Skin fibroblast model to study an impaired glutathione synthesis: consequences of a genetic polymorphism on the proteome.

An impaired glutathione (GSH) synthesis was observed in several multifactorial diseases, including schizophrenia and myocardial infarction. Genetic studies revealed an association between schizophrenia and a GAG trinucleotide repeat (TNR) polymorphism in the catalytic subunit (GCLC) of the glutamate cysteine ligase (GCL). Disease-associated genotypes of this polymorphism correlated with a decrease in GCLC protein expression, GCL activity and GSH content. To clarify consequences of a decreased GCL activity at the proteome level, three schizophrenia patients and three controls have been selected based on the GCLC GAG TNR polymorphism. Fibroblast cultures were obtained by skin biopsy and were challenged with tert-butylhydroquinone (t-BHQ), a substance known to induce oxidative stress. Proteome changes were analyzed by two dimensional gel electrophoresis (2-DE) and results revealed 10 spots that were upregulated in patients following t-BHQ treatment, but not in controls. Nine corresponding proteins could be identified by MALDI mass spectrometry and these proteins are involved in various cellular functions, including energy metabolism, oxidative stress response, and cytoskeletal reorganization. In conclusion, skin fibroblasts of subjects with an impaired GSH synthesis showed an altered proteome reaction in response to oxidative stress. Furthermore, the study corroborates the use of fibroblasts as an additional mean to study vulnerability factors of psychiatric diseases.

Saving our science from ourselves: the plight of biological classification

Saving our science from ourselves: the plight of biological classification. Biological classification ( nomenclature, taxonomy, and systematics) is being sold short. The desire for new technologies, faster and cheaper taxonomic descriptions, identifications, and revisions is symptomatic of a lack of appreciation and understanding of classification. The problem of gadget-driven science, a lack of best practice and the inability to accept classification as a descriptive and empirical science are discussed. The worst cases scenario is a future in which classifications are purely artificial and uninformative.

The p63 target HBP1 is required for skin differentiation and stratification.

Genetic experiments established that p63 is crucial for the development and maintenance of pluristratified epithelia. In the RNA interference (RNAi) screening for targets of p63 in keratinocytes, we identified the transcription factor, High Mobility Group (HMG) box protein 1 (HBP1). HBP1 is an HMG-containing repressor transiently induced during differentiation of several cell lineages. We investigated the relationship between the two factors: using RNAi, overexpression, chromatin immunoprecipitations and transient transfections with reporter constructs, we established that HBP1 is directly repressed by p63. This was further confirmed in vivo by evaluating expression in p63 knockout mice and in transgenics expressing p63 in basal keratinocytes. Consistent with these findings, expression of HBP1 increases upon differentiation of primary keratinocytes and HaCaT cells in culture, and it is higher in the upper layers of human skin. Inactivation of HBP1 by RNAi prevents differentiation of keratinocytes and stratification of organotypic skin cultures. Finally, we analyzed the keratinocyte transcriptomes after HBP1 RNAi; in addition to repression of growth-promoting genes, unexpected activation of differentiation genes was uncovered, coexisting with repression of other genes involved in epithelial cornification. Our data indicate that suppression of HBP1 is part of the growth-promoting strategy of p63 in the lower layers of epidermis and that HBP1 temporally coordinates expression of genes involved in stratification, leading to the formation of the skin barrier.

A data-dependent skeleton estimate and a scale-sensitive dimension for classification

The classical binary classification problem is investigatedwhen it is known in advance that the posterior probability function(or regression function) belongs to some class of functions. We introduceand analyze a method which effectively exploits this knowledge. The methodis based on minimizing the empirical risk over a carefully selected``skeleton'' of the class of regression functions. The skeleton is acovering of the class based on a data--dependent metric, especiallyfitted for classification. A new scale--sensitive dimension isintroduced which is more useful for the studied classification problemthan other, previously defined, dimension measures. This fact isdemonstrated by performance bounds for the skeleton estimate in termsof the new dimension.

Risk of prostate, breast and colorectal cancer after skin cancer diagnosis.

Ultraviolet radiation is the major cause of skin cancer, but promotes vitamin D synthesis, and vitamin D has been inversely related to the risk of several common cancers including prostate, breast and colorectum. We therefore computed the incidence of prostate, breast and colorectal cancer following skin cancer using the datasets of the Swiss cancer Registries of Vaud and Neuchâtel. Between 1974 and 2005, 6,985 histologically confirmed squamous cell skin cancers, 21,046 basal cell carcinomas and 3,346 cutaneous malignant melanomas were registered, and followed up to the end of 2005 for the occurrence of second primary cancer of the prostate, breast and colorectum. Overall, 680 prostate cancers were observed versus 568.3 expected (standardized incidence ratio (SIR) = 1.20; 95% confidence interval (CI): 1.11-1.29), 440 breast cancers were observed versus 371.5 expected (SIR = 1.18; 95% CI: 1.08-1.30) and 535 colorectal cancers were observed versus 464.6 expected (SIR = 1.15; 95% CI: 1.06-1.25). When basal cell, squamous cell and skin melanoma were considered separately, all the SIRs for prostate, breast and colorectal cancers were around or slightly above unity. Likewise, the results were consistent across strata of age at skin cancer diagnosis and location (head and neck versus others), and for male and female colorectal cancers. These findings, based on a population with a long tradition of systematic histologic examination of all surgically treated skin lesions, do not support the hypothesis that prostate, breast and colorectal cancer risk is decreased following skin cancer.

