Local spatial analysis and dynamic simulation of childhood obesity and neighbourhood walkability in a major Canadian city

Body weight is an important indicator of current and future health and it is even more critical in children, who are tomorrow’s adults. This paper analyzes the relationship between childhood obesity and neighbourhood walkability in Calgary, Canada. A multivariate analytical framework recognizes that childhood obesity is also associated with many factors, including socioeconomic status, foodscapes, and environmental factors, as well as less measurable factors, such as individual preferences, that could not be included in this analysis. In contrast with more conventional global analysis, this research employs localized analysis and assesses need-based interventions. The <i>one-size-fit-all </i>strategy may not effectively control obesity rates, since each neighbourhood has unique characteristics that need to be addressed individually. This paper presents an innovative framework combining local analysis with simulation modeling to analyze childhood obesity. Spatial models generally do not deal with simulation over time, making it cumbersome for health planners and policy makers to effectively design and implement interventions and to quantify their impact over time. This research fills this gap by integrating geographically weighted regression (GWR), which identifies vulnerable neighbourhoods and critical factors for childhood obesity, with simulation modeling, which evaluates the impact of the suggested interventions on the targeted neighbourhoods. Neighbourhood walkability was chosen as a potential target for localized interventions, owing to the crucial role of walking in developing a healthy lifestyle, as well as because increasing walkability is relatively more feasible and less expensive then modifying other factors, such as income. Simulation results suggest that local walkability interventions can achieve measurable declines in childhood obesity rates. The results are encouraging, as improvements are likely to compound over time. The results demonstrate that the integration of GWR and simulation modeling is effective, and the proposed framework can assist in designing local interventions to control and prevent childhood obesity

A successful defence strategy in grapevine cultivar ‘Tocai friulano’ provides compartmentation of grapevine Flavescence dorée phytoplasma

Background: Flavescence dorée (FD) is a grapevine disease caused by phytoplasma and it is one of the most destructive pathologies in Europe. Nowadays, the only strategies used to control the epidemics are insecticides against vector, but more sustainable techniques are required. Completely resistant Vitis vinifera varieties have not been uncovered yet, but differences in susceptibility among cultivars and spontaneous recovery from FD symptoms have been observed. The grapevine cultivar ‘Tocai friulano’ shows very low susceptibility to FD but its defence strategy to counteract the phytoplasma spread has not been deciphered yet. In this work, the mechanisms occurring within ‘Tocai friulano’ FD-infected plants were examined in depth to identify the phytoplasma distribution and the defence pathways involved. Results: In ‘Tocai friulano’ symptoms of FD-infection remained confined near the area where they appeared during all the vegetative season. Analyses of secondary phloem showed a total absence of FD phytoplasma (FDp) in the trunk and its disappearance in 2-year-old arms from July to November, which was different from ‘Pinot gris’, a highly susceptible variety. Diverse modulations of defence genes and accumulation of metabolites were revealed in 1-year-old canes of ‘Tocai friulano’ FD-infected plants, depending on the sanitary status. Symptomatic portions showed high activation of both jasmonate- and salicylate-mediated responses, together with a great accumulation of resveratrol. Whereas activation of jasmonate-mediated response and high content of ε-viniferin were identified in asymptomatic 1-year-old cane portions close to the symptomatic ones. Conclusion: Successful defence mechanisms activated near the symptomatic areas allowed the compartmentation of FD symptoms and phytoplasmas within the infected ‘Tocai friulano’ plants. These results could suggest specific agronomical practices to be adopted during FD management of this variety, and drive research of resistance genes against FD

Chitosan induces delayed grapevine defense mechanisms and protects grapevine against Botrytis cinerea

