Response plateau models fitting via isotonic regression

Within the nutritional context, the supplementation of microminerals in bird food is often made in quantities exceeding those required in the attempt to ensure the proper performance of the animals. The experiments of type dosage x response are very common in the determination of levels of nutrients in optimal food balance and include the use of regression models to achieve this objective. Nevertheless, the regression analysis routine, generally, uses a priori information about a possible relationship between the response variable. The isotonic regression is a method of estimation by least squares that generates estimates which preserves data ordering. In the theory of isotonic regression this information is essential and it is expected to increase fitting efficiency. The objective of this work was to use an isotonic regression methodology, as an alternative way of analyzing data of Zn deposition in tibia of male birds of Hubbard lineage. We considered the models of plateau response of polynomial quadratic and linear exponential forms. In addition to these models, we also proposed the fitting of a logarithmic model to the data and the efficiency of the methodology was evaluated by Monte Carlo simulations, considering different scenarios for the parametric values. The isotonization of the data yielded an improvement in all the fitting quality parameters evaluated. Among the models used, the logarithmic presented estimates of the parameters more consistent with the values reported in literature.

The Truncated Inflated Beta Distribution

The study of proportions is a common topic in many fields of study. The standard beta distribution or the inflated beta distribution may be a reasonable choice to fit a proportion in most situations. However, they do not fit well variables that do not assume values in the open interval (0, c), 0 < c < 1. For these variables, the authors introduce the truncated inflated beta distribution (TBEINF). This proposed distribution is a mixture of the beta distribution bounded in the open interval (c, 1) and the trinomial distribution. The authors present the moments of the distribution, its scoring vector, and Fisher information matrix, and discuss estimation of its parameters. The properties of the suggested estimators are studied using Monte Carlo simulation. In addition, the authors present an application of the TBEINF distribution for unemployment insurance data.

Intrinsic uncoupling in the ATP synthase of Escherichia coli. Studies on WT and ε-truncated mutants

The H+/ATP ratio in the catalysis of ATP synthase has generally been considered a fixed parameter. However, Melandri and coworkers have recently shown that, in the ATP synthase of the photosynthetic bacterium Rb.capsulatus, this ratio can significantly decrease during ATP hydrolysis when the concentration of either ADP or Pi is maintained at a low level (Turina et al., 2004). The present work has dealt with the ATP synthase of E.coli, looking for evidence of this phenomenon of intrinsic uncoupling in this organism as well. First of all, we have shown that the DCCD-sensitive ATP hydrolysis activity of E.coli internal membranes was strongly inhibited by ADP and Pi, with a half-maximal effect in the submicromolar range for ADP and at 140 µM for Pi. In contrast to this monotonic inhibition, however, the proton pumping activity of the enzyme, as estimated under the same conditions by the fluorescence quenching of the ΔpH-sensitive probe ACMA, showed a clearly biphasic progression, both for Pi, increasing from 0 up to approximately 200 µM, and for ADP, increasing from 0 up to a few µM. We have interpreted these results as indicating that the occupancy of ADP and Pi binding sites shifts the enzyme from a partially uncoupled state to a fully coupled state, and we expect that the ADP- and Pi-modulated intrinsic uncoupling is likely to be a general feature of prokaryotic ATP synthases. Moreover, the biphasicity of the proton pumping data suggested that two Pi binding sites are involved. In order to verify whether the same behaviour could be observed in the isolated enzyme, we have purified the ATP synthase of E.coli and reconstituted it into liposomes. Similarly as observed in the internal membrane preparation, in the isolated and reconstituted enzyme it was possible to observe inhibition of the hydrolytic activity by ADP and Pi (with half-maximal effects at few µM for ADP and at 400 µM for Pi) with a concomitant stimulation of proton pumping. Both the inhibition of ATP hydrolysis and the stimulation of proton pumping as a function of Pi were lost upon ADP removal by an ADP trap. These data have made it possible to conclude that the results obtained in E.coli internal membranes are not due to the artefactual interference of enzymatic activities other than the ones of the ATP synthase. In addition, data obtained with liposomes have allowed a calibration of the ACMA signal by ΔpH transitions of known extent, leading to a quantitative evaluation of the proton pumping data. Finally, we have focused our efforts on searching for a possible structural candidate involved in the phenomenon of intrinsic uncoupling. The ε-subunit of the ATP-synthase is known as an endogenous inhibitor of the hydrolysis activity of the complex and appears to undergo drastic conformational changes between a non-inhibitory form (down-state) and an inhibitory form (up-state)(Rodgers & Wilce, 2000; Gibbons et al., 2000). In addition, the results of Cipriano & Dunn (2006) indicated that the C-terminal domain of this subunit played an important role in the coupling mechanism of the pump, and those of Capaldi et al. (2001), Suzuki et al. (2003) were consistent with the down-state showing a higher hydrolysis-to-synthesis ratio than the up-state. Therefore, we decided to search for modulation of pumping efficiency in a C-terminally truncated ε mutant. A low copy number expression vector has been built, carrying an extra copy of uncC, with the aim of generating an ε-overexpressing E.coli strain in which normal levels of assembly of the mutated ATP-synthase complex would be promoted. We have then compared the ATP hydrolysis and the proton pumping activity in membranes prepared from these ε-overexpressing E.coli strains, which carried either the WT ε subunit or the ε88-stop truncated form. Both strains yielded well energized membranes. Noticeably, they showed a marked difference in the inhibition of hydrolysis by Pi, this effect being largely lost in the truncated mutant. However, pre-incubation of the mutated enzyme with ADP at low nanomolar concentrations (apparent Kd = 0.7nM) restored the hydrolysis inhibition, together with the modulation of intrinsic uncoupling by Pi, indicating that, contrary to wild-type, during membrane preparation the truncated mutant had lost the ADP bound at this high-affinity site, evidently due to a lower affinity (and/or higher release) for ADP of the mutant relative to wild type. Therefore, one of the effects of the C-terminal domain of ε appears to be to modulate the affinity of at least one of the binding sites for ADP. The lack of this domain does not appear so much to influence the modulability of coupling efficiency, but instead the extent of this modulation. At higher preincubated ADP concentrations (apparent Kd = 117nM), the only observed effects were inhibition of both hydrolysis and synthesis, providing a direct proof that two ADP-binding sites on the enzyme are involved in the inhibition of hydrolysis, of which only the one at higher affinity also modulates the coupling efficiency.