Natural classification of knots

The principal objective of the knot theory is to provide a simple way of classifying and ordering all the knot types. Here, we propose a natural classification of knots based on their intrinsic position in the knot space that is defined by the set of knots to which a given knot can be converted by individual intersegmental passages. In addition, we characterize various knots using a set of simple quantum numbers that can be determined upon inspection of minimal crossing diagram of a knot. These numbers include: crossing number; average three-dimensional writhe; number of topological domains; and the average relaxation value

Les diatomées : histoire naturelle, préparation, classification & description des principales espèces. avec une Introduction à l'étude des diatomées. et un Exposé de la classification des diatomées. 2, 2 / par Le Dr J. Pelletan ; par M. J. Deby ; par M. Paul Petit

Comprend : Introduction à l'étude des diatomées ; Exposé de la classification des diatomées

Les diatomées : histoire naturelle, préparation, classification & description des principales espèces. avec une Introduction à l'étude des diatomées. et un Exposé de la classification des diatomées. 1 / par Le Dr J. Pelletan ; par M. J. Deby ; par M. Paul Petit

Comprend : Introduction à l'étude des diatomées ; Exposé de la classification des diatomées

Les diatomées : histoire naturelle, préparation, classification & description des principales espèces. avec une Introduction à l'étude des diatomées. et un Exposé de la classification des diatomées. 2, 1 / par Le Dr J. Pelletan ; par M. J. Deby ; par M. Paul Petit

Comprend : Introduction à l'étude des diatomées ; Exposé de la classification des diatomées

Reconstruction fronto-orbitaire complexe avec prothèse en PEEK et expansion cutanée : à propos d'un cas [Complex fronto-orbital reconstruction with a PEEK prosthesis and skin expansion: about a case].

INTRODUCTION: Reconstructions of the fronto-orbital area remain a challenge to the reconstructive surgeon, due to the functional and esthetic impact. OBSERVATION: The authors present a case of a complex fronto-orbital reconstruction with a PEEK (PolyEtherEtherKetone) implant, associated with a skin expansion. DISCUSSION: With a follow-up of over three years, the cosmetic result is excellent. The authors believe that this technique is reliable, fast with long-term good results.

AWSum-Combining Classification with Knowledge Aquisition

Many classifiers achieve high levels of accuracy but have limited applicability in real world situations because they do not lead to a greater understanding or insight into the^way features influence the classification. In areas such as health informatics a classifier that clearly identifies the influences on classification can be used to direct research and formulate interventions. This research investigates the practical applications of Automated Weighted Sum, (AWSum), a classifier that provides accuracy comparable to other techniques whilst providing insight into the data. This is achieved by calculating a weight for each feature value that represents its influence on the class value. The merits of this approach in classification and insight are evaluated on a Cystic Fibrosis and Diabetes datasets with positive results.

Active learning methods for remote sensing image classification

In this paper, we propose two active learning algorithms for semiautomatic definition of training samples in remote sensing image classification. Based on predefined heuristics, the classifier ranks the unlabeled pixels and automatically chooses those that are considered the most valuable for its improvement. Once the pixels have been selected, the analyst labels them manually and the process is iterated. Starting with a small and nonoptimal training set, the model itself builds the optimal set of samples which minimizes the classification error. We have applied the proposed algorithms to a variety of remote sensing data, including very high resolution and hyperspectral images, using support vector machines. Experimental results confirm the consistency of the methods. The required number of training samples can be reduced to 10% using the methods proposed, reaching the same level of accuracy as larger data sets. A comparison with a state-of-the-art active learning method, margin sampling, is provided, highlighting advantages of the methods proposed. The effect of spatial resolution and separability of the classes on the quality of the selection of pixels is also discussed.

Minimal data requirements for a continuous monitoring of the quality of care using the DRG classification.

The DRG classification provides a useful tool for the evaluation of hospital care. Indicators such as readmissions and mortality rates adjusted for the hospital Casemix could be adopted in Switzerland at the price of minor additions to the hospital discharge record. The additional information required to build patients histories and to identify the deaths occurring after hospital discharge is detailed.