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Background: Nonpharmacological approaches are recommended as first-line treatment for the neuropsychiatric symptoms of persons living with dementia. Tailored Activity Program is a non-pharmacological approach which has shown to reduce neuropsychiatric symptoms and functional dependence of persons living with dementia at the community, as well as caregiver’s distress. The aim of the study is to assess the feasibility of a project in which the Tailored Activity Program is delivered partially at an acute hospital ward and partially at the patient's home. Material and Methods: The study is aimed at enrolling 50 dyads (patients/caregivers) from the memory clinic, acute geriatric ward, or short-stay emergency department at the IRCCS San Gerardo dei Tintori, Monza (Italy). Inclusion criteria are: the patient’s clinical diagnosis of dementia, clinically detectable neuropsychiatric symptoms (Neuro Psychiatric Inventory, NPI score >6 in at least one item), the ability to participate in activities of daily living (Katz’s index ≥ 2), the presence of a caregiver (aged ≥ 21 years; minimum 4 hours of daily care), and the willingness of the dyad patient/caregiver to participate in the project. Treatment is delivered both i) in hospital (2 sessions) and ii) at home (6 sessions), including occupational therapy, telemedicine and nursing care provided once a week. The following variables are measured: Neuro Psychiatric Inventory, Time Up and Go Test, Katz’s Index and the level of caregiver’s sense of competence with the Sense of Competence Questionnaire. Data are collected both at baseline, at the end of the treatment, with a follow-up scheduled at 4 months after enrollment. Primary outcome measure is the dyad's retention rate at 4-month follow-up. Secondary outcomes are the reduction in the patient’s frequency and severity of NPS; the improvement in the patient’s Time Up and Go Test score; the improvement in the patient's levels of autonomy and participation in activities of daily

Medulloblastoma (MB) is a childhood malignant brain tumour comprising four main subgroups characterized by different genetic alterations and rate of mortality. Among MB subgroups, patients with enhanced levels of the c-MYC oncogene (MBGroup3) have the poorest prognosis. Here we identify a previously unrecognized role of the pro-autophagy factor AMBRA1 in regulating MB. We demonstrate that AMBRA1 expression depends on c-MYC levels and correlates with Group 3 patient poor prognosis; also, knockdown of AMBRA1 reduces MB stem potential, growth and migration of MBGroup3 stem cells. At a molecular level, AMBRA1 mediates these effects by suppressing SOCS3, an inhibitor of STAT3 activation. Importantly, pharmacological inhibition of autophagy profoundly affects both stem and invasion potential of MBGroup3 stem cells, and a combined anti-autophagy and anti-STAT3 approach impacts the MBGroup3 outcome. Taken together, our data support the c-MYC/AMBRA1/STAT3 axis as a strong oncogenic signalling pathway with significance for both patient stratification strategies and targeted treatments of MBGroup3.

Medulloblastoma (MB) is the most common malignant brain tumor in children and among the subtypes, Group 3 MB has the worst outcome. Here, we perform an in vivo, patient-specific screen leading to the identification of Otx2 and c-MYC as strong Group 3 MB inducers. We validated our findings in human cerebellar organoids where Otx2/c-MYC give rise to MB-like organoids harboring a DNA methylation signature that clusters with human Group 3 tumors. Furthermore, we show that SMARCA4 is able to reduce Otx2/c-MYC tumorigenic activity in vivo and in human cerebellar organoids while SMARCA4 T910M, a mutant form found in human MB patients, inhibits the wild-type protein function. Finally, treatment with Tazemetostat, a EZH2-specific inhibitor, reduces Otx2/c-MYC tumorigenesis in ex vivo culture and human cerebellar organoids. In conclusion, human cerebellar organoids can be efficiently used to understand the role of genes found altered in cancer patients and represent a reliable tool for developing personalized therapies.

