Aboveground biomass density models for NASA's Global Ecosystem Dynamics Investigation (GEDI) lidar mission

NASA's Global Ecosystem Dynamics Investigation (GEDI) is collecting spaceborne full waveform lidar data with a primary science goal of producing accurate estimates of forest aboveground biomass density (AGBD). This paper presents the development of the models used to create GEDI's footprint-level (similar to 25 m) AGBD (GEDI04_A) product, including a description of the datasets used and the procedure for final model selection. The data used to fit our models are from a compilation of globally distributed spatially and temporally coincident field and airborne lidar datasets, whereby we simulated GEDI-like waveforms from airborne lidar to build a calibration database. We used this database to expand the geographic extent of past waveform lidar studies, and divided the globe into four broad strata by Plant Functional Type (PFT) and six geographic regions. GEDI's waveform-to-biomass models take the form of parametric Ordinary Least Squares (OLS) models with simulated Relative Height (RH) metrics as predictor variables. From an exhaustive set of candidate models, we selected the best input predictor variables, and data transformations for each geographic stratum in the GEDI domain to produce a set of comprehensive predictive footprint-level models. We found that model selection frequently favored combinations of RH metrics at the 98th, 90th, 50th, and 10th height above ground-level percentiles (RH98, RH90, RH50, and RH10, respectively), but that inclusion of lower RH metrics (e.g. RH10) did not markedly improve model performance. Second, forced inclusion of RH98 in all models was important and did not degrade model performance, and the best performing models were parsimonious, typically having only 1-3 predictors. Third, stratification by geographic domain (PFT, geographic region) improved model performance in comparison to global models without stratification. Fourth, for the vast majority of strata, the best performing models were fit using square root transformation of field AGBD and/or height metrics. There was considerable variability in model performance across geographic strata, and areas with sparse training data and/or high AGBD values had the poorest performance. These models are used to produce global predictions of AGBD, but will be improved in the future as more and better training data become available

Global patient outcomes after elective surgery: prospective cohort study in 27 low-, middle- and high-income countries.

BACKGROUND: As global initiatives increase patient access to surgical treatments, there remains a need to understand the adverse effects of surgery and define appropriate levels of perioperative care. METHODS: We designed a prospective international 7-day cohort study of outcomes following elective adult inpatient surgery in 27 countries. The primary outcome was in-hospital complications. Secondary outcomes were death following a complication (failure to rescue) and death in hospital. Process measures were admission to critical care immediately after surgery or to treat a complication and duration of hospital stay. A single definition of critical care was used for all countries. RESULTS: A total of 474 hospitals in 19 high-, 7 middle- and 1 low-income country were included in the primary analysis. Data included 44 814 patients with a median hospital stay of 4 (range 2-7) days. A total of 7508 patients (16.8%) developed one or more postoperative complication and 207 died (0.5%). The overall mortality among patients who developed complications was 2.8%. Mortality following complications ranged from 2.4% for pulmonary embolism to 43.9% for cardiac arrest. A total of 4360 (9.7%) patients were admitted to a critical care unit as routine immediately after surgery, of whom 2198 (50.4%) developed a complication, with 105 (2.4%) deaths. A total of 1233 patients (16.4%) were admitted to a critical care unit to treat complications, with 119 (9.7%) deaths. Despite lower baseline risk, outcomes were similar in low- and middle-income compared with high-income countries. CONCLUSIONS: Poor patient outcomes are common after inpatient surgery. Global initiatives to increase access to surgical treatments should also address the need for safe perioperative care. STUDY REGISTRATION: ISRCTN5181700

Aboveground biomass density models for NASA's Global Ecosystem Dynamics Investigation (GEDI) lidar mission

