153 resultados para Vegetation Classification


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Vulvar cancer is a rare disease and its screening is depending on the quality and the relevance of our clinical examination. Incidence of vulvar cancer and especially precancerous lesions, vulvar intraepithelial neoplasias (VIN), increased during these last years. The new terminology of vulvar intraepithelial neoplasia will help us to identify high risk groups which could develop a cancer: usual and differentiated VIN. An early diagnosis is essential to propose an adequate treatment. Management is a major point according to the rising incidence of these lesions in younger women. Until we can observe a benefit from the vaccination against human papillomavirus, we must increase the quality of screening by a careful examination of the vulva.

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INTRODUCTION: The 2004 version of the World Health Organization classification subdivides thymic epithelial tumors into A, AB, B1, B2, and B3 (and rare other) thymomas and thymic carcinomas (TC). Due to a morphological continuum between some thymoma subtypes and some morphological overlap between thymomas and TC, a variable proportion of cases may pose problems in classification, contributing to the poor interobserver reproducibility in some studies. METHODS: To overcome this problem, hematoxylin-eosin-stained and immunohistochemically processed sections of prototypic, "borderland," and "combined" thymomas and TC (n = 72) were studied by 18 pathologists at an international consensus slide workshop supported by the International Thymic Malignancy Interest Group. RESULTS: Consensus was achieved on refined criteria for decision making at the A/AB borderland, the distinction between B1, B2, and B3 thymomas and the separation of B3 thymomas from TCs. "Atypical type A thymoma" is tentatively proposed as a new type A thymoma variant. New reporting strategies for tumors with more than one histological pattern are proposed. CONCLUSION: These guidelines can set the stage for reproducibility studies and the design of a clinically meaningful grading system for thymic epithelial tumors.

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Based on conclusions drawn from general climatic impact assessment in mountain regions, the review synthesizes results relevant to the European Alps published mainly from 1994 onward in the fields of population genetics, ecophysiology, phenology, phytogeography, modeling, paleoecology and vegetation dynamics. Other important factors of global change interacting synergistically with climatic factors are also mentioned, such as atmospheric CO2 concentration, eutrophication, ozone or changes in land-use. Topics addressed are general species distribution and populations (persistence, acclimation, genetic variability, dispersal, fragmentation, plant/animal interaction, species richness, conservation), potential response of vegetation (ecotonal shift - area, physiography - changes in the composition, structural changes), phenology, growth and productivity, and landscape. In conclusion, the European Alps appear to have a natural inertia and thus to tolerate an increase of 1-2 K of mean air temperature as far as plant species and ecosystems are concerned in general. However, the impact of land-use is very likely to negate this buffer in many areas. For a change of the order of 3 K or more, profound changes may be expected.

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111 patients with acute leukemia, including 29 children, were classified according to the surface markers and cytochemistry of their blasts. The acute leukemias were separated into two majors groups (lymphoid and non-lymphoid) depending on the presence or absence of specific lymphoid markers. On the basis of these criteria a correlation of 94% with the hematological diagnosis was obtained. Acute lymphoblastic leukemia (ALL) was divisible into three sub-groups: 11 cases expressing T-cell specific markers were classified as T-ALL and 33 cases expressing the common ALL antigen (CALLA) as c-ALL. 18 of the latter expressed an additional marker, DSA (Daudi surface antigen), splitting c-ALL cases in two subgroups. Cytochemistry of the cases lacking specific surface markers (n = 67) served to diagnose 41 acute myeloid leukemia (AML) cases and 8 monoblastic leukemias. The remaining 18 cases could not be classified. The presence of absence of HLD-DR (Ia) antigens served to subdivide AML into two major subgroups. The prognostic significance of these new diagnostic splits is under active study.

