Applications Exploiting e-Infrastructures Across Europe and India Within the EU-IndiaGrid Project

In the last few years e-Infrastructures across Europe and India faced remarkable developments. Both national and international connectivity improved considerably and Grid Computing also profited of significant developments. As a consequence scientific applications were in the position of taking substantial benefits from this progress. The most relevant cases are represented by High Energy Physics (with the contribution to the program of Large Hadron Collider at CERN, Geneva) and Nano Science (exploiting NKN-TEIN3-GEANT interconnection for crystallography experiments with the remote access & control of experimental facility at the ESRF Synchrotron based in Grenoble, France directly from Mumbai, India). Other relevant application areas include Climate Change research, Biology, and several areas in Material Science. Within this framework, in the last five years period two specific EU funded projects (the EU-IndiaGrid and EU-IndiaGrid2) played a bridging role supporting several applications that exploited these advanced e-Infrastructures for the benefit of Euro-India common research programs. A crucial important part in the projects activity was the support offered to selected applications which ranges from the training the user communities behind up to the porting of their scientific applications on the grid computing infrastructure. This article aims to present and review the main e Infrastructures development in India and their full exploitation by scientific applications with a focus on the role played by the EUIndiaGrid and EU-IndiaGrid2 projects

Local icosahedral structures in binary-alloy clusters from molecular-dynamics simulation

We investigate the structure of 13-particle clusters in binary alloys for various size ratios and different concentrations via molecular-dynamics simulation. Our goal is to predict which systems are likely to form local icosahedral structures when rapidly supercooled from the melt. We calculate the energy spectrum of the minimal energy structures, and characterize all detected minima from both their relative probability and a structural point of view. We identify regions in our parameter space where the icosahedral structure is dominant (like in the corresponding monatomic case), regions where the icosahedral structure disappears, and others where icosahedral structures are present but not dominant. Finally, we compare our results with simulations reported in the literature and performed on extended binary systems with various size ratios and at different concentrations

The Substitution Principle within the REACH Regulation: Nuclear Receptor-Bound Endocrine Disruptors

Within the REACH Regulation (EC/1907/2006), the substitution principle for chemicals classified as Substances of Very High Concern (SVHC) for either human health or environmental risks has been implemented in order to support their replacement by suitable alternatives. Considering the thousands of chemicals to be tested within the frame of REACH, animal testing by internationally-accepted guidelines sounds unreasonable in terms of the required time, costs as well ethical issues. Hence, REACH recommended also the use of alternative methods to animal experimentation although no validated in silico or in vitro tools were available when regulation entried into force. To search for suitable alternatives to SVHC having an Endocrine Disruptor (ED)-like Mode-of-Action (MoA) by means of an integrated, tiered in silico-in vitro approach, the EU-granted project LIFE-EDESIA (contract no. LIFE12 ENV/IT/000633) is combining computational-based tools and cell-based bioassays, in order to develop a no-animal testing procedure to screen for chemicals having less or no toxicity in terms of endocrine disruption-like activities. A general view of the no-animal testing approach implementing REACH and the substitution principle will be given, emphasising ligand-nuclear receptor (NR) assessment by molecular docking (one of the LIFE-EDESIA in silico approaches) and the use of clinical biomarkers in in vitro toxicology to detect ED-like adverse effects in cell-based bioassays

La prima infrastruttura di data management per le nanoscienze

In questo articolo presentiamo la prima infrastruttura di data management per le nanoscienze. L’infrastruttura, denominata IDRP (Information and Data management Repository Platform), è stata sviluppata dal CNR-IOM (Istituto di Officina dei Materiali) di Trieste all’interno di NFFA-EUROPE (Nanoscience Foundries & Fine Analysis). Questo progetto europeo Horizon 2020, coordinato dal CNR-IOM, coinvolge 20 partner ed ha lo scopo di fornire una infrastruttura di ricerca distribuita per la comunità di nanoscienze. La IDRP è nata con l’obiettivo di gestire ed archiviare in maniera FAIR la grande varietà di dati generati dalla strumentazione NFFA-EUROPE offrendo un accesso standardizzato

The Studying of the Relationship of Li Yu’s Novels And Plays

李渔是中国文学史上少有的兼擅小说创作和戏曲创作的作家。他的戏曲和小说创作之间的关系向来是学术界关注的热点。而纵观以往的研究状况，虽然取得了很大成就，但在某些方面尚存在一些偏颇，如过分夸大李渔戏曲理论和创作技法对其小说创作的影响，曲解李渔的“无声戏”理论等等。本文以李渔小说戏曲的关系为研究对象，通过考察李渔小说戏曲的创作年代、以李渔的审美理论观照其创作实践、将李渔的小说戏曲创作放在中国古代小说戏曲发展的整体背景下考察等等，厘清李渔小说与其戏曲间的关系，澄清目前学术界在该问题上的理解差误。 论文的第一章《李渔小说改编的四种传奇》，从“增添辅线，展现广阔社会生活”、“丰盈肌肤，凸现人物个性特征”、...In the history of Chinese literature, Li Yu was one of the rare writers who did well both in novel and in play. Studying the relationship of his novel and play is always the hotspot in the academic field. And many disquisitions have discussed it. But there are some defects in these disquisitions, such as excessively exaggerating the influence of his opera theories and opera inditement skill to his...学位：文学硕士院系专业：人文学院中文系_戏剧戏曲学学号：20010102

Deep Learning, Feature Learning, and Clustering Analysis for SEM Image Classification

In this paper, we report upon our recent work aimed at improving and adapting machine learning algorithms to automatically classify nanoscience images acquired by the Scanning Electron Microscope (SEM). This is done by coupling supervised and unsupervised learning approaches. We first investigate supervised learning on a ten-category data set of images and compare the performance of the different models in terms of training accuracy. Then, we reduce the dimensionality of the features through autoencoders to perform unsupervised learning on a subset of images in a selected range of scales (from 1 $\mu$m to 2 $\mu$m). Finally, we compare different clustering methods to uncover intrinsic structures in the images

Chemogenomics of pyridoxal 5′-phosphate dependent enzymes

Pyridoxal 5'-phosphate (PLP) dependent enzymes comprise a large family that plays key roles in amino acid metabolism and are acquiring an increasing interest as drug targets. For the identification of compounds inhibiting PLP-dependent enzymes, a chemogenomics-based approach has been adopted in this work. Chemogenomics exploits the information coded in sequences and three-dimensional structures to define pharmacophore models. The analysis was carried out on a dataset of 65 high-resolution PLP-dependent enzyme structures, including representative members of four-fold types. Evolutionarily conserved residues relevant to coenzyme or substrate binding were identified on the basis of sequence-structure comparisons. A dataset was obtained containing the information on conserved residues at substrate and coenzyme binding site for each representative PLP-dependent enzyme. By linking coenzyme and substrate pharmacophores, bifunctional pharmacophores were generated that will constitute the basis for future development of small inhibitors targeting specific PLP-dependent enzymes

Emotion-based analysis of programming languages on Stack Overflow

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