally recurrent soft tissue sarcoma (LR-STS) patients treated with a multidisciplinary approach. Methods and Materials: Patients with a histologic diagnosis of LR-STS (extremity, 43%; trunk wall, 24%; retroperitoneum, 33%) and no distant metastases who underwent radical surgery and intraoperative electron radiation therapy (IOERT; median dose, 12.5 Gy) were considered eligible for participation in this study. In addition, 62% received external beam radiation therapy (EBRT; median dose, 50 Gy). Results: From 1986 to 2012, a totalmore » of 103 patients from 3 Spanish expert IOERT institutions were analyzed. With a median follow-up of 57 months (range, 2-311 months), 5-year local control (LC) was 60%. The 5-year IORT in-field control, disease-free survival (DFS), and overall survival were 73%, 43%, and 52%, respectively. In the multivariate analysis, no EBRT to treat the LR-STS (P=.02) and microscopically involved margin resection status (P=.04) retained significance in relation to LC. With regard to IORT in-field control, only not delivering EBRT to the LR-STS retained significance in the multivariate analysis (P=.03). Conclusion: This joint analysis revealed that surgical margin and EBRT affect LC but that, given the high risk of distant metastases, DFS remains modest. Intensified local treatment needs to be further tested in the context of more efficient concurrent, neoadjuvant, and adjuvant systemic therapy.« less

Extreme sea storm in the Mediterranean Sea. Trends during the 2nd half of the 20th century.

NASA Astrophysics Data System (ADS)

Pino, C.; Lionello, P.; Galati, M. B.

2009-04-01

Extreme sea storm in the Mediterranean Sea. Trends during the 2nd half of the 20th century Piero Lionello, University of Salento, piero.lionello@unisalento.it Maria Barbara Galati, University of Salento, mariabarbara.galati@unisalento.it Cosimo Pino, University of Salento, pino@le.infn.it The analysis of extreme Significant Wave Height (SWH) values and their trend is crucial for planning and managing coastal defences and off-shore activities. The analysis provided by this study covers a 44-year long period (1958-2001). First the WW3 (Wave Watch 3) model forced with the REMO-Hipocas regional model wind fields has been used for the hindcast of extreme SWH values over the Mediterranean basin with a 0.25 deg lat-lon resolution. Subsequently, the model results have been processed with an ad hoc software to detect storms. GEV analysis has been perfomed and a set of indicators for extreme SWH have been computed, using the Mann Kendall test for assessing statistical significance of trends for different parameter such as the number of extreme events, their duration and their intensity. Results suggest a transition towards weaker extremes and a milder climate over most of the Mediterranean Sea.

Research in Stochastic Processes

DTIC Science & Technology

1988-08-31

stationary sequence, Stochastic Proc. Appl. 29, 1988, 155-169 T. Hsing, J. Husler and M.R. Leadbetter, On the exceedance point process for a stationary...Nandagopalan, On exceedance point processes for "regular" sample functions, Proc. Volume, Oberxolfach Conf. on Extreme Value Theory, J. Husler and R. Reiss...exceedance point processes for stationary sequences under mild oscillation restrictions, Apr. 88. Obermotfach Conf. on Extremal Value Theory. Ed. J. HUsler

A direct method for computing extreme value (Gumbel) parameters for gapped biological sequence alignments.

PubMed

Quinn, Terrance; Sinkala, Zachariah

2014-01-01

We develop a general method for computing extreme value distribution (Gumbel, 1958) parameters for gapped alignments. Our approach uses mixture distribution theory to obtain associated BLOSUM matrices for gapped alignments, which in turn are used for determining significance of gapped alignment scores for pairs of biological sequences. We compare our results with parameters already obtained in the literature.

Ensemble-based evaluation of extreme water levels for the eastern Baltic Sea

NASA Astrophysics Data System (ADS)

Eelsalu, Maris; Soomere, Tarmo

2016-04-01

The risks and damages associated with coastal flooding that are naturally associated with an increase in the magnitude of extreme storm surges are one of the largest concerns of countries with extensive low-lying nearshore areas. The relevant risks are even more contrast for semi-enclosed water bodies such as the Baltic Sea where subtidal (weekly-scale) variations in the water volume of the sea substantially contribute to the water level and lead to large spreading of projections of future extreme water levels. We explore the options for using large ensembles of projections to more reliably evaluate return periods of extreme water levels. Single projections of the ensemble are constructed by means of fitting several sets of block maxima with various extreme value distributions. The ensemble is based on two simulated data sets produced in the Swedish Meteorological and Hydrological Institute. A hindcast by the Rossby Centre Ocean model is sampled with a resolution of 6 h and a similar hindcast by the circulation model NEMO with a resolution of 1 h. As the annual maxima of water levels in the Baltic Sea are not always uncorrelated, we employ maxima for calendar years and for stormy seasons. As the shape parameter of the Generalised Extreme Value distribution changes its sign and substantially varies in magnitude along the eastern coast of the Baltic Sea, the use of a single distribution for the entire coast is inappropriate. The ensemble involves projections based on the Generalised Extreme Value, Gumbel and Weibull distributions. The parameters of these distributions are evaluated using three different ways: maximum likelihood method and method of moments based on both biased and unbiased estimates. The total number of projections in the ensemble is 40. As some of the resulting estimates contain limited additional information, the members of pairs of projections that are highly correlated are assigned weights 0.6. A comparison of the ensemble-based projection of extreme water levels and their return periods with similar estimates derived from local observations reveals an interesting pattern of match and mismatch. The match is almost perfect in measurement sites where local effects (e.g., wave-induced set-up or local surge in very shallow areas that are not resolved by circulation models) do not contribute to the observed values of water level. There is, however, substantial mismatch between projected and observed extreme values for most of the Estonian coast. The mismatch is largest for sections that are open to high waves and for several bays that are deeply cut into mainland but open for predominant strong wind directions. Detailed quantification of this mismatch eventually makes it possible to develop substantially improved estimates of extreme water levels in sections where local effects considerably contribute into the total water level.

Extreme ultraviolet index due to broken clouds at a midlatitude site, Granada (southeastern Spain)

NASA Astrophysics Data System (ADS)

Antón, M.; Piedehierro, A. A.; Alados-Arboledas, L.; Wolfran, E.; Olmo, F. J.

2012-11-01

Cloud cover usually attenuates the ultraviolet (UV) solar radiation but, under certain sky conditions, the clouds may produce an enhancement effect increasing the UV levels at surface. The main objective of this paper is to analyze an extreme UV enhancement episode recorded on 16 June 2009 at Granada (southeastern Spain). This phenomenon was characterized by a quick and intense increase in surface UV radiation under broken cloud fields (5-7 oktas) in which the Sun was surrounded by cumulus clouds (confirmed with sky images). Thus, the UV index (UVI) showed an enhancement of a factor 4 in the course of only 30 min around midday, varying from 2.6 to 10.4 (higher than the corresponding clear-sky UVI value). Additionally, the UVI presented values higher than 10 (extreme erythemal risk) for about 20 min running, with a maximum value around 11.5. The use of an empirical model and the total ozone column (TOC) derived from the Global Ozone Monitoring Experiment (GOME) for the period 1995-2011 showed that the value of UVI ~ 11.5 is substantially larger than the highest index that could origin the natural TOC variations over Granada. Finally, the UV erythemal dose accumulated during the period of 20 min with the extreme UVI values under broken cloud fields was 350 J/m2 which surpass the energy required to produce sunburn of the most human skin types.

Isokinetic profile of elbow flexion and extension strength in elite junior tennis players.

PubMed

Ellenbecker, Todd S; Roetert, E Paul

2003-02-01

Descriptive study. To determine whether bilateral differences exist in concentric elbow flexion and extension strength in elite junior tennis players. The repetitive nature of tennis frequently produces upper extremity overuse injuries. Prior research has identified tennis-specific strength adaptation in the dominant shoulder and distal upper extremity musculature of elite players. No previous study has addressed elbow flexion and extension strength. Thirty-eight elite junior tennis players were bilaterally tested for concentric elbow flexion and extension muscle performance on a Cybex 6000 isokinetic dynamometer at 90 degrees/s, 210 degrees/s, and 300 degrees/s. Repeated-measures ANOVAs were used to test for differences between extremities, muscle groups, and speed. Significantly greater (P<0.002) dominant-arm elbow extension peak torque values were measured at 90 degrees/s, 210 degrees/s, and 300 degrees/s for males. Significantly greater (P<0.002) dominant-arm single-repetition work values were also measured at 90 degrees/s and 210 degrees/s for males. No significant difference was measured between extremities in elbow flexion muscular performance in males and for elbow flexion or extension peak torque and single-repetition work values in females. No significant difference between extremities was measured in elbow flexion/extension strength ratios in females and significant differences between extremities in this ratio were only present at 210 degrees/s in males (P<0.002). These data indicate muscular adaptations around the dominant elbow in male elite junior tennis players but not females. These data have ramifications for clinicians rehabilitating upper extremity injuries in patients from this population.

Climatic extremes improve predictions of spatial patterns of tree species

USGS Publications Warehouse

Zimmermann, N.E.; Yoccoz, N.G.; Edwards, T.C.; Meier, E.S.; Thuiller, W.; Guisan, Antoine; Schmatz, D.R.; Pearman, P.B.

2009-01-01

Understanding niche evolution, dynamics, and the response of species to climate change requires knowledge of the determinants of the environmental niche and species range limits. Mean values of climatic variables are often used in such analyses. In contrast, the increasing frequency of climate extremes suggests the importance of understanding their additional influence on range limits. Here, we assess how measures representing climate extremes (i.e., interannual variability in climate parameters) explain and predict spatial patterns of 11 tree species in Switzerland. We find clear, although comparably small, improvement (+20% in adjusted D2, +8% and +3% in cross-validated True Skill Statistic and area under the receiver operating characteristics curve values) in models that use measures of extremes in addition to means. The primary effect of including information on climate extremes is a correction of local overprediction and underprediction. Our results demonstrate that measures of climate extremes are important for understanding the climatic limits of tree species and assessing species niche characteristics. The inclusion of climate variability likely will improve models of species range limits under future conditions, where changes in mean climate and increased variability are expected.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ghosh, Subimal; Das, Debasish; Kao, Shih-Chieh

Recent studies disagree on how rainfall extremes over India have changed in space and time over the past half century, as well as on whether the changes observed are due to global warming or regional urbanization. Although a uniform and consistent decrease in moderate rainfall has been reported, a lack of agreement about trends in heavy rainfall may be due in part to differences in the characterization and spatial averaging of extremes. Here we use extreme value theory to examine trends in Indian rainfall over the past half century in the context of long-term, low-frequency variability.We show that when generalizedmore » extreme value theory is applied to annual maximum rainfall over India, no statistically significant spatially uniform trends are observed, in agreement with previous studies using different approaches. Furthermore, our space time regression analysis of the return levels points to increasing spatial variability of rainfall extremes over India. Our findings highlight the need for systematic examination of global versus regional drivers of trends in Indian rainfall extremes, and may help to inform flood hazard preparedness and water resource management in the region.« less

Sequences of extremal radially excited rotating black holes.

PubMed

Blázquez-Salcedo, Jose Luis; Kunz, Jutta; Navarro-Lérida, Francisco; Radu, Eugen

2014-01-10

In the Einstein-Maxwell-Chern-Simons theory the extremal Reissner-Nordström solution is no longer the single extremal solution with vanishing angular momentum, when the Chern-Simons coupling constant reaches a critical value. Instead a whole sequence of rotating extremal J=0 solutions arises, labeled by the node number of the magnetic U(1) potential. Associated with the same near horizon solution, the mass of these radially excited extremal solutions converges to the mass of the extremal Reissner-Nordström solution. On the other hand, not all near horizon solutions are also realized as global solutions.

Suitability of the isolated chicken eye test for classification of extreme pH detergents and cleaning products.

PubMed

Cazelle, Elodie; Eskes, Chantra; Hermann, Martina; Jones, Penny; McNamee, Pauline; Prinsen, Menk; Taylor, Hannah; Wijnands, Marcel V W

2015-04-01

A.I.S.E. investigated the suitability of the regulatory adopted ICE in vitro test method (OECD TG 438) with or without histopathology to identify detergent and cleaning formulations having extreme pH that require classification as EU CLP/UN GHS Category 1. To this aim, 18 extreme pH detergent and cleaning formulations were tested covering both alkaline and acidic extreme pHs. The ICE standard test method following OECD Test Guideline 438 showed good concordance with in vivo classification (83%) and good and balanced specificity and sensitivity values (83%) which are in line with the performances of currently adopted in vitro test guidelines, confirming its suitability to identify Category 1 extreme pH detergent and cleaning products. In contrast to previous findings obtained with non-extreme pH formulations, the use of histopathology did not improve the sensitivity of the assay whilst it strongly decreased its specificity for the extreme pH formulations. Furthermore, use of non-testing prediction rules for classification showed poor concordance values (33% for the extreme pH rule and 61% for the EU CLP additivity approach) with high rates of over-prediction (100% for the extreme pH rule and 50% for the additivity approach), indicating that these non-testing prediction rules are not suitable to predict Category 1 hazards of extreme pH detergent and cleaning formulations. Copyright © 2015 Elsevier Ltd. All rights reserved.

Long-term statistics of extreme tsunami height at Crescent City

NASA Astrophysics Data System (ADS)

Dong, Sheng; Zhai, Jinjin; Tao, Shanshan

2017-06-01

Historically, Crescent City is one of the most vulnerable communities impacted by tsunamis along the west coast of the United States, largely attributed to its offshore geography. Trans-ocean tsunamis usually produce large wave runup at Crescent Harbor resulting in catastrophic damages, property loss and human death. How to determine the return values of tsunami height using relatively short-term observation data is of great significance to assess the tsunami hazards and improve engineering design along the coast of Crescent City. In the present study, the extreme tsunami heights observed along the coast of Crescent City from 1938 to 2015 are fitted using six different probabilistic distributions, namely, the Gumbel distribution, the Weibull distribution, the maximum entropy distribution, the lognormal distribution, the generalized extreme value distribution and the generalized Pareto distribution. The maximum likelihood method is applied to estimate the parameters of all above distributions. Both Kolmogorov-Smirnov test and root mean square error method are utilized for goodness-of-fit test and the better fitting distribution is selected. Assuming that the occurrence frequency of tsunami in each year follows the Poisson distribution, the Poisson compound extreme value distribution can be used to fit the annual maximum tsunami amplitude, and then the point and interval estimations of return tsunami heights are calculated for structural design. The results show that the Poisson compound extreme value distribution fits tsunami heights very well and is suitable to determine the return tsunami heights for coastal disaster prevention.

Extreme geomagnetically induced currents

NASA Astrophysics Data System (ADS)

Kataoka, Ryuho; Ngwira, Chigomezyo

2016-12-01

We propose an emergency alert framework for geomagnetically induced currents (GICs), based on the empirically extreme values and theoretical upper limits of the solar wind parameters and of d B/d t, the time derivative of magnetic field variations at ground. We expect this framework to be useful for preparing against extreme events. Our analysis is based on a review of various papers, including those presented during Extreme Space Weather Workshops held in Japan in 2011, 2012, 2013, and 2014. Large-amplitude d B/d t values are the major cause of hazards associated with three different types of GICs: (1) slow d B/d t with ring current evolution (RC-type), (2) fast d B/d t associated with auroral electrojet activity (AE-type), and (3) transient d B/d t of sudden commencements (SC-type). We set "caution," "warning," and "emergency" alert levels during the main phase of superstorms with the peak Dst index of less than -300 nT (once per 10 years), -600 nT (once per 60 years), or -900 nT (once per 100 years), respectively. The extreme d B/d t values of the AE-type GICs are 2000, 4000, and 6000 nT/min at caution, warning, and emergency levels, respectively. For the SC-type GICs, a "transient alert" is also proposed for d B/d t values of 40 nT/s at low latitudes and 110 nT/s at high latitudes, especially when the solar energetic particle flux is unusually high.

Improving power and robustness for detecting genetic association with extreme-value sampling design.

PubMed

Chen, Hua Yun; Li, Mingyao

2011-12-01

Extreme-value sampling design that samples subjects with extremely large or small quantitative trait values is commonly used in genetic association studies. Samples in such designs are often treated as "cases" and "controls" and analyzed using logistic regression. Such a case-control analysis ignores the potential dose-response relationship between the quantitative trait and the underlying trait locus and thus may lead to loss of power in detecting genetic association. An alternative approach to analyzing such data is to model the dose-response relationship by a linear regression model. However, parameter estimation from this model can be biased, which may lead to inflated type I errors. We propose a robust and efficient approach that takes into consideration of both the biased sampling design and the potential dose-response relationship. Extensive simulations demonstrate that the proposed method is more powerful than the traditional logistic regression analysis and is more robust than the linear regression analysis. We applied our method to the analysis of a candidate gene association study on high-density lipoprotein cholesterol (HDL-C) which includes study subjects with extremely high or low HDL-C levels. Using our method, we identified several SNPs showing a stronger evidence of association with HDL-C than the traditional case-control logistic regression analysis. Our results suggest that it is important to appropriately model the quantitative traits and to adjust for the biased sampling when dose-response relationship exists in extreme-value sampling designs. © 2011 Wiley Periodicals, Inc.

Stability of Teacher Value-Added Rankings across Measurement Model and Scaling Conditions

ERIC Educational Resources Information Center

Hawley, Leslie R.; Bovaird, James A.; Wu, ChaoRong

2017-01-01

Value-added assessment methods have been criticized by researchers and policy makers for a number of reasons. One issue includes the sensitivity of model results across different outcome measures. This study examined the utility of incorporating multivariate latent variable approaches within a traditional value-added framework. We evaluated the…

An Exploration of Adult Career Interests and Work Values in Taiwan

ERIC Educational Resources Information Center

Tien, Hsiu-Lan Shelley

2011-01-01

The purpose of the study was to investigate the relationship between vocational interests and work values among 206 adults in Taiwan. The instruments were the Career Interest Inventory developed based on Holland's RIASEC typology and the Work Value Inventory developed based on Super's theory. The results of multivariate analysis of variance…

Total ozone patterns over the southern mid-latitudes: spatial correlations, extreme events and dynamical contributions

NASA Astrophysics Data System (ADS)

Rieder, Harald E.; di Rocco, Stefania; Staehelin, Johannes; Maeder, Jörg A.; Ribatet, Mathieu; Peter, Thomas; Davison, Anthony C.

2010-05-01

Tools from geostatistics and extreme value theory are applied to analyze spatial correlations in total ozone for the southern mid-latitudes. The dataset used in this study is the NIWA-assimilated total ozone dataset (Bodeker et al., 2001; Müller et al., 2008). Recently new tools from extreme value theory (Coles, 2001; Ribatet, 2007) have been applied to the world's longest total ozone record from Arosa, Switzerland (e.g. Staehelin 1998a,b) and 5 other long-term ground based stations to describe extreme events in low and high total ozone (Rieder et al., 2010a,b,c). Excursions in the frequency of extreme events reveal "fingerprints" of dynamical factors such as ENSO or NAO, and chemical factors, such as cold Arctic vortex ozone losses, as well as major volcanic eruptions of the 20th century (e.g. Gunung Agung, El Chichón, Mt. Pinatubo). Furthermore, atmospheric loading in ozone depleting substances lead to a continuous modification of column ozone in the northern hemisphere also with respect to extreme values (partly again in connection with polar vortex contributions). It is shown that application of extreme value theory allows the identification of many more of such fingerprints than conventional time series analysis on basis of annual and seasonal mean values. Especially, the analysis shows the strong influence of dynamics, revealing that even moderate ENSO and NAO events have a discernible effect on total ozone (Rieder et al., 2010b,c). Within the current study patterns in spatial correlation and frequency distributions of extreme events (e.g. ELOs and EHOs) are studied for the southern mid-latitudes. It is analyzed if "fingerprints"found for features in the northern hemisphere occur also in the southern mid-latitudes. New insights in spatial patterns of total ozone for the southern mid-latitudes are presented. Within this study the influence of changes in atmospheric dynamics (e.g. tropospheric and lower stratospheric pressure systems, ENSO) as well as influence of major volcanic eruptions (e.g. Mt. Pinatubo) and ozone depleting substances (ODS) on column ozone over the southern mid-latitudes is analyzed for the time period 1979-2007. References: Bodeker, G.E., J.C. Scott, K. Kreher, and R.L. McKenzie, Global ozone trends in potential vorticity coordinates using TOMS and GOME intercompared against the Dobson network: 1978-1998, J. Geophys. Res., 106 (D19), 23029-23042, 2001. Coles, S.: An Introduction to Statistical Modeling of Extreme Values, Springer Series in Statistics, ISBN:1852334592, Springer, Berlin, 2001. Müller, R., Grooß, J.-U., Lemmen, C., Heinze, D., Dameris, M., and Bodeker, G.: Simple measures of ozone depletion in the polar stratosphere, Atmos. Chem. Phys., 8, 251-264, 2008. Ribatet, M.: POT: Modelling peaks over a threshold, R News, 7, 34-36, 2007. Rieder ,H.E., Staehelin, J., Maeder, J.A., Ribatet, M., Stübi, R., Weihs, P., Holawe, F., Peter, T., and A.D., Davison (2010): Extreme events in total ozone over Arosa - Part I: Application of extreme value theory, to be submitted to ACPD. Rieder, H.E., Staehelin, J., Maeder, J.A., Ribatet, M., Stübi, R., Weihs, P., Holawe, F., Peter, T., and A.D., Davison (2010): Extreme events in total ozone over Arosa - Part II: Fingerprints of atmospheric dynamics and chemistry and effects on mean values and long-term changes, to be submitted to ACPD. Rieder, H.E., Jancso, L.M., Staehelin, J., Maeder, J.A., Ribatet, Peter, T., and A.D., Davison (2010): Extreme events in total ozone over the northern mid-latitudes: A case study based on long-term data sets from 5 ground-based stations, in preparation. Staehelin, J., Kegel, R., and Harris, N. R.: Trend analysis of the homogenized total ozone series of Arosa (Switzerland), 1929-1996, J. Geophys. Res., 103(D7), 8389-8400, doi:10.1029/97JD03650, 1998a. Staehelin, J., Renaud, A., Bader, J., McPeters, R., Viatte, P., Hoegger, B., Bugnion, V., Giroud, M., and Schill, H.: Total ozone series at Arosa (Switzerland): Homogenization and data comparison, J. Geophys. Res., 103(D5), 5827-5842, doi:10.1029/97JD02402, 1998b.

Decadal oscillations and extreme value distribution of river peak flows in the Meuse catchment

NASA Astrophysics Data System (ADS)

De Niel, Jan; Willems, Patrick

2017-04-01

In flood risk management, flood probabilities are often quantified through Generalized Pareto distributions of river peak flows. One of the main underlying assumptions is that all data points need to originate from one single underlying distribution (i.i.d. assumption). However, this hypothesis, although generally assumed to be correct for variables such as river peak flows, remains somehow questionable: flooding might indeed be caused by different hydrological and/or meteorological conditions. This study confirms these findings from previous research by showing a clear indication of the link between atmospheric conditions and flooding for the Meuse river in The Netherlands: decadal oscillations of river peak flows can (at least partially) be attributed to the occurrence of westerly weather types. The study further proposes a method to take this correlation between atmospheric conditions and river peak flows into account when calibrating an extreme value distribution for river peak flows. Rather than calibrating one single distribution to the data and potentially violating the i.i.d. assumption, weather type depending extreme value distributions are derived and composed. The study shows that, for the Meuse river in The Netherlands, such approach results in a more accurate extreme value distribution, especially with regards to extrapolations. Comparison of the proposed method with a traditional extreme value analysis approach and an alternative model-based approach for the same case study shows strong differences in the peak flow extrapolation. The design-flood for a 1,250 year return period is estimated at 4,800 m3s-1 for the proposed method, compared with 3,450 m3s-1 and 3,900 m3s-1 for the traditional method and a previous study. The methods were validated based on instrumental and documentary flood information of the past 500 years.

I know why you voted for Trump: (Over)inferring motives based on choice.

PubMed

Barasz, Kate; Kim, Tami; Evangelidis, Ioannis

2018-05-10

People often speculate about why others make the choices they do. This paper investigates how such inferences are formed as a function of what is chosen. Specifically, when observers encounter someone else's choice (e.g., of political candidate), they use the chosen option's attribute values (e.g., a candidate's specific stance on a policy issue) to infer the importance of that attribute (e.g., the policy issue) to the decision-maker. Consequently, when a chosen option has an attribute whose value is extreme (e.g., an extreme policy stance), observers infer-sometimes incorrectly-that this attribute disproportionately motivated the decision-maker's choice. Seven studies demonstrate how observers use an attribute's value to infer its weight-the value-weight heuristic-and identify the role of perceived diagnosticity: more extreme attribute values give observers the subjective sense that they know more about a decision-maker's preferences, and in turn, increase the attribute's perceived importance. The paper explores how this heuristic can produce erroneous inferences and influence broader beliefs about decision-makers. Copyright © 2018 Elsevier B.V. All rights reserved.

Epidemiologic methods in clinical trials.

PubMed

Rothman, K J

1977-04-01

Epidemiologic methods developed to control confounding in non-experimental studies are equally applicable for experiments. In experiments, most confounding is usually controlled by random allocation of subjects to treatment groups, but randomization does not preclude confounding except for extremely large studies, the degree of confounding expected being inversely related to the size of the treatment groups. In experiments, as in non-experimental studies, the extent of confounding for each risk indicator should be assessed, and if sufficiently large, controlled. Confounding is properly assessed by comparing the unconfounded effect estimate to the crude effect estimate; a common error is to assess confounding by statistical tests of significance. Assessment of confounding involves its control as a prerequisite. Control is most readily and cogently achieved by stratification of the data, though with many factors to control simultaneously, multivariate analysis or a combination of multivariate analysis and stratification might be necessary.

Bayesian multivariate Poisson abundance models for T-cell receptor data.

PubMed

Greene, Joshua; Birtwistle, Marc R; Ignatowicz, Leszek; Rempala, Grzegorz A

2013-06-07

A major feature of an adaptive immune system is its ability to generate B- and T-cell clones capable of recognizing and neutralizing specific antigens. These clones recognize antigens with the help of the surface molecules, called antigen receptors, acquired individually during the clonal development process. In order to ensure a response to a broad range of antigens, the number of different receptor molecules is extremely large, resulting in a huge clonal diversity of both B- and T-cell receptor populations and making their experimental comparisons statistically challenging. To facilitate such comparisons, we propose a flexible parametric model of multivariate count data and illustrate its use in a simultaneous analysis of multiple antigen receptor populations derived from mammalian T-cells. The model relies on a representation of the observed receptor counts as a multivariate Poisson abundance mixture (m PAM). A Bayesian parameter fitting procedure is proposed, based on the complete posterior likelihood, rather than the conditional one used typically in similar settings. The new procedure is shown to be considerably more efficient than its conditional counterpart (as measured by the Fisher information) in the regions of m PAM parameter space relevant to model T-cell data. Copyright © 2013 Elsevier Ltd. All rights reserved.

Systematic wavelength selection for improved multivariate spectral analysis

DOEpatents

Thomas, Edward V.; Robinson, Mark R.; Haaland, David M.

1995-01-01

Methods and apparatus for determining in a biological material one or more unknown values of at least one known characteristic (e.g. the concentration of an analyte such as glucose in blood or the concentration of one or more blood gas parameters) with a model based on a set of samples with known values of the known characteristics and a multivariate algorithm using several wavelength subsets. The method includes selecting multiple wavelength subsets, from the electromagnetic spectral region appropriate for determining the known characteristic, for use by an algorithm wherein the selection of wavelength subsets improves the model's fitness of the determination for the unknown values of the known characteristic. The selection process utilizes multivariate search methods that select both predictive and synergistic wavelengths within the range of wavelengths utilized. The fitness of the wavelength subsets is determined by the fitness function F=.function.(cost, performance). The method includes the steps of: (1) using one or more applications of a genetic algorithm to produce one or more count spectra, with multiple count spectra then combined to produce a combined count spectrum; (2) smoothing the count spectrum; (3) selecting a threshold count from a count spectrum to select these wavelength subsets which optimize the fitness function; and (4) eliminating a portion of the selected wavelength subsets. The determination of the unknown values can be made: (1) noninvasively and in vivo; (2) invasively and in vivo; or (3) in vitro.

A New Time-varying Concept of Risk in a Changing Climate.

PubMed

Sarhadi, Ali; Ausín, María Concepción; Wiper, Michael P

2016-10-20

In a changing climate arising from anthropogenic global warming, the nature of extreme climatic events is changing over time. Existing analytical stationary-based risk methods, however, assume multi-dimensional extreme climate phenomena will not significantly vary over time. To strengthen the reliability of infrastructure designs and the management of water systems in the changing environment, multidimensional stationary risk studies should be replaced with a new adaptive perspective. The results of a comparison indicate that current multi-dimensional stationary risk frameworks are no longer applicable to projecting the changing behaviour of multi-dimensional extreme climate processes. Using static stationary-based multivariate risk methods may lead to undesirable consequences in designing water system infrastructures. The static stationary concept should be replaced with a flexible multi-dimensional time-varying risk framework. The present study introduces a new multi-dimensional time-varying risk concept to be incorporated in updating infrastructure design strategies under changing environments arising from human-induced climate change. The proposed generalized time-varying risk concept can be applied for all stochastic multi-dimensional systems that are under the influence of changing environments.

Multi-hazard Assessment and Scenario Toolbox (MhAST): A Framework for Analyzing Compounding Effects of Multiple Hazards

NASA Astrophysics Data System (ADS)

Sadegh, M.; Moftakhari, H.; AghaKouchak, A.

2017-12-01

Many natural hazards are driven by multiple forcing variables, and concurrence/consecutive extreme events significantly increases risk of infrastructure/system failure. It is a common practice to use univariate analysis based upon a perceived ruling driver to estimate design quantiles and/or return periods of extreme events. A multivariate analysis, however, permits modeling simultaneous occurrence of multiple forcing variables. In this presentation, we introduce the Multi-hazard Assessment and Scenario Toolbox (MhAST) that comprehensively analyzes marginal and joint probability distributions of natural hazards. MhAST also offers a wide range of scenarios of return period and design levels and their likelihoods. Contribution of this study is four-fold: 1. comprehensive analysis of marginal and joint probability of multiple drivers through 17 continuous distributions and 26 copulas, 2. multiple scenario analysis of concurrent extremes based upon the most likely joint occurrence, one ruling variable, and weighted random sampling of joint occurrences with similar exceedance probabilities, 3. weighted average scenario analysis based on a expected event, and 4. uncertainty analysis of the most likely joint occurrence scenario using a Bayesian framework.

Spatiotemporal variation in heat-related out-of-hospital cardiac arrest during the summer in Japan.

PubMed

Onozuka, Daisuke; Hagihara, Akihito

2017-04-01

Although several studies have reported the impacts of extremely high temperature on cardiovascular diseases, few studies have investigated the spatiotemporal variation in the incidence of out-of-hospital cardiac arrest (OHCA) due to extremely high temperature in Japan. Daily OHCA data from 2005 to 2014 were acquired from all 47 prefectures of Japan. We used time-series Poisson regression analysis combined with a distributed lag non-linear model to assess the temporal variability in the effects of extremely high temperature on OHCA incidence in each prefecture, adjusted for time trends. Spatial variability in the relationships between extremely high temperature and OHCA between prefectures was estimated using a multivariate random-effects meta-analysis. We analyzed 166,496 OHCA cases of presumed cardiac origin occurring during the summer (June to September) that met the inclusion criteria. The minimum morbidity percentile (MMP) was the 51st percentile of temperature during the summer in Japan. The overall cumulative relative risk at the 99th percentile vs. the MMP over lags 0-10days was 1.21 (95% CI: 1.12-1.31). There was also a strong low temperature effect during the summer periods. No substantial difference in spatial or temporal variability was observed over the study period. Our study demonstrated spatiotemporal homogeneity in the risk of OHCA during periods of extremely high temperature between 2005 and 2014 in Japan. Our findings suggest that public health strategies for OHCA due to extremely high temperatures should be finely adjusted and should particularly account for the unchanging risk during the summer. Copyright © 2017 Elsevier B.V. All rights reserved.

Ergonomic stressors and upper extremity disorders in vehicle manufacturing: cross sectional exposure-response trends

PubMed Central

Punnett, L.

1998-01-01

OBJECTIVE: To evaluate the association between upper extremity soft tissue disorders and exposure to preventable ergonomic stressors in vehicle manufacturing operations. METHODS: A cross sectional study was conducted in one vehicle stamping plant and one engine assembly plant. A standardised physical examination of the upper extremities was performed on all subjects. An interviewer administered questionnaire obtained data on demographics, work history, musculoskeletal symptoms, non-occupational covariates, and psycho-physical (relative intensity) ratings of ergonomic stressors. The primary exposure score was computed by summing the responses to the psychophysical exposure items. Multivariate regression analysis was used to model the prevalence of disorders of the shoulders or upper arms, wrists or hands, and all upper extremity regions (each defined both by symptoms and by physical examination plus symptoms) as a function of exposure quartile. RESULTS: A total of 1315 workers (85% of the target population) was examined. The prevalence of symptom disorders was 22% for the wrists or hands and 15% for the shoulders or upper arms; cases defined on the basis of a physical examination were about 80% as frequent. Disorders of the upper extremities, shoulders, and wrists or hands all increased markedly with exposure score, after adjustment for plant, acute injury, sex, body mass index, systemic disease, and seniority. CONCLUSIONS: Musculoskeletal disorders of the upper extremities were strongly associated with exposure to combined ergonomic stressors. The exposure- response trend was very similar for symptom cases and for physical examination cases. It is important to evaluate all dimensions of ergonomic exposure in epidemiological studies, as exposures often occur in combination in actual workplaces.   PMID:9764102

Isotopic evidence for continental ice sheet in mid-latitude region in the supergreenhouse Early Cretaceous

PubMed Central

Yang, Wu-Bin; Niu, He-Cai; Sun, Wei-Dong; Shan, Qiang; Zheng, Yong-Fei; Li, Ning-Bo; Li, Cong-Ying; Arndt, Nicholas T.; Xu, Xing; Jiang, Yu-Hang; Yu, Xue-Yuan

2013-01-01

Cretaceous represents one of the hottest greenhouse periods in the Earth's history, but some recent studies suggest that small ice caps might be present in non-polar regions during certain periods in the Early Cretaceous. Here we report extremely negative δ18O values of −18.12‰ to −13.19‰ for early Aptian hydrothermal zircon from an A-type granite at Baerzhe in northeastern China. Given that A-type granite is anhydrous and that magmatic zircon of the Baerzhe granite has δ18O value close to mantle values, the extremely negative δ18O values for hydrothermal zircon are attributed to addition of meteoric water with extremely low δ18O, mostly likely transported by glaciers. Considering the paleoaltitude of the region, continental glaciation is suggested to occur in the early Aptian, indicating much larger temperature fluctuations than previously thought during the supergreenhouse Cretaceous. This may have impact on the evolution of major organism in the Jehol Group during this period. PMID:24061068

Inter-model variability in hydrological extremes projections for Amazonian sub-basins

NASA Astrophysics Data System (ADS)

Andres Rodriguez, Daniel; Garofolo, Lucas; Lázaro de Siqueira Júnior, José; Samprogna Mohor, Guilherme; Tomasella, Javier

2014-05-01

Irreducible uncertainties due to knowledge's limitations, chaotic nature of climate system and human decision-making process drive uncertainties in Climate Change projections. Such uncertainties affect the impact studies, mainly when associated to extreme events, and difficult the decision-making process aimed at mitigation and adaptation. However, these uncertainties allow the possibility to develop exploratory analyses on system's vulnerability to different sceneries. The use of different climate model's projections allows to aboard uncertainties issues allowing the use of multiple runs to explore a wide range of potential impacts and its implications for potential vulnerabilities. Statistical approaches for analyses of extreme values are usually based on stationarity assumptions. However, nonstationarity is relevant at the time scales considered for extreme value analyses and could have great implications in dynamic complex systems, mainly under climate change transformations. Because this, it is required to consider the nonstationarity in the statistical distribution parameters. We carried out a study of the dispersion in hydrological extremes projections using climate change projections from several climate models to feed the Distributed Hydrological Model of the National Institute for Spatial Research, MHD-INPE, applied in Amazonian sub-basins. This model is a large-scale hydrological model that uses a TopModel approach to solve runoff generation processes at the grid-cell scale. MHD-INPE model was calibrated for 1970-1990 using observed meteorological data and comparing observed and simulated discharges by using several performance coeficients. Hydrological Model integrations were performed for present historical time (1970-1990) and for future period (2010-2100). Because climate models simulate the variability of the climate system in statistical terms rather than reproduce the historical behavior of climate variables, the performances of the model's runs during the historical period, when feed with climate model data, were tested using descriptors of the Flow Duration Curves. The analyses of projected extreme values were carried out considering the nonstationarity of the GEV distribution parameters and compared with extremes events in present time. Results show inter-model variability in a broad dispersion on projected extreme's values. Such dispersion implies different degrees of socio-economic impacts associated to extreme hydrological events. Despite the no existence of one optimum result, this variability allows the analyses of adaptation strategies and its potential vulnerabilities.

