A colorectal cancer classification system that associates cellular phenotype and responses to therapy.

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.

Comparison between artificial neural networks and maximum likelihood classification in digital soil mapping

Soil surveys are the main source of spatial information on soils and have a range of different applications, mainly in agriculture. The continuity of this activity has however been severely compromised, mainly due to a lack of governmental funding. The purpose of this study was to evaluate the feasibility of two different classifiers (artificial neural networks and a maximum likelihood algorithm) in the prediction of soil classes in the northwest of the state of Rio de Janeiro. Terrain attributes such as elevation, slope, aspect, plan curvature and compound topographic index (CTI) and indices of clay minerals, iron oxide and Normalized Difference Vegetation Index (NDVI), derived from Landsat 7 ETM+ sensor imagery, were used as discriminating variables. The two classifiers were trained and validated for each soil class using 300 and 150 samples respectively, representing the characteristics of these classes in terms of the discriminating variables. According to the statistical tests, the accuracy of the classifier based on artificial neural networks (ANNs) was greater than of the classic Maximum Likelihood Classifier (MLC). Comparing the results with 126 points of reference showed that the resulting ANN map (73.81 %) was superior to the MLC map (57.94 %). The main errors when using the two classifiers were caused by: a) the geological heterogeneity of the area coupled with problems related to the geological map; b) the depth of lithic contact and/or rock exposure, and c) problems with the environmental correlation model used due to the polygenetic nature of the soils. This study confirms that the use of terrain attributes together with remote sensing data by an ANN approach can be a tool to facilitate soil mapping in Brazil, primarily due to the availability of low-cost remote sensing data and the ease by which terrain attributes can be obtained.

Damn Platonism! Concrete Persons' Skin, Flesh and Bodies: Three Phases of a Won Battle in Miquel Àngel Riera's poetry

The aim of this article is to show how, throughout M. A. Riera's poetry, an evident anti-metaphysical sensibility can be easily detected, which in its turn makes the poet to praise concrete person's skin, flesh and bodies, thus avoiding any personal Platonic or idealistic experience of human love. In the author's opinion, an accurate reading of his poems makes us discover Plato and Platonism as one of the great responsible thinkers for the contempt of carnal love, which has been undoubtedly the origin of a real human pain as a result of denying the somatic side of eros.

Using numerical classification of profiles based on Vis-NIR spectra to distinguish soils from the Piracicaba Region, Brazil

Considering that information from soil reflectance spectra is underutilized in soil classification, this paper aimed to evaluate the relationship of soil physical, chemical properties and their spectra, to identify spectral patterns for soil classes, evaluate the use of numerical classification of profiles combined with spectral data for soil classification. We studied 20 soil profiles from the municipality of Piracicaba, State of São Paulo, Brazil, which were morphologically described and classified up to the 3rd category level of the Brazilian Soil Classification System (SiBCS). Subsequently, soil samples were collected from pedogenetic horizons and subjected to soil particle size and chemical analyses. Their Vis-NIR spectra were measured, followed by principal component analysis. Pearson's linear correlation coefficients were determined among the four principal components and the following soil properties: pH, organic matter, P, K, Ca, Mg, Al, CEC, base saturation, and Al saturation. We also carried out interpretation of the first three principal components and their relationships with soil classes defined by SiBCS. In addition, numerical classification of the profiles based on the OSACA algorithm was performed using spectral data as a basis. We determined the Normalized Mutual Information (NMI) and Uncertainty Coefficient (U). These coefficients represent the similarity between the numerical classification and the soil classes from SiBCS. Pearson's correlation coefficients were significant for the principal components when compared to sand, clay, Al content and soil color. Visual analysis of the principal component scores showed differences in the spectral behavior of the soil classes, mainly among Argissolos and the others soils. The NMI and U similarity coefficients showed values of 0.74 and 0.64, respectively, suggesting good similarity between the numerical and SiBCS classes. For example, numerical classification correctly distinguished Argissolos from Latossolos and Nitossolos. However, this mathematical technique was not able to distinguish Latossolos from Nitossolos Vermelho férricos, but the Cambissolos were well differentiated from other soil classes. The numerical technique proved to be effective and applicable to the soil classification process.

Plasticity of protein expression during culture of fetal skin cells.

