1,721,063 research outputs found
Gaia, il pianeta delle piante e degli animali – umani compresi. Ecosistemi, ambienti vegetali e vita animale nell’Antropocene. Introduzione
No full textCome potrebbe il collettivo dei geografi italiani non sentirsi convocato da Gaia? A sentire Bruno Latour, Gaia non è la Terra come siamo abituati a pensarla. Forse un Congresso di geografi potrebbe contribuire allora a sciogliere l’enigma contenuto nella sibillina frase: «There is only one Gaia, but Gaia is not One». Secondo Latour (2020), non abbiamo a che fare con un superorganismo che tutto contiene e comprende. Gaia è piuttosto la risultante mobile, fluida, complessa e imprevedibile di una molteplicità di agenti e l’approccio geostorico appa- re idoneo al fine di non proporne una versione riduttiva. In ogni ecosistema si delinea, infatti, una complessa interazione tra forme di vita vegetale e animale – compresi gli umani
Temporal Network Motifs: Models, Limitations, Evaluation
No full textInvestigating the frequency and distribution of small subgraphs with a few nodes/edges, i.e., motifs, is an effective analysis method for static networks. Motif-driven analysis is also useful for temporal networks where the spectrum of motifs is significantly larger due to the additional temporal information on edges. This variety makes it challenging to design a temporal motif model that can consider all aspects of temporality. In the literature, previous works have introduced various models that handle different characteristics. In this work, we compare the existing temporal motif models and evaluate the facets of temporal networks that are overlooked in the literature. We first survey four temporal motif models and highlight their differences. Then, we evaluate the advantages and limitations of these models with respect to the temporal inducedness and timing constraints. In addition, we suggest a new lens, event pairs, to investigate temporal correlations. We believe that our comparative survey and extensive evaluation will catalyze the research on temporal network motif models
Nutraceutical Value of Pantelleria Capers (Capparis spinosa L.)
No full textAbstract: Unopened flower buds of Capparis spinosa L. (capers), generally used in the Mediterranean area as food flavoring, are known to be a good source of bioactive compounds. The aim of this work was to evaluate the nutraceutical value of salt-fermented capers collected from different areas of Pantelleria Island (Italy), testing their methylglyoxal and glyoxal trapping capacity and antioxidant activity by 2,2-diphenyl-1-picryl hydrazyl (DPPH), [2,2-azinobis(3-ethylben- zothiazoline-6-sulfonic acid)] diammonium salt (ABTS), and oxygen radical absorbance capacity (ORAC) assays. Hydrophilic extracts were also characterized by high-performance liquid chromatography–electrospray ionization/mass spectrometry. Among 24 detected compounds, several flavonol derivatives and glucosinolates were identified. The levels of kaempferol and quercetin derivatives varied considerably among the five accessions considered (6.46 to 267.93 and 22.39 to 367.14 mg kaempferol and quercetin equivalent /g fresh weight, respectively), with kaempferol derivatives more representative than quercetin ones. Person's coefficient indicated a high correlation between total phenolic content and anti-DPPH radical capacity (R2 = 0.665), as well as between total flavonoid content and antioxidant capacity (by ORAC assay; R2 = 0.888) and between total flavonoid content and glyoxal and methylglyoxal trapping capacity (R2 = 0.918). Results indicate that capers from Pantelleria Island represent a rich source of bioactive compounds with potential nutraceutical relevance. Practical Application: The findings of this study highlight the health benefits of Pantelleria capers consumption due to their composition in antioxidants and their biological properties (antiradical and alpha-dicarbonyls trapping) correlated with the development of a high number of chronic–degenerative diseases. These results are also important for the agricultural and commercial sectors involved in the production of capers from Pantelleria, which received the Protected Geographical Indications recognition
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
A multi-expert system to detect COVID-19 Cases in X-ray images
No full textThe year 2020 was marked by the worldwide COVID-19 pandemic, which caused over 2.5 million deaths by the end of February 2021. Different methods have been established since the beginning to identify infected patients and restrict the spread of the virus. In addition to laboratory analysis, used as the gold standard, several applications have been developed to apply deep learning algorithms to chest X-ray (CXR) images to diagnose patients affected by COVID-19. The literature shows that convolutional neural networks (CNNs) perform well on a single image dataset, but fail to generalize to other sources of data. To overcome this limitation, we present a late fusion approach in which multiple CNNs collaborate to diagnose the CXR scan of a patient, improving the generalizability. Experiments on three datasets publicly available show that the ensemble of CNNs outperforms stand-alone networks, achieving promising performance not only in cross-validation, but also when external validation is used, with an average accuracy of 95.18%
Inhibition of foodborne pathogen bacteria by essential oils extracted from citrus fruits cultivated in Sicily
No full textThe antagonistic activity of the essential oils (EOs) extracted by hydrodistillation from the fruit peel of
several citrus genotypes (pummelo, grapefruit, orange, kumquat, mandarin and lemon) was evaluated
against foodborne pathogen bacteria (43 strains of Listeria monocytogenes, 35 strains of Staphylococcus
aureus and 14 strains of Salmonella enterica). Five commercial EOs were used for comparison. Most of the
EOs were more effective against the Gram-positive bacteria rather than Salmonella. EOs of lemon genotypes
14 and 15 showed the best results in terms of number of strains inhibited and width of the inhibition
zone. The most susceptible strain of each species (L. monocytogenes 133, St. aureus 473 and Salmonella
Newport 50404)was used as indicator for the evaluation of the minimum inhibition concentration (MIC) of
the EOs 14 and 15. The last two EOs, EO 16 (showing the weakest inhibition power among lemon EOs), and
the commercial EO L (lemon) were analysed for their chemical composition. A total of 30major compounds
were identified by gas chromatography/mass spectrometry (GC/MS) and the oxygenated monoterpenes
were suggested to be implicated in the process of bacterial inhibition by citrus EOs
Pareto optimization of deep networks for COVID-19 diagnosis from chest X-rays
No full textThe year 2020 was characterized by the COVID-19 pandemic that has caused, by the end of March 2021, more than 2.5 million deaths worldwide. Since the beginning, besides the laboratory test, used as the gold standard, many applications have been applying deep learning algorithms to chest X-ray images to recognize COVID-19 infected patients. In this context, we found out that convolutional neural networks perform well on a single dataset but struggle to generalize to other data sources. To overcome this limitation, we propose a late fusion approach where we combine the outputs of several state-of-the-art CNNs, introducing a novel method that allows us to construct an optimum ensemble determining which and how many base learners should be aggregated. This choice is driven by a two-objective function that maximizes, on a validation set, the accuracy and the diversity of the ensemble itself. A wide set of experiments on several publicly available datasets, accounting for more than 92,000 images, shows that the proposed approach provides average recognition rates up to 93.54% when tested on external datasets
- …
