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As the need for ecosystem biodiversity assessment increases within the climate crisis framework, more and more studies using spectral variation hypothesis (SVH) are proposed to assess biodiversity at various scales. The SVH implies optical diversity (also called spectral diversity) is driven by light absorption dynamics associated with plant traits (PTs) variability (which is an indicator of functional diversity) which is, in turn, determined by biodiversity. In this study, we examined the relationship between PTs variability, optical diversity and α‐ and β‐diversity at different taxonomic ranks at the Monte Bondone grasslands, Trentino province, Italy. The results of the study showed that the PTs variability, at the α scale, was not correlated with biodiversity. On the other hand, the results observed at the community scale (β‐diversity) showed that the variation of some of the investigated biochemical and biophysical PTs was associated with the β‐diversity. We used the Mantel test to analyse the relationship between the PTs variability and species β‐diversity. The results showed a correlation coefficient of up to 0.50 between PTs variability and species β‐diversity. For higher taxonomic ranks such as family and functional groups, a slightly higher Spearman's correlation coefficient of up to 0.64 and 0.61 was observed, respectively. The SVH approach was also tested to estimate β‐diversity and we found that spectral diversity calculated by Spectral Angle Mapper showed to be a better proxy of biodiversity in the same ecosystem where the spectral diversity approach failed to estimate α‐diversity. These findings suggest that optical and PTs diversity approaches can be used to predict species diversity in the grasslands ecosystem where the species turnover is high. [ABSTRACT FROM AUTHOR]

N is known to be the most limiting element for vegetation growth in temperate and boreal forests. The expected increases in global temperature are predicted to accelerate N mineralization, therefore incrementing N availability in the soil and affecting the soil C cycle as well. While there is an abundance of C data collected to fulfill the requirements for national GHG accounting, more limited information is available for soil N accumulation and storage in relation to forest categories and altitudinal gradients. The data collected by the second Italian National Forest Inventory, spanning a wide range of temperature and precipitation values (10° latitudinal range), represented a unique opportunity to calculate N content and C/N ratio of the different soil layers to a depth of 30 cm. Boosted Regression Tree (BRT) models were applied to investigate the main determinants of soil N distribution and C/N ratio. Forest category was shown to be the main explanatory factor of soil N variability in seven out of eight models, both for forest floor and mineral soil layers. Moreover latitude explained a larger share of variability than single climate variables. BRT models explained, on average, the 49% of the data variability, with the remaining fraction likely due to soil-related variables that were unaccounted for. Accurate estimations of N pools and their determinants in a climate change perspective are consequently required to predict the potential impact of their degradation on forest soil N pools. [ABSTRACT FROM AUTHOR]

Objective: The aim of this study was to evaluate adverse effects of oral desmopressin lyophilisate (MELT) in enuretic children. Material and methods: We enrolled 260 children with nocturnal enuresis (NE) referred to the Pediatric Service, 'Campus Bio-Medico' University of Rome, from April 2014 to April 2017 in the study, of these 23 were excluded. The study was characterized by 2 phases. During Phase 1 a careful patient's medical history was obtained and physical examination was performed. After 3 months of treatment with MELT (Minirin/DDAVP®) at the dose of 120 mcg a day, a micturition diary was kept, adherence to therapy and any possible adverse effects were checked during the Phase 2. The study was carried out in compliance with the Helsinki Declaration. Results: Among 237 patients included in the study 11 male and 6 female (n=17; 7.2%) patients with a mean age 10.06±2.49 years, reported 22 adverse effects, with an absolute risk of 7.17%. In particular, 5 neurological symptoms, 3 gastrointestinal effects, 4 sleep disturbances, 8 psycho-behavioral disorders, 2 symptoms of fatigue were reported. Conclusion: In our study MELT with its higher bioavailability guaranteed lower frequency of adverse effects which resolved spontaneously and rapidly. The MELT formulation actually represents the first line and safe treatment for the NE. [ABSTRACT FROM AUTHOR]

Abstract: This study evaluated the correlation between scapula size and stature and developed standard equations in order to estimate stature by CT scan evaluation. A total of 200 healthy Italian subjects (100 men and 100 women, mean age 64.2±12.8years) underwent thoracic CT scan evaluation during pulmonary screening in our department; we measured the stature of each patient with standard anthropometric instruments and then analyzed images to calculate the longitudinal scapular length (LSL) and the transverse scapular length (TSL). The correlation between stature and each parameter measured was analyzed by dividing the population into two groups, males and females, and was examined by simple regression analysis using Pearson’s correlation coefficient (r). Each anthropometric variable showed a significant difference between males and females (p value <0.001). The correlation coefficients (r-values) were LSL=0.74 and TSL=0.51 in males and LSL=0.70 and TSL=0.48 in females. In both sexes the r-values showed a significant correlation between stature and LSL. Our study demonstrates that scapulae can be used for stature estimation; in our sample LSL was found to have a better correlation with stature then TSL. h m =4.247*LSL+93.74 and h f =4.031*LSL+92.38 are the formulae that provide the most accurate stature assessment in males and females respectively. [Copyright &y& Elsevier]

