Using structural analysis to investigate the function of Suppressor of IKK-epsilon (SIKE)

The innate immune system provides the body’s first line of defense against pathogenic challenge through pathogen recognition and initiation of the immune response. Among the various cellular mechanisms of pathogen recognition in mammals, Toll-like receptor 3 (TLR3) recognizes viral dsRNA. Stimulation of TLR3 signaling pathway leads to transcription of pro-inflammatory cytokines and type-1 Interferons. Suppressor of IKKε (SIKE) interacts with two kinases in the signaling pathway, IKKε and TANK binding kinase 1 (TBK1), inhibiting the transcription of type I interferons. Recently, the Bell Laboratory discovered that SIKE blocks TBK1-mediated activation of type I interferons by acting as a high affinity, alternative substrate of TBK1. To further characterize SIKE’s function within the antiviral response, this study focused on defining the overall SIKE structure. Using recombinant protein expressed from E. coli and purified via immobilized metal affinity chromatography, SIKE crystals were obtained from a sample concentrated to 15 mg/ml under several crystallization conditions. Yet, reproducing these results has been difficult. In this study, we have modified the purification scheme to remove an E. coli contaminant, SlyD. Purification under denaturing conditions, removal of soluble proteins, incorporation of ion exchange and different IMAC (immobilized metal ion affinity chromatography) resins has been tested. For each scheme, size exclusion chromatography and SDS-PAGE/Coomassie/silver stain were used to assess purity. Crystallization trials for samples from each purification scheme were completed. In addition to crystallization trials, hydrogen-deuterium exchange (HDX) was investigated, accompanied with pepsin digests, in order to further characterize the dynamic structure of SIKE

Orbital Decay in M82 X-2

© 2022. The Author(s). Published by the American Astronomical Society. This is an open access article distributed under the Creative Commons Attribution License, to view a copy of the license, https://creativecommons.org/licenses/by/4.0/M82 X-2 is the first pulsating ultraluminous X-ray source discovered. The luminosity of these extreme pulsars, if isotropic, implies an extreme mass transfer rate. An alternative is to assume a much lower mass transfer rate, but with an apparent luminosity boosted by geometrical beaming. Only an independent measurement of the mass transfer rate can help discriminate between these two scenarios. In this paper, we follow the orbit of the neutron star for 7 yr, measure the decay of the orbit ( Ṗorb/Porb≈−8·10−6yr−1 ), and argue that this orbital decay is driven by extreme mass transfer of more than 150 times the mass transfer limit set by the Eddington luminosity. If this is true, the mass available to the accretor is more than enough to justify its luminosity, with no need for beaming. This also strongly favors models where the accretor is a highly magnetized neutron star.Peer reviewe

Citizen science can improve conservation science, natural resource management, and environmental protection

Citizen science has advanced science for hundreds of years, contributed to many peer-reviewed articles, and informed land management decisions and policies across the United States. Over the last 10 years, citizen science has grown immensely in the United States and many other countries. Here, we show how citizen science is a powerful tool for tackling many of the challenges faced in the field of conservation biology. We describe the two interwoven paths bywhich citizen science can improve conservation efforts, natural resource management, and environmental protection. The first path includes building scientific knowledge, while the other path involves informing policy and encouraging public action. We explore how citizen science is currently used and describe the investments needed to create a citizen science program. We find that: 1. Citizen science already contributes substantially to many domains of science, including conservation, natural resource, and environmental science. Citizen science informs natural resource management, environmental protection, and policymaking and fosters public input and engagement. 2. Many types of projects can benefit fromcitizen science, but one must be careful tomatch the needs for science and public involvement with the right type of citizen science project and the right method of public participation. 3. Citizen science is a rigorous process of scientific discovery, indistinguishable from conventional science apart from the participation of volunteers.When properly designed, carried out, and evaluated, citizen science can provide sound science, efficiently generate high-quality data, and help solve problems

Federated learning enables big data for rare cancer boundary detection.

Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing

Author Correction: Federated learning enables big data for rare cancer boundary detection.

10.1038/s41467-023-36188-7NATURE COMMUNICATIONS14

Federated Learning Enables Big Data for Rare Cancer Boundary Detection

Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing

Proceedings of the Thirteenth International Society of Sports Nutrition (ISSN) Conference and Expo

Meeting Abstracts: Proceedings of the Thirteenth International Society of Sports Nutrition (ISSN) Conference and Expo Clearwater Beach, FL, USA. 9-11 June 201