In order to gain insight into the biology of fetal skin during culture, cellular proteins were studied during four culture passages (P00, P01, P04 as well as P10) using high-resolution two-dimensional (2-D) gel electrophoresis and mass spectrometry (MS). Bioinformatic analyses were focused on a region of each gel corresponding to pI between 4 and 8 and M(r) from 8000 to 35 000. In this area, 373 +/- 42 spots were detected (N = 18). Twenty-six spots presented an integrated intensity that increased in the higher passages, whereas five spots showed a progressively lower intensity in subsequent passaging. MS analysis was performed on spots that were unambiguously identified on preparative 2-D gels. Among the 26 spots showing an increased size between P00 and P10, 9 were identified, and corresponded to 3 proteins: (i) peptidyl-prolyl cis-trans isomerase A (P05092; cyclophilin A or cyclosporin A-binding protein), (ii) triosephosphate isomerase (P00938), and (iii) enoyl-CoA hydratase (P30084). Among these nine identified spots, three were absent at P00, but were present at P10. They corresponded to isoforms of peptidyl-prolyl cis-trans isomerase and triosephosphate isomerase, respectively. Liquid chromatography-tandem mass spectrometry (LC-MS/MS) analyses of the acidic isoforms of triosephosphate isomerase showed modifications of cysteine residues to cysteic acid. All these isoforms were clearly present in the skin cells of a 4-year-old child, as well as in skin cells from a 80-year-old man, at P00. These observations probably reflect either an oxidative stress related to cell culture, or, alternatively, maturation, differentiation and the aging of the cells.

Sentinel lymph node biopsy in nonmelanoma skin cancer patients.

The management of lymph nodes in nonmelanoma skin cancer patients is currently still debated. Merkel cell carcinoma (MCC), squamous cell carcinoma (SCC), pigmented epithelioid melanocytoma (PEM), and other rare skin neoplasms have a well-known risk to spread to regional lymph nodes. The use of sentinel lymph node biopsy (SLNB) could be a promising procedure to assess this risk in clinically N0 patients. Metastatic SNs have been observed in 4.5-28% SCC (according to risk factors), in 9-42% MCC, and in 14-57% PEM. We observed overall 30.8% positive SNs in 13 consecutive patients operated for high-risk nonmelanoma skin cancer between 2002 and 2011 in our institution. These high rates support recommendation to implement SLNB for nonmelanoma skin cancer especially for SCC patients. Completion lymph node dissection following positive SNs is also a matter of discussion especially in PEM. It must be remembered that a definitive survival benefit of SLNB in melanoma patients has not been proven yet. However, because of its low morbidity when compared to empiric elective lymph node dissection or radiation therapy of lymphatic basins, SLNB has allowed sparing a lot of morbidity and could therefore be used in nonmelanoma skin cancer patients, even though a significant impact on survival has not been demonstrated.

Expression of intercellular adhesion molecule-1 in UVA-irradiated human skin cells in vitro and in vivo.

Ultraviolet A (UVA) radiation represents an important oxidative stress to human skin and certain forms of oxidative stress have been shown to modulate intercellular adhesion molecule-1 (ICAM-1) expression. ICAM-1 has been shown to play an important part in many immune reactions and the perturbations of this molecule by ultraviolet radiation could have implications in many inflammatory responses. An enhancement immunohistochemical method with avidin/biotin was used for analysing the early effects of UVA radiation on human cell cultures and human skin (340-400 nm). Both in vitro and in vivo data show that ICAM-1 staining in epidermal keratinocytes, which was expressed constitutively, decreased in a UVA dose-dependent manner. The decrease was most noted at 3-6 h following UVA radiation with some ICAM-1 staining returning by 48 h post-UVA. ICAM-1 positive staining in the dermis was specific for vascular structures and was increased 24 h after UVA radiation. Cultured dermal fibroblasts exhibited ICAM-1 staining which increased slightly within 6-48 h post-UVA radiation. As epidermal ICAM-1 expression is depleted following UVA radiation and dermal expression increases due to an increase in the vascular structures, ICAM-1 provides a valuable marker following UVA radiation in human skin that can be readily measured in situ.