Legacy soil data have been produced over 70 years in nearly all countries of the world. Unfortunately, data, information and knowledge are still currently fragmented and at risk of getting lost if they remain in a paper format. To process this legacy data into consistent, spatially explicit and continuous global soil information, data are being rescued and compiled into databases. Thousands of soil survey reports and maps have been scanned and made available online. The soil profile data reported by these data sources have been captured and compiled into databases. The total number of soil profiles rescued in the selected countries is about 800,000. Currently, data for 117, 000 profiles are compiled and harmonized according to GlobalSoilMap specifications in a world level database (WoSIS). The results presented at the country level are likely to be an underestimate. The majority of soil data is still not rescued and this effort should be pursued. The data have been used to produce soil property maps. We discuss the pro and cons of top-down and bottom-up approaches to produce such maps and we stress their complementarity. We give examples of success stories. The first global soil property maps using rescued data were produced by a top-down approach and were released at a limited resolution of 1 km in 2014, followed by an update at a resolution of 250 m in 2017. By the end of 2020, we aim to deliver the first worldwide product that fully meets the GlobalSoilMap specifications.

There is an increasing demand for up-to-date soil organic carbon (OC) data for global environmental and climatic modelling. The aim of this study was to create a map of topsoil OC content at the European scale by applying digital soil mapping techniques to the first European harmonized geo-referenced topsoil (0–20 cm) database, which arises from the Land use/Cover Area frame statistical Survey (LUCAS). A map of the associated uncertainty was also produced to support careful use of the predicted OC contents. A generalized additive model (GAM)was fitted on 85% of the dataset (R2 =0.29), using OC content as dependent variable; a backward stepwise approach selected slope, land cover, temperature, net primary productivity, latitude and longitude as suitable covariates. The validation of the model (performed on 15% of the data-set) gave an overall R2 of 0.27 and an R2 of 0.21 for mineral soils and 0.06 for organic soils. Organic C content in most organic soils was under-predicted, probably because of the imposed unimodal distribution of our model, whose mean is tilted towards the prevalent mineral soils. This was also confirmed by the poor prediction in Scandinavia (where organic soils are more frequent), which gave an R2 of 0.09, whilst the prediction performance (R2) in non-Scandinavian countries was 0.28. Themap of predicted OC content had the smallest values in Mediterranean countries and in croplands across Europe, whereas largest OC contents were predicted in wetlands, woodlands and mountainous areas. The map of the predictions’ standard error had large uncertainty in northern latitudes, wetlands, moors and heathlands, whereas small uncertainty was mostly found in croplands. The map produced gives the most updated general picture of topsoil OC content at the European Union scale.

A reliable spatial assessment of the POPs contamination in soils is essential for burden studies and flux evaluations. Soil characteristics and properties vary enormously even within small spatial scale and over time; therefore soil capacity of accumulating POPs varies greatly. In order to include this very high spatial and temporal variability, models can be used for assessing soil accumulation capacity in a specific time and space and, from it, the spatial distribution and temporal trends of POPs concentrations. In this work, predictive contamination maps of the accumulation capacity of soils were developed at a space resolution of 1×1m with a time frame of one day, in a study area located in the central Alps. Physical algorithms for temperature and organic carbon estimation along the soil profile and across the year were fitted to estimate the horizontal, vertical and seasonal distribution of the contamination potential for PCBs in soil (Ksa maps).The resulting maps were cross-validated with an independent set of PCB contamination data, showing very good agreement (e.g. for CB-153, R2=0.80, p-value≤2.2·10-06). Slopes of the regression between predicted Ksa and experimental concentrations were used to map the soil contamination for the whole area, taking into account soil characteristics and temperature conditions. These maps offer the opportunity to evaluate burden (concentration maps) and fluxes (emission maps) with highly resolved temporal and spatial detail.In addition, in order to explain the observed low autumn PCB concentrations in soil related to the high Ksa values of this period, a dynamic model of seasonal variation of soil concentrations was developed basing on rate parameters fitted on measured concentrations. The model was able to describe, at least partially, the observed different behavior between the quite rapid discharge phase in summer and the slow recharge phase in autumn. © 2013 Elsevier B.V.

