Walsura robusta Roxb. (Meliaceae), a little-known tree with a rich limonoid profile

The plant Walsura robusta Roxb. (Meliaceae) is a robust tree largely distributed in south-east Asia, including provinces of southern China. A few traditional usages of the plant have been mentioned, notably for the treatment of microbial infections. But experimental studies using different types of plant extracts only revealed modest antibacterial effects, and no major antiparasitic activity. Walsura robusta Roxb. is a rich source of secondary metabolites. Several series of limonoids have been isolated from the leaves or the fruits of the plant, such as walsuronoid A-I, walsurins A-E, walsunoids A-I, walrobsins A-R and other cedrelone- or dihydrocedrelone-type limonoids, in addition to a few other terpenoids. All information about Walsura robusta Roxb. have been collated in this brief review. The analysis underlines the presence of two limonoids endowed with significant anticancer activities, walsuronoid B and cedrelone. They both activate the production of reactive oxygen species in cancer cells, modulate mitochondrial activities and induce apoptosis of cancer cells. Their molecular targets and mechanism of action are discussed. Walsura robusta Roxb. has a potential for the development of anticancer natural products. The use of the plant extracts could be further considered for the treatment of diseases with a cell proliferation component

Binding of the antibacterial drug clofoctol and analogues to the Cdc7/Dbf4 kinase complex. A computational study

Drugs targeting the cell division cycle kinase 7 (Cdc7) are actively searched for the treatment of different pathologies such as amyotrophic lateral sclerosis and cancer. Cdc7 interacts with multiple protein partners, including protein Dbf4 to form the Dbf4-dependent kinase (DDK) complex which regulates DNA replication initiation. Cdc7 and its activator Dbf4 are over-expressed in some cancers. The antibacterial drug clofoctol (CFT), used to treat respiratory tract infections, has been shown to block Cdc7 kinase activity, acting as a non-ATP-competitive inhibitor, capable of arresting DNA synthesis in cancer cells. We have modeled the interaction of CFT with the DDK complex and identified four potential binding sites at the interface of the Cdc7/Dbf4 heterodimer: at T109 and D128 (Cdc7), V220 and I330 (Dbf4). CFT behaves as an interfacial protein-protein inhibitor of the Cdc7/Dbf4 complex, limiting drug access to the proximal kinase site. Six CFT analogues have been tested for binding to the kinase complex. Two potent binders were analyzed in detail. The CFT structure was modulated to replace the two chlorine atoms with hydroxyl groups. The empirical potential energy of interaction (ΔE) calculated with hydroxylated compounds points to a more favorable interaction with the DDK complex, in particular at D128 site with the compound bearing two ortho-OH groups. Our work contributes to the identification of novel DDK inhibitors. DOI: http://dx.doi.org/10.5281/zenodo.552721

Modeling Perception-Action Loops: Comparing Sequential Models with Frame-Based Classifiers

International audienceModeling multimodal perception-action loops in face-to-face interactions is a crucial step in the process of building sensory-motor behaviors for social robots or users-aware Embodied Conversational Agents (ECA). In this paper, we compare trainable behavioral models based on sequential models (HMMs) and classifiers (SVMs and Decision Trees) inherently inappropriate to model sequential aspects. These models aim at giving pertinent perception/action skills for robots in order to generate optimal actions given the perceived actions of others and joint goals. We applied these models to parallel speech and gaze data collected from interacting dyads. The challenge was to predict the gaze of one subject given the gaze of the interlocutor and the voice activity of both. We show that Incremental Discrete HMM (IDHMM) generally outperforms classifiers and that injecting input context in the modeling process significantly improves the performances of all algorithms

Graphical models for social behavior modeling in face-to face interaction

International audienceThe goal of this paper is to model the coverbal behavior of a subject involved in face-to-face social interactions. For this end, we present a multimodal behavioral model based on a Dynamic Bayesian Network (DBN). The model was inferred from multimodal data of interacting dyads in a specific scenario designed to foster mutual attention and multimodal deixis of objects and places in a collaborative task. The challenge for this behavioral model is to generate coverbal actions (gaze, hand gestures) for the subject given his verbal productions, the current phase of the interaction and the perceived actions of the partner. In our work, the structure of the DBN was learned from data, which revealed an interesting causality graph describing precisely how verbal and coverbal human behaviors are coordinated during the studied interactions. Using this structure, DBN exhibits better performances compared to classical baseline models such as Hidden Markov Models (HMMs) and Hidden Semi-Markov Models (HSMMs). We outperform the baseline in both measures of performance, i.e. interaction unit recognition and behavior generation. DBN also reproduces more faithfully the coordination patterns between modalities observed in ground truth compared to the baseline models

Enhancing multi-sectoral collaboration in health: the open arena for public health as a model for bridging the knowledge-translation gap

