Factors Influencing Soil Organic Carbon Stock Variations in Italy During the Last Three Decades

Soils contain about three times the amount of carbon globally available in vegetation, and about twice the amount in the atmosphere. However, soil organic carbon (SOC) has been reduced in many areas, while an increase in atmospheric CO2 has been detected. Recent research works have shown that it is likely that past changes in land use history and land management were the main reasons for the loss of carbon rather than higher temperatures and changes of precipitation resulting from climate change. The primary scope of this work was to estimate soil organic carbon stock (CS) variations in Italy during the last three decades and to relate them to land use changes. The study was also aimed at finding relationships between SOC and factors of pedogenesis, namely pedoclimate, morphology, lithology, and land use, but also at verifying the possible bias on SOC estimation caused by the use of data coming from different sources and laboratories. The soil database of Italy was the main source of information in this study. In the national soil database is stored information for 20,702 georeferentiated and dated observations (soil pro- files and minipits) analysed for routine soil parameters. Although the observations were collected from different sources, soil description and analysis were similar, because all the sources made reference to the Soil Taxonomy and WRB classification systems, and soil analyses followed the Italian official methods. Besides horizon description and analysis, soil observations had a set of site information including topography, lithology, and land use. The SOC and bulk density referred to the first 50 cm, thus CS was calculated on the basis of the weighted percentage of SOC, rock fragments volume, and bulk density. A set of geographic attributes were considered to spatialize point information, in particular, DEM (100 m) and derived SOTER morphological classification, soil regions (reference scale 1:5,000,000) and soil systems lithological groups (reference scale 1:500,000), soil moisture and temperature regimes (raster maps of 1 km pixel size), land cover (CORINE project, reference scale 1:100,000) at three reference dates: years 1990 and 2000, and an originalupdate to 2008, obtained with field point observations. The interpolation methodology used a multiple linear regression (MLR). CS was the target variable, while predictive variables were the geographic attributes. Basic statistical analysis was performed first, to find the predictive variables statistically related to CS and to verify the bias caused by different laboratories and surveys. After excluding the biased datasets, the best predictors were selected using a step-wise regression method with Akaike Information Criterion (AIC) as selection and stop criterion. The obtained MLR model made use of the following categorical attributes: (i) decade, (ii) land use, (iii) SOTER morphological class, (iv) soil region, (v) soil temperature regime, (vi) soil moisture regime, (vii) soil system lithology, (viii) soil temperature, (ix) soil aridity index (dry days per year), and, (x) elevation. The interaction between decade and land use variables was also considered in the model. Results indicated that CS was highly correlated with the kind of main type of land use (forest, meadow, arable land), soil moisture and temperature regimes, lithology, as well as morphological classes, and decreased notably in the second decade but slightly increased in the third one, passing form 3.32 Pg, to 2.74 Pg and 2.93 Pg respectively. The bias caused by the variables like “laboratory” and “survey source” could be as large as the 190%

USING A.R.P. PROXIMAL SURVEY TO MAP CALCIC HORIZON DEPTH IN VINEYARDS

The investigation of spatial variability of soil water retention capacity and depth is essential for a correct and economical planning of water supply of a vineyard. The advantage of measuring soil electrical properties by proximal sensors is the ability to operate with mobile and non-destructive tools, quicker than the traditional soil survey. A.R.P. (Automatic Resistivity Profiling) is a mobile soil electrical resistivity (ER) mapping system conceived by Geocarta (Paris, France), and it is comprised by a couple of transmitter sprocket-wheels, which inject current within the soil, and three couples of receiver sprocket-wheels, which measure the voltage-drop at three different depths, about 0-50, 0-100 and 0-170 cm. Ten vineyards of “Villa Albius” farm in Sicily region (southern Italy) were chosen to carry out the A.R.P. survey, for a overall surface of 45 hectares. The vineyards were located in a wide Plio-Pleistocene marine terrace, characterized by a few meters level of calcarenite, overlying partially cemented by calcium carbonate yellow sands. During the A.R.P. survey, 12 boreholes were described and sampled for the laboratory analysis and other 6 boreholes were carried out to validade the map. All soils showed a calcic horizon (Bk, BCk or Ck) with the upper limit at variable depths. The depth of calcic horizon (Dk) of each boreholes resulted significantly correlated to ER, especially with the ER0-100 (R2 = 0.83). Dk map was interpolated using the regression kriging and validated by the boreholes (R2 = 0.71) and with a NDVI map of the same vintage (R2 = 0.95)

Comparing different approaches - data mining, geostatistic, and deterministic pedology - to assess the frequency of WRB Reference Soil Groups in the Italian soil regions

