HCFC1 is a common component of active human CpG-island promoters and coincides with ZNF143, THAP11, YY1, and GABP transcription factor occupancy.

In human transcriptional regulation, DNA-sequence-specific factors can associate with intermediaries that orchestrate interactions with a diverse set of chromatin-modifying enzymes. One such intermediary is HCFC1 (also known as HCF-1). HCFC1, first identified in herpes simplex virus transcription, has a poorly defined role in cellular transcriptional regulation. We show here that, in HeLa cells, HCFC1 is observed bound to 5400 generally active CpG-island promoters. Examination of the DNA sequences underlying the HCFC1-binding sites revealed three sequence motifs associated with the binding of (1) ZNF143 and THAP11 (also known as Ronin), (2) GABP, and (3) YY1 sequence-specific transcription factors. Subsequent analysis revealed colocalization of HCFC1 with these four transcription factors at ∼90% of the 5400 HCFC1-bound promoters. These studies suggest that a relatively small number of transcription factors play a major role in HeLa-cell transcriptional regulation in association with HCFC1.

Covalent homodimers of murine secretory component induced by epitope substitution unravel the capacity of the polymeric Ig receptor to dimerize noncovalently in the absence of IgA ligand.

Recombinant secretory immunoglobulin A containing a bacterial epitope in domain I of the secretory component (SC) moiety can serve as a mucosal delivery vehicle triggering both mucosal and systemic responses (Corthésy, B., Kaufmann, M., Phalipon, A., Peitsch, M., Neutra, M. R., and Kraehenbuhl, J.-P. (1996) J. Biol. Chem. 271, 33670-33677). To load recombinant secretory IgA with multiple B and T epitopes and extend its biological functions, we selected, based on molecular modeling, five surface-exposed sites in domains II and III of murine SC. Loops predicted to be exposed at the surface of SC domains were replaced with the DYKDDDDK octapeptide (FLAG). Another two mutants were obtained with the FLAG inserted in between domains II and III or at the carboxyl terminus of SC. As shown by mass spectrometry, internal substitution of the FLAG into four of the mutants induced the formation of disulfide-linked homodimers. Three of the dimers and two of the monomers from SC mutants could be affinity-purified using an antibody to the FLAG, mapping them as candidates for insertion. FLAG-induced dimerization also occurred with the polymeric immunoglobulin receptor (pIgR) and might reflect the so-far nondemonstrated capacity of the receptor to oligomerize. By co-expressing in COS-7 cells and epithelial Caco-2 cells two pIgR constructs tagged at the carboxyl terminus with hexahistidine or FLAG, we provide the strongest evidence reported to date that the pIgR dimerizes noncovalently in the plasma membrane in the absence of polymeric IgA ligand. The implication of this finding is discussed in terms of IgA transport and specific antibody response at mucosal surfaces.

Analysis, modelling and classification of geospatial data using machine learning

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.

Trends in mortality from coronary heart and cerebrovascular disease in Switzerland, 1969-87.

Trends in age-specific and age-standardized death certification rates from all ischaemic heart disease and cerebrovascular disease in Switzerland have been analysed for the period 1969-87, i.e. since the introduction of the Eighth Revision of the International Classification of Diseases for coding causes of death. For coronary heart disease, overall age-standardized rates of males in the mid-late 1980's were similar to those in the late 1960's, although some upward trend was evident up to the mid 1970's (with a peak rate of 120.4/100,000, World standard, in 1978) followed by steady declines in more recent years (103.8/100,000 in 1987). These falls were larger in truncated (35 to 64 years) rates. For females, overall age-standardized rates were stable around a value of 40/100,000, while truncated rates tended to decrease, particularly over most recent years, with an overall decline of over 25%. Examination of age-specific trends showed that in both sexes declines at younger ages were already evident in the earlier calendar period, while above age 50 some fall became evident only in most recent years. Thus, in a formal log-linear age/period/cohort model, both a period and a cohort component emerged. In relation to cerebrovascular diseases, the overall declines were around 40% in males (from 67.4 to 41.2/100,000, World standard) and 45% for females (from 56.6 to 31.7/100,000), and were proportionally comparable across subsequent age groups above age 45. The estimates for the age/period/cohort model were thus downwards both for the period and the cohort component although, in such a situation, it is difficult to disentangle the major underlying component.(ABSTRACT TRUNCATED AT 250 WORDS)

Attention-related potentials allow for a highly accurate discrimination of mild cognitive impairment subtypes.

