Quantifying the influence of global warming on unprecedented extreme climate events.

Efforts to understand the influence of historical global warming on individual extreme climate events have increased over the past decade. However, despite substantial progress, events that are unprecedented in the local observational record remain a persistent challenge. Leveraging observations and a large climate model ensemble, we quantify uncertainty in the influence of global warming on the severity and probability of the historically hottest month, hottest day, driest year, and wettest 5-d period for different areas of the globe. We find that historical warming has increased the severity and probability of the hottest month and hottest day of the year at >80% of the available observational area. Our framework also suggests that the historical climate forcing has increased the probability of the driest year and wettest 5-d period at 57% and 41% of the observed area, respectively, although we note important caveats. For the most protracted hot and dry events, the strongest and most widespread contributions of anthropogenic climate forcing occur in the tropics, including increases in probability of at least a factor of 4 for the hottest month and at least a factor of 2 for the driest year. We also demonstrate the ability of our framework to systematically evaluate the role of dynamic and thermodynamic factors such as atmospheric circulation patterns and atmospheric water vapor, and find extremely high statistical confidence that anthropogenic forcing increased the probability of record-low Arctic sea ice extent.

Recent amplification of the North American winter temperature dipole.

During the winters of 2013-2014 and 2014-2015, anomalously warm temperatures in western North America and anomalously cool temperatures in eastern North America resulted in substantial human and environmental impacts. Motivated by the impacts of these concurrent temperature extremes and the intrinsic atmospheric linkage between weather conditions in the western and eastern United States, we investigate the occurrence of concurrent "warm-West/cool-East" surface temperature anomalies, which we call the "North American winter temperature dipole." We find that, historically, warm-West/cool-East dipole conditions have been associated with anomalous mid-tropospheric ridging over western North America and downstream troughing over eastern North America. We also find that the occurrence and severity of warm-West/cool-East events have increased significantly between 1980 and 2015, driven largely by an increase in the frequency with which high-amplitude "ridge-trough" wave patterns result in simultaneous severe temperature conditions in both the West and East. Using a large single-model ensemble of climate simulations, we show that the observed positive trend in the warm-West/cool-East events is attributable to historical anthropogenic emissions including greenhouse gases, but that the co-occurrence of extreme western warmth and eastern cold will likely decrease in the future as winter temperatures warm dramatically across the continent, thereby reducing the occurrence of severely cold conditions in the East. Although our analysis is focused on one particular region, our analysis framework is generally transferable to the physical conditions shaping different types of extreme events around the globe.

Foundational Principles for Large-Scale Inference: Illustrations Through Correlation Mining.

When can reliable inference be drawn in fue "Big Data" context? This paper presents a framework for answering this fundamental question in the context of correlation mining, wifu implications for general large scale inference. In large scale data applications like genomics, connectomics, and eco-informatics fue dataset is often variable-rich but sample-starved: a regime where the number n of acquired samples (statistical replicates) is far fewer than fue number p of observed variables (genes, neurons, voxels, or chemical constituents). Much of recent work has focused on understanding the computational complexity of proposed methods for "Big Data". Sample complexity however has received relatively less attention, especially in the setting when the sample size n is fixed, and the dimension p grows without bound. To address fuis gap, we develop a unified statistical framework that explicitly quantifies the sample complexity of various inferential tasks. Sampling regimes can be divided into several categories: 1) the classical asymptotic regime where fue variable dimension is fixed and fue sample size goes to infinity; 2) the mixed asymptotic regime where both variable dimension and sample size go to infinity at comparable rates; 3) the purely high dimensional asymptotic regime where the variable dimension goes to infinity and the sample size is fixed. Each regime has its niche but only the latter regime applies to exa cale data dimension. We illustrate this high dimensional framework for the problem of correlation mining, where it is the matrix of pairwise and partial correlations among the variables fua t are of interest. Correlation mining arises in numerous applications and subsumes the regression context as a special case. we demonstrate various regimes of correlation mining based on the unifying perspective of high dimensional learning rates and sample complexity for different structured covariance models and different inference tasks.

