Sentinel nodes and and non sentinel nodes evaluation in T1 melanoma patients

Objective: Because increasing incidence of melanoma and dermatologicsystematic screening, more early superficial melanoma are discovered in Switzerland. Patients with Breslow index more than 1 mm. (T2) represent the classical indication to sentinel node (SN). It has been shown that some ''risky'' T1 patients may have micrometastatic SNs. T1b melanoma are defined by presence of ulceration,Clark IV (ormore) level, signs of melanoma regression (old classification) and high mitotic index (new TNM). The objective of the present study was to review the incidence and risk for metastatic SN in T1 patients and if radical lymph node dissection is justified (evaluation of non sentinel node [NSN]) compared with T2-4 patients.Methods: Retrospective review of a cohort of all patients operated for T1-4 clinically N0 and radiological M0 melanoma patients between 1997 and 2010 in a reference melanoma centre.Results: 599 melanoma patients have been operated with SNdissection. There were 98 T1 patients. Metastatic SN were observed in 2 out of 24 T1a patients and in 5 out of 74 T1b patients. This means overall 7% T1 patients were at least N1a. None of SN+ T1a or T1b patients had metastatic NSN after radical lymph node dissections (RLND). During the follow-up (1998-2010), no patients presented with locoregional disease and only one T1a N1a patient died of metastatic melanoma. These results contrast with the other 591 T2-4 patients: 150 were SN+ (25%) and among them 23 had metastatic NSN after RLND. Overall 23/136 (17%) had metastatic NSN.Conclusion: T1 melanoma patients are at significant risk (7%) for metastatic lymph node in the corresponding drainage basin. T1a and T1b did not differ regarding this risk. However, the benefit for a RLND must be reevaluated regarding surgical morbidity, because none of T1 patients had metastatic NSN.

Predicting positive food challenges in children sensitised to peanuts/tree nuts.

BACKGROUND: Children with atopic diseases in early life are frequently found with positive IgE tests to peanuts/tree nuts without a history of previous ingestion. We aimed to identify risk factors for reactions to nuts at first introduction. METHODS: A retrospective case-note and database analysis was performed. Recruitment criteria were: patients aged 3-16 yr who had a standardized food challenge to peanut and/or tree nuts due to sensitisation to the peanut/tree nut (positive spIgE or SPT) without previous consumption. A detailed assessment was performed of factors relating to food challenge outcome with univariate and multivariate logistic regression analysis. RESULTS: There were 98 food challenges (47 peanut, 51 tree nut) with 29 positive, 67 negative and 2 inconclusive outcomes. A positive maternal history of allergy and a specific IgE >5 kU/l were strongly associated with a significantly increased risk of a positive food challenge (OR 3.73; 95% CI 1.31-10.59; p = 0.013 and OR 3.35; 95% CI 1.23-9.11; p = 0.007, respectively). Adjusting for age, a three year-old with these criteria has a 67% probability of a positive challenge. There was no significant association between types of peanut/tree nut, other food allergies, atopic conditions or severity of previous food reactions and positive challenges. CONCLUSIONS: We have demonstrated an association between the presence of maternal atopic history and a specific IgE >5 kU/l, with a significant increase in the likelihood of a positive food challenge. Although requiring further prospective validation these easily identifiable components should be considered when deciding the need for a challenge.

“Improved Feature Extraction and Classification Techniques for Multispectral Brain Magnetic Resonance Images”

Magnetic Resonance Imaging (MRI) is a multi sequence medical imaging technique in which stacks of images are acquired with different tissue contrasts. Simultaneous observation and quantitative analysis of normal brain tissues and small abnormalities from these large numbers of different sequences is a great challenge in clinical applications. Multispectral MRI analysis can simplify the job considerably by combining unlimited number of available co-registered sequences in a single suite. However, poor performance of the multispectral system with conventional image classification and segmentation methods makes it inappropriate for clinical analysis. Recent works in multispectral brain MRI analysis attempted to resolve this issue by improved feature extraction approaches, such as transform based methods, fuzzy approaches, algebraic techniques and so forth. Transform based feature extraction methods like Independent Component Analysis (ICA) and its extensions have been effectively used in recent studies to improve the performance of multispectral brain MRI analysis. However, these global transforms were found to be inefficient and inconsistent in identifying less frequently occurred features like small lesions, from large amount of MR data. The present thesis focuses on the improvement in ICA based feature extraction techniques to enhance the performance of multispectral brain MRI analysis. Methods using spectral clustering and wavelet transforms are proposed to resolve the inefficiency of ICA in identifying small abnormalities, and problems due to ICA over-completeness. Effectiveness of the new methods in brain tissue classification and segmentation is confirmed by a detailed quantitative and qualitative analysis with synthetic and clinical, normal and abnormal, data. In comparison to conventional classification techniques, proposed algorithms provide better performance in classification of normal brain tissues and significant small abnormalities.

