Prediction of direct and indirect genetic gains and genotypic correlations in rubber tree progenies

The objective of this work was to estimate the genetic parameters, genotypic and phenotypic correlations, and direct and indirect genetic gains among and within rubber tree (Hevea brasiliensis) progenies. The experiment was set up at the Municipality of Jaú, SP, Brazil. A randomized complete block design was used, with 22 treatments (progenies), 6 replicates, and 10 plants per plot at a spacing of 3x3 m. Three‑year‑old progenies were assessed for girth, rubber yield, and bark thickness by direct and indirect gains and genotypic correlations. The number of latex vessel rings showed the best correlations, correlating positively and significantly with girth and bark thickness. Selection gains among progenies were greater than within progeny for all the variables analyzed. Total gains obtained were high, especially for girth increase and rubber yield, which were 93.38 and 105.95%, respectively. Young progeny selection can maximize the expected genetic gains, reducing the rubber tree selection cycle.

Pneumopathie interstitielle dans la polyarthrite rhumatoïde [Interstitial lung disease in rheumatoid arthritis].

Interstitial lung disease (ILD) is found in up to 30% of patients with rheumatoid arthritis (RA) and is clinically manifest in 5 to 10%, resulting in significant morbidity and mortality. The most frequent histopathological forms are usual interstitial pneumonia and nonspecific interstitial pneumonia. Another recently described presentation is combined pulmonary fibrosis and emphysema. Similarly to idiopathic pulmonary fibrosis, acute exacerbation of ILD may occur in RA and is associated with severe prognosis. Smoking is a known risk factor of RA and may also play a role in the pathogenesis of RA-associated ILD, in combination with genetic and immunologic mechanisms. Several treatments of RA may also lead to drug-induced ILD.

A comparison of Bayesian and frequentist approaches to incorporating external information for the prediction of prostate cancer risk.

We present the most comprehensive comparison to date of the predictive benefit of genetics in addition to currently used clinical variables, using genotype data for 33 single-nucleotide polymorphisms (SNPs) in 1,547 Caucasian men from the placebo arm of the REduction by DUtasteride of prostate Cancer Events (REDUCE®) trial. Moreover, we conducted a detailed comparison of three techniques for incorporating genetics into clinical risk prediction. The first method was a standard logistic regression model, which included separate terms for the clinical covariates and for each of the genetic markers. This approach ignores a substantial amount of external information concerning effect sizes for these Genome Wide Association Study (GWAS)-replicated SNPs. The second and third methods investigated two possible approaches to incorporating meta-analysed external SNP effect estimates - one via a weighted PCa 'risk' score based solely on the meta analysis estimates, and the other incorporating both the current and prior data via informative priors in a Bayesian logistic regression model. All methods demonstrated a slight improvement in predictive performance upon incorporation of genetics. The two methods that incorporated external information showed the greatest receiver-operating-characteristic AUCs increase from 0.61 to 0.64. The value of our methods comparison is likely to lie in observations of performance similarities, rather than difference, between three approaches of very different resource requirements. The two methods that included external information performed best, but only marginally despite substantial differences in complexity.

2nd ESMO Consensus Conference on Lung Cancer: non-small-cell lung cancer first-line/second and further lines of treatment in advanced disease.

To complement the existing treatment guidelines for all tumour types, ESMO organises consensus conferences to focus on specific issues in each type of tumour. The 2nd ESMO Consensus Conference on Lung Cancer was held on 11-12 May 2013 in Lugano. A total of 35 experts met to address several questions on non-small-cell lung cancer (NSCLC) in each of four areas: pathology and molecular biomarkers, first-line/second and further lines of treatment in advanced disease, early-stage disease and locally advanced disease. For each question, recommendations were made including reference to the grade of recommendation and level of evidence. This consensus paper focuses on first line/second and further lines of treatment in advanced disease.

Statistical methods for river runoff prediction

Methods used to analyze one type of nonstationary stochastic processes?the periodically correlated process?are considered. Two methods of one-step-forward prediction of periodically correlated time series are examined. One-step-forward predictions made in accordance with an autoregression model and a model of an artificial neural network with one latent neuron layer and with an adaptation mechanism of network parameters in a moving time window were compared in terms of efficiency. The comparison showed that, in the case of prediction for one time step for time series of mean monthly water discharge, the simpler autoregression model is more efficient.

Prediction of hemodynamic severity of coarctation by magnetic resonance imaging.

