Lymphocyte specificity to protein antigens. V. Conformational dependence of activation of cytochrome c-specific T cells.

Variations in the immunogenic and antigenic properties of native and denatured forms of cytochrome c were observed depending on the strain of mouse tested. In C57BL/6 and (C57BL/6 X DBA/2)F1 (BDF1) mice, priming with either native or denatured cytochrome c (apocytochrome c) gave rise to T cell blasts responding in a similar fashion to the two forms of the antigen and to different peptides derived from CNBr cleavage of the protein when tested for proliferation in the presence of C57BL/6 or BDF1 accessory cells. A different pattern of proliferation was observed when apocytochrome c-specific DBA/2 or BDF1 T cell blasts were tested with DBA/2 accessory cells. In this case, no response was obtained to heme peptide 1-65. This was not due to an inability of DBA/2 macrophages to process and present heme peptide 1-65, as they were able to present this antigen to native cytochrome c-specific BDF1 T cell blasts. Thus, it seems that different sets of clones are generated upon priming BDF1 mice with denatured cytochrome c which are able to recognize different sets of peptides depending on the nature of the accessory cells. The results obtained are consistent with the hypothesis that degradation and presentation of native and denatured cytochrome c by macrophages is dependent on the three-dimensional conformation of the protein.

PRECISION V: Randomisierte Phase II-Studie mit Drug Eluting Beads (DEB) zur Behandlung des hepatozelluläre Karzinoms (HCC) mittels transarterieller Chemoemblisation [PRECISION V: Randomized Phase II-Study with Drug Eluting Beads (DEB) for the Treatment of Hepatocellular cancer (HCC) using transarterial Chemoemblisation.]

Zielsetzung: Vergleich von Drug Eluting Bead (DEB)-TACE mit konventioneller TACE bei der Behandlung von ,,intermediate stage-HCC bei Patienten mit Zirrhose. Material und Methodik: 212 Patienten (185 ♂, 27 ♀; mittleres Alter, 67 Jahre) mit Child-Pugh A oder B Leberzirrhose und großem und/oder multinodulärem, irresektablen HCC wurden randomisiert, um das Therapieansprechen nach der Behandlung mit DEB (DC Bead; Biocompatibles, UK) beladen mit Doxorubicin oder konventioneller TACE mit Doxorubicin zu vergleichen. Die Randomisierung wurde nach Child-Pugh Status (A oder B), Performance Status (ECOG 0 oder 1), bilobärer Erkrankung (ja/nein) und frühere kurative Behandlung (ja/nein) stratifiziert. Der primäre Studienendpunkt war das 6-Monats-Tumoransprechen. Eine unabhängige verblindete MRT-Studie wurde durchgeführt, um das Tumoransprechen nach den RECIST Kriterien zu beurteilen. Ergebnisse: DEB-TACE mit Doxorubicin zeigte eine höhere Rate an komplettem Tumoransprechen, objektivem Ansprechen und Tumorkontrolle im Vergleich zur konventionellen TACE (27% vs 22%; 52% vs 44%; and 63% vs 52%; P>0.05). Patienten mit Child-Pugh B Zirrhose, ECOG 1 Performance Status, bilobärer Erkrankung und Rezidiven nach kurativer Behandlung zeigte einen signifikanten Anstieg des objektiven Ansprechens (p = 0.038) im Vergleich zur Kontrollgruppe. Bei Patienten, die mit DEB-TACE behandelt wurden, konnte eine deutliche Reduktion der gravierenden Lebertoxizität erreicht werden. Die Doxorubicin-Nebenwirkungsrate war in der DEB-TACE Gruppe deutlich geringer (p = 0.0001) als in der konventionellen TACEGruppe. Schlussfolgerung: DEB-TACE mit Doxorubicin ist sicher und effektiv in der Behandlung von ,,intermediate-stage HCC und bietet einen signifikanten Vorteil bei Patienten mit fortgeschrittener Erkrankung.

Selective requirement for c-Myc at an early stage of V(alpha)14i NKT cell development.

