Global assessment and mapping of ecological vulnerability to wildfires

Fire is a natural phenomenon that has played a critical role in transforming the environment and maintaining biodiversity at a global scale. However, the plants in some habitats have not developed strategies for recovery from fire or have not adapted to the changes taking place in their fire regimes. Maps showing ecological vulnerability to fires could contribute to environmental management policies in the face of global change scenarios. The main objective of this study is to assess and map ecological vulnerability to fires on a global scale. To this end, we created ecological value and post-fire regeneration delay indices on the basis of existing global databases. Two ecological value indices were identified: biological distinction and conservation status. For the post-fire regeneration delay index, various factors were taken into account, including the type of fire regime, the increase in the frequency and intensity of forest fires, and the potential soil erosion they can cause. These indices were combined by means of a qualitative cross-tabulation to create a new index evaluating ecological vulnerability to fire. The results showed that global ecological value could be reduced by as much as 50 % due to fire perturbation of poorly adapted ecosystems. The terrestrial biomes most affected are the tropical and subtropical moist broadleaf forest, tundra, mangroves, tropical and subtropical coniferous forests, and tropical and subtropical dry broadleaf forests.This research has been supported by the Ministerio de Ciencia, Innovación y Universidades (grant no. RTI2018- 097538-B-I00) and the Ministerio de Ciencia, Innovación y Universidades (grant no. PRE2019-089208)

El uso de las redes e influencia en la comunicación por parte de los estudiantes de La Universidad de El Salvador, Universidad Tecnológica, Universidad Dr. José Matías Delgado y Universidad Centroamericana José Simeón Cañas, en el periodo de febrero a julio de 2018.

Las redes sociales son herramientas tecnológicas para comunicarse con diferentes personas a nivel nacional e internacional; desde sus inicios se vio la aceptación de las personas, hoy en día las redes se han vuelto imprescindibles para cada estudiante en el uso académico, personal e inclusive de trabajo. Estas proporcionan diferentes gratificaciones y al mismo tiempo el uso inadecuado y excesivo produce diferentes problemáticas en las que se pueden ver afectados universitarios. Sin embargo, el poder estar relacionado con sus familiares, compañeros y amigos es importante para cada uno de ellos. Por otra parte, el mantenerse conectado demasiado tiempo en las redes puede crear distracción o inclusive ser víctima de violencia; en muchos de los casos no perciben ciertas problemáticas al menos que ya haya sido víctima por el uso inadecuado de las mismas. Por consiguiente, los estudiantes se convierten en público activo al aprovechar las herramientas que proporcionan las redes sociales, ya que el joven tiene la capacidad de pensar, actuar, canalizar y reflexionar sobre lo que él visualiza; sin embargo, existen las mediaciones, que son los grupos de amigos o entorno social que pueden incidir en la manera de actuar y en el contenido que comparten sus contactos. Por otra parte, la influencia de las redes sociales puede producirse de manera positiva en los estudiantes al compartir con sus contactos imágenes o mensajes de reflexión y académicos. Asimismo, pueden influir negativamente al acceder a las redes por mucho tiempo, convirtiéndose en una adicción. Tomando en consideración puede haber una influencia necesaria en el uso de cada una de ellas al reforzar los conocimientos adquiridos en las cátedras y finalmente su uso puede convertirse de una forma innecesaria al pasar mucho tiempo sin obtener provecho. En cuanto al uso profesional de los estudiantes que actualmente trabajan, las redes sociales se han convertido en una herramienta importante para generar publicidad y así lograr un acercamiento con las personas, por otra parte, para los estudiantes que aún no trabajan, el uso que le brindarían a las redes sociales es para influir positivamente de acuerdo a su carrera y de igual manera ven a las redes como una forma gratuita de realizar publicidad

First detection of the bla OXA - 23 gene in a multidrug - resistant A. baumannii clinical isolate from Cochabamba, Bolivia

The University of the Basque Country UPV/EHU was the Institution where the research was conducted although the clinical data and preliminary experiments were done in the University of San Simon (Universidad Mayor de San Simon UMSS) and Viedma Hospital within a collaborative project. This work was supported by grant AE14/23 from the University of the Basque Country UPV/EHU. Thanks to Dr. Bruno Lopes, School of Medicine & Dentistry, University of Aberdeen, UK, for critically reading the manuscript

Biobased polymers derived from itaconic acid bearing clickable groups with potent antibacterial activity and negligible hemolytic activity.

