Of housing and politics : mapping political opportunities for mobilising in Bangalore, India

Quelles sont les conditions pour l'émergence d'une mobilisation sociale en faveur du logement convenable dans la métropole de Bangalore (Inde)? Cette question, qui est au coeur de cette thèse, est particulièrement pertinente dans le contexte d'une ville où 1,7 million de personnes, soit un cinquième de la population, vit dans des bidonvilles. L'absence d'un mouvement mettant en cause l'échec des politiques publiques du logement est intéressante dans la mesure où l'Inde a hérité un système de gouvernance colonial et d'une tradition de mouvements sociaux. Pour répondre à ce questionnement, un cadre théorique issu de la littérature sur les mouvements sociaux est développé. Il s'articule autour des liens entre les opportunités politiques au niveau macro et les répertoires d'action des organisations de mouvement social (OMS) au niveau méso, de la tension entre la formalité de la loi et des politiques publiques et l'informalité des circuits d'échange, de la corruption et du clientélisme, et enfin, se focalise sur les systèmes de discours de caste et de la citoyenneté et de leur concrétisation dans des systèmes d'organisations et de réseaux sociaux. Ce cadre théorique permet d'étudier empiriquement la question à travers quatre OMS dans la ville de Bangalore. Les résultats mettent en avant l'existence de mécanismes complexes. Les opportunités politiques formelles n'étant ouvertes que sur le plan rhétorique, elles ne peuvent être véritablement utilisées que par des moyens légaux ou contentieux, ce qui nécessite des compétences sociales dont la plupart des habitants des bidonvilles sont dépourvus. L'inadéquation entre les ressources à disposition pour les logements sociaux et les besoins très importants des pauvres, donne un poids politique considérable aux acteurs en charge de l'attribution de ces ressources rares. Cet état de fait a des répercussions sur la politique électorale. Les habitants des bidonvilles représentant un poids électoral important, ils sont mobilisés à travers de pratiques clientélistes. La corruption et le clientélisme se nourrissent mutuellement pour maintenir une certaine dépendance des habitants. Les OMS qui développent un répertoire discursif remettant en cause le système de caste et qui encouragent une conscience citoyenne, se sont avérées les plus durables pour résister à la cooptation des forces politiques. Cette recherche empirique met en lumière l'inadéquation entre les prescriptions formelles dans le domaine de la gouvernance des besoins humains, tels que le logement, et les pratiques réelles sur le terrain. Cette recherche appelle à réfléchir au-delà de la diffusion du discours sur la « bonne gouvernance » vers des formes de « gouvernance vernaculaire » qui prendrait au sérieux l'informalité en développant une compréhension des avantages à court terme pour les personnes marginalisées dans la ville et les effets à long terme sur la pratique démocratique. - What are the conditions for the emergence of a social movement on the issue of adequate housing in the metropolitan city of Bangalore (India)? This question is at the heart of this dissertation and is particularly pertinent against the background that an estimated 1.7 million or about 20% of the city's population lives in slums. The absence of a movement addressing the failure of public housing policy despite India having inherited colonial systems of governance and traditions of movement is noteworthy. Answers are sought within a theoretical framework stemming from social movement theories that incorporates three linkages articulating around: Macro-level political opportunities and meso-level action repertoires of social movement organisations (SMOs), tensions between the formality of law, policy and the informality of exchange circuits of corruption and clientelism and finally around systems of discourses of caste and citizenship and their instantiation in concrete systems of social organisations and networks. This thesis is empirically investigated through a qualitative case study research design involving four sampled social movement organisations. The results bring complex mechanisms to the fore. Formal political opportunities are only rhetorically open and have to be cracked through legal weaponry or contentious escalation, which requires considerable social skills that slum-dwellers often lack. The inadequacy between the few housing resources and the vast number of slum-dwellers transform housing benefits and urban service provisions into political currency. Such a state of affairs has serious repercussions on conditions for mobilisation. They become imbricated with electoral logic, in which slum-dwellers represent large vote-banks and where corruption and clientelism feed each other to maintain a certain dependency of the poor. SMOs deploying a discursive repertoire that questioned the caste system and encouraged a pursuit of citizenship proved to be the most sustainable to resist co-option from political forces. This empirical investigation brings to light the mismatch between the formal prescriptions in the domain of the governance of basic human needs such as housing and the real practices on the ground. This research calls to reflect beyond the inadequacy of the diffused « good governance » discourse towards forms of « vernacular governance » that take informality seriously in understanding the short-term benefits for the marginalised in the city and the long-term effects on democratic practice.

