PWD and PCP are employees of Cynvenio Biosystems Inc. FB is employed by Menarini Silicon Biosystems. SK, MT, PDC, MWM, and SG are employees of ResearchDx. JU and KD are employees of Liquid Genomics. SR is an employee of NantHealth. PD is affiliated with Liquid Genomics. These companies all developed platforms used in this work.
A triad of 'rapidly growing lesion with predilection for oral mucosa, classical plasmablastic morphology and limited immunohistochemical panel' can render a reliable diagnosis of PBL, irrespective of HIV and EBV status, especially in developing countries with limited resources.
BackgroundAcute respiratory distress syndrome (ARDS) is a frequent complication of COVID-19 and is associated with a component of thrombo-inflammation and cytokine storm. COVID-19 also affects the hemostatic system causing multiple coagulation abnormalities that is a cause of concern and needs to be addressed.
Rapid development of renewable energy sources, particularly solar photovoltaics, is critical to mitigate climate change. As a result, India has set ambitious goals to install 300 gigawatts of solar energy capacity by 2030. Given the large footprint projected to meet these renewable energy targets the potential for land use conflicts over environmental and social values is high. To expedite development of solar energy, land use planners will need access to up-to-date and accurate geo-spatial information of PV infrastructure. The majority of recent studies use either predictions of resource suitability or databases that are either developed thru crowdsourcing that often have significant sampling biases or have time lags between when projects are permitted and when location data becomes available. Here, we address this shortcoming by developing a spatially explicit machine learning model to map utility-scale solar projects across India. Using these outputs, we provide a cumulative measure of the solar footprint across India and quantified the degree of land modification associated with land cover types that may cause conflicts. Our analysis indicates that over 74% of solar development In India was built on landcover types that have natural ecosystem preservation, and agricultural values. Thus, with a mean accuracy of 92% this method permits the identification of the factors driving land suitability for solar projects and will be of widespread interest for studies seeking to assess trade-offs associated with the global decarbonization of green-energy systems. In the same way, our model increases the feasibility of remote sensing and long-term monitoring of renewable energy deployment targets.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.