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Introducción: El embarazo constituye un período de grandes modificaciones fisiológicas en el organismo, que pueden conllevar a la aparición de trastornos en el estado de salud bucal. Objetivos: Describir el estado de salud periodontal en las embarazadas del área de salud Colón Oeste. Metodología: Se realizó un estudio observacional, descriptivo, transversal, retrospectivo, en el periodo comprendido desde enero del 2019 a marzo del 2020. EL universo se estudió en su totalidad y estuvo constituido por 291 embarazadas pertenecientes al área de salud Colón Oeste las cuales fueron visitadas para obtener el consentimiento informado para participar en la investigación. Los resultados se presentaron en tablas estadísticas y se utilizó como medida resumen el porciento. Resultados: La edad que más pacientes aportó fue la del grupo etario de 25 a 34 años con 37.5%. Predominaron las pacientes sanas para un 70.2% seguidas de las pacientes con gingivitis crónica con 28.5%. La higiene bucal inadecuada fue la más significativa con 52.3%, dentro de los factores de riesgo el antecedente de caries dental tuvo un mayor significado con un 61.8%, la inflación gingival prevaleció en el 100% de las pacientes enfermas periodontalmente. Conclusiones: La edad de 25 a 34 años fue la que más pacientes aportó, predominaron las pacientes sanas seguidas de las pacientes con gingivitis crónica. La higiene bucal inadecuada fue la más significativa, los factores de riesgo que prevalecieron fueron el antecedente de caries dental seguido del apiñamiento dentario. El signo más representativo fue la inflación gingival.

Coffee is a major global commodity whose production is sustained by and provides livelihoods for millions of smallholder families in the tropics. However, it is highly sensitive to climate change and the climate risk family farmer's face from direct impacts on coffee production are often compounded by further impacts on the physical and social landscapes and infrastructure. We examine the vulnerability (sensitivity and adaptive capacity) of smallholder coffee farmers in northeastern Peru via the lens of their central participation in a value chain that mediates access to livelihood assets, affecting their adaptive capacity and aspects of their sensitivity. Using a staged and participatory, mixed-methods approach, we sought to understand the territorial climate exposure, the structure of the regional value chain and role of different actors in supporting farmer adaptive capacity, and assess the vulnerability of the entire value chain (including other actors in addition to farmers). We found heterogeneity not only in the potential impact of climate change on coffee production, future adaptation needs and vulnerability of farmers across the territory (among elevational zones and regions), but in the distribution of vulnerability among value chain actors. Farmers are the most vulnerable actors, simultaneously the most sensitive and with the lowest adaptive capacity, issues stemming from their strong territorial dependence and pre-existing social and economic asymmetries with actors in the coffee value chain who are not as territorially dependent (e.g., private companies). We make the case that supporting the adaptation of smallholder farmers in the study region requires moving beyond a value-chain approach to a territorial systems perspective that more intentionally involves those actors with stronger, locally vested interests (e.g., local governments and institutions) in their adaptation and requires the strengthening capacities of these actors in various areas.

We report the psychometric properties of the Patient-Reported Impact of Symptoms in Schizophrenia Scale (PRISS), which assesses the impact of subjective experiences or qualia in outpatients with this condition.A cross-sectional study was carried out in 162 patients diagnosed with schizophrenia in Spain. The PRISS measures the presence, frequency, concern and interference with daily life of self-reported experiences related to the main symptoms observed in these patients. The psychometric analysis included test-retest reliability, internal consistency and structural and convergent validity.The 28-item PRISS showed good test-retest reliability as 64.3% of the intraclass correlation coefficient values were between 0.40 and 0.79, which were statistically significant (p 0.01). Analysis of the structural validity revealed a three-factor structure, (1) productive subjective experiences, (2) affective-negative subjective experiences and (3) excitation, which accounted for 56.11% of the variance. Of the Pearson's correlation coefficients analysed between the PRISS and the Positive and Negative Syndrome Scale (PANSS), Scale for Assessment of Negative Symptoms (SANS) and World Health Organization Disability Assessment Schedule (WHO-DAS), 72.2% were statistically significant (p 0.05) and ranged from 0.38-0.42, 0.32-0.42 and 0.40-0.42, respectively.Our results indicate that the PRISS appears to be a brief, reliable and valid scale to measure subjective experiences in schizophrenia and provides valuable information complementary to clinical evaluation.

