Fully automatic segmentation of AP pelvis X-rays via random forest regression with efficient feature selection and hierarchical sparse shape composition

In clinical practice, traditional X-ray radiography is widely used, and knowledge of landmarks and contours in anteroposterior (AP) pelvis X-rays is invaluable for computer aided diagnosis, hip surgery planning and image-guided interventions. This paper presents a fully automatic approach for landmark detection and shape segmentation of both pelvis and femur in conventional AP X-ray images. Our approach is based on the framework of landmark detection via Random Forest (RF) regression and shape regularization via hierarchical sparse shape composition. We propose a visual feature FL-HoG (Flexible- Level Histogram of Oriented Gradients) and a feature selection algorithm based on trace radio optimization to improve the robustness and the efficacy of RF-based landmark detection. The landmark detection result is then used in a hierarchical sparse shape composition framework for shape regularization. Finally, the extracted shape contour is fine-tuned by a post-processing step based on low level image features. The experimental results demonstrate that our feature selection algorithm reduces the feature dimension in a factor of 40 and improves both training and test efficiency. Further experiments conducted on 436 clinical AP pelvis X-rays show that our approach achieves an average point-to-curve error around 1.2 mm for femur and 1.9 mm for pelvis.

ROBREG: Stata module providing robust regression estimators

robreg provides a number of robust estimators for linear regression models. Among them are the high breakdown-point and high efficiency MM-estimator, the Huber and bisquare M-estimator, and the S-estimator, each supporting classic or robust standard errors. Furthermore, basic versions of the LMS/LQS (least median of squares) and LTS (least trimmed squares) estimators are provided. Note that the moremata package, also available from SSC, is required.

Variance estimation in ranked set sampling using a concomitant variable

We propose a nonparametric variance estimator when ranked set sampling (RSS) and judgment post stratification (JPS) are applied by measuring a concomitant variable. Our proposed estimator is obtained by conditioning on observed concomitant values and using nonparametric kernel regression.

Susceptibility of enamel to initial erosion in relation to tooth type, tooth surface and enamel depth

This study aimed at assessing the susceptibility of different tooth types (molar/premolar), surfaces (buccal/lingual) and enamel depths (100, 200, 400 and 600 μm) to initial erosion measured by surface microhardness loss (ΔSMH) and calcium (Ca) release. Twenty molars and 20 premolars were divided into experimental and control groups, cut into lingual/ buccal halves, and ground/polished, removing 100 μm of enamel. The initial surface microhardness (SMH 0 ) was measured on all halves. The experimental group was subjected to 3 consecutive erosive challenges (30 ml/tooth of 1% citric acid, pH 3.6, 25 ° C, 1 min). After each challenge, ΔSMH and Ca release were measured. The same teeth were consecutively ground to 200, 400 and 600 μm depths, and the experimental group underwent 3 erosive challenges at each depth. No difference was found in SMH 0 between experimental and control groups. Multivariate nonparametric ANOVA showed no significant differences between lingual and buccal surfaces in ΔSMH (p = 0.801) or Ca release (p = 0.370). ΔSMH was significantly greater in premolars than in molars (p < 0.05), but not different with respect to enamel depth. Ca release decreased significantly with increasing depth. Regression between Ca release and ΔSMH at 100 μm depth showed lower slope and r 2 value, associated with greater Ca release values. At 200-600 μm depths, moderately large r 2 values were observed (0.651-0.830). In conclusion, different teeth and enamel depths have different susceptibility to erosion, so when Ca release is used to measure erosion, the depth of the test facet in enamel should be standardized, whereas this is less important if ΔSMH is used.

Modeling absolute differences in life expectancy with a censored skew-normal regression approach.

Parameter estimates from commonly used multivariable parametric survival regression models do not directly quantify differences in years of life expectancy. Gaussian linear regression models give results in terms of absolute mean differences, but are not appropriate in modeling life expectancy, because in many situations time to death has a negative skewed distribution. A regression approach using a skew-normal distribution would be an alternative to parametric survival models in the modeling of life expectancy, because parameter estimates can be interpreted in terms of survival time differences while allowing for skewness of the distribution. In this paper we show how to use the skew-normal regression so that censored and left-truncated observations are accounted for. With this we model differences in life expectancy using data from the Swiss National Cohort Study and from official life expectancy estimates and compare the results with those derived from commonly used survival regression models. We conclude that a censored skew-normal survival regression approach for left-truncated observations can be used to model differences in life expectancy across covariates of interest.

