Robust Nonlinear Mapping of Soil Contamination Using Support Regression

Spatial data analysis mapping and visualization is of great importance in various fields: environment, pollution, natural hazards and risks, epidemiology, spatial econometrics, etc. A basic task of spatial mapping is to make predictions based on some empirical data (measurements). A number of state-of-the-art methods can be used for the task: deterministic interpolations, methods of geostatistics: the family of kriging estimators (Deutsch and Journel, 1997), machine learning algorithms such as artificial neural networks (ANN) of different architectures, hybrid ANN-geostatistics models (Kanevski and Maignan, 2004; Kanevski et al., 1996), etc. All the methods mentioned above can be used for solving the problem of spatial data mapping. Environmental empirical data are always contaminated/corrupted by noise, and often with noise of unknown nature. That's one of the reasons why deterministic models can be inconsistent, since they treat the measurements as values of some unknown function that should be interpolated. Kriging estimators treat the measurements as the realization of some spatial randomn process. To obtain the estimation with kriging one has to model the spatial structure of the data: spatial correlation function or (semi-)variogram. This task can be complicated if there is not sufficient number of measurements and variogram is sensitive to outliers and extremes. ANN is a powerful tool, but it also suffers from the number of reasons. of a special type ? multiplayer perceptrons ? are often used as a detrending tool in hybrid (ANN+geostatistics) models (Kanevski and Maignank, 2004). Therefore, development and adaptation of the method that would be nonlinear and robust to noise in measurements, would deal with the small empirical datasets and which has solid mathematical background is of great importance. The present paper deals with such model, based on Statistical Learning Theory (SLT) - Support Vector Regression. SLT is a general mathematical framework devoted to the problem of estimation of the dependencies from empirical data (Hastie et al, 2004; Vapnik, 1998). SLT models for classification - Support Vector Machines - have shown good results on different machine learning tasks. The results of SVM classification of spatial data are also promising (Kanevski et al, 2002). The properties of SVM for regression - Support Vector Regression (SVR) are less studied. First results of the application of SVR for spatial mapping of physical quantities were obtained by the authorsin for mapping of medium porosity (Kanevski et al, 1999), and for mapping of radioactively contaminated territories (Kanevski and Canu, 2000). The present paper is devoted to further understanding of the properties of SVR model for spatial data analysis and mapping. Detailed description of the SVR theory can be found in (Cristianini and Shawe-Taylor, 2000; Smola, 1996) and basic equations for the nonlinear modeling are given in section 2. Section 3 discusses the application of SVR for spatial data mapping on the real case study - soil pollution by Cs137 radionuclide. Section 4 discusses the properties of the modelapplied to noised data or data with outliers.

Autism Support Program Annual Report, December 2015

The Autism Support Program provides funding for applied behavioral analysis services for children under the age of nine who meet certain diagnostic and financial eligibility criteria.

Family Support Subsidy and Children at Home Program SFY 2015, October 30, 2015

The Family Support Subsidy (FSS) program provides a monthly payment to help families with the cost of raising a child with a developmental disability. Parents of children with disabilities were very active in getting state and federal policy makers to look at how they could divert some of the funds going to institutional care. Families with severely disabled children wanted to raise their children at home but were met with a lot of resistance and policy barriers when they tried to get home-based support.

Prospective study of the barriers to nutritional support in a paediatric intensive care unit : P16

Introduction and aim: Children hospitalised in a paediatric intensive care unit (PICU) are mainly fed by nutritional support (NS) which may often be interrupted. The aims of the study were to verify the relationship between prescribed (PEI) and actual energy intake (AEI) and to identify the reasons for NS interruption. Methods: Prospective study in a PICU. PEI and AEI from day 1 to 15, type of NS (enteral, parenteral, mixed), position of the feeding tube, interruptions in NS and reasons for these were noted. Inter - ruptions were classified in categories of barriers and their frequency and duration were analysed. Results: Fifteen children (24 ± 25.2 months) were studied for 84 days. The NS was exclusively enteral (69%) or mixed (31%). PEI were significantly higher than AEI (54.7 ± 32.9 vs 49.2 ± 33.6 kcal/kg, p = 0.0011). AEI represented 93% of the PEI. Ninety-eight interruptions were noted and lasted 189 h, i.e. 9.4% of the evaluated time. The most frequent barriers were nursing procedures, respiratory physiotherapy and unavailability of intravenous access. The longest were caused by the necessity to stop NS for surgery or diagnostic studies, to treat burns or to carry out medical procedures. Conclusion: AEI in PICU were inferior by 7% to PEI, considerably lower than in adult studies. Making these results available to medical staff for greater anticipation and compensation could reduce NS interruptions. Starving protocols should be reconsidered.

