Pedotransfer functions to predict water retention for soils of the humid tropics: a review

Over the past three decades, pedotransfer functions (PTFs) have been widely used by soil scientists to estimate soils properties in temperate regions in response to the lack of soil data for these regions. Several authors indicated that little effort has been dedicated to the prediction of soil properties in the humid tropics, where the need for soil property information is of even greater priority. The aim of this paper is to provide an up-to-date repository of past and recently published articles as well as papers from proceedings of events dealing with water-retention PTFs for soils of the humid tropics. Of the 35 publications found in the literature on PTFs for prediction of water retention of soils of the humid tropics, 91 % of the PTFs are based on an empirical approach, and only 9 % are based on a semi-physical approach. Of the empirical PTFs, 97 % are continuous, and 3 % (one) is a class PTF; of the empirical PTFs, 97 % are based on multiple linear and polynomial regression of n th order techniques, and 3 % (one) is based on the k-Nearest Neighbor approach; 84 % of the continuous PTFs are point-based, and 16 % are parameter-based; 97 % of the continuous PTFs are equation-based PTFs, and 3 % (one) is based on pattern recognition. Additionally, it was found that 26 % of the tropical water-retention PTFs were developed for soils in Brazil, 26 % for soils in India, 11 % for soils in other countries in America, and 11 % for soils in other countries in Africa.

Compared performance of penetrometers and effect of soil water content on penetration resistance measurements

Modern agriculture techniques have a great impact on crops and soil quality, especially by the increased machinery traffic and weight. Several devices have been developed for determining soil properties in the field, aimed at managing compacted areas. Penetrometry is a widely used technique; however, there are several types of penetrometers, which have different action modes that can affect the soil resistance measurement. The objective of this study was to compare the functionality of two penetrometry methods (manual and automated mode) in the field identification of compacted, highly mechanized sugarcane areas, considering the influence of soil water volumetric content (θ) on soil penetration resistance (PR). Three sugarcane fields on a Rhodic Eutrudrox were chosen, under a sequence of harvest systems: one manual harvest (1ManH), one mechanized harvest (1MH) and three mechanized harvests (3MH). The different degrees of mechanization were associated to cumulative compaction processes. An electronic penetrometer was used on PR measurements, so that the rod was introduced into the soil by hand (Manual) and by an electromechanical motor (Auto). The θ was measured in the field with a soil moisture sensor. Results showed an effect of θ on PR measurements and that regression models must be used to correct data before comparing harvesting systems. The rod introduction modes resulted in different mean PR values, where the "Manual" overestimated PR compared to the "Auto" mode at low θ.

Regression analysis of spatial data.

Many of the most interesting questions ecologists ask lead to analyses of spatial data. Yet, perhaps confused by the large number of statistical models and fitting methods available, many ecologists seem to believe this is best left to specialists. Here, we describe the issues that need consideration when analysing spatial data and illustrate these using simulation studies. Our comparative analysis involves using methods including generalized least squares, spatial filters, wavelet revised models, conditional autoregressive models and generalized additive mixed models to estimate regression coefficients from synthetic but realistic data sets, including some which violate standard regression assumptions. We assess the performance of each method using two measures and using statistical error rates for model selection. Methods that performed well included generalized least squares family of models and a Bayesian implementation of the conditional auto-regressive model. Ordinary least squares also performed adequately in the absence of model selection, but had poorly controlled Type I error rates and so did not show the improvements in performance under model selection when using the above methods. Removing large-scale spatial trends in the response led to poor performance. These are empirical results; hence extrapolation of these findings to other situations should be performed cautiously. Nevertheless, our simulation-based approach provides much stronger evidence for comparative analysis than assessments based on single or small numbers of data sets, and should be considered a necessary foundation for statements of this type in future.

Intraoperative training on the techniques of sentinel node biopsy in breast cancer

Principles: Surgeon's experience is crucial for proper application of sentinel node biopsy (SNB) in patients with breast cancer. A 20-30 cases learning curve of sentinel node (SN) and axillary lymph node dissection (ALND) was widely practiced. In order to speed up this learning curve, surgeons may be trained intraoperative by an experienced surgeon. The purpose of this report is to evaluate the results of this procedure. Methods: Patients with one primary invasive breast cancer (cT1-T2[<3 cm]cN0) underwent SNB based on lymphoscintigraphy using technetium Tc 99m colloid, intraoperative gamma probe detection, with or without blue dye mapping. This was followed by completion ALND when SN was positive or not found. SNB was performed by one experienced surgeon (teacher) or by 10 junior surgeons trained by the experienced surgeon (trainees). Four groups were defined: (i) SNB with immediate ALND for the teacher's learning curve, (ii) SNB by the teacher, (iii) SNB by the trainees under the teacher's supervision, and (iv) SNB by the trainees alone. Results: Between May 1999 and December 2007, a total of 808 évaluable patients underwent SNB. The SN identification rate was 98% in the teacher's group, and 99% in the trainees' group (p = 0.196). SN were positive in respectively 28% and 29% of patients (p = 0.196). The distribution of isolated tumor cells, micrometastases and metastases was not statistically different between the teacher's and the trainees' groups (p = 0.163). Conclusion: These comparable results confirm the success with which the SNB was taught. This strategy avoided the 20-30 SNB followed by immediate ALND early required per surgeon.

