Occupational Health and Safety Prevention Plan in Water Treatment Plant

The research was carried out at the "El Guarumo" drinking water plant located in Santa Ana, province of Manabí, Ecuador. The objective of the investigation was the proposal of a plan of prevention of occupational risks that allows the management of the labor risks in said plant. The main tools used were: survey, interview, checklist, LEST questionnaire for the diagnosis of the current situation in terms of working conditions, the risk identification matrix and the binary method of risk assessment. The main results obtained were the identification of the risks in their different categories, observing that the critical risk factors are related to the physical overexertion, the uncomfortable postures and the manual lifting of the load. Among the important risks are falling objects, skin contact with toxic substances and mental overwork, closely related to work pressures and job security? It was possible to carry out the proposal of preventive and corrective measures in order to properly manage the risks and contribute to the safety and health of the workers

Depresión y ansiedad en pacientes hospitalizados en el servicio de Medicina Interna del Hospital Escuela Universitario de Honduras

Antecedentes y objetivo: La hospitalización no siempre es bien asimilada por los pacientes, generan-do síntomas de depresión y ansiedad. Considerando esto, el presente estudio analizó la prevalencia de síntomas de depresión y ansiedad, y su relación, en pacientes del servicio de Medicina Interna del Hospital Escuela Universitario (HEU), en Honduras. Además, se compararon los puntajes de ansiedad y depresión en base al sexo, presencia de hábitos tóxicos y experiencias previas de hospitalización de los informantes. Materiales y métodos: La investigación se enmarcó en un enfoque cuantitativo, no experimental, de corte trasversal. Tomando una muestra de 92 pacientes de las salas de Medicina Interna del HEU. La información se recolectó por medio del Cuestionario de Salud del Paciente-9 (PHQ-9), la Escala del Trastorno de Ansiedad Generalizada-7 (GAD-7) y una ficha de datos sociodemográficos. Resultados: El 67.4% de los pacientes presentó algún grado de sintomatología depresiva, siendo prevalentes los síntomas moderados (29.3%). El 51.1% de los pacientes presentaba ansiedad, predo-minando los síntomas leves (29.3%). Existe relación moderada, pero significativa, entre la depresión y la ansiedad de los evaluados. No se encontró diferencia significativa en los puntajes de depresión y ansiedad según el sexo del informante, la presencia de hábitos tóxicos o las experiencias previas de hospitalización. Conclusiones: Es necesario que los entes de salud pública realicen abordajes integra-les, en donde no sólo se enfatice el componente fisiológico, sino, además, el bienestar psicológico en pacientes no-psiquiátricos hospitalizadosBackground and purpose: Hospitalization is not always well assimilated by individuals, producing anxiety and depression. However, these psychological reactions in hospitalized patients may be unde-restimated and not considered in treatment or recuperation processes. Considering this, the purpose of this study was to analyze the dynamic between depression and anxiety in patients of the Internal Medicine department of the Hospital Escuela Universitario (HEU), in Honduras. Additionally, anxie-ty and depression scores were compared regarding the respondent’s sex, toxic habits, and previous hospitalizations. Materials and methods: A quantitative, non-experimental, cross-sectional research method was used, taking a simple random sample of 92 patients from the Internal Medicine rooms of the HEU. Anxiety was measured through the Generalized Anxiety Disorder-7 Questionnaire (GAD-7) and depression was measured by the Patient Health Questionnaire- 9 (PHQ-9), demographic data was also collected by the researchers. Results: 67.4% of the patients presented some degree of depressive symptoms, particularly at a moderate level (29.3%). On the other hand, 51.1% of the patients presen-ted anxiety -at some level- with moderate symptoms being the most predominant (29.3%). Results suggest a moderate significant statistic relationship between depression and anxiety scores. No statis-tically significant difference was found in depression and anxiety scores regarding the respondent’s sex, presence of toxic habits or history of previous hospitalizations. Conclusions: Public health services should promote holistic approaches which not only rely on a physiological perspective, but also on the psychological wellbeing of non-psychiatric hospitalized patient

Insights Into the Effect of Verticillium dahliae Defoliating-Pathotype Infection on the Content of Phenolic and Volatile Compounds Related to the Sensory Properties of Virgin Olive Oil