How the aerosol particle number size distribution evolves during long range transport due to atmospheric chemistry and aerosol processes, and how aerosol sources contribute to this aerosol population is one of our greatest conundrums in aerosol science. One way to approach this challenge is to follow the movement of the aerosol with the wind in the lowest aerosol layer of the atmosphere and measure the aerosol particles and the precursor gases, which is called the Lagrangian approach.In this pilot Lagrangian project, measurements are made on a mobile platform with a road vehicle. Aerosol particle number size distribution is measured with an SMPS system, together with low cost-sensors measuring ozone, nitrogen oxides, carbon dioxide, PM1 mass concentration. The instruments are operated with an already operational battery-inverter system and not the combustion engine of the vehicle to avoid self-contamination. There is a limit to the time the measurements can go on and the number of instruments operated due to the capacity of the battery. To avoid isokinetic sampling issues in the pilot project, the measurements are performed during short stops with the vehicle. These short stopsa are a few kilometers away from major emission sources, to avoid short duration peaks.Measurements are performed from January to March 2021 in the first set of measurements to account for the winter background aerosol. The chase is initiated from southernmost Sweden, where the air is followed for several hours until the battery to support power to the instruments is depleted. Measurements in the car are validated with the ACTRIS infrastructure station Hyltemossa. If these first set of experiments will be accomplishable, further measurements during summer will be performed, possibly also in Denmark and Finland. Modelling with a Lagrangian aerosol dynamics and chemistry model is neccessary to account for the source emissions and to understand and account for coagulation, condensation, chemistry, air mixing, and to quantify the source contributions from various new emissions along the trajectory path. Qualitatively, however, it is anticipated that these processes can be understood and identified with the naked eye by observing changes in the measured parameters along the wind path.If the results of this pilot project are succesful, it will be a revolution in aerosol science to understand the process and emissions shaping the aerosol population. The project can hopefully be exported also to other continental environments worldwide.

Approximately two-thirds of continental precipitation is evaporated back into the atmosphere. This highlights the influence of terrestrial evaporation for the distribution of hydrological resources, from catchment to planetary scales. The ability to monitor terrestrial evaporation dynamics is critical for climatological applications, since evaporation directly affects air temperature, influences air humidity and cloud formation, and is intrinsically connected to photosynthesis. To date, terrestrial evaporation cannot be observed directly from space, and in situ networks remain too sparse for both research and practical activities, making terrestrial evaporation one of the most uncertain components of Earth’s energy and water balance. However, a range of approaches have been proposed over the last decade to indirectly derive evaporation by applying models that combine the satellite-observed environmental and climatic drivers of the flux. One of these pioneering methods is the Global Land Evaporation Amsterdam Model (GLEAM; Miralles et al. 2011). GLEAM combines global satellite observations of meteorological variables – (e.g.) precipitation, surface net radiation and air temperature – and surface characteristics – (e.g.) soil and vegetation water content and snow depth. Since its publication almost 10 years ago, the model has been widely used to analyse trends in the water cycle, study land–atmospheric feedbacks or benchmark climate models. Advantages of GLEAM over analogous methods are the estimation of evaporation under cloud conditions due to the exploitation of microwave data, the explicit estimation of root-zone soil moisture data, and the detailed calculation of rainfall interception. Current model development efforts concentrate on (a) the increase in spatial resolution for its application to water management and agricultural applications and (b) the assimilation of novel satellite observations. This presentation provides a general overview of the framework and concentrates on ongoing efforts that strive in the direction of assimilating Gravity Recovery and Climate Experiment (GRACE) and Soil Moisture Active–Passive (SMAP) observations to improve the root-zone soil moisture estimates. References Miralles, D. G., Holmes, T. R. H., De Jeu, R. A. M., Gash, J. H., Meesters, A. G. C. A. and Dolman, A. J.: Global land-surface evaporation estimated from satellite-based observations, Hydrol. Earth Syst. Sci., 15(2), 453–469, doi:10.5194/hess-15-453-2011, 2011.