Significance Of Classification Techniques In Prediction Of Learning Disabilities

The aim of this study is to show the importance of two classification techniques, viz. decision tree and clustering, in prediction of learning disabilities (LD) of school-age children. LDs affect about 10 percent of all children enrolled in schools. The problems of children with specific learning disabilities have been a cause of concern to parents and teachers for some time. Decision trees and clustering are powerful and popular tools used for classification and prediction in Data mining. Different rules extracted from the decision tree are used for prediction of learning disabilities. Clustering is the assignment of a set of observations into subsets, called clusters, which are useful in finding the different signs and symptoms (attributes) present in the LD affected child. In this paper, J48 algorithm is used for constructing the decision tree and K-means algorithm is used for creating the clusters. By applying these classification techniques, LD in any child can be identified

Global Acceleration and Dispossesion: Regression in the Right to Life and Public Health during Ecuador’s Neoliberal Decades

The present essay’s central argument or hypothesis is, consequently, that the mechanisms accelerating a wealth concentrating and exclusionary economy centred on the benefit and overprotection of big business—with a corresponding plundering of resources that are vital for life—generated forms of loss and regression in the right to healthcare and the dismantling of institutional protections. These are all expressed in indicators from 1990-2005, which point not only to the deterioration of healthcare programs and services but also to the undermining of the general conditions of life (social reproduction) and, in contrast to the reports and predictions of the era’s governments, a stagnation or deterioration in health indicators, especially for those most sensitive to the crisis. The present study’s argument is linked together across distinct chapters. First, we undertake the necessary clarification of the categories central to the understanding of a complex issue; clarifying the concept of health itself and its determinants, emphasizing the necessity of taking on an integral understanding as a fundamental prerequisite to unravelling what documents and reports from this era either leave unsaid or distort. Based on that analysis, we will explain the harmful effects of global economic acceleration, the monopolization and pillaging of strategic healthcare goods; not only those which directly place obstacles on the access to health services, but also those like the destructuration of small economies, linked to the impoverishment and worsening of living modes. Thinking epidemiologically, we intend to show signs of the deterioration of broad collectivities’ ways of life as a result of the mechanisms of acceleration and pillage. We will then collect disparate evidence of the deterioration of human health and ecosystems to, finally, establish the most urgent conclusions about this unfortunate period of our social and medical history.

Models to predict the distribution and abundance of breeding ducks in Canada

Detailed knowledge of waterfowl abundance and distribution across Canada is lacking, which limits our ability to effectively conserve and manage their populations. We used 15 years of data from an aerial transect survey to model the abundance of 17 species or species groups of ducks within southern and boreal Canada. We included 78 climatic, hydrological, and landscape variables in Boosted Regression Tree models, allowing flexible response curves and multiway interactions among variables. We assessed predictive performance of the models using four metrics and calculated uncertainty as the coefficient of variation of predictions across 20 replicate models. Maps of predicted relative abundance were generated from resulting models, and they largely match spatial patterns evident in the transect data. We observed two main distribution patterns: a concentrated prairie-parkland distribution and a more dispersed pan-Canadian distribution. These patterns were congruent with the relative importance of predictor variables and model evaluation statistics among the two groups of distributions. Most species had a hydrological variable as the most important predictor, although the specific hydrological variable differed somewhat among species. In some cases, important variables had clear ecological interpretations, but in some instances, e.g., topographic roughness, they may simply reflect chance correlations between species distributions and environmental variables identified by the model-building process. Given the performance of our models, we suggest that the resulting prediction maps can be used in future research and to guide conservation activities, particularly within the bounds of the survey area.