A published formula containing minimal aortic cross-sectional area and the flow deceleration pattern in the descending aorta obtained by cardiovascular magnetic resonance predicts significant coarctation of the aorta (CoA). However, the existing formula is complicated to use in clinical practice and has not been externally validated. Consequently, its clinical utility has been limited. The aim of this study was to derive a simple and clinically practical algorithm to predict severe CoA from data obtained by cardiovascular magnetic resonance. Seventy-nine consecutive patients who underwent cardiovascular magnetic resonance and cardiac catheterization for the evaluation of native or recurrent CoA at Children's Hospital Boston (n = 30) and the University of California, San Francisco (n = 49), were retrospectively reviewed. The published formula derived from data obtained at Children's Hospital Boston was first validated from data obtained at the University of California, San Francisco. Next, pooled data from the 2 institutions were analyzed, and a refined model was created using logistic regression methods. Finally, recursive partitioning was used to develop a clinically practical prediction tree to predict transcatheter systolic pressure gradient ≥ 20 mm Hg. Severe CoA was present in 48 patients (61%). Indexed minimal aortic cross-sectional area and heart rate-corrected flow deceleration time in the descending aorta were independent predictors of CoA gradient ≥ 20 mm Hg (p <0.01 for both). A prediction tree combining these variables reached a sensitivity and specificity of 90% and 76%, respectively. In conclusion, the presented prediction tree on the basis of cutoff values is easy to use and may help guide the management of patients investigated for CoA.

Transitoriness in cancer patients: a cross-sectional survey of lung and gastrointestinal cancer patients.

Despite earlier diagnosis and advancements in treatment, cancer remains a leading cause of death in the world (13% of all deaths according to the World Health Organization) among men and women. Cancer accounts for approximately 20% of the deaths in the USA every year. Here, we report the findings from a cross-sectional survey of psychosocial factors in lung and gastrointestinal cancer patients. The aim of the study was to explore the associations among transitoriness, uncertainty, and locus of control (LOC) with quality of life. Transitoriness is defined as a person's confrontation with life's finitude due to a cancer diagnosis. A total of 126 patients with lung or gastrointestinal cancer completed eight self-reporting questionnaires addressing demographics, spiritual perspective, symptom burden, transitoriness, uncertainty, LOC, and quality of life. Transitoriness, uncertainty, and LOC were significantly associated with one another (r = 0.3267, p = 0.0002/r = 0.1994, p = 0.0252, respectively). LOC/belief in chance has a significant inverse relationship with patients' quality of life (r = -0.2505, p = 0.0047). Transitoriness, uncertainty, and LOC were found to have a significant inverse relationship with patients' quality of life (transitoriness state: r = -0.5363, p = 0.0000/trait: r = -0.4629, p = 0.0000/uncertainty: r = -0.4929, p = 0.0000/internal LOC: r = 0.1759, p = 0.0489/chance LOC: r = -0.2505, p = 0.0047). Transitoriness, uncertainty, and LOC are important concepts as they adversely influence patients' quality of life. Incorporating this finding into the care of cancer patients may provide them with the support they need to cope with treatment and maintenance of a positive quality of life.

Prediction of soil orders with high spatial resolution: response of different classifiers to sampling density

The objective of this work was to evaluate sampling density on the prediction accuracy of soil orders, with high spatial resolution, in a viticultural zone of Serra Gaúcha, Southern Brazil. A digital elevation model (DEM), a cartographic base, a conventional soil map, and the Idrisi software were used. Seven predictor variables were calculated and read along with soil classes in randomly distributed points, with sampling densities of 0.5, 1, 1.5, 2, and 4 points per hectare. Data were used to train a decision tree (Gini) and three artificial neural networks: adaptive resonance theory, fuzzy ARTMap; self‑organizing map, SOM; and multi‑layer perceptron, MLP. Estimated maps were compared with the conventional soil map to calculate omission and commission errors, overall accuracy, and quantity and allocation disagreement. The decision tree was less sensitive to sampling density and had the highest accuracy and consistence. The SOM was the less sensitive and most consistent network. The MLP had a critical minimum and showed high inconsistency, whereas fuzzy ARTMap was more sensitive and less accurate. Results indicate that sampling densities used in conventional soil surveys can serve as a reference to predict soil orders in Serra Gaúcha.

Spatial prediction of monthly wind speeds in complex terrain with adaptive general regression neural networks

This paper presents the general regression neural networks (GRNN) as a nonlinear regression method for the interpolation of monthly wind speeds in complex Alpine orography. GRNN is trained using data coming from Swiss meteorological networks to learn the statistical relationship between topographic features and wind speed. The terrain convexity, slope and exposure are considered by extracting features from the digital elevation model at different spatial scales using specialised convolution filters. A database of gridded monthly wind speeds is then constructed by applying GRNN in prediction mode during the period 1968-2008. This study demonstrates that using topographic features as inputs in GRNN significantly reduces cross-validation errors with respect to low-dimensional models integrating only geographical coordinates and terrain height for the interpolation of wind speed. The spatial predictability of wind speed is found to be lower in summer than in winter due to more complex and weaker wind-topography relationships. The relevance of these relationships is studied using an adaptive version of the GRNN algorithm which allows to select the useful terrain features by eliminating the noisy ones. This research provides a framework for extending the low-dimensional interpolation models to high-dimensional spaces by integrating additional features accounting for the topographic conditions at multiple spatial scales. Copyright (c) 2012 Royal Meteorological Society.

