27 research outputs found

    Farmers as data sources: Cooperative framework for mapping soil properties for permanent crops in South Tyrol (Northern Italy)

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    Abstract Detailed knowledge of agricultural soil properties is a key element for high-quality food production. However, high-resolution soil data covering a large agricultural region are generally unavailable. This study explores a demand-driven cooperative framework for soil data sourcing that connects individual farmers to several stakeholders by means of a centralised database containing more than 16,000 records of soil information collected within the framework of an integrated production program for intensively managed permanent crops in the Adige/Etsch and Venosta/Vinschgau valleys in South Tyrol, Italy. Data for soil pH, soil organic matter (SOM), and soil texture were used to produce digital soil maps with a RMSE of 0.21, 1.25% and a cross-validation of 43%, respectively. Spatialisation was conducted using either regression-kriging or multinomial logistic regression. Collaboration among farmers, public administrators, and researchers provided a successful cooperative framework for digital soil mapping. The maps highlight the complex interplay of the postglacial evolution of these valleys due to the presence of a cluster of large alluvial fans and the anthropogenic influences of intense farming on pH, SOM, and soil texture. This study regarded a subset of the available soil properties, which can be dealt with using the geostatistical approaches presented herein. Thus, a long-term soil monitoring program and the combination of all available variables will allow digital assessment of the spatial patterns of nutrient availability, ecological risk assessments, change detection studies, and an overall long-term plan for soil security at larger spatial scales

    Copper and zinc as a window to past agricultural land-use.

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    Abstract Intensive agricultural management significantly affects soil chemical properties. Such impacts, depending on the intensity of agronomic practices, might persist for several decades. We tested how current soil properties, especially heavy metal concentrations, reflect the land-use history over a 24,000 ha area dominated by intensive apple orchards and viticulture (South Tyrol, ITA). We combined georeferenced soil analyses with land-use maps from 1850 to 2010 in a space-for-time approach to detect the accumulation rates of copper and zinc and understand how present-day soil heavy metal concentrations reflect land-use history. Soils under vineyards since the 1850s showed the highest available copper concentration (median of 314.0 mg kg-1, accumulation rate between 19.4 and 41.3 mg kg-1·10 y-1). Zinc reached the highest concentration in the same land-use type (median of 32.5 mg kg-1, accumulation rate between 1.8 and 4.4 mg kg-1·10 y-1). Using a random forest approach on 44,132 soil samples, we extrapolated land-use history on the permanent crop area of the region, reaching an accuracy of 0.72. This suggests that combining current soil analysis, historical management information, and machine learning models provides a valuable tool to predict land-use history and understand management legacies

    Electron-Beam-Induced Grafting of Chitosan onto HDPE/ATZ Composites for Biomedical Applications

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    The surface functionalisation of high-density polyethylene (HDPE) and HDPE/alumina-toughened zirconia (ATZ) surfaces with chitosan via electron-beam (EB) irradiation technique was exploited for preparing materials suitable for biomedical purposes. ATR–FTIR analysis and wettability measurements were employed for monitoring the surface changes after both irradiation and chitosan grafting reaction. Interestingly, the presence of ATZ loadings beyond 2 wt% influenced both the EB irradiation process and the chitosan functionalisation reaction, decreasing the oxidation of the surface and the chitosan grafting. The EB irradiation induced an increase in Young’s modulus and a decrease in the elongation at the break of all analysed systems, whereas the tensile strength was not affected in a relevant way. Biological assays indicated that electrostatic interactions between the negative charges of the surface of cell membranes and the –NH3+ sites on chitosan chains promoted cell adhesion, while some oxidised species produced during the irradiation process are thought to cause a detrimental effect on the cell viability

    Mechanical and Biological Characterization of PMMA/Al2O3 Composites for Dental Implant Abutments

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    The mechanical and biological behaviors of PMMA/Al2O3 composites incorporating 30 wt.%, 40 wt.%, and 50 wt.% of Al2O3 were thoroughly characterized as regards to their possible application in implant-supported prostheses. The Al2O3 particles accounted for an increase in the flexural modulus of PMMA. The highest value was recorded for the composite containing 40 wt.% Al2O3 (4.50 GPa), which was about 18% higher than that of its unfilled counterpart (3.86 GPa). The Al2O3 particles caused a decrease in the flexural strength of the composites, due to the presence of filler aggregates and voids, though it was still satisfactory for the intended application. The roughness (Ra) and water contact angle had the same trend, ranging from 1.94 microns and 77.2° for unfilled PMMA to 2.45 microns and 105.8° for the composite containing the highest alumina loading, respectively, hence influencing both the protein adsorption and cell adhesion. No cytotoxic effects were found, confirming that all the specimens are biocompatible and capable of sustaining cell growth and proliferation, without remarkable differences at 24 and 48 h. Finally, Al2O3 was able to cause strong cell responses (cell orientation), thus guiding the tissue formation in contact with the composite itself and not enhancing its osteoconductive properties, supporting the PMMA composite’s usage in the envisaged application

    High familial burden of cancer correlates with improved outcome from immunotherapy in patients with NSCLC independent of somatic DNA damage response gene status

