To cite this article: Francavilla R, Calasso M, Calace L, Siragusa S, Ndagijimana M, Vernocchi P, Brunetti L, Mancino G, Tedeschi G, Guerzoni E, Indrio F, Laghi L, Miniello VL, Gobbetti M, De Angelis M. Effect of lactose on gut microbiota and metabolome of infants with cow’s milk allergy. Pediatric Allergy Immunology 2012: 23: 420–427. Abstract Allergic infants have an unusual gastrointestinal microbiota with low numbers of Bifidobacterium/Lactobacilli and high levels of Clostridium, staphylococci and Escherichia coli. Hydrolyzed formula used to treat these infants is deprived of lactose that instead may influence the gut microbial composition. The aim of the present study is to investigate the influence of lactose on the composition of the gut microbiota and metabolome of infants with cow’s milk allergy. Infants prospectively enrolled received an extensively hydrolyzed formula with no lactose for 2 months followed by an identical lactose‐containing formula for an additional 2 months. Healthy, age‐gender‐matched infants were used as controls. The following determinations were performed before and after the introduction of lactose in the diet: enumeration of cells present in the feces using FISH, counts of viable bacterial cells and gas‐chromatography mass spectrometry/solid‐phase microextraction analysis. The addition of lactose to the diet significantly increases the counts of Bifidobacteria and lactic acid bacteria (p < 0.01), decreases that of Bacteroides/clostridia (p < 0.05) reaching counts found in healthy controls; lactose significantly increases the concentration of total short‐chain fatty acids (p < 0.05). The addition of lactose to an extensively hydrolyzed formula is able to positively modulate the composition of gut microbiota by increasing the total fecal counts of Lactobacillus/Bifidobacteria and decreasing that of Bacteroides/Clostridia. The positive effect is completed by the increase of median concentration of short chain fatty acids, especially for acetic and butyric acids demonstrated by the metabolomic analysis.

© iForest -Biogeosciences and Forestry IntroductionAssessment of natural forest expansion represents a crucial issue to elucidate several processes, including biogeochemical cycles, atmospheric composition related to climate change, and forest carbon uptake, as well as socio-economic processes and issues. Anthropogenic and naturally induced land cover changes affect spatial and temporal distribution and availability of environmental resources, and alter ecosystem composition and productivity. Globally, these processes can be considered the primary catalysts for change in biogeochemical cycling, atmospheric composition, and climate (Pielke 2005, Metz et al. 2007, Turner et al. 2007). Forest land-use and land-cover change (LU-LCC) were recognised as key issues in greenhouse gas removal/emission processes as specified by the Good Practices Guidance for Land Use, Land Use Change, and Forestry (GPG-LULUCF) during the Intergovernmental Panel on Climate Change (IPCC) established at the Kyoto Protocol (Penman et al. 2003). Observation and assessment of forest cover changes are crucial to elucidate the complexities inherent in feedback processes between forest distribution and human activities in sustainable forest development, natural resource management, biodiversity conservation, ecosystem functioning, and biogeochemical cycling (IPCC 2007). In Mediterranean regions, natural forest expansion is primarily related to the abandonment of agricultural practices and cattle-raising in marginal areas representing the principal change in Italy's Mediterranean rural landscape over the past five decades (Piussi 2005). These processes generally vary in terms of the vegetation successional series and time scale, however the expansion dynamics are shared, beginning with an initial phase of spontaneous shrub dominance, followed by tree colonisation (Biondi et al. 2006).In recent decades, satellite-based high-resolution observations with multispectral scanners provided the scientific community with consistent data to implement detailed thematic mapping for local and regional scale land classification (Friedl et al. 2002, 2010, Lu & Weng 2007, Giri 2012. The near infrared wavebands on the Landsat Thematic Mapper (TM) facilitates advanced land classification analyses based on differences in spectral reflectance of different land cover types. In particular, specific foliar reflectance, pigment absorptions, and foliar moisture wavelength ranges represent the basis of vegetation class analyses. Furthermore, the availability of such land cover data at different spatial and temporal scales promotes the development and implementation of vegetation change detection techniques, which furthers our understanding on vegetation and ecosystem dynamics (Cohen & Fiorella 1998, Coppin et al. 2004, Lu et al. 2004, Martinez & Gilabert 2009).In forest ecosystems, land cover change dynamic detection based on visual and statistical approaches represents a challenge to the scientific community due to the difficulties in remotely sensed image acquisition err...