A new framework for estimating return levels using regional frequency analysis

NASA Astrophysics Data System (ADS)

Winter, Hugo; Bernardara, Pietro; Clegg, Georgina

2017-04-01

We propose a new framework for incorporating more spatial and temporal information into the estimation of extreme return levels. Currently, most studies use extreme value models applied to data from a single site; an approach which is inefficient statistically and leads to return level estimates that are less physically realistic. We aim to highlight the benefits that could be obtained by using methodology based upon regional frequency analysis as opposed to classic single site extreme value analysis. This motivates a shift in thinking, which permits the evaluation of local and regional effects and makes use of the wide variety of data that are now available on high temporal and spatial resolutions. The recent winter storms over the UK during the winters of 2013-14 and 2015-16, which have caused wide-ranging disruption and damaged important infrastructure, provide the main motivation for the current work. One of the most impactful natural hazards is flooding, which is often initiated by extreme precipitation. In this presentation, we focus on extreme rainfall, but shall discuss other meteorological variables alongside potentially damaging hazard combinations. To understand the risks posed by extreme precipitation, we need reliable statistical models which can be used to estimate quantities such as the T-year return level, i.e. the level which is expected to be exceeded once every T-years. Extreme value theory provides the main collection of statistical models that can be used to estimate the risks posed by extreme precipitation events. Broadly, at a single site, a statistical model is fitted to exceedances of a high threshold and the model is used to extrapolate to levels beyond the range of the observed data. However, when we have data at many sites over a spatial domain, fitting a separate model for each separate site makes little sense and it would be better if we could incorporate all this information to improve the reliability of return level estimates. Here, we use the regional frequency analysis approach to define homogeneous regions which are affected by the same storms. Extreme value models are then fitted to the data pooled from across a region. We find that this approach leads to more spatially consistent return level estimates with reduced uncertainty bounds.

Research in Stochastic Processes

DTIC Science & Technology

1988-10-10

To appear in Proceedings Volume, Oberwolfach Conf. on Extremal Value Theory, Ed. J. HUsler and R. Reiss, Springer. 4. M.R. Leadbetter. The exceedance...Hsing, J. Husler and M.R. Leadbetter, On the exceedance point process for a stationary sequence, Probability Theor. Rel. Fields, 20, 1988, 97-112 Z.J...Oberwotfach Conf. on Extreme Value Theory. J. Husler and R. Reiss. eds.. Springer. to appear V. Mandrekar, On a limit theorem and invariance

Microstructure-Sensitive Extreme Value Probabilities for High Cycle Fatigue of Ni-Base Superalloy IN100 (Preprint)

DTIC Science & Technology

2009-03-01

transition fatigue regimes; however, microplasticity (i.e., heterogeneous plasticity at the scale of microstructure) is relevant to understanding fatigue...and Socie [57] considered the affect of microplastic 14 Microstructure-Sensitive Extreme Value Probabilities for High Cycle Fatigue of Ni-Base...considers the local stress state as affected by intergranular interactions and microplasticity . For the calculations given below, the volumes over which

Modeling annual extreme temperature using generalized extreme value distribution: A case study in Malaysia

NASA Astrophysics Data System (ADS)

Hasan, Husna; Salam, Norfatin; Kassim, Suraiya

2013-04-01

Extreme temperature of several stations in Malaysia is modeled by fitting the annual maximum to the Generalized Extreme Value (GEV) distribution. The Augmented Dickey Fuller (ADF) and Phillips Perron (PP) tests are used to detect stochastic trends among the stations. The Mann-Kendall (MK) test suggests a non-stationary model. Three models are considered for stations with trend and the Likelihood Ratio test is used to determine the best-fitting model. The results show that Subang and Bayan Lepas stations favour a model which is linear for the location parameters while Kota Kinabalu and Sibu stations are suitable with a model in the logarithm of the scale parameters. The return level is the level of events (maximum temperature) which is expected to be exceeded once, on average, in a given number of years, is obtained.

Spatial extreme value analysis to project extremes of large-scale indicators for severe weather

PubMed Central

Gilleland, Eric; Brown, Barbara G; Ammann, Caspar M

2013-01-01

Concurrently high values of the maximum potential wind speed of updrafts (Wmax) and 0–6 km wind shear (Shear) have been found to represent conducive environments for severe weather, which subsequently provides a way to study severe weather in future climates. Here, we employ a model for the product of these variables (WmSh) from the National Center for Atmospheric Research/United States National Center for Environmental Prediction reanalysis over North America conditioned on their having extreme energy in the spatial field in order to project the predominant spatial patterns of WmSh. The approach is based on the Heffernan and Tawn conditional extreme value model. Results suggest that this technique estimates the spatial behavior of WmSh well, which allows for exploring possible changes in the patterns over time. While the model enables a method for inferring the uncertainty in the patterns, such analysis is difficult with the currently available inference approach. A variation of the method is also explored to investigate how this type of model might be used to qualitatively understand how the spatial patterns of WmSh correspond to extreme river flow events. A case study for river flows from three rivers in northwestern Tennessee is studied, and it is found that advection of WmSh from the Gulf of Mexico prevails while elsewhere, WmSh is generally very low during such extreme events. © 2013 The Authors. Environmetrics published by JohnWiley & Sons, Ltd. PMID:24223482

Selection Indices and Multivariate Analysis Show Similar Results in the Evaluation of Growth and Carcass Traits in Beef Cattle

PubMed Central

Brito Lopes, Fernando; da Silva, Marcelo Corrêa; Magnabosco, Cláudio Ulhôa; Goncalves Narciso, Marcelo; Sainz, Roberto Daniel

2016-01-01

This research evaluated a multivariate approach as an alternative tool for the purpose of selection regarding expected progeny differences (EPDs). Data were fitted using a multi-trait model and consisted of growth traits (birth weight and weights at 120, 210, 365 and 450 days of age) and carcass traits (longissimus muscle area (LMA), back-fat thickness (BF), and rump fat thickness (RF)), registered over 21 years in extensive breeding systems of Polled Nellore cattle in Brazil. Multivariate analyses were performed using standardized (zero mean and unit variance) EPDs. The k mean method revealed that the best fit of data occurred using three clusters (k = 3) (P < 0.001). Estimates of genetic correlation among growth and carcass traits and the estimates of heritability were moderate to high, suggesting that a correlated response approach is suitable for practical decision making. Estimates of correlation between selection indices and the multivariate index (LD1) were moderate to high, ranging from 0.48 to 0.97. This reveals that both types of indices give similar results and that the multivariate approach is reliable for the purpose of selection. The alternative tool seems very handy when economic weights are not available or in cases where more rapid identification of the best animals is desired. Interestingly, multivariate analysis allowed forecasting information based on the relationships among breeding values (EPDs). Also, it enabled fine discrimination, rapid data summarization after genetic evaluation, and permitted accounting for maternal ability and the genetic direct potential of the animals. In addition, we recommend the use of longissimus muscle area and subcutaneous fat thickness as selection criteria, to allow estimation of breeding values before the first mating season in order to accelerate the response to individual selection. PMID:26789008

Selection Indices and Multivariate Analysis Show Similar Results in the Evaluation of Growth and Carcass Traits in Beef Cattle.

PubMed

Brito Lopes, Fernando; da Silva, Marcelo Corrêa; Magnabosco, Cláudio Ulhôa; Goncalves Narciso, Marcelo; Sainz, Roberto Daniel

2016-01-01

This research evaluated a multivariate approach as an alternative tool for the purpose of selection regarding expected progeny differences (EPDs). Data were fitted using a multi-trait model and consisted of growth traits (birth weight and weights at 120, 210, 365 and 450 days of age) and carcass traits (longissimus muscle area (LMA), back-fat thickness (BF), and rump fat thickness (RF)), registered over 21 years in extensive breeding systems of Polled Nellore cattle in Brazil. Multivariate analyses were performed using standardized (zero mean and unit variance) EPDs. The k mean method revealed that the best fit of data occurred using three clusters (k = 3) (P < 0.001). Estimates of genetic correlation among growth and carcass traits and the estimates of heritability were moderate to high, suggesting that a correlated response approach is suitable for practical decision making. Estimates of correlation between selection indices and the multivariate index (LD1) were moderate to high, ranging from 0.48 to 0.97. This reveals that both types of indices give similar results and that the multivariate approach is reliable for the purpose of selection. The alternative tool seems very handy when economic weights are not available or in cases where more rapid identification of the best animals is desired. Interestingly, multivariate analysis allowed forecasting information based on the relationships among breeding values (EPDs). Also, it enabled fine discrimination, rapid data summarization after genetic evaluation, and permitted accounting for maternal ability and the genetic direct potential of the animals. In addition, we recommend the use of longissimus muscle area and subcutaneous fat thickness as selection criteria, to allow estimation of breeding values before the first mating season in order to accelerate the response to individual selection.

Measures of precision for dissimilarity-based multivariate analysis of ecological communities.

PubMed

Anderson, Marti J; Santana-Garcon, Julia

2015-01-01

Ecological studies require key decisions regarding the appropriate size and number of sampling units. No methods currently exist to measure precision for multivariate assemblage data when dissimilarity-based analyses are intended to follow. Here, we propose a pseudo multivariate dissimilarity-based standard error (MultSE) as a useful quantity for assessing sample-size adequacy in studies of ecological communities. Based on sums of squared dissimilarities, MultSE measures variability in the position of the centroid in the space of a chosen dissimilarity measure under repeated sampling for a given sample size. We describe a novel double resampling method to quantify uncertainty in MultSE values with increasing sample size. For more complex designs, values of MultSE can be calculated from the pseudo residual mean square of a permanova model, with the double resampling done within appropriate cells in the design. R code functions for implementing these techniques, along with ecological examples, are provided. © 2014 The Authors. Ecology Letters published by John Wiley & Sons Ltd and CNRS.

A Framework for Establishing Standard Reference Scale of Texture by Multivariate Statistical Analysis Based on Instrumental Measurement and Sensory Evaluation.

PubMed

Zhi, Ruicong; Zhao, Lei; Xie, Nan; Wang, Houyin; Shi, Bolin; Shi, Jingye

2016-01-13

A framework of establishing standard reference scale (texture) is proposed by multivariate statistical analysis according to instrumental measurement and sensory evaluation. Multivariate statistical analysis is conducted to rapidly select typical reference samples with characteristics of universality, representativeness, stability, substitutability, and traceability. The reasonableness of the framework method is verified by establishing standard reference scale of texture attribute (hardness) with Chinese well-known food. More than 100 food products in 16 categories were tested using instrumental measurement (TPA test), and the result was analyzed with clustering analysis, principal component analysis, relative standard deviation, and analysis of variance. As a result, nine kinds of foods were determined to construct the hardness standard reference scale. The results indicate that the regression coefficient between the estimated sensory value and the instrumentally measured value is significant (R(2) = 0.9765), which fits well with Stevens's theory. The research provides reliable a theoretical basis and practical guide for quantitative standard reference scale establishment on food texture characteristics.

Development of a non-destructive method for determining protein nitrogen in a yellow fever vaccine by near infrared spectroscopy and multivariate calibration.

PubMed

Dabkiewicz, Vanessa Emídio; de Mello Pereira Abrantes, Shirley; Cassella, Ricardo Jorgensen

2018-08-05

Near infrared spectroscopy (NIR) with diffuse reflectance associated to multivariate calibration has as main advantage the replacement of the physical separation of interferents by the mathematical separation of their signals, rapidly with no need for reagent consumption, chemical waste production or sample manipulation. Seeking to optimize quality control analyses, this spectroscopic analytical method was shown to be a viable alternative to the classical Kjeldahl method for the determination of protein nitrogen in yellow fever vaccine. The most suitable multivariate calibration was achieved by the partial least squares method (PLS) with multiplicative signal correction (MSC) treatment and data mean centering (MC), using a minimum number of latent variables (LV) equal to 1, with the lower value of the square root of the mean squared prediction error (0.00330) associated with the highest percentage value (91%) of samples. Accuracy ranged 95 to 105% recovery in the 4000-5184 cm -1 region. Copyright © 2018 Elsevier B.V. All rights reserved.

Surface atmospheric extremes (Launch and transportation areas)

NASA Technical Reports Server (NTRS)

1972-01-01

The effects of extreme values of surface and low altitude atmospheric parameters on space vehicle design, tests, and operations are discussed. Atmospheric extremes from the surface to 150 meters for geographic locations of interest to NASA are given. Thermal parameters (temperature and solar radiation), humidity, pressure, and atmospheric electricity (lighting and static) are presented. Weather charts and tables are included.

Extreme Events: low and high total ozone over Arosa, Switzerland

NASA Astrophysics Data System (ADS)

Rieder, H. E.; Staehelin, J.; Maeder, J. A.; Ribatet, M.; Stübi, R.; Weihs, P.; Holawe, F.; Peter, T.; Davison, A. C.

2009-04-01

The frequency distribution of days with extreme low (termed ELOs) and high (termed EHOs) total ozone is analyzed for the world's longest total ozone record (Arosa, Switzerland - for details see Staehelin et al.,1998a,b), with new tools from extreme value theory (e.g. Coles, 2001; Ribatet, 2007). A heavy-tail focused approach is used through the fitting of the Generalized Pareto Distribution (GPD) to the Arosa time series. Asymptotic arguments (Pickands, 1975) justify the use of the GPD for modeling exceedances over a high (or below a low) enough threshold (Coles, 2001). The analysis shows that the GPD is appropriate for modeling the frequency distribution in total ozone above or below a mathematically well-defined threshold. While previous studies focused on so termed ozone mini-holes and mini-highs (e.g. Bojkov and Balis, 2001, Koch et al., 2005), this study is the first to present a mathematical description of extreme events in low and high total ozone for a northern mid-latitudes site (Rieder et al., 2009). The results show (a) an increase in days with extreme low (ELOs) and (b) a decrease in days with extreme high total ozone (EHOs) during the last decades, (c) that the general trend in total ozone is strongly determined by these extreme events and (d) that fitting the GPD is an appropriate method for the estimation of the frequency distribution of so-called ozone mini-holes. Furthermore, this concept allows one to separate the effect of Arctic ozone depletion from that of in situ mid-latitude ozone loss. As shown by this study, ELOs and EHOs have a strong influence on mean values in total ozone and the "extremes concept" could be further used also for validation of Chemistry-Climate-Models (CCMs) within the scientific community. References: Bojkov, R. D., and Balis, D.S.: Characteristics of episodes with extremely low ozone values in the northern middle latitudes 1975-2000, Ann. Geophys., 19, 797-807, 2001. Coles, S.: An Introduction to Statistical Modeling of Extreme Values, Springer Series in Statistics, ISBN:1852334592, Springer, Berlin, 2001. Koch, G., H. Wernli, C. Schwierz, J. Staehelin, and T. Peter (2005), A composite study on the structure and formation of ozone miniholes and minihighs over central Europe, Geophys. Res. Lett., 32, L12810, doi:10.1029/2004GL022062. Pickands, J.: Statistical-Inference using extreme order Statistics, Ann. Stat., 3, 1, 119-131, 1975. Ribatet, M.: POT: Modelling peaks over a threshold, R News, 7, 34-36, 2007. Rieder, H.E., Staehelin, J., Maeder, J.A., Ribatet, M., Stübi, R., Weihs, P., Holawe, F., Peter, T., and Davison, A.C.: From ozone mini holes and mini highs towards extreme value theory: New insights from extreme events and non stationarity, submitted to J. Geophys. Res., 2009. Staehelin, J., Kegel, R., and Harris, N. R.: Trend analysis of the homogenized total ozone series of Arosa (Switzerland), 1929-1996, J. Geophys. Res., 103(D7), 8389-8400, doi:10.1029/97JD03650, 1998a. Staehelin, J., Renaud, A., Bader, J., McPeters, R., Viatte, P., Hoegger, B., Bugnion, V., Giroud, M., and Schill, H.: Total ozone series at Arosa (Switzerland): Homogenization and data comparison, J. Geophys. Res., 103(D5), 5827-5842, doi:10.1029/97JD02402, 1998b.

The bothersomeness of sciatica: patients' self-report of paresthesia, weakness and leg pain.

PubMed

Grøvle, Lars; Haugen, Anne Julsrud; Keller, Anne; Natvig, Bård; Brox, Jens Ivar; Grotle, Margreth

2010-02-01

The objective of the study was to investigate how patients with sciatica due to disc herniation rate the bothersomeness of paresthesia and weakness as compared to leg pain, and how these symptoms are associated with socio-demographic and clinical characteristics. A cross-sectional study was conducted on 411 patients with clinical signs of radiculopathy. Items from the Sciatica Bothersomeness Index (0 = none to 6 = extremely) were used to establish values for paresthesia, weakness and leg pain. Associations with socio-demographic and clinical variables were analyzed by multiple linear regression. Mean scores (SD) were 4.5 (1.5) for leg pain, 3.4 (1.8) for paresthesia and 2.6 (2.0) for weakness. Women reported higher levels of bothersomeness for all three symptoms with mean scores approximately 10% higher than men. In the multivariate models, more severe symptoms were associated with lower physical function and higher emotional distress. Muscular paresis explained 19% of the variability in self-reported weakness, sensory findings explained 10% of the variability in paresthesia, and straight leg raising test explained 9% of the variability in leg pain. In addition to leg pain, paresthesia and weakness should be assessed when measuring symptom severity in sciatica.

Heavy metal pollution of coal mine-affected agricultural soils in the northern part of Bangladesh.

PubMed

Bhuiyan, Mohammad A H; Parvez, Lutfar; Islam, M A; Dampare, Samuel B; Suzuki, Shigeyuki

2010-01-15

Total concentrations of heavy metals in the soils of mine drainage and surrounding agricultural fields in the northern part of Bangladesh were determined to evaluate the level of contamination. The average concentrations of Ti, Mn, Zn, Pb, As, Fe, Rb, Sr, Nb and Zr exceeded the world normal averages and, in some cases, Mn, Zn, As and Pb exceeded the toxic limit of the respective metals. Soil pollution assessment was carried out using enrichment factor (EF), geoaccumulation index (I(geo)) and pollution load index (PLI). The soils show significant enrichment with Ti, Mn, Zn, Pb, As, Fe, Sr and Nb, indicating inputs from mining activities. The I(geo) values have revealed that Mn (1.24+/-0.38), Zn (1.49+/-0.58) and Pb (1.63+/-0.38) are significantly accumulated in the study area. The PLIs derived from contamination factors indicate that the distal part of the coal mine-affected area is the most polluted (PLI of 4.02). Multivariate statistical analyses, principal component and cluster analyses, suggest that Mn, Zn, Pb and Ti are derived from anthropogenic sources, particularly coal mining activities, and the extreme proximal and distal parts are heavily contaminated with maximum heavy metals.

Obesity in pediatric trauma.

PubMed

Witt, Cordelie E; Arbabi, Saman; Nathens, Avery B; Vavilala, Monica S; Rivara, Frederick P

2017-04-01

The implications of childhood obesity on pediatric trauma outcomes are not clearly established. Anthropomorphic data were recently added to the National Trauma Data Bank (NTDB) Research Datasets, enabling a large, multicenter evaluation of the effect of obesity on pediatric trauma patients. Children ages 2 to 19years who required hospitalization for traumatic injury were identified in the 2013-2014 NTDB Research Datasets. Age and gender-specific body mass indices (BMI) were calculated. Outcomes included injury patterns, operative procedures, complications, and hospital utilization parameters. Data from 149,817 pediatric patients were analyzed; higher BMI percentiles were associated with significantly more extremity injuries, and fewer injuries to the head, abdomen, thorax and spine (p values <0.001). On multivariable analysis, higher BMI percentiles were associated with significantly increased likelihood of death, deep venous thrombosis, pulmonary embolus and pneumonia; although there was no difference in risk of overall complications. Obese children also had significantly longer lengths of stay and more frequent ventilator requirement. Among children admitted after trauma, increased BMI percentile is associated with increased risk of death and potentially preventable complications. These findings suggest that obese children may require different management than nonobese counterparts to prevent complications. Level III; prognosis study. Copyright © 2017 Elsevier Inc. All rights reserved.

Analysis of Sequence Data Under Multivariate Trait-Dependent Sampling.

PubMed

Tao, Ran; Zeng, Donglin; Franceschini, Nora; North, Kari E; Boerwinkle, Eric; Lin, Dan-Yu

2015-06-01

High-throughput DNA sequencing allows for the genotyping of common and rare variants for genetic association studies. At the present time and for the foreseeable future, it is not economically feasible to sequence all individuals in a large cohort. A cost-effective strategy is to sequence those individuals with extreme values of a quantitative trait. We consider the design under which the sampling depends on multiple quantitative traits. Under such trait-dependent sampling, standard linear regression analysis can result in bias of parameter estimation, inflation of type I error, and loss of power. We construct a likelihood function that properly reflects the sampling mechanism and utilizes all available data. We implement a computationally efficient EM algorithm and establish the theoretical properties of the resulting maximum likelihood estimators. Our methods can be used to perform separate inference on each trait or simultaneous inference on multiple traits. We pay special attention to gene-level association tests for rare variants. We demonstrate the superiority of the proposed methods over standard linear regression through extensive simulation studies. We provide applications to the Cohorts for Heart and Aging Research in Genomic Epidemiology Targeted Sequencing Study and the National Heart, Lung, and Blood Institute Exome Sequencing Project.

Global Weirding? - Using Very Large Ensembles and Extreme Value Theory to assess Changes in Extreme Weather Events Today

NASA Astrophysics Data System (ADS)

Otto, F. E. L.; Mitchell, D.; Sippel, S.; Black, M. T.; Dittus, A. J.; Harrington, L. J.; Mohd Saleh, N. H.

2014-12-01

A shift in the distribution of socially-relevant climate variables such as daily minimum winter temperatures and daily precipitation extremes, has been attributed to anthropogenic climate change for various mid-latitude regions. However, while there are many process-based arguments suggesting also a change in the shape of these distributions, attribution studies demonstrating this have not currently been undertaken. Here we use a very large initial condition ensemble of ~40,000 members simulating the European winter 2013/2014 using the distributed computing infrastructure under the weather@home project. Two separate scenarios are used:1. current climate conditions, and 2. a counterfactual scenario of "world that might have been" without anthropogenic forcing. Specifically focusing on extreme events, we assess how the estimated parameters of the Generalized Extreme Value (GEV) distribution vary depending on variable-type, sampling frequency (daily, monthly, …) and geographical region. We find that the location parameter changes for most variables but, depending on the region and variables, we also find significant changes in scale and shape parameters. The very large ensemble allows, furthermore, to assess whether such findings in the fitted GEV distributions are consistent with an empirical analysis of the model data, and whether the most extreme data still follow a known underlying distribution that in a small sample size might otherwise be thought of as an out-lier. The ~40,000 member ensemble is simulated using 12 different SST patterns (1 'observed', and 11 best guesses of SSTs with no anthropogenic warming). The range in SSTs, along with the corresponding changings in the NAO and high-latitude blocking inform on the dynamics governing some of these extreme events. While strong tele-connection patterns are not found in this particular experiment, the high number of simulated extreme events allows for a more thorough analysis of the dynamics than has been performed before. Therefore, combining extreme value theory with very large ensemble simulations allows us to understand the dynamics of changes in extreme events which is not possible just using the former but also shows in which cases statistics combined with smaller ensembles give as valid results as very large initial conditions.

Quantifying uncertainties in wind energy assessment

NASA Astrophysics Data System (ADS)

Patlakas, Platon; Galanis, George; Kallos, George

2015-04-01

The constant rise of wind energy production and the subsequent penetration in global energy markets during the last decades resulted in new sites selection with various types of problems. Such problems arise due to the variability and the uncertainty of wind speed. The study of the wind speed distribution lower and upper tail may support the quantification of these uncertainties. Such approaches focused on extreme wind conditions or periods below the energy production threshold are necessary for a better management of operations. Towards this direction, different methodologies are presented for the credible evaluation of potential non-frequent/extreme values for these environmental conditions. The approaches used, take into consideration the structural design of the wind turbines according to their lifespan, the turbine failures, the time needed for repairing as well as the energy production distribution. In this work, a multi-parametric approach for studying extreme wind speed values will be discussed based on tools of Extreme Value Theory. In particular, the study is focused on extreme wind speed return periods and the persistence of no energy production based on a weather modeling system/hind cast/10-year dataset. More specifically, two methods (Annual Maxima and Peaks Over Threshold) were used for the estimation of extreme wind speeds and their recurrence intervals. Additionally, two different methodologies (intensity given duration and duration given intensity, both based on Annual Maxima method) were implied to calculate the extreme events duration, combined with their intensity as well as the event frequency. The obtained results prove that the proposed approaches converge, at least on the main findings, for each case. It is also remarkable that, despite the moderate wind speed climate of the area, several consequent days of no energy production are observed.

Predictors of clinical outcome following lumbar disc surgery: the value of historical, physical examination, and muscle function variables.

PubMed

Hebert, Jeffrey J; Fritz, Julie M; Koppenhaver, Shane L; Thackeray, Anne; Kjaer, Per

2016-01-01

Explore the relationships between preoperative findings and clinical outcome following lumbar disc surgery, and investigate the prognostic value of physical examination findings after accounting for information acquired from the clinical history. We recruited 55 adult patients scheduled for first time, single-level lumbar discectomy. Participants underwent a standardized preoperative evaluation including real-time ultrasound imaging assessment of lumbar multifidus function, and an 8-week postoperative rehabilitation programme. Clinical outcome was defined by change in disability, and leg and low back pain (LBP) intensity at 10 weeks. Linear regression models were used to identify univariate and multivariate predictors of outcome. Univariate predictors of better outcome varied depending on the outcome measure. Clinical history predictors included a greater proportion of leg pain to LBP, pain medication use, greater time to surgery, and no history of previous physical or injection therapy. Physical examination predictors were a positive straight or cross straight leg raise test, diminished lower extremity strength, sensation or reflexes, and the presence of postural abnormality or pain peripheralization. Preoperative pain peripheralization remained a significant predictor of improved disability (p = 0.04) and LBP (p = 0.02) after accounting for information from the clinical history. Preoperative lumbar multifidus function was not associated with clinical outcome. Information gleaned from the clinical history and physical examination helps to identify patients more likely to succeed with lumbar disc surgery. While this study helps to inform clinical practice, additional research confirming these results is required prior to confident clinical implementation.

The intervals method: a new approach to analyse finite element outputs using multivariate statistics

PubMed Central

De Esteban-Trivigno, Soledad; Püschel, Thomas A.; Fortuny, Josep

2017-01-01

Background In this paper, we propose a new method, named the intervals’ method, to analyse data from finite element models in a comparative multivariate framework. As a case study, several armadillo mandibles are analysed, showing that the proposed method is useful to distinguish and characterise biomechanical differences related to diet/ecomorphology. Methods The intervals’ method consists of generating a set of variables, each one defined by an interval of stress values. Each variable is expressed as a percentage of the area of the mandible occupied by those stress values. Afterwards these newly generated variables can be analysed using multivariate methods. Results Applying this novel method to the biological case study of whether armadillo mandibles differ according to dietary groups, we show that the intervals’ method is a powerful tool to characterize biomechanical performance and how this relates to different diets. This allows us to positively discriminate between specialist and generalist species. Discussion We show that the proposed approach is a useful methodology not affected by the characteristics of the finite element mesh. Additionally, the positive discriminating results obtained when analysing a difficult case study suggest that the proposed method could be a very useful tool for comparative studies in finite element analysis using multivariate statistical approaches. PMID:29043107

Combination of radar and daily precipitation data to estimate meaningful sub-daily point precipitation extremes

NASA Astrophysics Data System (ADS)

Bárdossy, András; Pegram, Geoffrey

2017-01-01

The use of radar measurements for the space time estimation of precipitation has for many decades been a central topic in hydro-meteorology. In this paper we are interested specifically in daily and sub-daily extreme values of precipitation at gauged or ungauged locations which are important for design. The purpose of the paper is to develop a methodology to combine daily precipitation observations and radar measurements to estimate sub-daily extremes at point locations. Radar data corrected using precipitation-reflectivity relationships lead to biased estimations of extremes. Different possibilities of correcting systematic errors using the daily observations are investigated. Observed gauged daily amounts are interpolated to unsampled points and subsequently disaggregated using the sub-daily values obtained by the radar. Different corrections based on the spatial variability and the subdaily entropy of scaled rainfall distributions are used to provide unbiased corrections of short duration extremes. Additionally a statistical procedure not based on a matching day by day correction is tested. In this last procedure as we are only interested in rare extremes, low to medium values of rainfall depth were neglected leaving a small number of L days of ranked daily maxima in each set per year, whose sum typically comprises about 50% of each annual rainfall total. The sum of these L day maxima is first iterpolated using a Kriging procedure. Subsequently this sum is disaggregated to daily values using a nearest neighbour procedure. The daily sums are then disaggregated by using the relative values of the biggest L radar based days. Of course, the timings of radar and gauge maxima can be different, so the method presented here uses radar for disaggregating daily gauge totals down to 15 min intervals in order to extract the maxima of sub-hourly through to daily rainfall. The methodologies were tested in South Africa, where an S-band radar operated relatively continuously at Bethlehem from 1998 to 2003, whose scan at 1.5 km above ground [CAPPI] overlapped a dense (10 km spacing) set of 45 pluviometers recording in the same 6-year period. This valuable set of data was obtained from each of 37 selected radar pixels [1 km square in plan] which contained a pluviometer not masked out by the radar foot-print. The pluviometer data were also aggregated to daily totals, for the same purpose. The extremes obtained using disaggregation methods were compared to the observed extremes in a cross validation procedure. The unusual and novel goal was not to obtain the reproduction of the precipitation matching in space and time, but to obtain frequency distributions of the point extremes, which we found to be stable.

Estimation and model selection of semiparametric multivariate survival functions under general censorship.

PubMed

Chen, Xiaohong; Fan, Yanqin; Pouzo, Demian; Ying, Zhiliang

2010-07-01

We study estimation and model selection of semiparametric models of multivariate survival functions for censored data, which are characterized by possibly misspecified parametric copulas and nonparametric marginal survivals. We obtain the consistency and root- n asymptotic normality of a two-step copula estimator to the pseudo-true copula parameter value according to KLIC, and provide a simple consistent estimator of its asymptotic variance, allowing for a first-step nonparametric estimation of the marginal survivals. We establish the asymptotic distribution of the penalized pseudo-likelihood ratio statistic for comparing multiple semiparametric multivariate survival functions subject to copula misspecification and general censorship. An empirical application is provided.

Estimation and model selection of semiparametric multivariate survival functions under general censorship

PubMed Central

Chen, Xiaohong; Fan, Yanqin; Pouzo, Demian; Ying, Zhiliang

2013-01-01

We study estimation and model selection of semiparametric models of multivariate survival functions for censored data, which are characterized by possibly misspecified parametric copulas and nonparametric marginal survivals. We obtain the consistency and root-n asymptotic normality of a two-step copula estimator to the pseudo-true copula parameter value according to KLIC, and provide a simple consistent estimator of its asymptotic variance, allowing for a first-step nonparametric estimation of the marginal survivals. We establish the asymptotic distribution of the penalized pseudo-likelihood ratio statistic for comparing multiple semiparametric multivariate survival functions subject to copula misspecification and general censorship. An empirical application is provided. PMID:24790286

[Value of Neutrophil/Lymphocyte Ratio and Platelet/Lymphocyte Ratio for Prognostic Evaluation of Diffuse Large B-cell Lymphoma].

PubMed

Ni, Jing; Wang, Yong-Qing; Zhang, Ying-Ping; Wu, Wei; Zeng, Qing-Shu; Yang, Ming-Zhen; Xia, Rui-Xiang

2016-04-01

To investigate the predictive value of neutrophil/lymphocyte ratio (NLR) and platelet/lymphocyte ratio (PLR) for the patients with diffuse large B-cell lymphoma (DLBCL). The clinical data of 57 DLBCL patients admitted in the First Affiliated hospital of Anhui Medical University were analyzed retrospectively. According to ROC curve, the cut-off value for NLR and PLR was deterimined, and the patients were divided into high and low NLR/PLR groups before first chamotherapy. Then the relation of NLR and PLR with overall survival (OS) and progression-free survival (PFS) was analyzed by univariate and multivariate COX regression. The optimal cut-off value for NLR and PLR was 2.915 and 270.27, respectively. NLR at the diagnosis was found to be an independent predictor for OS and PFS by univariate and multivariate analysis, while the PLR was an independent predictor for PFS, but did not affect the OS. NLR and PLR may provide additional prognostic information for DLBCL patients.

Exploring multivariate representations of indices along linear geographic features

NASA Astrophysics Data System (ADS)

Bleisch, Susanne; Hollenstein, Daria

2018-05-01

A study of the walkability of a Swiss town required finding suitable representations of multivariate geographical da-ta. The goal was to represent multiple indices of walkability concurrently and visualizing the data along the street network it relates to. Different indices of pedestrian friendliness were assessed for short street sections and then mapped to an overlaid grid. Basic and composite glyphs were designed using square- or triangle-areas to display one to four index values concurrently within the grid structure. Color was used to indicate different indices. Implement-ing visualizations for different combinations of index sets, we find that single values can be emphasized or de-emphasized by selecting the color scheme accordingly and that different color selections either allow perceiving sin-gle values or overall trends over the evaluated area. Values for up to four indices can be displayed in combination within the resulting geovisualizations and the underlying gridded road network references the data to its real world locations.