Statistical methods are extensively used by hydrogeologists for assessing groundwater vulnerability. Several of these methods require to express the response variable as binary and to select a threshold distinguishing between positive and negative indicators of contamination that are usually identified as occurrences and non-occurrences, respectively. In this study, both occurrences and non-occurrences were alternately used as training points (TPs) in the Weights of Evidence (WofE) for assessing groundwater vulnerability to nitrate contamination of a shallow, unconfined, porous aquifer. This was done to better understand the individual role and the combined effect of explanatory variables in both protecting and exposing groundwater from and to nitrate contamination in the study area. The idea behind this approach is that, for a given aquifer, each explanatory variable should have an unequivocal effect on the physical process of groundwater contamination. As part of this study, a procedure for multi-class generalization was developed. Results showed that an evidential theme, even if it appears to be a statistically significant predictor of occurrences, can show an equivocal spatial relationship with the positive and the negative indicators of contamination due to the presence of a sampling bias between the TPs and the evidential theme. It was demonstrated that, if sampling bias is not recognized and corrected, the use of such evidential theme in the analysis could lead to obtain unreliable groundwater vulnerability maps. In order to deal with this issue, a quantitative methodology to correct the effects of sampling bias was successfully tested. Indeed, once the spatial relationships between the different type of TPs and the considered evidential themes were corrected for the effects of sampling bias, the WofE method was found to be a reliable modeling technique for assessing groundwater vulnerability and proved to be capable of identifying areas characterized by diff

The influence of organisms on pedogenesis is acknowledged in the scorpan model; however organisms, plants in particular, might be seen in a different light within the scorpan model. In fact, in minimally managed terrestrial ecosystems, biota coexists with soil as part of a feedback system, in which the biota not only influences soil development, but is also in turn influenced by it. This means that in natural environments a particular soil is usually associated with a typical combination of plant species which thrive in the biotope defined by the soil physical and chemical properties. Changes in soil features will favor certain species over others, thus modifying the structure of the resident plant communities. This makes plant communities very effective proxies of soil properties, effectively acting as widespread biological sensors. In this paper we will show how plant communities can be utilized to improve the quality of digital soil maps, effectively reducing the amount of field work needed by soil surveys, through a combination of relatively swifter and cheaper vegetation surveys and remote sensing data. The approach we propose is based on the spectral and textural properties of plant communities which can be summarized from high resolution remotely sensed images and LIDAR data through the use of geostatistical, spectral and geomorphometric descriptors. These descriptors are then associated with the scores obtained from the ordination of the plant communities' relative coverage. Ordination projects the high dimensional plant cover data into a lesser dimensional space, thus making easier to establish a relation between ecological space and geostatistical descriptors. Once established this relation can be exploited through the use of regression techniques in a regression kriging framework. In this case study, we applied the proposed model to the prediction of soil organic carbon content in an alpine grassland. The use of plant communities cover almost doubled the

The aim of this study is the application of support vector machines (SVM) to landslide susceptibility mapping. SVM are a set of machine learning methods in which model capacity matches data complexity. The research is based on a conceptual framework targeted to apply and test all the procedural steps for landslide susceptibility modeling from model selection, to investigation of predictive variables, from empirical cross-validation of results, to analysis of predicted patterns. SVM were successfully applied and the final susceptibility map was interpreted via success and prediction rate curves and receiver operating characteristic (ROC) curves, to support the modeling results and assess the robustness of the model. SVM appeared to be very specific learners, able to discriminate between the informative input and random noise. About 78% of occurrences was identified within the 20% of the most susceptible study area for the cross-validation set. Then the final susceptibility map was compared with other maps, addressed by different statistical approaches, commonly used in susceptibility mapping, such as logistic regression, linear discriminant analysis, and naive Bayes classifier. The SVM procedure was found feasible and able to outperform other techniques in terms of accuracy and generalization capacity. The over-performance of SVM against the other techniques was around 18% for the cross-validation set, considering the 20% of the most susceptible area. Moreover, by analyzing receiver operating characteristic (ROC) curves, SVM appeared to be less prone to false positives than the other models. The study was applied in the Staffora river basin (Lombardy, Northern Italy), an area of about 275 km 2 characterized by a very high density of landslides, mainly superficial slope failures triggered by intense rainfall events.