A protein interaction atlas for the nuclear receptors: properties and quality of a hub-based dimerisation network.

BACKGROUND: The nuclear receptors are a large family of eukaryotic transcription factors that constitute major pharmacological targets. They exert their combinatorial control through homotypic heterodimerisation. Elucidation of this dimerisation network is vital in order to understand the complex dynamics and potential cross-talk involved. RESULTS: Phylogeny, protein-protein interactions, protein-DNA interactions and gene expression data have been integrated to provide a comprehensive and up-to-date description of the topology and properties of the nuclear receptor interaction network in humans. We discriminate between DNA-binding and non-DNA-binding dimers, and provide a comprehensive interaction map, that identifies potential cross-talk between the various pathways of nuclear receptors. CONCLUSION: We infer that the topology of this network is hub-based, and much more connected than previously thought. The hub-based topology of the network and the wide tissue expression pattern of NRs create a highly competitive environment for the common heterodimerising partners. Furthermore, a significant number of negative feedback loops is present, with the hub protein SHP [NR0B2] playing a major role. We also compare the evolution, topology and properties of the nuclear receptor network with the hub-based dimerisation network of the bHLH transcription factors in order to identify both unique themes and ubiquitous properties in gene regulation. In terms of methodology, we conclude that such a comprehensive picture can only be assembled by semi-automated text-mining, manual curation and integration of data from various sources.

Broadening the horizon--level 2.5 of the HUPO-PSI format for molecular interactions.

BACKGROUND: Molecular interaction Information is a key resource in modern biomedical research. Publicly available data have previously been provided in a broad array of diverse formats, making access to this very difficult. The publication and wide implementation of the Human Proteome Organisation Proteomics Standards Initiative Molecular Interactions (HUPO PSI-MI) format in 2004 was a major step towards the establishment of a single, unified format by which molecular interactions should be presented, but focused purely on protein-protein interactions. RESULTS: The HUPO-PSI has further developed the PSI-MI XML schema to enable the description of interactions between a wider range of molecular types, for example nucleic acids, chemical entities, and molecular complexes. Extensive details about each supported molecular interaction can now be captured, including the biological role of each molecule within that interaction, detailed description of interacting domains, and the kinetic parameters of the interaction. The format is supported by data management and analysis tools and has been adopted by major interaction data providers. Additionally, a simpler, tab-delimited format MITAB2.5 has been developed for the benefit of users who require only minimal information in an easy to access configuration. CONCLUSION: The PSI-MI XML2.5 and MITAB2.5 formats have been jointly developed by interaction data producers and providers from both the academic and commercial sector, and are already widely implemented and well supported by an active development community. PSI-MI XML2.5 enables the description of highly detailed molecular interaction data and facilitates data exchange between databases and users without loss of information. MITAB2.5 is a simpler format appropriate for fast Perl parsing or loading into Microsoft Excel.

Interactome mapping of the phosphatidylinositol 3-kinase-mammalian target of rapamycin pathway identifies deformed epidermal autoregulatory factor-1 as a new glycogen synthase kinase-3 interactor.