Multisite rainfall downscaling and disaggregation in a tropical urban area

NASA Astrophysics Data System (ADS)

Lu, Y.; Qin, X. S.

2014-02-01

A systematic downscaling-disaggregation study was conducted over Singapore Island, with an aim to generate high spatial and temporal resolution rainfall data under future climate-change conditions. The study consisted of two major components. The first part was to perform an inter-comparison of various alternatives of downscaling and disaggregation methods based on observed data. This included (i) single-site generalized linear model (GLM) plus K-nearest neighbor (KNN) (S-G-K) vs. multisite GLM (M-G) for spatial downscaling, (ii) HYETOS vs. KNN for single-site disaggregation, and (iii) KNN vs. MuDRain (Multivariate Rainfall Disaggregation tool) for multisite disaggregation. The results revealed that, for multisite downscaling, M-G performs better than S-G-K in covering the observed data with a lower RMSE value; for single-site disaggregation, KNN could better keep the basic statistics (i.e. standard deviation, lag-1 autocorrelation and probability of wet hour) than HYETOS; for multisite disaggregation, MuDRain outperformed KNN in fitting interstation correlations. In the second part of the study, an integrated downscaling-disaggregation framework based on M-G, KNN, and MuDRain was used to generate hourly rainfall at multiple sites. The results indicated that the downscaled and disaggregated rainfall data based on multiple ensembles from HadCM3 for the period from 1980 to 2010 could well cover the observed mean rainfall amount and extreme data, and also reasonably keep the spatial correlations both at daily and hourly timescales. The framework was also used to project future rainfall conditions under HadCM3 SRES A2 and B2 scenarios. It was indicated that the annual rainfall amount could reduce up to 5% at the end of this century, but the rainfall of wet season and extreme hourly rainfall could notably increase.

Process-informed extreme value statistics- Why and how?

NASA Astrophysics Data System (ADS)

Schumann, Andreas; Fischer, Svenja

2017-04-01

In many parts of the world, annual maximum series (AMS) of runoff consist of flood peaks, which differ in their genesis. There are several aspects why these differences should be considered: Often multivariate flood characteristics (volumes, shapes) are of interest. These characteristics depend on the flood types. For regionalization, the main impacts on the flood regime has to be specified. If this regime depends on different flood types, type-specific hydro-meteorological and/or watershed characteristics are relevant. The ratios between event types often change over the range of observations. If a majority of events, which belongs to certain flood type, dominates the extrapolation of a probability distribution function (pdf), it is a problem if this more frequent type would not be typical for extraordinary large extremes, determining the right tail of the pdf. To consider differences in flood origin, several problems has to be solved. The events have to be separated into different groups according to their genesis. This can be a problem for long past events where e.g. precipitation data are not available. Another problem consists in the flood type-specific statistics. If block maxima are used, the sample of floods belong to a certain type is often incomplete as other events are overlaying smaller events. Some practical useable statistical tools to solve this and other problems are presented in a case study. Seasonal models were developed which differ between winter and summer floods but also between events with long and short timescales. The pdfs of the two groups of summer floods are combined via a new mixing model. The application to German watersheds demonstrates the advantages of the new model, giving specific influence to flood types.

Pituitary, gonadal and adrenal hormones after prolonged residence at extreme altitude in man.

PubMed

Basu, M; Pal, K; Prasad, R; Malhotra, A S; Rao, K S; Sawhney, R C

1997-06-01

High altitude-induced alterations in pituitary, gonadal and adrenal hormones were studied in (i) eugonadal men from the armed forces who were resident at sea level (SL), (ii) SL residents staying at an altitude of 3542 m for periods ranging from 3 to 12 months (acclimatized lowlanders, ALL), (iii) ALL who stayed at 6300 m for 6 months, (iv) ALL who trekked from 3542 to 5080 m and stayed at an altitude of more than 6300 m in the glacier region for 6 months, and (v) high-altitude natives (HAN) resident at an altitude of 3300-3700 m. Circulating levels of LH, FSH, prolactin, cortisol, testosterone, dihydrotestosterone (DHT) and progesterone in ALL at 3542 m and in HAN were not significantly different (p > 0.05) from the SL control values. When the ALL living at 3542 m trekked to an extreme altitude of 5080 m, their testosterone levels showed a significant decrease (p < 0.01) compared to the preceding altitude values but had returned to SL values when measured after 6 months' continuous stay at 6300 m. As with testosterone, the levels of DHT and oestradiol-17 beta (E2) after prolonged stay at extreme altitude were also not significantly different (p > 0.05) from the SL values. The LH levels after trekking to 5080 m were significantly higher (p < 0.01) than at an altitude of 3542 m, but decreased to levels found at 3542 m or SL after prolonged residence at extreme altitude. Plasma levels of ACTH, prolactin, FSH and cortisol on arrival at 5080 m, and after a 6-month stay at extreme altitude, were not significantly different (p > 0.05) from the SL values. Plasma progesterone levels tended to increase on arrival at 5080 m but a significant increase (p < 0.001) was evident only after a 6-month stay at extreme altitude. These observations suggest that prolonged residence at lower as well as at extreme altitude does not appreciably alter blood levels of pituitary, gonadal or adrenal hormones except for plasma levels of progesterone. The exact mechanism and significance of this increase remains unknown, but may be important in increasing the sensitivity of the hypoxic ventilatory response and activation of haemoglobin synthesis.

Clinical features and risk factor analysis for lower extremity deep venous thrombosis in Chinese neurosurgical patients

PubMed Central

Guo, Fuyou; Shashikiran, Tagilapalli; Chen, Xi; Yang, Lei; Liu, Xianzhi; Song, Laijun

2015-01-01

Background: Deep venous thrombosis (DVT) contributes significantly to the morbidity and mortality of neurosurgical patients; however, no data regarding lower extremity DVT in postoperative Chinese neurosurgical patients have been reported. Materials and Methods: From January 2012 to December 2013, 196 patients without preoperative DVT who underwent neurosurgical operations were evaluated by color Doppler ultrasonography and D-dimer level measurements on the 3rd, 7th, and 14th days after surgery. Follow-up clinical data were recorded to determine the incidence of lower extremity DVT in postoperative neurosurgical patients and to analyze related clinical features. First, a single factor analysis, Chi-square test, was used to select statistically significant factors. Then, a multivariate analysis, binary logistic regression analysis, was used to determine risk factors for lower extremity DVT in postoperative neurosurgical patients. Results: Lower extremity DVT occurred in 61 patients, and the incidence of DVT was 31.1% in the enrolled Chinese neurosurgical patients. The common symptoms of DVT were limb swelling and lower extremity pain as well as increased soft tissue tension. The common sites of venous involvement were the calf muscle and peroneal and posterior tibial veins. The single factor analysis showed statistically significant differences in DVT risk factors, including age, hypertension, smoking status, operation time, a bedridden or paralyzed state, the presence of a tumor, postoperative dehydration, and glucocorticoid treatment, between the two groups (P < 0.05). The binary logistic regression analysis showed that an age greater than 50 years, hypertension, a bedridden or paralyzed state, the presence of a tumor, and postoperative dehydration were risk factors for lower extremity DVT in postoperative neurosurgical patients. Conclusions: Lower extremity DVT was a common complication following craniotomy in the enrolled Chinese neurosurgical patients. Multiple factors were identified as predictive of DVT in neurosurgical patients, including the presence of a tumor, an age greater than 50 years, hypertension, and immobility. PMID:26752303

Coping with matrix effects in headspace solid phase microextraction gas chromatography using multivariate calibration strategies.

PubMed

Ferreira, Vicente; Herrero, Paula; Zapata, Julián; Escudero, Ana

2015-08-14

SPME is extremely sensitive to experimental parameters affecting liquid-gas and gas-solid distribution coefficients. Our aims were to measure the weights of these factors and to design a multivariate strategy based on the addition of a pool of internal standards, to minimize matrix effects. Synthetic but real-like wines containing selected analytes and variable amounts of ethanol, non-volatile constituents and major volatile compounds were prepared following a factorial design. The ANOVA study revealed that even using a strong matrix dilution, matrix effects are important and additive with non-significant interaction effects and that it is the presence of major volatile constituents the most dominant factor. A single internal standard provided a robust calibration for 15 out of 47 analytes. Then, two different multivariate calibration strategies based on Partial Least Square Regression were run in order to build calibration functions based on 13 different internal standards able to cope with matrix effects. The first one is based in the calculation of Multivariate Internal Standards (MIS), linear combinations of the normalized signals of the 13 internal standards, which provide the expected area of a given unit of analyte present in each sample. The second strategy is a direct calibration relating concentration to the 13 relative areas measured in each sample for each analyte. Overall, 47 different compounds can be reliably quantified in a single fully automated method with overall uncertainties better than 15%. Copyright © 2015 Elsevier B.V. All rights reserved.

Relationship Between Central and Peripheral Atherosclerosis and Left Ventricular Dysfunction in a Community Population

PubMed Central

Tsao, Connie W.; Gona, Philimon; Salton, Carol; Murabito, Joanne M.; Oyama, Noriko; Danias, Peter G.; O’Donnell, Christopher J.; Manning, Warren J.; Yeon, Susan B.

2011-01-01

We aimed to determine the relationships between resting left ventricular (LV) wall motion abnormalities (WMAs), aortic plaque, and PAD in a community cohort. 1726 Framingham Heart Study Offspring Cohort participants (806 males, 65±9 years) underwent cardiovascular magnetic resonance with quantification of aortic plaque volume and assessment of regional LV systolic function. Claudication, lower extremity revascularization, and ankle-brachial index (ABI) were recorded at Examination 7. WMAs were associated with greater aortic plaque burden, decreased ABI, and claudication in age- and sex-adjusted analyses (all p<0.001), which were not significant after adjustment for cardiovascular risk factors. In age- and sex-adjusted analyses, both the presence (p<0.001) and volume of aortic plaque were associated with decreased ABI (p<0.001). After multivariable adjustment, ABI≤0.9 or prior revascularization was associated with a three-fold odds of aortic plaque (p=0.0083). Plaque volume significantly increased with decreasing ABI in multivariable-adjusted analyses (p<0.0001). In this free-living population, associations of WMAs with aortic plaque burden and clinical measures of PAD were attenuated after adjustment for coronary heart disease risk factors. Aortic plaque volume and ABI remained strongly negatively correlated after multivariable adjustment. Our findings suggest that the association between coronary heart disease and non-coronary atherosclerosis is explained by cardiovascular risk factors. Aortic atherosclerosis and PAD remain strongly associated after multivariable adjustment suggesting shared mechanisms beyond those captured by traditional risk factors. PMID:21708875

Flap choice does not affect complication rates or functional outcomes following extremity soft tissue sarcoma reconstruction.

PubMed

Slump, Jelena; Hofer, Stefan O P; Ferguson, Peter C; Wunder, Jay S; Griffin, Anthony M; Hoekstra, Harald J; Bastiaannet, Esther; O'Neill, Anne C

2018-04-12

Flap reconstruction plays an essential role in facilitating limb preservation in patients with extremity soft tissue sarcoma (ESTS). However, the effect of flap choice on the rates of postoperative complications and functional outcomes has not been clearly established. This study directly compares the outcomes of free and pedicled flap reconstructions in patients with ESTS. Two hundred sixty-six patients who underwent flap reconstruction following ESTS resection were included. Associations between flap type and complications were determined using logistic regression analyses. Functional outcome was evaluated using the Toronto Extremity Salvage Score (TESS) and the Musculoskeletal Tumor Society Scales (MSTS). There was no significant difference between complication rates in the pedicled and free flap groups (32% vs. 38%, p = 0.38). In the lower limb, pedicled flaps had complication rates similar to those of free flaps on univariate analysis (odds ratio [OR] = 1.12, 95% confidence interval [CI] = 0.56-2.26, p = 0.75). Conversely, in the upper limb, pedicled flaps were associated with fewer complications on univariate analysis (OR = 0.31, 95% CI = 0.11-0.86, p = 0.03), but this was not significant on multivariate analysis (OR = 0.45, 95% CI = 0.13-1.59, p = 0.22). Obesity was a strong predictor of complications in the upper limb group on multivariate analysis (body mass index [BMI] ≥ 30 kg/m 2 , OR = 7.01, 95% CI = 1.28-38.51, p = 0.03). There was no significant difference in functional outcomes between both flap groups in either upper or lower limbs. Postoperative complications and functional outcomes for patients undergoing free and pedicled flaps are similar in ESTS reconstruction. Selecting the most suitable reconstructive option in each individual case is paramount to preserving function while minimizing postoperative morbidity. Copyright © 2018 British Association of Plastic, Reconstructive and Aesthetic Surgeons. Published by Elsevier Ltd. All rights reserved.

Radiation Therapy for Control of Soft-Tissue Sarcomas Resected With Positive Margins

DOE Office of Scientific and Technical Information (OSTI.GOV)

DeLaney, Thomas F.; Kepka, Lucyna; Goldberg, Saveli I.

Purpose: Positive margins (PM) remain after surgery in some soft-tissue sarcoma (STS) patients. We investigated the efficacy of radiation therapy (RT) in STS patients with PM. Methods and Materials: A retrospective chart review was performed on 154 patients with STS at various anatomic sites with PM, defined as tumor on ink, who underwent RT with curative intent between 1970 and 2001. Local control (LC), disease-free survival (DFS), and overall survival (OS) rates were evaluated by univariate (log-rank) and multivariate analysis of prognostic and treatment factors. Results: At 5 years, actuarial LC, DFS, and OS rates were: 76%, 46.7%, and 65.2%,more » respectively. LC was highest with extremity lesions (p < 0.01), radiation dose >64 Gy (p < 0.05), microscopically (vs. grossly visible) positive margin (p = 0.03), and superficial lesions (p = 0.05). Patients receiving >64 Gy had higher 5-year LC, DFS, and OS rates of 85%, 52.1%, and 67.8% vs. 66.1%, 41.8%, and 62.9% if {<=}64 Gy, p < 0.04. OS was worse in patients with G2/G3 tumors with local failure (LF), p < 0.001. Other known prognostic factors, including grade, stage, size, and age (>50), also significantly influenced OS. By multivariate analysis, the best predictors of LC were site (extremity vs. other), p < 0.01 and dose (>64 vs. {<=}64 Gy), p < 0.05; the best predictors for OS were size, p < 0.001, gross vs. microscopic PM, p < 0.05, and LF, p < 0.01. Conclusion: Local control is achieved in most PM STS patients undergoing RT. Doses >64 Gy, superficial location, and extremity site are associated with improved LC. OS is worse in patients with tumors with lesions >5 cm, grossly positive margins, and after local failure.« less

On measures of association among genetic variables

PubMed Central

Gianola, Daniel; Manfredi, Eduardo; Simianer, Henner

2012-01-01

Summary Systems involving many variables are important in population and quantitative genetics, for example, in multi-trait prediction of breeding values and in exploration of multi-locus associations. We studied departures of the joint distribution of sets of genetic variables from independence. New measures of association based on notions of statistical distance between distributions are presented. These are more general than correlations, which are pairwise measures, and lack a clear interpretation beyond the bivariate normal distribution. Our measures are based on logarithmic (Kullback-Leibler) and on relative ‘distances’ between distributions. Indexes of association are developed and illustrated for quantitative genetics settings in which the joint distribution of the variables is either multivariate normal or multivariate-t, and we show how the indexes can be used to study linkage disequilibrium in a two-locus system with multiple alleles and present applications to systems of correlated beta distributions. Two multivariate beta and multivariate beta-binomial processes are examined, and new distributions are introduced: the GMS-Sarmanov multivariate beta and its beta-binomial counterpart. PMID:22742500

Outcomes of Extremely Low Birth Weight Infants with Acidosis at Birth

PubMed Central

Randolph, David A.; Nolen, Tracy L.; Ambalavanan, Namasivayam; Carlo, Waldemar A.; Peralta-Carcelen, Myriam; Das, Abhik; Bell, Edward F.; Davis, Alexis S.; Laptook, Abbot R.; Stoll, Barbara J.; Shankaran, Seetha; Higgins, Rosemary D.

2014-01-01

OBJECTIVES To test the hypothesis that acidosis at birth is associated with the combined primary outcome of death or neurodevelopmental impairment (NDI) in extremely low birth weight (ELBW) infants, and to develop a predictive model of death/NDI exploring perinatal acidosis as a predictor variable. STUDY DESIGN The study population consisted of ELBW infants born between 2002-2007 at NICHD Neonatal Research Network hospitals. Infants with cord blood gas data and documentation of either mortality prior to discharge or 18-22 month neurodevelopmental outcomes were included. Multiple logistic regression analysis was used to determine the contribution of perinatal acidosis, defined as a cord blood gas with a pH<7 or base excess (BE)<-12, to death/NDI in ELBW infants. In addition, a multivariable model predicting death/NDI was developed. RESULTS 3979 patients were identified of whom 249 had a cord gas pH<7 or BE<-12 mEq/L. 2124 patients (53%) had the primary outcome of death/NDI. After adjustment for confounding variables, pH<7 and BE<-12 mEq/L were each significantly associated with death/NDI (OR=2.5[1.6,4.2]; and OR=1.5[1.1,2.0], respectively). However, inclusion of pH or BE did not improve the ability of the multivariable model to predict death/NDI. CONCLUSIONS Perinatal acidosis is significantly associated with death/NDI in ELBW infants. Perinatal acidosis is infrequent in ELBW infants, however, and other factors are more important in predicting death/NDI. PMID:24554564

Extreme values and fat tails of multifractal fluctuations

NASA Astrophysics Data System (ADS)

Muzy, J. F.; Bacry, E.; Kozhemyak, A.

2006-06-01

In this paper we discuss the problem of the estimation of extreme event occurrence probability for data drawn from some multifractal process. We also study the heavy (power-law) tail behavior of probability density function associated with such data. We show that because of strong correlations, the standard extreme value approach is not valid and classical tail exponent estimators should be interpreted cautiously. Extreme statistics associated with multifractal random processes turn out to be characterized by non-self-averaging properties. Our considerations rely upon some analogy between random multiplicative cascades and the physics of disordered systems and also on recent mathematical results about the so-called multifractal formalism. Applied to financial time series, our findings allow us to propose an unified framework that accounts for the observed multiscaling properties of return fluctuations, the volatility clustering phenomenon and the observed “inverse cubic law” of the return pdf tails.

Probability distribution of extreme share returns in Malaysia

NASA Astrophysics Data System (ADS)

Zin, Wan Zawiah Wan; Safari, Muhammad Aslam Mohd; Jaaman, Saiful Hafizah; Yie, Wendy Ling Shin

2014-09-01

The objective of this study is to investigate the suitable probability distribution to model the extreme share returns in Malaysia. To achieve this, weekly and monthly maximum daily share returns are derived from share prices data obtained from Bursa Malaysia over the period of 2000 to 2012. The study starts with summary statistics of the data which will provide a clue on the likely candidates for the best fitting distribution. Next, the suitability of six extreme value distributions, namely the Gumbel, Generalized Extreme Value (GEV), Generalized Logistic (GLO) and Generalized Pareto (GPA), the Lognormal (GNO) and the Pearson (PE3) distributions are evaluated. The method of L-moments is used in parameter estimation. Based on several goodness of fit tests and L-moment diagram test, the Generalized Pareto distribution and the Pearson distribution are found to be the best fitted distribution to represent the weekly and monthly maximum share returns in Malaysia stock market during the studied period, respectively.

[The informative value of the functional step test for the purpose of computed optical topography in the children presenting with the functional disorders of the musculoskeletal system].

PubMed

Trukhmanov, I M; Suslova, G A; Ponomarenko, G N

This paper is devoted to the characteristic of the informative value of the functional step test with the application of the heel cushions in the children for the purpose of differential diagnostics of anatomic and functional differences in the length of the lower extremities. A total of 85 schoolchildren with different length of the lower extremities have been examined. The comparative evaluation of the results of clinical and instrumental examinations was undertaken. The data obtained with the help of the functional step test give evidence of its very high sensitivity, specificity, and clinical significant as a tool for the examination of the children with different length of the low extremities. It is concluded that the test is one of the most informative predictors of the effectiveness of rehabilitation in the children with different length of the lower extremities.

Extreme value laws for fractal intensity functions in dynamical systems: Minkowski analysis

NASA Astrophysics Data System (ADS)

Mantica, Giorgio; Perotti, Luca

2016-09-01

Typically, in the dynamical theory of extremal events, the function that gauges the intensity of a phenomenon is assumed to be convex and maximal, or singular, at a single, or at most a finite collection of points in phase-space. In this paper we generalize this situation to fractal landscapes, i.e. intensity functions characterized by an uncountable set of singularities, located on a Cantor set. This reveals the dynamical rôle of classical quantities like the Minkowski dimension and content, whose definition we extend to account for singular continuous invariant measures. We also introduce the concept of extremely rare event, quantified by non-standard Minkowski constants and we study its consequences to extreme value statistics. Limit laws are derived from formal calculations and are verified by numerical experiments. Dedicated to the memory of Joseph Ford, on the twentieth anniversary of his departure.

Functional recovery patterns in seriously injured automotive crash victims.

PubMed

McMurry, Timothy L; Poplin, Gerald S; Crandall, Jeff

2016-09-01

The functional capacity index (FCI) is designed to predict functional loss 12 months post-injury for each injury in the 2008 Abbreviated Injury Scale (AIS) manual on a scale from 0 (death) to 100 (full recovery), but FCI has never been validated. This study compared FCI predicted loss with patient-reported 12-month outcomes as measured through the Short Form 36 (SF-36) health assessment survey. Using follow-up data collected on 2,858 adult car crash occupants in the Crash Injury Research and Engineering Network (CIREN) database, we compared FCI predicted outcomes to occupants' Physical Component Summary (PCS) scores, which are weighted averages of the SF-36 items addressing physical function. Our analyses included descriptive statistics, plots of typical recovery patterns, and a mixed effects regression model that describes PCS as a function of FCI, demographics, comorbidities, and injury pattern while also adjusting for the occupants' pre-crash physical capabilities. We further examined injuries in patients who report a significant drop in PCS 12 months post-crash despite being predicted to fully recover. At baseline, the CIREN population exhibited PCS scores similar to the overall population (mean = 51.1, SD = 10.3). Twelve months post-crash, occupants with predicted impairment (FCI < 100) report a substantial decrease in physical function, and those who were predicted to fully recover still report some, albeit less, impairment. In the multivariate mixed-effects regression model, FCI is a strongly significant (P-value <.0001) predictor of PCS, with each 1-point drop in FCI predicting a 0.27-point drop in PCS. Maximum AIS severities in the head, spine, and lower extremity body regions were also significantly associated with PCS (P-values <.05). Among occupants who were expected to fully recover but who report a significant drop in PCS at 12 months, spinal fractures without cord involvement account for 5 of the 10 most common AIS 2+ injuries. FCI was associated with 12-month outcomes but may not adequately describe the recovery from some head, spine, and lower extremity injuries. Some occupants who were expected to recover still report functional loss 12 months post-injury.

A New Approach to Extreme Value Estimation Applicable to a Wide Variety of Random Variables

NASA Technical Reports Server (NTRS)

Holland, Frederic A., Jr.

1997-01-01

Designing reliable structures requires an estimate of the maximum and minimum values (i.e., strength and load) that may be encountered in service. Yet designs based on very extreme values (to insure safety) can result in extra material usage and hence, uneconomic systems. In aerospace applications, severe over-design cannot be tolerated making it almost mandatory to design closer to the assumed limits of the design random variables. The issue then is predicting extreme values that are practical, i.e. neither too conservative or non-conservative. Obtaining design values by employing safety factors is well known to often result in overly conservative designs and. Safety factor values have historically been selected rather arbitrarily, often lacking a sound rational basis. To answer the question of how safe a design needs to be has lead design theorists to probabilistic and statistical methods. The so-called three-sigma approach is one such method and has been described as the first step in utilizing information about the data dispersion. However, this method is based on the assumption that the random variable is dispersed symmetrically about the mean and is essentially limited to normally distributed random variables. Use of this method can therefore result in unsafe or overly conservative design allowables if the common assumption of normality is incorrect.

Impacts of Model Bias on the Climate Change Signal and Effects of Weighted Ensembles of Regional Climate Model Simulations: A Case Study over Southern Québec, Canada

DOE PAGES

Eum, Hyung-Il; Gachon, Philippe; Laprise, René

2016-01-01

This study examined the impact of model biases on climate change signals for daily precipitation and for minimum and maximum temperatures. Through the use of multiple climate scenarios from 12 regional climate model simulations, the ensemble mean, and three synthetic simulations generated by a weighting procedure, we investigated intermodel seasonal climate change signals between current and future periods, for both median and extreme precipitation/temperature values. A significant dependence of seasonal climate change signals on the model biases over southern Québec in Canada was detected for temperatures, but not for precipitation. This suggests that the regional temperature change signal is affectedmore » by local processes. Seasonally, model bias affects future mean and extreme values in winter and summer. In addition, potentially large increases in future extremes of temperature and precipitation values were projected. For three synthetic scenarios, systematically less bias and a narrow range of mean change for all variables were projected compared to those of climate model simulations. In addition, synthetic scenarios were found to better capture the spatial variability of extreme cold temperatures than the ensemble mean scenario. Finally, these results indicate that the synthetic scenarios have greater potential to reduce the uncertainty of future climate projections and capture the spatial variability of extreme climate events.« less

Impacts of Model Bias on the Climate Change Signal and Effects of Weighted Ensembles of Regional Climate Model Simulations: A Case Study over Southern Québec, Canada

DOE Office of Scientific and Technical Information (OSTI.GOV)

Eum, Hyung-Il; Gachon, Philippe; Laprise, René

This study examined the impact of model biases on climate change signals for daily precipitation and for minimum and maximum temperatures. Through the use of multiple climate scenarios from 12 regional climate model simulations, the ensemble mean, and three synthetic simulations generated by a weighting procedure, we investigated intermodel seasonal climate change signals between current and future periods, for both median and extreme precipitation/temperature values. A significant dependence of seasonal climate change signals on the model biases over southern Québec in Canada was detected for temperatures, but not for precipitation. This suggests that the regional temperature change signal is affectedmore » by local processes. Seasonally, model bias affects future mean and extreme values in winter and summer. In addition, potentially large increases in future extremes of temperature and precipitation values were projected. For three synthetic scenarios, systematically less bias and a narrow range of mean change for all variables were projected compared to those of climate model simulations. In addition, synthetic scenarios were found to better capture the spatial variability of extreme cold temperatures than the ensemble mean scenario. Finally, these results indicate that the synthetic scenarios have greater potential to reduce the uncertainty of future climate projections and capture the spatial variability of extreme climate events.« less

From ozone mini-holes and mini-highs towards extreme value theory: New insights from extreme events and non-stationarity

NASA Astrophysics Data System (ADS)

Rieder, H. E.; Staehelin, J.; Maeder, J. A.; Ribatet, M.; Stübi, R.; Weihs, P.; Holawe, F.; Peter, T.; Davison, A. C.

2009-04-01

Over the last few decades negative trends in stratospheric ozone have been studied because of the direct link between decreasing stratospheric ozone and increasing surface UV-radiation. Recently a discussion on ozone recovery has begun. Long-term measurements of total ozone extending back earlier than 1958 are limited and only available from a few stations in the northern hemisphere. The world's longest total ozone record is available from Arosa, Switzerland (Staehelin et al., 1998a,b). At this site total ozone measurements have been made since late 1926 through the present day. Within this study (Rieder et al., 2009) new tools from extreme value theory (e.g. Coles, 2001; Ribatet, 2007) are applied to select mathematically well-defined thresholds for extreme low and extreme high total ozone. A heavy-tail focused approach is used by fitting the Generalized Pareto Distribution (GPD) to the Arosa time series. Asymptotic arguments (Pickands, 1975) justify the use of the GPD for modeling exceedances over a sufficiently high (or below a sufficiently low) threshold (Coles, 2001). More precisely, the GPD is the limiting distribution of normalized excesses over a threshold, as the threshold approaches the endpoint of the distribution. In practice, GPD parameters are fitted, to exceedances by maximum likelihood or other methods - such as the probability weighted moments. A preliminary step consists in defining an appropriate threshold for which the asymptotic GPD approximation holds. Suitable tools for threshold selection as the MRL-plot (mean residual life plot) and TC-plot (stability plot) from the POT-package (Ribatet, 2007) are presented. The frequency distribution of extremes in low (termed ELOs) and high (termed EHOs) total ozone and their influence on the long-term changes in total ozone are analyzed. Further it is shown that from the GPD-model the distribution of so-called ozone mini holes (e.g. Bojkov and Balis, 2001) can be precisely estimated and that the "extremes concept" provides new information on the data distribution and variability within the Arosa record as well as on the influence of ELOs and EHOs on the long-term trends of the ozone time series. References: Bojkov, R. D., and Balis, D.S.: Characteristics of episodes with extremely low ozone values in the northern middle latitudes 1975-2000, Ann. Geophys., 19, 797-807, 2001. Coles, S.: An Introduction to Statistical Modeling of Extreme Values, Springer Series in Statistics, ISBN:1852334592, Springer, Berlin, 2001. Pickands, J.: Statistical inference using extreme order statistics, Ann. Stat., 3, 1, 119-131, 1975. Ribatet, M.: POT: Modelling peaks over a threshold, R News, 7, 34-36, 2007. Rieder, H.E., Staehelin, J., Maeder, J.A., Stübi, R., Weihs, P., Holawe, F., and M. Ribatet: From ozone mini holes and mini highs towards extreme value theory: New insights from extreme events and non stationarity, submitted to J. Geophys. Res., 2009. Staehelin, J., Kegel, R., and Harris, N. R.: Trend analysis of the homogenized total ozone series of Arosa (Switzerland), 1929-1996, J. Geophys. Res., 103(D7), 8389-8400, doi:10.1029/97JD03650, 1998a. Staehelin, J., Renaud, A., Bader, J., McPeters, R., Viatte, P., Hoegger, B., Bugnion, V., Giroud, M., and Schill, H.: Total ozone series at Arosa (Switzerland): Homogenization and data comparison, J. Geophys. Res., 103(D5), 5827-5842, doi:10.1029/97JD02402, 1998b.

Factors that impact expectations before total knee arthroplasty.

PubMed

Hepinstall, Matthew S; Rutledge, John R; Bornstein, Lindsey J; Mazumdar, Madhu; Westrich, Geoffrey H

2011-09-01

This study examined the effect of patient attributes on expectations before total knee arthroplasty (TKA). A total of 1943 patients completed an Expectations Survey before TKA. Demographics, surgical history, baseline Medical Outcomes Study Short Form 36 (SF-36) score, Knee injury and Osteoarthritis Outcome Score (KOOS), and Lower Extremity Activity Scale score were obtained. On univariate analysis, expectations (mean score, 77.6) correlated with SF-36 General Health, age, SF-36 Vitality, KOOS Quality-of-Life, and Lower Extremity Activity Scale. Living alone and history of joint arthroplasty were associated with significantly lower expectations, whereas male sex and white race were associated with higher expectations. On multivariate regression analysis, age, living situation, history of joint arthroplasty, SF-36 General Health, and KOOS Quality-of-Life remained significant predictors of expectations. Our results suggest that high, possibly unrealistic, expectations of TKA are common and should be moderated to maintain patient satisfaction. Copyright © 2011 Elsevier Inc. All rights reserved.

Poor safety climate, long work hours, and musculoskeletal discomfort among Latino horse farm workers.

PubMed

Swanberg, Jennifer; Clouser, Jessica Miller; Gan, Wenqi; Flunker, John C; Westneat, Susan; Browning, Steven R

2017-09-03

This study investigated the prevalence of self-reported musculoskeletal discomfort (MSD) and work-related factors associated with elevated MSD among Latino thoroughbred farm workers. Participants (N = 225) were recruited using a community-based purposive sampling approach to participate in in-person interviews. Of these workers, 85% experienced MSD. MSD was divided into tertiles; the upper tertile was defined as elevated. Multivariable Poisson regression revealed associations between any elevated MSD and longer tenure on horse farms, longer work hours, and poor safety climate. Elevated neck/back MSD was associated with longer tenure, longer work hours, and poor safety climate. Elevated upper extremity MSD was associated with age and poor safety climate. Elevated lower extremity MSD was associated with longer tenure, longer work hours, and being female. Musculoskeletal discomfort is common among these workers. Improving safety climate and minimizing long work hours is recommended.

Method for enhanced accuracy in predicting peptides using liquid separations or chromatography

DOEpatents

Kangas, Lars J.; Auberry, Kenneth J.; Anderson, Gordon A.; Smith, Richard D.

2006-11-14

A method for predicting the elution time of a peptide in chromatographic and electrophoretic separations by first providing a data set of known elution times of known peptides, then creating a plurality of vectors, each vector having a plurality of dimensions, and each dimension representing the elution time of amino acids present in each of these known peptides from the data set. The elution time of any protein is then be predicted by first creating a vector by assigning dimensional values for the elution time of amino acids of at least one hypothetical peptide and then calculating a predicted elution time for the vector by performing a multivariate regression of the dimensional values of the hypothetical peptide using the dimensional values of the known peptides. Preferably, the multivariate regression is accomplished by the use of an artificial neural network and the elution times are first normalized using a transfer function.

400 Years of summer hydroclimate from stable isotopes in Iberian trees

NASA Astrophysics Data System (ADS)

Andreu-Hayles, Laia; Ummenhofer, Caroline C.; Barriendos, Mariano; Schleser, Gerhard H.; Helle, Gerhard; Leuenberger, Markus; Gutiérrez, Emilia; Cook, Edward R.

2017-07-01

Tree rings are natural archives that annually record distinct types of past climate variability depending on the parameters measured. Here, we use ring-width and stable isotopes in cellulose of trees from the northwestern Iberian Peninsula (IP) to understand regional summer hydroclimate over the last 400 years and the associated atmospheric patterns. Correlations between tree rings and climate data demonstrate that isotope signatures in the targeted Iberian pine forests are very sensitive to water availability during the summer period, and are mainly controlled by stomatal conductance. Non-linear methods based on extreme events analysis allow for capturing distinct seasonal climatic variability recorded by tree-ring parameters and asymmetric signals of the associated atmospheric features. Moreover, years with extreme high (low) values in the tree-ring records were characterised by coherent large-scale atmospheric circulation patterns with reduced (enhanced) moisture transport onto the northwestern IP. These analyses of extremes revealed that high/low proxy values do not necessarily correspond to mirror images in the atmospheric anomaly patterns, suggesting different drivers of these patterns and the corresponding signature recorded in the proxies. Regional hydroclimate features across the broader IP and western Europe during extreme wet/dry summers detected by the northwestern IP trees compare favourably to independent multicentury sea level pressure and drought reconstructions for Europe. Historical records also validate our findings that attribute non-linear moisture signals recorded by extreme tree-ring values to distinct large-scale atmospheric patterns and allow for 400-year reconstructions of the frequency of occurrence of extreme conditions in late spring and summer hydroclimate.

