Geomatics and epidemiology: Associating oxidative stress and greenness in urban areas.

Green spaces may benefit human health mainly by mitigating noise and air pollution, promoting physical or social activities and improving mental health. Based on the influence that green space exposure seems to exert on Public Health and using a multidisciplinary approach, we investigated, the association between oxidative stress (OS) and green exposure in children. Overall, 207 subjects (10-13 yrs) living in Torino (NW- Italy) were involved in this study. Each participant provided a urinary sample, used to quantify a reliable OS biomarker (15-F2t-IsoP), and their residence addresses, used for geocoding. Green exposure was characterised by calculating i) the Soil Adjusted Vegetation Index (SAVI) within fixed buffers around each participant's home, using remotely-sensed data; ii) Tree Map accounting for evergreen/broadleaf species; iii) The percentage of green cover (PGC). Significant negative correlation (Pearson's r = -0.758, p < 0.001) between PGC and 15-F2t-IsoP was found. Greater SAVI was associated with lower OS (Pearson's r = -0.717, p < 0.001). Noticeably, evergreens seemed to determine a significant OS reduction compared to broadleaves (slope = -0.12 and -0.02, respectively; Warton-test F = 12.48, p = 0.0011). Finally, a spatial distribution of 15-F2t-IsoP estimates map, overlying with 2011 Census Data on same-aged dwellers of Torino, was generated. Predictive models accounting for green spaces influence on OS can be useful tool derived from geomatic employ in the Public Health field. Future developments of such a multidisciplinary approach should be considered in urban planning and policy-makers decisions to better define priority zones to requalify in urban settings.

Ultrasonographic features of adrenal gland lesions in dogs can aid in diagnosis.

BACKGROUND: Ultrasonography to visualize adrenal gland lesions and evaluate incidentally discovered adrenal masses in dogs has become more reliable with advances in imaging techniques. However, correlations between sonographic and histopathological changes have been elusive. The goal of our study was to investigate which ultrasound features of adrenal gland abnormalities could aid in discriminating between benign and malignant lesions. To this end, we compared diagnosis based on ultrasound appearance and histological findings and evaluated ultrasound criteria for predicting malignancy. RESULTS: Clinical records of 119 dogs that had undergone ultrasound adrenal gland and histological examination were reviewed. Of these, 50 dogs had normal adrenal glands whereas 69 showed pathological ones. Lesions based on histology were classified as cortical adrenal hyperplasia (n = 67), adenocarcinoma (n = 17), pheochromocytoma (n = 10), metastases (n = 7), adrenal adenoma (n = 4), and adrenalitis (n = 4). Ultrasonographic examination showed high specificity (100%) but low sensitivity (63.7%) for identifying the adrenal lesions, which improved with increasing lesion size. Analysis of ultrasonographic predictive parameters showed a significant association between lesion size and malignant tumors. All adrenal gland lesions >20 mm in diameter were histologically confirmed as malignant neoplasms (pheochromocytoma and adenocarcinoma). Vascular invasion was a specific but not sensitive predictor of malignancy. As nodular shape was associated with benign lesions and irregular enlargement with malignant ones, this parameter could be used as diagnostic tool. Bilaterality of adrenal lesions was a useful ultrasonographic criterion for predicting benign lesions, as cortical hyperplasia. CONCLUSIONS: Abnormal appearance of structural features on ultrasound images (e.g., adrenal gland lesion size, shape, laterality, and echotexture) may aid in diagnosis, but these features alone were not pathognomic. Lesion size was the most direct ultrasound predictive criterion. Large and irregular masses seemed to be better predictors of malignant neoplasia and lesions <20 mm in diameter and nodular in shape were often identified as cortical hyperplastic nodules or adenomas.

Estimation of Intercepted Solar Radiation and Stem Water Potential in a Table Grape Vineyard Covered by Plastic Film Using Sentinel-2 Data: A Comparison of OLS-, MLR-, and ML-Based Methods.

In the framework of precision viticulture, satellite data have been demonstrated to significantly support many tasks. Specifically, they enable the rapid, large-scale estimation of some viticultural parameters like vine stem water potential (Ψstem) and intercepted solar radiation (ISR) that traditionally require time-consuming ground surveys. The practice of covering table grape vineyards with plastic films introduces an additional challenge for estimation, potentially affecting vine spectral responses and, consequently, the accuracy of estimations from satellites. This study aimed to address these challenges with a special focus on the exploitation of Sentinel-2 Level 2A and meteorological data to monitor a plastic-covered vineyard in Southern Italy. Estimates of Ψstem and ISR were obtained using different algorithms, namely, Ordinary Least Square (OLS), Multivariate Linear Regression (MLR), and machine learning (ML) techniques, which rely on Random Forest Regression, Support Vector Regression, and Partial Least Squares. The results proved that, despite the potential spectral interference from the plastic coverings, ISR and Ψstem can be locally estimated with a satisfying accuracy. In particular, (i) the OLS regression-based approach showed a good performance in providing accurate ISR estimates using the near-infrared spectral bands (RMSE < 8%), and (ii) the MLR and ML algorithms could estimate both the ISR and vine water status with a higher accuracy (RMSE < 7 for ISR and RMSE < 0.14 MPa for Ψstem). These results encourage the adoption of medium-high resolution multispectral satellite imagery for deriving satisfying estimates of key crop parameters even in anomalous situations like the ones where plastic films cover the monitored vineyard, thus marking a significant advancement in precision viticulture.