The phosphatidylinositol 3-kinase-mammalian target of rapamycin (PI3K-mTOR) pathway plays pivotal roles in cell survival, growth, and proliferation downstream of growth factors. Its perturbations are associated with cancer progression, type 2 diabetes, and neurological disorders. To better understand the mechanisms of action and regulation of this pathway, we initiated a large scale yeast two-hybrid screen for 33 components of the PI3K-mTOR pathway. Identification of 67 new interactions was followed by validation by co-affinity purification and exhaustive literature curation of existing information. We provide a nearly complete, functionally annotated interactome of 802 interactions for the PI3K-mTOR pathway. Our screen revealed a predominant place for glycogen synthase kinase-3 (GSK3) A and B and the AMP-activated protein kinase. In particular, we identified the deformed epidermal autoregulatory factor-1 (DEAF1) transcription factor as an interactor and in vitro substrate of GSK3A and GSK3B. Moreover, GSK3 inhibitors increased DEAF1 transcriptional activity on the 5-HT1A serotonin receptor promoter. We propose that DEAF1 may represent a therapeutic target of lithium and other GSK3 inhibitors used in bipolar disease and depression.

Molecular and therapeutic characterization of anti-ectodysplasin A receptor (EDAR) agonist monoclonal antibodies.

The TNF family ligand ectodysplasin A (EDA) and its receptor EDAR are required for proper development of skin appendages such as hair, teeth, and eccrine sweat glands. Loss of function mutations in the Eda gene cause X-linked hypohidrotic ectodermal dysplasia (XLHED), a condition that can be ameliorated in mice and dogs by timely administration of recombinant EDA. In this study, several agonist anti-EDAR monoclonal antibodies were generated that cross-react with the extracellular domains of human, dog, rat, mouse, and chicken EDAR. Their half-life in adult mice was about 11 days. They induced tail hair and sweat gland formation when administered to newborn EDA-deficient Tabby mice, with an EC(50) of 0.1 to 0.7 mg/kg. Divalency was necessary and sufficient for this therapeutic activity. Only some antibodies were also agonists in an in vitro surrogate activity assay based on the activation of the apoptotic Fas pathway. Activity in this assay correlated with small dissociation constants. When administered in utero in mice or at birth in dogs, agonist antibodies reverted several ectodermal dysplasia features, including tooth morphology. These antibodies are therefore predicted to efficiently trigger EDAR signaling in many vertebrate species and will be particularly suited for long term treatments.

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.

Molecular underpinning of extranodal NK/T-cell lymphoma.

Peripheral NK/T-cell lymphoma (PTCL) is a heterogeneous group of uncommon hematologic malignancies with aggressive clinical course and unfavorable prognosis. Extranodal NK/T-cell lymphoma, nasal type (NKTCL) is the most common extranodal entity worldwide, with heterogeneous geographic distribution, and it is characterized by its association with EBV, a nasal or less often extranasal presentation and aggressive behavior. Recent works using array-based technologies have provided novel insights into the pathogenesis and discovered new biomarkers with diagnostic and therapeutic implications in NKTCL. Gene expression profiling identified that most of the NKTCL are derived from activated natural killer cells with distinctively high expression of granzyme H compared to other PTCLs, which might serve as a new diagnostic biomarker. Frequent deletions and promoter methylations in PRDM1, ATG5, AIM1, FOXO3, HACE1 mapping to 6q21-q25, suggest their roles as potential tumor suppressors. The deregulation of oncogenic pathways (PDGF, JAK-STAT, AKT) provides a rationale for developing targeted therapies in the future.

Cono-spondylar dysplasia: Clinical, radiographic, and molecular findings of a previously unreported disorder.

We report on a consanguineous Arab family in which three sibs had an unusual skeletal dysplasia characterized by anterior defects of the spine leading to severe lumbar kyphosis and marked brachydactyly with cone epiphyses. The clinical phenotype also included dysmorphic facial features, epilepsy, and developmental delay. This constellation likely represents a previously undescribed skeletal dysplasia, most probably inherited in an autosomal recessive pattern. A homozygosity mapping approach has thus far failed to unearth the responsible gene as the region shared by these three sibs is 27.7 Mb in size and contains over 200 genes with no obvious candidate.

cDNA cloning and mapping of a novel islet-brain/JNK-interacting protein.