The Importance of Scale for Spatial-confounding Bias and Precision of Spatial Regression Estimators

Increasingly, regression models are used when residuals are spatially correlated. Prominent examples include studies in environmental epidemiology to understand the chronic health effects of pollutants. I consider the effects of residual spatial structure on the bias and precision of regression coefficients, developing a simple framework in which to understand the key issues and derive informative analytic results. When the spatial residual is induced by an unmeasured confounder, regression models with spatial random effects and closely-related models such as kriging and penalized splines are biased, even when the residual variance components are known. Analytic and simulation results show how the bias depends on the spatial scales of the covariate and the residual; bias is reduced only when there is variation in the covariate at a scale smaller than the scale of the unmeasured confounding. I also discuss how the scales of the residual and the covariate affect efficiency and uncertainty estimation when the residuals can be considered independent of the covariate. In an application on the association between black carbon particulate matter air pollution and birth weight, controlling for large-scale spatial variation appears to reduce bias from unmeasured confounders, while increasing uncertainty in the estimated pollution effect.

Success rate and efficiency of activator treatment

In a retrospective multicentre study, the success rate and efficiency of activator treatment were analysed. All patients from two University clinics (Giessen, Germany and Berne, Switzerland) that fulfilled the selection criteria (Class II division 1 malocclusion, activator treatment, no aplasia, no extraction of permanent teeth, no syndromes, no previous orthodontic treatment except transverse maxillary expansion, full available records) were included in the study. The subject material amounted to 222 patients with a mean age of 10.6 years. Patient records, lateral head films, and dental casts were evaluated. Treatment was classified as successful if the molar relationship improved by at least half to three-fourths cusp width depending on whether or not the leeway space was used during treatment. Group comparisons were carried out using Wilcoxon two-sample and Kruskal-Wallis tests. For discrete data, chi-square analysis was used and Fisher's exact test when the sample size was small. Stepwise logistic regression was also employed. The success rate was 64 per cent in Giessen and 66 per cent in Berne. The only factor that significantly (P < 0.001) influenced treatment success was the level of co-operation. In approximately 27 per cent of the patients at both centres, the post-treatment occlusion was an 'ideal' Class I. In an additional 38 per cent of the patients, marked improvements in occlusal relationships were found. In subjects with Class II division 1 malocclusions, in which orthodontic treatment is performed by means of activators, a marked improvement of the Class II dental arch relationships can be expected in approximately 65 per cent of subjects. Activator treatment is more efficient in the late than in the early mixed dentition.

A Combinatorial Approach to the Variable Selection in Multiple Linear Regression: Analysis of Selwood et al Data set -A Case Study.