The three most frequent forms of mild cognitive impairment (MCI) are single-domain amnestic MCI (sd-aMCI), single-domain dysexecutive MCI (sd-dMCI) and multiple-domain amnestic MCI (md-aMCI). Brain imaging differences among single domain subgroups of MCI were recently reported supporting the idea that electroencephalography (EEG) functional hallmarks can be used to differentiate these subgroups. We performed event-related potential (ERP) measures and independent component analysis in 18 sd-aMCI, 13 sd-dMCI and 35 md-aMCI cases during the successful performance of the Attentional Network Test. Sensitivity and specificity analyses of ERP for the discrimination of MCI subgroups were also made. In center-cue and spatial-cue warning stimuli, contingent negative variation (CNV) was elicited in all MCI subgroups. Two independent components (ICA1 and 2) were superimposed in the time range on the CNV. The ICA2 was strongly reduced in sd-dMCI compared to sd-aMCI and md-aMCI (4.3 vs. 7.5% and 10.9% of the CNV component). The parietal P300 ERP latency increased significantly in sd-dMCI compared to md-aMCI and sd-aMCI for both congruent and incongruent conditions. This latency for incongruent targets allowed for a highly accurate separation of sd-dMCI from both sd-aMCI and md-aMCI with correct classification rates of 90 and 81%, respectively. This EEG parameter alone performed much better than neuropsychological testing in distinguishing sd-dMCI from md-aMCI. Our data reveal qualitative changes in the composition of the neural generators of CNV in sd-dMCI. In addition, they document an increased latency of the executive P300 component that may represent a highly accurate hallmark for the discrimination of this MCI subgroup in routine clinical settings.

Mutations in CTC1, encoding conserved telomere maintenance component 1, cause Coats plus.

Coats plus is a highly pleiotropic disorder particularly affecting the eye, brain, bone and gastrointestinal tract. Here, we show that Coats plus results from mutations in CTC1, encoding conserved telomere maintenance component 1, a member of the mammalian homolog of the yeast heterotrimeric CST telomeric capping complex. Consistent with the observation of shortened telomeres in an Arabidopsis CTC1 mutant and the phenotypic overlap of Coats plus with the telomeric maintenance disorders comprising dyskeratosis congenita, we observed shortened telomeres in three individuals with Coats plus and an increase in spontaneous γH2AX-positive cells in cell lines derived from two affected individuals. CTC1 is also a subunit of the α-accessory factor (AAF) complex, stimulating the activity of DNA polymerase-α primase, the only enzyme known to initiate DNA replication in eukaryotic cells. Thus, CTC1 may have a function in DNA metabolism that is necessary for but not specific to telomeric integrity.

Automatic Mapping and Classification of Spatial Environmental Data

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.

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.

Bacterial-induced protection against allergy through a novel multi-component immunoregulatory mechanism

Airborne microbial products have been reported to promote immune responses that suppress asthma, yet how these beneficial effects take place remains controversial and poorly understood. We have found that pulmonary exposure with the bacterium Escherichia coli leads to a suppression of allergic airway inflammation, characterized by reduced airway-hyperresponsiveness, eosinophilia and cytokine production by T cells in the lung. This immune modulation was neither mediated by the induction of a Th1 response nor regulatory T cells; was dependent on TLR-4 but did not involve TLR-desensitization. Dendritic cell migration to the draining lymph nodes and subsequent activation of T cells was unaffected by prior exposure to E.coli indicating that the immunomodulation was limited to the lung environment. In non-treated control mice ovalbumin was primarily presented by airway CD11b+ CD11c+ DCs expressing high levels of MHC class II molecules whilst the DCs in E.coli-treated mice displayed a less activated phenotype and had impaired antigen presentation capacity. Consequently, in situ Th2 cytokine production by ovalbuminspecific effector T cells recruited to the airways was significantly reduced. The suppression of airways hyper responsiveness was mediated through the recruitment of IL-17-producing gd-T cells; however, the suppression of dendritic cells and T cells was mediated through a distinct mechanism that could not be overcome by the local administration of activated dendritic cells, or by the in vivo administration of TNF-alpha. Taken together, these data reveal a novel multi-component immunoregulatory pathway that acts to protect the airways from allergic inflammation.

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.

Cholesterol is the major component of native lipoproteins activating the p38 mitogen-activated protein kinases.

Elevated low-density lipoprotein (LDL) levels induce activation of the p38 mitogen-activated protein kinase (MAPK), a stress-activated protein kinase potentially participating in the development of atherosclerosis. The nature of the lipoprotein components inducing p38 MAPK activation has remained unclear however. We show here that both LDLs and high-density lipoproteins (HDLs) have the ability to stimulate the p38 MAPKs with potencies that correlate with their cholesterol content. Cholesterol solubilized in methyl-beta-cyclodextrin was sufficient to activate the p38 MAPK pathway. Liposomes made of phosphatidylcholine (PC) or sphingomyelin, the two main phospholipids found in lipoproteins, were unable to stimulate the p38 MAPKs. In contrast, PC liposomes loaded with cholesterol potently activated this pathway. Reducing the cholesterol content of LDL particles lowered their ability to activate the p38 MAPKs. Cell lines representative of the three main cell types found in blood vessels (endothelial cells, smooth muscle cells and fibroblasts) all activated their p38 MAPK pathway in response to LDLs or cholesterol-loaded PC liposomes. These results indicate that elevated cholesterol content in lipoproteins, as seen in hypercholesterolemia, favors the activation of the stress-activated p38 MAPK pathway in cells from the vessel wall, an event that might contribute to the development of atherosclerosis.