IB1/JIP-1 is a scaffold protein that regulates the c-Jun NH(2)-terminal kinase (JNK) signaling pathway, which is activated by environmental stresses and/or by treatment with proinflammatory cytokines including IL-1beta and TNF-alpha. The JNKs play an essential role in many biological processes, including the maturation and differentiation of immune cells and the apoptosis of cell targets of the immune system. IB1 is expressed predominantly in brain and pancreatic beta-cells where it protects cells from proapoptotic programs. Recently, a mutation in the amino-terminus of IB1 was associated with diabetes. A novel isoform, IB2, was cloned and characterized. Overall, both IB1 and IB2 proteins share a very similar organization, with a JNK-binding domain, a Src homology 3 domain, a phosphotyrosine-interacting domain, and polyacidic and polyproline stretches located at similar positions. The IB2 gene (HGMW-approved symbol MAPK8IP2) maps to human chromosome 22q13 and contains 10 coding exons. Northern and RT-PCR analyses indicate that IB2 is expressed in brain and in pancreatic cells, including insulin-secreting cells. IB2 interacts with both JNK and the JNK-kinase MKK7. In addition, ectopic expression of the JNK-binding domain of IB2 decreases IL-1beta-induced pancreatic beta-cell death. These data establish IB2 as a novel scaffold protein that regulates the JNK signaling pathway in brain and pancreatic beta-cells and indicate that IB2 represents a novel candidate gene for diabetes.

Subtelomeric deletion of chromosome 10p15.3: clinical findings and molecular cytogenetic characterization.

We describe 19 unrelated individuals with submicroscopic deletions involving 10p15.3 characterized by chromosomal microarray (CMA). Interestingly, to our knowledge, only two individuals with isolated, submicroscopic 10p15.3 deletion have been reported to date; however, only limited clinical information is available for these probands and the deleted region has not been molecularly mapped. Comprehensive clinical history was obtained for 12 of the 19 individuals described in this study. Common features among these 12 individuals include: cognitive/behavioral/developmental differences (11/11), speech delay/language disorder (10/10), motor delay (10/10), craniofacial dysmorphism (9/12), hypotonia (7/11), brain anomalies (4/6) and seizures (3/7). Parental studies were performed for nine of the 19 individuals; the 10p15.3 deletion was de novo in seven of the probands, not maternally inherited in one proband and inherited from an apparently affected mother in one proband. Molecular mapping of the 19 individuals reported in this study has identified two genes, ZMYND11 (OMIM 608668) and DIP2C (OMIM 611380; UCSC Genome Browser), mapping within 10p15.3 which are most commonly deleted. Although no single gene has been identified which is deleted in all 19 individuals studied, the deleted region in all but one individual includes ZMYND11 and the deleted region in all but one other individual includes DIP2C. There is not a clearly identifiable phenotypic difference between these two individuals and the size of the deleted region does not generally predict clinical features. Little is currently known about these genes complicating a direct genotype/phenotype correlation at this time. These data however, suggest that ZMYND11 and/or DIP2C haploinsufficiency contributes to the clinical features associated with 10p15 deletions in probands described in this study.

Data-driven topo-climatic mapping with machine learning methods

Automatic environmental monitoring networks enforced by wireless communication technologies provide large and ever increasing volumes of data nowadays. The use of this information in natural hazard research is an important issue. Particularly useful for risk assessment and decision making are the spatial maps of hazard-related parameters produced from point observations and available auxiliary information. The purpose of this article is to present and explore the appropriate tools to process large amounts of available data and produce predictions at fine spatial scales. These are the algorithms of machine learning, which are aimed at non-parametric robust modelling of non-linear dependencies from empirical data. The computational efficiency of the data-driven methods allows producing the prediction maps in real time which makes them superior to physical models for the operational use in risk assessment and mitigation. Particularly, this situation encounters in spatial prediction of climatic variables (topo-climatic mapping). In complex topographies of the mountainous regions, the meteorological processes are highly influenced by the relief. The article shows how these relations, possibly regionalized and non-linear, can be modelled from data using the information from digital elevation models. The particular illustration of the developed methodology concerns the mapping of temperatures (including the situations of Föhn and temperature inversion) given the measurements taken from the Swiss meteorological monitoring network. The range of the methods used in the study includes data-driven feature selection, support vector algorithms and artificial neural networks.