400 years of summer hydroclimate from stable isotopes in Iberian trees

NASA Astrophysics Data System (ADS)

Andreu-Hayles, Laia; Ummenhofer, Caroline C.; Barriendos, Mariano; Schleser, Gerhard H.; Helle, Gerhard; Leuenberger, Markus; Gutierrez, Emilia; Cook, Edward R.

2017-04-01

Tree rings are natural archives that annually record distinct types of past climate variability depending on the parameters measured. Here, we use ring-width and stable isotopes in cellulose of trees from the northwestern Iberian Peninsula (IP) to understand regional summer hydroclimate over the last 400 years and the associated atmospheric patterns. Correlations between tree rings and climate data demonstrate that isotope signatures in the targeted Iberian pine forests are very sensitive to water availability during the summer period, and are mainly controlled by stomatal conductance. Non-linear methods based on extreme events analysis allow for capturing distinct seasonal climatic variability recorded by tree-ring parameters and asymmetric signals of the associated atmospheric features. Moreover, years with extreme high (low) values in the tree-ring records were characterised by coherent large-scale atmospheric circulation patterns with reduced (enhanced) moisture transport onto the northwestern IP. These analyses of extremes revealed that high/low proxy values do not necessarily correspond to mirror images in the atmospheric anomaly patterns, suggesting different drivers of these patterns and the corresponding signature recorded in the proxies. Regional hydroclimate features across the broader IP and western Europe during extreme wet/dry summers detected by the northwestern IP trees compare favourably to an independent multicentury sea level pressure and drought reconstruction for Europe. Historical records also validate our findings that attribute non-linear moisture signals recorded by extreme tree-ring values to distinct large-scale atmospheric patterns and allow for 400-yr reconstructions of the frequency of occurrence of extreme conditions in summer hydroclimate. We will discuss how the results for Lillo compare with other records.

Analysis and modeling of extreme temperatures in several cities in northwestern Mexico under climate change conditions

NASA Astrophysics Data System (ADS)

García-Cueto, O. Rafael; Cavazos, M. Tereza; de Grau, Pamela; Santillán-Soto, Néstor

2014-04-01

The generalized extreme value distribution is applied in this article to model the statistical behavior of the maximum and minimum temperature distribution tails in four cities of Baja California in northwestern Mexico, using data from 1950-2010. The approach used of the maximum of annual time blocks. Temporal trends were included as covariates in the location parameter (μ), which resulted in significant improvements to the proposed models, particularly for the extreme maximum temperature values in the cities of Mexicali, Tijuana, and Tecate, and the extreme minimum temperature values in Mexicali and Ensenada. These models were used to estimate future probabilities over the next 100 years (2015-2110) for different time periods, and they were compared with changes in the extreme (P90th and P10th) percentiles of maximum and minimum temperature scenarios for a set of six general circulation models under low (RCP4.5) and high (RCP8.5) radiative forcings. By the end of the twenty-first century, the scenarios of the changes in extreme maximum summer temperature are of the same order in both the statistical model and the high radiative scenario (increases of 4-5 °C). The low radiative scenario is more conservative (increases of 2-3 °C). The winter scenario shows that minimum temperatures could be less severe; the temperature increases suggested by the probabilistic model are greater than those projected for the end of the century by the set of global models under RCP4.5 and RCP8.5 scenarios. The likely impacts on the region are discussed.

Predictive value of sperm morphology and progressively motile sperm count for pregnancy outcomes in intrauterine insemination.

PubMed

Lemmens, Louise; Kos, Snjezana; Beijer, Cornelis; Brinkman, Jacoline W; van der Horst, Frans A L; van den Hoven, Leonie; Kieslinger, Dorit C; van Trooyen-van Vrouwerff, Netty J; Wolthuis, Albert; Hendriks, Jan C M; Wetzels, Alex M M

2016-06-01

To investigate the value of sperm parameters to predict an ongoing pregnancy outcome in couples treated with intrauterine insemination (IUI), during a methodologically stable period of time. Retrospective, observational study with logistic regression analyses. University hospital. A total of 1,166 couples visiting the fertility laboratory for their first IUI episode, including 4,251 IUI cycles. None. Sperm morphology, total progressively motile sperm count (TPMSC), and number of inseminated progressively motile spermatozoa (NIPMS); odds ratios (ORs) of the sperm parameters after the first IUI cycle and the first finished IUI episode; discriminatory accuracy of the multivariable model. None of the sperm parameters was of predictive value for pregnancy after the first IUI cycle. In the first finished IUI episode, a positive relationship was found for ≤4% of morphologically normal spermatozoa (OR 1.39) and a moderate NIPMS (5-10 million; OR 1.73). Low NIPMS showed a negative relation (≤1 million; OR 0.42). The TPMSC had no predictive value. The multivariable model (i.e., sperm morphology, NIPMS, female age, male age, and the number of cycles in the episode) had a moderate discriminatory accuracy (area under the curve 0.73). Intrauterine insemination is especially relevant for couples with moderate male factor infertility (sperm morphology ≤4%, NIPMS 5-10 million). In the multivariable model, however, the predictive power of these sperm parameters is rather low. Copyright © 2016 American Society for Reproductive Medicine. Published by Elsevier Inc. All rights reserved.

Extreme Mean and Its Applications

NASA Technical Reports Server (NTRS)

Swaroop, R.; Brownlow, J. D.

1979-01-01

Extreme value statistics obtained from normally distributed data are considered. An extreme mean is defined as the mean of p-th probability truncated normal distribution. An unbiased estimate of this extreme mean and its large sample distribution are derived. The distribution of this estimate even for very large samples is found to be nonnormal. Further, as the sample size increases, the variance of the unbiased estimate converges to the Cramer-Rao lower bound. The computer program used to obtain the density and distribution functions of the standardized unbiased estimate, and the confidence intervals of the extreme mean for any data are included for ready application. An example is included to demonstrate the usefulness of extreme mean application.

Simulation-Based Extreme Value Marked Correlations in Fatigue of Advanced Engineering Alloys (PREPRINT)

DTIC Science & Technology

2010-04-01

000 the response of damage dependent processes like fatigue crack formation, a framework is needed that accounts for the extreme value life...many different damage processes (e.g. fatigue, creep, fracture). In this work, multiple material volumes for both IN100 and Ti-6Al-4V are simulated via...polycrystalline P/M Ni-base superalloy IN100 Typically, fatigue damage formation in polycrystalline superalloys has been linked to the existence of

Impact of possible climate changes on river runoff under different natural conditions

NASA Astrophysics Data System (ADS)

Gusev, Yeugeniy M.; Nasonova, Olga N.; Kovalev, Evgeny E.; Ayzel, Georgy V.

2018-06-01

The present study was carried out within the framework of the International Inter-Sectoral Impact Model Intercomparison Project (ISI-MIP) for 11 large river basins located in different continents of the globe under a wide variety of natural conditions. The aim of the study was to investigate possible changes in various characteristics of annual river runoff (mean values, standard deviations, frequency of extreme annual runoff) up to 2100 on the basis of application of the land surface model SWAP and meteorological projections simulated by five General Circulation Models (GCMs) according to four RCP scenarios. Analysis of the obtained results has shown that changes in climatic runoff are different (both in magnitude and sign) for the river basins located in different regions of the planet due to differences in natural (primarily climatic) conditions. The climatic elasticities of river runoff to changes in air temperature and precipitation were estimated that makes it possible, as the first approximation, to project changes in climatic values of annual runoff, using the projected changes in mean annual air temperature and annual precipitation for the river basins. It was found that for most rivers under study, the frequency of occurrence of extreme runoff values increases. This is true both for extremely high runoff (when the projected climatic runoff increases) and for extremely low values (when the projected climatic runoff decreases).

Natural Hazards characterisation in industrial practice

NASA Astrophysics Data System (ADS)

Bernardara, Pietro

2017-04-01

The definition of rare hydroclimatic extremes (up to 10-4 annual probability of occurrence) is of the utmost importance for the design of high value industrial infrastructures, such as grids, power plants, offshore platforms. The underestimation as well as the overestimation of the risk may lead to huge costs (ex. mid-life expensive works or overdesign) which may even prevent the project to happen. Nevertheless, the uncertainty associated to the extrapolation towards the rare frequencies are huge and manifold. They are mainly due to the scarcity of observations, the lack of quality on the extreme value records and on the arbitrary choice of the models used for extrapolations. This often put the design engineers in uncomfortable situations when they must choose the design values to use. Providentially, the recent progresses in the earth observation techniques, information technology, historical data collection and weather and ocean modelling are making huge datasets available. A careful use of big datasets of observations and modelled data are leading towards a better understanding of the physics of the underlying phenomena, the complex interactions between them and thus of the extreme events frequency extrapolations. This will move the engineering practice from the single site, small sample, application of statistical analysis to a more spatially coherent, physically driven extrapolation of extreme values. Few examples, from the EDF industrial practice are given to illustrate these progresses and their potential impact on the design approaches.

Univariate and multivariate skewness and kurtosis for measuring nonnormality: Prevalence, influence and estimation.

PubMed

Cain, Meghan K; Zhang, Zhiyong; Yuan, Ke-Hai

2017-10-01

Nonnormality of univariate data has been extensively examined previously (Blanca et al., Methodology: European Journal of Research Methods for the Behavioral and Social Sciences, 9(2), 78-84, 2013; Miceeri, Psychological Bulletin, 105(1), 156, 1989). However, less is known of the potential nonnormality of multivariate data although multivariate analysis is commonly used in psychological and educational research. Using univariate and multivariate skewness and kurtosis as measures of nonnormality, this study examined 1,567 univariate distriubtions and 254 multivariate distributions collected from authors of articles published in Psychological Science and the American Education Research Journal. We found that 74 % of univariate distributions and 68 % multivariate distributions deviated from normal distributions. In a simulation study using typical values of skewness and kurtosis that we collected, we found that the resulting type I error rates were 17 % in a t-test and 30 % in a factor analysis under some conditions. Hence, we argue that it is time to routinely report skewness and kurtosis along with other summary statistics such as means and variances. To facilitate future report of skewness and kurtosis, we provide a tutorial on how to compute univariate and multivariate skewness and kurtosis by SAS, SPSS, R and a newly developed Web application.

Multi-variant study of obesity risk genes in African Americans: The Jackson Heart Study.

PubMed

Liu, Shijian; Wilson, James G; Jiang, Fan; Griswold, Michael; Correa, Adolfo; Mei, Hao

2016-11-30

Genome-wide association study (GWAS) has been successful in identifying obesity risk genes by single-variant association analysis. For this study, we designed steps of analysis strategy and aimed to identify multi-variant effects on obesity risk among candidate genes. Our analyses were focused on 2137 African American participants with body mass index measured in the Jackson Heart Study and 657 common single nucleotide polymorphisms (SNPs) genotyped at 8 GWAS-identified obesity risk genes. Single-variant association test showed that no SNPs reached significance after multiple testing adjustment. The following gene-gene interaction analysis, which was focused on SNPs with unadjusted p-value<0.10, identified 6 significant multi-variant associations. Logistic regression showed that SNPs in these associations did not have significant linear interactions; examination of genetic risk score evidenced that 4 multi-variant associations had significant additive effects of risk SNPs; and haplotype association test presented that all multi-variant associations contained one or several combinations of particular alleles or haplotypes, associated with increased obesity risk. Our study evidenced that obesity risk genes generated multi-variant effects, which can be additive or non-linear interactions, and multi-variant study is an important supplement to existing GWAS for understanding genetic effects of obesity risk genes. Copyright © 2016 Elsevier B.V. All rights reserved.

Projected changes to short- and long-duration precipitation extremes over the Canadian Prairie Provinces

NASA Astrophysics Data System (ADS)

Masud, M. B.; Khaliq, M. N.; Wheater, H. S.

2017-09-01

The effects of climate change on April-October short- and long-duration precipitation extremes over the Canadian Prairie Provinces were evaluated using a multi-Regional Climate Model (RCM) ensemble available through the North American Regional Climate Change Assessment Program. Simulations considered include those performed with six RCMs driven by the National Centre for Environmental Prediction (NCEP) reanalysis II product for the 1981-2000 period and those driven by four Atmosphere-Ocean General Circulation Models (AOGCMs) for the current 1971-2000 and future 2041-2070 periods (i.e. a total of 11 current-to-future period simulation pairs). A regional frequency analysis approach was used to develop 2-, 5-, 10-, 25-, and 50-year return values of precipitation extremes from NCEP and AOGCM-driven current and future period simulations that respectively were used to study the performance of RCMs and projected changes for selected return values at regional, grid-cell and local scales. Performance errors due to internal dynamics and physics of RCMs studied for the 1981-2000 period reveal considerable variation in the performance of the RCMs. However, the performance errors were found to be much smaller for RCM ensemble averages than for individual RCMs. Projected changes in future climate to selected regional return values of short-duration (e.g. 15- and 30-min) precipitation extremes and for longer return periods (e.g. 50-year) were found to be mostly larger than those to the longer duration (e.g. 24- and 48-h) extremes and short return periods (e.g. 2-year). Overall, projected changes in precipitation extremes were larger for southeastern regions followed by southern and northern regions and smaller for southwestern and western regions of the study area. The changes to return values were also found to be statistically significant for the majority of the RCM-AOGCM simulation pairs. These projections might be useful as a key input for the future planning of urban drainage infrastructure and development of strategic climate change adaptation measures.

Perceived ability to perform daily hand activities after stroke and associated factors: a cross-sectional study.

PubMed

Ekstrand, Elisabeth; Rylander, Lars; Lexell, Jan; Brogårdh, Christina

2016-11-02

Despite that disability of the upper extremity is common after stroke, there is limited knowledge how it influences self-perceived ability to perform daily hand activities. The aim of this study was to describe which daily hand activities that persons with mild to moderate impairments of the upper extremity after stroke perceive difficult to perform and to evaluate how several potential factors are associated with the self-perceived performance. Seventy-five persons (72 % male) with mild to moderate impairments of the upper extremity after stroke (4 to 116 months) participated. Self-perceived ability to perform daily hand activities was rated with the ABILHAND Questionnaire. The perceived ability to perform daily hand activities and the potentially associated factors (age, gender, social and vocational situation, affected hand, upper extremity pain, spasticity, grip strength, somatosensation of the hand, manual dexterity, perceived participation and life satisfaction) were evaluated by linear regression models. The activities that were perceived difficult or impossible for a majority of the participants were bimanual tasks that required fine manual dexterity of the more affected hand. The factor that had the strongest association with perceived ability to perform daily hand activities was dexterity (p < 0.001), which together with perceived participation (p = 0.002) explained 48 % of the variance in the final multivariate model. Persons with mild to moderate impairments of the upper extremity after stroke perceive that bimanual activities requiring fine manual dexterity are the most difficult to perform. Dexterity and perceived participation are factors specifically important to consider in the rehabilitation of the upper extremity after stroke in order to improve the ability to use the hands in daily life.

Within-summer variation in out-of-hospital cardiac arrest due to extremely long sunshine duration.

PubMed

Onozuka, Daisuke; Hagihara, Akihito

2017-03-15

Although several studies have reported the impacts of extremely high temperatures on cardiovascular diseases, no studies have examined whether variation in out-of-hospital cardiac arrest (OHCA) due to extremely long sunshine duration changes during the summer. We obtained daily data on all cases of OHCA and weather variations for all 47 prefectures of Japan during the summer (June to September) between 2005 and 2014. A distributed lag non-linear model combined with a quasi-Poisson regression model was used to estimate within-summer variation in OHCA due to extremely long sunshine duration for each prefecture. Then, multivariate random-effects meta-analysis was performed to derive overall effect estimates of sunshine duration at the national level. A total of 166,496 OHCAs of presumed cardiac origin met the inclusion criteria. The minimum morbidity percentile (MMP) was the 0th percentile of sunshine duration at the national level. The overall cumulative relative risk (RR) at the 99th percentile vs. the MMP was 1.15 (95% CI: 1.05-1.27) during the summer. The effect of extremely long sunshine duration on OHCA in early summer was acute and did not persist, whereas an identical effect was observed in late summer, but it was delayed and lasted for several days. During summer periods, excessive sunshine duration could increase the risk of OHCA. Timely preventive measures to reduce the OHCA risk due to extremely long sunshine duration are important in early summer, whereas these measures could include a wider time window of several days to reduce the risk in late summer. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Analysis of extreme values of the economic efficiency indicators of transport infrastructure projects

NASA Astrophysics Data System (ADS)

Korytárová, J.; Vaňková, L.

2017-10-01

Paper builds on previous research of the authors into the evaluation of economic efficiency of transport infrastructure projects evaluated by the economic efficiency ratio - NPV, IRR and BCR. Values of indicators and subsequent outputs of the sensitivity analysis show extremely favourable values in some cases. The authors dealt with the analysis of these indicators down to the level of the input variables and examined which inputs have a larger share of these extreme values. NCF for the calculation of above mentioned ratios is created by benefits that arise as the difference between zero and investment options of the project (savings in travel and operating costs, savings in travel time costs, reduction in accident costs and savings in exogenous costs) as well as total agency costs. Savings in travel time costs which contribute to the overall utility of projects by more than 70% appear to be the most important benefits in the long term horizon. This is the reason why this benefit emphasized. The outcome of the article has resulted how the particular basic variables contributed to the total robustness of economic efficiency of these project.

Evaluation of NASA's MERRA Precipitation Product in Reproducing the Observed Trend and Distribution of Extreme Precipitation Events in the United States

NASA Technical Reports Server (NTRS)

Ashouri, Hamed; Sorooshian, Soroosh; Hsu, Kuo-Lin; Bosilovich, Michael G.; Lee, Jaechoul; Wehner, Michael F.; Collow, Allison

2016-01-01

This study evaluates the performance of NASA's Modern-Era Retrospective Analysis for Research and Applications (MERRA) precipitation product in reproducing the trend and distribution of extreme precipitation events. Utilizing the extreme value theory, time-invariant and time-variant extreme value distributions are developed to model the trends and changes in the patterns of extreme precipitation events over the contiguous United States during 1979-2010. The Climate Prediction Center (CPC) U.S.Unified gridded observation data are used as the observational dataset. The CPC analysis shows that the eastern and western parts of the United States are experiencing positive and negative trends in annual maxima, respectively. The continental-scale patterns of change found in MERRA seem to reasonably mirror the observed patterns of change found in CPC. This is not previously expected, given the difficulty in constraining precipitation in reanalysis products. MERRA tends to overestimate the frequency at which the 99th percentile of precipitation is exceeded because this threshold tends to be lower in MERRA, making it easier to be exceeded. This feature is dominant during the summer months. MERRA tends to reproduce spatial patterns of the scale and location parameters of the generalized extreme value and generalized Pareto distributions. However, MERRA underestimates these parameters, particularly over the Gulf Coast states, leading to lower magnitudes in extreme precipitation events. Two issues in MERRA are identified: 1) MERRA shows a spurious negative trend in Nebraska and Kansas, which is most likely related to the changes in the satellite observing system over time that has apparently affected the water cycle in the central United States, and 2) the patterns of positive trend over the Gulf Coast states and along the East Coast seem to be correlated with the tropical cyclones in these regions. The analysis of the trends in the seasonal precipitation extremes indicates that the hurricane and winter seasons are contributing the most to these trend patterns in the southeastern United States. In addition, the increasing annual trend simulated by MERRA in the Gulf Coast region is due to an incorrect trend in winter precipitation extremes.

Evaluation of NASA’s MERRA Precipitation Product in Reproducing the Observed Trend and Distribution of Extreme Precipitation Events in the United States

DOE PAGES

Ashouri, Hamed; Sorooshian, Soroosh; Hsu, Kuo-Lin; ...

2016-02-03

This study evaluates the performance of NASA's Modern-Era Retrospective Analysis for Research and Applications (MERRA) precipitation product in reproducing the trend and distribution of extreme precipitation events. Utilizing the extreme value theory, time-invariant and time-variant extreme value distributions are developed to model the trends and changes in the patterns of extreme precipitation events over the contiguous United States during 1979-2010. The Climate Prediction Center (CPC)U.S.Unified gridded observation data are used as the observational dataset. The CPC analysis shows that the eastern and western parts of the United States are experiencing positive and negative trends in annual maxima, respectively. The continental-scalemore » patterns of change found in MERRA seem to reasonably mirror the observed patterns of change found in CPC. This is not previously expected, given the difficulty in constraining precipitation in reanalysis products. MERRA tends to overestimate the frequency at which the 99th percentile of precipitation is exceeded because this threshold tends to be lower in MERRA, making it easier to be exceeded. This feature is dominant during the summer months. MERRAtends to reproduce spatial patterns of the scale and location parameters of the generalized extreme value and generalized Pareto distributions. However, MERRA underestimates these parameters, particularly over the Gulf Coast states, leading to lower magnitudes in extreme precipitation events. Two issues in MERRA are identified: 1)MERRAshows a spurious negative trend in Nebraska andKansas, which ismost likely related to the changes in the satellite observing system over time that has apparently affected the water cycle in the central United States, and 2) the patterns of positive trend over theGulf Coast states and along the East Coast seem to be correlated with the tropical cyclones in these regions. The analysis of the trends in the seasonal precipitation extremes indicates that the hurricane and winter seasons are contributing the most to these trend patterns in the southeastern United States. The increasing annual trend simulated by MERRA in the Gulf Coast region is due to an incorrect trend in winter precipitation extremes.« less

[Multi-temporal scale analysis of impacts of extreme high temperature on net carbon uptake in subtropical coniferous plantation.

PubMed

Zhang, Mi; Wen, Xue Fa; Zhang, Lei Ming; Wang, Hui Min; Guo, Yi Wen; Yu, Gui Rui

2018-02-01

Extreme high temperature is one of important extreme weathers that impact forest ecosystem carbon cycle. In this study, applying CO 2 flux and routine meteorological data measured during 2003-2012, we examined the impacts of extreme high temperature and extreme high temperature event on net carbon uptake of subtropical coniferous plantation in Qianyanzhou. Combining with wavelet analysis, we analyzed environmental controls on net carbon uptake at different temporal scales, when the extreme high temperature and extreme high temperature event happened. The results showed that mean daily cumulative NEE decreased by 51% in the days with daily maximum air temperature range between 35 ℃ and 40 ℃, compared with that in the days with the range between 30 ℃ and 34 ℃. The effects of the extreme high temperature and extreme high temperature event on monthly NEE and annual NEE related to the strength and duration of extreme high tempe-rature event. In 2003, when strong extreme high temperature event happened, the sum of monthly cumulative NEE in July and August was only -11.64 g C·m -2 ·(2 month) -1 . The value decreased by 90%, compared with multi-year average value. At the same time, the relative variation of annual NEE reached -6.7%. In July and August, when the extreme high temperature and extreme high temperature event occurred, air temperature (T a ) and vapor press deficit (VPD) were the dominant controller for the daily variation of NEE. The coherency between NEE T a and NEE VPD was 0.97 and 0.95, respectively. At 8-, 16-, and 32-day periods, T a , VPD, soil water content at 5 cm depth (SWC), and precipitation (P) controlled NEE. The coherency between NEE SWC and NEE P was higher than 0.8 at monthly scale. The results indicated that atmospheric water deficit impacted NEE at short temporal scale, when the extreme high temperature and extreme high temperature event occurred, both of atmospheric water deficit and soil drought stress impacted NEE at long temporal scales in this ecosystem.

Individualism-Collectivism: Links to Occupational Plans and Work Values

ERIC Educational Resources Information Center

Hartung, Paul J.; Fouad, Nadya A.; Leong, Frederick T. L.; Hardin, Erin E.

2010-01-01

Individualism-collectivism (IC) constitutes a cultural variable thought to influence a wide variety of variables including career planning and decision making. To examine this possibility, college students (216 women, 106 men, 64% racial-ethnic minorities) responded to measures of IC, occupational plans, and work values. Multivariate analysis of…

Moving Average Models with Bivariate Exponential and Geometric Distributions.

DTIC Science & Technology

1985-03-01

ordinary time series and of point processes. Developments in Statistics, Vol. 1, P.R. Krishnaiah , ed. Academic Press, New York. [9] Esary, J.D. and...valued and discrete - valued time series with ARMA correlation structure. Multivariate Analysis V, P.R. Krishnaiah , ed. North-Holland. 151-166. [28

Evaluation of dynamically downscaled extreme temperature using a spatially-aggregated generalized extreme value (GEV) model

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wang, Jiali; Han, Yuefeng; Stein, Michael L.

2016-02-10

The Weather Research and Forecast (WRF) model downscaling skill in extreme maximum daily temperature is evaluated by using the generalized extreme value (GEV) distribution. While the GEV distribution has been used extensively in climatology and meteorology for estimating probabilities of extreme events, accurately estimating GEV parameters based on data from a single pixel can be difficult, even with fairly long data records. This work proposes a simple method assuming that the shape parameter, the most difficult of the three parameters to estimate, does not vary over a relatively large region. This approach is applied to evaluate 31-year WRF-downscaled extreme maximummore » temperature through comparison with North American Regional Reanalysis (NARR) data. Uncertainty in GEV parameter estimates and the statistical significance in the differences of estimates between WRF and NARR are accounted for by conducting bootstrap resampling. Despite certain biases over parts of the United States, overall, WRF shows good agreement with NARR in the spatial pattern and magnitudes of GEV parameter estimates. Both WRF and NARR show a significant increase in extreme maximum temperature over the southern Great Plains and southeastern United States in January and over the western United States in July. The GEV model shows clear benefits from the regionally constant shape parameter assumption, for example, leading to estimates of the location and scale parameters of the model that show coherent spatial patterns.« less

Extreme storm surge and wind wave climate scenario simulations at the Venetian littoral

NASA Astrophysics Data System (ADS)

Lionello, P.; Galati, M. B.; Elvini, E.

Scenario climate projections for extreme marine storms producing storm surges and wind waves are very important for the northern flat coast of the Adriatic Sea, where the area at risk includes a unique cultural and environmental heritage, and important economic activities. This study uses a shallow water model and a spectral wave model for computing the storm surge and the wind wave field, respectively, from the sea level pressure and wind fields that have been computed by the RegCM regional climate model. Simulations cover the period 1961-1990 for the present climate (control simulations) and the period 2071-2100 for the A2 and B2 scenarios. Generalized Extreme Value analysis is used for estimating values for the 10 and 100 year return times. The adequacy of these modeling tools for a reliable estimation of the climate change signal, without needing further downscaling is shown. However, this study has mainly a methodological value, because issues such as interdecadal variability and intermodel variability cannot be addressed, since the analysis is based on single model 30-year long simulations. The control simulation looks reasonably accurate for extreme value analysis, though it overestimates/underestimates the frequency of high/low surge and wind wave events with respect to observations. Scenario simulations suggest higher frequency of intense storms for the B2 scenario, but not for the A2. Likely, these differences are not the effect of climate change, but of climate multidecadal variability. Extreme storms are stronger in future scenarios, but differences are not statistically significant. Therefore this study does not provide convincing evidence for more stormy conditions in future scenarios.

Multivariate Feature Selection of Image Descriptors Data for Breast Cancer with Computer-Assisted Diagnosis

PubMed Central

Galván-Tejada, Carlos E.; Zanella-Calzada, Laura A.; Galván-Tejada, Jorge I.; Celaya-Padilla, José M.; Gamboa-Rosales, Hamurabi; Garza-Veloz, Idalia; Martinez-Fierro, Margarita L.

2017-01-01

Breast cancer is an important global health problem, and the most common type of cancer among women. Late diagnosis significantly decreases the survival rate of the patient; however, using mammography for early detection has been demonstrated to be a very important tool increasing the survival rate. The purpose of this paper is to obtain a multivariate model to classify benign and malignant tumor lesions using a computer-assisted diagnosis with a genetic algorithm in training and test datasets from mammography image features. A multivariate search was conducted to obtain predictive models with different approaches, in order to compare and validate results. The multivariate models were constructed using: Random Forest, Nearest centroid, and K-Nearest Neighbor (K-NN) strategies as cost function in a genetic algorithm applied to the features in the BCDR public databases. Results suggest that the two texture descriptor features obtained in the multivariate model have a similar or better prediction capability to classify the data outcome compared with the multivariate model composed of all the features, according to their fitness value. This model can help to reduce the workload of radiologists and present a second opinion in the classification of tumor lesions. PMID:28216571

Multivariate Feature Selection of Image Descriptors Data for Breast Cancer with Computer-Assisted Diagnosis.

PubMed

Galván-Tejada, Carlos E; Zanella-Calzada, Laura A; Galván-Tejada, Jorge I; Celaya-Padilla, José M; Gamboa-Rosales, Hamurabi; Garza-Veloz, Idalia; Martinez-Fierro, Margarita L

2017-02-14

Breast cancer is an important global health problem, and the most common type of cancer among women. Late diagnosis significantly decreases the survival rate of the patient; however, using mammography for early detection has been demonstrated to be a very important tool increasing the survival rate. The purpose of this paper is to obtain a multivariate model to classify benign and malignant tumor lesions using a computer-assisted diagnosis with a genetic algorithm in training and test datasets from mammography image features. A multivariate search was conducted to obtain predictive models with different approaches, in order to compare and validate results. The multivariate models were constructed using: Random Forest, Nearest centroid, and K-Nearest Neighbor (K-NN) strategies as cost function in a genetic algorithm applied to the features in the BCDR public databases. Results suggest that the two texture descriptor features obtained in the multivariate model have a similar or better prediction capability to classify the data outcome compared with the multivariate model composed of all the features, according to their fitness value. This model can help to reduce the workload of radiologists and present a second opinion in the classification of tumor lesions.

[Quantitative Evaluation of Metal Artifacts on CT Images on the Basis of Statistics of Extremes].

PubMed

Kitaguchi, Shigetoshi; Imai, Kuniharu; Ueda, Suguru; Hashimoto, Naomi; Hattori, Shouta; Saika, Takahiro; Ono, Yoshifumi

2016-05-01

It is well-known that metal artifacts have a harmful effect on the image quality of computed tomography (CT) images. However, the physical property remains still unknown. In this study, we investigated the relationship between metal artifacts and tube currents using statistics of extremes. A commercially available phantom for measuring CT dose index 160 mm in diameter was prepared and a brass rod 13 mm in diameter was placed at the centerline of the phantom. This phantom was used as a target object to evaluate metal artifacts and was scanned using an area detector CT scanner with various tube currents under a constant tube voltage of 120 kV. Sixty parallel line segments with a length of 100 pixels were placed to cross metal artifacts on CT images and the largest difference between two adjacent CT values in each of 60 CT value profiles of these line segments was employed as a feature variable for measuring metal artifacts; these feature variables were analyzed on the basis of extreme value theory. The CT value variation induced by metal artifacts was statistically characterized by Gumbel distribution, which was one of the extreme value distributions; namely, metal artifacts have the same statistical characteristic as streak artifacts. Therefore, Gumbel evaluation method makes it possible to analyze not only streak artifacts but also metal artifacts. Furthermore, the location parameter in Gumbel distribution was shown to be in inverse proportion to the square root of a tube current. This result suggested that metal artifacts have the same dose dependence as image noises.

Heterogeneity Coefficients for Mahalanobis' D as a Multivariate Effect Size.

PubMed

Del Giudice, Marco

2017-01-01

The Mahalanobis distance D is the multivariate generalization of Cohen's d and can be used as a standardized effect size for multivariate differences between groups. An important issue in the interpretation of D is heterogeneity, that is, the extent to which contributions to the overall effect size are concentrated in a small subset of variables rather than evenly distributed across the whole set. Here I present two heterogeneity coefficients for D based on the Gini coefficient, a well-known index of inequality among values of a distribution. I discuss the properties and limitations of the two coefficients and illustrate their use by reanalyzing some published findings from studies of gender differences.

Assessing Regional Scale Variability in Extreme Value Statistics Under Altered Climate Scenarios

DOE Office of Scientific and Technical Information (OSTI.GOV)

Brunsell, Nathaniel; Mechem, David; Ma, Chunsheng

Recent studies have suggested that low-frequency modes of climate variability can significantly influence regional climate. The climatology associated with extreme events has been shown to be particularly sensitive. This has profound implications for droughts, heat waves, and food production. We propose to examine regional climate simulations conducted over the continental United States by applying a recently developed technique which combines wavelet multi–resolution analysis with information theory metrics. This research is motivated by two fundamental questions concerning the spatial and temporal structure of extreme events. These questions are 1) what temporal scales of the extreme value distributions are most sensitive tomore » alteration by low-frequency climate forcings and 2) what is the nature of the spatial structure of variation in these timescales? The primary objective is to assess to what extent information theory metrics can be useful in characterizing the nature of extreme weather phenomena. Specifically, we hypothesize that (1) changes in the nature of extreme events will impact the temporal probability density functions and that information theory metrics will be sensitive these changes and (2) via a wavelet multi–resolution analysis, we will be able to characterize the relative contribution of different timescales on the stochastic nature of extreme events. In order to address these hypotheses, we propose a unique combination of an established regional climate modeling approach and advanced statistical techniques to assess the effects of low-frequency modes on climate extremes over North America. The behavior of climate extremes in RCM simulations for the 20th century will be compared with statistics calculated from the United States Historical Climatology Network (USHCN) and simulations from the North American Regional Climate Change Assessment Program (NARCCAP). This effort will serve to establish the baseline behavior of climate extremes, the validity of an innovative multi–resolution information theory approach, and the ability of the RCM modeling framework to represent the low-frequency modulation of extreme climate events. Once the skill of the modeling and analysis methodology has been established, we will apply the same approach for the AR5 (IPCC Fifth Assessment Report) climate change scenarios in order to assess how climate extremes and the the influence of lowfrequency variability on climate extremes might vary under changing climate. The research specifically addresses the DOE focus area 2. Simulation of climate extremes under a changing climate. Specific results will include (1) a better understanding of the spatial and temporal structure of extreme events, (2) a thorough quantification of how extreme values are impacted by low-frequency climate teleconnections, (3) increased knowledge of current regional climate models ability to ascertain these influences, and (4) a detailed examination of the how the distribution of extreme events are likely to change under different climate change scenarios. In addition, this research will assess the ability of the innovative wavelet information theory approach to characterize extreme events. Any and all of these results will greatly enhance society’s ability to understand and mitigate the regional ramifications of future global climate change.« less

Risk factors for lower extremity injuries among half marathon and marathon runners of the Lage Landen Marathon Eindhoven 2012: A prospective cohort study in the Netherlands.

PubMed

van Poppel, D; de Koning, J; Verhagen, A P; Scholten-Peeters, G G M

2016-02-01

To determine risk factors for running injuries during the Lage Landen Marathon Eindhoven 2012. Prospective cohort study. Population-based study. This study included 943 runners. Running injuries after the Lage Landen Marathon. Sociodemographic and training-related factors as well as lifestyle factors were considered as potential risk factors and assessed in a questionnaire 1 month before the running event. The association between potential risk factors and injuries was determined, per running distance separately, using univariate and multivariate logistic regression analysis. In total, 154 respondents sustained a running injury. Among the marathon runners, in the univariate model, body mass index ≥ 26 kg/m(2), ≤ 5 years of running experience, and often performing interval training, were significantly associated with running injuries, whereas in the multivariate model only ≤ 5 years of running experience and not performing interval training on a regular basis were significantly associated with running injuries. Among marathon runners, no multivariate model could be created because of the low number of injuries and participants. This study indicates that interval training on a regular basis may be recommended to marathon runners to reduce the risk of injury. © 2015 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

FGWAS: Functional genome wide association analysis.

