Spatiotemporal dynamics and modelling support the case for area-wide management of citrus greasy spot in a Brazilian smallholder farming region

Citrus greasy spot (CGS), caused by Zasmidium citri, induces premature defoliation and yield loss in Citrus spp. The epidemiology of CGS is well understood in high humidity areas, but remains unaddressed in Brazil, despite differing climatic conditions and disease management practices. The spatiotemporal dynamics of CGS was characterized in the Recôncavo of Bahia (Brazil) at four hierarchical levels (quadrant, plant, grove and region). A survey conducted in 19 municipalities found the disease to be present throughout the region with an incidence of 100% in groves and plants, and higher than 70% on leaves. Index of dispersion (D) values suggest the spatial pattern of symptomatic units lies between random and regular. This was confirmed by the parameters of the binary power law for plants and their quadrants (log(A)<0 and b<1). No consistent differences were observed in the disease incidence at different plant heights. We introduce a compartmental model synthesizing CGS epidemiology. The collected data allow such a model to be parameterised, albeit with some ambiguity over the proportion of new infections that result from inoculum produced within the grove vs. external sources of infection. By extending the model to include two populations of growers – those who control and those who do not – coupled by the airborne inoculum, we investigate likely performance of cultural controls accessible to citrus growers in Northeastern Brazil. The results show that control via removal of fallen leaves can be very effective. However, successful control is likely to require area-wide strategies, in which a large proportion of growers actively manage disease

Biomimetic Analogs for Collagen Biomineralization

Inability of chemical phosphorylation of sodium trimetaphosphate to induce intrafibrillar mineralization of type I collagen may be due to the failure to incorporate a biomimetic analog to stabilize amorphous calcium phosphates (ACP) as nanoprecursors. This study investigated adsorption/desorption characteristics of hydrolyzed and pH-adjusted sodium trimetaphosphate (HPA-Na3P3O9) to collagen. Based on those results, a 5-minute treatment time with 2.8 wt% HPA-Na3P3O9 was used in a single-layer reconstituted collagen model to confirm that both the ACP-stabilization analog and matrix phosphoprotein analog must be present for intrafibrillar mineralization. The results of that model were further validated by complete remineralization of phosphoric-acid-etched dentin treated with the matrix phosphoprotein analog and lined with a remineralizing lining composite, and with the ACP-stabilization analog supplied in simulated body fluid. An understanding of the basic processes involved in intrafibrillar mineralization of reconstituted collagen fibrils facilitates the design of novel tissue engineering materials for hard tissue repair and regeneration

Multivariate linear regression with non-normal errors: a solution based on mixture models

In some situations, the distribution of the error terms of a multivariate linear regression model may depart from normality. This problem has been addressed, for example, by specifying a different parametric distribution family for the error terms, such as multivariate skewed and/or heavy-tailed distributions. A new solution is proposed, which is obtained by modelling the error term distribution through a finite mixture of multi-dimensional Gaussian components. The multivariate linear regression model is studied under this assumption. Identifiability conditions are proved and maximum likelihood estimation of the model parameters is performed using the EM algorithm. The number of mixture components is chosen through model selection criteria; when this number is equal to one, the proposal results in the classical approach. The performances of the proposed approach are evaluated through Monte Carlo experiments and compared to the ones of other approaches. In conclusion, the results obtained from the analysis of a real dataset are presented