A combinatorial protocol (CP) is introduced here to interface it with the multiple linear regression (MLR) for variable selection. The efficiency of CP-MLR is primarily based on the restriction of entry of correlated variables to the model development stage. It has been used for the analysis of Selwood et al data set [16], and the obtained models are compared with those reported from GFA [8] and MUSEUM [9] approaches. For this data set CP-MLR could identify three highly independent models (27, 28 and 31) with Q2 value in the range of 0.632-0.518. Also, these models are divergent and unique. Even though, the present study does not share any models with GFA [8], and MUSEUM [9] results, there are several descriptors common to all these studies, including the present one. Also a simulation is carried out on the same data set to explain the model formation in CP-MLR. The results demonstrate that the proposed method should be able to offer solutions to data sets with 50 to 60 descriptors in reasonable time frame. By carefully selecting the inter-parameter correlation cutoff values in CP-MLR one can identify divergent models and handle data sets larger than the present one without involving excessive computer time.

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.

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.

Technical Efficiency of State Owned Enterprises in China (1980-1989): An Assessment of the Economic Reforms

This paper uses Data Envelopment Analysis to examine changes in levels of technical efficiency over time in China's state-owned enterprises (SOEs) during 1980-1989. Our paper adds to the growing body of literature in this area by obtaining measures of technical efficiency of individual SOEs over years and by identifying how different aspects of the reforms have affected efficiency. We estimate a Tobit regression model, using the technical efficiency score as the dependent variable and a set of reform variables and firm attributes as regressors. We find that specific aspects of the reforms were very effective in improving technical efficiency.

Input Aggregation in Models of Data Envelopment Analysis: A Statistical Test with an Application to Indian Manufacturing

A problem frequently encountered in Data Envelopment Analysis (DEA) is that the total number of inputs and outputs included tend to be too many relative to the sample size. One way to counter this problem is to combine several inputs (or outputs) into (meaningful) aggregate variables reducing thereby the dimension of the input (or output) vector. A direct effect of input aggregation is to reduce the number of constraints. This, in its turn, alters the optimal value of the objective function. In this paper, we show how a statistical test proposed by Banker (1993) may be applied to test the validity of a specific way of aggregating several inputs. An empirical application using data from Indian manufacturing for the year 2002-03 is included as an example of the proposed test.

Biomass of seston in the benthic layer of the Southwestern Indian Ocean

At stations to 1530 m depth in the Mozambique Channel and on the Saya-de-Malha and Walters banks seston biomass 2 m above the bottom was lower than at 30 m. Above the Walters shoal this difference was 13.2 mg/m**3 and was not equal to zero for P < 0.001. These results contradict previous ideas of biomass increase in benthic layers. The most likely cause of the observed impoverishment of plankton may be predominant consumption of living zooplankton component of seston by bottom and near-bottom predators. In the area of the Walters shoal this consumption is estimated as being about 300 mg/m**2 per day. Animals inhabiting this area live mainly on plankton brought in by horizontal advection, so that existence of faunal assemblages even on shallow-water submarine elevations is supported not mainly by local photosynthesis, but by primary production of surrounding waters.

Particle flux studies of sediment traps from the northern and equatorial Indian Ocean

The Asian monsoon system governs seasonality and fundamental environmental characteristics in the study area from which two distinct peculiarities are most notable: upwelling and convective mixing in the Arabian Sea and low surface salinity and stratification in the Bay of Bengal due to high riverine input and monsoonal precipitation. The respective oceanography sets the framework for nutrient availability and productivity. Upwelling ensures high nitrate concentration with temporal/spatial Si limitation; freshwater-induced stratification leads to reduced nitrogen input from the subsurface but Si enrichment in surface waters. Ultimately, both environments support high abundance of diatoms, which play a central role in the export of organic matter. It is speculated that, additional to eddy pumping, nitrogen fixation is a source of N in stratified waters and contributes to the low-d15N signal in sinking particles formed under riverine impact. Organic carbon fluxes are best correlated to opal but not to carbonate, which is explained by low foraminiferal carbonate fluxes within the river-impacted systems. This observation points to the necessity of differentiating between carbonate sources for carbon flux modeling. As evident from a compilation of previously published and new data on labile organic matter composition (amino acids and carbohydrates), organic matter fluxes are mainly driven by direct input from marine production, except the site off Pakistan where sedimentary input of (marine) organic matter is dominant during the NE monsoon. The explanation of apparently different organic carbon export efficiency calls for further investigations of, for example, food web structure and water column processes.