Patterns of pollen and seed dispersal in a small, fragmented population of the wind-pollinated tree Araucaria angustifolia in southern Brazil

Paternity analysis based on eight microsatellite loci was used to investigate pollen and seed dispersal patterns of the dioecious wind- pollinated tree, Araucaria angustifolia. The study sites were a 5.4 ha isolated forest fragment and a small tree group situated 1.7 km away, located in Paran alpha State, Brazil. In the forest fragment, 121 males, 99 females, 66 seedlings and 92 juveniles were mapped and genotyped, together with 210 seeds. In the tree group, nine male and two female adults were mapped and genotyped, together with 20 seeds. Paternity analysis within the forest fragment indicated that at least 4% of the seeds, 3% of the seedlings and 7% of the juveniles were fertilized by pollen from trees in the adjacent group, and 6% of the seeds were fertilized by pollen from trees outside these stands. The average pollination distance within the forest fragment was 83 m; when the tree group was included the pollination distance was 2006m. The average number of effective pollen donors was estimated as 12.6. Mother- trees within the fragment could be assigned to all seedlings and juveniles, suggesting an absence of seed immigration. The distance of seedlings and juveniles from their assigned mother- trees ranged from 0.35 to 291m ( with an average of 83m). Significant spatial genetic structure among adult trees, seedlings, and juveniles was detected up to 50m, indicating seed dispersal over a short distance. The effective pollination neighborhood ranged from 0.4 to 3.3 ha. The results suggest that seed dispersal is restricted but that there is longdistance pollen dispersal between the forest fragment and the tree group; thus, the two stands of trees are not isolated.

Landscape context and habitat type as drivers of bee diversity in European annual crops

To better understand the dynamics of bee populations in crops, we assessed the effect of landscape context and habitat type on bee communities in annual entomophilous crops in Europe. We quantified bee communities in five pairs of crop-country: buckwheat in Poland, cantaloupe in France, field beans in the UK, spring oilseed rape in Sweden, and strawberries in Germany. For each country, 7-10 study fields were sampled over a gradient of increasing proportion of semi-natural habitats in the surrounding landscape. The CORINE land cover classification was used to characterize the landscape over a 3 km radius around each study field and we used multivariate and regression analyses to quantify the impact of landscape features on bee abundance and diversity at the sub-generic taxonomic level. Neither overall wild bee abundance nor diversity, taken as the number of sub-genera. was significantly affected by the proportion of semi-natural habitat. Therefore, we used the most precise level of the CORINE classification to examine the possible links between specific landscape features and wild bee communities. Bee community composition fell into three distinct groups across Europe: group I included Poland, Germany, and Sweden, group 2 the UK, and group 3 France. Among all three groups, wild bee abundance and sub-generic diversity were affected by 17 landscape elements including some semi-natural habitats (e.g., transitional wood land-shrub), some urban habitats (e.g., sport and leisure facilities) and some crop habitats (e.g., non-irrigated arable land). Some bee taxa were positively affected by urban habitats only, others by semi-natural habitats only, and others by a combination of semi-natural, urban and crop habitats. Bee sub-genera favoured by urban and crop habitats were more resistant to landscape change than those favoured only by semi-natural habitats. In agroecosystems, the agricultural intensification defined as the loss of semi-natural habitats does not necessarily cause a decline in evenness at the local level, but can change community composition towards a bee fauna dominated by common taxa. (C) 2009 Elsevier B.V. All rights reserved.

Chemical composition and in vitro fermentation of tannin-rich tree fruits

Dry and mature tree fruits are a potential source of protein for goats in the semi-arid areas of southern Africa, but their chemical composition and feeding value is largely unknown. This study presents the chemical composition and in vitro fermentation of indehiscent whole fruits and separated seed and hull fractions from Acacia nilotica, Acacia erubescens, Acacia sieberiana, Acacia erioloba, Piliostigma thonningii and Dichrostachys cinerea trees. Results indicate that the N contents of whole fruits ranged between 13.5 g/kg DM (A. nilotica) and 27.1 g/kg DM (A. erubescens). Seeds had a higher N content than hulls for all tree species. A. nilotica, D. cinerea and P thonningii fruits had high levels of extractable phenolics (758, 458 and 299 g/kg DM, respectively). Soluble phenolics (SPh) and ytterbium precipitable phenolics (YbPh) levels were negatively correlated to in vitro gas production but positively correlated to in vitro organic matter degradability (iOMD). Partition factors for whole fruits at 48 h ranged between 3.6 mg/ml for A. erioloba and 7.8 mg/ml for A. nilotica. Seeds of A. erioloba, A. erubescens and P thonningii were consistently fermented more efficiently throughout the incubation period compared to their whole fruits or hulls. Estimating in vitro degradability of phenolic-rich substrates through filtration procedures can give erroneous results due to the loss of soluble phenolics, which are not necessarily degradable. The feeding value of fruits from D. cinerea and A. nilotica tree species may be reduced due to the presence of high levels of phenolics. (C) 2007 Elsevier B.V. All rights reserved.