Estimating frequency of soil classes in map unit is always affected by some degree of uncertainty, especially at small scales, with a larger generalization. The aim of this study was to compare different possible approaches - data mining, geostatistic, deterministic pedology - to assess the frequency of WRB Reference Soil Groups (RSG) in the major Italian soil regions. In the soil map of Italy (Costantini et al., 2012), a list of the first five RSG was reported in each major 10 soil regions. The soil map was produced using the national soil geodatabase, which stored 22,015 analyzed and classified pedons, 1,413 soil typological unit (STU) and a set of auxiliary variables (lithology, land-use, DEM). Other variables were added, to better consider the influence of soil forming factors (slope, soil aridity index, carbon stock, soil inorganic carbon content, clay, sand, geography of soil regions and soil systems) and a grid at 1 km mesh was set up. The traditional deterministic pedology assessed the STU frequency according to the expert judgment presence in every elementary landscape which formed the mapping unit. Different data mining techniques were firstly compared in their ability to predict RSG through auxiliary variables (neural networks, random forests, boosted tree, supported vector machine (SVM)). We selected SVM according to the result of a testing set. A SVM model is a representation of the examples as points in space, mapped so that examples of separate categories are divided by a clear gap that is as wide as possible. The geostatistic algorithm we used was an indicator collocated cokriging. The class values of the auxiliary variables, available at all the points of the grid, were transformed in indicator variables (values 0, 1). A principal component analysis allowed us to select the variables that were able to explain the largest variability, and to correlate each RSG with the first principal component, which explained the 51% of the total variability. The principal component was used as collocated variable. The results were as many probability maps as the estimated WRB classes. They were summed up in a unique map, with the most probable class at each pixel. The first five more frequent RSG resulting from the three methods were compared. The outcomes were validated with a subset of the 10% of the pedons, kept out before the elaborations. The error estimate was produced for each estimated RSG. The first results, obtained in one of the most widespread soil region (plains and low hills of central and southern Italy) showed that the first two frequency classes were the same for all the three methods. The deterministic method differed from the others at the third position, while the statistical methods inverted the third and fourth position. An advantage of the SVM was the possibility to use in the same elaboration numeric and categorical variable, without any previous transformation, which reduced the processing time. A Bayesian validation indicated that the SVM method was as reliable as the indicator collocated cokriging, and better than the deterministic pedological approach

Comparing Different Approaches - Data Mining, Geostatistic, and Deterministic Pedology - to Assess the Frequency of WRB Reference Soil Groups in the Italian Soil Regions

The assessment of class frequency in soil map legends is affected by uncertainty, especially at small scales, where generalization is larger. The aim of this study was to test the hypothesis that data mining or geostatistic techniques provide better estimation of class frequency than traditional deterministic pedology in a national soil map. In the map of Italian soil regions compiled at 1:5,000,000 reference scale, soil classes were the WRB Reference Soil Groups (RSGs). Different data mining techniques, namely neural networks, random forests, boosted tree, classification and regression tree, supported vector machine (SVM), were tested and the last one gave the best RSGs predictions, using selected auxiliary variables and 22,015 classified soil profiles. Given the categorical target variable, the multi-collocated indicator cokriging was the algorithm chosen for the geostatistic approach. The first five more frequent RSGs resulting from the three methods were compared. The outcomes were validated with a Bayesian approach on a subset of 10% of geographically representative profiles, kept out before the elaborations. The most frequent classes were uniformly predicted by the three methods, which instead differentiated notably for the classes with a lower occurrence. The Bayesian validation indicated that the SVM method was as reliable as the multi-collocated indicator cokriging, and both more than the deterministic pedological approach. An advantage of the SVM was the possibility to use numeric and categorical variable in the same elaboration, without any previous transformation, which notably reduced the processing time

Ozone effects on blood biomarkers of systemic inflammation, oxidative stress, endothelial function, and thrombosis: The Multicenter Ozone Study in oldEr Subjects (MOSES).

The evidence that exposure to ozone air pollution causes acute cardiovascular effects is mixed. We postulated that exposure to ambient levels of ozone would increase blood markers of systemic inflammation, prothrombotic state, oxidative stress, and vascular dysfunction in healthy older subjects, and that absence of the glutathione S-transferase Mu 1 (GSTM1) gene would confer increased susceptibility. This double-blind, randomized, crossover study of 87 healthy volunteers 55-70 years of age was conducted at three sites using a common protocol. Subjects were exposed for 3 h in random order to 0 parts per billion (ppb) (filtered air), 70 ppb, and 120 ppb ozone, alternating 15 min of moderate exercise and rest. Blood was obtained the day before, approximately 4 h after, and approximately 22 h after each exposure. Linear mixed effect and logistic regression models evaluated the impact of exposure to ozone on pre-specified primary and secondary outcomes. The definition of statistical significance was p<0.01. There were no effects of ozone on the three primary markers of systemic inflammation and a prothrombotic state: C-reactive protein, monocyte-platelet conjugates, and microparticle-associated tissue factor activity. However, among the secondary endpoints, endothelin-1, a potent vasoconstrictor, increased from pre- to post-exposure with ozone concentration (120 vs 0 ppb: 0.07 pg/mL, 95% confidence interval [CI] 0.01, 0.14; 70 vs 0 ppb: -0.03 pg/mL, CI -0.09, 0.04; p = 0.008). Nitrotyrosine, a marker of oxidative and nitrosative stress, decreased with increasing ozone concentrations, with marginal significance (120 vs 0 ppb: -41.5, CI -70.1, -12.8; 70 vs 0 ppb: -14.2, CI -42.7, 14.2; p = 0.017). GSTM1 status did not modify the effect of ozone exposure on any of the outcomes. These findings from healthy older adults fail to identify any mechanistic basis for the epidemiologically described cardiovascular effects of exposure to ozone. The findings, however, may not be applicable to adults with cardiovascular disease

First Morphological and Molecular Evidence of the Negative Impact of Diatom-Derived Hydroxyacids on the Sea Urchin Paracentrotus lividus.

