Integrating an epidemic spread model with remote sensing for Xylella fastidiosa detection

Trabajo presentado en la 3rd European Conference on Xylella fastidiosa (Building knowledge, protecting plant health), celebrada online el 29 y 30 de abril de 2021.Xylella fastidiosa (Xf) causes plant diseases that lead to massive economic losses in agricultural crops, making it one of the pathogens of greatest concern to agriculture nowadays. Detecting Xf at early stages of infection is crucial to prevent and manage outbreaks of this vector-borne bacterium. Recent remote sensing (RS) studies at different scales have shown that Xf-infected olive trees have distinct spectral features in the visible and infrared regions (VNIR). However, RS-based forecasting of Xf outbreaks requires tools that account for their spatiotemporal dynamics. Here, we show how coupling a spatial Xf-spread model with the probability of Xf-infection predicted by an RS-driven modeling algorithm based on a Support Vector Machine (RS-SVM) helps detecting the spatial Xf distribution in a landscape. To optimize such model, we investigated which RS plant traits (i.e., pigments, structural or leaf protein content) derived from high-resolution hyperspectral imagery and biophysical modelling are most responsive to Xf infection and damage. For that, we combined a field campaign in almond orchards in Alicante province (Spain) affected by Xf (n=1,426 trees), with an airborne campaign over the same area to acquire high-resolution thermal and hyperspectral images in the visible-near-infrared (400-850 nm) and short-wave infrared regions (SWIR, 950-1700 nm). We found that coupling the epidemic spread model and the RS-based model increased accuracy by around 5% (OA = 80%, kappa = 0.48 and AUC = 0.81); compared to the best performing RS-SVM model (OA = 75%; kappa = 0.50) that included as predictors leaf protein content, nitrogen indices (NIs), fluorescence and a thermal indicator, alongside pigments and structural parameters. The parameters with the greatest explanatory power of the RS model were leaf protein content together with NI (28%), followed by chlorophyll (22%), structural parameters (LAI and LIDFa), and chlorophyll indicators of photosynthetic efficiency. In the subset of almond trees where the presence of Xf was tested by qPCR (n=318 tress), the combined RS-spread model yielded the best performance (OA of 71% and kappa = 0.33). Conversely, the best-performing RS-SVM model and visual inspections produced OA and kappa values of 65% and 0.31, respectively. This study shows for the first time the potential of combining spatial epidemiological models and remote sensing to monitor Xf-disease distribution in almond trees

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.Data collection was partially supported by the European Union's Horizon 2020 research and innovation program through grant agreements POnTE (635646) and XF-ACTORS (727987). R. Calderón was supported by a post-doctoral research fellowship from the Alfonso Martin Escudero Foundation (Spain)

Genome-wide association studies for methane production in dairy cattle

© 2019 by the authors. Licensee MDPI, Basel, Switzerland. Genomic selection has been proposed for the mitigation of methane (CH4) emissions by cattle because there is considerable variability in CH4 emissions between individuals fed on the same diet. The genome-wide association study (GWAS) represents an important tool for the detection of candidate genes, haplotypes or single nucleotide polymorphisms (SNP) markers related to characteristics of economic interest. The present study included information for 280 cows in three dairy production systems in Mexico: 1) Dual Purpose (n = 100), 2) Specialized Tropical Dairy (n = 76), 3) Familiar Production System (n = 104). Concentrations of CH4 in a breath of individual cows at the time of milking (MEIm) were estimated through a system of infrared sensors. After quality control analyses, 21,958 SNPs were included. Associations of markers were made using a linear regression model, corrected with principal component analyses. In total, 46 SNPs were identified as significant for CH4 production. Several SNPs associated with CH4 production were found at regions previously described for quantitative trait loci of composition characteristics of meat, milk fatty acids and characteristics related to feed intake. It was concluded that the SNPs identified could be used in genomic selection programs in developing countries and combined with other datasets for global selection

Divergent abiotic spectral pathways unravel pathogen stress signals across species

