@article {67314, title = {Alpha-1 adrenergic receptor antagonists to prevent hyperinflammation and death from lower respiratory tract infection.}, journal = {eLife}, volume = {10}, year = {2021}, month = {2021 Jun 11}, abstract = {

In severe viral pneumonia, including Coronavirus disease 2019 (COVID-19), the viral replication phase is often followed by hyperinflammation, which can lead to acute respiratory distress syndrome, multi-organ failure, and death. We previously demonstrated that alpha-1 adrenergic receptor (⍺-AR) antagonists can prevent hyperinflammation and death in mice. Here, we conducted retrospective analyses in two cohorts of patients with acute respiratory distress (ARD, n = 18,547) and three cohorts with pneumonia (n = 400,907). Federated across two ARD cohorts, we find that patients exposed to ⍺-AR antagonists, as compared to unexposed patients, had a 34\% relative risk reduction for mechanical ventilation and death (OR = 0.70, p = 0.021). We replicated these methods on three pneumonia cohorts, all with similar effects on both outcomes. All results were robust to sensitivity analyses. These results highlight the urgent need for prospective trials testing whether prophylactic use of ⍺-AR antagonists ameliorates lower respiratory tract infection-associated hyperinflammation and death, as observed in COVID-19.

}, issn = {2050-084X}, doi = {10.7554/eLife.61700}, author = {Koenecke, Allison and Powell, Michael and Xiong, Ruoxuan and Shen, Zhu and Fischer, Nicole and Huq, Sakibul and Khalafallah, Adham M and Trevisan, Marco and Sparen, P{\"a}r and Carrero, Juan J and Nishimura, Akihiko and Caffo, Brian and Stuart, Elizabeth A and Bai, Renyuan and Staedtke, Verena and Thomas, David L and Papadopoulos, Nickolas and Kinzler, Ken W and Vogelstein, Bert and Zhou, Shibin and Bettegowda, Chetan and Konig, Maximilian F and Mensh, Brett D and Vogelstein, Joshua T and Athey, Susan} } @article {65721, title = {Preventing cytokine storm syndrome in COVID-19 using α-1 adrenergic receptor antagonists.}, journal = {The Journal of Clinical Investigatio}, volume = {130}, year = {2020}, month = {2020 Jul 01}, pages = {3345-47}, keywords = {Adrenergic alpha-1 Receptor Antagonists, Clinical Trials as Topic, Coronavirus Infections, Cytokine Release Syndrome, Humans, Pandemics, Pneumonia, Viral}, issn = {1558-8238}, doi = {10.1172/JCI139642}, author = {Konig, Maximilian F and Powell, Mike and Staedtke, Verena and Bai, Ren-Yuan and Thomas, David L and Fischer, Nicole and Huq, Sakibul and Khalafallah, Adham M and Koenecke, Allison and Xiong, Ruoxuan and Mensh, Brett and Papadopoulos, Nickolas and Kinzler, Kenneth W and Vogelstein, Bert and Vogelstein, Joshua T and Athey, Susan and Zhou, Shibin and Bettegowda, Chetan} } @article {49149, title = {A community-developed Open-Source computational ecosystem for big neuro data.}, journal = {Nature Methods}, volume = {15}, year = {2018}, month = {2018 Nov}, pages = {846-7}, abstract = {

Big imaging data is becoming more prominent in brain sciences across spatiotemporal scales and phylogenies. We have developed a computational ecosystem that enables storage, visualization, and analysis of these data in the cloud, thusfar spanning 20+ publications and 100+ terabytes including nanoscale ultrastructure, microscale synaptogenetic diversity, and mesoscale whole brain connectivity, making NeuroData the largest and most diverse open repository of brain data.

