1,774 research outputs found

    SYNTHESIS AND EVALUATION OF ANTIMICROBIAL ACTIVITY OF PHENYL AND FURAN-2-YL[1,2,4] TRIAZOLO[4,3-a]QUINOXALIN-4(5H)-ONE AND THEIR HYDRAZONE PRECURSORS

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    A variety of 1-(s-phenyl)-[1,2,4]triazolo[4,3-a]quinoxalin-4(5H)-one (3a-3h) and 1-(s-furan-2-yl)-[1,2,4]triazolo[4,3- a]quinoxalin-4(5H)-one (5a-d) were synthesized from thermal annelation of corresponding hydrazones (2a-h) and (4a-d) respectively in the presence of ethylene glycol which is a high boiling solvent. The structures of the compounds prepared were confirmed by analytical and spectral data. Also, the newly synthesized compounds were evaluated for possible antimicrobial activity. 3-(2-(4-hydroxylbenzylidene)hydrazinyl)quinoxalin-2(1H)-one (2e) was the most active antibacterial agent while 1-(5-Chlorofuran-2-yl)-[1,2,4]triazolo[4,3-a]quinoxalin-4(5H)-one (5c) stood out as the most potent antifungal agent

    European HYdropedological Data Inventory (EU-HYDI)

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    There is a common need for reliable hydropedological information in Europe. In the last decades research institutes, universities and government agencies have developed local, regional and national datasets containing soil physical, chemical, hydrological and taxonomic information often combined with land use and landform data. A hydrological database for western European soils was also created in the mid-1990s. However, a comprehensive European hydropedological database, with possible additional information on chemical parameters and land use is still missing. A comprehensive joint European hydropedological inventory can serve multiple purposes, including scientific research, modelling and application of models on different geographical scales. The objective of the joint effort of the participants is to establish the European Hydropedological Data Inventory (EU-HYDI). This database holds data from European soils focusing on soil physical, chemical and hydrological properties. It also contains information on geographical location, soil classification and land use/cover at the time of sampling. It was assembled with the aim of encompassing the soil variability in Europe. It contains data from 18 countries with contributions from 29 institutions. This report presents an overview of the database, details the individual contributed datasets and explains the quality assurance and harmonization process that lead to the final database

    On Complex Dependence Structures in Bayesian Nonparametrics: a Distance–based Approach

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    No abstract availableRandom vectors of measures are the main building block to a major portion of Bayesian nonparametric models. The introduction of infinite–dimensional parameter spaces guarantees notable flexibility and generality to the models but makes their treatment and interpretation more demanding. To overcome these issues we seek a deep understanding of infinite–dimensional random objects and their role in modeling complex dependence structures in the data. Comparisons with baseline models play a major role in the learning process and are expressed through the introduction of suitable distances. In particular, we define a distance between the laws of random vectors of measures that builds on the Wasserstein distance and combines intuitive geometric properties with analytical tractability. This is first used to evaluate approximation errors in posterior sampling schemes and then culminates in the definition of a new principled and non model–specific measure of dependence for partial exchangeability, going beyond current measures of linear dependence. The study of dependence is complemented by the investigation of asymptotic properties for partially exchangeable mixture models from a frequentist perspective. We extend Schwartz theory to a multisample framework by relying on natural distances between vectors of densities and leverage it to find optimal contraction rates for a wide class of hierarchical models

    On Complex Dependence Structures in Bayesian Nonparametrics: a Distance–based Approach

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    No abstract availableRandom vectors of measures are the main building block to a major portion of Bayesian nonparametric models. The introduction of infinite–dimensional parameter spaces guarantees notable flexibility and generality to the models but makes their treatment and interpretation more demanding. To overcome these issues we seek a deep understanding of infinite–dimensional random objects and their role in modeling complex dependence structures in the data. Comparisons with baseline models play a major role in the learning process and are expressed through the introduction of suitable distances. In particular, we define a distance between the laws of random vectors of measures that builds on the Wasserstein distance and combines intuitive geometric properties with analytical tractability. This is first used to evaluate approximation errors in posterior sampling schemes and then culminates in the definition of a new principled and non model–specific measure of dependence for partial exchangeability, going beyond current measures of linear dependence. The study of dependence is complemented by the investigation of asymptotic properties for partially exchangeable mixture models from a frequentist perspective. We extend Schwartz theory to a multisample framework by relying on natural distances between vectors of densities and leverage it to find optimal contraction rates for a wide class of hierarchical models

    Twentieth conference on stochastic processes and their applications

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