Internal Oblique and Transversus Abdominis Muscle Fatigue Induced by Slumped Sitting Posture after 1 Hour of Sitting in Office Workers.

BACKGROUND: Prolonged sitting leads to low back discomfort and lumbopelvic muscle fatigue. This study examined the characteristics of body perceived discomfort and trunk muscle fatigue during 1 hour of sitting in three postures in office workers. METHODS: Thirty workers sat for 1 hour in one of three sitting postures (i.e., upright, slumped, and forward leaning postures). Body discomfort was assessed using the Body Perceived Discomfort scale at the beginning and after 1 hour of sitting. Electromyographic (EMG) signals were recorded from superficial lumbar multifidus, iliocostalis lumborum pars thoracis, internal oblique (IO)/transversus abdominis (TrA), and rectus abdominis muscles during 1 hour of sitting. The median frequency (MDF) of the EMG power spectrum was calculated. RESULTS: Regardless of the sitting posture, the Body Perceived Discomfort scores in the neck, shoulder, upper back, low back, and buttock significantly increased after 1 hour of sitting compared with baseline values (t (9) = -11.97 to -2.69, p < 0.05). The MDF value of the EMG signal of rectus abdominis, iliocostalis lumborum pars thoracis, and multifidus muscles was unchanged over time in all three sitting postures. Only the right and left IO/TrA in the slumped sitting posture was significantly associated with decreased MDF over time (p = 0.019 to 0.041). CONCLUSION: Prolonged sitting led to increased body discomfort in the neck, shoulder, upper back, low back, and buttock. No sign of trunk muscle fatigue was detected over 1 hour of sitting in the upright and forward leaning postures. Prolonged slumped sitting may relate to IO/TrA muscle fatigue, which may compromise the stability of the spine, making it susceptible to injury.

Perceived body discomfort and trunk muscle activity in three prolonged sitting postures.

[Purpose] This study aimed to investigate the perceived discomfort and trunk muscle activity in three different 1-hour sitting postures. [Subjects] A repeated-measures design study was conducted on 10 healthy subjects. [Methods] Each subject sat for an hour in three sitting postures (i.e., upright, slumped, and forward leaning sitting postures). Subjects rated perceived body discomfort using Borg's CR-10 scale at the beginning and after 1 hour sitting. The electromyographic activity of the trunk muscle activity was recorded during the 1-hour period of sitting. [Results] The forward leaning sitting posture led to higher Borg scores in the low back than those in the upright (p = 0.002) and slumped sitting postures (p < 0.001). The forward leaning posture was significantly associated with increased iliocostalis lumborum pars thoracis (ICL) and superficial lumbar multifidus (MF) muscle activity compared with the upright and slumped sitting postures. The upright sitting posture was significantly associated with increased internal oblique (IO)/transversus abdominis (TrA) and ICL muscle activity compared with the slumped sitting posture. [Conclusion] The sitting posture with the highest low back discomfort after prolonged sitting was the forward leaning posture. Sitting in an upright posture is recommended because it increases IO/TrA muscle activation and induces only relatively moderate ICL and MF muscle activation.

Contribution of changes in atmospheric circulation patterns to extreme temperature trends.

Surface weather conditions are closely governed by the large-scale circulation of the Earth's atmosphere. Recent increases in the occurrence of some extreme weather phenomena have led to multiple mechanistic hypotheses linking changes in atmospheric circulation to increasing probability of extreme events. However, observed evidence of long-term change in atmospheric circulation remains inconclusive. Here we identify statistically significant trends in the occurrence of atmospheric circulation patterns, which partially explain observed trends in surface temperature extremes over seven mid-latitude regions of the Northern Hemisphere. Using self-organizing map cluster analysis, we detect robust circulation pattern trends in a subset of these regions during both the satellite observation era (1979-2013) and the recent period of rapid Arctic sea-ice decline (1990-2013). Particularly substantial influences include the contribution of increasing trends in anticyclonic circulations to summer and autumn hot extremes over portions of Eurasia and North America, and the contribution of increasing trends in northerly flow to winter cold extremes over central Asia. Our results indicate that although a substantial portion of the observed change in extreme temperature occurrence has resulted from regional- and global-scale thermodynamic changes, the risk of extreme temperatures over some regions has also been altered by recent changes in the frequency, persistence and maximum duration of regional circulation patterns.