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    Family history of cancer (FHC) is a hallmark of cancer risk and an independent predictor of outcome, albeit with uncertain biologic foundations. We previously showed that FHC-high patients experienced prolonged overall (OS) and progression-free survival (PFS) following PD-1/PD-L1 checkpoint inhibitors. To validate our findings in patients with NSCLC, we evaluated two multicenter cohorts of patients with metastatic NSCLC receiving either first-line pembrolizumab or chemotherapy. From each cohort, 607 patients were randomly case-control matched accounting for FHC, age, performance status, and disease burden. Compared to FHC-low/negative, FHC-high patients experienced longer OS (HR 0.67 [95% CI 0.46-0.95], p\u2009=\u20090.0281), PFS (HR 0.65 [95% CI 0.48-0.89]; p\u2009=\u20090.0074) and higher disease control rates (DCR, 86.4% vs 67.5%, p\u2009=\u20090.0096), within the pembrolizumab cohort. No significant associations were found between FHC and OS/PFS/DCR within the chemotherapy cohort. We explored the association between FHC and somatic DNA damage response (DDR) gene alterations as underlying mechanism to our findings in a parallel cohort of 118 NSCLC, 16.9% of whom were FHC-high. The prevalence of\u2009 65\u20091 somatic DDR gene mutation was 20% and 24.5% (p\u2009=\u20090.6684) in FHC-high vs. FHC-low/negative, with no differences in tumor mutational burden (6.0 vs. 7.6 Mut/Mb, p\u2009=\u20090.6018) and tumor cell PD-L1 expression. FHC-high status identifies NSCLC patients with improved outcomes from pembrolizumab but not chemotherapy, independent of somatic DDR gene status. Prospective studies evaluating FHC alongside germline genetic testing are warranted

    APOLLO 11 Project, Consortium in Advanced Lung Cancer Patients Treated With Innovative Therapies: Integration of Real-World Data and Translational Research

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    Introduction: Despite several therapeutic efforts, lung cancer remains a highly lethal disease. Novel therapeutic approaches encompass immune-checkpoint inhibitors, targeted therapeutics and antibody-drug conjugates, with different results. Several studies have been aimed at identifying biomarkers able to predict benefit from these therapies and create a prediction model of response, despite this there is a lack of information to help clinicians in the choice of therapy for lung cancer patients with advanced disease. This is primarily due to the complexity of lung cancer biology, where a single or few biomarkers are not sufficient to provide enough predictive capability to explain biologic differences; other reasons include the paucity of data collected by single studies performed in heterogeneous unmatched cohorts and the methodology of analysis. In fact, classical statistical methods are unable to analyze and integrate the magnitude of information from multiple biological and clinical sources (eg, genomics, transcriptomics, and radiomics). Methods and objectives: APOLLO11 is an Italian multicentre, observational study involving patients with a diagnosis of advanced lung cancer (NSCLC and SCLC) treated with innovative therapies. Retrospective and prospective collection of multiomic data, such as tissue- (eg, for genomic, transcriptomic analysis) and blood-based biologic material (eg, ctDNA, PBMC), in addition to clinical and radiological data (eg, for radiomic analysis) will be collected. The overall aim of the project is to build a consortium integrating different datasets and a virtual biobank from participating Italian lung cancer centers. To face with the large amount of data provided, AI and ML techniques will be applied will be applied to manage this large dataset in an effort to build an R-Model, integrating retrospective and prospective population-based data. The ultimate goal is to create a tool able to help physicians and patients to make treatment decisions. Conclusion: APOLLO11 aims to propose a breakthrough approach in lung cancer research, replacing the old, monocentric viewpoint towards a multicomprehensive, multiomic, multicenter model. Multicenter cancer datasets incorporating common virtual biobank and new methodologic approaches including artificial intelligence, machine learning up to deep learning is the road to the future in oncology launched by this project

    Meteo Browser South Tyrol: A Shiny App to download the meteorological time series from the Open Data Catalogue of the Province of Bolzano/Bozen - Italy

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    Meteo Browser South Tyrol is a user-friendly web-based application that helps to visualize and download the hydro-meteorological time series freely available in South Tyrol, Italy. It is designed for a wide range of users, from common citizens to students as well as researchers, private companies and the public administration. Meteo Browser South Tyrol is a Shiny App inside an R package and can be used on a local machine or accessed on-line. Drop down menus allow the user to select hydro-meteorological station and measurements. A simple map shows where the monitoring stations are, the latest measurements available, and lets the user subset the selected stations geographically by drawing a polygon

    Soil Organic Carbon Modelling with Digital Soil Mapping and Remote Sensing for Permanently Vegetated Areas

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    Soil organic matter is essential for preserving and maintaining a range of soil and ecosystem functions as well as supply and store carbon for climate change mitigation. Digital Soil Mapping techniques will be used to obtain a spatially continuous product, especially over permanently vegetated areas. Recently available satellite remote sensing data, with among other systems the Copernicus Sentinel, will be used as input for environmental covariates. Digital Soil Mapping, coupled together with Remote Sensing products, is a powerful tool to produce soil properties maps and monitoring the changes in soil conditions over time

    A Hierarchical Multivariate Spatio-Temporal Model for Clustered Climate data with Annual Cycles

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    We introduce a Bayesian multivariate hierarchical framework to estimate a space-time model for a joint series of monthly extreme temperatures and amounts of precipitation. Data are available for 360 monitoring stations over 60 years, with missing data affecting almost all series. Model components account for spatio-temporal correlation and annual cycles, dependence on covariates and between responses. Spatio-temporal dependence is modeled by the nearest neighbor Gaussian process (GP), response multivariate depen- dencies are represented by the linear model of coregionalization and effects of annual cycles are included by a circular representation of time. The proposed approach allows imputation of missing values and interpolation of climate surfaces at the national level. It also provides a characterization of the so called Italian ecoregions, namely broad and discrete ecologically homogeneous areas of similar potential as regards the climate, physiography, hydrography, vegetation, and wildlife. To now, Italian ecoregions are hierarchically classified into 4 tiers that go from 2 Divisions to 35 Subsections and are defined by informed expert judgments. The current climatic characterization of Italian ecoregions is based on bioclimatic indices for the period 1955–2000
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