Bootstrapping pairs in Distance-Based Regression

La regressió basada en distàncies és un mètode de predicció que consisteix en dos passos: a partir de les distàncies entre observacions obtenim les variables latents, les quals passen a ser els regressors en un model lineal de mínims quadrats ordinaris. Les distàncies les calculem a partir dels predictors originals fent us d'una funció de dissimilaritats adequada. Donat que, en general, els regressors estan relacionats de manera no lineal amb la resposta, la seva selecció amb el test F usual no és possible. En aquest treball proposem una solució a aquest problema de selecció de predictors definint tests estadístics generalitzats i adaptant un mètode de bootstrap no paramètric per a l'estimació dels p-valors. Incluim un exemple numèric amb dades de l'assegurança d'automòbils.

Probe-specific mixed-model approach to detect copy number differences using multiplex ligation-dependent probe amplification (MLPA)

Background: MLPA method is a potentially useful semi-quantitative method to detect copy number alterations in targeted regions. In this paper, we propose a method for the normalization procedure based on a non-linear mixed-model, as well as a new approach for determining the statistical significance of altered probes based on linear mixed-model. This method establishes a threshold by using different tolerance intervals that accommodates the specific random error variability observed in each test sample.Results: Through simulation studies we have shown that our proposed method outperforms two existing methods that are based on simple threshold rules or iterative regression. We have illustrated the method using a controlled MLPA assay in which targeted regions are variable in copy number in individuals suffering from different disorders such as Prader-Willi, DiGeorge or Autism showing the best performace.Conclusion: Using the proposed mixed-model, we are able to determine thresholds to decide whether a region is altered. These threholds are specific for each individual, incorporating experimental variability, resulting in improved sensitivity and specificity as the examples with real data have revealed.

Assessment of Weathering Steel Bridge Performance in Iowa and Development of Inspection and Maintenance Techniques, RB17-012, 2013

Weathering steel is commonly used as a cost-effective alternative for bridge superstructures, as the costs and environmental impacts associated with the maintenance/replacement of paint coatings are theoretically eliminated. The performance of weathering steel depends on the proper formation of a surface patina, which consists of a dense layer of corrosion product used to protect the steel from further atmospheric corrosion. The development of the weathering steel patina may be hindered by environmental factors such as humid environments, wetting/drying cycles, sheltering, exposure to de-icing chlorides, and design details that permit water to pond on steel surfaces. Weathering steel bridges constructed over or adjacent to other roadways could be subjected to sufficient salt spray that would impede the development of an adequate patina. Addressing areas of corrosion on a weathering steel bridge superstructure where a protective patina has not formed is often costly and negates the anticipated cost savings for this type of steel superstructure. Early detection of weathering steel corrosion is important to extending the service life of the bridge structure; however, written inspection procedures are not available for inspectors to evaluate the performance or quality of the patina. This project focused on the evaluation of weathering steel bridge structures, including possible methods to assess the quality of the weathering steel patina and to properly maintain the quality of the patina. The objectives of this project are summarized as follows:  Identify weathering steel bridge structures that would be most vulnerable to chloride contamination, based on location, exposure, environment, and other factors.  Identify locations on an individual weathering steel bridge structure that would be most susceptible to chloride contamination, such as below joints, splash/spray zones, and areas of ponding water or debris.  Identify possible testing methods and/or inspection techniques for inspectors to evaluate the quality of the weathering steel patina at locations discussed above.  Identify possible methods to measure and evaluate the level of chloride contamination at the locations discussed above.  Evaluate the effectiveness of water washing on removing chlorides from the weathering steel patina.  Develop a general prioritization for the washing of bridge structures based on the structure’s location, environment, inspection observations, patina evaluation findings, and chloride test results.

Robust accelerated failure time regression

Robust estimators for accelerated failure time models with asymmetric (or symmetric) error distribution and censored observations are proposed. It is assumed that the error model belongs to a log-location-scale family of distributions and that the mean response is the parameter of interest. Since scale is a main component of mean, scale is not treated as a nuisance parameter. A three steps procedure is proposed. In the first step, an initial high breakdown point S estimate is computed. In the second step, observations that are unlikely under the estimated model are rejected or down weighted. Finally, a weighted maximum likelihood estimate is computed. To define the estimates, functions of censored residuals are replaced by their estimated conditional expectation given that the response is larger than the observed censored value. The rejection rule in the second step is based on an adaptive cut-off that, asymptotically, does not reject any observation when the data are generat ed according to the model. Therefore, the final estimate attains full efficiency at the model, with respect to the maximum likelihood estimate, while maintaining the breakdown point of the initial estimator. Asymptotic results are provided. The new procedure is evaluated with the help of Monte Carlo simulations. Two examples with real data are discussed.

La validation des techniques en chirurgie laparoscopique [Validation of laparoscopic surgical techniques].

The use of laparoscopic surgery has increased rapidly. However, a technically feasible procedure is not automatically recommendable. Thus, if cholecystectomy and fundoplication are currently fully validated techniques, this does not hold true for gastroplasty and kidney harvesting for transplantation: these operations are feasible indeed but their efficacy remains to be proved. Laparoscopic oncology has been shown to be feasible too, but its efficacy has not been documented yet.