Detecting outlying studies in meta-regression models using a forward search algorithm.

When considering data from many trials, it is likely that some of them present a markedly different intervention effect or exert an undue influence on the summary results. We develop a forward search algorithm for identifying outlying and influential studies in meta-analysis models. The forward search algorithm starts by fitting the hypothesized model to a small subset of likely outlier-free studies and proceeds by adding studies into the set one-by-one that are determined to be closest to the fitted model of the existing set. As each study is added to the set, plots of estimated parameters and measures of fit are monitored to identify outliers by sharp changes in the forward plots. We apply the proposed outlier detection method to two real data sets; a meta-analysis of 26 studies that examines the effect of writing-to-learn interventions on academic achievement adjusting for three possible effect modifiers, and a meta-analysis of 70 studies that compares a fluoride toothpaste treatment to placebo for preventing dental caries in children. A simple simulated example is used to illustrate the steps of the proposed methodology, and a small-scale simulation study is conducted to evaluate the performance of the proposed method. Copyright © 2016 John Wiley & Sons, Ltd.

Regression of coronary atherosclerosis: Current evidence and future perspectives

Coronary atherosclerosis has been considered a chronic disease characterized by ongoing progression in response to systemic risk factors and local pro-atherogenic stimuli. As our understanding of the pathobiological mechanisms implicated in atherogenesis and plaque progression is evolving, effective treatment strategies have been developed that led to substantial reduction of the clinical manifestations and acute complications of coronary atherosclerotic disease. More recently, intracoronary imaging modalities have enabled detailed in vivo quantification and characterization of coronary atherosclerotic plaque, serial evaluation of atherosclerotic changes over time, and assessment of vascular responses to effective anti-atherosclerotic medications. The use of intracoronary imaging modalities has demonstrated that intensive lipid lowering can halt plaque progression and may even result in regression of coronary atheroma when the highest doses of the most potent statins are used. While current evidence indicates the feasibility of atheroma regression and of reversal of presumed high-risk plaque characteristics in response to intensive anti-atherosclerotic therapies, these changes of plaque size and composition are modest and their clinical implications remain largely elusive. Growing interest has focused on achieving more pronounced regression of coronary plaque using novel anti-atherosclerotic medications, and more importantly on elucidating ways toward clinical translation of favorable changes of plaque anatomy into more favorable clinical outcomes for our patients.

Systematic analysis of factors associated with progression and regression of ulcerative colitis in 918 patients.

BACKGROUND Studies that systematically assess change in ulcerative colitis (UC) extent over time in adult patients are scarce. AIM To assess changes in disease extent over time and to evaluate clinical parameters associated with this change. METHODS Data from the Swiss IBD cohort study were analysed. We used logistic regression modelling to identify factors associated with a change in disease extent. RESULTS A total of 918 UC patients (45.3% females) were included. At diagnosis, UC patients presented with the following disease extent: proctitis [199 patients (21.7%)], left-sided colitis [338 patients (36.8%)] and extensive colitis/pancolitis [381 (41.5%)]. During a median disease duration of 9 [4-16] years, progression and regression was documented in 145 patients (15.8%) and 149 patients (16.2%) respectively. In addition, 624 patients (68.0%) had a stable disease extent. The following factors were identified to be associated with disease progression: treatment with systemic glucocorticoids [odds ratio (OR) 1.704, P = 0.025] and calcineurin inhibitors (OR: 2.716, P = 0.005). No specific factors were found to be associated with disease regression. CONCLUSIONS Over a median disease duration of 9 [4-16] years, about two-thirds of UC patients maintained the initial disease extent; the remaining one-third had experienced either progression or regression of the disease extent.

Directional statistics: introduction

In many of the natural and physical sciences, measurements are directions, either in two or three dimensions. The analysis of directional data relies on specific statistical models and procedures, which differ from the usual models and methodologies of Cartesian data. This chapter briefly introduces statistical models and inference for this type of data. The basic von Mises-Fisher distribution is introduced and nonparametric methods such as goodness-of-fit tests are presented. Further references are given for exploring related topics such as correlation and regression.