PubMed

Huang, Chao; Thompson, Paul; Wang, Yalin; Yu, Yang; Zhang, Jingwen; Kong, Dehan; Colen, Rivka R; Knickmeyer, Rebecca C; Zhu, Hongtu

2017-10-01

Functional phenotypes (e.g., subcortical surface representation), which commonly arise in imaging genetic studies, have been used to detect putative genes for complexly inherited neuropsychiatric and neurodegenerative disorders. However, existing statistical methods largely ignore the functional features (e.g., functional smoothness and correlation). The aim of this paper is to develop a functional genome-wide association analysis (FGWAS) framework to efficiently carry out whole-genome analyses of functional phenotypes. FGWAS consists of three components: a multivariate varying coefficient model, a global sure independence screening procedure, and a test procedure. Compared with the standard multivariate regression model, the multivariate varying coefficient model explicitly models the functional features of functional phenotypes through the integration of smooth coefficient functions and functional principal component analysis. Statistically, compared with existing methods for genome-wide association studies (GWAS), FGWAS can substantially boost the detection power for discovering important genetic variants influencing brain structure and function. Simulation studies show that FGWAS outperforms existing GWAS methods for searching sparse signals in an extremely large search space, while controlling for the family-wise error rate. We have successfully applied FGWAS to large-scale analysis of data from the Alzheimer's Disease Neuroimaging Initiative for 708 subjects, 30,000 vertices on the left and right hippocampal surfaces, and 501,584 SNPs. Copyright © 2017 Elsevier Inc. All rights reserved.

Generation of synthetic flood hydrographs by hydrological donors (SHYDONHY method)

NASA Astrophysics Data System (ADS)

Paquet, Emmanuel

2017-04-01

For the design of hydraulic infrastructures like dams, a design hydrograph is required in most of the cases. Some of its features (e.g. peak value, duration, volume) corresponding to a given return period are computed thanks to a wide range of methods: historical records, mono or multivariate statistical analysis, stochastic simulation, etc. Then various methods have been proposed to construct design hydrographs having such characteristics, ranging from traditional unit-hydrograph to statistical methods (Yue et al., 2002). A new method to build design hydrographs (or more generally synthetic hydrographs) is introduced here, named SHYDONHY, French acronym for "Synthèse d'HYdrogrammes par DONneurs HYdrologiques". It is based on an extensive database of 100 000 flood hydrographs recorded at hourly time-step on 1300 gauging stations in France and Switzerland, covering a wide range of catchment size and climatology. For each station, an average of two hydrographs per year of record has been selected by a peak-over-threshold (POT) method with independence criteria (Lang et al., 1999). This sampling ensures that only hydrographs of intense floods are gathered in the dataset. For a given catchment, where few or no hydrograph is available at the outlet, a sub-set of 10 "donor stations" is selected within the complete dataset, considering several criteria: proximity, size, mean annual values and regimes for both total runoff and POT-selected floods. This sub-set of stations (and their corresponding flood hydrographs) will allow to: • Estimate a characteristic duration of flood hydrographs (e.g. duration for which the discharge is above 50% of the peak value). • For a given duration (e.g. one day), estimate the average peak-to- volume ratio of floods. • For a given duration and peak-to-volume ratio, generation of a synthetic reference hydrograph by combining appropriate hydrographs of the sub-set. • For a given daily discharge sequence, being observed or generated for extreme flood estimation, generate a suitable synthetic hydrograph, also by combining selected hydrographs of the sub-set. The reliability of the method is assessed by performing a jackknife validation on the whole dataset of stations, in particular by reconstructing the hydrograph of the biggest flood of each station and comparing it to the actual one. Some applications are presented, e.g. the coupling of SHYDONHY with the SCHADEX method (Paquet et al., 2003) for the stochastic simulation of extreme reservoir level in dams. References: Lang, M., Ouarda, T. B. M. J., & Bobée, B. (1999). Towards operational guidelines for over-threshold modeling. Journal of hydrology, 225(3), 103-117. Paquet, E., Garavaglia, F., Garçon, R., & Gailhard, J. (2013). The SCHADEX method: A semi-continuous rainfall-runoff simulation for extreme flood estimation. Journal of Hydrology, 495, 23-37. Yue, S., Ouarda, T. B., Bobée, B., Legendre, P., & Bruneau, P. (2002). Approach for describing statistical properties of flood hydrograph. Journal of hydrologic engineering, 7(2), 147-153.

A multivariate multilevel Gaussian model with a mixed effects structure in the mean and covariance part.

PubMed

Li, Baoyue; Bruyneel, Luk; Lesaffre, Emmanuel

2014-05-20

A traditional Gaussian hierarchical model assumes a nested multilevel structure for the mean and a constant variance at each level. We propose a Bayesian multivariate multilevel factor model that assumes a multilevel structure for both the mean and the covariance matrix. That is, in addition to a multilevel structure for the mean we also assume that the covariance matrix depends on covariates and random effects. This allows to explore whether the covariance structure depends on the values of the higher levels and as such models heterogeneity in the variances and correlation structure of the multivariate outcome across the higher level values. The approach is applied to the three-dimensional vector of burnout measurements collected on nurses in a large European study to answer the research question whether the covariance matrix of the outcomes depends on recorded system-level features in the organization of nursing care, but also on not-recorded factors that vary with countries, hospitals, and nursing units. Simulations illustrate the performance of our modeling approach. Copyright © 2013 John Wiley & Sons, Ltd.

Complex numbers in chemometrics: examples from multivariate impedance measurements on lipid monolayers.

PubMed

Geladi, Paul; Nelson, Andrew; Lindholm-Sethson, Britta

2007-07-09

Electrical impedance gives multivariate complex number data as results. Two examples of multivariate electrical impedance data measured on lipid monolayers in different solutions give rise to matrices (16x50 and 38x50) of complex numbers. Multivariate data analysis by principal component analysis (PCA) or singular value decomposition (SVD) can be used for complex data and the necessary equations are given. The scores and loadings obtained are vectors of complex numbers. It is shown that the complex number PCA and SVD are better at concentrating information in a few components than the naïve juxtaposition method and that Argand diagrams can replace score and loading plots. Different concentrations of Magainin and Gramicidin A give different responses and also the role of the electrolyte medium can be studied. An interaction of Gramicidin A in the solution with the monolayer over time can be observed.

A Multivariate Methodological Workflow for the Analysis of FTIR Chemical Mapping Applied on Historic Paint Stratigraphies

PubMed Central

Sciutto, Giorgia; Oliveri, Paolo; Catelli, Emilio; Bonacini, Irene

2017-01-01

In the field of applied researches in heritage science, the use of multivariate approach is still quite limited and often chemometric results obtained are often underinterpreted. Within this scenario, the present paper is aimed at disseminating the use of suitable multivariate methodologies and proposes a procedural workflow applied on a representative group of case studies, of considerable importance for conservation purposes, as a sort of guideline on the processing and on the interpretation of this FTIR data. Initially, principal component analysis (PCA) is performed and the score values are converted into chemical maps. Successively, the brushing approach is applied, demonstrating its usefulness for a deep understanding of the relationships between the multivariate map and PC score space, as well as for the identification of the spectral bands mainly involved in the definition of each area localised within the score maps. PMID:29333162

Spatial distribution of precipitation extremes in Norway

NASA Astrophysics Data System (ADS)

Verpe Dyrrdal, Anita; Skaugen, Thomas; Lenkoski, Alex; Thorarinsdottir, Thordis; Stordal, Frode; Førland, Eirik J.

2015-04-01

Estimates of extreme precipitation, in terms of return levels, are crucial in planning and design of important infrastructure. Through two separate studies, we have examined the levels and spatial distribution of daily extreme precipitation over catchments in Norway, and hourly extreme precipitation in a point. The analyses were carried out through the development of two new methods for estimating extreme precipitation in Norway. For daily precipitation we fit the Generalized Extreme Value (GEV) distribution to areal time series from a gridded dataset, consisting of daily precipitation during the period 1957-today with a resolution of 1x1 km². This grid-based method is more objective and less manual and time-consuming compared to the existing method at MET Norway. In addition, estimates in ungauged catchments are easier to obtain, and the GEV approach includes a measure of uncertainty, which is a requirement in climate studies today. Further, we go into depth on the debated GEV shape parameter, which plays an important role for longer return periods. We show that it varies according to dominating precipitation types, having positive values in the southeast and negative values in the southwest. We also find indications that the degree of orographic enhancement might affect the shape parameter. For hourly precipitation, we estimate return levels on a 1x1 km² grid, by linking GEV distributions with latent Gaussian fields in a Bayesian hierarchical model (BHM). Generalized linear models on the GEV parameters, estimated from observations, are able to incorporate location-specific geographic and meteorological information and thereby accommodate these effects on extreme precipitation. Gaussian fields capture additional unexplained spatial heterogeneity and overcome the sparse grid on which observations are collected, while a Bayesian model averaging component directly assesses model uncertainty. We find that mean summer precipitation, mean summer temperature, latitude, longitude, mean annual precipitation and elevation are good covariate candidates for hourly precipitation in our model. Summer indices succeed because hourly precipitation extremes often occur during the convective season. The spatial distribution of hourly and daily precipitation differs in Norway. Daily precipitation extremes are larger along the southwestern coast, where large-scale frontal systems dominate during fall season and the mountain ridge generates strong orographic enhancement. The largest hourly precipitation extremes are mostly produced by intense convective showers during summer, and are thus found along the entire southern coast, including the Oslo-region.

Normative Data for the Cognitively Intact Oldest-Old: The Framingham Heart Study.

PubMed

Miller, Ivy N; Himali, Jayandra J; Beiser, Alexa S; Murabito, Joanne M; Seshadri, Sudha; Wolf, Philip A; Au, Rhoda

2015-01-01

BACKGROUND/STUDY CONTEXT: The number of individuals who reach extreme age is quickly increasing. Much of the current literature focuses on impaired cognition in extreme age, and debate continues regarding what constitutes "normal" cognition in extreme age. This study aimed to provide oldest-old normative data and to compare cognitive performances of cognitively intact elderly individuals from the Framingham Heart Study. A total of 1302 individuals aged 65+ years from the Framingham Heart Study were separated into 5-year age bands and compared on cognitive tests. Multivariate linear regression analyses were conducted, adjusting for gender, the Wide Range Achievement Test-Third Edition (WRAT-III) Reading score, and cohort. Analyses also included comparisons between 418 individuals aged 80+ and 884 individuals aged 65-79, and comparisons within oldest-old age bands. Normative data for all participants are presented. Significant differences were found on most tests between age groups in the overall analysis between young-old and oldest-old, and analysis of oldest-old age bands also revealed select significant differences (all ps <.05). As aging increases, significant cognitive differences and increased variability in performances are evident. These results support the use of age-appropriate normative data for oldest-old individuals.

Normative Data for the Cognitively-Intact Oldest-Old: The Framingham Heart Study

PubMed Central

Miller, Ivy N.; Himali, Jayandra J.; Beiser, Alexa S.; Murabito, Joanne M.; Seshadri, Sudha; Wolf, Philip A.; Au, Rhoda

2017-01-01

Background The number of individuals who reach extreme age is quickly increasing. Much of the current literature focuses on impaired cognition in extreme age, and debate continues regarding what constitutes “normal” cognition in extreme age. This study aimed to provide oldest-old normative data and to compare cognitive performances of cognitively-intact elderly individuals from the Framingham Heart Study. Methods 1302 individuals ages 65+ years old from the Framingham Heart Study were separated into five-year age bands and compared on cognitive tests. Multivariate linear regression analyses were conducted, adjusting for gender, the WRAT-III Reading score, and cohort. Analyses also included comparisons between 418 individuals ages 80+ and 884 individuals ages 65–79, and comparisons within oldest-old age bands. Results Normative data for all participants are presented. Significant differences were found on most tests between age groups in the overall analysis between young-old and oldest-old, and analysis of oldest-old age bands also revealed select significant differences (all p’s <.05). Conclusion As aging increases, significant cognitive differences and increased variability in performances are evident. These results support the use of age appropriate normative data for oldest-old individuals. PMID:26214098

Combination of radar and daily precipitation data to estimate meaningful sub-daily point precipitation extremes

NASA Astrophysics Data System (ADS)

Pegram, Geoff; Bardossy, Andras; Sinclair, Scott

2017-04-01

The use of radar measurements for the space time estimation of precipitation has for many decades been a central topic in hydro-meteorology. In this presentation we are interested specifically in daily and sub-daily extreme values of precipitation at gauged or ungauged locations which are important for design. The purpose of the presentation is to develop a methodology to combine daily precipitation observations and radar measurements to estimate sub-daily extremes at point locations. Radar data corrected using precipitation-reflectivity relationships lead to biased estimations of extremes. Different possibilities of correcting systematic errors using the daily observations are investigated. Observed gauged daily amounts are interpolated to un-sampled points and subsequently disaggregated using the sub-daily values obtained by the radar. Different corrections based on the spatial variability and the sub-daily entropy of scaled rainfall distributions are used to provide unbiased corrections of short duration extremes. In addition, a statistical procedure not based on a matching day by day correction is tested. In this last procedure, as we are only interested in rare extremes, low to medium values of rainfall depth were neglected leaving 12 days of ranked daily maxima in each set per year, whose sum typically comprises about 50% of each annual rainfall total. The sum of these 12 day maxima is first interpolated using a Kriging procedure. Subsequently this sum is disaggregated to daily values using a nearest neighbour procedure. The daily sums are then disaggregated by using the relative values of the biggest 12 radar based days in each year. Of course, the timings of radar and gauge maxima can be different, so the new method presented here uses radar for disaggregating daily gauge totals down to 15 min intervals in order to extract the maxima of sub-hourly through to daily rainfall. The methodologies were tested in South Africa, where an S-band radar operated relatively continuously at Bethlehem from 1998 to 2003, whose scan at 1.5 km above ground [CAPPI] overlapped a dense [10 km spacing] set of 45 pluviometers recording in the same 6-year period. This valuable set of data was obtained from each of 37 selected radar pixels [1 km square in plan] which contained a pluviometer, not masked out by the radar foot-print. The pluviometer data were also aggregated to daily totals, for the same purpose. The extremes obtained using disaggregation methods were compared to the observed extremes in a cross validation procedure. The unusual and novel goal was not to obtain the reproduction of the precipitation matching in space and time, but to obtain frequency distributions of the point extremes, which we found to be stable. Published as: Bárdossy, A., and G. G. S. Pegram (2017) Journal of Hydrology, Volume 544, pp 397-406

Ethical research as the target of animal extremism: an international problem.

PubMed

Conn, P Michael; Rantin, F T

2010-02-01

Animal extremism has been increasing worldwide; frequently researchers are the targets of actions by groups with extreme animal rights agendas. Sometimes this targeting is violent and may involve assaults on family members or destruction of property. In this article, we summarize recent events and suggest steps that researchers can take to educate the public on the value of animal research both for people and animals.

Estimating the extreme low-temperature event using nonparametric methods

NASA Astrophysics Data System (ADS)

D'Silva, Anisha

This thesis presents a new method of estimating the one-in-N low temperature threshold using a non-parametric statistical method called kernel density estimation applied to daily average wind-adjusted temperatures. We apply our One-in-N Algorithm to local gas distribution companies (LDCs), as they have to forecast the daily natural gas needs of their consumers. In winter, demand for natural gas is high. Extreme low temperature events are not directly related to an LDCs gas demand forecasting, but knowledge of extreme low temperatures is important to ensure that an LDC has enough capacity to meet customer demands when extreme low temperatures are experienced. We present a detailed explanation of our One-in-N Algorithm and compare it to the methods using the generalized extreme value distribution, the normal distribution, and the variance-weighted composite distribution. We show that our One-in-N Algorithm estimates the one-in- N low temperature threshold more accurately than the methods using the generalized extreme value distribution, the normal distribution, and the variance-weighted composite distribution according to root mean square error (RMSE) measure at a 5% level of significance. The One-in- N Algorithm is tested by counting the number of times the daily average wind-adjusted temperature is less than or equal to the one-in- N low temperature threshold.

A multivariate mixed model system for wood specific gravity and moisture content of planted loblolly pine stands in the southern United States

Treesearch

Finto Antony; Laurence R. Schimleck; Alex Clark; Richard F. Daniels

2012-01-01

Specific gravity (SG) and moisture content (MC) both have a strong influence on the quantity and quality of wood fiber. We proposed a multivariate mixed model system to model the two properties simultaneously. Disk SG and MC at different height levels were measured from 3 trees in 135 stands across the natural range of loblolly pine and the stand level values were used...

The Logic of Values Clarification

ERIC Educational Resources Information Center

Kazepides, A. C.

1977-01-01

Traces the origin of the Values Clarification movement in education in Carl Roger's clien-centered therapy and exposes its unwarranted extreme ethical stance. Examines a model episode of values clarification and shows how the theoretical confusions of the Values Clarification proponents are reflected in their actual teaching strategies. (Editor/RK)

A Metastatistical Approach to Satellite Estimates of Extreme Rainfall Events

NASA Astrophysics Data System (ADS)

Zorzetto, E.; Marani, M.

2017-12-01

The estimation of the average recurrence interval of intense rainfall events is a central issue for both hydrologic modeling and engineering design. These estimates require the inference of the properties of the right tail of the statistical distribution of precipitation, a task often performed using the Generalized Extreme Value (GEV) distribution, estimated either from a samples of annual maxima (AM) or with a peaks over threshold (POT) approach. However, these approaches require long and homogeneous rainfall records, which often are not available, especially in the case of remote-sensed rainfall datasets. We use here, and tailor it to remotely-sensed rainfall estimates, an alternative approach, based on the metastatistical extreme value distribution (MEVD), which produces estimates of rainfall extreme values based on the probability distribution function (pdf) of all measured `ordinary' rainfall event. This methodology also accounts for the interannual variations observed in the pdf of daily rainfall by integrating over the sample space of its random parameters. We illustrate the application of this framework to the TRMM Multi-satellite Precipitation Analysis rainfall dataset, where MEVD optimally exploits the relatively short datasets of satellite-sensed rainfall, while taking full advantage of its high spatial resolution and quasi-global coverage. Accuracy of TRMM precipitation estimates and scale issues are here investigated for a case study located in the Little Washita watershed, Oklahoma, using a dense network of rain gauges for independent ground validation. The methodology contributes to our understanding of the risk of extreme rainfall events, as it allows i) an optimal use of the TRMM datasets in estimating the tail of the probability distribution of daily rainfall, and ii) a global mapping of daily rainfall extremes and distributional tail properties, bridging the existing gaps in rain gauges networks.

Assessing the Implications of Changing Extreme Value Distributions of Weather on Carbon and Water Cycling in Grasslands

NASA Astrophysics Data System (ADS)

Brunsell, N. A.; Nippert, J. B.

2011-12-01

As the climate warms, it is generally acknowledged that the number and magnitude of extreme weather events will increase. We examined an ecophysiological model's responses to precipitation and temperature anomalies in relation to the mean and variance of annual precipitation along a pronounced precipitation gradient from eastern to western Kansas. This natural gradient creates a template of potential responses for both the mean and variance of annual precipitation to compare the timescales of carbon and water fluxes. Using data from several Ameriflux sites (KZU and KFS) and a third eddy covariance tower (K4B) along the gradient, BIOME-BGC was used to characterize water and carbon cycle responses to extreme weather events. Changes in the extreme value distributions were based on SRES A1B and A2 scenarios using an ensemble mean of 21 GCMs for the region, downscaled using a stochastic weather generator. We focused on changing the timing and magnitude of precipitation and altering the diurnal and seasonal temperature ranges. Biome-BGC was then forced with daily output from the stochastic weather generator, and we examined how potential changes in these extreme value distributions impact carbon and water cycling at the sites across the Kansas precipitation gradient at time scales ranging from daily to interannual. To decompose the time scales of response, we applied a wavelet based information theory analysis approach. Results indicate impacts in soil moisture memory and carbon allocation processes, which vary in response to both the mean and variance of precipitation along the precipitation gradient. These results suggest a more pronounced focus ecosystem responses to extreme events across a range of temporal scales in order to fully characterize the water and carbon cycle responses to global climate change.

Cerebral Small Vessel Disease Burden Is Associated with Motor Performance of Lower and Upper Extremities in Community-Dwelling Populations

PubMed Central

Su, Ning; Zhai, Fei-Fei; Zhou, Li-Xin; Ni, Jun; Yao, Ming; Li, Ming-Li; Jin, Zheng-Yu; Gong, Gao-Lang; Zhang, Shu-Yang; Cui, Li-Ying; Tian, Feng; Zhu, Yi-Cheng

2017-01-01

Objective: To investigate the correlation between cerebral small vessel disease (CSVD) burden and motor performance of lower and upper extremities in community-dwelling populations. Methods: We performed a cross-sectional analysis on 770 participants enrolled in the Shunyi study, which is a population-based cohort study. CSVD burden, including white matter hyperintensities (WMH), lacunes, cerebral microbleeds (CMBs), perivascular spaces (PVS), and brain atrophy were measured using 3T magnetic resonance imaging. All participants underwent quantitative motor assessment of lower and upper extremities, which included 3-m walking speed, 5-repeat chair-stand time, 10-repeat pronation–supination time, and 10-repeat finger-tapping time. Data on demographic characteristics, vascular risk factors, and cognitive functions were collected. General linear model analysis was performed to identify potential correlations between motor performance measures and imaging markers of CSVD after controlling for confounding factors. Results: For motor performance of the lower extremities, WMH was negatively associated with gait speed (standardized β = -0.092, p = 0.022) and positively associated with chair-stand time (standardized β = 0.153, p < 0.0001, surviving FDR correction). For motor performance of the upper extremities, pronation–supination time was positively associated with WMH (standardized β = 0.155, p < 0.0001, surviving FDR correction) and negatively with brain parenchymal fraction (BPF; standardized β = -0.125, p = 0.011, surviving FDR correction). Only BPF was found to be negatively associated with finger-tapping time (standardized β = -0.123, p = 0.012). However, lacunes, CMBs, or PVS were not found to be associated with motor performance of lower or upper extremities in multivariable analysis. Conclusion: Our findings suggest that cerebral microstructural changes related to CSVD may affect motor performance of both lower and upper extremities. WMH and brain atrophy are most strongly associated with motor function deterioration in community-dwelling populations. PMID:29021757

Cerebral Small Vessel Disease Burden Is Associated with Motor Performance of Lower and Upper Extremities in Community-Dwelling Populations.

PubMed

Su, Ning; Zhai, Fei-Fei; Zhou, Li-Xin; Ni, Jun; Yao, Ming; Li, Ming-Li; Jin, Zheng-Yu; Gong, Gao-Lang; Zhang, Shu-Yang; Cui, Li-Ying; Tian, Feng; Zhu, Yi-Cheng

2017-01-01

Objective: To investigate the correlation between cerebral small vessel disease (CSVD) burden and motor performance of lower and upper extremities in community-dwelling populations. Methods: We performed a cross-sectional analysis on 770 participants enrolled in the Shunyi study, which is a population-based cohort study. CSVD burden, including white matter hyperintensities (WMH), lacunes, cerebral microbleeds (CMBs), perivascular spaces (PVS), and brain atrophy were measured using 3T magnetic resonance imaging. All participants underwent quantitative motor assessment of lower and upper extremities, which included 3-m walking speed, 5-repeat chair-stand time, 10-repeat pronation-supination time, and 10-repeat finger-tapping time. Data on demographic characteristics, vascular risk factors, and cognitive functions were collected. General linear model analysis was performed to identify potential correlations between motor performance measures and imaging markers of CSVD after controlling for confounding factors. Results: For motor performance of the lower extremities, WMH was negatively associated with gait speed (standardized β = -0.092, p = 0.022) and positively associated with chair-stand time (standardized β = 0.153, p < 0.0001, surviving FDR correction). For motor performance of the upper extremities, pronation-supination time was positively associated with WMH (standardized β = 0.155, p < 0.0001, surviving FDR correction) and negatively with brain parenchymal fraction (BPF; standardized β = -0.125, p = 0.011, surviving FDR correction). Only BPF was found to be negatively associated with finger-tapping time (standardized β = -0.123, p = 0.012). However, lacunes, CMBs, or PVS were not found to be associated with motor performance of lower or upper extremities in multivariable analysis. Conclusion: Our findings suggest that cerebral microstructural changes related to CSVD may affect motor performance of both lower and upper extremities. WMH and brain atrophy are most strongly associated with motor function deterioration in community-dwelling populations.

Exact extreme-value statistics at mixed-order transitions.

PubMed

Bar, Amir; Majumdar, Satya N; Schehr, Grégory; Mukamel, David

2016-05-01

We study extreme-value statistics for spatially extended models exhibiting mixed-order phase transitions (MOT). These are phase transitions that exhibit features common to both first-order (discontinuity of the order parameter) and second-order (diverging correlation length) transitions. We consider here the truncated inverse distance squared Ising model, which is a prototypical model exhibiting MOT, and study analytically the extreme-value statistics of the domain lengths The lengths of the domains are identically distributed random variables except for the global constraint that their sum equals the total system size L. In addition, the number of such domains is also a fluctuating variable, and not fixed. In the paramagnetic phase, we show that the distribution of the largest domain length l_{max} converges, in the large L limit, to a Gumbel distribution. However, at the critical point (for a certain range of parameters) and in the ferromagnetic phase, we show that the fluctuations of l_{max} are governed by novel distributions, which we compute exactly. Our main analytical results are verified by numerical simulations.

Estimating maximum instantaneous distortion from inlet total pressure rms and PSD measurements. [Root Mean Square and Power Spectral Density methods

NASA Technical Reports Server (NTRS)

Melick, H. C., Jr.; Ybarra, A. H.; Bencze, D. P.

1975-01-01

An inexpensive method is developed to determine the extreme values of instantaneous inlet distortion. This method also provides insight into the basic mechanics of unsteady inlet flow and the associated engine reaction. The analysis is based on fundamental fluid dynamics and statistical methods to provide an understanding of the turbulent inlet flow and quantitatively relate the rms level and power spectral density (PSD) function of the measured time variant total pressure fluctuations to the strength and size of the low pressure regions. The most probable extreme value of the instantaneous distortion is then synthesized from this information in conjunction with the steady state distortion. Results of the analysis show the extreme values to be dependent upon the steady state distortion, the measured turbulence rms level and PSD function, the time on point, and the engine response characteristics. Analytical projections of instantaneous distortion are presented and compared with data obtained by a conventional, highly time correlated, 40 probe instantaneous pressure measurement system.

Time-varying nonstationary multivariate risk analysis using a dynamic Bayesian copula

NASA Astrophysics Data System (ADS)

Sarhadi, Ali; Burn, Donald H.; Concepción Ausín, María.; Wiper, Michael P.

2016-03-01

A time-varying risk analysis is proposed for an adaptive design framework in nonstationary conditions arising from climate change. A Bayesian, dynamic conditional copula is developed for modeling the time-varying dependence structure between mixed continuous and discrete multiattributes of multidimensional hydrometeorological phenomena. Joint Bayesian inference is carried out to fit the marginals and copula in an illustrative example using an adaptive, Gibbs Markov Chain Monte Carlo (MCMC) sampler. Posterior mean estimates and credible intervals are provided for the model parameters and the Deviance Information Criterion (DIC) is used to select the model that best captures different forms of nonstationarity over time. This study also introduces a fully Bayesian, time-varying joint return period for multivariate time-dependent risk analysis in nonstationary environments. The results demonstrate that the nature and the risk of extreme-climate multidimensional processes are changed over time under the impact of climate change, and accordingly the long-term decision making strategies should be updated based on the anomalies of the nonstationary environment.

Shape model of the maxillary dental arch using Fourier descriptors with an application in the rehabilitation for edentulous patient.

PubMed

Rijal, Omar M; Abdullah, Norli A; Isa, Zakiah M; Noor, Norliza M; Tawfiq, Omar F

2013-01-01

The knowledge of teeth positions on the maxillary arch is useful in the rehabilitation of the edentulous patient. A combination of angular (θ), and linear (l) variables representing position of four teeth were initially proposed as the shape descriptor of the maxillary dental arch. Three categories of shape were established, each having a multivariate normal distribution. It may be argued that 4 selected teeth on the standardized digital images of the dental casts could be considered as insufficient with respect to representing shape. However, increasing the number of points would create problems with dimensions and proof of existence of the multivariate normal distribution is extremely difficult. This study investigates the ability of Fourier descriptors (FD) using all maxillary teeth to find alternative shape models. Eight FD terms were sufficient to represent 21 points on the arch. Using these 8 FD terms as an alternative shape descriptor, three categories of shape were verified, each category having the complex normal distribution.

A Non-Stationary Approach for Estimating Future Hydroclimatic Extremes Using Monte-Carlo Simulation

NASA Astrophysics Data System (ADS)

Byun, K.; Hamlet, A. F.

2017-12-01

There is substantial evidence that observed hydrologic extremes (e.g. floods, extreme stormwater events, and low flows) are changing and that climate change will continue to alter the probability distributions of hydrologic extremes over time. These non-stationary risks imply that conventional approaches for designing hydrologic infrastructure (or making other climate-sensitive decisions) based on retrospective analysis and stationary statistics will become increasingly problematic through time. To develop a framework for assessing risks in a non-stationary environment our study develops a new approach using a super ensemble of simulated hydrologic extremes based on Monte Carlo (MC) methods. Specifically, using statistically downscaled future GCM projections from the CMIP5 archive (using the Hybrid Delta (HD) method), we extract daily precipitation (P) and temperature (T) at 1/16 degree resolution based on a group of moving 30-yr windows within a given design lifespan (e.g. 10, 25, 50-yr). Using these T and P scenarios we simulate daily streamflow using the Variable Infiltration Capacity (VIC) model for each year of the design lifespan and fit a Generalized Extreme Value (GEV) probability distribution to the simulated annual extremes. MC experiments are then used to construct a random series of 10,000 realizations of the design lifespan, estimating annual extremes using the estimated unique GEV parameters for each individual year of the design lifespan. Our preliminary results for two watersheds in Midwest show that there are considerable differences in the extreme values for a given percentile between conventional MC and non-stationary MC approach. Design standards based on our non-stationary approach are also directly dependent on the design lifespan of infrastructure, a sensitivity which is notably absent from conventional approaches based on retrospective analysis. The experimental approach can be applied to a wide range of hydroclimatic variables of interest.

Spatial variability of extreme rainfall at radar subpixel scale

NASA Astrophysics Data System (ADS)

Peleg, Nadav; Marra, Francesco; Fatichi, Simone; Paschalis, Athanasios; Molnar, Peter; Burlando, Paolo

2018-01-01

Extreme rainfall is quantified in engineering practice using Intensity-Duration-Frequency curves (IDF) that are traditionally derived from rain-gauges and more recently also from remote sensing instruments, such as weather radars. These instruments measure rainfall at different spatial scales: rain-gauge samples rainfall at the point scale while weather radar averages precipitation on a relatively large area, generally around 1 km2. As such, a radar derived IDF curve is representative of the mean areal rainfall over a given radar pixel and neglects the within-pixel rainfall variability. In this study, we quantify subpixel variability of extreme rainfall by using a novel space-time rainfall generator (STREAP model) that downscales in space the rainfall within a given radar pixel. The study was conducted using a unique radar data record (23 years) and a very dense rain-gauge network in the Eastern Mediterranean area (northern Israel). Radar-IDF curves, together with an ensemble of point-based IDF curves representing the radar subpixel extreme rainfall variability, were developed fitting Generalized Extreme Value (GEV) distributions to annual rainfall maxima. It was found that the mean areal extreme rainfall derived from the radar underestimate most of the extreme values computed for point locations within the radar pixel (on average, ∼70%). The subpixel variability of rainfall extreme was found to increase with longer return periods and shorter durations (e.g. from a maximum variability of 10% for a return period of 2 years and a duration of 4 h to 30% for 50 years return period and 20 min duration). For the longer return periods, a considerable enhancement of extreme rainfall variability was found when stochastic (natural) climate variability was taken into account. Bounding the range of the subpixel extreme rainfall derived from radar-IDF can be of major importance for different applications that require very local estimates of rainfall extremes.

The Economic Value of Career Counseling Services for College Students in South Korea

ERIC Educational Resources Information Center

Choi, Bo Young; Lee, Ji Hee; Kim, Areum; Kim, Boram; Cho, Daeyeon; Lee, Sang Min

2013-01-01

This study investigated college students' perception of the monetary value of career counseling services by using the contingent valuation method. The results of a multivariate survival analysis based on interviews with a convenience sample of 291 undergraduate students in South Korea indicate that, on average, participants' expressed willingness…

Motivations of Traditional and Nontraditional College Students: From Self-Determination and Attributions, to Expectancy and Values

ERIC Educational Resources Information Center

Johnson, Marcus Lee; Taasoobshirazi, Gita; Clark, Lauren; Howell, Leah; Breen, Mishele

2016-01-01

We surveyed 139 (88 traditional, 51 nontraditional) students on various motivational measures of self-determination, attribution, and expectancy-value to (a) investigate motivational differences by student status and (b) identify the motivational variables that best predict academic achievement by student status. Results of a multivariate analysis…

Relationship between central and peripheral atherosclerosis and left ventricular dysfunction in a community population.

PubMed

Tsao, Connie W; Gona, Philimon; Salton, Carol; Murabito, Joanne M; Oyama, Noriko; Danias, Peter G; O'Donnell, Christopher J; Manning, Warren J; Yeon, Susan B

2011-08-01

We aimed to determine the relationships between resting left ventricular (LV) wall motion abnormalities (WMAs), aortic plaque, and peripheral artery disease (PAD) in a community cohort. A total of 1726 Framingham Heart Study Offspring Cohort participants (806 males, 65 ± 9 years) underwent cardiovascular magnetic resonance with quantification of aortic plaque volume and assessment of regional left ventricular systolic function. Claudication, lower extremity revascularization, and ankle-brachial index (ABI) were recorded at the most contemporaneous examination visit. WMAs were associated with greater aortic plaque burden, decreased ABI, and claudication in age- and sex-adjusted analyses (all p < 0.001), which were not significant after adjustment for cardiovascular risk factors. In age- and sex-adjusted analyses, both the presence (p < 0.001) and volume of aortic plaque were associated with decreased ABI (p < 0.001). After multivariable adjustment, an ABI ≤ 0.9 or prior revascularization was associated with a threefold odds of aortic plaque (p = 0.0083). Plaque volume significantly increased with decreasing ABI in multivariable-adjusted analyses (p < 0.0001). In this free-living population, associations of WMAs with aortic plaque burden and clinical measures of PAD were attenuated after adjustment for coronary heart disease risk factors. Aortic plaque volume and ABI remained strongly negatively correlated after multivariable adjustment. Our findings suggest that the association between coronary heart disease and non-coronary atherosclerosis is explained by cardiovascular risk factors. Aortic atherosclerosis and PAD remain strongly associated after multivariable adjustment, suggesting shared mechanisms beyond those captured by traditional risk factors.

