Small satellite technologies, particularly CubeSats, are enabling breakthrough research in space. Over the past 15 years, NASA Ames Research Center has developed and flown half a dozen biological CubeSats in low Earth orbit (LEO) to conduct space biology and astrobiology research investigating the effects of the space environment on microbiological organisms. These studies of the impacts of radiation and reduced gravity on cellular processes include dose-dependent interactions with antimicrobial drugs, measurements of gene expression and signaling, and assessment of radiation damage. BioSentinel, the newest addition to this series, will be the first deep space biological CubeSat, its heliocentric orbit extending far beyond the radiation-shielded environment of low Earth orbit. BioSentinel's 4U biosensing payload, the first living biology space experiment ever conducted beyond the Earth-Moon system, will use a microbial bioassay to assess repair of radiation-induced DNA damage in eukaryotic cells over a duration of 6-12 months. Part of a special collection of articles focused on BioSentinel and its science mission, this article describes the design, development, and testing of the biosensing payload's microfluidics and optical systems, highlighting improvements relative to previous CubeSat life-support and bioanalytical measurement technologies.
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http://dx.doi.org/10.1089/ast.2020.2305 | DOI Listing |
Alzheimers Dement
December 2024
Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
Background: Cerebral small vessel disease (CSVD), which includes cerebral amyloid angiopathy (CAA) and arteriolosclerosis, often co-occurs with Alzheimer's disease (AD) pathology. The medial temporal lobe (MTL) is susceptible to hosting multiple AD pathologies, such as neurofibrillary tangles (NFTs), amyloid-β plaques, phospho-Tar-DNA-Binding-Protein-43 (pTDP-43), as well as CSVD. Whether a causal relationship between these pathologies exists remains largely unknown, but one potential linking mechanism is the dysfunction of perivascular clearance.
View Article and Find Full Text PDFBackground: The aging and dementia field has long been interested in understanding disease heterogeneity, subtypes, and progression. Work has progressed from clinical, to neuroimaging to biomedical devices to neuropathological data, and now brain and blood omic data.
Method: The AMP-AD consortium generated and/or annotated genomic, epigenomic, transcriptomic, proteomic, and metabolomic data from brain and/or blood from thousands of study participants and patients across the 8 teams.
Phys Rev Lett
December 2024
Department of Physics, Harvard University, Cambridge, Massachusetts 02138, USA.
Quantum error correction is believed to be essential for scalable quantum computation, but its implementation is challenging due to its considerable space-time overhead. Motivated by recent experiments demonstrating efficient manipulation of logical qubits using transversal gates [Bluvstein et al., Nature (London) 626, 58 (2024)NATUAS0028-083610.
View Article and Find Full Text PDFSci Rep
January 2025
Physics Department, Science College, Princess Nourah Bint Abdulrahman University, Riyadh, Saudi Arabia.
Semantic segmentation of high-resolution images from remote sensing is crucial across various sectors. However, due to limitations in computational resources and the complexity of network architectures, many sophisticated semantic segmentation models struggle with efficiency in real-world applications, leading to an interest in developing lightweight model like borders. These models often employ a dual-branch structure, which balances processing speed and performance effectively.
View Article and Find Full Text PDFNat Commun
January 2025
Department of Electronic Engineering, Tsinghua University, Beijing, China.
Deep generative models have garnered significant attention for their efficiency in drug discovery, yet the synthesis of proposed molecules remains a challenge. Retrosynthetic planning, a part of computer-assisted synthesis planning, addresses this challenge by recursively decomposing molecules using symbolic rules and machine-trained scoring functions. However, current methods often treat each molecule independently, missing the opportunity to utilize shared synthesis patterns and repeat pathways, which may contribute from known synthesis routes to newly emerging, similar molecules, a notable challenge with AI-generated small molecules.
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