Insulin resistance: regression and clustering.

In this paper we try to define insulin resistance (IR) precisely for a group of Chinese women. Our definition deliberately does not depend upon body mass index (BMI) or age, although in other studies, with particular random effects models quite different from models used here, BMI accounts for a large part of the variability in IR. We accomplish our goal through application of Gauss mixture vector quantization (GMVQ), a technique for clustering that was developed for application to lossy data compression. Defining data come from measurements that play major roles in medical practice. A precise statement of what the data are is in Section 1. Their family structures are described in detail. They concern levels of lipids and the results of an oral glucose tolerance test (OGTT). We apply GMVQ to residuals obtained from regressions of outcomes of an OGTT and lipids on functions of age and BMI that are inferred from the data. A bootstrap procedure developed for our family data supplemented by insights from other approaches leads us to believe that two clusters are appropriate for defining IR precisely. One cluster consists of women who are IR, and the other of women who seem not to be. Genes and other features are used to predict cluster membership. We argue that prediction with "main effects" is not satisfactory, but prediction that includes interactions may be.

A Methodology for Robust Multiproxy Paleoclimate Reconstructions and Modeling of Temperature Conditional Quantiles.

Great strides have been made in the field of reconstructing past temperatures based on models relating temperature to temperature-sensitive paleoclimate proxies. One of the goals of such reconstructions is to assess if current climate is anomalous in a millennial context. These regression based approaches model the conditional mean of the temperature distribution as a function of paleoclimate proxies (or vice versa). Some of the recent focus in the area has considered methods which help reduce the uncertainty inherent in such statistical paleoclimate reconstructions, with the ultimate goal of improving the confidence that can be attached to such endeavors. A second important scientific focus in the subject area is the area of forward models for proxies, the goal of which is to understand the way paleoclimate proxies are driven by temperature and other environmental variables. One of the primary contributions of this paper is novel statistical methodology for (1) quantile regression with autoregressive residual structure, (2) estimation of corresponding model parameters, (3) development of a rigorous framework for specifying uncertainty estimates of quantities of interest, yielding (4) statistical byproducts that address the two scientific foci discussed above. We show that by using the above statistical methodology we can demonstrably produce a more robust reconstruction than is possible by using conditional-mean-fitting methods. Our reconstruction shares some of the common features of past reconstructions, but we also gain useful insights. More importantly, we are able to demonstrate a significantly smaller uncertainty than that from previous regression methods. In addition, the quantile regression component allows us to model, in a more complete and flexible way than least squares, the conditional distribution of temperature given proxies. This relationship can be used to inform forward models relating how proxies are driven by temperature.

Control strategies to re-establish glenohumeral stability after shoulder injury.

BACKGROUND: Muscles are important "sensors of the joint instability". The aim of this study was to identify the neuro-motor control strategies adopted by patients with anterior shoulder instability during overhead shoulder elevation in two planes. METHODS: The onset, time of peak activation, and peak magnitude of seven shoulder muscles (posterior deltoid, bilateral upper trapezius, biceps brachii, infraspinatus, supraspinatus and teres major) were identified using electromyography as 19 pre-operative patients with anterior shoulder instability (mean 27.95 years, SD = 7.796) and 25 age-matched asymptomatic control subjects (mean 23.07 years, SD = 2.952) elevated their arm above 90 degrees in the sagittal and coronal planes. RESULTS: Temporal characteristics of time of muscle onsets were significantly different between groups expect for teres major in the coronal plane (t = 1.1220, p = 0.2646) Patients recruited the rotator cuff muscles earlier and delayed the onset of ipsilateral upper trapezius compared with subjects (p<0.001) that control subjects. Furthermore, significant alliances existed between the onsets of infraspinatus and supraspinatus (sagittal: r = 0.720; coronal: r = 0.756 at p<0.001) and ipsilateral upper trapezius and infraspinatus (sagittal: r = -0.760, coronal: r = -0.818 at p<0.001). The peak activation of all seven muscles occurred in the mid-range of elevation among patients with anterior shoulder instability whereas subjects spread peak activation of all 7 muscles throughout range. Peak magnitude of patients' infraspinatus muscle was six times higher (sagittal: t = -8.6428, coronal: t = -54.1578 at p<0.001) but magnitude of their supraspinatus was lower (sagittal: t = 36.2507, coronal: t = 35.9350 at p<0.001) that subjects. CONCLUSIONS: Patients with anterior shoulder instability adopted a "stability before mobility" neuro-motor control strategy to initiate elevation and a "stability at all cost" strategy to ensure concavity compression in the mid-to-150 degrees of elevation in both sagittal and coronal planes.

