92 research outputs found
Le dinamiche partenariali inter-istituzionali del Corso di laurea in Scienze della Formazione primaria dell’Aquila: il tirocinio diretto e indiretto come azione di coordinamento locale Università, Scuola, Territorio in una prospettiva internazionale
No full textEconomic and Monetary Union and Switzerland: two models compared in the light of the economic crisis
No full textLooking at the origin and the reasons behind and considering the impact of the 2007 economic crisis in the EU countries and in Switzerland, the author describes the initiatives taken and the key role played by the European Central Bank to mitigate financial and monetary risks within the Eurozone, and compares it to the Swiss approach, namely addressing the set of measures adopted by the Swiss National Bank to tackle the economic crisis
Le dinamiche partenariali inter-istituzionali del Corso di laurea in Scienze della Formazione primaria dell'Aquila: il Tirocinio diretto e indiretto come azione di coordinamento locale università, scuola, territorio in una prospettiva internazionale;
No full textExosome derived from murine adipose-derived stromal cells: Neuroprotective effect onin vitromodel of amyotrophic lateral sclerosis
No full textTherapeutic strategies for the fatal neurodegenerative disease amyotrophic lateral sclerosis (ALS) have not yet provided satisfactory results. Interest in stem cells for the treatment of neurodegenerative diseases is increasing and their beneficial action seems to be due to a paracrine effect via the release of exosomes, main mediators of cell-cell communication. Here we wished to assess, in vitro, the efficacy of a novel non-cell therapeutic approach based on the use of exosomes derived from murine adipose-derived stromal cells on motoneuron-like NSC-34 cells expressing ALS mutations, and used as in vitro models of disease. In particular, we set out to investigate the effect of exosomes on NSC-34 naïve cells and NSC-34 cells overexpressing human SOD1(G93A) or SOD1(G37R) or SOD1(A4V) mutants, exposed to oxidative stress. The data presented here indicate for the first time that exosomes (0.2μg/ml) are able to protect NSC-34 cells from oxidative damage, which is one of the main mechanism of damage in ALS, increasing cell viability. These data highlight a promising role of exosomes derived from stem cells for potential therapeutic applications in motoneuron disease
Novel insights on intake of meat and prevention of sarcopenia: all reasons for an adequate consumption
No full textIntroducion: sarcopenia is defined as a syndrome characterized by progressive and generalized loss of muscle mass and strength. The main cause of sarcopenia is the alteration of protein metabolism, in which the proteolytic processes are not accompanied by an appropriate protein synthesis and muscle cells lose progressively the sensitivity to the anabolic stimulus. The most rational approach to delay the progression of sarcopenia and counteract the anabolic resistance is proper nutrition. Meat contains biologically active compounds, such as creatine, carnitine, Conjugated Linoleic Acid (CLA) which have significant
impacts upon human protein metabolism. Methods: we performed a narrative literature review
to evaluate the till-now evidence regarding: 1. adequate intake of meat in elderly as a topic for prevention of sarcopenia; 2. the correct intake of biologically active compounds contain in meat, which have significant impacts upon human protein metabolism and so have beneficial effects on prevention of sarcopenia. This review included 62 eligible studies. Results: the results demonstrated that in elderly the optimum diet therapy for the sarcopenia prevention and treatment, which must aim at achieving specific metabolic goals, must recommend the consumption of 113 g of meat (220 kcal; 30 g protein) five time a week. Conclusion: in a varied and balanced diet, for preventing sarcopenia, it is recommended to assume meat 4-5 times a week (white meat 2 times per week, lean red meat less than 2 times per week, processed meat less than 1 time per week), as suggested in the diet pyramid for elderly
A knowledge graph embeddings based approach for author name disambiguation using literals