Coeliac disease (CD) is a chronic intolerance to gluten, contained mainly in wheat, rye and barley. The only therapy at present is the lifelong exclusion of gluten from the diet. Whether oats can be considered safe for CD patients has long been debated, and oats have been included among gluten-free ingredients only recently (EU Regulation 41/2009), provided the gluten content does not exceed 20 ppm. The aim of this study was to evaluate the suitability of 36 different oat cultivars for CD patients using biochemical and immunochemical approaches. The cross-reactivity between avenins and gliadins was evaluated by both SDS-PAGE/Immunoblotting and ELISA. The protein pattern of each oat cultivar showed both qualitative and quantitative differences that correlated with different binding affinity for specific anti-gliadin antibodies in immunoblotting. In most oat samples, the content of cross-reactive proteins measured by ELISA was below 20 ppm, but in a few varieties was above 80 ppm. Although the taxonomic and biochemical characteristics of oats allow to conclude that their use could be safe for CD patients, it is essential to select those cultivars having the lowest level of gluten-like proteins.

Statistical methods are widely used in environmental studies to evaluate natural hazards. Within groundwater vulnerability in particular, statistical methods are used to support decisions about environmental planning and management. The production of vulnerability maps obtained by statistical methods can greatly help decision making. One of the key points in all of these studies is the validation of the model outputs, which is performed through the application of various techniques to analyze the quality and reliability of the final results and to evaluate the model having the best performance. In this study, a groundwater vulnerability assessment to nitrate contamination was performed for the shallow aquifer located in the Province of Milan (Italy). The Weights of Evidence modeling technique was used to generate six model outputs, each one with a different number of input predictive factors. Considering that a vulnerability map is meaningful and useful only if it represents the study area through a limited number of classes with different degrees of vulnerability, the spatial agreement of different reclassified maps has been evaluated through the kappa statistics and a series of validation procedures has been proposed and applied to evaluate the reliability of the reclassified maps. Results show that performance is not directly related to the number of input predictor factors and that is possible to identify, among apparently similar maps, those best representing groundwater vulnerability in the study area. Thus, vulnerability maps generated using statistical modeling techniques have to be carefully handled before they are disseminated. Indeed, the results may appear to be excellent and final maps may perform quite well when, in fact, the depicted spatial distribution of vulnerability is greatly different from the actual one. For this reason, it is necessary to carefully evaluate the obtained results using multiple statistical techniques that are capable of providi

The seasonal and spatial variability of polychlorinated biphenyls (PCBs) in vegetation and cow milk was studied in a high altitude pasture in the Alps (1900 m a.s.l.). PCB contamination in vegetation shows a concentration peak in June, which is mainly interpreted as the consequence of a temporary PCB enrichment of the air layer above the ground due to net emission fluxes from the soil. A three compartment dynamic model was developed to test this hypothesis. The North/South enrichment factor in the vegetation was 1.5–1.6 for penta- and hexa-substituted congeners and 1.7 for hepta- and octa-PCBs, according to the effect of temperature on compounds having higher Koa values. Milk concentrations followed the vegetation seasonal trend. The congener abundance in milk is in agreement with the biotransformation susceptibility, absorption efficiency and residence time of the different congeners in dairy cows., The seasonal and spatial variability of polychlorinated biphenyls (PCBs) in vegetation and cow milk was studied in a high altitude pasture in the Alps (1900 m a.s.l.). PCB contamination in vegetation shows a concentration peak in June, which is mainly interpreted as the consequence of a temporary PCB enrichment of the air layer above the ground due to net emission fluxes from the soil. A three compartment dynamic model was developed to test this hypothesis. The North/South enrichment factor in the vegetation was 1.5-1.6 for penta- and hexa-substituted congeners and 1.7 for hepta- and octa-PCBs, according to the effect of temperature on compounds having higher K oa values. Milk concentrations followed the vegetation seasonal trend. The congener abundance in milk is in agreement with the biotransformation susceptibility, absorption efficiency and residence time of the different congeners in dairy cows. © 2010 Published by Elsevier Ltd.