Advanced Kernel Methods For Remote Sensing Image Classification

Résumé Suite aux recentes avancées technologiques, les archives d'images digitales ont connu une croissance qualitative et quantitative sans précédent. Malgré les énormes possibilités qu'elles offrent, ces avancées posent de nouvelles questions quant au traitement des masses de données saisies. Cette question est à la base de cette Thèse: les problèmes de traitement d'information digitale à très haute résolution spatiale et/ou spectrale y sont considérés en recourant à des approches d'apprentissage statistique, les méthodes à noyau. Cette Thèse étudie des problèmes de classification d'images, c'est à dire de catégorisation de pixels en un nombre réduit de classes refletant les propriétés spectrales et contextuelles des objets qu'elles représentent. L'accent est mis sur l'efficience des algorithmes, ainsi que sur leur simplicité, de manière à augmenter leur potentiel d'implementation pour les utilisateurs. De plus, le défi de cette Thèse est de rester proche des problèmes concrets des utilisateurs d'images satellite sans pour autant perdre de vue l'intéret des méthodes proposées pour le milieu du machine learning dont elles sont issues. En ce sens, ce travail joue la carte de la transdisciplinarité en maintenant un lien fort entre les deux sciences dans tous les développements proposés. Quatre modèles sont proposés: le premier répond au problème de la haute dimensionalité et de la redondance des données par un modèle optimisant les performances en classification en s'adaptant aux particularités de l'image. Ceci est rendu possible par un système de ranking des variables (les bandes) qui est optimisé en même temps que le modèle de base: ce faisant, seules les variables importantes pour résoudre le problème sont utilisées par le classifieur. Le manque d'information étiquétée et l'incertitude quant à sa pertinence pour le problème sont à la source des deux modèles suivants, basés respectivement sur l'apprentissage actif et les méthodes semi-supervisées: le premier permet d'améliorer la qualité d'un ensemble d'entraînement par interaction directe entre l'utilisateur et la machine, alors que le deuxième utilise les pixels non étiquetés pour améliorer la description des données disponibles et la robustesse du modèle. Enfin, le dernier modèle proposé considère la question plus théorique de la structure entre les outputs: l'intègration de cette source d'information, jusqu'à présent jamais considérée en télédétection, ouvre des nouveaux défis de recherche. Advanced kernel methods for remote sensing image classification Devis Tuia Institut de Géomatique et d'Analyse du Risque September 2009 Abstract The technical developments in recent years have brought the quantity and quality of digital information to an unprecedented level, as enormous archives of satellite images are available to the users. However, even if these advances open more and more possibilities in the use of digital imagery, they also rise several problems of storage and treatment. The latter is considered in this Thesis: the processing of very high spatial and spectral resolution images is treated with approaches based on data-driven algorithms relying on kernel methods. In particular, the problem of image classification, i.e. the categorization of the image's pixels into a reduced number of classes reflecting spectral and contextual properties, is studied through the different models presented. The accent is put on algorithmic efficiency and the simplicity of the approaches proposed, to avoid too complex models that would not be used by users. The major challenge of the Thesis is to remain close to concrete remote sensing problems, without losing the methodological interest from the machine learning viewpoint: in this sense, this work aims at building a bridge between the machine learning and remote sensing communities and all the models proposed have been developed keeping in mind the need for such a synergy. Four models are proposed: first, an adaptive model learning the relevant image features has been proposed to solve the problem of high dimensionality and collinearity of the image features. This model provides automatically an accurate classifier and a ranking of the relevance of the single features. The scarcity and unreliability of labeled. information were the common root of the second and third models proposed: when confronted to such problems, the user can either construct the labeled set iteratively by direct interaction with the machine or use the unlabeled data to increase robustness and quality of the description of data. Both solutions have been explored resulting into two methodological contributions, based respectively on active learning and semisupervised learning. Finally, the more theoretical issue of structured outputs has been considered in the last model, which, by integrating outputs similarity into a model, opens new challenges and opportunities for remote sensing image processing.

Prognostic relevance of Masaoka and Müller-Hermelink classification in patients with thymic tumors.