Herein, we report, for the first time, the synthesis of clickable polymers derived from biobased itaconic acid, which was then used for the preparation of novel cationic polymers with antibacterial properties and low hemotoxicity via click chemistry. Itaconic acid (IA) was subjected to chemical modification by incorporating clickable alkyne groups on the carboxylic acids. The resulting monomer with pendant alkyne groups was easily polymerized and copolymerized with dimethyl itaconate (DMI) by radical polymerization. The feed molar ratio of comonomers was varied to precisely tune the content of alkyne groups in the copolymers and the amphiphilic balance. Subsequently, an azide with a thiazole group, which is a component of the vitamin thiamine (B1), was attached onto the polymers by copper-catalyzed azidealkyne cycloaddition (CuAAC) click chemistry leading to triazole linkages. N-Alkylation reactions of the thiazole and triazole groups with methyl and butyl iodides provide the corresponding itaconate derivatives with pendant azolium groups. The copolymers with variable cationic charge densities and hydrophobic/ hydrophilic balances, depending on the comonomer feed ratio, display potent antibacterial activity against Gram-positive bacteria, whereas the activity was almost null against Gram-negative bacteria. Hemotoxicity assays demonstrated that the copolymers exhibited negligible hemolysis and excellent selectivity, more than 1000-fold, for Gram-positive bacteria over human red blood cells.post-print1945 K

Enfermedad renal en empleados que laboran en la fábrica industrial AGAVE S.A. de C.V. Hacienda El Platanar, municipio de Moncagua, departamento de San Miguel

RESUMEN: La Enfermedad Renal es un proceso o trastorno, infeccioso, inflamatorio, obstructivo, vascular o neoplásico del riñón. La prevalencia en nuestro medio representa un porcentaje de mucha importancia por ser una enfermedad silenciosa que en la última década constituye la primera causa de muerte. Este estudio tiene como objetivo: Determinar la prevalencia de Enfermedad Renal en los empleados que laboran en la Fábrica Industrial Agave S.A. de C.V. Hacienda El Platanar, municipio de Moncagua, departamento de San Miguel. Metodología: El estudio fue de tipo prospectivo, transversal, descriptivo, y de laboratorio; la población estuvo constituida por 200 empleados de la fábrica Agave, a quienes se solicitó firmar un consentimiento informado y se realizó una entrevista previa con preguntas abiertas y cerradas, referentes a la variable de interés para recopilar información necesaria. Se tomó el peso de cada empleado y se recolectó muestra de sangre y de orina para realizar pruebas de laboratorio: Determinación de creatinina sérica y glicemia, examen general de orina, proteínas en orina con ácido sulfosalicílico y microalbuminuria. Estos fueron procesados en el Laboratorio Clínico de la Unidad Comunitaria de Salud Familiar de Moncagua, se calculó teóricamente el índice de filtración glomerular aplicando la fórmula de Cockcroft- Gault. Resultados: Se encontraron casos para los cinco estadios en donde el 33.7% se encontró con estadio 1 y 2, el 17.2% en estadio 3, 4 y 5. Se observó disminución del índice de filtración glomerular para los siguientes factores: Diabetes mellitus (63.6%), sin embargo el 28.6% no saben si son diabéticos; la población que consume mucha sal el 51.9% presenta disminución; el consumo de agua independientemente de la cantidad, alrededor de 40% a 50% presentan disminución. El 55.3% que trabajan bajo el sol y el 75% que trabaja a altas temperaturas presenta disminución del IFG. Conclusión: La prevalencia de Enfermedad Renal fue de 51.5% para esta población. Y se encontró el 39.4% con Enfermedad Renal oculta. ABSTRACT: The renal illness is a process or disorder, infectious, inflammatory, obstructive, vascular or neoplastic of the kidneys. The prevalence in our environment represent a very important percentage because it is a silence disease that in the last decade constitute the first cause of death. This study has as objective: determine the prevalence of renal illness in employed that work in the industrial factory AGAVE S.A. de C.V. Hacienda El Platanar, municipality of Moncagua, department of San Miguel. Methodology: The study was of prospective, cross-sectional, descriptive and of laboratory type; the population was formed by 200 workers of the AGAVE factory, who was requested to sign an informed consent form and it was gathered sample blood and of urine for make laboratory tests: serum creatinine and glycemia determination, general urine test, urine proteins with sulfosalicylic acid and microalbuminuria. These were processed in the clinical laboratory of community unit of family health Moncagua, it was calculated theoretically the glomerular filtration rate making use of Cockcroft-Gault formule. Results: cases were found for the five stage where 33.7% it was found with the stage 1 and 2, el 17.2% in the stage 3, 4 and 5. It is saw a glomerular filtration rate reduction for the following factors mellitus diabetes (63.6%), however 28.6% don’t know if they are diabetics; the population that consume to much salt 51.9% present reduction; water consumption (independently of the quantity) around 40% to 50% presented reduction. The 55.3% that worked under the sun and the 75% that work in high temperatures they present IFG reduction. Conclusion: the prevalence of renal illness was of 51.55% for this population. I was found that the 39.4% with hidden kidney disease