Orthogonal-least-squares regression: A unified approach for data modelling

A unified approach is proposed for data modelling that includes supervised regression and classification applications as well as unsupervised probability density function estimation. The orthogonal-least-squares regression based on the leave-one-out test criteria is formulated within this unified data-modelling framework to construct sparse kernel models that generalise well. Examples from regression, classification and density estimation applications are used to illustrate the effectiveness of this generic data-modelling approach for constructing parsimonious kernel models with excellent generalisation capability. (C) 2008 Elsevier B.V. All rights reserved.

Global climate change and tree nutrition: effects of elevated CO2 and temperature

Although tree nutrition has not been the primary focus of large climate change experiments on trees, we are beginning to understand its links to elevated atmospheric CO2 and temperature changes. This review focuses on the major nutrients, namely N and P, and deals with the effects of climate change on the processes that alter their cycling and availability. Current knowledge regarding biotic and abiotic agents of weathering, mobilization and immobilization of these elements will be discussed. To date, controlled environment studies have identified possible effects of climate change on tree nutrition. Only some of these findings, however, were verified in ecosystem scale experiments. Moreover, to be able to predict future effects of climate change on tree nutrition at this scale, we need to progress from studying effects of single factors to analysing interactions between factors such as elevated CO2, temperature or water availability.

Analysis of full-waveform LiDAR data for classification of an orange orchard scene

Full-waveform laser scanning data acquired with a Riegl LMS-Q560 instrument were used to classify an orange orchard into orange trees, grass and ground using waveform parameters alone. Gaussian decomposition was performed on this data capture from the National Airborne Field Experiment in November 2006 using a custom peak-detection procedure and a trust-region-reflective algorithm for fitting Gauss functions. Calibration was carried out using waveforms returned from a road surface, and the backscattering coefficient c was derived for every waveform peak. The processed data were then analysed according to the number of returns detected within each waveform and classified into three classes based on pulse width and c. For single-peak waveforms the scatterplot of c versus pulse width was used to distinguish between ground, grass and orange trees. In the case of multiple returns, the relationship between first (or first plus middle) and last return c values was used to separate ground from other targets. Refinement of this classification, and further sub-classification into grass and orange trees was performed using the c versus pulse width scatterplots of last returns. In all cases the separation was carried out using a decision tree with empirical relationships between the waveform parameters. Ground points were successfully separated from orange tree points. The most difficult class to separate and verify was grass, but those points in general corresponded well with the grass areas identified in the aerial photography. The overall accuracy reached 91%, using photography and relative elevation as ground truth. The overall accuracy for two classes, orange tree and combined class of grass and ground, yielded 95%. Finally, the backscattering coefficient c of single-peak waveforms was also used to derive reflectance values of the three classes. The reflectance of the orange tree class (0.31) and ground class (0.60) are consistent with published values at the wavelength of the Riegl scanner (1550 nm). The grass class reflectance (0.46) falls in between the other two classes as might be expected, as this class has a mixture of the contributions of both vegetation and ground reflectance properties.

Tree-structured smooth transition models

The goal of this paper is to introduce a class of tree-structured models that combines aspects of regression trees and smooth transition regression models. The model is called the Smooth Transition Regression Tree (STR-Tree). The main idea relies on specifying a multiple-regime parametric model through a tree-growing procedure with smooth transitions among different regimes. Decisions about splits are entirely based on a sequence of Lagrange Multiplier (LM) tests of hypotheses.

Tree population and community dynamics in a Brazilian tropical semideciduous forest

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

BODE Index and GOLD Staging as Predictors of 1-Year Exacerbation Risk in Chronic Obstructive Pulmonary Disease

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)