Condition Number Regularized Covariance Estimation.

Estimation of high-dimensional covariance matrices is known to be a difficult problem, has many applications, and is of current interest to the larger statistics community. In many applications including so-called the "large p small n" setting, the estimate of the covariance matrix is required to be not only invertible, but also well-conditioned. Although many regularization schemes attempt to do this, none of them address the ill-conditioning problem directly. In this paper, we propose a maximum likelihood approach, with the direct goal of obtaining a well-conditioned estimator. No sparsity assumption on either the covariance matrix or its inverse are are imposed, thus making our procedure more widely applicable. We demonstrate that the proposed regularization scheme is computationally efficient, yields a type of Steinian shrinkage estimator, and has a natural Bayesian interpretation. We investigate the theoretical properties of the regularized covariance estimator comprehensively, including its regularization path, and proceed to develop an approach that adaptively determines the level of regularization that is required. Finally, we demonstrate the performance of the regularized estimator in decision-theoretic comparisons and in the financial portfolio optimization setting. The proposed approach has desirable properties, and can serve as a competitive procedure, especially when the sample size is small and when a well-conditioned estimator is required.

Participating in a virtual reality balance exercise program can reduce risk and fear of falls.

OBJECTIVE: The objective of this study was to quantify the effectiveness of virtual reality balance games (VRBG) to decrease risk and fear of falls among women. METHODS: Thirty six community dwelling women aged 56 and above were randomly divided into experimental (exercises using VRBG focus on improving balance) and control (conventional balance exercises) groups. Both groups attended a twice 6 weekly exercise session for an hour. Risk and fear of falls were measured with Physiological Profile Approach (PPA) and Activity Specific Balance Scale (ABC-6). Pre and post intervention differences between the groups were examined using two way repeated measures ANOVA. RESULTS: Both VRBG and conventional balance exercise groups had significant decrease in PPA (p<0.001) and ABC-6 (p<0.01) after the interventions. However, no significant effects were demonstrated between the groups in PPA (p=0.18) and ABC-6 (p=0.25) post intervention. Time and group interaction effect were not significant for PPA (p=0.18) and ABC-6 (p=0.45). CONCLUSIONS: Practising VRBG can increase balance confidence and decrease risk of falls among community dwelling women.

Successive Standardization of Rectangular Arrays.

In this note we illustrate and develop further with mathematics and examples, the work on successive standardization (or normalization) that is studied earlier by the same authors in [1] and [2]. Thus, we deal with successive iterations applied to rectangular arrays of numbers, where to avoid technical difficulties an array has at least three rows and at least three columns. Without loss, an iteration begins with operations on columns: first subtract the mean of each column; then divide by its standard deviation. The iteration continues with the same two operations done successively for rows. These four operations applied in sequence completes one iteration. One then iterates again, and again, and again, … In [1] it was argued that if arrays are made up of real numbers, then the set for which convergence of these successive iterations fails has Lebesgue measure 0. The limiting array has row and column means 0, row and column standard deviations 1. A basic result on convergence given in [1] is true, though the argument in [1] is faulty. The result is stated in the form of a theorem here, and the argument for the theorem is correct. Moreover, many graphics given in [1] suggest that except for a set of entries of any array with Lebesgue measure 0, convergence is very rapid, eventually exponentially fast in the number of iterations. Because we learned this set of rules from Bradley Efron, we call it "Efron's algorithm". More importantly, the rapidity of convergence is illustrated by numerical examples.