BACKGROUND: To compare the prognostic relevance of Masaoka and Müller-Hermelink classifications. METHODS: We treated 71 patients with thymic tumors at our institution between 1980 and 1997. Complete follow-up was achieved in 69 patients (97%) with a mean follow up-time of 8.3 years (range, 9 months to 17 years). RESULTS: Masaoka stage I was found in 31 patients (44.9%), stage II in 17 (24.6%), stage III in 19 (27.6%), and stage IV in 2 (2.9%). The 10-year overall survival rate was 83.5% for stage I, 100% for stage IIa, 58% for stage IIb, 44% for stage III, and 0% for stage IV. The disease-free survival rates were 100%, 70%, 40%, 38%, and 0%, respectively. Histologic classification according to Müller-Hermelink found medullary tumors in 7 patients (10.1%), mixed in 18 (26.1%), organoid in 14 (20.3%), cortical in 11 (15.9%), well-differentiated thymic carcinoma in 14 (20.3%), and endocrine carcinoma in 5 (7.3%), with 10-year overall survival rates of 100%, 75%, 92%, 87.5%, 30%, and 0%, respectively, and 10-year disease-free survival rates of 100%, 100%, 77%, 75%, 37%, and 0%, respectively. Medullary, mixed, and well-differentiated organoid tumors were correlated with stage I and II, and well-differentiated thymic carcinoma and endocrine carcinoma with stage III and IV (p < 0.001). Multivariate analysis showed age, gender, myasthenia gravis, and postoperative adjuvant therapy not to be significant predictors of overall and disease-free survival after complete resection, whereas the Müller-Hermelink and Masaoka classifications were independent significant predictors for overall (p < 0.05) and disease-free survival (p < 0.004; p < 0.0001). CONCLUSIONS: The consideration of staging and histology in thymic tumors has the potential to improve recurrence prediction and patient selection for combined treatment modalities.

Loss of serum response factor in keratinocytes results in hyperproliferative skin disease in mice.

The transcription factor serum response factor (SRF) plays a crucial role in the development of several organs. However, its role in the skin has not been explored. Here, we show that keratinocytes in normal human and mouse skin expressed high levels of SRF but that SRF expression was strongly downregulated in the hyperproliferative epidermis of wounded and psoriatic skin. Keratinocyte-specific deletion within the mouse SRF locus during embryonic development caused edema and skin blistering, and all animals died in utero. Postnatal loss of mouse SRF in keratinocytes resulted in the development of psoriasis-like skin lesions. These lesions were characterized by inflammation, hyperproliferation, and abnormal differentiation of keratinocytes as well as by disruption of the actin cytoskeleton. Ultrastructural analysis revealed markedly reduced cell-cell and cell-matrix contacts and loss of cell compaction in all epidermal layers. siRNA-mediated knockdown of SRF in primary human keratinocytes revealed that the cytoskeletal abnormalities and adhesion defects were a direct consequence of the loss of SRF. In contrast, the hyperproliferation observed in vivo was an indirect effect that was most likely a consequence of the inflammation. These results reveal that loss of SRF disrupts epidermal homeostasis and strongly suggest its involvement in the pathogenesis of hyperproliferative skin diseases, including psoriasis.

Genesis, Characterization, and Classification of Mangrove Soils in the Subaé River Basin, Bahia, Brazil

ABSTRACT Preservation of mangroves, a very significant ecosystem from a social, economic, and environmental viewpoint, requires knowledge on soil composition, genesis, morphology, and classification. These aspects are of paramount importance to understand the dynamics of sustainability and preservation of this natural resource. In this study mangrove soils in the Subaé river basin were described and classified and inorganic waste concentrations evaluated. Seven pedons of mangrove soil were chosen, five under fluvial influence and two under marine influence and analyzed for morphology. Samples of horizons and layers were collected for physical and chemical analyses, including heavy metals (Pb, Cd, Mn, Zn, and Fe). The moist soils were suboxidic, with Eh values below 350 mV. The pH level of the pedons under fluvial influence ranged from moderately acid to alkaline, while the pH in pedons under marine influence was around 7.0 throughout the profile. The concentration of cations in the sorting complex for all pedons, independent of fluvial or marine influence, indicated the following order: Na+>Mg2+>Ca2+>K+. Mangrove soils from the Subaé river basin under fluvial and marine influence had different morphological, physical, and chemical characteristics. The highest Pb and Cd concentrations were found in the pedons under fluvial influence, perhaps due to their closeness to the mining company Plumbum, while the concentrations in pedon P7 were lowest, due to greater distance from the factory. For containing at least one metal above the reference levels established by the National Oceanic and Atmospheric Administration (United States Environmental Protection Agency), the pedons were classified as potentially toxic. The soils were classified as Gleissolos Tiomórficos Órticos (sálicos) sódico neofluvissólico in according to the Brazilian Soil Classification System, indicating potential toxicity and very poor drainage, except for pedon P7, which was classified in the same subgroup as the others, but different in that the metal concentrations met acceptable standards.

Inmate Custody Classification Study Brings Changes, Data Download, Iowa Department of Corrections, July 2008

As part of its 2006 systemic evaluation of DOC’s facilities, operations and programming, the Durrant/PBA consulting group found several shortcomings with the Department’s inmate custody classification system. Specifically, the consultants found that the system: