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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 AG

Aging is the single largest risk factor for most chronic diseases, and thus possesses large socioeconomic interest to continuously aging societies. Consequently, the field of aging research is expanding alongside a growing focus from the industry and investors in aging research. This year's 8th Annual Aging Research and Drug Discovery (ARDD) meeting was organized as a hybrid meeting from August 30th to September 3rd 2021 with more than 130 attendees participating on-site at the Ceremonial Hall at University of Copenhagen, Denmark, and 1800 engaging online. The conference comprised of presentations from 75 speakers focusing on new research in topics including mechanisms of aging and how these can be modulated as well as the use of AI and new standards of practices within aging research. This year, a longevity workshop was included to build stronger connections with the clinical community.

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 AG

© 2018, Biomedical Engineering Society. Purpose: Advanced morphology analysis and image-based hemodynamic simulations are increasingly used to assess the rupture risk of intracranial aneurysms (IAs). However, the accuracy of those results strongly depends on the quality of the vessel wall segmentation. Methods: To evaluate state-of-the-art segmentation approaches, the Multiple Aneurysms AnaTomy CHallenge (MATCH) was announced. Participants carried out segmentation in three anonymized 3D DSA datasets (left and right anterior, posterior circulation) of a patient harboring five IAs. Qualitative and quantitative inter-group comparisons were carried out with respect to aneurysm volumes and ostia. Further, over- and undersegmentation were evaluated based on highly resolved 2D images. Finally, clinically relevant morphological parameters were calculated. Results: Based on the contributions of 26 participating groups, the findings reveal that no consensus regarding segmentation software or underlying algorithms exists. Qualitative similarity of the aneurysm representations was obtained. However, inter-group differences occurred regarding the luminal surface quality, number of vessel branches considered, aneurysm volumes (up to 20%) and ostium surface areas (up to 30%). Further, a systematic oversegmentation of the 3D surfaces was observed with a difference of approximately 10% to the highly resolved 2D reference image. Particularly, the neck of the ruptured aneurysm was overrepresented by all groups except for one. Finally, morphology parameters (e.g., undulation and non-sphericity) varied up to 25%. Conclusions: MATCH provides an overview of segmentation methodologies for IAs and highlights the variability of surface reconstruction. Further, the study emphasizes the need for c

© 2018, The Author(s). Purpose: Image-based computational fluid dynamics (CFD) is widely used to predict intracranial aneurysm wall shear stress (WSS), particularly with the goal of improving rupture risk assessment. Nevertheless, concern has been expressed over the variability of predicted WSS and inconsistent associations with rupture. Previous challenges, and studies from individual groups, have focused on individual aspects of the image-based CFD pipeline. The aim of this Challenge was to quantify the total variability of the whole pipeline. Methods: 3D rotational angiography image volumes of five middle cerebral artery aneurysms were provided to participants, who were free to choose their segmentation methods, boundary conditions, and CFD solver and settings. Participants were asked to fill out a questionnaire about their solution strategies and experience with aneurysm CFD, and provide surface distributions of WSS magnitude, from which we objectively derived a variety of hemodynamic parameters. Results: A total of 28 datasets were submitted, from 26 teams with varying levels of self-assessed experience. Wide variability of segmentations, CFD model extents, and inflow rates resulted in interquartile ranges of sac average WSS up to 56%, which reduced to < 30% after normalizing by parent artery WSS. Sac-maximum WSS and low shear area were more variable, while rank-ordering of cases by low or high shear showed only modest consensus among teams. Experience was not a significant predictor of variability. Conclusions: Wide variability exists in the prediction of intracranial aneurysm WSS. While segmentation and CFD solver techniques may be difficult to standardize across groups, our findings suggest that some of the variability in image-based CFD could be reduced by esta

