New structural and petrologic data on Mesozoic schists in the Rhodope (Bulgaria): geodynamic implications

Structural analysis of low-grade rocks highlights the allochthonous character of Mesozoic schists in southeastern Rhodope, Bulgaria. The deformation can be related to the Late Jurassic-Early Cretaceous thrusting and Tertiary detachment faulting. Petrologic and geochemical data show a volcanic arc origin of the greenschists and basaltic rocks. These results are interpreted as representing an island arc-accretionary complex related to the southward subduction of the Meliata-Maliac Ocean under the supra-subduction back-arc Vardar ocean/island arc system. This arc-trench system collided with the Rhodope in Late Jurassic times. (C) 2003 Academie des sciences. Published by Editions scientifiques et medicales Elsevier SAS. All rights reserved.

Global surveillance of cancer survival 1995-2009 : analysis of individual data for 25 676 887 patients from 279 population-based registries in 67 countries (CONCORD-2)

BACKGROUND: Worldwide data for cancer survival are scarce. We aimed to initiate worldwide surveillance of cancer survival by central analysis of population-based registry data, as a metric of the effectiveness of health systems, and to inform global policy on cancer control. METHODS: Individual tumour records were submitted by 279 population-based cancer registries in 67 countries for 25·7 million adults (age 15-99 years) and 75 000 children (age 0-14 years) diagnosed with cancer during 1995-2009 and followed up to Dec 31, 2009, or later. We looked at cancers of the stomach, colon, rectum, liver, lung, breast (women), cervix, ovary, and prostate in adults, and adult and childhood leukaemia. Standardised quality control procedures were applied; errors were corrected by the registry concerned. We estimated 5-year net survival, adjusted for background mortality in every country or region by age (single year), sex, and calendar year, and by race or ethnic origin in some countries. Estimates were age-standardised with the International Cancer Survival Standard weights. FINDINGS: 5-year survival from colon, rectal, and breast cancers has increased steadily in most developed countries. For patients diagnosed during 2005-09, survival for colon and rectal cancer reached 60% or more in 22 countries around the world; for breast cancer, 5-year survival rose to 85% or higher in 17 countries worldwide. Liver and lung cancer remain lethal in all nations: for both cancers, 5-year survival is below 20% everywhere in Europe, in the range 15-19% in North America, and as low as 7-9% in Mongolia and Thailand. Striking rises in 5-year survival from prostate cancer have occurred in many countries: survival rose by 10-20% between 1995-99 and 2005-09 in 22 countries in South America, Asia, and Europe, but survival still varies widely around the world, from less than 60% in Bulgaria and Thailand to 95% or more in Brazil, Puerto Rico, and the USA. For cervical cancer, national estimates of 5-year survival range from less than 50% to more than 70%; regional variations are much wider, and improvements between 1995-99 and 2005-09 have generally been slight. For women diagnosed with ovarian cancer in 2005-09, 5-year survival was 40% or higher only in Ecuador, the USA, and 17 countries in Asia and Europe. 5-year survival for stomach cancer in 2005-09 was high (54-58%) in Japan and South Korea, compared with less than 40% in other countries. By contrast, 5-year survival from adult leukaemia in Japan and South Korea (18-23%) is lower than in most other countries. 5-year survival from childhood acute lymphoblastic leukaemia is less than 60% in several countries, but as high as 90% in Canada and four European countries, which suggests major deficiencies in the management of a largely curable disease. INTERPRETATION: International comparison of survival trends reveals very wide differences that are likely to be attributable to differences in access to early diagnosis and optimum treatment. Continuous worldwide surveillance of cancer survival should become an indispensable source of information for cancer patients and researchers and a stimulus for politicians to improve health policy and health-care systems. FUNDING: Canadian Partnership Against Cancer (Toronto, Canada), Cancer Focus Northern Ireland (Belfast, UK), Cancer Institute New South Wales (Sydney, Australia), Cancer Research UK (London, UK), Centers for Disease Control and Prevention (Atlanta, GA, USA), Swiss Re (London, UK), Swiss Cancer Research foundation (Bern, Switzerland), Swiss Cancer League (Bern, Switzerland), and University of Kentucky (Lexington, KY, USA).

Data-driven clustering : new methods and applications

Abstract : This work is concerned with the development and application of novel unsupervised learning methods, having in mind two target applications: the analysis of forensic case data and the classification of remote sensing images. First, a method based on a symbolic optimization of the inter-sample distance measure is proposed to improve the flexibility of spectral clustering algorithms, and applied to the problem of forensic case data. This distance is optimized using a loss function related to the preservation of neighborhood structure between the input space and the space of principal components, and solutions are found using genetic programming. Results are compared to a variety of state-of--the-art clustering algorithms. Subsequently, a new large-scale clustering method based on a joint optimization of feature extraction and classification is proposed and applied to various databases, including two hyperspectral remote sensing images. The algorithm makes uses of a functional model (e.g., a neural network) for clustering which is trained by stochastic gradient descent. Results indicate that such a technique can easily scale to huge databases, can avoid the so-called out-of-sample problem, and can compete with or even outperform existing clustering algorithms on both artificial data and real remote sensing images. This is verified on small databases as well as very large problems. Résumé : Ce travail de recherche porte sur le développement et l'application de méthodes d'apprentissage dites non supervisées. Les applications visées par ces méthodes sont l'analyse de données forensiques et la classification d'images hyperspectrales en télédétection. Dans un premier temps, une méthodologie de classification non supervisée fondée sur l'optimisation symbolique d'une mesure de distance inter-échantillons est proposée. Cette mesure est obtenue en optimisant une fonction de coût reliée à la préservation de la structure de voisinage d'un point entre l'espace des variables initiales et l'espace des composantes principales. Cette méthode est appliquée à l'analyse de données forensiques et comparée à un éventail de méthodes déjà existantes. En second lieu, une méthode fondée sur une optimisation conjointe des tâches de sélection de variables et de classification est implémentée dans un réseau de neurones et appliquée à diverses bases de données, dont deux images hyperspectrales. Le réseau de neurones est entraîné à l'aide d'un algorithme de gradient stochastique, ce qui rend cette technique applicable à des images de très haute résolution. Les résultats de l'application de cette dernière montrent que l'utilisation d'une telle technique permet de classifier de très grandes bases de données sans difficulté et donne des résultats avantageusement comparables aux méthodes existantes.

