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

Monitorage par doppler tranosesophagien au cours de l'ansethésie pour craniostenose chez le nourrison (adaptation cardiaque à l'hémodilution normovolémique)

Introduction : la répartition des craniosténoses est une chirurgie à haut risque hémorragique, qui se réalise avant douze mois de vie. L'hémodilution normovolémique figure parmi les stratégies d'épargne sanguine, mais son setentissement n'a pas été étudié chez l'enfant de moins de un an. Grâce au monitorage par doppler transoesophagien (DTO), nous avons évalué les mécanismes d'adaptation cardiovasculaire à l'hémodilution normovolémique chez le nourrisson. Méthodes : nous avons inclus tous les enfants candidats à une chirurgie réparatrice de craniosténose dans notre centre. Les enfants ont subi une hémodilution normovolémique peropératoire jusqu'à une hémoglobinémie minimale de 8 g.dl-1.Les différents paramètres hémodynamiques (cliniques et issus du DTO) et d'oxygénation tissulaire ont été recueillis successivement en situation hémodynamique stable puis comparés aux valeurs de référence. Résultats : entre 2006 et 2011, 38 patients ont été retenus dans l'étude. Durant l'intervention, l'hémoglobine a diminué de 11,3+-1,3 à 8,4+-1,5 g.dl-1 (p<0,001), associée à une augmentation de l'index cardiaque de 2,8+-0,8 à 3,0+-0,4l.min-1;m-2 (p=0,027) liée à une hausse du volume d'éjection systolique sans variation de la fréquence cardiaque, et une chute des résistances vasculaires systématiques indexées de 1537+-417 à 1082+-215 dynes.s;cm-5 (p=0,012). Le transport en O2 a diminué de 14% (p=0,024) mais la consommation d'O2 est restée conservée, alors que l'extraction de O2 a augmenté de 33% (p=0,004) et la saturation veineuse centrale en O2 perdu 12% (p=0,007) des valeurs de références, sans augmentation des lactates. Conclusion : le nourrisson semble s'adapter parfaitement à une hémodilution normovolémique peropératoire modérée, par une augmentation de débit cardiaque qui compense partiellement la chute du transport en O2 et par une augmentation de l'extraction tissulaire en O2 ; ces mécanismes permettent le maintien de l'équlibre entre apports et besoins en O2MONTPELLIER-BU Médecine UPM (341722108) / SudocMONTPELLIER-BU Médecine (341722104) / SudocSudocFranceF

Aggravation hémorragique secondaire des contusions cérébrales chez le traumatisme crânien (Facteurs de risque et critères prédictifs cliniques, biologiques et radiologiques)

Introduction : Les Aggravations secondaires des Contusions Cérébrales Hémorragiques (ACCH) retentissent à la fois sur le pronostic fonctionnel et vital des traumatisés crâniens, et nécessitent un dépistage précoce pour adapter la prise en charge des patients. Nous avons cherché des facteurs de risques d'ACCH afin de proposer un score prédictif applicable à la prise en charge initiale. Matériel et Méthode : Nous avons réalisé une étude rétrospective monocentrique en incluant les traumatisés crâniens sévères admis durant 7 ans dans notre service, et ayant bénéficié de deux scanners comparatifs à 24-48h d'intervalle. Nous avons colligé et comparé les données démographiques, cliniques, biologiques, radiologiques, thérapeutiques et pronostiques des patients au sein de deux groupes: avec ACCH et sans ACCH. Un score prédictif a été établi à partir d'une régression logistique multivariée. Résultats : 337 traumatisés crâniens ont été inclus, majoritairement polytraumatisés (ISS moyen: 33 +- 9), dont 95 patients (28%) avec ACCH et 242 patients (72%) sans ACCH. Dans le groupe ACCH, l'âge et l'ISS étaient plus élevés, les lésions crânio-cérébrales associées plus importantes et les taux de fibrinogène plus bas (p < 0,01). Le score de Glasgow, les lésions extra-crâniennes, ou les autres paramètres d'hémostase n'étaient pas différents dans les deux groupes. Le Glasgow Outcome Scale à la fin du séjour et la survie étaient plus altérés dans le groupe ACCH (p < 0,01). L'analyse multivariée a identifié 6 variables prédictives d'ACCH (r'=0,28): fracture du crâne (OR 3,7 [2,3-6,1] ; p < 0,0 1), engagement cérébral (OR 4 [2-7,9] ; p < 0,01), hémorragie intraventriculaire (OR 2 [1 ,2-3 ,4] ; p = 0,01), localisation frontale (OR 6,1 [3,6-10,2] ;p < 0,01) ou temporale (OR 7 [3,4-11,8] ;p < 0,01), fibrinogène < Ig/L (OR 3,9 [1,7-8,6] ;p < 0,01), regroupées au sein d'un score prédictif allant de à 7 points (aire sous la courbe ROC=0,82 [0 ,77-0,87];p < 0,01). Conclusion: La survenue des ACCH est associée à des stigmates scannographiques de l'énergie cinétique du traumatisme crânien, et potentialisée par une coagulopathie spécifique. Ces facteurs de risque rejoignent la vision physiopathologique récente des ACCH, et sont à la base d'un score prédictif à la fois simple et pertinent qui permet de dépister précocemment (dès le bilan initial) le risque de lésions secondMONTPELLIER-BU Médecine UPM (341722108) / SudocMONTPELLIER-BU Médecine (341722104) / SudocSudocFranceF

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

Aboveground Biomass Density Models for NASA’s Global Ecosystem Dynamics Investigation (GEDI) Lidar Mission

NASAs Global Ecosystem Dynamics Investigation (GEDI) is collecting space-borne 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 GEDIs 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. GEDIs 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 select the best input predictor variables, 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 favors 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) does not markedly improve model performance. Second, forced inclusion of RH98 in all models was important and did not degrade model performance, and that 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

The impact of surgical delay on resectability of colorectal cancer: An international prospective cohort study

AimThe SARS-CoV-2 pandemic has provided a unique opportunity to explore the impact of surgical delays on cancer resectability. This study aimed to compare resectability for colorectal cancer patients undergoing delayed versus non-delayed surgery.MethodsThis was an international prospective cohort study of consecutive colorectal cancer patients with a decision for curative surgery (January-April 2020). Surgical delay was defined as an operation taking place more than 4 weeks after treatment decision, in a patient who did not receive neoadjuvant therapy. A subgroup analysis explored the effects of delay in elective patients only. The impact of longer delays was explored in a sensitivity analysis. The primary outcome was complete resection, defined as curative resection with an R0 margin.ResultsOverall, 5453 patients from 304 hospitals in 47 countries were included, of whom 6.6% (358/5453) did not receive their planned operation. Of the 4304 operated patients without neoadjuvant therapy, 40.5% (1744/4304) were delayed beyond 4 weeks. Delayed patients were more likely to be older, men, more comorbid, have higher body mass index and have rectal cancer and early stage disease. Delayed patients had higher unadjusted rates of complete resection (93.7% vs. 91.9%, P = 0.032) and lower rates of emergency surgery (4.5% vs. 22.5%, P ConclusionOne in 15 colorectal cancer patients did not receive their planned operation during the first wave of COVID-19. Surgical delay did not appear to compromise resectability, raising the hypothesis that any reduction in long-term survival attributable to delays is likely to be due to micro-metastatic disease

Delaying surgery for patients with a previous SARS-CoV-2 infection

Not availabl