Valpha14 invariant (Valpha14i) NKT cells are a subset of regulatory T cells that utilize a semi-invariant TCR to recognize glycolipids associated with monomorphic CD1d molecules. During development in the thymus, CD4(+)CD8(+) Valpha14i NKT precursors recognizing endogenous CD1d-associated glycolipids on other CD4(+)CD8(+) thymocytes are selected to undergo a maturation program involving sequential expression of CD44 and NK-related markers such as NK1.1. The molecular requirements for Valpha14i NKT cell maturation, particularly at early developmental stages, remain poorly understood. In this study, we show that CD4-Cre-mediated T cell-specific inactivation of c-Myc, a broadly expressed transcription factor with a wide range of biological activities, selectively impairs Valpha14i NKT cell development without perturbing the development of conventional T cells. In the absence of c-Myc, Valpha14i NKT cell precursors are blocked at an immature CD44(low)NK1.1(-) stage in a cell autonomous fashion. Residual c-Myc-deficient immature Valpha14i NKT cells appear to proliferate normally, cannot be rescued by transgenic expression of BCL-2, and exhibit characteristic features of immature Valpha14i NKT cells such as high levels of preformed IL-4 mRNA and the transcription factor promyelocytic leukemia zinc finger. Collectively our data identify c-Myc as a critical transcription factor that selectively acts early in Valpha14i NKT cell development to promote progression beyond the CD44(low)NK1.1(-) precursor stage.

New infectious mammary tumor virus superantigen with V beta-specificity identical to staphylococcal enterotoxin B (SEB).

Only few infectious mouse mammary tumor viruses (MMTV) have been characterized which induce a potent superantigen response in vivo. Here we describe the characterization of an MMTV which was isolated from milk of the highly mammary tumor-prone SHN mouse strain. Exposure of newborn mice to milk-borne MMTV (SHN) results in a very slow deletion of V beta 7, 8.1, 8.2 and 8.3 expressing peripheral T cells. Subcutaneous injection of adult mice with this virus induces a rapid and strong stimulation of all four affected V beta-subsets in vivo. Besides the strong T cell effect we observed an early proliferation and activation of the local B cell pool leading to the initial secretion of IgM followed by preferential secretion of IgG2a by day 6. Sequence comparison of the polymorphic C terminus with known open reading frames revealed high homology to the endogenous provirus Mtv-RCS. This is the first report of a virus having a complete overlap in V beta-specificity with a bacterial superantigen stimulating as many as 35% of the whole CD4+ T cell repertoire including V beta 8.2.

Kernel-based mapping of orographic rainfall enhancement in the Swiss Alps as detected by weatherradar

In this paper, we develop a data-driven methodology to characterize the likelihood of orographic precipitation enhancement using sequences of weather radar images and a digital elevation model (DEM). Geographical locations with topographic characteristics favorable to enforce repeatable and persistent orographic precipitation such as stationary cells, upslope rainfall enhancement, and repeated convective initiation are detected by analyzing the spatial distribution of a set of precipitation cells extracted from radar imagery. Topographic features such as terrain convexity and gradients computed from the DEM at multiple spatial scales as well as velocity fields estimated from sequences of weather radar images are used as explanatory factors to describe the occurrence of localized precipitation enhancement. The latter is represented as a binary process by defining a threshold on the number of cell occurrences at particular locations. Both two-class and one-class support vector machine classifiers are tested to separate the presumed orographic cells from the nonorographic ones in the space of contributing topographic and flow features. Site-based validation is carried out to estimate realistic generalization skills of the obtained spatial prediction models. Due to the high class separability, the decision function of the classifiers can be interpreted as a likelihood or susceptibility of orographic precipitation enhancement. The developed approach can serve as a basis for refining radar-based quantitative precipitation estimates and short-term forecasts or for generating stochastic precipitation ensembles conditioned on the local topography.

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.

A V beta 4-specific superantigen encoded by a new exogenous mouse mammary tumor virus.

The superantigen (SAg) expressed by mouse mammary tumor virus (MMTV) has been shown to play an essential role in the course of the viral life cycle. In the present study, we describe a V beta 4-specific SAg encoded by a new exogenous MMTV carried by the SIM mouse strain. This is the first report of a viral or bacterial SAg reacting with mouse V beta 4+ T cells. Injection of MMTV(SIM) into adult BALB/c mice leads to a rapid and strong stimulation of V beta 4+ CD4+ T cells, followed by a slow deletion of these cells. Neonatal exposure to the virus also leads to a progressive deletion of V beta 4+ T cells. In contrast to other strong MMTV SAg, this new SAg requires the presence of major histocompatibility complex class II I-E molecules to be presented efficiently to T cells. Sequence analysis revealed a new predicted amino acid sequence in the C-terminal polymorphic region of this SAg. Furthermore, sequence comparisons to the most closely related SAg with different V beta specificities hint at the specific residues involved in the interaction with the T cell receptor.