Kernel-based Mapping of Meteorological Fields in Complex Orography

Due to the advances in sensor networks and remote sensing technologies, the acquisition and storage rates of meteorological and climatological data increases every day and ask for novel and efficient processing algorithms. A fundamental problem of data analysis and modeling is the spatial prediction of meteorological variables in complex orography, which serves among others to extended climatological analyses, for the assimilation of data into numerical weather prediction models, for preparing inputs to hydrological models and for real time monitoring and short-term forecasting of weather.In this thesis, a new framework for spatial estimation is proposed by taking advantage of a class of algorithms emerging from the statistical learning theory. Nonparametric kernel-based methods for nonlinear data classification, regression and target detection, known as support vector machines (SVM), are adapted for mapping of meteorological variables in complex orography.With the advent of high resolution digital elevation models, the field of spatial prediction met new horizons. In fact, by exploiting image processing tools along with physical heuristics, an incredible number of terrain features which account for the topographic conditions at multiple spatial scales can be extracted. Such features are highly relevant for the mapping of meteorological variables because they control a considerable part of the spatial variability of meteorological fields in the complex Alpine orography. For instance, patterns of orographic rainfall, wind speed and cold air pools are known to be correlated with particular terrain forms, e.g. convex/concave surfaces and upwind sides of mountain slopes.Kernel-based methods are employed to learn the nonlinear statistical dependence which links the multidimensional space of geographical and topographic explanatory variables to the variable of interest, that is the wind speed as measured at the weather stations or the occurrence of orographic rainfall patterns as extracted from sequences of radar images. Compared to low dimensional models integrating only the geographical coordinates, the proposed framework opens a way to regionalize meteorological variables which are multidimensional in nature and rarely show spatial auto-correlation in the original space making the use of classical geostatistics tangled.The challenges which are explored during the thesis are manifolds. First, the complexity of models is optimized to impose appropriate smoothness properties and reduce the impact of noisy measurements. Secondly, a multiple kernel extension of SVM is considered to select the multiscale features which explain most of the spatial variability of wind speed. Then, SVM target detection methods are implemented to describe the orographic conditions which cause persistent and stationary rainfall patterns. Finally, the optimal splitting of the data is studied to estimate realistic performances and confidence intervals characterizing the uncertainty of predictions.The resulting maps of average wind speeds find applications within renewable resources assessment and opens a route to decrease the temporal scale of analysis to meet hydrological requirements. Furthermore, the maps depicting the susceptibility to orographic rainfall enhancement can be used to improve current radar-based quantitative precipitation estimation and forecasting systems and to generate stochastic ensembles of precipitation fields conditioned upon the orography.

Systems Genetics of Metabolism: The Use of the BXD Murine Reference Panel for Multiscalar Integration of Traits.

Metabolic homeostasis is achieved by complex molecular and cellular networks that differ significantly among individuals and are difficult to model with genetically engineered lines of mice optimized to study single gene function. Here, we systematically acquired metabolic phenotypes by using the EUMODIC EMPReSS protocols across a large panel of isogenic but diverse strains of mice (BXD type) to study the genetic control of metabolism. We generated and analyzed 140 classical phenotypes and deposited these in an open-access web service for systems genetics (www.genenetwork.org). Heritability, influence of sex, and genetic modifiers of traits were examined singly and jointly by using quantitative-trait locus (QTL) and expression QTL-mapping methods. Traits and networks were linked to loci encompassing both known variants and novel candidate genes, including alkaline phosphatase (ALPL), here linked to hypophosphatasia. The assembled and curated phenotypes provide key resources and exemplars that can be used to dissect complex metabolic traits and disorders.