Nonparametric location estimators in the randomized complete block design

Several tests for the comparison of different groups in the randomized complete block design exist. However, there is a lack of robust estimators for the location difference between one group and all the others on the original scale. The relative marginal effects are commonly used in this situation, but they are more difficult to interpret and use by less experienced people because of the different scale. In this paper two nonparametric estimators for the comparison of one group against the others in the randomized complete block design will be presented. Theoretical results such as asymptotic normality, consistency, translation invariance, scale preservation, unbiasedness, and median unbiasedness are derived. The finite sample behavior of these estimators is derived by simulations of different scenarios. In addition, possible confidence intervals with these estimators are discussed and their behavior derived also by simulations.

ALTERATIONS IN HYPOTHALAMIC CONTENT OF LUTEINIZING HORMONE RELEASING HORMONE AND PITUITARY RESPONSIVENESS ASSOCIATED WITH TESTICULAR REGRESSION INDUCED BY PINEAL RELATED TREATMENTS IN THE GOLDEN HAMSTER

The adult male golden hamster, when exposed to blinding (BL), short photoperiod (SP), or daily melatonin injections (MEL) demonstrates dramatic reproductive collapse. This collapse can be blocked by removal of the pineal gland prior to treatment. Reproductive collapse is characterized by a dramatic decrease in both testicular weight and serum gonadotropin titers. The present study was designed to examine the interactions of the hypothalamus and pituitary gland during testicular regression, and to specifically compare and contrast changes caused by the three commonly employed methods of inducing testicular regression (BL,SP,MEL). Hypothalamic LHRH content was altered by all three treatments. There was an initial increase in content of LHRH that occurred concomitantly with the decreased serum gonadotropin titers, followed by a precipitous decline in LHRH content which reflected the rapid increases in both serum LH and FSH which occur during spontaneous testicular recrudescence. In vitro pituitary responsiveness was altered by all three treatments: there was a decline in basal and maximally stimulatable release of both LH and FSH which paralleled the fall of serum gonadotropins. During recrudescence both basal and maximal release dramatically increased in a manner comparable to serum hormone levels. While all three treatments were equally effective in their ability to induce changes at all levels of the endocrine system, there were important temporal differences in the effects of the various treatments. Melatonin injections induced the most rapid changes in endocrine parameters, followed by exposure to short photoperiod. Blinding required the most time to induce the same changes. This study has demonstrated that pineal-mediated testicular regression is a process which involves dynamic changes in multiply-dependent endocrine relationships, and proper evaluation of these changes must be performed with specific temporal events in mind. ^

Direct and Indirect Measures of Capacity Utilization: A Nonparametric Analysis of U.S. Manufacturing

We measure the capacity output of a firm as the maximum amount producible by a firm given a specific quantity of the quasi-fixed input and an overall expenditure constraint for its choice of variable inputs. We compute this indirect capacity utilization measure for the total manufacturing sector in the US as well as for a number of disaggregated industries, for the period 1970-2001. We find considerable variation in capacity utilization rates both across industries and over years within industries. Our results suggest that the expenditure constraint was binding, especially in periods of high interest rates.

Liberalization, Ownership, and Efficiency in Indian Banking: A Nonparametric Approach

This paper empirically estimates and analyzes various efficiency scores of Indian banks during 1997-2003 using data envelopment analysis (DEA). During the 1990s India's financial sector underwent a process of gradual liberalization aimed at strengthening and improving the operational efficiency of the financial system. It is observed, none the less, that Indian banks are still not much differentiated in terms of input or output oriented technical efficiency and cost efficiency. However, they differ sharply in respect of revenue and profit efficiencies. The results provide interesting insight into the empirical correlates of efficiency scores of Indian banks. Bank size, ownership, and the fact of its being listed on the stock exchange are some of the factors that are found to have positive impact on the average profit efficiency and to some extent revenue efficiency scores are. Finally, we observe that the median efficiency scores of Indian banks in general and of bigger banks in particular have improved considerably during the post-reform period.

Are Indian Firms too Small? A Nonparametric Analysis of Cost Efficiency and Industry Structure of Indian Manufacturing

In this paper we use the 2004-05 Annual Survey of Industries data to estimate the levels of cost efficiency of Indian manufacturing firms in the various states and also get state level measures of industrial organization (IO) efficiency. The empirical results show the presence of considerable cost inefficiency in a majority of the states. Further, we also find that, on average, Indian firms are too small. Consolidating them to attain the optimal scale would further enhance efficiency and lower average cost.