No full textScholarly data is growing continuously containing information about the articles from a plethora of venues including conferences, journals, etc. Many initiatives have been taken to make scholarly data available in the form of Knowledge Graphs (KGs). These efforts to standardize these data and make them accessible have also led to many challenges such as exploration of scholarly articles, ambiguous authors, etc. This study more specifically targets the problem of Author Name Disambiguation (AND) on Scholarly KGs and presents a novel framework, Literally Author Name Disambiguation (LAND), which utilizes Knowledge Graph Embeddings (KGEs) using multimodal literal information generated from these KGs. This framework is based on three components: (1) multimodal KGEs, (2) a blocking procedure, and finally, (3) hierarchical Agglomerative Clustering. Extensive experiments have been conducted on two newly created KGs: (i) KG containing information from Scientometrics Journal from 1978 onwards (OC-782K), and (ii) a KG extracted from a well-known benchmark for AND provided by AMiner (AMiner-534K). The results show that our proposed architecture outperforms our baselines of 8–14% in terms of F1 score and shows competitive performances on a challenging benchmark such as AMiner. The code and the datasets are publicly available through Github (https://github.com/sntcristian/and-kge) and Zenodo (https://doi.org/10.5281/zenodo.6309855) respectively
A Knowledge Graph Embeddings based Approach for Author Name Disambiguation using Literals
No full textScholarly data is growing continuously containing information about the
articles from a plethora of venues including conferences, journals, etc. Many
initiatives have been taken to make scholarly data available as Knowledge
Graphs (KGs). These efforts to standardize these data and make them accessible
have also led to many challenges such as exploration of scholarly articles,
ambiguous authors, etc. This study more specifically targets the problem of
Author Name Disambiguation (AND) on Scholarly KGs and presents a novel
framework, Literally Author Name Disambiguation (LAND), which utilizes
Knowledge Graph Embeddings (KGEs) using multimodal literal information
generated from these KGs. This framework is based on three components: 1)
Multimodal KGEs, 2) A blocking procedure, and finally, 3) Hierarchical
Agglomerative Clustering. Extensive experiments have been conducted on two
newly created KGs: (i) KG containing information from Scientometrics Journal
from 1978 onwards (OC-782K), and (ii) a KG extracted from a well-known
benchmark for AND provided by AMiner (AMiner-534K). The results show that our
proposed architecture outperforms our baselines of 8-14% in terms of the F1
score and shows competitive performances on a challenging benchmark such as
AMiner. The code and the datasets are publicly available through Github:
https://github.com/sntcristian/and-kge and
Zenodo:https://doi.org/10.5281/zenodo.6309855 respectively
BiblioDAP'21: The 1st Workshop on Bibliographic Data Analysis and Processing
No full textAutomatic processing of bibliographic data becomes very important in digital libraries, data science and machine learning due to its importance in keeping pace with the significant increase of published papers every year from one side and to the inherent challenges from the other side. This processing has several aspects including but not limited to I) Automatic extraction of references from PDF documents, II) Building an accurate citation graph, III) Author name disambiguation, etc. Bibliographic data is heterogeneous by nature and occurs in both structured (e.g. citation graph) and unstructured (e.g. publications) formats. Therefore, it requires data science and machine learning techniques to be processed and analysed. Here we introduce BiblioDAP'21: The 1st Workshop on Bibliographic Data Analysis and Processing
A Semantic Web Approach To Everyday Overlapping Markup
No full textOverlapping structures in XML are not symptoms of a misunderstanding of the intrinsic characteristics of a text document nor evidence of extreme scholarly requirements far beyond those needed by the most common XML-based applications. On the contrary, overlaps have started to appear in a large number of incredibly popular applications hidden under the guise of syntactical tricks to the basic hierarchy of the XML data format. Unfortunately, syntactical tricks have the drawback that the affected structures require complicated workarounds to support even the simplest query or usage. In this article, we present Extremely Annotational Resource Description Framework (RDF) Markup (EARMARK), an approach to overlapping markup that simplifies and streamlines the management of multiple hierarchies on the same content, and provides an approach to sophisticated queries and usages over such structures without the need of ad-hoc applications, simply by using Semantic Web tools and languages. We compare how relevant tasks (e.g., the identification of the contribution of an author in a word processor document) are of some substantial complexity when using the original data format and become more or less trivial when using EARMARK. We finally evaluate positively the memory and disk requirements of EARMARK documents in comparison to Open Office and Microsoft Word XML-based formats
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