Frequência da Anafilaxia Induzida pelo Exercício numa Consulta de Imunoalergologia

Introdução: A anafilaxia induzida pelo exercício (AIE) é uma forma rara de alergia física que ocorre na sequência de esforços físicos. A verdadeira incidência e prevalência da AIE permanecem por esclarecer, não existindo até à data dados publicados a nível nacional. Objectivos: Estimar a frequência da AIE no ambulatório de um serviço de Imunoalergologia e incrementar o conhecimento em relação a esta patologia. Métodos: De 7699 doentes observados na consulta de Imunoalergologia durante o período de um ano, incluímos os correspondentes a quadros de anafilaxia notificados pelo corpo clínico (“pelo menos um episódio de reacção sistémica grave”). Resultados: A AIE foi reportada em 5 de 103 doentes com história de anafilaxia; correspondendo a uma frequência de 0,06% na população observada na consulta. A média etária destes doentes era de 20,2 ± 10,3 anos (entre 10 e 37 anos) e a distribuição por sexo masculino/feminino de 4:1. Todos tinham história pessoal de atopia e de rinite alérgica; dois doentes (40%) tinham asma. As actividades desencadeantes das crises foram a corrida, o futebol, a natação e a dança. Todos os doentes tinham sintomas com o exercício dependente da ingestão prévia de alimentos: cereais em três doentes (trigo – dois, cevada – um), leguminosas em dois (amendoim – um, feijão -frade e feijão -verde – um); com teste cutâneo por picada positivo para os referidos alimentos. Conclusões: A AIE representa 5% dos casos de anafi laxia reportados. Todos os casos identifi cados apresentavam AIE dependente de alimentos, encontrando-se os doentes controlados com a evicção dos alimentos referidos 6 horas antes da prática de exercício e sendo portadores de dispositivo para autoadministração de adrenalina

Grapevine bioclimatic indices in relation to climate change: a case study in the Portuguese Douro Demarcated Region

Climate change is of major relevance to wine production as most of the wine-growing regions of the world, in particular the Douro region, are located within relatively narrow latitudinal bands with average growing season temperatures limited to 13-21ºC. This study focuses on the temporal variability of three grapevine bioclimatic indices, which are commonly used as part of the Geoviticulture Multicriteria Climatic Classification System (MCC) to classify the climate of wine producing regions worldwide. Dynamical downscaling of MPI-ESM-LR global data forced with RCP8.5 climatic scenario is performed with the Weather Research and Forecast (WRF) model to a regional scale including the Douro valley of Portugal for recent-past (1986-2005) and future periods (2046-2065; 2081-2100). Results indicate significant shifts towards warmer and dryer conditions during the growing season and higher night temperatures during the grape ripening period. An assessment on the statistical significance of the differences between the recent-past and the future scenarios and the potential impact on wine production in the study area is performed. These results will provide evidence for future strategies aimed to preserve the high-quality wines in the region and their typicality in a sustainable way.The authors wish to thank the financial support of the DOUROZONE project (PTDC/AAG-MAA/3335/2014; POCI- 01-0145-FEDER-016778) through the Project 3599 – Promoting the scientific production and the technological development, and thematic networks (3599-PPCDT) and through FEDER, and the national funds from FCT – Science and Technology Portuguese Foundation for the doc grant of C. Silveira (SFRH/BD/112343/2015).info:eu-repo/semantics/publishedVersio

Analysis of climate change indices in relation to wine production: a case study in the Douro region (Portugal)

Climate change is of major relevance to wine production as most of the wine-growing regions of the world, in particular the Douro region, are located within relatively narrow latitudinal bands with average growing season temperatures limited to 13–21◦C. This study focuses on the incidence of climate variables and indices that are relevant both for climate change detection and for grape production with particular emphasis on extreme events (e.g. cold waves, storms, heat waves). Dynamical downscaling ofMPI-ESM-LR global data forced with RCP8.5 climatic scenario is performed with the Weather Research and Forecast (WRF) model to a regional scale including the Douro valley of Portugal for recent-past (1986–2005) and future periods (2046– 2065; 2081–2100). The number, duration and intensity of events are superimposed over critical phenological phases of the vine (dormancy, bud burst, flowering, v´eraison, and maturity) in order to assess their positive or negative implications on wine production in the region. An assessment on the statistical significance of climatic indices, their differences between the recent-past and the future scenarios and the potential impact on wine production is performed. Preliminary results indicate increased climatic stress on the Douro region wine production and increased vulnerability of its vine varieties. These results will provide evidence for future strategies aimed to preserve the high-quality wines in the region and their typicality in a sustainable way.The authors wish to thank the financial support of the DOUROZONE project (PTDC/AAG-MAA/3335/2014; POCI- 01-0145-FEDER-016778) through the Project 3599 – Promoting the scientific production and the technological development, and thematic networks (3599-PPCDT) and through FEDER.info:eu-repo/semantics/publishedVersio

