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

Global patient outcomes after elective surgery: prospective cohort study in 27 low-, middle- and high-income countries.

BACKGROUND: As global initiatives increase patient access to surgical treatments, there remains a need to understand the adverse effects of surgery and define appropriate levels of perioperative care. METHODS: We designed a prospective international 7-day cohort study of outcomes following elective adult inpatient surgery in 27 countries. The primary outcome was in-hospital complications. Secondary outcomes were death following a complication (failure to rescue) and death in hospital. Process measures were admission to critical care immediately after surgery or to treat a complication and duration of hospital stay. A single definition of critical care was used for all countries. RESULTS: A total of 474 hospitals in 19 high-, 7 middle- and 1 low-income country were included in the primary analysis. Data included 44 814 patients with a median hospital stay of 4 (range 2-7) days. A total of 7508 patients (16.8%) developed one or more postoperative complication and 207 died (0.5%). The overall mortality among patients who developed complications was 2.8%. Mortality following complications ranged from 2.4% for pulmonary embolism to 43.9% for cardiac arrest. A total of 4360 (9.7%) patients were admitted to a critical care unit as routine immediately after surgery, of whom 2198 (50.4%) developed a complication, with 105 (2.4%) deaths. A total of 1233 patients (16.4%) were admitted to a critical care unit to treat complications, with 119 (9.7%) deaths. Despite lower baseline risk, outcomes were similar in low- and middle-income compared with high-income countries. CONCLUSIONS: Poor patient outcomes are common after inpatient surgery. Global initiatives to increase access to surgical treatments should also address the need for safe perioperative care. STUDY REGISTRATION: ISRCTN5181700

Management of Soil-Borne Diseases of Grain Legumes Through Broad-Spectrum Actinomycetes Having Plant Growth-Promoting and Biocontrol Traits

Chickpea (Cicer arietinum L.) and pigeonpea (Cajanus cajan L.) are the two important grain legumes grown extensively in the semiarid tropics (SAT) of the world, where soils are poor in nutrients and receive inadequate/erratic rainfall. SAT regions are commonly found in Africa, Australia, and South Asia. Chickpea and pigeonpea suffer from about 38 pathogens that cause soil-borne diseases including wilt, collar rot, dry root rot, damping off, stem canker, and Ascochyta/Phytophthora blight, and of which three of them, wilt, collar rot, and dry root rot, are important in SAT regions. Management of these soil-borne diseases are hard, as no one control measure is completely effective. Advanced/delayed sowing date, solarization of soil, and use of fungicides are some of the control measures usually employed for these diseases but with little success. The use of disease-resistant cultivar is the best efficient and economical control measure, but it is not available for most of the soil-borne diseases. Biocontrol of soil-borne plant pathogens has been managed using antagonistic actinobacteria, bacteria, and fungi. Actinobacterial strains of Streptomyces, Amycolatopsis, Micromonospora, Frankia, and Nocardia were reported to exert effective control on soil-borne pathogens and help the host plants to mobilize and acquire macro- and micronutrients. Such novel actinomycetes with wide range of plant growth-promoting (PGP) and antagonistic traits need to be exploited for sustainable agriculture. This chapter gives a comprehensive analysis of important soil-borne diseases of chickpea and pigeonpea and how broad-spectrum actinomycetes, particularly Streptomyces spp., could be exploited for managing them

Guidelines for the reliable use of high throughput sequencing technologies to detect plant pathogens and pests

High-throughput sequencing (HTS) technologies have the potential to become one of the most significant advances in molecular diagnostics. Their use by researchers to detect and characterize plant pathogens and pests has been growing steadily for more than a decade and they are now envisioned as a routine diagnostic test to be deployed by plant pest diagnostics laboratories. Nevertheless, HTS technologies and downstream bioinformatics analysis of the generated datasets represent a complex process including many steps whose reliability must be ensured. The aim of the present guidelines is to provide recommendations for researchers and diagnosticians aiming to reliably use HTS technologies to detect plant pathogens and pests. These guidelines are generic and do not depend on the sequencing technology or platform. They cover all the adoption processes of HTS technologies from test selection to test validation as well as their routine implementation. A special emphasis is given to key elements to be considered: undertaking a risk analysis, designing sample panels for validation, using proper controls, evaluating performance criteria, confirming and interpreting results. These guidelines cover any HTS test used for the detection and identification of any plant pest (viroid, virus, bacteria, phytoplasma, fungi and fungus-like protists, nematodes, arthropods, plants) from any type of matrix. Overall, their adoption by diagnosticians and researchers should greatly improve the reliability of pathogens and pest diagnostics and foster the use of HTS technologies in plant health