SUCCESSIVE NORMALIZATION OF RECTANGULAR ARRAYS.

Standard statistical techniques often require transforming data to have mean 0 and standard deviation 1. Typically, this process of "standardization" or "normalization" is applied across subjects when each subject produces a single number. High throughput genomic and financial data often come as rectangular arrays, where each coordinate in one direction concerns subjects, who might have different status (case or control, say); and each coordinate in the other designates "outcome" for a specific feature, for example "gene," "polymorphic site," or some aspect of financial profile. It may happen when analyzing data that arrive as a rectangular array that one requires BOTH the subjects and features to be "on the same footing." Thus, there may be a need to standardize across rows and columns of the rectangular matrix. There arises the question as to how to achieve this double normalization. We propose and investigate the convergence of what seems to us a natural approach to successive normalization, which we learned from colleague Bradley Efron. We also study the implementation of the method on simulated data and also on data that arose from scientific experimentation.

TRUST-TECH-based expectation maximization for learning finite mixture models.

In spite of the initialization problem, the Expectation-Maximization (EM) algorithm is widely used for estimating the parameters of finite mixture models. Most popular model-based clustering techniques might yield poor clusters if the parameters are not initialized properly. To reduce the sensitivity of initial points, a novel algorithm for learning mixture models from multivariate data is introduced in this paper. The proposed algorithm takes advantage of TRUST-TECH (TRansformation Under STability-reTaining Equilibra CHaracterization) to compute neighborhood local maxima on likelihood surface using stability regions. Basically, our method coalesces the advantages of the traditional EM with that of the dynamic and geometric characteristics of the stability regions of the corresponding nonlinear dynamical system of the log-likelihood function. Two phases namely, the EM phase and the stability region phase, are repeated alternatively in the parameter space to achieve improvements in the maximum likelihood. The EM phase obtains the local maximum of the likelihood function and the stability region phase helps to escape out of the local maximum by moving towards the neighboring stability regions. The algorithm has been tested on both synthetic and real datasets and the improvements in the performance compared to other approaches are demonstrated. The robustness with respect to initialization is also illustrated experimentally.

Predictability of simple clinical tests to identify shoulder pain after stroke.

OBJECTIVE: To identify simple diagnostic musculoskeletal tests that can be performed early after stroke to predict patients' likelihood of reporting early signs of hemiplegic shoulder pain. DESIGN: Case control. SETTING: Multicenter acute care hospitals. PARTICIPANTS: A total of 152 adults after a first episode of stroke, of whom 135 met the inclusion criteria. Thirty patients were assigned to the experimental group because they reported moderate intensity of hemiplegic shoulder pain at rest. The remaining 105 patients made up the control group. INTERVENTIONS: Not applicable. MAIN OUTCOME MEASURES: Therapists measured the performance of combined upper-limb movement including the hand-behind-neck (HBN) maneuver, passive pain-free ranges of shoulder motion, 3 musculoskeletal tests, and the strength of deltoid muscles during each patient's hospital stay. The numeric rating scale (NRS) identified those who reported moderate or greater intensities of hemiplegic shoulder pain during rest and during assessment. RESULTS: In our study, 22.2% (95% confidence interval, 15.5-30.2) of the patients reported hemiplegic shoulder pain, on average 1 week after the onset of stroke. Positive Neer test (NRS score >or=5) during the HBN maneuver and a difference of more than 10 degrees of passive range of external rotation between shoulders had a 98% probability of predicting the presence of hemiplegic shoulder pain (receiver operating characteristic, .994; sensitivity, 96.7%; specificity, 99.0%; positive predictive value, 96.7%; negative predictive value, 99.0%; P<.001). CONCLUSIONS: Three diagnostic clinical tests that can be performed during a bedside evaluation increase the likelihood of determining those who complain of hemiplegic shoulder pain after an acute episode of stroke.