Oxylipins (including polyunsaturated aldehydes PUAs, hydoxyacids and epoxyalcohols) are the end-products of a lipoxygenase/hydroperoxide lyase metabolic pathway in diatoms. To date very little information is available on oxylipins other than PUAs, even though they represent the most common oxylipins produced by diatoms. Here, we report, for the first time, on the effects of two hydroxyacids, 5-and 15-HEPE, which have never been tested before, using the sea urchin Paracentrotus lividus as a model organism. We show that HEPEs do induce developmental malformations but at concentrations higher when compared to PUAs. Interestingly, HEPEs also induced a marked developmental delay in sea urchin embryos, which has not hitherto been reported for PUAs. Recovery experiments revealed that embryos do not recover following treatment with HEPEs. Finally, we report the expression levels of 35 genes (involved in stress, development, differentiation, skeletogenesis and detoxification processes) to identify the molecular targets affected by HEPEs. We show that the two HEPEs have very few common molecular targets, specifically affecting different classes of genes and at different times of development. In particular, 15-HEPE switched on fewer genes than 5-HEPE, up-regulating mainly stress-related genes at a later pluteus stage of development. 5-HEPE was stronger than 15-HEPE, targeting twenty-four genes, mainly at the earliest stages of embryo development (at the blastula and swimming blastula stages). These findings highlight the differences between HEPEs and PUAs and also have important ecological implications because many diatom species do not produce PUAs but rather these other chemicals derived from the oxidation of fatty acids

Superconducting tunable flux qubit with direct readout scheme

We describe a simple and efficient scheme for the readout of a tunable flux qubit, and present preliminary experimental tests for the preparation, manipulation and final readout of the qubit state, performed in incoherent regime at liquid Helium temperature. The tunable flux qubit is realized by a double SQUID with an extra Josephson junction inserted in the large superconducting loop, and the readout is performed by applying a current ramp to the junction and recording the value for which there is a voltage response, depending on the qubit state. This preliminary work indicates the feasibility and efficiency of the scheme.Comment: 10 pages, 5 figure

The inhibition of the highly expressed miR-221 and miR-222 impairs the growth of prostate carcinoma xenografts in mice

MiR-221 and miR-222 are two highly homologous microRNAs whose upregulation has been recently described in several types of human tumors, for some of which their oncogenic role was explained by the discovery of their target p27, a key cell cycle regulator. We previously showed this regulatory relationship in prostate carcinoma cell lines in vitro, underlying the role of miR-221/222 as inducers of proliferation and tumorigenicity

Impact of migraine on fibromyalgia symptoms

Background: Fibromyalgia (FMS) and high frequency episodic/chronic migraine (M) very frequently co-occur, suggesting common pathophysiological mechanisms; both conditions display generalized somatic hyperalgesia. In FMS-M comorbidity we assessed if: a different level of hyperalgesia is present compared to one condition only; hyperalgesia is a function of migraine frequency; migraine attacks trigger FMS symptoms. Methods: Female patients with fibromyalgia (FMS)(n.40), high frequency episodic migraine (M1)(n.41), chronic migraine (M2)(n.40), FMS + M1 (n.42) and FMS + M2 (n.40) underwent recording of: −electrical pain thresholds in skin, subcutis and muscle and pressure pain thresholds in control sites, −pressure pain thresholds in tender points (TePs), −number of monthly migraine attacks and fibromyalgia flares (3-month diary). Migraine and FMS parameters were evaluated before and after migraine prophylaxis, or no prophylaxis, for 3 months with calcium-channel blockers, in two further FMS + H1 groups (n.49, n.39). 1-way ANOVA was applied to test trends among groups, Student’s t-test for paired samples was used to compare pre and post-treatment values. Results: The lowest electrical and pressure thresholds at all sites and tissues were found in FMS + M2, followed by FMS + H1, FMS, M2 and M1 (trend: p < 0.0001). FMS monthly flares were progressively higher in FMS, FMS + M1 and FMS + M2 (p < 0.0001); most flares (86–87 %) occurred within 12 h from a migraine attack in co-morbid patients (p < 0.0001). Effective migraine prophylaxis vs no prophylaxis also produced a significant improvement of FMS symptoms (decreased monthly flares, increased pain thresholds)(0.0001 < p < 0.003). Conclusions: Co-morbidity between fibromyalgia and migraine involves heightened somatic hyperalgesia compared to one condition only. Increased migraine frequency – with shift towards chronicity – enhances both hyperalgesia and spontaneous FMS pain, which is reversed by effective migraine prophylaxis. These results suggest different levels of central sensitization in patients with migraine, fibromyalgia or both conditions and a role for migraine as a triggering factor for FMS