Land Use Land Cover Labeling of GLOBE Images Using a Deep Learning Fusion Model
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Genomic and transcriptomic studies generate a rich source of molecular information; however, neither a static genome nor the presence of a messenger RNA provides the most direct insight into the functional state of a microbial cell at any given time. Rather, it is the ensemble of proteins (i.e., proteome) that is the primary workhorse for most biological processes that are concurrent and coordinately active in a living cell. Currently, mass-spectrometry-based (MS) technologies provide unprecedented information into the composition of the proteome, shedding light on the numerous complex biological processes actively shaping observed phenotypes. Herein, we detail a protocol for the exploration of intracellular proteomes through the large-scale, unbiased identification of proteins and their relative abundances using liquid chromatography (LC) coupled to tandem mass spectrometry (MS/MS). In general, this protocol is applicable to both microbial and eukaryotic systems.
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To address the unprecedented scale of HL-LHC data, the Exa.TrkX project is investigating a variety of machine learning approaches to particle track reconstruction. The most promising of these solutions, graph neural networks (GNN), process the event as a graph that connects track measurements (detector hits corresponding to nodes) with candidate line segments between the hits (corresponding to edges). Detector information can be associated with nodes and edges, enabling a GNN to propagate the embedded parameters around the graph and predict node-, edge- and graph-level observables. Previously, message-passing GNNs have shown success in predicting doublet likelihood, and we here report updates on the state-of-the-art architectures for this task. In addition, the Exa.TrkX project has investigated innovations in both graph construction, and embedded representations, in an effort to achieve fully learned end-to-end track finding. Hence, we present a suite of extensions to the original model, with encouraging results for hitgraph classification. In addition, we explore increased performance by constructing graphs from learned representations which contain non-linear metric structure, allowing for efficient clustering and neighborhood queries of data points. We demonstrate how this framework fits in with both traditional clustering pipelines, and GNN approaches. The embedded graphs feed into high-accuracy doublet and triplet classifiers, or can be used as an end-to-end track classifier by clustering in an embedded space. A set of post-processing methods improve performance with knowledge of the detector physics. Finally, we present numerical results on the TrackML particle tracking challenge dataset, where our framework shows favorable results in both seeding and track finding.
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A significant enhancement of detection capabilities of the room temperature MPQ is seen using optical lithography-defined, ferromagnetic iron-nickel alloy microdisks. Irreversible transitions between strongly non-collinear (vortex) and a collinear single domain states, driven by an ac magnetic field, translate into a nonlinear magnetic response that enables ultrasensitive detection of material at relatively small magnetic fields.
A system and method for tracking a plurality of objects. Unlabeled measurement data identifying a plurality of targets corresponding to the plurality of objects is received. A multi-target likelihood function is generated using a persistent target density, a birth target density, and a clutter density. The multi-target likelihood function is used to associate persistent targets with the unlabeled measurement data to update persistent tracks and to initiate a new track for each target in the plurality of targets in the measurement data that is not associated with a persistent target.
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Covalently cross-linked pilus polymers displayed on the cell surface of Gram-positive bacteria are assembled by class C sortase enzymes. These pilus-specific transpeptidases located on the bacterial membrane catalyze a two-step protein ligation reaction—first, cleaving the LPXTG motif of one pilin protomer to form an acyl-enzyme intermediate, and second, joining the terminal threonine to the nucleophilic lysine residue residing within the pilin motif of another pilin protomer. Informed by the high-resolution crystal structures of corynebacterial pilus-specific sortase (SrtA) and by developing structural variants of the sortase enzyme whose catalytic pocket has been unmasked by activating mutations, we have developed new reagents capable of forming isopeptide bonds in vitro. The reagents disclosed herein can catalyze ligation of isolated SpaA domains in vitro provide a facile and versatile new platform for protein engineering and bio-conjugation that has major implications for biotechnology.
A significant enhancement of detection capabilities of the room temperature MPQ is seen using optical lithography-defined, ferromagnetic iron-nickel alloy microdisks. Irreversible transitions between strongly non-collinear (vortex) and a collinear single domain states, driven by an ac magnetic field, translate into a nonlinear magnetic response that enables ultrasensitive detection of material at relatively small magnetic fields.