Calculating p-values and their significances with the Energy Test for large datasets

NASA Astrophysics Data System (ADS)

Barter, W.; Burr, C.; Parkes, C.

2018-04-01

The energy test method is a multi-dimensional test of whether two samples are consistent with arising from the same underlying population, through the calculation of a single test statistic (called the T-value). The method has recently been used in particle physics to search for samples that differ due to CP violation. The generalised extreme value function has previously been used to describe the distribution of T-values under the null hypothesis that the two samples are drawn from the same underlying population. We show that, in a simple test case, the distribution is not sufficiently well described by the generalised extreme value function. We present a new method, where the distribution of T-values under the null hypothesis when comparing two large samples can be found by scaling the distribution found when comparing small samples drawn from the same population. This method can then be used to quickly calculate the p-values associated with the results of the test.

Determining octanol-water partition coefficients for extremely hydrophobic chemicals by combining "slow stirring" and solid-phase microextraction.

PubMed

Jonker, Michiel T O

2016-06-01

Octanol-water partition coefficients (KOW ) are widely used in fate and effects modeling of chemicals. Still, high-quality experimental KOW data are scarce, in particular for very hydrophobic chemicals. This hampers reliable assessments of several fate and effect parameters and the development and validation of new models. One reason for the limited availability of experimental values may relate to the challenging nature of KOW measurements. In the present study, KOW values for 13 polycyclic aromatic hydrocarbons were determined with the gold standard "slow-stirring" method (log KOW 4.6-7.2). These values were then used as reference data for the development of an alternative method for measuring KOW . This approach combined slow stirring and equilibrium sampling of the extremely low aqueous concentrations with polydimethylsiloxane-coated solid-phase microextraction fibers, applying experimentally determined fiber-water partition coefficients. It resulted in KOW values matching the slow-stirring data very well. Therefore, the method was subsequently applied to a series of 17 moderately to extremely hydrophobic petrochemical compounds. The obtained KOW values spanned almost 6 orders of magnitude, with the highest value measuring 10(10.6) . The present study demonstrates that the hydrophobicity domain within which experimental KOW measurements are possible can be extended with the help of solid-phase microextraction and that experimentally determined KOW values can exceed the proposed upper limit of 10(9) . Environ Toxicol Chem 2016;35:1371-1377. © 2015 SETAC. © 2015 SETAC.

Changes in seasonal streamflow extremes experienced in rivers of Northwestern South America (Colombia)

NASA Astrophysics Data System (ADS)

Pierini, J. O.; Restrepo, J. C.; Aguirre, J.; Bustamante, A. M.; Velásquez, G. J.

2017-04-01

A measure of the variability in seasonal extreme streamflow was estimated for the Colombian Caribbean coast, using monthly time series of freshwater discharge from ten watersheds. The aim was to detect modifications in the streamflow monthly distribution, seasonal trends, variance and extreme monthly values. A 20-year length time moving window, with 1-year successive shiftments, was applied to the monthly series to analyze the seasonal variability of streamflow. The seasonal-windowed data were statistically fitted through the Gamma distribution function. Scale and shape parameters were computed using the Maximum Likelihood Estimation (MLE) and the bootstrap method for 1000 resample. A trend analysis was performed for each windowed-serie, allowing to detect the window of maximum absolute values for trends. Significant temporal shifts in seasonal streamflow distribution and quantiles (QT), were obtained for different frequencies. Wet and dry extremes periods increased significantly in the last decades. Such increase did not occur simultaneously through the region. Some locations exhibited continuous increases only at minimum QT.

Precipitation extremes and their relation to climatic indices in the Pacific Northwest USA

NASA Astrophysics Data System (ADS)

Zarekarizi, Mahkameh; Rana, Arun; Moradkhani, Hamid

2018-06-01

There has been focus on the influence of climate indices on precipitation extremes in the literature. Current study presents the evaluation of the precipitation-based extremes in Columbia River Basin (CRB) in the Pacific Northwest USA. We first analyzed the precipitation-based extremes using statistically (ten GCMs) and dynamically downscaled (three GCMs) past and future climate projections. Seven precipitation-based indices that help inform about the flood duration/intensity are used. These indices help in attaining first-hand information on spatial and temporal scales for different service sectors including energy, agriculture, forestry etc. Evaluation of these indices is first performed in historical period (1971-2000) followed by analysis of their relation to large scale tele-connections. Further we mapped these indices over the area to evaluate the spatial variation of past and future extremes in downscaled and observational data. The analysis shows that high values of extreme indices are clustered in either western or northern parts of the basin for historical period whereas the northern part is experiencing higher degree of change in the indices for future scenario. The focus is also on evaluating the relation of these extreme indices to climate tele-connections in historical period to understand their relationship with extremes over CRB. Various climate indices are evaluated for their relationship using Principal Component Analysis (PCA) and Singular Value Decomposition (SVD). Results indicated that, out of 13 climate tele-connections used in the study, CRB is being most affected inversely by East Pacific (EP), Western Pacific (WP), East Atlantic (EA) and North Atlaentic Oscillation (NAO).

Independent Prognostic Value of Serum Markers in Diffuse Large B-Cell Lymphoma in the Era of the NCCN-IPI.

PubMed

Melchardt, Thomas; Troppan, Katharina; Weiss, Lukas; Hufnagl, Clemens; Neureiter, Daniel; Tränkenschuh, Wolfgang; Schlick, Konstantin; Huemer, Florian; Deutsch, Alexander; Neumeister, Peter; Greil, Richard; Pichler, Martin; Egle, Alexander

2015-12-01

Several serum parameters have been evaluated for adding prognostic value to clinical scoring systems in diffuse large B-cell lymphoma (DLBCL), but none of the reports used multivariate testing of more than one parameter at a time. The goal of this study was to validate widely available serum parameters for their independent prognostic impact in the era of the National Comprehensive Cancer Network-International Prognostic Index (NCCN-IPI) score to determine which were the most useful. This retrospective bicenter analysis includes 515 unselected patients with DLBCL who were treated with rituximab and anthracycline-based chemoimmunotherapy between 2004 and January 2014. Anemia, high C-reactive protein, and high bilirubin levels had an independent prognostic value for survival in multivariate analyses in addition to the NCCN-IPI, whereas neutrophil-to-lymphocyte ratio, high gamma-glutamyl transferase levels, and platelets-to-lymphocyte ratio did not. In our cohort, we describe the most promising markers to improve the NCCN-IPI. Anemia and high C-reactive protein levels retain their power in multivariate testing even in the era of the NCCN-IPI. The negative role of high bilirubin levels may be associated as a marker of liver function. Further studies are warranted to incorporate these markers into prognostic models and define their role opposite novel molecular markers. Copyright © 2015 by the National Comprehensive Cancer Network.

The association of 83 plasma proteins with CHD mortality, BMI, HDL-, and total-cholesterol in men: applying multivariate statistics to identify proteins with prognostic value and biological relevance.

PubMed

Heidema, A Geert; Thissen, Uwe; Boer, Jolanda M A; Bouwman, Freek G; Feskens, Edith J M; Mariman, Edwin C M

2009-06-01

In this study, we applied the multivariate statistical tool Partial Least Squares (PLS) to analyze the relative importance of 83 plasma proteins in relation to coronary heart disease (CHD) mortality and the intermediate end points body mass index, HDL-cholesterol and total cholesterol. From a Dutch monitoring project for cardiovascular disease risk factors, men who died of CHD between initial participation (1987-1991) and end of follow-up (January 1, 2000) (N = 44) and matched controls (N = 44) were selected. Baseline plasma concentrations of proteins were measured by a multiplex immunoassay. With the use of PLS, we identified 15 proteins with prognostic value for CHD mortality and sets of proteins associated with the intermediate end points. Subsequently, sets of proteins and intermediate end points were analyzed together by Principal Components Analysis, indicating that proteins involved in inflammation explained most of the variance, followed by proteins involved in metabolism and proteins associated with total-C. This study is one of the first in which the association of a large number of plasma proteins with CHD mortality and intermediate end points is investigated by applying multivariate statistics, providing insight in the relationships among proteins, intermediate end points and CHD mortality, and a set of proteins with prognostic value.

Comparison of connectivity analyses for resting state EEG data

NASA Astrophysics Data System (ADS)

Olejarczyk, Elzbieta; Marzetti, Laura; Pizzella, Vittorio; Zappasodi, Filippo

2017-06-01

Objective. In the present work, a nonlinear measure (transfer entropy, TE) was used in a multivariate approach for the analysis of effective connectivity in high density resting state EEG data in eyes open and eyes closed. Advantages of the multivariate approach in comparison to the bivariate one were tested. Moreover, the multivariate TE was compared to an effective linear measure, i.e. directed transfer function (DTF). Finally, the existence of a relationship between the information transfer and the level of brain synchronization as measured by phase synchronization value (PLV) was investigated. Approach. The comparison between the connectivity measures, i.e. bivariate versus multivariate TE, TE versus DTF, TE versus PLV, was performed by means of statistical analysis of indexes based on graph theory. Main results. The multivariate approach is less sensitive to false indirect connections with respect to the bivariate estimates. The multivariate TE differentiated better between eyes closed and eyes open conditions compared to DTF. Moreover, the multivariate TE evidenced non-linear phenomena in information transfer, which are not evidenced by the use of DTF. We also showed that the target of information flow, in particular the frontal region, is an area of greater brain synchronization. Significance. Comparison of different connectivity analysis methods pointed to the advantages of nonlinear methods, and indicated a relationship existing between the flow of information and the level of synchronization of the brain.

Additive genetic variation and evolvability of a multivariate trait can be increased by epistatic gene action.

PubMed

Griswold, Cortland K

2015-12-21

Epistatic gene action occurs when mutations or alleles interact to produce a phenotype. Theoretically and empirically it is of interest to know whether gene interactions can facilitate the evolution of diversity. In this paper, we explore how epistatic gene action affects the additive genetic component or heritable component of multivariate trait variation, as well as how epistatic gene action affects the evolvability of multivariate traits. The analysis involves a sexually reproducing and recombining population. Our results indicate that under stabilizing selection conditions a population with a mixed additive and epistatic genetic architecture can have greater multivariate additive genetic variation and evolvability than a population with a purely additive genetic architecture. That greater multivariate additive genetic variation can occur with epistasis is in contrast to previous theory that indicated univariate additive genetic variation is decreased with epistasis under stabilizing selection conditions. In a multivariate setting, epistasis leads to less relative covariance among individuals in their genotypic, as well as their breeding values, which facilitates the maintenance of additive genetic variation and increases a population׳s evolvability. Our analysis involves linking the combinatorial nature of epistatic genetic effects to the ancestral graph structure of a population to provide insight into the consequences of epistasis on multivariate trait variation and evolution. Copyright © 2015 Elsevier Ltd. All rights reserved.

MEMD-enhanced multivariate fuzzy entropy for the evaluation of complexity in biomedical signals.

PubMed

Azami, Hamed; Smith, Keith; Escudero, Javier

2016-08-01

Multivariate multiscale entropy (mvMSE) has been proposed as a combination of the coarse-graining process and multivariate sample entropy (mvSE) to quantify the irregularity of multivariate signals. However, both the coarse-graining process and mvSE may not be reliable for short signals. Although the coarse-graining process can be replaced with multivariate empirical mode decomposition (MEMD), the relative instability of mvSE for short signals remains a problem. Here, we address this issue by proposing the multivariate fuzzy entropy (mvFE) with a new fuzzy membership function. The results using white Gaussian noise show that the mvFE leads to more reliable and stable results, especially for short signals, in comparison with mvSE. Accordingly, we propose MEMD-enhanced mvFE to quantify the complexity of signals. The characteristics of brain regions influenced by partial epilepsy are investigated by focal and non-focal electroencephalogram (EEG) time series. In this sense, the proposed MEMD-enhanced mvFE and mvSE are employed to discriminate focal EEG signals from non-focal ones. The results demonstrate the MEMD-enhanced mvFE values have a smaller coefficient of variation in comparison with those obtained by the MEMD-enhanced mvSE, even for long signals. The results also show that the MEMD-enhanced mvFE has better performance to quantify focal and non-focal signals compared with multivariate multiscale permutation entropy.

Correlation between ultrafiltration rate and phase angle measured by BIA in chronic kidney disease patients on regular hemodialysis

NASA Astrophysics Data System (ADS)

Nasution, B. R.; Lubis, A. R.

2018-03-01

Chronic Kidney Disease (CKD) patients with regular hemodialysis have high rates of morbidity and mortality that may be related to the hemodynamic effects of rapid UFR and low PhA value. In this study, we investigated whether high UFR is associated with a low value of PhA thus indirectly affect the risk of morbidity and mortality. UFR and Bioelectrical Impedance Analysis (BIA) examination on 92 subjects were recorded shortly after HD and analyzed by using Pearson correlation test. Multivariate analysis was also conducted to identify several factors that can affect the value of Phase angle. The number of HD regular CKD patients with PhA<4 based on the division of the UFR (cc/kg/h) <10, 10-13, ≥ 13, respectively were3, 10 and 6, whereas patients with ≥ 4 PhA <10, 10-13, ≥ 13respectively were 60, 11, and 2. The results showed a significant relationship between UFR with PhA. In CKD patients with regular HD, UFR has aninverse relationship with the value of PhA. After multivariate analysis, the UFR and the etiology of HD are still significantly affect the value of PhA. UFR optimal value in patients with CKD with regular HD is <10 cc/kg/h.

A comparison of bivariate, multivariate random-effects, and Poisson correlated gamma-frailty models to meta-analyze individual patient data of ordinal scale diagnostic tests.

PubMed

Simoneau, Gabrielle; Levis, Brooke; Cuijpers, Pim; Ioannidis, John P A; Patten, Scott B; Shrier, Ian; Bombardier, Charles H; de Lima Osório, Flavia; Fann, Jesse R; Gjerdingen, Dwenda; Lamers, Femke; Lotrakul, Manote; Löwe, Bernd; Shaaban, Juwita; Stafford, Lesley; van Weert, Henk C P M; Whooley, Mary A; Wittkampf, Karin A; Yeung, Albert S; Thombs, Brett D; Benedetti, Andrea

2017-11-01

Individual patient data (IPD) meta-analyses are increasingly common in the literature. In the context of estimating the diagnostic accuracy of ordinal or semi-continuous scale tests, sensitivity and specificity are often reported for a given threshold or a small set of thresholds, and a meta-analysis is conducted via a bivariate approach to account for their correlation. When IPD are available, sensitivity and specificity can be pooled for every possible threshold. Our objective was to compare the bivariate approach, which can be applied separately at every threshold, to two multivariate methods: the ordinal multivariate random-effects model and the Poisson correlated gamma-frailty model. Our comparison was empirical, using IPD from 13 studies that evaluated the diagnostic accuracy of the 9-item Patient Health Questionnaire depression screening tool, and included simulations. The empirical comparison showed that the implementation of the two multivariate methods is more laborious in terms of computational time and sensitivity to user-supplied values compared to the bivariate approach. Simulations showed that ignoring the within-study correlation of sensitivity and specificity across thresholds did not worsen inferences with the bivariate approach compared to the Poisson model. The ordinal approach was not suitable for simulations because the model was highly sensitive to user-supplied starting values. We tentatively recommend the bivariate approach rather than more complex multivariate methods for IPD diagnostic accuracy meta-analyses of ordinal scale tests, although the limited type of diagnostic data considered in the simulation study restricts the generalization of our findings. © 2017 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Invited Article: Visualisation of extreme value events in optical communications

NASA Astrophysics Data System (ADS)

Derevyanko, Stanislav; Redyuk, Alexey; Vergeles, Sergey; Turitsyn, Sergei

2018-06-01

Fluctuations of a temporal signal propagating along long-haul transoceanic scale fiber links can be visualised in the spatio-temporal domain drawing visual analogy with ocean waves. Substantial overlapping of information symbols or use of multi-frequency signals leads to strong statistical deviations of local peak power from an average signal power level. We consider long-haul optical communication systems from this unusual angle, treating them as physical systems with a huge number of random statistical events, including extreme value fluctuations that potentially might affect the quality of data transmission. We apply the well-established concepts of adaptive wavefront shaping used in imaging through turbid medium to detect the detrimental phase modulated sequences in optical communications that can cause extreme power outages (rare optical waves of ultra-high amplitude) during propagation down the ultra-long fiber line. We illustrate the concept by a theoretical analysis of rare events of high-intensity fluctuations—optical freak waves, taking as an example an increasingly popular optical frequency division multiplexing data format where the problem of high peak to average power ratio is the most acute. We also show how such short living extreme value spikes in the optical data streams are affected by nonlinearity and demonstrate the negative impact of such events on the system performance.

Characteristics and present trends of wave extremes in the Mediterranean Sea

NASA Astrophysics Data System (ADS)

Pino, Cosimo; Lionello, Piero; Galati, Maria Barbara

2010-05-01

Wind generated surface waves are an important factor characterizing marine storminess and the marine environment. This contribution considers characteristics and trends of SWH (Significant Wave Height) extremes (both high and low extremes, such as dead calm duration are analyzed). The data analysis is based on a 44-year long simulation (1958-2001) of the wave field in the Mediterranean Sea. The quality of the model simulation is controlled using satellite data. The results show the different characteristics of the different parts of the basin with the variability being higher in the western (where the highest values are produced) than in the eastern areas of the basin (where absence of wave is a rare condition). In fact, both duration of storms and of dead calm episodes is larger in the east than in the west part of the Mediterranean. The African coast and the southern Ionian Sea are the areas were exceptional values of SWH are expected to occur in correspondence with exceptional meteorological events. Significant trends of storm characteristics are present only in sparse areas and suggest a decrease of both storm intensity and duration (a marginal increase of storm intensity is present in the center of the Mediterranean). The statistics of extremes and high SWH values is substantially steady during the second half of the 20th century. The influence of the large-scale teleconnection patterns (TlcP) that are known to be relevant for the Mediterranean climate on the intensity and spatial distribution of extreme SWH (Significant Wave Height) has been investigated. The analysis was focused on the monthly scale analysing the variability of links along the annual cycle. The considered TlcP are the North Atlantic Oscillation, the East-Atlantic / West-Russian pattern and the Scandinavian pattern and their effect on the intensity and the frequency of high/low SWH conditions. The results show it is difficult to establish a dominant TlcP for SWH extremes, because all 4 patterns considered are important for at least few months in the year and none of them is important for the whole year. High extremes in winter and fall are more influenced by the TlcPs than in other seasons and low extremes.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ashouri, Hamed; Sorooshian, Soroosh; Hsu, Kuo-Lin

This study evaluates the performance of NASA's Modern-Era Retrospective Analysis for Research and Applications (MERRA) precipitation product in reproducing the trend and distribution of extreme precipitation events. Utilizing the extreme value theory, time-invariant and time-variant extreme value distributions are developed to model the trends and changes in the patterns of extreme precipitation events over the contiguous United States during 1979-2010. The Climate Prediction Center (CPC)U.S.Unified gridded observation data are used as the observational dataset. The CPC analysis shows that the eastern and western parts of the United States are experiencing positive and negative trends in annual maxima, respectively. The continental-scalemore » patterns of change found in MERRA seem to reasonably mirror the observed patterns of change found in CPC. This is not previously expected, given the difficulty in constraining precipitation in reanalysis products. MERRA tends to overestimate the frequency at which the 99th percentile of precipitation is exceeded because this threshold tends to be lower in MERRA, making it easier to be exceeded. This feature is dominant during the summer months. MERRAtends to reproduce spatial patterns of the scale and location parameters of the generalized extreme value and generalized Pareto distributions. However, MERRA underestimates these parameters, particularly over the Gulf Coast states, leading to lower magnitudes in extreme precipitation events. Two issues in MERRA are identified: 1)MERRAshows a spurious negative trend in Nebraska andKansas, which ismost likely related to the changes in the satellite observing system over time that has apparently affected the water cycle in the central United States, and 2) the patterns of positive trend over theGulf Coast states and along the East Coast seem to be correlated with the tropical cyclones in these regions. The analysis of the trends in the seasonal precipitation extremes indicates that the hurricane and winter seasons are contributing the most to these trend patterns in the southeastern United States. The increasing annual trend simulated by MERRA in the Gulf Coast region is due to an incorrect trend in winter precipitation extremes.« less

The Use of Opioid Analgesics following Common Upper Extremity Surgical Procedures: A National, Population-Based Study.

PubMed

Waljee, Jennifer F; Zhong, Lin; Hou, Hechuan; Sears, Erika; Brummett, Chad; Chung, Kevin C

2016-02-01

The misuse of opioid analgesics is a major public health concern, and guidelines regarding postoperative opioid use are sparse. The authors examined the use of opioids following outpatient upper extremity procedures to discern the variation by procedure type and patient factors. The authors studied opioid prescriptions among 296,452 adults older than 18 years who underwent carpal tunnel release, trigger finger release, cubital tunnel release, or thumb carpometacarpal arthroplasty from 2009 to 2013 using insurance claims drawn from the Truven Health MarketScan Commercial Claims and Encounters, which encompasses over 100 health plans in the United States. Using multivariable regression, the authors compared the receipt of opioids, number of days supplied, indicators of inappropriate prescriptions, and number of refills by patient factors. In this cohort, 59 percent filled a postoperative prescription for opioid medication, and 8.8 percent of patients had an indicator of inappropriate prescribing. The probability of filling an opioid prescription declined linearly with advancing age. On multivariate analysis, patients who had previously received opioids were more likely to fill a postoperative opioid prescription (66 percent versus 59 percent), receive longer prescriptions (24 versus 5 days), receive refills following surgery (24 percent versus 5 percent), and have at least one indicator of potentially inappropriate prescribing (19 percent versus 6 percent). Current opioid users are more likely to require postoperative opioid analgesics for routine procedures and more likely to receive inappropriate prescriptions. More evidence is needed to identify patients who derive the greatest benefit from opioids to curb opioid prescriptions when alternative analgesics may be equally effective and available. Risk, III.

A proposed case-control framework to probabilistically classify individual deaths as expected or excess during extreme hot weather events.

PubMed

Henderson, Sarah B; Gauld, Jillian S; Rauch, Stephen A; McLean, Kathleen E; Krstic, Nikolas; Hondula, David M; Kosatsky, Tom

2016-11-15

Most excess deaths that occur during extreme hot weather events do not have natural heat recorded as an underlying or contributing cause. This study aims to identify the specific individuals who died because of hot weather using only secondary data. A novel approach was developed in which the expected number of deaths was repeatedly sampled from all deaths that occurred during a hot weather event, and compared with deaths during a control period. The deaths were compared with respect to five factors known to be associated with hot weather mortality. Individuals were ranked by their presence in significant models over 100 trials of 10,000 repetitions. Those with the highest rankings were identified as probable excess deaths. Sensitivity analyses were performed on a range of model combinations. These methods were applied to a 2009 hot weather event in greater Vancouver, Canada. The excess deaths identified were sensitive to differences in model combinations, particularly between univariate and multivariate approaches. One multivariate and one univariate combination were chosen as the best models for further analyses. The individuals identified by multiple combinations suggest that marginalized populations in greater Vancouver are at higher risk of death during hot weather. This study proposes novel methods for classifying specific deaths as expected or excess during a hot weather event. Further work is needed to evaluate performance of the methods in simulation studies and against clinically identified cases. If confirmed, these methods could be applied to a wide range of populations and events of interest.

The Utilization of Opioid Analgesics Following Common Upper Extremity Surgical Procedures: A National, Population-Based Study

PubMed Central

Waljee, Jennifer F.; Zhong, Lin; Hou, Hechuan; Sears, Erika; Brummet, Chad; Chung, Kevin C.

2016-01-01

Background The misuse of opioid analgesics is a major public health concern, and guidelines regarding postoperative opioid use are sparse. We examined the use of opioids following outpatient upper extremity procedures. We hypothesized that opioid use varies widely by procedure and patient factors. Methods We studied opioid prescriptions among 296,452 adults ages ≥ 18 years who underwent carpal tunnel release, trigger finger release, cubital tunnel release, and thumb carpometacarpal (CMC) arthroplasty from 2009 to 2013. We analyzed insurance claims drawn using Truven Health MarketScan Commercial Claims and Encounters, which encompasses over 100 health plans in the United States. Using multivariable regression, we compared the receipt of opioids, number of days supplied, indicators of inappropriate prescriptions, and number of refills by patient factors. Results In this cohort, 59% filled a postoperative prescription for opioid medication, and 8.8% patients had an indicator of inappropriate prescribing. The probability of filling an opioid prescription declined linearly with advancing age. In multivariate analysis, patients who had previously received opioids were more likely to fill a postoperative opioid prescription (66% vs. 59%), receive longer prescriptions (24 vs. 5 days), receive refills following surgery (24% vs. 5%), and have at least one indicator of potentially inappropriate prescribing (19% vs 6%). Conclusions Current opioid users are more likely to require postoperative opioid analgesics for routine procedures, and more likely to receive inappropriate prescriptions. More evidence is needed to identify patients who derive the greatest benefit from opioids in order to curb opioids prescriptions when alternative analgesics may be equally effective and available. PMID:26818326

Cumulative hazard: The case of nuisance flooding

NASA Astrophysics Data System (ADS)

Moftakhari, Hamed R.; AghaKouchak, Amir; Sanders, Brett F.; Matthew, Richard A.

2017-02-01

The cumulative cost of frequent events (e.g., nuisance floods) over time may exceed the costs of the extreme but infrequent events for which societies typically prepare. Here we analyze the likelihood of exceedances above mean higher high water and the corresponding property value exposure for minor, major, and extreme coastal floods. Our results suggest that, in response to sea level rise, nuisance flooding (NF) could generate property value exposure comparable to, or larger than, extreme events. Determining whether (and when) low cost, nuisance incidents aggregate into high cost impacts and deciding when to invest in preventive measures are among the most difficult decisions for policymakers. It would be unfortunate if efforts to protect societies from extreme events (e.g., 0.01 annual probability) left them exposed to a cumulative hazard with enormous costs. We propose a Cumulative Hazard Index (CHI) as a tool for framing the future cumulative impact of low cost incidents relative to infrequent extreme events. CHI suggests that in New York, NY, Washington, DC, Miami, FL, San Francisco, CA, and Seattle, WA, a careful consideration of socioeconomic impacts of NF for prioritization is crucial for sustainable coastal flood risk management.

Extreme events and event size fluctuations in biased random walks on networks.

PubMed

Kishore, Vimal; Santhanam, M S; Amritkar, R E

2012-05-01

Random walk on discrete lattice models is important to understand various types of transport processes. The extreme events, defined as exceedences of the flux of walkers above a prescribed threshold, have been studied recently in the context of complex networks. This was motivated by the occurrence of rare events such as traffic jams, floods, and power blackouts which take place on networks. In this work, we study extreme events in a generalized random walk model in which the walk is preferentially biased by the network topology. The walkers preferentially choose to hop toward the hubs or small degree nodes. In this setting, we show that extremely large fluctuations in event sizes are possible on small degree nodes when the walkers are biased toward the hubs. In particular, we obtain the distribution of event sizes on the network. Further, the probability for the occurrence of extreme events on any node in the network depends on its "generalized strength," a measure of the ability of a node to attract walkers. The generalized strength is a function of the degree of the node and that of its nearest neighbors. We obtain analytical and simulation results for the probability of occurrence of extreme events on the nodes of a network using a generalized random walk model. The result reveals that the nodes with a larger value of generalized strength, on average, display lower probability for the occurrence of extreme events compared to the nodes with lower values of generalized strength.

When personality and culture clash: the psychological distress of allocentrics in an individualist culture and idiocentrics in a collectivist culture.

PubMed

Caldwell-Harris, Catherine L; Ayçiçegi, Ayse

2006-09-01

Because humans need both autonomy and interdependence, persons with either an extreme collectivist orientation (allocentrics) or extreme individualist values (idiocentrics) may be at risk for possession of some features of psychopathology. Is an extreme personality style a risk factor primarily when it conflicts with the values of the surrounding society? Individualism-collectivism scenarios and a battery of clinical and personality scales were administered to nonclinical samples of college students in Boston and Istanbul. For students residing in a highly individualistic society (Boston), collectivism scores were positively correlated with depression, social anxiety, obsessive-compulsive disorder and dependent personality. Individualism scores, particularly horizontal individualism, were negatively correlated with these same scales. A different pattern was obtained for students residing in a collectivist culture, Istanbul. Here individualism (and especially horizontal individualism) was positively correlated with scales for paranoid, schizoid, narcissistic, borderline and antisocial personality disorder. Collectivism (particularly vertical collectivism) was associated with low report of symptoms on these scales. These results indicate that having a personality style which conflicts with the values of society is associated with psychiatric symptoms. Having an orientation inconsistent with societal values may thus be a risk factor for poor mental health.

Projected Changes in Temperature and Precipitation Extremes over China as Measured by 50-yr Return Values and Periods Based on a CMIP5 Ensemble

NASA Astrophysics Data System (ADS)

Xu, Ying; Gao, Xuejie; Giorgi, Filippo; Zhou, Botao; Shi, Ying; Wu, Jie; Zhang, Yongxiang

2018-04-01

Future changes in the 50-yr return level for temperature and precipitation extremes over mainland China are investigated based on a CMIP5 multi-model ensemble for RCP2.6, RCP4.5 and RCP8.5 scenarios. The following indices are analyzed: TXx and TNn (the annual maximum and minimum of daily maximum and minimum surface temperature), RX5day (the annual maximum consecutive 5-day precipitation) and CDD (maximum annual number of consecutive dry days). After first validating the model performance, future changes in the 50-yr return values and return periods for these indices are investigated along with the inter-model spread. Multi-model median changes show an increase in the 50-yr return values of TXx and a decrease for TNn, more specifically, by the end of the 21st century under RCP8.5, the present day 50-yr return period of warm events is reduced to 1.2 yr, while extreme cold events over the country are projected to essentially disappear. A general increase in RX5day 50-yr return values is found in the future. By the end of the 21st century under RCP8.5, events of the present RX5day 50-yr return period are projected to reduce to < 10 yr over most of China. Changes in CDD-50 show a dipole pattern over China, with a decrease in the values and longer return periods in the north, and vice versa in the south. Our study also highlights the need for further improvements in the representation of extreme events in climate models to assess the future risks and engineering design related to large-scale infrastructure in China.

Extreme air-sea surface turbulent fluxes in mid latitudes - estimation, origins and mechanisms

NASA Astrophysics Data System (ADS)

Gulev, Sergey; Natalia, Tilinina

2014-05-01

Extreme turbulent heat fluxes in the North Atlantic and North Pacific mid latitudes were estimated from the modern era and first generation reanalyses (NCEP-DOE, ERA-Interim, MERRA NCEP-CFSR, JRA-25) for the period from 1979 onwards. We used direct surface turbulent flux output as well as reanalysis state variables from which fluxes have been computed using COARE-3 bulk algorithm. For estimation of extreme flux values we analyzed surface flux probability density distribution which was approximated by Modified Fisher-Tippett distribution. In all reanalyses extreme turbulent heat fluxes amount to 1500-2000 W/m2 (for the 99th percentile) and can exceed 2000 W/m2 for higher percentiles in the western boundary current extension (WBCE) regions. Different reanalyses show significantly different shape of MFT distribution, implying considerable differences in the estimates of extreme fluxes. The highest extreme turbulent latent heat fluxes are diagnosed in NCEP-DOE, ERA-Interim and NCEP-CFSR reanalyses with the smallest being in MERRA. These differences may not necessarily reflect the differences in mean values. Analysis shows that differences in statistical properties of the state variables are the major source of differences in the shape of PDF of fluxes and in the estimates of extreme fluxes while the contribution of computational schemes used in different reanalyses is minor. The strongest differences in the characteristics of probability distributions of surface fluxes and extreme surface flux values between different reanalyses are found in the WBCE extension regions and high latitudes. In the next instance we analyzed the mechanisms responsible for forming surface turbulent fluxes and their potential role in changes of midlatitudinal heat balance. Midlatitudinal cyclones were considered as the major mechanism responsible for extreme turbulent fluxes which are typically occur during the cold air outbreaks in the rear parts of cyclones when atmospheric conditions provide locally high winds and air-sea temperature gradients. For this purpose we linked characteristics of cyclone activity over the midlatitudinal oceans with the extreme surface turbulent heat fluxes. Cyclone tracks and parameters of cyclone life cycle (deepening rates, propagation velocities, life time and clustering) were derived from the same reanalyses using state of the art numerical tracking algorithm. The main questions addressed in this study are (i) through which mechanisms extreme surface fluxes are associated with cyclone activity? and (ii) which types of cyclones are responsible for forming extreme turbulent fluxes? Our analysis shows that extreme surface fluxes are typically associated not with cyclones themselves but rather with cyclone-anticyclone interaction zones. This implies that North Atlantic and North Pacific series of intense cyclones do not result in the anomalous surface fluxes. Alternatively, extreme fluxes are most frequently associated with blocking situations, particularly with the intensification of the Siberian and North American Anticyclones providing cold-air outbreaks over WBC regions.

Can multivariate models based on MOAKS predict OA knee pain? Data from the Osteoarthritis Initiative

NASA Astrophysics Data System (ADS)

Luna-Gómez, Carlos D.; Zanella-Calzada, Laura A.; Galván-Tejada, Jorge I.; Galván-Tejada, Carlos E.; Celaya-Padilla, José M.

2017-03-01

Osteoarthritis is the most common rheumatic disease in the world. Knee pain is the most disabling symptom in the disease, the prediction of pain is one of the targets in preventive medicine, this can be applied to new therapies or treatments. Using the magnetic resonance imaging and the grading scales, a multivariate model based on genetic algorithms is presented. Using a predictive model can be useful to associate minor structure changes in the joint with the future knee pain. Results suggest that multivariate models can be predictive with future knee chronic pain. All models; T0, T1 and T2, were statistically significant, all p values were < 0.05 and all AUC > 0.60.

Monitoring task loading with multivariate EEG measures during complex forms of human-computer interaction

NASA Technical Reports Server (NTRS)

Smith, M. E.; Gevins, A.; Brown, H.; Karnik, A.; Du, R.

2001-01-01

Electroencephalographic (EEG) recordings were made while 16 participants performed versions of a personal-computer-based flight simulation task of low, moderate, or high difficulty. As task difficulty increased, frontal midline theta EEG activity increased and alpha band activity decreased. A participant-specific function that combined multiple EEG features to create a single load index was derived from a sample of each participant's data and then applied to new test data from that participant. Index values were computed for every 4 s of task data. Across participants, mean task load index values increased systematically with increasing task difficulty and differed significantly between the different task versions. Actual or potential applications of this research include the use of multivariate EEG-based methods to monitor task loading during naturalistic computer-based work.

A Brief Critique of the TATES Procedure.