Ecosystem Services Assessment for Their Integration in the Analysis of Landslide Risk

Landslides are disasters that cause damage to anthropic activities, innumerable loss of human life, and affect the natural ecosystem and its services globally. The landslide risk evaluated by integrating susceptibility and vulnerability maps has recently become a manner of studying sites prone to landslide events and managing these regions well. Developing countries, where the impact of landslides is frequent, need risk assessment tools to address these disasters, starting with their prevention, with free spatial data and appropriate models. However, to correctly understand their interrelationships and social affection, studying the different ecosystem services that relate to them is necessary. This study is the first that has been attempted in which an integrated application methodology of ecosystem services is used to know in a systematic way if the information that ecosystem services provide is useful for landslide risk assessment. For the integration of ecosystem services into the landslide risk evaluation, (1) eight ecosystem services were chosen and mapped to improve understanding of the spatial relationships between these services in the Guerrero State (México), and (2) areas of synergies and trade-offs were identified through a principal component analysis, to understand their influence on risk analysis better. These are extracted from the models of the ARIES platform, artificial intelligence, and big data platform. Finally, (3) the similarity between the risk characteristics (susceptibility and vulnerability, already mapped by the authors) and the ecosystem services assessment was analysed. The results showed that the ecosystem services that most affect the synergy are organic carbon mass and the potential value of outdoor recreation; meanwhile, the possible removed soil mass was the most important trade-off. Furthermore, the lowest similarity value was found between landslide vulnerability and ecosystem services synergy, indicating the importance of including these ecosystem services as a source of valuable information in the risk analysis methodologies, especially with respect to risk vulnerability

Ecosystem Services Assessment for Their Integration in the Analysis of Landslide Risk