Climate change impact on a wine-producing region using a dynamical downscaling approach: Climate parameters, bioclimatic indices and extreme indices

Climate change is of major relevance to wine production as most of the winegrowing regions of the world are located within relatively narrow latitudinal bands with average growing-season temperatures (GSTs) limited to 13–21 C. This study focuses on the incidence of climate variables and indices that are relevant both for climate change assessment and for grape production, with emphasis on grapevine bioclimatic indices and extreme events (e.g., cold waves, storms, heatwaves). Dynamical downscaling of European Reanalysis-Interim and Max Planck Institute Earth System low-resolution global simulations forced with a Representative Concentration Pathway 8.5 (RCP8.5) greenhouse gas emission scenario was performed with the Weather Research and Forecast (WRF) model to a regional scale including the Douro Valley of Portugal for recent-past (1986–2005) and future periods (2046–2065, 2081–2100). The number, duration and intensity of events were superimposed over critical phenological phases estimated by using a specific local grapevine varietal phenological model in order to assess their positive or negative implications for wine production in the region. An assessment of the relevance of climate parameters and indices and their progression in recent-past and future climate scenarios with regard to the potential impact on wine production was performed. Results indicate a positive relation between higher growing-season heat accumulations and greater vintage yields. A moderate incidence of very hot days (daily maximum temperature above 35 C) and drought from pre-véraison phenological conditions have a positive association with vintage ratings. However, the mid- and long-term WRF-MPI RCP8.5 future climate scenarios reveal shifts to warmer and drier conditions, with the mean GST not remaining within range for quality wine production in the long-term future climate scenario. These results indicate potential impacts that suggest a range of strategies to maintain wine production and quality in the region.The authors wish to thank the DOUROZONE project (PTDC/AAG-MAA/3335/2014; POCI-01-0145-FEDER- 016778) for financial support through Project 3599 – Promoting the Scientific Production and the Technological Development, and Thematic Networks (3599-PPCDT) – and through FEDER, and the national funds from FCT-Science and Technology Portuguese Foundation for the doctoral grant of D. Blanco-Ward (SFRH/BD/139193/2018). Thanks are also due for the financial support to CESAM (UID/AMB/50017 - POCI-01-0145-FEDER-007638), to FCT/MEC through national funds, and the co-funding by FEDER within the PT2020 Partnership Agreement and Compete 2020.info:eu-repo/semantics/publishedVersio

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)

Numerical and physical assessment of control measures to mitigate fugitive dust emissions from harbor activities

In recent years, the industrial demand for petcoke—a solid residue derived from the refinement of crude oil—has been growing due to its low cost. The use of petcoke is causing environmental concern associated with its high level of contaminants and air pollutant emissions, mainly particulate matter (PM). Given the impact of petcoke on the environment and human health, increased attention has been given to its production, storage, transportation, and application processes. The main goal of this work was to assess the effectiveness of placing a barrier to reduce PM emissions from petcoke in a harbor area. The Port of Aveiro, Portugal, was used as case study. Firstly, wind tunnel experiments were performed for different types of barrier to (i) assess the effect on PM emissions of different types of barriers, namely solid, porous, and raised porous barriers; (ii) determine the optimal size and location of the barrier to achieve maximum reduction of PM emissions; and (iii) estimate the impact of placing such barrier in the attenuation of petcoke emissions over the harbor area. Secondly, the numerical model VADIS (pollutant DISpersion in the atmosphere under VAriable wind conditions) was run to evaluate the effect of implementing the barrier on the local air quality. Results showed that the best solution would be the implementation of two solid barriers: a main barrier of 109 m length plus a second barrier of 30 m length. This measure produced the best results in terms of reduction of the dispersion of particulate matter from the petcoke stockpile and minimization of the PM concentrations in the harbor surrounding area.publishe

Quince (Cydonia oblonga) in vitro plant root formation through an automated temporary inmersion system, and its acclimation

