Abstract

The symbiotic diazotrophs comprise with a very diverse group of Gram negative soil bacteria, collectively called as rhizobia found in nodule of legume plant. Rhizobia adopt themselves in different environment including soil, rhizosphere and grown within legume roots, where they fix nitrogen. The establishment of symbiosis is a very complicated process involving a coordinated exchange of signal between legume plants and the symbionts. The nodule development requires synthesis of signal molecules such as Nod factors that are important for induction of nodule development. There are different types of surface polysaccharides such as lipopolysaccharides, capsular polysaccharides, neutral and acidic polysaccharides found in rhizobia. The production of symbiotically active polysaccharides may allow rhizobial strains to adapt themselves to changing environmental conditions and interact efficiently with legume plants. Despite extensive research, the actual molecular function of the surface polysaccharides of rhizobia in symbiosis remains unclear. This review emphasized on the structural composition of extracellular polysaccharide of different rhizobia isolated from different legume plants. The compositions of extracellular polysaccharides are different in different rhizobia. The various compositions of extracellular polysaccharides produced by the symbionts are considered as the signaling molecules essential for determining host plant specificity. The present status of the biological functions of the exo-polysaccharide in symbiosis such as host specificity, successful invasion, formation of infection thread and induction of nodule formation in legume plants is also summarized here.

Abstract

Analysing the errors committed by Calderón himself during his writing amplify the perspective on his complex composition process. The aim of this paper is to offer an initial approximation on autograph errors located in En la vida todo es verdad y todo mentira, La desdicha de la voz and Cada uno para sí. A first typology considering its production, its magnitude and its posterior survival is deduced from the examples exposed.

Abstract

PUBLISHED We have investigated the properties and conduction limitations of spray pyrolysis grown, low-cost transparent conducting oxide ZnO thin films doped with indium. We analyze the optical, electrical, and crystallographic properties as functions of In content with a specific focus on postgrowth heat treatment of these thin films at 320?C in an inert, nitrogen atmosphere, which improves the films electrical properties considerably. The effect was found to be dominated by nitrogen-induced grain-boundary passivation, identified by a combined study using in situ resistance measurement upon annealing, x-ray photoelectron spectroscopy, photoluminescence, and x-ray diffraction studies. We also highlight the chemical mechanism of morphologic and crystallographic changes found in films with high indium content. By optimizing growth conditions according to these findings, ZnO:In with a resistivity as low as 2?10?3?cm, high optical quality (T?90%), and sheet resistance of 32?/? has been obtained without any need for postgrowth treatments. Document type: Part of book or chapter of book

Abstract

International audience; The Machine Learning (ML) world is in constant evolution, as the amount of different algorithms in this context is evolving quickly. Until now, it is the responsibility of data scientists to create ad-hoc ML pipelines for each situation they encounter, gaining knowledge about the adequacy between their context and the chosen pipeline. Considering that it is not possible at a human scale to analyze the exponential number of potential pipelines, picking the right pipeline that combines the proper preprocessing and algorithms is a hard task that requires knowledge and experience. In front of the complexity of building a right ML pipeline, algorithm portfolios aim to drive algorithm selection, learning from the past in a continuous process. However, building a portfolio requires that (i) data scientists develop and test pipelines and (ii) portfolio maintainers ensure the quality of the portfolio and enrich it. The firsts are the developers, while the seconds are the operators. In this paper, we present a set of criteria to be respected, and propose a pipeline-based meta-model, to support a DevOps approach in the context of Machine Learning Pipelines. The exploitation of this meta-model, both as a graph and as a logical expression, serves to ensure continuity between Dev and Ops. We depict our proposition through the simplified study of two primary use cases, one with developer's point-of-view, the other with ops'.