In the present study, a commercial chitosan soluble in acid solution and obtained from shrimp shell waste, with a molecular weight of 173 kDa and a degree of acetylation of 17%, named as chitosan (173/17), was investigated. Chitosan is a well-known biopolymer whose antimicrobial properties are highly influenced by the molecular weight, degree of acetylation as well as the preparation and derivatization methods used. Chitosan (173/17) was applied on grapevine leaves before Botrytis cinerea inoculation to verify its effectiveness as a preventive treatment against the fungal infection. The expression of a set of defense marker genes, as well as accumulation of stilbene phytoalexins, was investigated. Thanks to its fungistatic and filmogenic properties, chitosan (173/17) protected grapevine leaves against B. cinerea. Moreover, it induced grapevine defense response: three days after the treatment an induction of the jasmonic acid and ethylene-mediated response, a repression of the salicylic acid-mediated signaling, and a transient accumulation of trans-resveratrol were registered. Our data indicate that chitosan (173/17), when used in preventive application, is able to protect grapevine against B. cinerea infection. The effectiveness of chitosan (173/17) as a natural ecofriendly product for the control of B. cinerea on grapevine was demonstrated

Comparison of distance measures in spatial analytical modeling for health service planning

<p>Abstract</p> <p>Background</p> <p>Several methodological approaches have been used to estimate distance in health service research. In this study, focusing on cardiac catheterization services, Euclidean, Manhattan, and the less widely known Minkowski distance metrics are used to estimate distances from patient residence to hospital. Distance metrics typically produce less accurate estimates than actual measurements, but each metric provides a single model of travel over a given network. Therefore, distance metrics, unlike actual measurements, can be directly used in spatial analytical modeling. Euclidean distance is most often used, but unlikely the most appropriate metric. Minkowski distance is a more promising method. Distances estimated with each metric are contrasted with road distance and travel time measurements, and an optimized Minkowski distance is implemented in spatial analytical modeling.</p> <p>Methods</p> <p>Road distance and travel time are calculated from the postal code of residence of each patient undergoing cardiac catheterization to the pertinent hospital. The Minkowski metric is optimized, to approximate travel time and road distance, respectively. Distance estimates and distance measurements are then compared using descriptive statistics and visual mapping methods. The optimized Minkowski metric is implemented, via the spatial weight matrix, in a spatial regression model identifying socio-economic factors significantly associated with cardiac catheterization.</p> <p>Results</p> <p>The Minkowski coefficient that best approximates road distance is 1.54; 1.31 best approximates travel time. The latter is also a good predictor of road distance, thus providing the best single model of travel from patient's residence to hospital. The Euclidean metric and the optimal Minkowski metric are alternatively implemented in the regression model, and the results compared. The Minkowski method produces more reliable results than the traditional Euclidean metric.</p> <p>Conclusion</p> <p>Road distance and travel time measurements are the most accurate estimates, but cannot be directly implemented in spatial analytical modeling. Euclidean distance tends to underestimate road distance and travel time; Manhattan distance tends to overestimate both. The optimized Minkowski distance partially overcomes their shortcomings; it provides a single model of travel over the network. The method is flexible, suitable for analytical modeling, and more accurate than the traditional metrics; its use ultimately increases the reliability of spatial analytical models.</p

Spatial analysis of the bioaccumulation of distributed pollutants in clams Tapes philipinarum: the Venetian Lagoon (Italy)

Exposure characterization is a central step in Ecological Risk Assessment (ERA). Exposure level is a function of the spatial factors linking contaminants and receptors, yet exposure estimation models are traditionally non-spatial. Non-spatial models are prone to the adverse effects of spatial dependence: inflated variance and biased inferential procedures, which can result in unreliable and potentially misleading models. Such negative effects can be amended by spatial regression modelling: we propose an integration of geostatistics and multivariate spatial regression to compute efficient spatial regression parameters and to characterize exposure at under-sampled locations. The method is applied to estimate bioaccumulation models of organic and inorganic micropollutants in the tissues of the clam Tapes philipinarum. The models link bioaccumulation of micropollutants in clam tissue to a set of environmental variables sampled in the lagoon sediment. The Venetian lagoon case study exemplifies the problem of multiple variables sampled at different locations or spatial units: we propose and test an effective solution to this common and serious problem in environmental as well as socio-economic multivariate analysis