4 research outputs found
Complex interactions of HIV-1 nucleocapsid protein with oligonucleotides
The HIV-1 nucleocapsid (NC) protein is a small, basic protein containing two retroviral zinc fingers. It is a highly active nucleic acid chaperone; because of this activity, it plays a crucial role in virus replication as a cofactor during reverse transcription, and is probably important in other steps of the replication cycle as well. We previously reported that NC binds with high-affinity to the repeating sequence d(TG)(n). We have now analyzed the interaction between NC and d(TG)(4) in considerable detail, using surface plasmon resonance (SPR), tryptophan fluorescence quenching (TFQ), fluorescence anisotropy (FA), isothermal titration calorimetry (ITC) and electrospray ionization Fourier transform mass spectrometry (ESI-FTMS). Our results show that the interactions between these two molecules are surprisngly complex: while the K(d) for binding of a single d(TG)(4) molecule to NC is only ∼5 nM in 150 mM NaCl, a single NC molecule is capable of interacting with more than one d(TG)(4) molecule, and conversely, more than one NC molecule can bind to a single d(TG)(4) molecule. The strengths of these additional binding reactions are quantitated. The implications of this multivalency for the functions of NC in virus replication are discussed
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MIDRC-MetricTree: a decision tree-based tool for recommending performance metrics in artificial intelligence-assisted medical image analysis.
PURPOSE: The Medical Imaging and Data Resource Center (MIDRC) was created to facilitate medical imaging machine learning (ML) research for tasks including early detection, diagnosis, prognosis, and assessment of treatment response related to the coronavirus disease 2019 pandemic and beyond. The purpose of this work was to create a publicly available metrology resource to assist researchers in evaluating the performance of their medical image analysis ML algorithms. APPROACH: An interactive decision tree, called MIDRC-MetricTree, has been developed, organized by the type of task that the ML algorithm was trained to perform. The criteria for this decision tree were that (1) users can select information such as the type of task, the nature of the reference standard, and the type of the algorithm output and (2) based on the user input, recommendations are provided regarding appropriate performance evaluation approaches and metrics, including literature references and, when possible, links to publicly available software/code as well as short tutorial videos. RESULTS: Five types of tasks were identified for the decision tree: (a) classification, (b) detection/localization, (c) segmentation, (d) time-to-event (TTE) analysis, and (e) estimation. As an example, the classification branch of the decision tree includes two-class (binary) and multiclass classification tasks and provides suggestions for methods, metrics, software/code recommendations, and literature references for situations where the algorithm produces either binary or non-binary (e.g., continuous) output and for reference standards with negligible or non-negligible variability and unreliability. CONCLUSIONS: The publicly available decision tree is a resource to assist researchers in conducting task-specific performance evaluations, including classification, detection/localization, segmentation, TTE, and estimation tasks
Complex interactions of HIV-1 nucleocapsid protein with oligonucleotides-0
<p><b>Copyright information:</b></p><p>Taken from "Complex interactions of HIV-1 nucleocapsid protein with oligonucleotides"</p><p>Nucleic Acids Research 2006;34(3):1082-1082.</p><p>Published online 13 Feb 2006</p><p>PMCID:PMC1369284.</p><p>© The Author 2006. Published by Oxford University Press. All rights reserved</p