Antiphospholipid antibodies in patients with stroke during COVID-19: A role in the signaling pathway leading to platelet activation

Background: Several viral and bacterial infections, including COVID-19, may lead to both thrombotic and hemorrhagic complications. Previously, it has been demonstrated an "in vitro " pathogenic effect of "antiphospholipid " antibodies (aPLs), which are able to activate a proinflammatory and procoagulant phenotype in monocytes, endothelial cells and platelets. This study analyzed the occurrence of aPL IgG in patients with acute ischemic stroke (AIS) during COVID-19, evaluating the effect of Ig fractions from these patients on signaling and functional activation of platelets. Materials and methods: Sera from 10 patients with AIS during COVID-19, 10 non-COVID-19 stroke patients, 20 COVID-19 and 30 healthy donors (HD) were analyzed for anti-cardiolipin, anti-beta 2-GPI, anti-phosphatidylserine/prothrombin and anti-vimentin/CL antibodies by ELISA. Platelets from healthy donors were incubated with Ig fractions from these patients or with polyclonal anti-beta 2-GPI IgG and analyzed for phospho-ERK and phospho-p38 by western blot. Platelet secretion by ATP release dosage was also evaluated. Results: We demonstrated the presence of aPLs IgG in sera of patients with AIS during COVID-19. Treatment with the Ig fractions from these patients or with polyclonal anti-beta 2-GPI IgG induced a significant increase of phospho-ERK and phospho-p38 expression. In the same vein, platelet activation was supported by the increase of adenyl nucleotides release induced by Ig fractions. Conclusions: This study demonstrates the presence of aPLs in a subgroup of COVID-19 patients who presented AIS, suggesting a role in the mechanisms contributing to hypercoagulable state in these patients. Detecting these antibodies as a serological marker to check and monitor COVID-19 may contribute to improve the risk stratification of thromboembolic manifestations in these patients

Linking Ecomechanical Models and Functional Traits to Understand Phenotypic Diversity

Physical principles and laws determine the set of possible organismal phenotypes. Constraints arising from development, the environment, and evolutionary history then yield workable, integrated phenotypes. We propose a theoretical and practical framework that considers the role of changing environments. This \u27ecomechanical approach\u27 integrates functional organismal traits with the ecological variables. This approach informs our ability to predict species shifts in survival and distribution and provides critical insights into phenotypic diversity. We outline how to use the ecomechanical paradigm using drag-induced bending in trees as an example. Our approach can be incorporated into existing research and help build interdisciplinary bridges. Finally, we identify key factors needed for mass data collection, analysis, and the dissemination of models relevant to this framework

Searching for a Stochastic Background of Gravitational Waves with LIGO

The Laser Interferometer Gravitational-wave Observatory (LIGO) has performed the fourth science run, S4, with significantly improved interferometer sensitivities with respect to previous runs. Using data acquired during this science run, we place a limit on the amplitude of a stochastic background of gravitational waves. For a frequency independent spectrum, the new limit is $\Omega_{\rm GW} < 6.5 \times 10^{-5}$. This is currently the most sensitive result in the frequency range 51-150 Hz, with a factor of 13 improvement over the previous LIGO result. We discuss complementarity of the new result with other constraints on a stochastic background of gravitational waves, and we investigate implications of the new result for different models of this background.Comment: 37 pages, 16 figure

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