Landslides are disasters that cause damage to anthropic activities, innumerable loss of human life, and affect the natural ecosystem and its services globally. The landslide risk evaluated by integrating susceptibility and vulnerability maps has recently become a manner of studying sites prone to landslide events and managing these regions well. Developing countries, where the impact of landslides is frequent, need risk assessment tools to address these disasters, starting with their prevention, with free spatial data and appropriate models. However, to correctly understand their interrelationships and social affection, studying the different ecosystem services that relate to them is necessary. This study is the first that has been attempted in which an integrated application methodology of ecosystem services is used to know in a systematic way if the information that ecosystem services provide is useful for landslide risk assessment. For the integration of ecosystem services into the landslide risk evaluation, (1) eight ecosystem services were chosen and mapped to improve understanding of the spatial relationships between these services in the Guerrero State (México), and (2) areas of synergies and trade-offs were identified through a principal component analysis, to understand their influence on risk analysis better. These are extracted from the models of the ARIES platform, artificial intelligence, and big data platform. Finally, (3) the similarity between the risk characteristics (susceptibility and vulnerability, already mapped by the authors) and the ecosystem services assessment was analysed. The results showed that the ecosystem services that most affect the synergy are organic carbon mass and the potential value of outdoor recreation; meanwhile, the possible removed soil mass was the most important trade-off. Furthermore, the lowest similarity value was found between landslide vulnerability and ecosystem services synergy, indicating the importance of including these ecosystem services as a source of valuable information in the risk analysis methodologies, especially with respect to risk vulnerability

Evaluation of Conditioning Factors of Slope Instability and Continuous Change Maps in the Generation of Landslide Inventory Maps Using Machine Learning (ML) Algorithms

Landslides are recognized as high-impact natural hazards in different regions around the world; therefore, they are extensively researched by experts. Landslide inventories are essential to identify areas that are likely to be affected in the future, thereby enabling interventions to prevent loss of life. Today, through combined approaches, such as remote sensing and machine learning techniques, it is possible to apply algorithms that use data derived from satellite images to produce landslide inventories. This work presents the performance of five machine learning methods—k-nearest neighbor (KNN), stochastic gradient descendent (SGD), support vector machine radial basis function (SVM RBF Kernel), support vector machine (SVM linear kernel), and AdaBoost—in landslide detection in a zone of the state of Guerrero in southern Mexico, using continuous change maps and primary landslide factors, such as slope angle, terrain orientation (aspect), and lithology, as inputs. The models were trained with 2/3 of ground truth samples of 671 slidden/non-slidden polygons. The obtained inventory maps were evaluated with the remaining 1/3 of ground truth samples by generating a confusion matrix and applying the Kappa concordance coefficient, accuracy, precision, recall, and F1 score as evaluation metrics, as well as omission and commission errors. According to the results, the AdaBoost classifier reached greater spatial and statistical coherence than the other implemented methods. The best input layer combination for detection was the continuous change maps obtained by the linear regression and image differencing detection methods, together with the slope angle, aspect, and lithology conditioning factors

Evaluation of Conditioning Factors of Slope Instability and Continuous Change Maps in the Generation of Landslide Inventory Maps Using Machine Learning (ML) Algorithms

Landslides are recognized as high-impact natural hazards in different regions around the world; therefore, they are extensively researched by experts. Landslide inventories are essential to identify areas that are likely to be affected in the future, thereby enabling interventions to prevent loss of life. Today, through combined approaches, such as remote sensing and machine learning techniques, it is possible to apply algorithms that use data derived from satellite images to produce landslide inventories. This work presents the performance of five machine learning methods—k-nearest neighbor (KNN), stochastic gradient descendent (SGD), support vector machine radial basis function (SVM RBF Kernel), support vector machine (SVM linear kernel), and AdaBoost—in landslide detection in a zone of the state of Guerrero in southern Mexico, using continuous change maps and primary landslide factors, such as slope angle, terrain orientation (aspect), and lithology, as inputs. The models were trained with 2/3 of ground truth samples of 671 slidden/non-slidden polygons. The obtained inventory maps were evaluated with the remaining 1/3 of ground truth samples by generating a confusion matrix and applying the Kappa concordance coefficient, accuracy, precision, recall, and F1 score as evaluation metrics, as well as omission and commission errors. According to the results, the AdaBoost classifier reached greater spatial and statistical coherence than the other implemented methods. The best input layer combination for detection was the continuous change maps obtained by the linear regression and image differencing detection methods, together with the slope angle, aspect, and lithology conditioning factors