Background: The values such as participation/empathy, communication/sharing, self-awareness, moral integrity, sensitivity/trustfulness, commitment to ongoing professional development, and sense of duty linked to the practice of the medical professionalism were defined by various professional oaths. Aims: The aim of this study was to evaluate how these values are considered by the students of the degree course of medicine. Materials and Methods: Four hundred twenty three students (254 females, 169 males) taking part of the first, fourth, and fifth years of the degree course in medicine were asked to answer seven questions. Pearson's Chi-square, Wilcoxon rank sum test, and Kruskal-Wallis test were used for the statistical analysis. Results: The survey showed a high level of knowledge and self-awareness about the values and skills of medical profession. In particular, the respect, accountability, and the professional skills of competence were considered fundamental in clinical practice. However, the students considered that these values not sufficiently present in their educational experience. Conclusions: Teaching methods should be harmonized with the contents and with the educational needs to ensure a more complex patient-based approach and the classical lectures of teachers should be more integrated with learning through experience methods. [ABSTRACT FROM AUTHOR]

In this paper, we present a study on the efficiency of multi-return LIDAR (Light Detection Ranging) data in the estimation of forest stem volume over a multi-layered forest area in the Italian Alps. The goals of this paper are (1) to verify the usefulness of multi-return LIDAR data compared to single-return data in forest volume estimation and (2) to define the optimal resolution of a stem volume distribution raster map over the investigated area. To achieve these goals, raw data were segmented into a net, and different cell dimensions were investigated to maximize the relationship between the LIDAR data and the ground-truth information. Twenty predicting variables (e.g., mean height, coefficient of variation) have been extracted from multi-return LIDAR data, and a multiple linear regression analysis has been used for predicting tree stem volume. Experimental results found that the optimal resolutions of the net square cells were 40 m. The analysis indicated that in a mixed multi-layered forest, characterized by a complex vertical structure, the correct selection of the map spatial resolution and the inclusion of the secondary-return data were important factors for improving the effectiveness of the laser scanning approach in forest inventories. The experimental tests showed that the chosen model is effective for the estimation of stem volume over the analyzed area, providing good results on all the three considered validation methods. [ABSTRACT FROM AUTHOR]

Abstract: The aim of this inventory (acronym: INFOCARB) was to measure the organic carbon stored in the forest ecosystems of the Trento region (Provincia Autonoma di Trento, Northern Italy) in both above- and belowground pools, according to the Kyoto protocol and IPCC requirements. A total of 150 forest sampling points were selected on the entire regional area (6206km2) with a statistical sampling approach, based on the timber volume as a proxy variable for a stratified sampling. Each sampling point was located with a GPS receiver and a 600m2 circular plot was delimited around each point. Inside the plots, the biomass of trees, shrubs and herbaceous vegetation was measured, while litter was collected in systematically placed subplots. Topsoil (down to 30cm depth) was sampled with the excavation method on three systematically located pits, to determine the organic carbon content, the bulk density and the volume occupied by stones and roots. The inventory estimated the regional total carbon content of the forests as 71.9±5.2Tg C, with an average carbon density of 207.01±14.5Mg Cha−1. The aboveground biomass and the soil had a similar carbon content, 43.2% and 44.6% of the total ecosystem carbon, respectively, whereas the root systems and the litter accounted for 9.6% and 2.6%, respectively. Due to the high inter-site variability, only weak statistical relationships were found between the soil carbon content and main ecosystem and climatic variables. However, when dividing the plots into different species-dominated forests, the beech sites differed significantly from the conifer sites in the carbon stock and the C/N ratio in the soil organic layers. [Copyright &y& Elsevier]

In areas with complex three-dimensional features, slope and aspect interact with light conditions and significantly affect the spatial structure of images acquired by remote sensing instruments (for example, by changing the distribution of shadows and affecting the texture of high resolution imagery). In this scenario, this paper analyses the potential and the effectiveness of an automatic classification system to identify three fundamental vegetation classes (forest, grassland and crops) in the complex topography of the Italian Alps (Autonomous Province of Trento, Italy). This classification system is based on the fusion of spectral information provided by the SPOT-5 multi-spectral channels (Ground Instantaneous Field of View, GIFOV, equal to 10 m) and textural information extracted from airborne digital orthophotos (GIFOV equal to 1 m) and is designed to be user-friendly. The texture of the digital orthophotos was modelled using defined bidirectional variograms, thereby extracting additional information unavailable in first-order texture analyses. Using SPOT-5 multi-spectral information alone, the classification accuracy in the investigated alpine area was equal to 87.5%, but increased to 92.1% when texture information was included. In particular, the texture information significantly increased the classification accuracy for crops (from 68.9% to 87.9%), especially orchards that tend to be classified as lowland deciduous forests, and herbaceous crops (such as maize) that are often misclassified as grasslands. A further simple majority analysis increased the ability of detecting grassland, crops and urban zones. The combination of the majority analysis and the proposed automatic classification system seems an effective approach to classifying vegetation types in highly fragmented and complex Alpine landscapes on a regional scale. [ABSTRACT FROM AUTHOR]