NASA's Global Ecosystem Dynamics Investigation (GEDI) is collecting spaceborne full waveform lidar data with a primary science goal of producing accurate estimates of forest aboveground biomass density (AGBD). This paper presents the development of the models used to create GEDI's footprint-level (~25 m) AGBD (GEDI04_A) product, including a description of the datasets used and the procedure for final model selection. The data used to fit our models are from a compilation of globally distributed spatially and temporally coincident field and airborne lidar datasets, whereby we simulated GEDI-like waveforms from airborne lidar to build a calibration database. We used this database to expand the geographic extent of past waveform lidar studies, and divided the globe into four broad strata by Plant Functional Type (PFT) and six geographic regions. GEDI's waveform-to-biomass models take the form of parametric Ordinary Least Squares (OLS) models with simulated Relative Height (RH) metrics as predictor variables. From an exhaustive set of candidate models, we selected the best input predictor variables, and data transformations for each geographic stratum in the GEDI domain to produce a set of comprehensive predictive footprint-level models. We found that model selection frequently favored combinations of RH metrics at the 98th, 90th, 50th, and 10th height above ground-level percentiles (RH98, RH90, RH50, and RH10, respectively), but that inclusion of lower RH metrics (e.g. RH10) did not markedly improve model performance. Second, forced inclusion of RH98 in all models was important and did not degrade model performance, and the best performing models were parsimonious, typically having only 1-3 predictors. Third, stratification by geographic domain (PFT, geographic region) improved model performance in comparison to global models without stratification. Fourth, for the vast majority of strata, the best performing models were fit using square root transformation of field AGBD and/or height metrics. There was considerable variability in model performance across geographic strata, and areas with sparse training data and/or high AGBD values had the poorest performance. These models are used to produce global predictions of AGBD, but will be improved in the future as more and better training data become available

Seguimiento de las guías españolas para el manejo del asma por el médico de atención primaria: un estudio observacional ambispectivo

Objetivo Evaluar el grado de seguimiento de las recomendaciones de las versiones de la Guía española para el manejo del asma (GEMA 2009 y 2015) y su repercusión en el control de la enfermedad. Material y métodos Estudio observacional y ambispectivo realizado entre septiembre del 2015 y abril del 2016, en el que participaron 314 médicos de atención primaria y 2.864 pacientes. Resultados Utilizando datos retrospectivos, 81 de los 314 médicos (25, 8% [IC del 95%, 21, 3 a 30, 9]) comunicaron seguir las recomendaciones de la GEMA 2009. Al inicio del estudio, 88 de los 314 médicos (28, 0% [IC del 95%, 23, 4 a 33, 2]) seguían las recomendaciones de la GEMA 2015. El tener un asma mal controlada (OR 0, 19, IC del 95%, 0, 13 a 0, 28) y presentar un asma persistente grave al inicio del estudio (OR 0, 20, IC del 95%, 0, 12 a 0, 34) se asociaron negativamente con tener un asma bien controlada al final del seguimiento. Por el contrario, el seguimiento de las recomendaciones de la GEMA 2015 se asoció de manera positiva con una mayor posibilidad de que el paciente tuviera un asma bien controlada al final del periodo de seguimiento (OR 1, 70, IC del 95%, 1, 40 a 2, 06). Conclusiones El escaso seguimiento de las guías clínicas para el manejo del asma constituye un problema común entre los médicos de atención primaria. Un seguimiento de estas guías se asocia con un control mejor del asma. Existe la necesidad de actuaciones que puedan mejorar el seguimiento por parte de los médicos de atención primaria de las guías para el manejo del asma. Objective: To assess the degree of compliance with the recommendations of the 2009 and 2015 versions of the Spanish guidelines for managing asthma (Guía Española para el Manejo del Asma [GEMA]) and the effect of this compliance on controlling the disease. Material and methods: We conducted an observational ambispective study between September 2015 and April 2016 in which 314 primary care physicians and 2864 patients participated. Results: Using retrospective data, we found that 81 of the 314 physicians (25.8%; 95% CI 21.3–30.9) stated that they complied with the GEMA2009 recommendations. At the start of the study, 88 of the 314 physicians (28.0%; 95% CI 23.4–33.2) complied with the GEMA2015 recommendations. Poorly controlled asthma (OR, 0.19; 95% CI 0.13–0.28) and persistent severe asthma at the start of the study (OR, 0.20; 95% CI 0.12–0.34) were negatively associated with having well-controlled asthma by the end of the follow-up. In contrast, compliance with the GEMA2015 recommendations was positively associated with a greater likelihood that the patient would have well-controlled asthma by the end of the follow-up (OR, 1.70; 95% CI 1.40–2.06). Conclusions: Low compliance with the clinical guidelines for managing asthma is a common problem among primary care physicians. Compliance with these guidelines is associated with better asthma control. Actions need to be taken to improve primary care physician compliance with the asthma management guidelines

