Research priorities to fill knowledge gaps in wild boar management measures that could improve the control of African swine fever in wild boar populations

The European Commission asked EFSA to provide study designs for the investigation of four research domains (RDs) according to major gaps in knowledge identified by EFSA in a report published in 2019: (RD 1) African swine fever (ASF) epidemiology in wild boar; (RD 2) ASF transmission by vectors; (RD 3) African swine fever virus (ASFV) survival in the environment, and (RD 4) the patterns of seasonality of ASF in wild boar and domestic pigs in the EU. In this Scientific Opinion, the second RD on ASF epidemiology in wild boar is addressed. Twenty-nine research objectives were proposed by the working group and broader ASF expert networks and 23 of these research objectives met a prespecified inclusion criterion. Fourteen of these 23 research objectives met the predefined threshold for selection and so were prioritised based on the following set of criteria: (1) the impact on ASF management; (2) the feasibility or practicality to carry out the study; (3) the potential implementation of study results in practice; (4) a possible short time-frame study (< 1 year); (5) the novelty of the study; and (6) if it was a priority for risk managers. Finally, after further elimination of three of the proposed research objectives due to overlapping scope of studies published during the development of this opinion, 11 research priorities were elaborated into short research proposals, considering the potential impact on ASF management and the period of one year for the research activities

The application of digital volume correlation (DVC) to evaluate strain predictions generated by finite element models of the osteoarthritic humeral head

Continuum-level finite element models (FEMs) of the humerus offer the ability to evaluate joint replacement designs preclinically; however, experimental validation of these models is critical to ensure accuracy. The objective of the current study was to quantify experimental full-field strain magnitudes within osteoarthritic (OA) humeral heads by combining mechanical loading with volumetric microCT imaging and digital volume correlation (DVC). The experimental data was used to evaluate the accuracy of corresponding FEMs. Six OA humeral head osteotomies were harvested from patients being treated with total shoulder arthroplasty and mechanical testing was performed within a microCT scanner. MicroCT images (33.5 µm isotropic voxels) were obtained in a pre- and post-loaded state and BoneDVC was used to quantify full-field experimental strains (≈ 1 mm nodal spacing, accuracy = 351 µstrain, precision = 518 µstrain). Continuum-level FEMs with two types of boundary conditions (BCs) were simulated: DVC-driven and force-driven. Accuracy of the FEMs was found to be sensitive to the BC simulated with better agreement found with the use of DVC-driven BCs (slope = 0.83, r2 = 0.80) compared to force-driven BCs (slope = 0.22, r2 = 0.12). This study quantified mechanical strain distributions within OA trabecular bone and demonstrated the importance of BCs to ensure the accuracy of predictions generated by corresponding FEMs