PubMed

Aliev, Fazil; Salvatore, Jessica E; Agrawal, Arpana; Almasy, Laura; Chan, Grace; Edenberg, Howard J; Hesselbrock, Victor; Kuperman, Samuel; Meyers, Jacquelyn; Dick, Danielle M

2018-03-01

The Trait-based test that uses the Extended Simes procedure (TATES) was developed as a method for conducting multivariate GWAS for correlated phenotypes whose underlying genetic architecture is complex. In this paper, we provide a brief methodological critique of the TATES method using simulated examples and a mathematical proof. Our simulated examples using correlated phenotypes show that the Type I error rate is higher than expected, and that more TATES p values fall outside of the confidence interval relative to expectation. Thus the method may result in systematic inflation when used with correlated phenotypes. In a mathematical proof we further demonstrate that the distribution of TATES p values deviates from expectation in a manner indicative of inflation. Our findings indicate the need for caution when using TATES for multivariate GWAS of correlated phenotypes.

Linearized stability of extreme black holes

NASA Astrophysics Data System (ADS)

Burko, Lior M.; Khanna, Gaurav

2018-03-01

Extreme black holes have been argued to be unstable, in the sense that under linearized gravitational perturbations of the extreme Kerr spacetime the Weyl scalar ψ4 blows up along their event horizons at very late advanced times. We show numerically, by solving the Teukolsky equation in 2 +1 D , that all algebraically independent curvature scalar polynomials approach limits that exist when advanced time along the event horizon approaches infinity. Therefore, the horizons of extreme black holes are stable against linearized gravitational perturbations. We argue that the divergence of ψ4 is a consequence of the choice of a fixed tetrad, and that in a suitable dynamical tetrad all Weyl scalars, including ψ4, approach their background extreme Kerr values. We make similar conclusions also for the case of scalar field perturbations of extreme Kerr.

Discriminant factors for adolescent sexual offending: On the usefulness of considering both victim age and sibling incest.

PubMed

Joyal, Christian C; Carpentier, Julie; Martin, Caroline

2016-04-01

Understanding the pathways and circumstances of juvenile sexual offending is of utmost importance. However, juvenile sexual offenders (JSO) represent an especially diverse group of individuals, and several categorizations have been proposed to obtain more homogeneous subgroups. Victim age-based and family relation-based categorizations are particularly promising because they seem theoretically and clinically relevant. Empirical results however are still inconsistent, and most studies have not considered these two dimensions jointly. The first goal of this study was to further examine the value of subgrouping JSO according to the age of their victim. A second goal was to determine the supplementary value, if any, of considering sibling incest. Based on a sample of 351 male JSO, it was first confirmed that sexual abuse of children was more strongly related to asociality (social skill deficits) than sexual abuse of peers, the latter being more closely associated with antisociality (general delinquency). The relevance of considering mixed-type JSO (with both child and peer victims) separately was also confirmed. More importantly, multivariate statistical analyses demonstrated that adding sibling incest to the equation was useful. JSO of intra-familial child were significantly more likely to have been victimized during their own childhood compared to JSO with extra-familial victims. Nevertheless, adolescents who had committed sibling incest obtained middle ground results on most variables (except for crime severity), suggesting that they constitute a distinct but not extreme, subgroup. This study confirmed the utility of using both the age and the family relation with the victim in characterizing juvenile sexual offending. Copyright © 2016 Elsevier Ltd. All rights reserved.

Detection of Extremes with AIRS and CrIS

NASA Technical Reports Server (NTRS)

Aumann, Hartmut H.; Manning, Evan M.; Behrangi, Ali

2013-01-01

Climate change is expected to be detected first as changes in extreme values rather than in mean values. The availability of data of from two instruments in the same orbit, AIRS data for the past eleven years and AIRS and CrIS data from the past year, provides an opportunity to evaluate this using examples of climate relevance: Desertification, seen as changes in hot extremes, severe storm, seen as a change in extremely cold clouds and the warming of the polar zone. We use AIRS to establish trends for the 1%tile, the mean and 99%tile brightness temperatures measured with the 900 cm(exp -1) channel from AIRS for the past 11 years. This channel is in the clearest part of the 11 micron atmospheric window. Substantial trends are seen for land and ocean, which in the case of the 1%tile (cold) extremes are related to the current shift of deep convection from ocean to land. Changes are also seen in the 99%tile for day tropical land, but their interpretation is at present unclear. We also see dramatic changes for the mean and 99%tile of the North Polar area. The trends are an order of magnitude larger than the instrument trend of about 3 mK/year. We use the statistical distribution from the past year derived from AIRS to evaluate the accuracy of continuing the trends established with AIRS with CrIS data. We minimize the concern about differences in the spectral response functions by limiting the analysis to the channel at 900 cm(exp -1).While the two instruments agree within 100 mK for the global day/night land/ocean mean values, there are significant differences when evaluating the1% and 99%tiles. We see a consistent warm bias in the CrIS data relative to AIRS for the 1%tile (extremely cold, cloudy) data in the tropical zone, particularly for tropical land, but the bias is not day/night land/ocean consistent. At this point the difference appears to be due to differences in the radiometric response of AIRS and CrIS to differences in the day/night land/ocean cloud types. Unless the effect can be mitigated by a future reprocessing the CrIS data, it will significantly complicate the concatenation of the AIRS and CrIS data records for the continuation of trends in extreme values.

Decreased Visual Function Scores on a Low Luminance Questionnaire Is Associated with Impaired Dark Adaptation.

PubMed

Yazdanie, Mohammad; Alvarez, Jason; Agrón, Elvira; Wong, Wai T; Wiley, Henry E; Ferris, Frederick L; Chew, Emily Y; Cukras, Catherine

2017-09-01

We investigate whether responses on a Low Luminance Questionnaire (LLQ) in patients with a range of age-related macular degeneration (AMD) severity are associated with their performance on focal dark adaptation (DA) testing and with choroidal thickness. Cross-sectional, single-center, observational study. A total of 113 participants older than 50 years of age with a range of AMD severity. Participants answered the LLQ on the same day they underwent DA testing using a focal dark adaptometer measuring rod intercept time (RIT). We performed univariable and multivariable analyses of the LLQ scores and age, RIT, AMD severity, subfoveal choroidal thickness [SFCT], phakic status, and best-corrected visual acuity. The primary outcome of this study was the score on the 32-question LLQ. Each item in the LLQ is designated to 1 of 6 subscales describing functional problems in low luminance: driving, emotional distress, mobility, extreme lighting, peripheral vision, and general dim lighting. Scores were computed for each subscale, in addition to a weighted total mean score. Responses from 113 participants (mean age, 76.2±9.3 years; 58.4% were female) and 113 study eyes were analyzed. Univariable analysis demonstrated that lower scores on all LLQ subscales were correlated with prolonged DA testing (longer RIT) and decreased choroidal thickness. All associations were statistically significant except for the association of choroidal thickness and "peripheral vision." The strongest association was the LLQ subscale of driving with RIT (r =-0.97, P < 0.001). Multivariable analysis for each of the LLQ subscale outcomes, adjusted for age, included RIT, with total LLQ score, "driving," "extreme lighting," and "mobility" also including choroidal thickness. In all multivariable analyses, RIT had a stronger association than choroidal thickness. This cross-sectional analysis demonstrates associations of patient-reported functional deficits, as assessed on the LLQ, with both reduced DA and reduced choroidal thickness, in a population of older adults with varying degrees of AMD severity and good visual acuity in at least 1 eye. These analyses suggest that local functional measurements of DA testing (RIT) and choroidal thickness are associated with patient-reported functional deficits. Published by Elsevier Inc.

Robust multivariate nonparametric tests for detection of two-sample location shift in clinical trials

PubMed Central

Jiang, Xuejun; Guo, Xu; Zhang, Ning; Wang, Bo

2018-01-01

This article presents and investigates performance of a series of robust multivariate nonparametric tests for detection of location shift between two multivariate samples in randomized controlled trials. The tests are built upon robust estimators of distribution locations (medians, Hodges-Lehmann estimators, and an extended U statistic) with both unscaled and scaled versions. The nonparametric tests are robust to outliers and do not assume that the two samples are drawn from multivariate normal distributions. Bootstrap and permutation approaches are introduced for determining the p-values of the proposed test statistics. Simulation studies are conducted and numerical results are reported to examine performance of the proposed statistical tests. The numerical results demonstrate that the robust multivariate nonparametric tests constructed from the Hodges-Lehmann estimators are more efficient than those based on medians and the extended U statistic. The permutation approach can provide a more stringent control of Type I error and is generally more powerful than the bootstrap procedure. The proposed robust nonparametric tests are applied to detect multivariate distributional difference between the intervention and control groups in the Thai Healthy Choices study and examine the intervention effect of a four-session motivational interviewing-based intervention developed in the study to reduce risk behaviors among youth living with HIV. PMID:29672555

Storms or cold fronts: what is really responsible for the extreme waves regime in the Colombian Caribbean coastal region?

NASA Astrophysics Data System (ADS)

Otero, L. J.; Ortiz-Royero, J. C.; Ruiz-Merchan, J. K.; Higgins, A. E.; Henriquez, S. A.

2016-02-01

The aim of this study is to determine the contribution and importance of cold fronts and storms to extreme waves in different areas of the Colombian Caribbean in an attempt to determine the extent of the threat posed by the flood processes to which these coastal populations are exposed. Furthermore, the study wishes to establish the actions to which coastal engineering constructions should be subject. In the calculation of maritime constructions, the most important parameter is the height of the wave. For this reason, it is necessary to establish the design wave height to which a coastal engineering structure should be resistant. This wave height varies according to the return period considered. The significant height values for the areas focused on in the study were calculated in accordance with Gumbel's extreme value methodology. The methodology was evaluated using data from the reanalysis of the spectral National Oceanic and Atmospheric Administration (NOAA) WAVEWATCH III® (WW3) model for 15 points along the 1600 km of the Colombian Caribbean coastline (continental and insular) between the years 1979 and 2009. The results demonstrated that the extreme waves caused by tropical cyclones and those caused by cold fronts have different effects along the Colombian Caribbean coast. Storms and hurricanes are of greater importance in the Guajira Peninsula (Alta Guajira). In the central area (consisting of Baja Guajira, and the cities of Santa Marta, Barranquilla, and Cartagena), the strong impact of cold fronts on extreme waves is evident. However, in the southern region of the Colombian Caribbean coast (ranging from the Gulf of Morrosquillo to the Gulf of Urabá), the extreme values of wave heights are lower than in the previously mentioned regions, despite being dominated mainly by the passage of cold fronts. Extreme waves in the San Andrés and Providencia insular region present a different dynamic from that in the continental area due to their geographic location. The wave heights in the extreme regime are similar in magnitude to those found in Alta Guajira, but the extreme waves associated with the passage of cold fronts in this region have lower return periods than those associated with the hurricane season.

Assessment of climate change downscaling and non-stationarity on the spatial pattern of a mangrove ecosystem in an arid coastal region of southern Iran

NASA Astrophysics Data System (ADS)

Etemadi, Halimeh; Samadi, S. Zahra; Sharifikia, Mohammad; Smoak, Joseph M.

2016-10-01

Mangrove wetlands exist in the transition zone between terrestrial and marine environments and have remarkable ecological and socio-economic value. This study uses climate change downscaling to address the question of non-stationarity influences on mangrove variations (expansion and contraction) within an arid coastal region. Our two-step approach includes downscaling models and uncertainty assessment, followed by a non-stationary and trend procedure using the Extreme Value Analysis (extRemes code). The Long Ashton Research Station Weather Generator (LARS-WG) model along with two different general circulation model (GCMs) (MIRH and HadCM3) were used to downscale climatic variables during current (1968-2011) and future (2011-2030, 2045-2065, and 2080-2099) periods. Parametric and non-parametric bootstrapping uncertainty tests demonstrated that the LARS-WGS model skillfully downscaled climatic variables at the 95 % significance level. Downscaling results using MIHR model show that minimum and maximum temperatures will increase in the future (2011-2030, 2045-2065, and 2080-2099) during winter and summer in a range of +4.21 and +4.7 °C, and +3.62 and +3.55 °C, respectively. HadCM3 analysis also revealed an increase in minimum (˜+3.03 °C) and maximum (˜+3.3 °C) temperatures during wet and dry seasons. In addition, we examined how much mangrove area has changed during the past decades and, thus, if climate change non-stationarity impacts mangrove ecosystems. Our results using remote sensing techniques and the non-parametric Mann-Whitney two-sample test indicated a sharp decline in mangrove area during 1972,1987, and 1997 periods ( p value = 0.002). Non-stationary assessment using the generalized extreme value (GEV) distributions by including mangrove area as a covariate further indicated that the null hypothesis of the stationary climate (no trend) should be rejected due to the very low p values for precipitation ( p value = 0.0027), minimum ( p value = 0.000000029) and maximum ( p value = 0.00016) temperatures. Based on non-stationary analysis and an upward trend in downscaled temperature extremes, climate change may control mangrove development in the future.

Randolph AFB, San Antonio, Texas. Revised Uniform Summary of Surface Weather Observations (RUSSWO). Parts A-F.

DTIC Science & Technology

1982-02-08

is printed in any year-month block when the extreme value Is based on an in- complete month (at least one day missing for the month). When a month has...means, standard deviations, and total number of valid observations for each month and annual (all months). An asterisk (*) is printed n each data block...becomes the extreme or monthly total in any of these tables it is printed as "TRACE." Continued on Reverse Side Values ’or means and standard

Optical rogue-wave-like extreme value fluctuations in fiber Raman amplifiers.

PubMed

Hammani, Kamal; Finot, Christophe; Dudley, John M; Millot, Guy

2008-10-13

We report experimental observation and characterization of rogue wave-like extreme value statistics arising from pump-signal noise transfer in a fiber Raman amplifier. Specifically, by exploiting Raman amplification with an incoherent pump, the amplified signal is shown to develop a series of temporal intensity spikes whose peak power follows a power-law probability distribution. The results are interpreted using a numerical model of the Raman gain process using coupled nonlinear Schrödinger equations, and the numerical model predicts results in good agreement with experiment.

Extreme value problems without calculus: a good link with geometry and elementary maths

NASA Astrophysics Data System (ADS)

Ganci, Salvatore

2016-11-01

Some classical examples of problem solving, where an extreme value condition is required, are here considered and/or revisited. The search for non-calculus solutions appears pedagogically useful and intriguing as shown through a rich literature. A teacher, who teaches both maths and physics, (as happens in Italian High schools) can find in these kinds of problems a mind stimulating exercise compared with the standard solution obtained by the differential calculus. A good link between the geometric and analytical explanations is so established.

Random walkers with extreme value memory: modelling the peak-end rule

NASA Astrophysics Data System (ADS)

Harris, Rosemary J.

2015-05-01

Motivated by the psychological literature on the ‘peak-end rule’ for remembered experience, we perform an analysis within a random walk framework of a discrete choice model where agents’ future choices depend on the peak memory of their past experiences. In particular, we use this approach to investigate whether increased noise/disruption always leads to more switching between decisions. Here extreme value theory illuminates different classes of dynamics indicating that the long-time behaviour is dependent on the scale used for reflection; this could have implications, for example, in questionnaire design.

The bothersomeness of sciatica: patients’ self-report of paresthesia, weakness and leg pain

PubMed Central

Haugen, Anne Julsrud; Keller, Anne; Natvig, Bård; Brox, Jens Ivar; Grotle, Margreth

2009-01-01

The objective of the study was to investigate how patients with sciatica due to disc herniation rate the bothersomeness of paresthesia and weakness as compared to leg pain, and how these symptoms are associated with socio-demographic and clinical characteristics. A cross-sectional study was conducted on 411 patients with clinical signs of radiculopathy. Items from the Sciatica Bothersomeness Index (0 = none to 6 = extremely) were used to establish values for paresthesia, weakness and leg pain. Associations with socio-demographic and clinical variables were analyzed by multiple linear regression. Mean scores (SD) were 4.5 (1.5) for leg pain, 3.4 (1.8) for paresthesia and 2.6 (2.0) for weakness. Women reported higher levels of bothersomeness for all three symptoms with mean scores approximately 10% higher than men. In the multivariate models, more severe symptoms were associated with lower physical function and higher emotional distress. Muscular paresis explained 19% of the variability in self-reported weakness, sensory findings explained 10% of the variability in paresthesia, and straight leg raising test explained 9% of the variability in leg pain. In addition to leg pain, paresthesia and weakness should be assessed when measuring symptom severity in sciatica. PMID:19488793

Radiographic progression of large joint damage in patients with rheumatoid arthritis treated with biological disease-modifying anti-rheumatic drugs.

PubMed

Nakajima, Arata; Aoki, Yasuchika; Sonobe, Masato; Takahashi, Hiroshi; Saito, Masahiko; Terayama, Keiichiro; Nakagawa, Koichi

2016-07-01

Radiographic progression of damage to the small joints in patients with rheumatoid arthritis (RA) is well known; however, it has not been studied fully in the large joints. In this study, we looked at the prevalence of radiographic progression of large joint damage in patients with RA treated with biological disease-modifying anti-rheumatic drugs (bDMARDs). A total of 273 large joints in the upper and lower extremities of 67 patients with RA treated with bDMARDs were investigated. Radiographs for tender and/or swollen large joints were taken at least twice during the study period (mean 18.6 months), and the progression of damage was evaluated. Progressive damage was found in 20.9% of patients and 6.2% of joints. A multivariate analysis revealed that the Larsen grade (LG) alone was a risk factor for progressive damage. The LG cutoff value was determined to be 2.5 (sensitivity: 0.529, specificity: 0.805). The only factor to predict progressive damage was the LG of the joints with symptoms, and the damage must be stopped within LG II. Regular radiographic examinations for large joints should be performed in addition to routine examinations for small joints, such as the hand and foot.

Trends and Cost-Analysis of Lower Extremity Nerve Injury Using the National Inpatient Sample.

PubMed

Foster, Chase H; Karsy, Michael; Jensen, Michael R; Guan, Jian; Eli, Ilyas; Mahan, Mark A

2018-06-08

Peripheral nerve injuries (PNIs) of the lower extremities have been assessed in small cohort studies; however, the actual incidence, national trends, comorbidities, and cost of care in lower extremity PNI are not defined. Lack of sufficient data limits discussion on national policies, payors, and other aspects fundamental to the delivery of care in the US. To establish estimates of lower extremity PNIs incidence, associated diagnoses, and cost in the US using a comprehensive database with a minimum of a decade of data. The National Inpatient Sample was utilized to evaluate International Classification of Disease codes for specific lower extremity PNIs (9560-9568) between 2001 and 2013. Lower extremity PNIs occurred with a mean incidence of 13.3 cases per million population annually, which declined minimally from 2001 to 2013. The mean ± SEM age was 41.6 ± 0.1 yr; 61.1% of patients were males. Most were admitted via the emergency department (56.0%). PNIs occurred to the sciatic (16.6%), femoral (10.7%), tibial (6.0%), peroneal (33.4%), multiple nerves (1.3%), and other (32.0%). Associated diagnoses included lower extremity fracture (13.4%), complications of care (11.2%), open wounds (10.3%), crush injury (9.7%), and other (7.2%). Associated procedures included tibial fixation (23.3%), closure of skin (20.1%), debridement of open fractures (15.4%), fixation of other bones (13.5%), and wound debridement (14.5%). The mean annual unadjusted compounded growth rate of charges was 8.8%. The mean ± SEM annual charge over the time period was $64 031.20 ± $421.10, which was associated with the number of procedure codes (β = 0.2), length of stay (β = 0.6), and year (β = 0.1) in a multivariable analysis (P = .0001). These data describe associations in the treatment of lower extremity PNIs, which are important for considering national policies, costs, research and the delivery of care.

Food Security and Extreme Events: Evidence from Smallholder Farmers in Central America

NASA Astrophysics Data System (ADS)

Saborio-Rodriguez, M.; Alpizar, F.; Harvey, C.; Martinez, R.; Vignola, R.; Viguera, B.; Capitan, T.

2016-12-01

Extreme weather events, which are expected to increase in magnitude and frequency due to climate change, are one of the main threats for smallholder farmers in Central America. Using a rich dataset from carefully selected subsistence farm households, we explore the determinants and severity of food insecurity resulting from extreme hydrometeorological hazards. In addition, we analyze farmerś coping strategies. Our analysis sheds light over food insecurity as an expression of vulnerability in a region that is expected to be increasingly exposed to extreme events and in a population already stressed by poverty and lack of opportunities. Regarding food insecurity, multivariate analyses indicate that education, having at least one migrant in the household, labor allocation, number of plots, and producing coffee are determinants of the probability of experiencing lack of food after an extreme weather event. Once the household is lacking food, the duration of the episode is related to access to credit, number of plots, producing coffee, ownership of land and gender of the head of the household. This results are in line with previous literature on the determinants of food insecurity in particular, and vulnerability, in general. Our dataset also allows us to analyze coping strategies. Households experiencing lack of food after an extreme weather event report mainly changes in their habits, as decreasing the amount of food consumed (54%) and modifying their diet (35%). A low proportion of household (between 10% and 15%, depending on the nature of the event) use their assets, by redirecting their savings, migrating, and selling items from the house. Asking money or food from family and friends or from an organization is reported for 4% of the households. This general results are connected to the specific coping strategies related to damages in crops, which are explored in detail. Our results indicate that there are patterns among the household experiencing lack of food after an extreme weather event. These patterns create opportunities for directing help, and preparing farmers in advance. The coping strategies used are precarious. Therefore, there is a need for rethinking policies that effectively help farmers to cope with extreme weather events with sustainable responses that reduce their vulnerability.

Clinical factors associated with extreme sleep apnoea [AHI>100 events per hour] in Peruvian patients: A case-control study–A preliminary report

PubMed Central

Rey de Castro, Jorge; Huamaní, Charles; Escobar-Córdoba, Franklin; Liendo, Cesar

2015-01-01

Purpose The severity of obstructive sleep apnoea (OSA) ranges from mild or moderate to severe sleep apnoea. However, there is no information available on the clinical characteristics associated with cases involving more than 100 events per hour. This is a preliminary report and our goal was to characterise the demographics and sleep characteristics of patients with Extreme OSA and compare with patients with sleep apnoea of lesser severity. We hypothesised that patients with Extreme OSA (AHI>100) is associated with an increased comorbidities and/or risk factors. Methods We carried out a case-control study on male patients with OSA who were seen in a private hospital in Lima, Peru between 2006 and 2012. Cases were identified if their apnoea/hypopnea index (AHI) was higher than 100 (Extreme OSA), and four controls were selected per case: two with 15–29 AHI and two with 30–50 AHI, matched according to case diagnosis dates. We evaluated demographic, past medical history, and oxygen saturation variables Results We identified 19 cases that were matched with 54 controls. In the multivariate model, only arterial hypertension, neck circumference, age, and over 10% in SatO2Hb≤90% in total sleep time (T90) were associated with Extreme OSA. Arterial hypertension had an OR=6.31 (CI95%: 1.71–23.23) of Extreme OSA. Each 5-cm increment in neck circumference was associated with an increase of OR=4.34 (CI95%: 1.32–14.33), while T90>10% had an OR=19.68 (CI95%: 4.33–89.49). Age had a marginal relevance (OR=0.95; CI95%: 0.92–0.99) Conclusion Our results suggest that arterial hypertension, neck circumference, and over 10% SatO2Hb≤90% in total sleep time were associated with a higher probability of Extreme OSA. We recommend investigators to study this population of Extreme OSA looking for an early diagnosis and the identification of prognostic factors in comparison with moderate to severe levels. PMID:26483940

An operational-oriented approach to the assessment of low probability seismic ground motions for critical infrastructures

NASA Astrophysics Data System (ADS)

Garcia-Fernandez, Mariano; Assatourians, Karen; Jimenez, Maria-Jose

2018-01-01

Extreme natural hazard events have the potential to cause significant disruption to critical infrastructure (CI) networks. Among them, earthquakes represent a major threat as sudden-onset events with limited, if any, capability of forecast, and high damage potential. In recent years, the increased exposure of interdependent systems has heightened concern, motivating the need for a framework for the management of these increased hazards. The seismic performance level and resilience of existing non-nuclear CIs can be analyzed by identifying the ground motion input values leading to failure of selected key elements. Main interest focuses on the ground motions exceeding the original design values, which should correspond to low probability occurrence. A seismic hazard methodology has been specifically developed to consider low-probability ground motions affecting elongated CI networks. The approach is based on Monte Carlo simulation, which allows for building long-duration synthetic earthquake catalogs to derive low-probability amplitudes. This approach does not affect the mean hazard values and allows obtaining a representation of maximum amplitudes that follow a general extreme-value distribution. This facilitates the analysis of the occurrence of extremes, i.e., very low probability of exceedance from unlikely combinations, for the development of, e.g., stress tests, among other applications. Following this methodology, extreme ground-motion scenarios have been developed for selected combinations of modeling inputs including seismic activity models (source model and magnitude-recurrence relationship), ground motion prediction equations (GMPE), hazard levels, and fractiles of extreme ground motion. The different results provide an overview of the effects of different hazard modeling inputs on the generated extreme motion hazard scenarios. This approach to seismic hazard is at the core of the risk analysis procedure developed and applied to European CI transport networks within the framework of the European-funded INFRARISK project. Such an operational seismic hazard framework can be used to provide insight in a timely manner to make informed risk management or regulating further decisions on the required level of detail or on the adoption of measures, the cost of which can be balanced against the benefits of the measures in question.

A Conservative Inverse Normal Test Procedure for Combining P-Values in Integrative Research.

ERIC Educational Resources Information Center

Saner, Hilary

1994-01-01

The use of p-values in combining results of studies often involves studies that are potentially aberrant. This paper proposes a combined test that permits trimming some of the extreme p-values. The trimmed statistic is based on an inverse cumulative normal transformation of the ordered p-values. (SLD)

Permafrost as an additional driving factor for the extreme fire event in the boreal Baikal region in 2003

NASA Astrophysics Data System (ADS)

Forkel, M.; Thonicke, K.; Beer, C.; Cramer, W.; Bartalev, S.; Schmullius, C.

2012-04-01

Wildfires are a natural and important element in the functioning of boreal forests. However, in some years, fires with extreme spread and severity occur. Such severe fires degrade the forest, affect human values, emit huge amount of carbon and aerosols and alter the land surface albedo. Usually, wind, slope, and dry conditions have been recognized as factors determining fire spread. In the Baikal region, 127,000 km2 burned in 2003, while the annual average burned area is approx. 8100 km2. In average years, 16% of the burned area occurred in the continuous permafrost zone but in 2003, 33% of these burned areas coincide with the existence of permanently frozen grounds. Permafrost and the associated upper active layer, which thaws during summer and refreezes during winter, is an important supply for soil moisture in boreal ecosystems. This leads to the question if permafrost hydrology is a potential additional driving factor for extreme fire events in boreal forests. Using temperature and precipitation data, we calculated the Nesterov index as indicator for fire weather conditions. Further, we used satellite observations of burned area and surface moisture, a digital elevation model, a land cover and a permafrost map to evaluate drivers for the temporal dynamic and spatial variability of surface moisture conditions and burned area in spring 2003. On the basis of time series decomposition, we separated the effect of drivers for fire activity on different time scales. We next computed cross-correlations to identify potential time lags between weather conditions, surface moisture and fire activity. Finally, we assessed the predictive capability of different combinations of driving variables for surface moisture conditions and burned area using multivariate spatial-temporal regression models. The results from this study demonstrate that permafrost in larch-dominated ecosystems regulates the inter-annual variability of surface moisture and thus increases the inter-annual variability of burned area. The drought conditions in spring 2003 were accelerated by the presence of permafrost because less water was stored in the upper active layer from the dry previous summer 2002 and the permafrost table prevents vegetative water uptake from deeper layers. In contrast, weather conditions (precipitation anomaly, Nesterov index) are weaker predictors for the 2003 fire event. Our analysis advances the understanding of complex interactions between the atmosphere, vegetation and soil on how feedback mechanisms can lead to extreme fire events. These findings emphasize the importance of a mechanistic coupling of soil thermodynamics, hydrology, and fire activity in earth system models for projecting climate change impacts over the next century.

Slice sampling technique in Bayesian extreme of gold price modelling

NASA Astrophysics Data System (ADS)

Rostami, Mohammad; Adam, Mohd Bakri; Ibrahim, Noor Akma; Yahya, Mohamed Hisham

2013-09-01

In this paper, a simulation study of Bayesian extreme values by using Markov Chain Monte Carlo via slice sampling algorithm is implemented. We compared the accuracy of slice sampling with other methods for a Gumbel model. This study revealed that slice sampling algorithm offers more accurate and closer estimates with less RMSE than other methods . Finally we successfully employed this procedure to estimate the parameters of Malaysia extreme gold price from 2000 to 2011.

The Differential Contribution of Maternal and Paternal Values to Social Competence of Preschoolers

ERIC Educational Resources Information Center

Bigras, Marc; Crepaldi, Maria Aparecida

2013-01-01

Multivariate analyses were conducted to clarify the nature of the influences of parental values on social behaviours of kindergarteners in the context of socio-demographic variables and sex of participants. This study included 217 mothers and 172 fathers from the same families, who completed a socio-demographic questionnaire and a new Q-sort that…

A Multivariate Analysis of Personality, Values and Expectations as Correlates of Career Aspirations of Final Year Medical Students

ERIC Educational Resources Information Center

Rogers, Mary E.; Searle, Judy; Creed, Peter A.; Ng, Shu-Kay

2010-01-01

This study reports on the career intentions of 179 final year medical students who completed an online survey that included measures of personality, values, professional and lifestyle expectations, and well-being. Logistic regression analyses identified the determinants of preferred medical specialty, practice location and hours of work.…

Nitrogen cycling in an extreme hyperarid environment inferred from δ(15)N analyses of plants, soils and herbivore diet.

PubMed

Díaz, Francisca P; Frugone, Matías; Gutiérrez, Rodrigo A; Latorre, Claudio

2016-03-09

Climate controls on the nitrogen cycle are suggested by the negative correlation between precipitation and δ(15)N values across different ecosystems. For arid ecosystems this is unclear, as water limitation among other factors can confound this relationship. We measured herbivore feces, foliar and soil δ(15)N and δ(13)C values and chemically characterized soils (pH and elemental composition) along an elevational/climatic gradient in the Atacama Desert, northern Chile. Although very positive δ(15)N values span the entire gradient, soil δ(15)N values show a positive correlation with aridity as expected. In contrast, foliar δ(15)N values and herbivore feces show a hump-shaped relationship with elevation, suggesting that plants are using a different N source, possibly of biotic origin. Thus at the extreme limits of plant life, biotic interactions may be just as important as abiotic processes, such as climate in explaining ecosystem δ(15)N values.

Nitrogen cycling in an extreme hyperarid environment inferred from δ15N analyses of plants, soils and herbivore diet

NASA Astrophysics Data System (ADS)

Díaz, Francisca P.; Frugone, Matías; Gutiérrez, Rodrigo A.; Latorre, Claudio

2016-03-01

Climate controls on the nitrogen cycle are suggested by the negative correlation between precipitation and δ15N values across different ecosystems. For arid ecosystems this is unclear, as water limitation among other factors can confound this relationship. We measured herbivore feces, foliar and soil δ15N and δ13C values and chemically characterized soils (pH and elemental composition) along an elevational/climatic gradient in the Atacama Desert, northern Chile. Although very positive δ15N values span the entire gradient, soil δ15N values show a positive correlation with aridity as expected. In contrast, foliar δ15N values and herbivore feces show a hump-shaped relationship with elevation, suggesting that plants are using a different N source, possibly of biotic origin. Thus at the extreme limits of plant life, biotic interactions may be just as important as abiotic processes, such as climate in explaining ecosystem δ15N values.

Nitrogen cycling in an extreme hyperarid environment inferred from δ15N analyses of plants, soils and herbivore diet

PubMed Central

Díaz, Francisca P.; Frugone, Matías; Gutiérrez, Rodrigo A.; Latorre, Claudio

2016-01-01

Climate controls on the nitrogen cycle are suggested by the negative correlation between precipitation and δ15N values across different ecosystems. For arid ecosystems this is unclear, as water limitation among other factors can confound this relationship. We measured herbivore feces, foliar and soil δ15N and δ13C values and chemically characterized soils (pH and elemental composition) along an elevational/climatic gradient in the Atacama Desert, northern Chile. Although very positive δ15N values span the entire gradient, soil δ15N values show a positive correlation with aridity as expected. In contrast, foliar δ15N values and herbivore feces show a hump-shaped relationship with elevation, suggesting that plants are using a different N source, possibly of biotic origin. Thus at the extreme limits of plant life, biotic interactions may be just as important as abiotic processes, such as climate in explaining ecosystem δ15N values. PMID:26956399

The prediction of gross calorific value using infrared (IR) spectroscopy and multivariate analysis

Treesearch

Chi-Leung So; Thomas L. Eberhardt

2011-01-01

The gross calorific value (GCV) of a fuel, also known as the higher heating value (HHV) or gross heat of combustion, is the amount of heat released by a specified quantity (initially at 25°C) once it is com-busted and the products returned to that temperature. Fuwape (1989) noted that extractive-free wood from Gmelina arborea (Roxb), a hardwood, had a lower gross heat...

Using Statistical Process Control for detecting anomalies in multivariate spatiotemporal Earth Observations

NASA Astrophysics Data System (ADS)

Flach, Milan; Mahecha, Miguel; Gans, Fabian; Rodner, Erik; Bodesheim, Paul; Guanche-Garcia, Yanira; Brenning, Alexander; Denzler, Joachim; Reichstein, Markus

2016-04-01

The number of available Earth observations (EOs) is currently substantially increasing. Detecting anomalous patterns in these multivariate time series is an important step in identifying changes in the underlying dynamical system. Likewise, data quality issues might result in anomalous multivariate data constellations and have to be identified before corrupting subsequent analyses. In industrial application a common strategy is to monitor production chains with several sensors coupled to some statistical process control (SPC) algorithm. The basic idea is to raise an alarm when these sensor data depict some anomalous pattern according to the SPC, i.e. the production chain is considered 'out of control'. In fact, the industrial applications are conceptually similar to the on-line monitoring of EOs. However, algorithms used in the context of SPC or process monitoring are rarely considered for supervising multivariate spatio-temporal Earth observations. The objective of this study is to exploit the potential and transferability of SPC concepts to Earth system applications. We compare a range of different algorithms typically applied by SPC systems and evaluate their capability to detect e.g. known extreme events in land surface processes. Specifically two main issues are addressed: (1) identifying the most suitable combination of data pre-processing and detection algorithm for a specific type of event and (2) analyzing the limits of the individual approaches with respect to the magnitude, spatio-temporal size of the event as well as the data's signal to noise ratio. Extensive artificial data sets that represent the typical properties of Earth observations are used in this study. Our results show that the majority of the algorithms used can be considered for the detection of multivariate spatiotemporal events and directly transferred to real Earth observation data as currently assembled in different projects at the European scale, e.g. http://baci-h2020.eu/index.php/ and http://earthsystemdatacube.net/. Known anomalies such as the Russian heatwave are detected as well as anomalies which are not detectable with univariate methods.

How Historical Information Can Improve Extreme Value Analysis of Coastal Water Levels

NASA Astrophysics Data System (ADS)

Le Cozannet, G.; Bulteau, T.; Idier, D.; Lambert, J.; Garcin, M.