Snow Metrics as Proxy to Assess Sarcoptic Mange in Wild Boar: Preliminary Results in Aosta Valley (Italy).

The widespread diffusion of the wild boar on the Italian territory and its consistent use for hunting have created the possibility to conduct multiple studies on the pathologies afflicting this ungulate. Nevertheless, in the last two decades, only some pathologies such as classical and African Swine Fever, Tuberculosis, Brucellosis from Brucella suis have benefited from substantial public funding and the consequent great interest from the scientific world, while less attention was addressed to parasitic diseases including sarcoptic mange. Therefore, to fill this gap, the purpose of this study was to contribute to the knowledge of sarcoptic mange in the wild boar population in Aosta Valley in the Northwest of Italy, including sympatric species as foxes. Due to past field surveys, it has been possible to find a possible role of snow metrics in the spread of this pathogen. Even if there are only empirical evidence and the mechanism remain unknown remote sensing analysis considering snow metrics were performed to provide to veterinarians, foresters, biologists, and ecologists new tools to better understand wield board dynamics and join to ordinary tool an instrument to enhance management and planning strategies. The snow metrics (SM) were derived from USGS NASA Landsat 8 L2A retrieved from Theia CNES platform and processed in Orfeo Toolbox LIS extension package. The relationship between SM and the disease spread was tested per each Aosta Valley municipality obtaining LISA maps for each hunting season. The results have showed that this parasite is present in an endemic form even if with rather low prevalence values, equal to 1.2% in the season hunting season 2013/2014, and equal to 7.5% in the hunting season 2014/2015. Moreover, within simultaneous given values of SM, sarcoptic mange seem to find good conditions for spreading.

An Integrated, Tentative Remote-Sensing Approach Based on NDVI Entropy to Model Canine Distemper Virus in Wildlife and to Prompt Science-Based Management Policies.

Changes in land use and land cover as well as feedback on the climate deeply affect the landscape worldwide. This phenomenon has also enlarged the human-wildlife interface and amplified the risk of potential new zoonoses. The expansion of the human settlement is supposed to affect the spread and distribution of wildlife diseases such as canine distemper virus (CDV), by shaping the distribution, density, and movements of wildlife. Nevertheless, there is very little evidence in the scientific literature on how remote sensing and GIS tools may help the veterinary sector to better monitor the spread of CDV in wildlife and to enforce ecological studies and new management policies in the near future. Thus, we perform a study in Northwestern Italy (Aosta Valley Autonomous Region), focusing on the relative epidemic waves of CDV that cause a virulent disease infecting different animal species with high host mortality. CDV has been detected in several mammalian from Canidae, Mustelidae, Procyonidae, Ursidae, and Viverridae families. In this study, the prevalence is determined at 60% in red fox (Vulpes vulpes, n = 296), 14% in wolf (Canis lupus, n = 157), 47% in badger (Meles meles, n = 103), and 51% in beech marten (Martes foina, n = 51). The detection of CDV is performed by means of real-time PCR. All the analyses are done using the TaqMan approach, targeting the chromosomal gene for phosphoprotein, gene P, that is involved in the transcription and replication of the virus. By adopting Earth Observation Data, we notice that CDV trends are strongly related to an altitude gradient and NDVI entropy changes through the years. A tentative model is developed concerning the ground data collected in the Aosta Valley region. According to our preliminary study, entropy computed from remote-sensing data can represent a valuable tool to monitor CDV spread as a proxy data predictor of the intensity of fragmentation of a given landscape and therefore also to monitor CDV. In conclusion, the evaluation from space of the landscape variations regarding the wildlife ecological corridors due to anthropic or natural disturbances may assist veterinarians and wildlife ecologists to enforce management health policies in a One Health perspective by pointing out the time and spatial conditions of interaction between wildlife. Surveillance and disease control actions are supposed to be carried out to strengthen the usage of geospatial analysis tools and techniques. These tools and techniques can deeply assist in better understanding and monitoring diseases affecting wildlife thanks to an integrated management approach.