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BACKGROUND: Extensive research exists estimating the effect hazardous alcohol¦use on morbidity and mortality, but little research quantifies the association between¦alcohol consumption and utility scores in patients with alcohol dependence.¦In the context of comparative research, the World Health Organisation (WHO)¦proposed to categorise the risk for alcohol-related acute and chronic harm according¦to patients' average daily alcohol consumption. OBJECTIVES: To estimate utility¦scores associated with each category of the WHO drinking risk-level classification¦in patients with alcohol dependence (AD). METHODS: We used data from¦CONTROL, an observational cohort study including 143 AD patients from the Alcohol¦Treatment Center at Lausanne University Hospital, followed for 12 months.¦Average daily alcohol consumption was assessed monthly using the Timeline Follow-¦back method and patients were categorised according to the WHO drinking¦risk-level classification: abstinent, low, medium, high and very high. Other measures¦as sociodemographic characteristics and utility scores derived from the EuroQoL¦5-Dimensions questionnaire (EQ-5D) were collected every three months.¦Mixed models for repeated measures were used to estimate mean utility scores¦associated with WHO drinking risk-level categories. RESULTS: A total of 143 patients¦were included and the 12-month follow-up permitting the assessment of¦1318 person-months. At baseline the mean age of the patients was 44.6 (SD 11.8)¦and the majority of patients was male (63.6%). Using repeated measures analysis,¦utility scores decreased with increasing drinking levels, ranging from 0.80 in abstinent¦patients to 0.62 in patients with very high risk drinking level (p_0.0001).¦CONCLUSIONS: In this sample of patients with alcohol dependence undergoing¦specialized care, utility scores estimated from the EQ-5D appeared to substantially¦and consistently vary according to patients' WHO drinking level.

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CD34/QBEND10 immunostaining has been assessed in 150 bone marrow biopsies (BMB) including 91 myelodysplastic syndromes (MDS), 16 MDS-related AML, 25 reactive BMB, and 18 cases where RA could neither be established nor ruled out. All cases were reviewed and classified according to the clinical and morphological FAB criteria. The percentage of CD34-positive (CD34 +) hematopoietic cells and the number of clusters of CD34+ cells in 10 HPF were determined. In most cases the CD34+ cell count was similar to the blast percentage determined morphologically. In RA, however, not only typical blasts but also less immature hemopoietic cells lying morphologically between blasts and promyelocytes were stained with CD34. The CD34+ cell count and cluster values were significantly higher in RA than in BMB with reactive changes (p<0.0001 for both), in RAEB than in RA (p=0.0006 and p=0.0189, respectively), in RAEBt than in RAEB (p=0.0001 and p=0.0038), and in MDS-AML than in RAEBt (p<0.0001 and p=0.0007). Presence of CD34+ cell clusters in RA correlated with increased risk of progression of the disease. We conclude that CD34 immunostaining in BMB is a useful tool for distinguishing RA from other anemias, assessing blast percentage in MDS cases, classifying them according to FAB, and following their evolution.

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BACKGROUND: Several studies have established Glioblastoma Multiforme (GBM) prognostic and predictive models based on age and Karnofsky Performance Status (KPS), while very few studies evaluated the prognostic and predictive significance of preoperative MR-imaging. However, to date, there is no simple preoperative GBM classification that also correlates with a highly prognostic genomic signature. Thus, we present for the first time a biologically relevant, and clinically applicable tumor Volume, patient Age, and KPS (VAK) GBM classification that can easily and non-invasively be determined upon patient admission. METHODS: We quantitatively analyzed the volumes of 78 GBM patient MRIs present in The Cancer Imaging Archive (TCIA) corresponding to patients in The Cancer Genome Atlas (TCGA) with VAK annotation. The variables were then combined using a simple 3-point scoring system to form the VAK classification. A validation set (N = 64) from both the TCGA and Rembrandt databases was used to confirm the classification. Transcription factor and genomic correlations were performed using the gene pattern suite and Ingenuity Pathway Analysis. RESULTS: VAK-A and VAK-B classes showed significant median survival differences in discovery (P = 0.007) and validation sets (P = 0.008). VAK-A is significantly associated with P53 activation, while VAK-B shows significant P53 inhibition. Furthermore, a molecular gene signature comprised of a total of 25 genes and microRNAs was significantly associated with the classes and predicted survival in an independent validation set (P = 0.001). A favorable MGMT promoter methylation status resulted in a 10.5 months additional survival benefit for VAK-A compared to VAK-B patients. CONCLUSIONS: The non-invasively determined VAK classification with its implication of VAK-specific molecular regulatory networks, can serve as a very robust initial prognostic tool, clinical trial selection criteria, and important step toward the refinement of genomics-based personalized therapy for GBM patients.

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A semisupervised support vector machine is presented for the classification of remote sensing images. The method exploits the wealth of unlabeled samples for regularizing the training kernel representation locally by means of cluster kernels. The method learns a suitable kernel directly from the image and thus avoids assuming a priori signal relations by using a predefined kernel structure. Good results are obtained in image classification examples when few labeled samples are available. The method scales almost linearly with the number of unlabeled samples and provides out-of-sample predictions.