The gut microbiome of solitary bees is mainly affected by pathogen assemblage and partially by land use

Pollinators, including solitary bees, are drastically declining worldwide. Among the factors contributing to this decline, bee pathogens and different land uses are of relevance. The link between the gut microbiome composition and host health has been recently studied for social pollinators (e.g. honeybees), whereas the information related to solitary bees is sparse. This work aimed at the characterization of the gut microbiome of the solitary bees Xylocopa augusti, Eucera fervens and Lasioglossum and attempted to correlate the gut microbial composition with the presence and load of different pathogens and land uses. Solitary bees were sampled in different sites (i.e. a farm, a natural reserve, and an urban plant nursery) showing different land uses. DNA was extracted from the gut, 16S rRNA gene amplified and sequenced. Eight pathogens, known for spillover from managed bees to wild ones, were quantified with qPCR. The results showed that the core microbiome profile of the three solitary bees significantly varied in the different species. Pseudomonas was found as the major core taxa in all solitary bees analyzed, whereas Lactobacillus, Spiroplasma and Sodalis were the second most abundant taxa in X. augusti, E. fervens and Lasioglossum, respectively. The main pathogens detected with qPCR were Nosema ceranae, Nosema bombi and Crithidia bombi, although differently abundant in the different bee species and sampling sites. Most microbial taxa did not show any correlation with the land use, apart from Snodgrassella and Nocardioides, showing higher abundances on less anthropized sites. Conversely, the pathogens species and load strongly affected the gut microbial composition, with Bifidobacterium, Apibacter, Serratia, Snodgrassella and Sodalis abundance that positively or negatively correlated with the detected pathogens load. Therefore, pathogens presence and load appear to be the main factor shaping the gut microbiome of solitary bees in Argentina

Continuous surveillance of a pregnancy clinical guideline : An early experience

To date there is no consensus about the optimal strategy for keeping clinical guidelines (CGs) up-to-date. The aims of this study were (1) to develop a continuous surveillance and updating strategy for CGs and (2) to test the strategy in a specific CG. The main steps were as follows: (1) recruiting members for the CG Updating Working Group, (2) mapping the CG, (3) identifying new evidence from the CG Updating Working Group, (4) designing and running restricted literature searches, (5) reviewing drugs and medical devices alerts, (6) screening and assessing the new evidence, (7) reviewing and, if necessary, modifying clinical questions and recommendations, and (8) updating the CG document. The Pregnancy CG Updating Working Group consisted of 29 members, including clinicians, patients and caregivers, and clinical guideline methodology experts. We selected 69 clinical questions (123 recommendations) from the "Assistance during pregnancy" section. For the first update cycle (32-month duration), 9710 references were identified. Of these, 318 were pertinent, 289 were relevant, and 55 were classified as potential key references. For the second and third update cycles (6-month duration each), 2160 and 2010 references were retrieved, respectively. The continuous surveillance and updating strategy has not yet been completely implemented. Further resources are needed in updating the CG field, both for implementing updating strategies and for developing methodological research