Methadone maintenance treatment in the Swiss Canton of Vaud: demographic and clinical data on 1,782 ambulatory patients.

Using data from the Public Health Service, we studied the demographic and clinical characteristics of 1,782 patients enrolled in methadone maintenance treatment (MMT) during 2001 in the Swiss Canton of Vaud, comparing our findings with the results of a previous study from 1976 to 1986. In 2001, most patients (76.9%) were treated in general practice. Mortality is low in this MMT population (1%/year). While patient age and sex profiles were similar to those found in the earlier study, we did observe a substantial increase in the number of patients and the number of practitioners treating MMT patients, probably reflecting the low-threshold governmental policies and the creation of specialized centers. In conclusion, easier access to MMT enhances the number of patients, but new concerns about the quality of management emerge: benzodiazepine as a concomitant prescription; low rates of screening for hepatitis B, C and HIV, and social and psychiatric preoccupations.

Will climate change increase the risk of plant invasions into mountains?

Mountain ecosystems have been less adversely affected by invasions of non-native plants than most other ecosystems, partially because most invasive plants in the lowlands are limited by climate and cannot grow under harsher high-elevation conditions. However, with ongoing climate change, invasive species may rapidly move upwards and threaten mid- then high-elevation mountain ecosystems. We evaluated this threat by predicting current and future potential distributions of 48 invasive plant species distributed in Switzerland (CH) and New South Wales (NSW), two areas where climate interacts differently with the elevation gradient. Using a species distribution modeling approach combining two scales, which builds on high-resolution data (< 250 m) but accounts for the global climatic niche of species, we found that different environmental drivers limit the elevation range of invasive species in the two regions, leading to region-specific species responses to climate change. Whereas the optimal suitability for plant invaders is predicted to markedly shift from the lowland to the montane or subalpine zone in CH, such an upward shift is far less pronounced in NSW where montane and subalpine elevations are currently already suitable. Non-native species able to invade the upper reaches of mountains in a future climate will be cold-tolerant in the Swiss Alps but preferring wet soils in the Australian Alps. Other plant traits were only marginally associated with elevation limits. These results demonstrate that a more systematic consideration of future distributions of invasive species is required in conservation plans of not yet invaded mountainous ecosystems.

Technical note: Implementation of a geodatabase of published and unpublished data on the catastrophic Vaiont landslide

On 9 October 1963 a catastrophic landslide suddenly occurred on the southern slope of the Vaiont dam reservoir. A mass of approximately 270 million m3 collapsed into the reservoir generating a wave that overtopped the dam and hit the town of Longarone and other villages nearby. Several investigations and interpretations of the slope collapse have been carried out during the last 45 years, however, a comprehensive explanation of both the triggering and the dynamics of the phenomenon has yet to be provided. In order to re-evaluate the currently existing information on the slide, an electronic bibliographic database and an ESRI-geodatabase have been developed. The chronology of the collected documentation showed that most of the studies for re-evaluating the failure mechanisms were conducted in the last decade, as a consequence of knowledge, methods and techniques recently acquired. The current contents of the geodatabase will improve definition of the structural setting that influenced the slide and led to the the propagation of the displaced rock mass. The objectives, structure and contents of the e-bibliography and Geodatabase are indicated, together with a brief description on the possible use of the alphanumeric and spatial contents of the databases.

EUROCAT website data on prenatal detection rates of congenital anomalies.

The EUROCAT website www.eurocat-network.eu publishes prenatal detection rates for major congenital anomalies using data from European population-based congenital anomaly registers, covering 28% of the EU population as well as non-EU countries. Data are updated annually. This information can be useful for comparative purposes to clinicians and public health service managers involved in the antenatal care of pregnant women as well as those interested in perinatal epidemiology.

GeoKernels: Modeling of Spatial Data on GeoManifolds

This paper presents a review of methodology for semi-supervised modeling with kernel methods, when the manifold assumption is guaranteed to be satisfied. It concerns environmental data modeling on natural manifolds, such as complex topographies of the mountainous regions, where environmental processes are highly influenced by the relief. These relations, possibly regionalized and nonlinear, can be modeled from data with machine learning using the digital elevation models in semi-supervised kernel methods. The range of the tools and methodological issues discussed in the study includes feature selection and semisupervised Support Vector algorithms. The real case study devoted to data-driven modeling of meteorological fields illustrates the discussed approach.

Tunisian population data on 10 Y-chromosomal loci.

Alleles and haplotypes frequencies for 10 Y-chromosome STR loci (DYS19, DYS385 I/II, DYS389I, DYS389II, DYS390, DYS391, DYS392, DYS393, DYS438 and DYS439), included in the Y-Plex6 and Y-Plex5 kits were determined for a Tunisian population sample of 100 male individuals.