}, doi = {10.1038/s41592-018-0181-1}, author = {Vogelstein, Joshua T and Perlman, Eric and Falk, Benjamin and Baden, Alex and Roncal, William Gray and Chandrashekhar, Vikram and Collman, Forrest and Seshamani, Sharmishtaa and Patsolic, Jesse and Lillaney, Kunal and Kazhdan, Michael and Hider Jr., Robert and Pryor, Derek and Matelsky, Jordan and Gion, Timothy and Manavalan, Priya and Wester, Brock and Chevillet, Mark and Trautman, Eric T and Khairy, Khaled} } @article {48495, title = {Semiparametric spectral modeling of the Drosophila connectome.}, journal = {arXiv}, year = {2017}, month = {2017 May 9}, pages = {1705.03297}, abstract = {

We present semiparametric spectral modeling of the complete larval Drosophila mushroom body connectome. Motivated by a thorough exploratory data analysis of the network via Gaussian mixture modeling (GMM) in the adjacency spectral embedding (ASE) representation space, we introduce the latent structure model (LSM) for network modeling and inference. LSM is a generalization of the stochastic block model (SBM) and a special case of the random dot product graph (RDPG) latent position model, and is amenable to semiparametric GMM in the ASE representation space. The resulting connectome code derived via semiparametric GMM composed with ASE captures latent connectome structure and elucidates biologically relevant neuronal properties.

}, url = {https://arxiv.org/abs/1705.03297}, author = {Priebe, Carey E. and Park, Youngser and Tang, Minh and Athreya, Avanti and Lyzinski, Vince and Vogelstein, Joshua T and Qin, Yichen and Cocanougher, Benjamin and Eichler, Katharina and Zlatic, Marta and Cardona, Albert} } @article {6911, title = {Discovery of brainwide neural-behavioral maps via multiscale unsupervised structure learning}, journal = {Science}, volume = {344}, year = {2014}, month = {2014 Mar 27}, pages = {386-92}, chapter = {386}, abstract = {

A single nervous system can generate many distinct motor patterns. Identifying which neurons and circuits control which behaviors has been a laborious piecemeal process, usually for one observer-defined behavior at a time. We present a fundamentally different approach to neuron-behavior mapping. We optogenetically activated 1,054 identified neuron lines in Drosophila larva and tracked the behavioral responses from 37,780 animals. Applying multiscale unsupervised structure learning methods to the behavioral data identified 29 discrete statistically distinguishable and observer-unbiased behavioral phenotypes. Mapping the neural lines to the behavior(s) they evoke provides a behavioral reference atlas for neuron subsets covering a large fraction of larval neurons. This atlas is a starting point for connectivity- and activity-mapping studies to further investigate the mechanisms by which neurons mediate diverse behaviors.

}, issn = {1095-9203}, doi = {10.1126/science.1250298}, author = {Vogelstein, Joshua T and Park, Youngser and Ohyama, Tomoko and Kerr, Rex and Truman, James W and Priebe, Carey E. and Zlatic, Marta} } @article {6904, title = {The Open Connectome Project Data Cluster: Scalable analysis and vision for high-throughput neuroscience.}, journal = {Scientific and Statistical Database Management: International Conference, SSDBM ... : Proceedings. International Conference on Scientific and Statistical Database Management}, year = {2013}, month = {2013 Jun 18}, abstract = {

We describe a scalable database cluster for the spatial analysis and annotation of high-throughput brain imaging data, initially for 3-d electron microscopy image stacks, but for time-series and multi-channel data as well. The system was designed primarily for workloads that build connectomes- neural connectivity maps of the brain-using the parallel execution of computer vision algorithms on high-performance compute clusters. These services and open-science data sets are publicly available at openconnecto.me. The system design inherits much from NoSQL scale-out and data-intensive computing architectures. We distribute data to cluster nodes by partitioning a spatial index. We direct I/O to different systems-reads to parallel disk arrays and writes to solid-state storage-to avoid I/O interference and maximize throughput. All programming interfaces are RESTful Web services, which are simple and stateless, improving scalability and usability. We include a performance evaluation of the production system, highlighting the effec-tiveness of spatial data organization.

}, doi = {10.1145/2484838.2484870}, author = {Burns, Randal and Roncal, William Gray and Kleissas, Dean and Lillaney, Kunal and Manavalan, Priya and Perlman, Eric and Berger, Daniel R. and Bock, Davi D and Chung, Kwanghun and Grosenick, Logan and Kasthuri, Narayanan and Weiler, Nicholas C and Deisseroth, Karl and Kazhdan, Michael and Lichtman, Jeff and Reid, R Clay and Smith, Stephen J and Szalay, Alexander S and Vogelstein, Joshua T and Vogelstein, R Jacob} }