2016-12-01

The knowledge of extreme coastal water levels is useful for coastal flooding studies or the design of coastal defences. While deriving such extremes with standard analyses using tide gauge measurements, one often needs to deal with limited effective duration of observation which can result in large statistical uncertainties. This is even truer when one faces outliers, those particularly extreme values distant from the others. In a recent work (Bulteau et al., 2015), we investigated how historical information of past events reported in archives can reduce statistical uncertainties and relativize such outlying observations. We adapted a Bayesian Markov Chain Monte Carlo method, initially developed in the hydrology field (Reis and Stedinger, 2005), to the specific case of coastal water levels. We applied this method to the site of La Rochelle (France), where the storm Xynthia in 2010 generated a water level considered so far as an outlier. Based on 30 years of tide gauge measurements and 8 historical events since 1890, the results showed a significant decrease in statistical uncertainties on return levels when historical information is used. Also, Xynthia's water level no longer appeared as an outlier and we could have reasonably predicted the annual exceedance probability of that level beforehand (predictive probability for 2010 based on data until the end of 2009 of the same order of magnitude as the standard estimative probability using data until the end of 2010). Such results illustrate the usefulness of historical information in extreme value analyses of coastal water levels, as well as the relevance of the proposed method to integrate heterogeneous data in such analyses.

Lower Extremity Stiffness Changes after Concussion in Collegiate Football Players.

PubMed

Dubose, Dominique F; Herman, Daniel C; Jones, Deborah L; Tillman, Susan M; Clugston, James R; Pass, Anthony; Hernandez, Jorge A; Vasilopoulos, Terrie; Horodyski, Marybeth; Chmielewski, Terese L

2017-01-01

Recent research indicates that a concussion increases the risk of musculoskeletal injury. Neuromuscular changes after concussion might contribute to the increased risk of injury. Many studies have examined gait postconcussion, but few studies have examined more demanding tasks. This study compared changes in stiffness across the lower extremity, a measure of neuromuscular function, during a jump-landing task in athletes with a concussion (CONC) to uninjured athletes (UNINJ). Division I football players (13 CONC and 26 UNINJ) were tested pre- and postseason. A motion capture system recorded subjects jumping on one limb from a 25.4-cm step onto a force plate. Hip, knee, and ankle joint stiffness were calculated from initial contact to peak joint flexion using the regression line slopes of the joint moment versus the joint angle plots. Leg stiffness was (peak vertical ground reaction force [PVGRF]/lower extremity vertical displacement) from initial contact to peak vertical ground reaction force. All stiffness values were normalized to body weight. Values from both limbs were averaged. General linear models compared group (CONC, UNINJ) differences in the changes of pre- and postseason stiffness values. Average time from concussion to postseason testing was 49.9 d. The CONC group showed an increase in hip stiffness (P = 0.03), a decrease in knee (P = 0.03) and leg stiffness (P = 0.03), but no change in ankle stiffness (P = 0.65) from pre- to postseason. Lower extremity stiffness is altered after concussion, which could contribute to an increased risk of lower extremity injury. These data provide further evidence of altered neuromuscular function after concussion.

Lower Extremity Stiffness Changes following Concussion in Collegiate Football Players

PubMed Central

DuBose, Dominique F.; Herman, Daniel C.; Jones, Debi L.; Tillman, Susan M.; Clugston, James R.; Pass, Anthony; Hernandez, Jorge A.; Vasilopoulos, Terrie; Horodyski, MaryBeth; Chmielewski, Terese L.

2016-01-01

Purpose Recent research indicates that a concussion increases risk of musculoskeletal injury. Neuromuscular changes following concussion might contribute to the increased risk of injury. Many studies have examined gait post-concussion, but few studies have examined more demanding tasks. This study compared changes in stiffness across the lower extremity, a measure of neuromuscular function, during a jump-landing task in athletes with a concussion (CONC) to uninjured athletes (UNINJ). Methods Division I football players (13 CONC, 26 UNINJ) were tested pre- and post-season. A motion-capture system recorded subjects jumping on one limb from a 25.4 cm step onto a force plate. Hip, knee, and ankle joint stiffness were calculated from initial contact to peak joint flexion using the regression line slopes of the joint moment versus joint angle plots. Leg stiffness was (peak vertical ground reaction force (PVGRF)/lower extremity vertical displacement) from initial contact to PVGRF. All stiffness values were normalized to bodyweight. Values from both limbs were averaged. General linear models compared group (CONC, UNINJ) differences in the changes of pre- and post-season stiffness values. Results Average time from concussion to post-season testing was 49.9 days. The CONC group showed an increase in hip stiffness (p=0.03), a decrease in knee (p=0.03) and leg stiffness (p=0.03), but no change in ankle stiffness (p=0.65) from pre- to post-season. Conclusion Lower extremity stiffness is altered following concussion, which could contribute to an increased risk of lower extremity injury. These data provide further evidence of altered neuromuscular function after concussion. PMID:27501359

Selecting and applying indicators of ecosystem collapse for risk assessments.

PubMed

Rowland, Jessica A; Nicholson, Emily; Murray, Nicholas J; Keith, David A; Lester, Rebecca E; Bland, Lucie M

2018-03-12

Ongoing ecosystem degradation and transformation are key threats to biodiversity. Measuring ecosystem change towards collapse relies on monitoring indicators that quantify key ecological processes. Yet little guidance is available on selecting and implementing indicators for ecosystem risk assessment. Here, we reviewed indicator use in ecological studies of decline towards collapse in marine pelagic and temperate forest ecosystems. We evaluated the use of indicator selection methods, indicator types (geographic distribution, abiotic, biotic), methods of assessing multiple indicators, and temporal quality of time series. We compared these ecological studies to risk assessments in the International Union for the Conservation of Nature Red List of Ecosystems (RLE), where indicators are used to estimate ecosystem collapse risk. We found that ecological studies and RLE assessments rarely reported how indicators were selected, particularly in terrestrial ecosystems. Few ecological studies and RLE assessments quantified ecosystem change with all three indicator types, and indicators types used varied between marine and terrestrial ecosystem. Several studies used indices or multivariate analyses to assess multiple indicators simultaneously, but RLE assessments did not, as RLE guidelines advise against them. Most studies and RLE assessments used time series spanning at least 30 years, increasing the chance of reliably detecting change. Limited use of indicator selection protocols and infrequent use of all three indicator types may hamper the ability to accurately detect changes. To improve the value of risk assessments for informing policy and management, we recommend using: (i) explicit protocols, including conceptual models, to identify and select indicators; (ii) a range of indicators spanning distributional, abiotic and biotic features; (iii) indices and multivariate analyses with extreme care until guidelines are developed; (iv) time series with sufficient data to increase ability to accurately diagnose directional change; (v) data from multiple sources to support assessments; and (vi) explicitly reporting steps in the assessment process. This article is protected by copyright. All rights reserved. This article is protected by copyright. All rights reserved.

An Incremental Type-2 Meta-Cognitive Extreme Learning Machine.

PubMed

Pratama, Mahardhika; Zhang, Guangquan; Er, Meng Joo; Anavatti, Sreenatha

2017-02-01

Existing extreme learning algorithm have not taken into account four issues: 1) complexity; 2) uncertainty; 3) concept drift; and 4) high dimensionality. A novel incremental type-2 meta-cognitive extreme learning machine (ELM) called evolving type-2 ELM (eT2ELM) is proposed to cope with the four issues in this paper. The eT2ELM presents three main pillars of human meta-cognition: 1) what-to-learn; 2) how-to-learn; and 3) when-to-learn. The what-to-learn component selects important training samples for model updates by virtue of the online certainty-based active learning method, which renders eT2ELM as a semi-supervised classifier. The how-to-learn element develops a synergy between extreme learning theory and the evolving concept, whereby the hidden nodes can be generated and pruned automatically from data streams with no tuning of hidden nodes. The when-to-learn constituent makes use of the standard sample reserved strategy. A generalized interval type-2 fuzzy neural network is also put forward as a cognitive component, in which a hidden node is built upon the interval type-2 multivariate Gaussian function while exploiting a subset of Chebyshev series in the output node. The efficacy of the proposed eT2ELM is numerically validated in 12 data streams containing various concept drifts. The numerical results are confirmed by thorough statistical tests, where the eT2ELM demonstrates the most encouraging numerical results in delivering reliable prediction, while sustaining low complexity.

Spatial analysis of extreme precipitation deficit as an index for atmospheric drought in Belgium

NASA Astrophysics Data System (ADS)

Zamani, Sepideh; Van De Vyver, Hans; Gobin, Anne

2014-05-01

The growing concern among the climate scientists is that the frequency of weather extremes will increase as a result of climate change. European society, for example, is particularly vulnerable to changes in the frequency and intensity of extreme events such as heat waves, heavy precipitation, droughts, and wind storms, as seen in recent years [1,2]. A more than 50% of the land is occupied by managed ecosystem (agriculture, forestry) in Belgium. Moreover, among the many extreme weather conditions, drought counts to have a substantial impact on the agriculture and ecosystem of the affected region, because its most immediate consequence is a fall in crop production. Besides the technological advances, a reliable estimation of weather conditions plays a crucial role in improving the agricultural productivity. The above mentioned reasons provide a strong motivation for a research on the drought and its impacts on the economical and agricultural aspects in Belgium. The main purpose of the presented work is to map atmospheric drought Return-Levels (RL), as first insight for agricultural drought, employing spatial modelling approaches. The likelihood of future drought is studied on the basis of precipitation deficit indices for four vegetation types: water (W), grass (G), deciduous (D) and coniferous forests (C) is considered. Extreme Value Theory (EVT) [3,4,5] as a branch of probability and statistics, is dedicated to characterize the behaviour of extreme observations. The tail behaviour of the EVT distributions provide important features about return levels. EVT distributions are applicable in many study areas such as: hydrology, environmental research and meteorology, insurance and finance. Spatial Generalized Extreme Value (GEV) distributions, as a branch of EVT, are applied to annual maxima of drought at 13 hydro-meteorological stations across Belgium. Superiority of the spatial GEV model is that a region can be modelled merging the individual time series of observations from isolated sites and using a common regression model based on climatological/geographical covariates. The behaviour of the fitted spatial GEV-distribution is heavy-tailed with γ ≡ 0.3 over Belgium. A comparison between the RL-maps using GEV model and the ones obtained from Universal Kriging (UK) confirms the reliability of the spatial GEV model in explaining atmospheric drought in Belgium. References [1] Beniston, M., Stephenson, D. B., Christensen, O. B., Ferro, C. A. T., Frei, C., Goyette, S., Halsnaes, K., Holt, T., Jylhü, K., Koffi, B., Palutikoff, J., Schöll, R., Semmler, T., and Woth, K. (2007), Future extreme events in European climate; an exploration of Regional Climate Model projections. Climatic Change, 81, 71-95. [2] Solomon, S., D. Qin, M. Manning, Z. Chen, M. Marquis, K.B. Averyt, M. Tignor and H.L. Miller (Eds.)] (2007), king Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 996 pp. [3] Coles, S. (2001), An Introduction to Statistical Modeling of Extreme Values, Springer-Verlag Heidelberg, Germany. [4] Embrechts, P., C. Klüppelberg, and T. Mikosch (1997), Modelling Extremal Events for Insurance and Finance, Springer-Verlag, Berlin. [5] Smith, R., (2004), Statistics of extremes, with application in environment, insurance and finance, in : Extreme Values in Finance, Telecommunications and the Environment, edited by: Finkenstadt, B. and Rootzen, H., 373-388, Chapman and Hall CRC Press, London.

Control-group feature normalization for multivariate pattern analysis of structural MRI data using the support vector machine.

PubMed

Linn, Kristin A; Gaonkar, Bilwaj; Satterthwaite, Theodore D; Doshi, Jimit; Davatzikos, Christos; Shinohara, Russell T

2016-05-15

Normalization of feature vector values is a common practice in machine learning. Generally, each feature value is standardized to the unit hypercube or by normalizing to zero mean and unit variance. Classification decisions based on support vector machines (SVMs) or by other methods are sensitive to the specific normalization used on the features. In the context of multivariate pattern analysis using neuroimaging data, standardization effectively up- and down-weights features based on their individual variability. Since the standard approach uses the entire data set to guide the normalization, it utilizes the total variability of these features. This total variation is inevitably dependent on the amount of marginal separation between groups. Thus, such a normalization may attenuate the separability of the data in high dimensional space. In this work we propose an alternate approach that uses an estimate of the control-group standard deviation to normalize features before training. We study our proposed approach in the context of group classification using structural MRI data. We show that control-based normalization leads to better reproducibility of estimated multivariate disease patterns and improves the classifier performance in many cases. Copyright © 2016 Elsevier Inc. All rights reserved.

Experimental analysis of multivariate female choice in gray treefrogs (Hyla versicolor): evidence for directional and stabilizing selection.

PubMed

Gerhardt, H Carl; Brooks, Robert

2009-10-01

Even simple biological signals vary in several measurable dimensions. Understanding their evolution requires, therefore, a multivariate understanding of selection, including how different properties interact to determine the effectiveness of the signal. We combined experimental manipulation with multivariate selection analysis to assess female mate choice on the simple trilled calls of male gray treefrogs. We independently and randomly varied five behaviorally relevant acoustic properties in 154 synthetic calls. We compared response times of each of 154 females to one of these calls with its response to a standard call that had mean values of the five properties. We found directional and quadratic selection on two properties indicative of the amount of signaling, pulse number, and call rate. Canonical rotation of the fitness surface showed that these properties, along with pulse rate, contributed heavily to a major axis of stabilizing selection, a result consistent with univariate studies showing diminishing effects of increasing pulse number well beyond the mean. Spectral properties contributed to a second major axis of stabilizing selection. The single major axis of disruptive selection suggested that a combination of two temporal and two spectral properties with values differing from the mean should be especially attractive.

Bayesian Treed Multivariate Gaussian Process with Adaptive Design: Application to a Carbon Capture Unit

DOE Office of Scientific and Technical Information (OSTI.GOV)

Konomi, Bledar A.; Karagiannis, Georgios; Sarkar, Avik

2014-05-16

Computer experiments (numerical simulations) are widely used in scientific research to study and predict the behavior of complex systems, which usually have responses consisting of a set of distinct outputs. The computational cost of the simulations at high resolution are often expensive and become impractical for parametric studies at different input values. To overcome these difficulties we develop a Bayesian treed multivariate Gaussian process (BTMGP) as an extension of the Bayesian treed Gaussian process (BTGP) in order to model and evaluate a multivariate process. A suitable choice of covariance function and the prior distributions facilitates the different Markov chain Montemore » Carlo (MCMC) movements. We utilize this model to sequentially sample the input space for the most informative values, taking into account model uncertainty and expertise gained. A simulation study demonstrates the use of the proposed method and compares it with alternative approaches. We apply the sequential sampling technique and BTMGP to model the multiphase flow in a full scale regenerator of a carbon capture unit. The application presented in this paper is an important tool for research into carbon dioxide emissions from thermal power plants.« less

Multivariate linear regression analysis to identify general factors for quantitative predictions of implant stability quotient values

PubMed Central

Huang, Hairong; Xu, Zanzan; Shao, Xianhong; Wismeijer, Daniel; Sun, Ping; Wang, Jingxiao

2017-01-01

Objectives This study identified potential general influencing factors for a mathematical prediction of implant stability quotient (ISQ) values in clinical practice. Methods We collected the ISQ values of 557 implants from 2 different brands (SICace and Osstem) placed by 2 surgeons in 336 patients. Surgeon 1 placed 329 SICace implants, and surgeon 2 placed 113 SICace implants and 115 Osstem implants. ISQ measurements were taken at T1 (immediately after implant placement) and T2 (before dental restoration). A multivariate linear regression model was used to analyze the influence of the following 11 candidate factors for stability prediction: sex, age, maxillary/mandibular location, bone type, immediate/delayed implantation, bone grafting, insertion torque, I-stage or II-stage healing pattern, implant diameter, implant length and T1-T2 time interval. Results The need for bone grafting as a predictor significantly influenced ISQ values in all three groups at T1 (weight coefficients ranging from -4 to -5). In contrast, implant diameter consistently influenced the ISQ values in all three groups at T2 (weight coefficients ranging from 3.4 to 4.2). Other factors, such as sex, age, I/II-stage implantation and bone type, did not significantly influence ISQ values at T2, and implant length did not significantly influence ISQ values at T1 or T2. Conclusions These findings provide a rational basis for mathematical models to quantitatively predict the ISQ values of implants in clinical practice. PMID:29084260

Dimensions of Problem Drinking among Young Adult Restaurant Workers

PubMed Central

Moore, Roland S.; Cunradi, Carol B.; Duke, Michael R.; Ames, Genevieve M.

2009-01-01

Background Nationwide surveys identify food service workers as heavy alcohol users. Objectives This article analyzes dimensions and correlates of problem drinking among young adult food service workers. Methods A telephone survey of national restaurant chain employees yielded 1294 completed surveys. Results Hazardous alcohol consumption patterns were seen in 80% of men and 64% of women. Multivariate analysis showed that different dimensions of problem drinking measured by the AUDIT were associated with workers' demographic characteristics, smoking behavior and job category. Conclusions & Scientific Significance These findings offer evidence of extremely high rates of alcohol misuse among young adult restaurant workers. PMID:20180660

Butterflies, Black swans and Dragon kings: How to use the Dynamical Systems Theory to build a "zoology" of mid-latitude circulation atmospheric extremes?

NASA Astrophysics Data System (ADS)

Faranda, D.; Yiou, P.; Alvarez-Castro, M. C. M.

2015-12-01

A combination of dynamical systems and statistical techniques allows for a robust assessment of the dynamical properties of the mid-latitude atmospheric circulation. Extremes at different spatial and time scales are not only associated to exceptionally intense weather structures (e.g. extra-tropical cyclones) but also to rapid changes of circulation regimes (thunderstorms, supercells) or the extreme persistence of weather structure (heat waves, cold spells). We will show how the dynamical systems theory of recurrence combined to the extreme value theory can take into account the spatial and temporal dependence structure of the mid-latitude circulation structures and provide information on the statistics of extreme events.

Various forms of indexing HDMR for modelling multivariate classification problems

DOE Office of Scientific and Technical Information (OSTI.GOV)

Aksu, Çağrı; Tunga, M. Alper

2014-12-10

The Indexing HDMR method was recently developed for modelling multivariate interpolation problems. The method uses the Plain HDMR philosophy in partitioning the given multivariate data set into less variate data sets and then constructing an analytical structure through these partitioned data sets to represent the given multidimensional problem. Indexing HDMR makes HDMR be applicable to classification problems having real world data. Mostly, we do not know all possible class values in the domain of the given problem, that is, we have a non-orthogonal data structure. However, Plain HDMR needs an orthogonal data structure in the given problem to be modelled.more » In this sense, the main idea of this work is to offer various forms of Indexing HDMR to successfully model these real life classification problems. To test these different forms, several well-known multivariate classification problems given in UCI Machine Learning Repository were used and it was observed that the accuracy results lie between 80% and 95% which are very satisfactory.« less

Practical robustness measures in multivariable control system analysis. Ph.D. Thesis

NASA Technical Reports Server (NTRS)

Lehtomaki, N. A.

1981-01-01

The robustness of the stability of multivariable linear time invariant feedback control systems with respect to model uncertainty is considered using frequency domain criteria. Available robustness tests are unified under a common framework based on the nature and structure of model errors. These results are derived using a multivariable version of Nyquist's stability theorem in which the minimum singular value of the return difference transfer matrix is shown to be the multivariable generalization of the distance to the critical point on a single input, single output Nyquist diagram. Using the return difference transfer matrix, a very general robustness theorem is presented from which all of the robustness tests dealing with specific model errors may be derived. The robustness tests that explicitly utilized model error structure are able to guarantee feedback system stability in the face of model errors of larger magnitude than those robustness tests that do not. The robustness of linear quadratic Gaussian control systems are analyzed.

Calibrated Multivariate Regression with Application to Neural Semantic Basis Discovery.

PubMed

Liu, Han; Wang, Lie; Zhao, Tuo

2015-08-01

We propose a calibrated multivariate regression method named CMR for fitting high dimensional multivariate regression models. Compared with existing methods, CMR calibrates regularization for each regression task with respect to its noise level so that it simultaneously attains improved finite-sample performance and tuning insensitiveness. Theoretically, we provide sufficient conditions under which CMR achieves the optimal rate of convergence in parameter estimation. Computationally, we propose an efficient smoothed proximal gradient algorithm with a worst-case numerical rate of convergence O (1/ ϵ ), where ϵ is a pre-specified accuracy of the objective function value. We conduct thorough numerical simulations to illustrate that CMR consistently outperforms other high dimensional multivariate regression methods. We also apply CMR to solve a brain activity prediction problem and find that it is as competitive as a handcrafted model created by human experts. The R package camel implementing the proposed method is available on the Comprehensive R Archive Network http://cran.r-project.org/web/packages/camel/.

Multivariate longitudinal data analysis with censored and intermittent missing responses.

PubMed

Lin, Tsung-I; Lachos, Victor H; Wang, Wan-Lun

2018-05-08

The multivariate linear mixed model (MLMM) has emerged as an important analytical tool for longitudinal data with multiple outcomes. However, the analysis of multivariate longitudinal data could be complicated by the presence of censored measurements because of a detection limit of the assay in combination with unavoidable missing values arising when subjects miss some of their scheduled visits intermittently. This paper presents a generalization of the MLMM approach, called the MLMM-CM, for a joint analysis of the multivariate longitudinal data with censored and intermittent missing responses. A computationally feasible expectation maximization-based procedure is developed to carry out maximum likelihood estimation within the MLMM-CM framework. Moreover, the asymptotic standard errors of fixed effects are explicitly obtained via the information-based method. We illustrate our methodology by using simulated data and a case study from an AIDS clinical trial. Experimental results reveal that the proposed method is able to provide more satisfactory performance as compared with the traditional MLMM approach. Copyright © 2018 John Wiley & Sons, Ltd.

Comment on Pisarenko et al., "Characterization of the Tail of the Distribution of Earthquake Magnitudes by Combining the GEV and GPD Descriptions of Extreme Value Theory"

NASA Astrophysics Data System (ADS)

Raschke, Mathias

2016-02-01

In this short note, I comment on the research of Pisarenko et al. (Pure Appl. Geophys 171:1599-1624, 2014) regarding the extreme value theory and statistics in the case of earthquake magnitudes. The link between the generalized extreme value distribution (GEVD) as an asymptotic model for the block maxima of a random variable and the generalized Pareto distribution (GPD) as a model for the peaks over threshold (POT) of the same random variable is presented more clearly. Inappropriately, Pisarenko et al. (Pure Appl. Geophys 171:1599-1624, 2014) have neglected to note that the approximations by GEVD and GPD work only asymptotically in most cases. This is particularly the case with truncated exponential distribution (TED), a popular distribution model for earthquake magnitudes. I explain why the classical models and methods of the extreme value theory and statistics do not work well for truncated exponential distributions. Consequently, these classical methods should be used for the estimation of the upper bound magnitude and corresponding parameters. Furthermore, I comment on various issues of statistical inference in Pisarenko et al. and propose alternatives. I argue why GPD and GEVD would work for various types of stochastic earthquake processes in time, and not only for the homogeneous (stationary) Poisson process as assumed by Pisarenko et al. (Pure Appl. Geophys 171:1599-1624, 2014). The crucial point of earthquake magnitudes is the poor convergence of their tail distribution to the GPD, and not the earthquake process over time.

Inter-examiner classification reliability of Mechanical Diagnosis and Therapy for extremity problems - Systematic review.

PubMed

Takasaki, Hiroshi; Okuyama, Kousuke; Rosedale, Richard

2017-02-01

Mechanical Diagnosis and Therapy (MDT) is used in the treatment of extremity problems. Classifying clinical problems is one method of providing effective treatment to a target population. Classification reliability is a key factor to determine the precise clinical problem and to direct an appropriate intervention. To explore inter-examiner reliability of the MDT classification for extremity problems in three reliability designs: 1) vignette reliability using surveys with patient vignettes, 2) concurrent reliability, where multiple assessors decide a classification by observing someone's assessment, 3) successive reliability, where multiple assessors independently assess the same patient at different times. Systematic review with data synthesis in a quantitative format. Agreement of MDT subgroups was examined using the Kappa value, with the operational definition of acceptable reliability set at ≥ 0.6. The level of evidence was determined considering the methodological quality of the studies. Six studies were included and all studies met the criteria for high quality. Kappa values for the vignette reliability design (five studies) were ≥ 0.7. There was data from two cohorts in one study for the concurrent reliability design and the Kappa values ranged from 0.45 to 1.0. Kappa values for the successive reliability design (data from three cohorts in one study) were < 0.6. The current review found strong evidence of acceptable inter-examiner reliability of MDT classification for extremity problems in the vignette reliability design, limited evidence of acceptable reliability in the concurrent reliability design and unacceptable reliability in the successive reliability design. Copyright © 2017 Elsevier Ltd. All rights reserved.

Postpartum contraceptive use among women with a recent preterm birth.

PubMed

Robbins, Cheryl L; Farr, Sherry L; Zapata, Lauren B; D'Angelo, Denise V; Callaghan, William M

2015-10-01

The objective of the study was to evaluate the associations between postpartum contraception and having a recent preterm birth. Population-based data from the Pregnancy Risk Assessment Monitoring System in 9 states were used to estimate the postpartum use of highly or moderately effective contraception (sterilization, intrauterine device, implants, shots, pills, patch, and ring) and user-independent contraception (sterilization, implants, and intrauterine device) among women with recent live births (2009-2011). We assessed the differences in contraception by gestational age (≤27, 28-33, or 34-36 weeks vs term [≥37 weeks]) and modeled the associations using multivariable logistic regression with weighted data. A higher percentage of women with recent extreme preterm birth (≤27 weeks) reported using no postpartum method (31%) compared with all other women (15-16%). Women delivering extreme preterm infants had a decreased odds of using highly or moderately effective methods (adjusted odds ratio, 0.5; 95% confidence interval, 0.4-0.6) and user-independent methods (adjusted odds ratio, 0.5; 95% confidence interval, 0.4-0.7) compared with women having term births. Wanting to get pregnant was more frequently reported as a reason for contraceptive nonuse by women with an extreme preterm birth overall (45%) compared with all other women (15-18%, P < .0001). Infant death occurred in 41% of extreme preterm births and more than half of these mothers (54%) reported wanting to become pregnant as the reason for contraceptive nonuse. During contraceptive counseling with women who had recent preterm births, providers should address an optimal pregnancy interval and consider that women with recent extreme preterm birth, particularly those whose infants died, may not use contraception because they want to get pregnant. Published by Elsevier Inc.

Effectiveness of community psychosocial and pharmacological treatments for alcohol use disorder: A national observational cohort study in England.

PubMed

Peacock, Amy; Eastwood, Brian; Jones, Andrew; Millar, Tim; Horgan, Patrick; Knight, Jonathan; Randhawa, Kulvir; White, Martin; Marsden, John

2018-05-01

This was a national English observational cohort study using administrative data to estimate the effectiveness of community pharmacological and psychosocial treatment for alcohol use disorder (AUD). All adults commencing AUD treatment in the community reported to the National Drug Treatment Monitoring System (April 1 2014-March 31 2015; N = 52,499). Past 28-day admission drinking pattern included drinks per drinking day (DDD): 0 ('Abstinent'), 1-15 ('Low-High'), 16-30 ('High-Extreme') and over 30 DDD ('Extreme'). The primary outcome was successful completion of treatment within 12 months of commencement with no re-presentation (SCNR) in the subsequent six months, analysed by multi-level, mixed effects, multivariable logistic regression. The majority reported DDD in the 'Low-High' (n = 17,698, 34%) and 'High-Extreme' (n = 21,383, 41%) range. Smaller proportions were categorised 'Extreme' (n = 7759, 15%) and 'Abstinent' (n = 5661, 11%). Three-fifths (58%) achieved SCNR. Predictors of SCNR were older age, black/minority ethnic group, employment, criminal justice system referral, and longer treatment exposure. Predictors of negative outcome were AUD treatment history, lower socio-economic status, housing problems, and 'Extreme' drinking at admission. In addition to psychosocial interventions, pharmacological interventions and recovery support increased the likelihood of SCNR. Pharmacological treatment was only beneficial for the 'Low-High' groups with recovery support. Over half of all patients admitted for community AUD treatment in England are reported to successfully complete treatment within 12 months and are not re-admitted for further treatment in the following 6 months. Study findings underscore efforts to tailor AUD treatment to the severity of alcohol consumption and using recovery support. Copyright © 2018 Elsevier B.V. All rights reserved.

Comparison of work-related musculoskeletal symptoms between male cameramen and male office workers.

PubMed

Jeong, Han-Seur; Suh, Byung-Seong; Kim, Soo-Geun; Kim, Won-Sool; Lee, Won-Cheol; Son, Kyung-Hun; Nam, Min-Woo

2018-01-01

Previous studies have classified cameramen's job as physiologically heavy work and identified the risk factors of work-related musculoskeletal disorders (WRMDs) in cameramen. However, those studies limited their research subjects to cameramen. In this study, we compared the frequency and severity of WRMDs between cameramen and office workers. A total of 293 subjects working in four broadcasting companies in Korea were recruited. A questionnaire survey was conducted for a month, starting in October 2016. The subjects were divided into cameramen and office workers according to their occupation. We compared the frequency and severity of WRMDs and ergonomic risk assessment results between the two groups. The high-risk WRMD group had a higher proportion of cameramen than office workers. Moreover, the high ergonomic risk group also had a higher proportion of cameramen than office workers for WRMDs in the upper extremities and waist+lower extremities. In the multivariable-adjusted model comparing cameramen and office workers, the odds ratio (OR) with 95% confidence interval (95% CI) for high-risk WRMDs was 3.50 (95% CI: 1.92-7.72) for the upper extremities and 3.18 (95% CI: 1.62-6.21) for the waist and the lower extremities. The ORs by body parts were 3.11 (95% CI: 1.28-7.57) for the neck, 3.90 (95% CI: 1.79-8.47) for the shoulders, and 4.23 (95% CI: 1.04-17.18) for the legs and feet. Our study suggests that cameramen are at high risk of WRMDs. Workplace improvements and management of the neck, shoulders, and lower extremities, which are susceptible to WRMDs, are necessary to prevent musculoskeletal disorders among cameramen.

Uncertainty Modeling for Robustness Analysis of Control Upset Prevention and Recovery Systems

NASA Technical Reports Server (NTRS)

Belcastro, Christine M.; Khong, Thuan H.; Shin, Jong-Yeob; Kwatny, Harry; Chang, Bor-Chin; Balas, Gary J.

2005-01-01

Formal robustness analysis of aircraft control upset prevention and recovery systems could play an important role in their validation and ultimate certification. Such systems (developed for failure detection, identification, and reconfiguration, as well as upset recovery) need to be evaluated over broad regions of the flight envelope and under extreme flight conditions, and should include various sources of uncertainty. However, formulation of linear fractional transformation (LFT) models for representing system uncertainty can be very difficult for complex parameter-dependent systems. This paper describes a preliminary LFT modeling software tool which uses a matrix-based computational approach that can be directly applied to parametric uncertainty problems involving multivariate matrix polynomial dependencies. Several examples are presented (including an F-16 at an extreme flight condition, a missile model, and a generic example with numerous crossproduct terms), and comparisons are given with other LFT modeling tools that are currently available. The LFT modeling method and preliminary software tool presented in this paper are shown to compare favorably with these methods.

Effects of participatory ergonomic intervention on the development of upper extremity musculoskeletal disorders and disability in office employees using a computer.

PubMed

Baydur, Hakan; Ergör, Alp; Demiral, Yücel; Akalın, Elif

2016-06-16

To evaluate the participatory ergonomic method on the development of upper extremity musculoskeletal disorders and disability in office employees. This study is a randomized controlled intervention study. It comprised 116 office workers using computers. Those in the intervention group were taught office ergonomics and the risk assessment method. Cox proportional hazards model and generalized estimating equations (GEEs) were used. In the 10-month postintervention follow-up, the possibility of developing symptoms was 50.9%. According to multivariate analysis results, the possibility of developing symptoms on the right side of the neck and in the right wrist and hand was significantly less in the intervention group than in the control group (p<0.05). Neck disability/symptom scores over time were significantly lower in the intervention group compared with the control group (p<0.05). The participatory ergonomic intervention decreases the possibility of musculoskeletal complaints and disability/symptom level in office workers.

Effects of participatory ergonomic intervention on the development of upper extremity musculoskeletal disorders and disability in office employees using a computer

PubMed Central

Baydur, Hakan; Ergör, Alp; Demiral, Yücel; Akalın, Elif

2016-01-01

Objective: To evaluate the participatory ergonomic method on the development of upper extremity musculoskeletal disorders and disability in office employees. Methods: This study is a randomized controlled intervention study. It comprised 116 office workers using computers. Those in the intervention group were taught office ergonomics and the risk assessment method. Cox proportional hazards model and generalized estimating equations (GEEs) were used. Results: In the 10-month postintervention follow-up, the possibility of developing symptoms was 50.9%. According to multivariate analysis results, the possibility of developing symptoms on the right side of the neck and in the right wrist and hand was significantly less in the intervention group than in the control group (p<0.05). Neck disability/symptom scores over time were significantly lower in the intervention group compared with the control group (p<0.05). Conclusion: The participatory ergonomic intervention decreases the possibility of musculoskeletal complaints and disability/symptom level in office workers. PMID:27108647

Factors associated with small head circumference at birth among infants born before the 28th week

PubMed Central

McElrath, Thomas F.; Allred, Elizabeth N.; Kuban, Karl; Hecht, Jonathan L.; Onderdonk, Andrew; O’Shea, T. Michael; Paneth, Nigel; Leviton, Alan

2010-01-01

OBJECTIVE We sought to identify risk factors for congenital microcephaly in extremely low gestational age newborns. STUDY DESIGN Demographic, clinical, and placental characteristics of 1445 infants born before the 28th week were gathered and evaluated for their relationship with congenital microcephaly. RESULTS Almost 10% of newborns (n = 138), rather than the expected 2.2%, had microcephaly defined as a head circumference >2 SD below the median. In multivariable models, microcephaly was associated with nonwhite race, severe intrauterine growth restriction, delivery for preeclampsia, placental infarction, and being female. The risk factors for a head circumference between <1 and >2 SD below the median were similar to those of microcephaly. CONCLUSION Characteristics associated with fetal growth restriction and preeclampsia are among the strongest correlates of microcephaly among children born at extremely low gestational ages. The elevated risk of a small head among nonwhites and females might reflect the lack of appropriate head circumference standards. PMID:20541727

A lexicon based method to search for extreme opinions

PubMed Central

Gamallo, Pablo

2018-01-01

Studies in sentiment analysis and opinion mining have been focused on many aspects related to opinions, namely polarity classification by making use of positive, negative or neutral values. However, most studies have overlooked the identification of extreme opinions (most negative and most positive opinions) in spite of their vast significance in many applications. We use an unsupervised approach to search for extreme opinions, which is based on the automatic construction of a new lexicon containing the most negative and most positive words. PMID:29799867