Fusing Structural and Functional MRIs using Graph Convolutional Networks for Autism Classification

Geometric deep learning methods such as graph convolutional networks have recently proven to deliver generalized solutions in disease prediction using medical imaging. In this paper, we focus particularly on their use in autism classification. Most of the recent methods use graphs to leverage phenotypic information about subjects (patients or healthy controls) as additional contextual information. To do so, metadata such as age, gender and acquisition sites are utilized to define intricate relations (edges) between the subjects. We alleviate the use of such non-imaging metadata and propose a fully imaging-based approach where information from structural and functional Magnetic Resonance Imaging (MRI) data are fused to construct the edges and nodes of the graph. To characterize each subject, we employ brain summaries. These are 3D images obtained from the 4D spatiotemporal resting-state fMRI data through summarization of the temporal activity of each voxel using neuroscientifically informed temporal measures such as amplitude low frequency fluctuations and entropy. Further, to extract features from these 3D brain summaries, we propose a 3D CNN model. We perform analysis on the open dataset for autism research (full ABIDE I-II) and show that by using simple brain summary measures and incorporating sMRI information, there is a noticeable increase in the generalizability and performance values of the framework as compared to state-of-the-art graph-based models

Revisiting the Local Scaling Hypothesis in Stably Stratified Atmospheric Boundary Layer Turbulence: an Integration of Field and Laboratory Measurements with Large-eddy Simulations

The `local scaling' hypothesis, first introduced by Nieuwstadt two decades ago, describes the turbulence structure of stable boundary layers in a very succinct way and is an integral part of numerous local closure-based numerical weather prediction models. However, the validity of this hypothesis under very stable conditions is a subject of on-going debate. In this work, we attempt to address this controversial issue by performing extensive analyses of turbulence data from several field campaigns, wind-tunnel experiments and large-eddy simulations. Wide range of stabilities, diverse field conditions and a comprehensive set of turbulence statistics make this study distinct

Pathophysiology, risk, diagnosis, and management of venous thrombosis in space: where are we now?

The recent incidental discovery of an asymptomatic venous thrombosis (VT) in the internal jugular vein of an astronaut on the International Space Station prompted a necessary, immediate response from the space medicine community. The European Space Agency formed a topical team to review the pathophysiology, risk and clinical presentation of venous thrombosis and the evaluation of its prevention, diagnosis, mitigation, and management strategies in spaceflight. In this article, we discuss the findings of the ESA VT Topical Team over its 2-year term, report the key gaps as we see them in the above areas which are hindering understanding VT in space. We provide research recommendations in a stepwise manner that build upon existing resources, and highlight the initial steps required to enable further evaluation of this newly identified pertinent medical risk