The use of the SeDeM diagram expert system for the formulation of Captopril SR matrix tablets by direct compression

The SeDeM Diagram Expert System has been used to study excipients, Captopril and designed formulations for their galenic characterization and to ascertain the critical points of the formula affecting product quality to obtain suitable formulations of Captopril Direct Compression SR Matrix Tablets. The application of the Sedem Diagram Expert System enables selecting excipients with in order to optimize the formula in the preformulation and formulation studies. The methodology is based on the implementation of ICH Q8, establishing the design space of the formula with the use of experiment design, using the parameters of the SeDeM Diagram Expert System as system responses.

Evaluation of the multicriteria approval method for timber-harvesting group decision support

Abstract

Different transcriptional control of metabolism and extracellular matrix in visceral and subcutaneous fat of obese and rimonabant treated mice.

BACKGROUND: The visceral (VAT) and subcutaneous (SCAT) adipose tissues play different roles in physiology and obesity. The molecular mechanisms underlying their expansion in obesity and following body weight reduction are poorly defined. METHODOLOGY: C57Bl/6 mice fed a high fat diet (HFD) for 6 months developed low, medium, or high body weight as compared to normal chow fed mice. Mice from each groups were then treated with the cannabinoid receptor 1 antagonist rimonabant or vehicle for 24 days to normalize their body weight. Transcriptomic data for visceral and subcutaneous adipose tissues from each group of mice were obtained and analyzed to identify: i) genes regulated by HFD irrespective of body weight, ii) genes whose expression correlated with body weight, iii) the biological processes activated in each tissue using gene set enrichment analysis (GSEA), iv) the transcriptional programs affected by rimonabant. PRINCIPAL FINDINGS: In VAT, "metabolic" genes encoding enzymes for lipid and steroid biosynthesis and glucose catabolism were down-regulated irrespective of body weight whereas "structure" genes controlling cell architecture and tissue remodeling had expression levels correlated with body weight. In SCAT, the identified "metabolic" and "structure" genes were mostly different from those identified in VAT and were regulated irrespective of body weight. GSEA indicated active adipogenesis in both tissues but a more prominent involvement of tissue stroma in VAT than in SCAT. Rimonabant treatment normalized most gene expression but further reduced oxidative phosphorylation gene expression in SCAT but not in VAT. CONCLUSION: VAT and SCAT show strikingly different gene expression programs in response to high fat diet and rimonabant treatment. Our results may lead to identification of therapeutic targets acting on specific fat depots to control obesity.

Mutations in the TGFβ binding-protein-like domain 5 of FBN1 are responsible for acromicric and geleophysic dysplasias.

Geleophysic (GD) and acromicric dysplasia (AD) belong to the acromelic dysplasia group and are both characterized by severe short stature, short extremities, and stiff joints. Although AD has an unknown molecular basis, we have previously identified ADAMTSL2 mutations in a subset of GD patients. After exome sequencing in GD and AD cases, we selected fibrillin 1 (FBN1) as a candidate gene, even though mutations in this gene have been described in Marfan syndrome, which is characterized by tall stature and arachnodactyly. We identified 16 heterozygous FBN1 mutations that are all located in exons 41 and 42 and encode TGFβ-binding protein-like domain 5 (TB5) of FBN1 in 29 GD and AD cases. Microfibrillar network disorganization and enhanced TGFβ signaling were consistent features in GD and AD fibroblasts. Importantly, a direct interaction between ADAMTSL2 and FBN1 was demonstrated, suggesting a disruption of this interaction as the underlying mechanism of GD and AD phenotypes. Although enhanced TGFβ signaling caused by FBN1 mutations can trigger either Marfan syndrome or GD and AD, our findings support the fact that TB5 mutations in FBN1 are responsible for short stature phenotypes.

Mixed spectral-structural classification of very high resolution images with summation kernels

In this paper, mixed spectral-structural kernel machines are proposed for the classification of very-high resolution images. The simultaneous use of multispectral and structural features (computed using morphological filters) allows a significant increase in classification accuracy of remote sensing images. Subsequently, weighted summation kernel support vector machines are proposed and applied in order to take into account the multiscale nature of the scene considered. Such classifiers use the Mercer property of kernel matrices to compute a new kernel matrix accounting simultaneously for two scale parameters. Tests on a Zurich QuickBird image show the relevance of the proposed method : using the mixed spectral-structural features, the classification accuracy increases of about 5%, achieving a Kappa index of 0.97. The multikernel approach proposed provide an overall accuracy of 98.90% with related Kappa index of 0.985.