Plant pathogens pose increasing threats to global food security, causing yield losses that exceed 30% in food-deficit regions. Xylella fastidiosa (Xf) represents the major transboundary plant pest and one of the world’s most damaging pathogens in terms of socioeconomic impact. Spectral screening methods are critical to detect non-visual symptoms of early infection and prevent spread. However, the subtle pathogen-induced physiological alterations that are spectrally detectable are entangled with the dynamics of abiotic stresses. Here, using airborne spectroscopy and thermal scanning of areas covering more than one million trees of different species, infections and water stress levels, we reveal the existence of divergent pathogen- and host-specific spectral pathways that can disentangle biotic-induced symptoms. We demonstrate that uncoupling this biotic–abiotic spectral dynamics diminishes the uncertainty in the Xf detection to below 6% across different hosts. Assessing these deviating pathways against another harmful vascular pathogen that produces analogous symptoms, Verticillium dahliae, the divergent routes remained pathogen- and host-specific, revealing detection accuracies exceeding 92% across pathosystems. These urgently needed hyperspectral methods advance early detection of devastating pathogens to reduce the billions in crop losses worldwide.The study was partially funded by the European Union’s Horizon 2020 Research and Innovation Programme through grant agreements POnTE (635646) and XF-ACTORS (727987), as well as by projects AGL2009-13105 from the Spanish Ministry of Education and Science, P08-AGR-03528 from the Regional Government of Andalusia and the European Social Fund, project E-RTA2017-00004-02 from ‘Programa Estatal de I + D + I Orientada a los Retos de la Sociedad’ of Spain and FEDER, Intramural Project 201840E111 from CSIC, and Project ITS2017-095 Consejeria de Medio Ambiente, Agricultura y Pesca de las Islas Baleares, Spain. The views expressed are purely those of the writers and may not in any circumstance be regarded as stating an official position of the European Commission

Progress and achievements on the early detection of Xylella fastidiosa infection and symptom development with hyperspectral and thermal remote sensing imagery

Trabajo presentado en la 3rd European Conference on Xylella fastidiosa (Building knowledge, protecting plant health), celebrada online el 29 y 30 de abril de 2021.Remote sensing efforts made as part of European initiatives via POnTE, XF-ACTORS and the JRC, as well as through regional programs, have focused, among others, on the development of algorithms for the early detection of Xylella fastidiosa (Xf)-induced symptoms. Airborne campaigns carried out between 2016 and 2019 collected high-resolution hyperspectral and thermal images from infected areas in the Apulia region (Italy), in the province of Alicante and on the island of Mallorca (Spain). The remote sensing imagery collections were performed alongside field surveys and laboratory analyses to assess the presence of Xf, and the severity and incidence of disease in olive and almond trees. Radiative transfer models and machine learning algorithms were used to quantify spectral plant traits for each individual infected tree, assessing their importance as pre visual indicators of Xf-induced stress. These studies conducted across species have demonstrated that specific spectral plant traits successfully revealed Xf induced symptoms at early stages, i.e., before visual symptoms appear. The results show that spectral plant traits contribute differently to symptom detection across host species (olive vs. almond), and that abiotic-induced stress affects the performance of the algorithms used for detecting infected trees. Together, the different European initiatives studying the use of remote sensing to support the monitoring of landscapes for Xylella fastidiosa detection lead us to conclude that the early detection of Xf-induced symptoms is feasible when high-resolution hyperspectral imagery and physically-based plant trait retrievals are used, obtaining accuracies exceeding 92% (kappa>0.8). These results are essential to enable the implementation of effective control and management of plant diseases using airborne- droneand satellite-based remote sensing technologies. Moreover, these large-scale hyperspectral and thermal imaging methods greatly contribute to the future operational monitoring of infected areas at large scales, well beyond what is possible from field surveys and laboratory analyses alone

Social Entrepreneurship and Social Business: Retrospective and Prospective Research

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Social Entrepreneurship and Social Business: Retrospective and Prospective Research

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Revista de Vertebrados de la Estación Biológica de Doñana

