Effect of long-term abandonment and spring grazing on floristic and functional composition of dry grasslands in a central Apennine farmland

Semi-natural dry grasslands host some of the most valuable habitats in Europe, due to their biodiversity heritage. Nevertheless, a strong decline in their extension, due to the cessation of traditional management, has been observed in the last decades. The aim of the study was to assess plant community changes due to abandonment and the effect of spring grazing in sub-Mediterranean dry grasslands, focusing on the plant functional traits involved in this turnover. The study area is located in the central Apennines (Italy), where grasslands were grazed by sheep in late winter and spring until 1980 and are nowadays abandoned. Relevés sampled (using the Braun-Blanquet method) in different years, namely in 1976–1980 (grazed pasture) and again in 2010 (abandoned pasture) were compared. Results indicated that abandonment leads to the increase of species richness. Traits and strategies indicator sets were: therophyte for the grazed pasture; geophytes, flower palatability, early flowering strategy, clonal ability and presence of storage organs for the abandoned ones. Traits related to low levels of stress (tolerance strategies) are heavily reduced in grazed systems, and thus the functional composition of plant community is mostly characterised by traits promoting avoidance strategies. In abandoned conditions a higher number of species can co-exist thanks to the micro-scale variation of soil features and niche diversification. The research findings also revealed grazing timing as a key factor for understanding changes of plant functional trait patterns and spring grazing as a threat for orchid species

The geosynphytosociological approach as a tool for agriculture innovation: the study case of saffron (Crocus sativus L.) cultivation suitability assessment in the Macerata district (central Italy)

The maintenance of open areas as grasslands and croplands has become a vital issue addressed to biodiversity conservation. For this purpose, innovation in agricultural activities may be a key factor. To achieve this goal, it is essential to identify the agronomic suitability and the most appropriate spatial pattern for the proposed cultivation. Therefore, the definition of land suitability classes and of their boundaries is a key step. For this purpose we used the phytosociological approach since it is based on an ecological definition and hierarchical classification of plant communities and landscapes and can be considered as an indirect way to assess the variation of the environmental conditions. Starting from the Marche Region vegetation geo-database, for each vegetation series a draft of the main ecological factors matching with the ecological needs of Crocus sativus L. was carried out. Afterwards, two intermediate maps were drawn: the “Climatic suitability map” and the “Soil suitability map”. Finally, the “Crocus sativus cultivation suitability map” was drawn by overlapping these two maps. Results were tested by agronomic experimentations. The synphytosociological approach proved to be a very valuable method. In fact, the areas belonging to the highlighted different suitability classes (that is the different vegetation series) showed substantial differences in the saffron productivity. Moreover using the vegetation mapping procedures also the definition of the borders of each suitability class has been easily solved at the landscape scale

Functional differentiation of central Apennine grasslands under mowing and grazing disturbance regimes

This research dealt to two grasslands potentially developing the same vegetation type because sited in the same environmental contest (bioclimate, substratum, soil, slope, altitude) but under diverse management regimes (grazing and mowing) for many decades. The evidenced differentiation between the two pastoral vegetations can be attributed to disturbance type and the statistical functional analysis performed through seven plant traits (prostrate form, early flowering, storage organs, clonal ability, basal meristems, chemical defences and hairs), revealed the distinguishing patterns. Discriminant analysis pointed out typical biological attributes for each disturbance conditions, while from correlation analysis emerged different possible traits combinations which do not follow the previous traits separation. Such outcomes are explainable because both grazing and mowing provoke aboveground phytomass removal, although grazing is a selective pressure, while mowing gives to all the species the same development chances. It is reasonable to conclude that convergent strategies within the two systems are possible and frequent

Decreasing incidence of gastric MALT lymphomas in the era of anti-Helicobacter pylori interventions: results from a population-based study on extranodal marginal zone lymphomas

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From deceased to bioengineered graft: New frontiers in liver transplantation

none6siopenCesaretti M.; Zarzavajian Le Bian A.; Moccia S.; Iannelli A.; Schiavo L.; Diaspro A.Cesaretti, M.; Zarzavajian Le Bian, A.; Moccia, S.; Iannelli, A.; Schiavo, L.; Diaspro, A

Contributo alla quantificazione della fitomassa epigea di alcuni pascoli dell’Appennino umbro-marchigiano (Italia centrale)