Abstract: Development of new methods for estimating biophysical parameters can be considered one of the most important targets for the improvement of grassland parameters estimation at full canopy cover. In fact, accurate assessment methods of biophysical characteristics of vegetation are needed in order to avoid the uncertainties of carbon terrestrial sinks. Remote sensing is a valid tool for scaling up ecosystem measurements towards landscape levels serving a wide range of applications, many of them being related to carbon-cycle models. The aim of this study was to test the suitability of satellite platform sensors in estimating grassland biophysical parameters such as LAI, biomass, phytomass, and Green herbage ratio (GR). Also, we wanted to compare some of the most common NIR and red/green-based vegetation indices with ones that also make use of the MIR band, in relation to their ability to predict grassland biophysical parameters. Ground-truth measurements were taken on July 2003 and 2004 on the Monte Bondone plateau (Italian Alps, Trento district) in grasslands varying in land use and management intensities. From satellite platforms, an IRS-1C-LISS III image (18/07/2003; 25m resolution in the visible-NIR and 70m resolution in the MIR) and a SPOT 5 image (27/07/2004, 10m resolution in the visible-NIR and MIR) were used. LAI, biomass, and phytomass measurements showed logarithmic relationships with the investigated NIR and red/green-based indices. GreenNDVI showed the highest R 2 values (0.59, IRS 2003; 0.60, SPOT 2004). Index saturation occurred above approximately 100–150gm−2 of biomass (LAI 1.5–2). On the other hand, GR relationships were shown to be linear. MIR-based indices performed better than NIR and red/green-based ones in estimating biophysical variables, with no saturation effect. Biomass showed a linear regression with Canopy Index (MIR/green ratio) and with the Normalised Canopy Index (NCI) calculated as a normalised difference between MIR and green bands (IRS: R 2 =0.91 and 0.90, respectively. SPOT: R 2 =0.63 and 0.64). Similar correlations could also be found for LAI and phytomass, and GR predictability was shown to be higher than NDVI and GreenNDVI. According to these results obtained in the investigated areas, phytomass, biomass, LAI, and GR are linearly correlated with the investigated MIR band indices and as a result, these parameters could be estimated from the adopted satellite platforms with limited saturation problems. [Copyright &y& Elsevier]

Red-edge (RE) spectral vegetation indices (SVIs)—combining bands on the sharp change region between near infrared (NIR) and visible (VIS) bands—alongside with SVIs solely based on NIR-shoulder bands (wavelengths 750–900 nm) have been shown to perform well in estimating leaf area index (LAI) from proximal and remote sensors. In this work, we used RE and NIR-shoulder SVIs to assess the full potential of bands provided by Sentinel-2 (S-2) and Sentinel-3 (S-3) sensors at both temporal and spatial scales for grassland LAI estimations. Ground temporal and spatial observations of hyperspectral reflectance and LAI were carried out at two grassland sites (Monte Bondone, Italy, and Neustift, Austria). A strong correlation (R2 > 0.8) was observed between grassland LAI and both RE and NIR-shoulder SVIs on a temporal basis, but not on a spatial basis. Using the PROSAIL Radiative Transfer Model (RTM), we demonstrated that grassland structural heterogeneity strongly affects the ability to retrieve LAI, with high uncertainties due to structural and biochemical PTs co-variation. The RENDVI783.740 SVI was the least affected by traits co-variation, and more studies are needed to confirm its potential for heterogeneous grasslands LAI monitoring using S-2, S-3, or Gaofen-5 (GF-5) and PRISMA bands. [ABSTRACT FROM AUTHOR]

The measurement of human behavior is a complex task, both for psychologists and human sciences researchers and with respect to technology, since advanced and sophisticated instruments may have to be implemented to manage the plurality of variables involved. In this article, an observational study is presented in which a quantitative procedure, the external variables method (Duncan & Fiske, 1977), was integrated with a structural analysis (Magnusson, 1993, 2000) in order to detect the hidden organization of nonverbal behavior in Italian and Icelandic interactions. To this aim, Theme software was introduced and employed. The results showed that both the frequency and the typology of gestures deeply change as a function of culture. Moreover, a high number of patterns was detected in both Italian and Icelandic interactions: They appeared to be complex sequences in which a huge number of events were constantly happening and recurring. In this domain, Theme software provides a methodological progression from the quantitative to the structural approach.