Robust Nonlinear Mapping of Soil Contamination Using Support Regression

Spatial data analysis mapping and visualization is of great importance in various fields: environment, pollution, natural hazards and risks, epidemiology, spatial econometrics, etc. A basic task of spatial mapping is to make predictions based on some empirical data (measurements). A number of state-of-the-art methods can be used for the task: deterministic interpolations, methods of geostatistics: the family of kriging estimators (Deutsch and Journel, 1997), machine learning algorithms such as artificial neural networks (ANN) of different architectures, hybrid ANN-geostatistics models (Kanevski and Maignan, 2004; Kanevski et al., 1996), etc. All the methods mentioned above can be used for solving the problem of spatial data mapping. Environmental empirical data are always contaminated/corrupted by noise, and often with noise of unknown nature. That's one of the reasons why deterministic models can be inconsistent, since they treat the measurements as values of some unknown function that should be interpolated. Kriging estimators treat the measurements as the realization of some spatial randomn process. To obtain the estimation with kriging one has to model the spatial structure of the data: spatial correlation function or (semi-)variogram. This task can be complicated if there is not sufficient number of measurements and variogram is sensitive to outliers and extremes. ANN is a powerful tool, but it also suffers from the number of reasons. of a special type ? multiplayer perceptrons ? are often used as a detrending tool in hybrid (ANN+geostatistics) models (Kanevski and Maignank, 2004). Therefore, development and adaptation of the method that would be nonlinear and robust to noise in measurements, would deal with the small empirical datasets and which has solid mathematical background is of great importance. The present paper deals with such model, based on Statistical Learning Theory (SLT) - Support Vector Regression. SLT is a general mathematical framework devoted to the problem of estimation of the dependencies from empirical data (Hastie et al, 2004; Vapnik, 1998). SLT models for classification - Support Vector Machines - have shown good results on different machine learning tasks. The results of SVM classification of spatial data are also promising (Kanevski et al, 2002). The properties of SVM for regression - Support Vector Regression (SVR) are less studied. First results of the application of SVR for spatial mapping of physical quantities were obtained by the authorsin for mapping of medium porosity (Kanevski et al, 1999), and for mapping of radioactively contaminated territories (Kanevski and Canu, 2000). The present paper is devoted to further understanding of the properties of SVR model for spatial data analysis and mapping. Detailed description of the SVR theory can be found in (Cristianini and Shawe-Taylor, 2000; Smola, 1996) and basic equations for the nonlinear modeling are given in section 2. Section 3 discusses the application of SVR for spatial data mapping on the real case study - soil pollution by Cs137 radionuclide. Section 4 discusses the properties of the modelapplied to noised data or data with outliers.

Gene transfer of the Ly-3 chain gene of the mouse CD8 molecular complex: co-transfer with the Ly-2 polypeptide gene results in detectable cell surface expression of the Ly-3 antigenic determinants.

The CD8 molecule is a glycoprotein expressed on a subset of mature T lymphocytes. It has been postulated to be a receptor for class I major histocompatibility complex molecules. In the mouse, CD8 is a heterodimer composed of Ly-2 and Ly-3 chains. We have isolated and analyzed cDNA and cosmid clones corresponding to the Ly-3 subunit. One of the isolated, cosmid clones was subsequently transfected, alone or in combination with the Ly-2 gene, into mouse Ltk- cells. Analysis of the Ly-2,3 molecules expressed at the surface of the double transfectants indicated that they are serologically and biochemically indistinguishable from their normal counterparts expressed on lymphoid cells. Ltk- cells transfected with the Ly-2 gene alone were shown to react with a subset of anti-CD8 monoclonal antibodies whereas Ly-3 transfectants did not stain with any of the anti-Ly-3 antibodies employed in this study. Since at least one of these antibodies (53-5.8) has been previously shown to recognize an epitope which is retained on the Ly-3 subunit after dissociation of the heterodimeric Ly-2,3 complex, these observations suggest that the expression of the Ly-2 polypeptide is required to permit the detectable cell surface expression of the antigenic determinants carried by the Ly-3 subunit.