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The research considers the problem of spatial data classification using machine learning algorithms: probabilistic neural networks (PNN) and support vector machines (SVM). As a benchmark model simple k-nearest neighbor algorithm is considered. PNN is a neural network reformulation of well known nonparametric principles of probability density modeling using kernel density estimator and Bayesian optimal or maximum a posteriori decision rules. PNN is well suited to problems where not only predictions but also quantification of accuracy and integration of prior information are necessary. An important property of PNN is that they can be easily used in decision support systems dealing with problems of automatic classification. Support vector machine is an implementation of the principles of statistical learning theory for the classification tasks. Recently they were successfully applied for different environmental topics: classification of soil types and hydro-geological units, optimization of monitoring networks, susceptibility mapping of natural hazards. In the present paper both simulated and real data case studies (low and high dimensional) are considered. The main attention is paid to the detection and learning of spatial patterns by the algorithms applied.

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Intensive agriculture, in which detrimental farming practices lessen food abundance and/or reduce food accessibility for many animal species, has led to a widespread collapse of farmland biodiversity. Vineyards in central and southern Europe are intensively cultivated; though they may still harbour several rare plant and animal species, they remain little studied. Over the past decades, there has been a considerable reduction in the application of insecticides in wine production, with a progressive shift to biological control (integrated production) and, to a lesser extent, organic production. Spraying of herbicides has also diminished, which has led to more vegetation cover on the ground, although most vineyards remain bare, especially in southern Europe. The effects of these potentially positive environmental trends upon biodiversity remain mostly unknown as regards vertebrates. The Woodlark (Lullula arborea) is an endangered, short-distance migratory bird that forages and breeds on the ground. In southern Switzerland (Valais), it occurs mostly in vineyards. We used radiotracking and mixed effects logistic regression models to assess Woodlark response to modern vineyard farming practices, study factors driving foraging micro-habitat selection, and determine optimal habitat profile to inform management. The presence of ground vegetation cover was the main factor dictating the selection of foraging locations, with an optimum around 55% at the foraging patch scale. These conditions are met in integrated production vineyards, but only when grass is tolerated on part of the ground surface, which is the case on ca. 5% of the total Valais vineyard area. In contrast, conventionally managed vineyards covering a parts per thousand yen95% of the vineyard area are too bare because of systematic application of herbicides all over the ground, whilst the rare organic vineyards usually have a too-dense sward. The optimal mosaic with ca. 50% ground vegetation cover is currently achieved in integrated production vineyards where herbicide is applied every second row. In organic production, ca. 50% ground vegetation cover should be promoted, which requires regular mechanical removal of ground vegetation. These measures are likely to benefit general biodiversity in vineyards.

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The paper deals with the development and application of the generic methodology for automatic processing (mapping and classification) of environmental data. General Regression Neural Network (GRNN) is considered in detail and is proposed as an efficient tool to solve the problem of spatial data mapping (regression). The Probabilistic Neural Network (PNN) is considered as an automatic tool for spatial classifications. The automatic tuning of isotropic and anisotropic GRNN/PNN models using cross-validation procedure is presented. Results are compared with the k-Nearest-Neighbours (k-NN) interpolation algorithm using independent validation data set. Real case studies are based on decision-oriented mapping and classification of radioactively contaminated territories.

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Colorectal cancer (CRC) is a major cause of cancer mortality. Whereas some patients respond well to therapy, others do not, and thus more precise, individualized treatment strategies are needed. To that end, we analyzed gene expression profiles from 1,290 CRC tumors using consensus-based unsupervised clustering. The resultant clusters were then associated with therapeutic response data to the epidermal growth factor receptor-targeted drug cetuximab in 80 patients. The results of these studies define six clinically relevant CRC subtypes. Each subtype shares similarities to distinct cell types within the normal colon crypt and shows differing degrees of 'stemness' and Wnt signaling. Subtype-specific gene signatures are proposed to identify these subtypes. Three subtypes have markedly better disease-free survival (DFS) after surgical resection, suggesting these patients might be spared from the adverse effects of chemotherapy when they have localized disease. One of these three subtypes, identified by filamin A expression, does not respond to cetuximab but may respond to cMET receptor tyrosine kinase inhibitors in the metastatic setting. Two other subtypes, with poor and intermediate DFS, associate with improved response to the chemotherapy regimen FOLFIRI in adjuvant or metastatic settings. Development of clinically deployable assays for these subtypes and of subtype-specific therapies may contribute to more effective management of this challenging disease.