Verticillium wilt, caused by the defoliating pathotype of Verticillium dahliae, is the most devastating soil-borne fungal disease of olive trees, and leads to low yields and high rates of tree mortality in highly susceptible cultivars. The disease is widely distributed throughout the Mediterranean olive-growing region and is one of the major limiting factors of olive oil production. Other than effects on crop yield, little is known about the effect of the disease on the content of volatile compounds and phenolics that are produced during the oil extraction process and determine virgin olive oil (VOO) quality and commercial value. Here, we aim to study the effect of Verticillium wilt of the olive tree on the content of phenolic and volatile compounds related to the sensory properties of VOO. Results showed that synthesis of six and five straight-chain carbon volatile compounds were higher and lower, respectively, in oils extracted from infected trees. Pathogen infection affected volatile compounds known to be contributors to VOO aroma: average content of one of the main positive contributors to VOO aroma, (E)-hex-2-enal, was 38% higher in oils extracted from infected trees, whereas average content of the main unpleasant volatile compound, pent-1-en-3-one, was almost 50% lower. In contrast, there was a clear effect of pathogen infection on the content of compounds responsible for VOO taste, where average content of the main bitterness contributor, oleuropein aglycone, was 18% lower in oil extracted from infected plants, and content of oleocanthal, the main contributor to pungency, was 26% lower. We believe this is the first evidence of the effect of Verticillium wilt infection of olive trees on volatile compounds and phenolics that are responsible of the aroma, taste, and commercial value of VOO

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)

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

A multi-decade record of high quality fCO2 data in version 3 of the Surface Ocean CO2 Atlas (SOCAT)

The Surface Ocean CO2 Atlas (SOCAT) is a synthesis of quality-controlled fCO2 (fugacity of carbon dioxide) values for the global surface oceans and coastal seas with regular updates. Version 3 of SOCAT has 14.7 million fCO2 values from 3646 data sets covering the years 1957 to 2014. This latest version has an additional 4.6 million fCO2 values relative to version 2 and extends the record from 2011 to 2014. Version 3 also significantly increases the data availability for 2005 to 2013. SOCAT has an average of approximately 1.2 million surface water fCO2 values per year for the years 2006 to 2012. Quality and documentation of the data has improved. A new feature is the data set quality control (QC) flag of E for data from alternative sensors and platforms. The accuracy of surface water fCO2 has been defined for all data set QC flags. Automated range checking has been carried out for all data sets during their upload into SOCAT. The upgrade of the interactive Data Set Viewer (previously known as the Cruise Data Viewer) allows better interrogation of the SOCAT data collection and rapid creation of high-quality figures for scientific presentations. Automated data upload has been launched for version 4 and will enable more frequent SOCAT releases in the future. High-profile scientific applications of SOCAT include quantification of the ocean sink for atmospheric carbon dioxide and its long-term variation, detection of ocean acidification, as well as evaluation of coupled-climate and ocean-only biogeochemical models. Users of SOCAT data products are urged to acknowledge the contribution of data providers, as stated in the SOCAT Fair Data Use Statement. This ESSD (Earth System Science Data) “living data” publication documents the methods and data sets used for the assembly of this new version of the SOCAT data collection and compares these with those used for earlier versions of the data collection (Pfeil et al., 2013; Sabine et al., 2013; Bakker et al., 2014). Individual data set files, included in the synthesis product, can be downloaded here: doi:10.1594/PANGAEA.849770. The gridded products are available here: doi:10.3334/CDIAC/OTG.SOCAT_V3_GRID

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

Peabody Picture Vocabulary Test-III: Normative data for Spanish-speaking pediatric population

OBJECTIVE: To generate normative data for the Peabody Picture Vocabulary Test-III (PPVT-III) in Spanish-speaking pediatric populations. METHOD: The sample consisted of 4,373 healthy children from nine countries in Latin America (Chile, Cuba, Ecuador, Honduras, Guatemala, Mexico, Paraguay, Peru, and Puerto Rico) and Spain. Each participant was administered the PPVT-III as part of a larger neuropsychological battery. PPVT-III scores were normed using multiple linear regressions and standard deviations of residual values. Age, age2, sex, and mean level of parental education (MLPE) were included as predictors in the analyses. RESULTS: The final multiple linear regression models showed main effects for age in all countries, such that scores increased linearly as a function of age. In addition, age2 had a significant effect in all countries, except Guatemala and Paraguay. Models showed that children whose parent(s) had a MLPE >12 years obtained higher scores compared to children whose parent(s) had a MLPE ≤12 years in all countries, except for Cuba, Peru, and Puerto Rico. Sex affected scores for Chile, Ecuador, Guatemala, Mexico, and Spain. CONCLUSIONS: This is the largest Spanish-speaking pediatric normative study in the world, and it will allow neuropsychologists from these countries to have a more accurate interpretation of the PPVT-III when used in pediatric populations