Detection of Xylella fastidiosa in almond orchards by synergic use of an epidemic spread model and remotely sensed plant traits.

The early detection of Xylella fastidiosa (Xf) infections is critical to the management of this dangerous plan pathogen across the world. Recent studies with remote sensing (RS) sensors at different scales have shown that Xf-infected olive trees have distinct spectral features in the visible and infrared regions (VNIR). However, further work is needed to integrate remote sensing in the management of plant disease epidemics. Here, we research how the spectral changes picked up by different sets of RS plant traits (i.e., pigments, structural or leaf protein content), can help capture the spatial dynamics of Xf spread. We coupled a spatial spread model with the probability of Xf-infection predicted by a RS-driven support vector machine (RS-SVM) model. Furthermore, we analyzed which RS plant traits contribute most to the output of the prediction models. For that, in almond orchards affected by Xf (n = 1426 trees), we conducted a field campaign simultaneously with an airborne campaign to collect high-resolution thermal images and hyperspectral images in the visible-near-infrared (VNIR, 400-850 nm) and short-wave infrared regions (SWIR, 950-1700 nm). The best performing RS-SVM model (OA = 75%; kappa = 0.50) included as predictors leaf protein content, nitrogen indices (NIs), fluorescence and a thermal indicator (Tc), alongside pigments and structural parameters. Leaf protein content together with NIs contributed 28% to the explanatory power of the model, followed by chlorophyll (22%), structural parameters (LAI and LIDFa), and chlorophyll indicators of photosynthetic efficiency. Coupling the RS model with an epidemic spread model increased the accuracy (OA = 80%; kappa = 0.48). In the almond trees where the presence of Xf was assayed by qPCR (n = 318 trees), the combined RS-spread model yielded an OA of 71% and kappa = 0.33, which is higher than the RS-only model and visual inspections (both OA = 64-65% and kappa = 0.26-31). Our work demonstrates how combining spatial epidemiological models and remote sensing can lead to highly accurate predictions of plant disease spatial distribution.

Treatment of periprosthetic tibial plateau fractures in unicompartmental knee arthroplasty: plates versus cannulated screws.

INTRODUCTION: Periprosthetic tibial plateau fractures (TPF) are rare but represent a serious complication in unicompartmental knee arthroplasty. The most common treatment for these fractures is osteosynthesis with cannulated screws or plates. The aim of this study was to evaluate two different treatment options for periprosthetic fractures. The hypothesis was that angle-stable plates show significantly higher fracture loads than fixation with cannulated screws. MATERIALS AND METHODS: Twelve matched, paired fresh-frozen tibiae with periprosthetic TPF were used for this study. In Group A, osteosyntheses with cannulated screws were performed, whereas in Group B plates fixated the periprosthetic fracture. DEXA bone density measurement and standard X-rays (AP and lateral) were performed before loading the tibiae under standardised conditions with a maximum load of up to 10.0 kN. After the specimens had been loaded, fracture patterns and fracture loads were analysed and correlated with BMD, BMI, bodyweight (BW), age and size of the tibial implant. RESULTS: In the plate group all tibiae fracture occured with a median load of F (max) = 2.64 (0.45-5.68) kN, whereas in the group with cannulated screws fractures occurred at a mean load of F (max) = 1.50 (0.27-3.51) kN. The difference was statistically significant at p < 0.05. DISCUSSION: Angle-stable plates showed significantly higher fracture loads than fixation with cannulated screws. Cannulated screws show a reduced stability of the tibial plateau. Therefore in periprosthetic TPF, osteosyntheses with angle-stable plates should be recommended instead of cannulated screws.