The aim of the study is to assess the degree of spatial agreement among different patterns of landslide susceptibility maps with almost similar success and prediction rate curves, obtained using different combinations of predictive factors. Our approach was tested in an alpine environment (Italian Alps) where debris flows represent one of the most frequent dangerous processes. A data-driven Bayesian method (the Weights of Evidence modelling technique) was successfully applied, and success and prediction rate curves were computed for supporting the modelling results and assessing the robustness of the models. The values of the area under curves were very similar for different models, ranging from 84.36% to 86.49% for the success rate curves and from 82.46% to 85.66% for the prediction rate curves. Then, the post-probability output maps were classified into rank-based maps, by using an equal-area criterion, to compare the predicted results.Afterwards, appropriate statistical techniques (kappa statistic, principal component analysis, and distance weighted entropy) were applied. Kappa statistic and principal component analysis outcomes called for significant differences within the output spatial patterns of the predicted maps as well as within the highest susceptibility classes. Moreover, the estimated distance weighted entropy values showed a very low overall entropy at the valley bottom, as all models predicted this area equally as low susceptible. In contrast, areas characterised by the highest values of entropy were more concentrated in the northern and southern slopes of the study site, lying in zones where landslide density was higher.Consequently, susceptibility maps with similar predictive power may not have the same meaning in terms of spatial pattern of predicted results. It is for this reason that landslide susceptibility maps should be distributed together with map documents aimed at defining the level of accuracy of the predicted results to provide the end

Support vector regression (SVR) is a powerful machine learning technique in the framework of the statistical learning theory; while Kriging is a well-established prediction method traditionally used in the spatial statistics field. However, the two techniques share the same background of reproducing kernel Hilbert space (RKHS). SVR has recently shown promising performance in different spatial mapping tasks. In the present work, the problem of spatial data mapping is addressed using a multi-scale SVR (MS-SVR) approach. This can be considered as a multi-resolution analysis of the observed process. The multi-scale SVR approach is particularly attractive for its capability to deal, at the same time, with the nonlinear regression of the dependent variable on auxiliary variables and with the spatial interpolation. This capability makes the MS-SVR an optimal choice for automatic mapping system. In the present work MS-SVR was applied to soil heavy metal content mapping, in a study area site in the Italian Alps. The area complex landscape, modelled by both glacial and karsts phenomena, along with an heterogeneous nature of the parent material, makes the mapping of heavy metal content a difficult task to approach with linear regression or mixed geostatistical techniques. The result obtained outlines the Multi-scale SVR as a powerful technique for general inference and automatic mapping, with the only constraint of the requirement of a multi-parameter optimization

Polycyclic Aromatic Hydrocarbons (PAHs) are a major group of pollutants whose occurrence in the environment is mainly of anthropogenic origin. In this paper,we examine the effect of topographical slope exposure on PAH contamination and seasonal change in PAH concentrations in soils.We collected soil samples on three dates in 2007 (early May, end of July and beginning of November) from south- and north-facing aspects at 1900ma.s.l. in the central Italian Alps.We found greater PAH contamination in soils from a north-facing slope than in those from a south-facing slope at all seasons. We calculated North–South Enrichment Factors as the ratio between the concentrations measured in soils from northern and southern aspects. These ratios ranged from 1.4 to 1.9 for lighter PAHs (from 2 to 4 rings). These values are consistent with theoretical calculations based on temperature-specific octanol-air partition coefficients (predicted North-South Enrichment Factors range from 1.6 to 2.0). For heavier PAHs (from 5 to 6 rings), smaller differences were observed between soils from northern and southern aspects, due to the gas/particle distribution of these compounds. We also found consistent differences in normalised PAH concentrations across the three sampling periods. The majority of compounds showed a significant decreasing trend from the beginning of May to the end of July, due to the annual cycles of physical processes (deposition vs. volatilisation) and biological processes (uptake and/or biotransformation). Only a few compounds showed different trends, presumably due to season-specific local emission sources.