Il presente studio rappresenta un primo approccio alla descrizione, in termini quantitativi, della risorsa foraggera ovvero della produttività che caratterizza alcune tipologie di pascolo nell’Appennino Umbro-Marchigiano. Seppur ancora perfettibili i risultati ottenuti sono coerenti con l’ipotesi di base e cioè con la constatazione che la produttività aumenta passando dalle comunità xeriche a quelle semimesofile ed ai prati-pascolo. La produttività dipende infatti, da diverse variabili quali: disponibilità di risorse ambientali, intensità con cui le diverse componenti delle piante vengono consumate dagli erbivori (GRIME, 2001), composizione floristica del pascolo (le specie vegetali influenzano la fisionomia e la struttura architettonica del manto erboso e quindi la produzione), caratteristiche geomorfologiche, pedologiche e climatiche del sito di studio, ecc

La regionalizzazione biogeografica quale elemento per una migliore comprensione del valore degli habitat: il caso della Regione Marche

Habitat Directive provides the subdivision of the habitats indicated in Annex I in different biogegraphic regions on the basis of a classification of the U.E. territory extremely simplified. On the contrary, in the opinion of the Authors, a precise definition of the different hierarchical levels of the territory could allow to recognise, for each habitat, a more precise collocation and to make e new classification of them in the regional context. Within the REM Project, the biogeographical classification of the regional territory was done to the vegetation series level and the results are here presented. The hierarchical scheme utilised, follows that one proposed by Rivas-Martinez and for the upper hierarchical levels, from Region to subprovince, the unities defined in the European Biogeographic map have been taken. For Marche Region 5 sectors, 10 subsectors, 14 districts, 96 plant landscape units and 146 plant landscape elements within them a high number of tessera have been found. For the upper biogeographic levels, the maps are here presented while the plant landscape elements are listed following the hierarchical order

Computer-assisted liver graft steatosis assessment via learning-based texture analysis

Purpose: Fast and accurate graft hepatic steatosis (HS) assessment is of primary importance for lowering liver dysfunction risks after transplantation. Histopathological analysis of biopsied liver is the gold standard for assessing HS, despite being invasive and time consuming. Due to the short time availability between liver procurement and transplantation, surgeons perform HS assessment through clinical evaluation (medical history, blood tests) and liver texture visual analysis. Despite visual analysis being recognized as challenging in the clinical literature, few efforts have been invested to develop computer-assisted solutions for HS assessment. The objective of this paper is to investigate the automatic analysis of liver texture with machine learning algorithms to automate the HS assessment process and offer support for the surgeon decision process. Methods: Forty RGB images of forty different donors were analyzed. The images were captured with an RGB smartphone camera in the operating room (OR). Twenty images refer to livers that were accepted and 20 to discarded livers. Fifteen randomly selected liver patches were extracted from each image. Patch size was 100 × 100. This way, a balanced dataset of 600 patches was obtained. Intensity-based features (INT), histogram of local binary pattern (HLBPriu2), and gray-level co-occurrence matrix (FGLCM) were investigated. Blood-sample features (Blo) were included in the analysis, too. Supervised and semisupervised learning approaches were investigated for feature classification. The leave-one-patient-out cross-validation was performed to estimate the classification performance. Results: With the best-performing feature set (HLBPriu2+INT+Blo) and semisupervised learning, the achieved classification sensitivity, specificity, and accuracy were 95, 81, and 88%, respectively. Conclusions: This research represents the first attempt to use machine learning and automatic texture analysis of RGB images from ubiquitous smartphone cameras for the task of graft HS assessment. The results suggest that is a promising strategy to develop a fully automatic solution to assist surgeons in HS assessment inside the OR

A COMPARISON OF PRE-PROCESSING APPROACHES FOR REMOTELY SENSED TIME SERIES CLASSIFICATION BASED ON FUNCTIONAL ANALYSIS

Satellite remote sensing has gained a key role for vegetation mapping distribution. Given the availability of multi-temporal satellite data, seasonal variations in vegetation dynamics can be used trough time series analysis for vegetation distribution mapping. These types of data have a very high variability within them and are subjected by artifacts. Therefore, a pre-processing phase must be performed to properly detect outliers, for data smoothing process and to correctly interpolate the data. In this work, we compare four pre-processing approaches for functional analysis on 4-years of remotely sensed images, resulting in four time series datasets. The methodologies presented are the results of the combination of two outlier detection methods, namely tsclean and boxplot functions in R and two discrete data smoothing approaches (Generalized Additive Model ”GAM” on daily and aggregated data). The approaches proposed are: tsclean-GAM on aggregated data (M01), boxplot-GAM on aggregated data (M02), tsclean-GAM on daily data (M03), boxplot-GAM on daily data (M04). Our results prove that the approach which involves tsclean function and GAM applied to daily data (M03) is ameliorative to the logic of the procedure and leads to better model performance in terms of Overall Accuracy (OA) which is always among the highest when compared with the others obtained from the other three different approaches