Contribución al estudio de la bermejuela Rutilus arcasi, Steindachner, 1866 de la cuenca del Júcar (Osteichthyes: Cyprinidae)II. Edad y crecimientoSobre la taxonomía de Barbus comiza Steindachner, 1865 (Ostariophysi: Cyprinidae)Fenología de una comunidad de anfibios asociada a cursos fluviales temporales.Nueva especie para la ciencia de Anolis (Lacertilia: Iguanidae) de Cuba pertenecient eal complejo argillaceusSegregación ecológica en una comunidad de ofidios.El Aguila Imperial (Aquila adalberti): dispersión de los jóvenes, estructura de edades y mortalidaSobre diferencias individuales en la alimentación de Tyto albaInfluencia de las condiciones ambientales sobre la organización de la comunidad de aves invernantes en un bosque subalpino mediterráneoVariaciones en la agregación y distribución de la cabra montés (Capra pyrenaica Schinz,1838) detectadas con un muestreo de excrementosAlimentación del conejo (Oryctolagus cuniculus L. 1758) en Doñana. SO, EspañaSobre la distribución de Barbus meridionales Risso, 1826 (Ostariophysi: Cyprinidae) en la Península IbéricaSobre la distribución de Barbus meridionales Risso, 1826 (Ostariophysi: Cyprinidae) en la Península IbéricaNueva cita de Barbus microcephalus Almaça (Pisces, Cyprinidae) en España.Revisión taxonómica y distribución de Cobitis maroccana Pellegrin, 1929 (Osteichthyes, Cobitidae)Datos sobre una población de Lacerta viviparaSobre la presencia de Emys orbicularis en la provincia de León.Algunas observaciones sobre la captura de quirópteros por Falco subbuteo y Falco tinunculusNyctalus leisleri (Kuhk, 1818) (Mammalia: Chiroptera). Una nueva especie para las islas CanariaNuevos datos acerca de la distribución del topillo campesino Microtus arvalis, PALLAS 1778, en la Península IbéricaPeer reviewe

Effects on short term outcome of non-invasive ventilation use in the emergency department to treat patients with acute heart failure: A propensity score-based analysis of the EAHFE Registry

Objective: To assess the effects of non-invasive ventilation (NIV) in emergency department (ED) patients with acute heart failure (AHF) on short term outcomes. Methods: Patients from the EAHFE Registry (a multicenter, observational, multipurpose, cohort-designed database including consecutive AHF patients in 41 Spanish EDs) were grouped based on NIV treatment (NIV+ and NIV–groups). Using propensity score (PS) methodology, we identified two subgroups of patients matched by 38 covariates and compared regarding 30-day survival (primary outcome). Interaction was investigated for age, sex, ischemic cardiomyopathy, chronic obstructive pulmonary disease, AHF precipitated by an acute coronary syndrome (ACS), AHF classified as hypertensive or acute pulmonary edema (APE), and systolic blood pressure (SBP). Secondary outcomes were intensive care unit (ICU) admission; mechanical ventilation; in-hospital, 3-day and 7-day mortality; and prolonged hospitalization (>7 days). Results: Of 11, 152 patients from the EAHFE (age (SD): 80 (10) years; 55.5% women), 718 (6.4%) were NIV+ and had a higher 30-day mortality (HR = 2.229; 95%CI = 1.861–2.670) (p 85 years, p < 0.001), AHF associated with ACS (p = 0.045), and SBP < 100 mmHg (p < 0.001). No significant differences were found in the secondary endpoints except for more prolonged hospitalizations in NIV+ patients (OR = 1.445; 95%CI = 1.122–1.862) (p = 0.004). Conclusion: The use of NIV to treat AHF in ED is not associated with improved mortality outcomes and should be cautious in old patients and those with ACS and hypotension

Biomimetic, Mild Chemical Synthesis of CdTe-GSH Quantum Dots with Improved Biocompatibility

Multiple applications of nanotechnology, especially those involving highly fluorescent nanoparticles (NPs) or quantum dots (QDs) have stimulated the research to develop simple, rapid and environmentally friendly protocols for synthesizing NPs exhibiting novel properties and increased biocompatibility