Effective public health interventions at local level must involve communities and stakeholders beyond the health services spectrum. A dedicated venue for structured discussion will ensure ongoing multi-sectoral collaboration more effectively than convening ad hoc meetings. Such a venue can be created using existing resources, at minimal extra cost. The University Hospital in Nice (France) has established an Open Arena for Public Health which can serve as a model for promoting collaborative partnerships at local level. The Arena has been successful in implementing sustainable interventions thanks to a set of principles, including: non-hierarchical governance and operating, fair representation of stakeholders, consensus as to best available evidence internationally and locally, policy dialogues: open, free-flowing discussions without preconceived solutions, and an experimental approach to interventions

Analyse de sensibilité globale d'un modèle d'évaluation économique du risque d'inondation

International audienceVariance-based Sobol' global sensitivity analysis (GSA) was initially designed for the study of models with scalar inputs and outputs, while many models in the environmental eld are spatially explicit. As a result, GSA is not a common practise in environmental modelling. In this paper we describe a detailed case study where GSA is performed on a spatially dependent model for flood risk economic assessment on the Orb valley (southeast France). The realisations of random input maps can be generated by any method including geostatistical simulation techniques, allowing for spatial structure and auto-correlation to be taken into account. The estimation of sensitivity indices on ACB-DE spatial outputs makes it possible to produce maps of sensitivity indices. These maps describe the spatial variability of Sobol' indices. Sensitivity maps of di fferent resolutions are then compared to discuss the relative influence of uncertain input factors at diff erent scales

Latin Hypercube Sampling of Gaussian random field for Sobol' global sensitivity analysis of models with spatial inputs and scalar output

4 pagesInternational audienceThe variance-based Sobol' approach is one of the few global sensitivity analysis methods that is suitable for complex models with spatially distributed inputs. Yet it needs a large number of model runs to compute sensitivity indices: in the case of models where some inputs are 2D Gaussian random fields, it is of great importance to generate a relatively small set of map realizations capturing most of the variability of the spatial inputs. The purpose of this paper is to discuss the use of Latin Hypercube Sampling (LHS) of geostatistical simulations to reach better efficiency in the computation of Sobol' sensitivity indices on spatial models. Sensitivity indices are estimated on a simple analytical model with a spatial input, for increasing sample size, using either Simple Random Sampling (SRS) or LHS to generate input map realizations. Results show that using LHS rather than SRS yields sensitivity indices estimates which are slightly more precise (smaller variance), with no significant improvement of bias

Towards New Aortic Tissues Analogue Materials: Micro-mechanical Modelling and Experiments

Human abdominal aortic tissue is a complex cylindrical soft sandwich structure, arranged in three different concentric layers. Within these layers, distribution and arrangement of all components display a double-helix architecture of wavy fibres, characterised by distinctive preferred orientations. The macroscopic mechanical behaviour of human healthy abdominal aorta (AA) and aneurysmal (AAA) tissues is highly non-linear, anisotropic and essentially hyperelastic. The global objective of this work is to design and process new artificial hy- perelastic and anisotropic membranes mimicking the macroscopic histological and mechanical features of AA and AAA tissues. These materials will be then used to build more realistic phantoms of AAA for in vitro experiments. The aim of the present study is (i) to develop a theoretical framework able to predict the optimal microstructure and mechanical behaviour of such AA/AAA analogues, and (ii) to provide experimental validation of micro-mechanical modelling

Sensitivity analysis of spatial models using geostatistical simulation

International audienceGeostatistical simulations are used to perform a global sensitivity analysis on a model Y = f(X1 ... Xk) where one of the model inputs Xi is a continuous 2D-field. Geostatistics allow specifying uncertainty on Xi with a spatial covariance model and generating random realizations of Xi. These random realizations are used to propagate uncertainty through model f and estimate global sensitivity indices. Focusing on variance-based global sensitivity analysis (GSA), we assess in this paper how sensitivity indices vary with covariance parameters (range, sill, nugget). Results give a better understanding on how and when to use geostatistical simulations for sensitivity analysis of spatially distributed models

Ranking sources of uncertainty in flood damage modelling: a case study on the cost-benefit analysis of a flood mitigation project in the Orb Delta, France

International audienceCost-benefit analyses (CBA) of flood management plans usually require estimating expected annual flood damages on a study area, and rely on a complex modelling chain including hydrological, hydraulic and economic modelling as well as GIS-based spatial analysis. As most model-based assessments, these CBA are fraught with uncertainty. In this paper, we consider as a case-study the CBA of a set of flood control structural measures on the Orb Delta, France. We demonstrate the use of variance-based global sensitivity analysis (VB-GSA) to i) propagate uncertainty sources through the modelling chain and assess their overall impact on the outcomes of the CBA, and ii) rank uncertainty sources according to their contribution to the variance of the CBA outcomes. All uncertainty sources prove to explain a significant share of the overall output variance. Results show that the ranking of uncertainty sources depends not only on the economic sector considered (private housing, agricultural land, other economic activities), but also on a number of averaging-out effects controlled by the number and surface area of the assets considered, the number of land use types or the number of damage functions