Successful migration of three tracers without identification of sentinel nodes during intraoperative lymphatic mapping for non-small cell lung cancer.

Prospective comparative evaluation of patent V blue, fluorescein and (99m)TC-nanocolloids for intraoperative sentinel lymph node (SLN) mapping during surgery for non-small cell lung cancer (NSCLC). Ten patients with peripherally localised clinical stage I NSCLC underwent thoracotomy and peritumoral subpleural injection of 2 ml of patent V blue dye, 1 ml of 10% fluorescein and 1ml of (99m)Tc-nanocolloids (0.4 mCi). The migration and spatial distribution pattern of the tracers was assessed by direct visualisation (patent V blue), visualisation of fluorescence signalling by a lamp of Wood (fluorescein) and radioactivity counting with a hand held gamma-probe ((99m)Tc-nanocolloids). Lymph nodes at interlobar (ATS 11), hilar (ATS 10) and mediastinal (right ATS 2,4,7; left ATS 5,6,7) levels were systematically assessed every 10 min up to 60 min after injection, followed by lobectomy and formal lymph node dissection. Successful migration from the peritumoral area to the mediastinum was observed for all three tracers up to 60 min after injection. The interlobar lympho-fatty tissue (station ATS 11) revealed an early and preferential accumulation of all three tracers for all tumours assessed and irrespective of the tumour localisation. However, no preferential accumulation in one or two distinct lymph nodes was observed up to 60 min after injection for all three tracers assessed. Intraoperative SLN mapping revealed successful migration of the tracers from the site of peritumoral injection to the mediastinum, but in a diffuse pattern without preferential accumulation in sentinel lymph nodes.

Subjectively adapted high capacity lossless image data hiding based on prediction errors

This article reports on a lossless data hiding scheme for digital images where the data hiding capacity is either determined by minimum acceptable subjective quality or by the demanded capacity. In the proposed method data is hidden within the image prediction errors, where the most well-known prediction algorithms such as the median edge detector (MED), gradient adjacent prediction (GAP) and Jiang prediction are tested for this purpose. In this method, first the histogram of the prediction errors of images are computed and then based on the required capacity or desired image quality, the prediction error values of frequencies larger than this capacity are shifted. The empty space created by such a shift is used for embedding the data. Experimental results show distinct superiority of the image prediction error histogram over the conventional image histogram itself, due to much narrower spectrum of the former over the latter. We have also devised an adaptive method for hiding data, where subjective quality is traded for data hiding capacity. Here the positive and negative error values are chosen such that the sum of their frequencies on the histogram is just above the given capacity or above a certain quality.

Early prediction of response to sunitinib after imatinib failure by 18F-fluorodeoxyglucose positron emission tomography in patients with gastrointestinal stromal tumor.

PURPOSE: Positron emission tomography with (18)F-fluorodeoxyglucose (FDG-PET) was used to evaluate treatment response in patients with gastrointestinal stromal tumors (GIST) after administration of sunitinib, a multitargeted tyrosine kinase inhibitor, after imatinib failure. PATIENTS AND METHODS: Tumor metabolism was assessed with FDG-PET before and after the first 4 weeks of sunitinib therapy in 23 patients who received one to 12 cycles of sunitinib therapy (4 weeks of 50 mg/d, 2 weeks off). Treatment response was expressed as the percent change in maximal standardized uptake values (SUV). The primary end point of time to tumor progression was compared with early PET results on the basis of traditional Response Evaluation Criteria in Solid Tumors (RECIST) criteria. RESULTS: Progression-free survival (PFS) was correlated with early FDG-PET metabolic response (P < .0001). Using -25% and +25% thresholds for SUV variations from baseline, early FDG-PET response was stratified in metabolic partial response, metabolically stable disease, or metabolically progressive disease; median PFS rates were 29, 16, and 4 weeks, respectively. Similarly, when a single FDG-PET positive/negative was considered after 4 weeks of sunitinib, the median PFS was 29 weeks for SUVs less than 8 g/mL versus 4 weeks for SUVs of 8 g/mL or greater (P < .0001). None of the patients with metabolically progressive disease subsequently responded according to RECIST criteria. Multivariate analysis showed shorter PFS in patients who had higher residual SUVs (P < .0001), primary resistance to imatinib (P = .024), or nongastric GIST (P = .002), regardless of the mutational status of the KIT and PDGFRA genes. CONCLUSION: Week 4 FDG-PET is useful for early assessment of treatment response and for the prediction of clinical outcome. Thus, it offers opportunities to individualize and optimize patient therapy.

Linear prediction application for modelling the relationships between a large number of stand characteristics of Norway spruce stands

Abstract