The environmentally sensitive area (ESA) methodology (originally proposed in the framework of MEDALUS–Mediterranean Desertification and Land Use—a series of international cooperation research projects funded by the European Union) is used worldwide to identify 'sensitive areas' that are potentially threatened by land degradation and desertification (LDD). The distinctive outcome of this approach is a multidimensional index (the ESA index) composed of partial indicators of climate, soil, vegetation, and management quality that are derived from the elaboration of 15 elementary variables. In this study, we propose (a) a major update of the ESA methodology, as presented in the MEDALUS project, for global LDD assessment, (b) a global map of ESAs to LDD, and (c) a global environmentally critical factors map. The results of the updated ESA framework confirm the efficiency and applicability of the ESA methodology in different worldwide areas, allowing for the harmonization of regional/country level studies and applications, and the more efficient use of global level datasets. In this study, we provide examples for analysis of LDD patterns and processes at a global level, as well as for identification of the main risk factors over time and space. Global‐ESA and global‐environmentally critical factors maps also support regional‐scale knowledge on LDD processes and sustainable land management practices for LDD mitigation. High‐resolution illustrative maps and other information are available on a dedicated website (http://web.unibas.it/global-esa/).

The temporal speeds and spatial scales at which ecosystem processes operate are often at odds with the scale and speed at which natural resources such as soil, water and vegetation are managed those. Scale mismatches often occur as a result of the time-lag between policy development, implementation and observable changes in natural capital in particular. In this study, we analyse some of the transformations that can occur in complex forest-shrubland socio-ecological systems undergoing biophysical and socioeconomic change. We use a Multiway Factor Analysis (MFA) applied to a representative set of variables to assess changes in components of natural, economic and social capitals over time. Our results indicate similarities among variables and spatial units (i.e. municipalities) which allows us to rank the variables used to describe the SES according to their rapidity of change. The novelty of the proposed framework lies in the fact that the assessment of rapidity-to-change, based on the MFA, takes into account the multivariate relationships among the system's variables, identifying the net rate of change for the whole system, and the relative impact that individual variables exert on the system itself. The aim of this study was to assess the influence of fast and slow variables on the evolution of socio-economic systems based on simplified multivariate procedures applicable to vastly different socio-economic contexts and conditions. This study also contributes to quantitative analysis methods for long-established socio-ecological systems, which may help in designing more effective, and sustainable land management strategies in environmentally sensitive areas.

Landsat 8 is the most recent generation of Landsat satellite missions that provides remote sensing imagery for earth observation. The Landsat 7 Enhanced Thematic Mapper Plus (ETM+) images, together with Landsat-8 Operational Land Imager (OLI) and Thermal Infrared sensor (TIRS) represent fundamental tools for earth observation due to the optimal combination of the radiometric and geometric images resolution provided by these sensors. However, there are substantial differences between the information provided by Landsat 7 and Landsat 8. In order to perform a multi-temporal analysis, a cross-comparison between image from different Landsat satellites is required. The present study is based on the evaluation of specific intercalibration functions for the standardization of main vegetation indices calculated from the two Landsat generation images, with respect to main land use types. The NDVI (Normalized Difference Vegetation Index), NDWI (Normalized Difference Water Index), LSWI (Land Surface Water Index), NBR (Normalized Burn Ratio), VIgreen (Green Vegetation Index), SAVI (Soil Adjusted Vegetation Index), and EVI (Enhanced Vegetation Index) have been derived from August 2017 ETM+ and OLI images (path: 188; row: 32) for the study area (Basilicata Region, located in the southern part of Italy) selected as a highly representative of Mediterranean environment. Main results show slight differences in the values of average reflectance for each band: OLI shows higher values in the near-infrared (NIR) wavelength for all the land use types, while in the short-wave infrared (SWIR) the ETM+ shows higher reflectance values. High correlation coefficients between different indices (in particular NDVI and NDWI) show that ETM+ and OLI can be used as complementary data. The best correlation in terms of cross-comparison was found for NDVI, NDWI, SAVI, and EVI indices; while according to land use classes, statistically significant differences were found for almost all the considered indices calculated with the two sensors.