The role of the pathologist in tissue banking: European Consensus Expert Group Report.

Human tissue biobanking encompasses a wide range of activities and study designs and is critical for application of a wide range of new technologies (-"omics") to the discovery of molecular patterns of disease and for implementation of novel biomarkers into clinical trials. Pathology is the cornerstone of hospital-based tissue biobanking. Pathologists not only provide essential information identifying the specimen but also make decisions on what should be biobanked, making sure that the timing of all operations is consistent with both the requirements of clinical diagnosis and the optimal preservation of biological products. This document summarizes the conclusions of a Pathology Expert Group Meeting within the European Biological and Biomolecular Research Infrastructure (BBMRI) Program. These recommendations are aimed at providing guidance for pathologists as well as for institutions hosting biobanks on how to better integrate and support pathological activities within the framework of biobanks that fulfill international standards.

Maximising the duration of disease control in metastatic renal cell carcinoma with targeted agents: an expert agreement.

With six targeted agents approved (sorafenib, sunitinib, temsirolimus, bevacizumab [+interferon], everolimus and pazopanib), many patients with metastatic renal cell carcinoma (mRCC) will receive multiple therapies. However, the optimum sequencing approach has not been defined. A group of European experts reviewed available data and shared their clinical experience to compile an expert agreement on the sequential use of targeted agents in mRCC. To date, there are few prospective studies of sequential therapy. The mammalian target of rapamycin (mTOR) inhibitor everolimus was approved for use in patients who failed treatment with inhibitors of vascular endothelial growth factor (VEGF) and VEGF receptors (VEGFR) based on the results from a Phase III placebo-controlled study; however, until then, the only licensed agents across the spectrum of mRCC were VEGF(R) inhibitors (sorafenib, sunitinib and bevacizumab + interferon), and as such, a large body of evidence has accumulated regarding their use in sequence. Data show that sequential use of VEGF(R) inhibitors may be an effective treatment strategy to achieve prolonged clinical benefit. The optimal place of each targeted agent in the treatment sequence is still unclear, and data from large prospective studies are needed. The Phase III AXIS study of second-line sorafenib vs. axitinib (including post-VEGF(R) inhibitors) has completed, but the data are not yet published; other ongoing studies include the Phase III SWITCH study of sorafenib-sunitinib vs. sunitinib-sorafenib (NCT00732914); the Phase III 404 study of temsirolimus vs. sorafenib post-sunitinib (NCT00474786) and the Phase II RECORD 3 study of sunitinib-everolimus vs. everolimus-sunitinib (NCT00903175). Until additional data are available, consideration of patient response and tolerability to treatment may facilitate current decision-making regarding when to switch and which treatment to switch to in real-life clinical practice.

Catalogue d'estampes anciennes... provenant du cabinet de M. R. D... vente... 19 novembre 1858... / [expert] Vignères

[Vente. Estampes. 1858-11-19 - 1858-11-20. Paris]

Childhood epilepsy with neuropsychological regression and continuous spike waves during sleep: epilepsy surgery in a young adult.

We describe the case of a man with a history of complex partial seizures and severe language, cognitive and behavioural regression during early childhood (3.5 years), who underwent epilepsy surgery at the age of 25 years. His early epilepsy had clinical and electroencephalogram features of the syndromes of epilepsy with continuous spike waves during sleep and acquired epileptic aphasia (Landau-Kleffner syndrome), which we considered initially to be of idiopathic origin. Seizures recurred at 19 years and presurgical investigations at 25 years showed a lateral frontal epileptic focus with spread to Broca's area and the frontal orbital regions. Histopathology revealed a focal cortical dysplasia, not visible on magnetic resonance imaging. The prolonged but reversible early regression and the residual neuropsychological disorders during adulthood were probably the result of an active left frontal epilepsy, which interfered with language and behaviour during development. Our findings raise the question of the role of focal cortical dysplasia as an aetiology in the syndromes of epilepsy with continuous spike waves during sleep and acquired epileptic aphasia.

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.

Early detection of lung cancer: a statement from an expert panel of the Swiss university hospitals on lung cancer screening.

The discussion about setting up a program for lung cancer screening was launched with the publication of the results of the National Lung Screening Trial, which suggested reduced mortality in high-risk subjects undergoing CT screening. However, important questions about the benefit-harm balance and the details of a screening program and its cost-effectiveness remain unanswered. A panel of specialists in chest radiology, respiratory medicine, epidemiology, and thoracic surgery representing all Swiss university hospitals prepared this joint statement following several meetings. The panel argues that premature and uncontrolled introduction of a lung cancer screening program may cause substantial harm that may remain undetected without rigorous quality control. This position paper focuses on the requirements of running such a program with the objective of harmonizing efforts across the involved specialties and institutions and defining quality standards. The underlying statement includes information on current evidence for a reduction in mortality with lung cancer screening and the potential epidemiologic implications of such a program in Switzerland. Furthermore, requirements for lung cancer screening centers are defined, and recommendations for both the CT technique and the algorithm for lung nodule assessment are provided. In addition, related issues such as patient management, registry, and funding are addressed. Based on the current state of the knowledge, the panel concludes that lung cancer screening in Switzerland should be undertaken exclusively within a national observational study in order to provide answers to several critical questions before considering broad population-based screening for lung cancer.

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.

Appendix: expert paper

Nanotechnology has been heralded as a "revolution" in science, for two reasons: first, because of its revolutionary view of the way in which chemicals and elements, such as gold and silver, behave, compared to traditional scientific understanding of their properties. Second, the impact of these new discoveries, as applied to commerce, can transform the daily life of consumer products ranging from sun tan lotions and cosmetics, food packaging and paints and coatings for cars, housing and fabrics, medicine and thousands of industrial processes.9 Beneficial consumer use of nanotechnologies, already in the stream of commerce, improves coatings on inks and paints in everything from food packaging to cars. Additionally, "Nanomedicine" offers the promise of diagnosis and treatment at the molecular level in order to detect and treat presymptomatic disease,10 or to rebuild neurons in Alzheimer's and Parkinson's disease. There is a possibility that severe complications such as stroke or heart attack may be avoided by means of prophylactic treatment of people at risk, and bone regeneration may keep many people active who never expected rehabilitation. Miniaturisation of diagnostic equipment can also reduce the amount of sampling materials required for testing and medical surveillance. Miraculous developments, that sound like science fiction to those people who eagerly anticipate these medical products, combined with the emerging commercial impact of nanotechnology applications to consumer products will reshape civil society - permanently. Thus, everyone within the jurisdiction of the Council of Europe is an end-user of nanotechnology, even without realising that nanotechnology has touched daily life.

Stability and adaptability of runner peanut genotypes based on nonlinear regression and AMMI analysis

The objective of this work was to estimate the stability and adaptability of pod and seed yield in runner peanut genotypes based on the nonlinear regression and AMMI analysis. Yield data from 11 trials, distributed in six environments and three harvests, carried out in the Northeast region of Brazil during the rainy season were used. Significant effects of genotypes (G), environments (E), and GE interactions were detected in the analysis, indicating different behaviors among genotypes in favorable and unfavorable environmental conditions. The genotypes BRS Pérola Branca and LViPE‑06 are more stable and adapted to the semiarid environment, whereas LGoPE‑06 is a promising material for pod production, despite being highly dependent on favorable environments.

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.

High capacity robust audio watermarking scheme based on FFT and linear regression

Peer-reviewed