Artículo científicoQuince (Cydonia oblonga) is a non-traditional fruit tree found in Costa Rica that has therapeutic and nutritional properties; however its slow growth and root formation prevents the production of a homogeneous population when using conventional farming techniques. Hence, the aim of this research project was to generate uniform plant material in a reduced time span using a temporary immersion bioreactor system (RITAS ®). A semisolid rooting MS culture medium supplemented with 0.1 mg L-1 NAA; 0.3 mg L-1 IBA and 3% sucrose (pH 6.5), developed in the Centro de Investigación en Biotecnología (CIB), Instituto Tecnológico de Costa Rica (ITCR), in Cartago, was used as a reference medium. Four different variations in the sucrose concentration (1%, 2%, 3%, and 4%) were performed in liquid medium. Each trial was evaluated with in vitro plants which had been previously exposed to the culture medium of the corresponding treatments, in a stationary mode and for a 15 day long period, and with in vitro plants without any previous treatment (a total of eight treatments). The comparison of the root formation percentages evidenced the clear effect of sucrose concentration used, with the best results obtained when using the 2% sucrose trial with no pre-treatment (73.3%). The in vitro plants were acclimated in cylinders made out of peat, have previously been disinfected with fungicide, and placed in a humidity chamber at a 20.5°C average temperature and a 75,5% relative humidity for the establishment of weekly fertilizing cycles. The acclimation process generated an 80% survival rate, since several seedlings experienced stem strangulation caused by a fungal attack. The conidiophores identified through optical and scanning electron microscopy evidenced the presence of Cladosporium spp., which was controlled with carbendazim and iprodione fungicides

Comprehensive Analysis of NRG1 Common and Rare Variants in Hirschsprung Patients

Hirschsprung disease (HSCR, OMIM 142623) is a developmental disorder characterized by the absence of ganglion cells along variable lengths of the distal gastrointestinal tract, which results in tonic contraction of the aganglionic gut segment and functional intestinal obstruction. The RET proto-oncogene is the major gene for HSCR with differential contributions of its rare and common, coding and noncoding mutations to the multifactorial nature of this pathology. Many other genes have been described to be associated with the pathology, as NRG1 gene (8p12), encoding neuregulin 1, which is implicated in the development of the enteric nervous system (ENS), and seems to contribute by both common and rare variants. Here we present the results of a comprehensive analysis of the NRG1 gene in the context of the disease in a series of 207 Spanish HSCR patients, by both mutational screening of its coding sequence and evaluation of 3 common tag SNPs as low penetrance susceptibility factors, finding some potentially damaging variants which we have functionally characterized. All of them were found to be associated with a significant reduction of the normal NRG1 protein levels. The fact that those mutations analyzed alter NRG1 protein would suggest that they would be related with HSCR disease not only in Chinese but also in a Caucasian population, which reinforces the implication of NRG1 gene in this pathology

Detecting Xylella fastidiosa in a machine learning framework using Vcmax and leaf biochemistry quantified with airborne hyperspectral imagery

The bacterium Xylella fastidiosa (Xf) is a plant pathogen that can block the flow of water and nutrients through the xylem. Xf symptoms may be confounded with generic water stress responses. Here, we assessed changes in biochemical, biophysical and photosynthetic traits, inferred using biophysical models, in Xf-affected almond orchards under rainfed and irrigated conditions on the Island of Majorca (Balearic Islands, Spain). Recent research has demonstrated the early detection of Xf-infections by monitoring spectral changes associated with pigments, canopy structural traits, fluorescence emission and transpiration. Nevertheless, there is still a need to make further progress in monitoring physiological processes (e.g., photosynthesis rate) to be able to efficiently detect when Xf-infection causes subtle spectral changes in photosynthesis. This paper explores the ability of parsimonious machine learning (ML) algorithms to detect Xf-infected trees operationally, when considering a proxy of photosynthetic capacity, namely the maximum carboxylation rate (Vcmax), along with carbon-based constituents (CBC, including lignin), and leaf biochemical traits and tree-crown temperature (Tc) as an indicator of transpiration rates. The ML framework proposed here reduced the uncertainties associated with the extraction of reflectance spectra and temperature from individual tree crowns using high-resolution hyperspectral and thermal images. We showed that the relative importance of Vcmax and leaf biochemical constituents (e.g., CBC) in the ML model for the detection of Xf at early stages of development were intrinsically associated with the water and nutritional conditions of almond trees. Overall, the functional traits that were most consistently altered by Xf-infection were Vcmax, pigments, CBC, and Tc, and, particularly in rainfed-trees, anthocyanins, and Tc. The parsimonious ML model for Xf detection yielded accuracies exceeding 90% (kappa = 0.80). This study brings progress in the development of an operational ML framework for the detection of Xf outbreaks based on plant traits related to photosynthetic capacity, plant biochemistry and structural decay parameters.This research was supported by grant: ITS2017-095: Design and Implementation of control strategies for Xylella fastidiosa, Project 5. Government of the Balearic Islands, Spain. Data collection was partially supported by the European Union's Horizon 2020 research and innovation program through gran agreement XF-ACTORS (727987).Peer reviewe