The maternal perinatal environment modulates brain formation, and altered maternal nutrition has been linked to the development of metabolic and psychiatric disorders in the offspring. Here, we showed that maternal high-fat diet (HFD) feeding during lactation in mice elicits long-lasting changes in gene expression in the offspring's dopaminergic circuitry. This translated into silencing of dopaminergic midbrain neurons, reduced connectivity to their downstream targets, and reduced stimulus-evoked dopamine (DA) release in the striatum. Despite the attenuated activity of DA midbrain neurons, offspring from mothers exposed to HFD feeding exhibited a sexually dimorphic expression of DA-related phenotypes, i.e., hyperlocomotion in males and increased intake of palatable food and sucrose in females. These phenotypes arose from concomitantly increased spontaneous activity of D1 medium spiny neurons (MSNs) and profoundly decreased D2 MSN projections. Overall, we have unraveled a fundamental restructuring of dopaminergic circuitries upon time-restricted altered maternal nutrition to induce persistent behavioral changes in the offspring.

Obesity promotes the development of insulin resistance and increases the incidence of colitis-associated cancer (CAC), but whether a blunted insulin action specifically in intestinal epithelial cells (IECs) affects CAC is unknown. Here, we show that obesity impairs insulin sensitivity in IECs and that mice with IEC-specific inactivation of the insulin and IGF1 receptors exhibit enhanced CAC development as a consequence of impaired restoration of gut barrier function. Blunted insulin signalling retains the transcription factor FOXO1 in the nucleus to inhibit expression of Dsc3, thereby impairing desmosome formation and epithelial integrity. Both IEC-specific nuclear FoxO1ADA expression and IEC-specific Dsc3 inactivation recapitulate the impaired intestinal integrity and increased CAC burden. Spontaneous colonic tumour formation and compromised intestinal integrity are also observed upon IEC-specific coexpression of FoxO1ADA and a stable Myc variant, thus suggesting a molecular mechanism through which impaired insulin action and nuclear FOXO1 in IECs promotes CAC.

Objective: Obesity represents a major risk factor for the development of type 2 diabetes mellitus, atherosclerosis and certain cancer entities. Treatment of obesity is hindered by the long-term maintenance of initially reduced body weight, and it remains unclear whether all pathologies associated with obesity are fully reversible even upon successfully maintained weight loss. Methods: We compared high fat diet-fed, weight reduced and lean mice in terms of body weight development, adipose tissue and liver insulin sensitivity as well as inflammatory gene expression. Moreover, we assessed similar parameters in a human cohort before and after bariatric surgery. Results: Compared to lean animals, mice that demonstrated successful weight reduction showed increased weight gain following exposure to ad libitum control diet. However, pair-feeding weight-reduced mice with lean controls efficiently stabilized body weight, indicating that hyperphagia was the predominant cause for the observed weight regain. Additionally, whereas glucose tolerance improved rapidly after weight loss, systemic insulin resistance was retained and ameliorated only upon prolonged pair-feeding. Weight loss enhanced insulin action and resolved pro-inflammatory gene expression exclusively in the liver, whereas visceral adipose tissue displayed no significant improvement of metabolic and inflammatory parameters compared to obese mice. Similarly, bariatric surgery in humans (n = 55) resulted in massive weight reduction, improved hepatic inflammation and systemic glucose homeostasis, while adipose tissue inflammation remained unaffected and adipocyte-autonomous insulin action only exhibit minor improvements in a subgroup of patients (42%). Conclusions: These results demonstrate that although sustained weight loss improves systemic glucose homeostasis, primarily through improved inflammation and insulin action in liver, a remarkable obesogenic memory can confer long-term increases in adipose tissue inflamma

© 2015 Human Kinetics, Inc. Much sport-for-development (SFD) research has focused on the impact initiatives have on participants, and not on other stakeholders such as volunteers. Some research suggests volunteerism enables social capital gains, while other scholars have been skeptical, with even less known about how volunteers are impacted by working for SFD events rather than for ongoing programs. Therefore, the purpose of this study was to investigate how, if at all, a large, multinational SFD event contributed to social capital development of volunteers. Findings revealed volunteers experienced social capital development through building relationships, learning, and enhanced motivation to work for social change and reciprocity. As very little research has examined the efficacy of SFD events in contributing to social capital development, the findings extend the literature on SFD events. It would be prudent for SFD events to target programming to impact the experience of volunteers to retain them and contribute to social capital development.