Complex interplay of kinetic factors governs the synergistic properties of HIV-1 entry inhibitors.

The homotrimeric HIV-1 envelope glycoprotein (Env) undergoes receptor-triggered structural changes that mediate viral entry through membrane fusion. This process is inhibited by chemokine receptor antagonists (CoRAs) that block Env-receptor interactions and by fusion inhibitors (FIs) that disrupt Env conformational transitions. Synergy between CoRAs and FIs has been attributed to a CoRA-dependent decrease in the rate of viral membrane fusion that extends the lifetime of the intermediate state targeted by FIs. Here, we demonstrated that the magnitude of CoRA/FI synergy unexpectedly depends on FI-binding affinity and the stoichiometry of chemokine receptor binding to trimeric Env. For C-peptide FIs (clinically represented by enfuvirtide), synergy waned as binding strength decreased until inhibitor combinations behaved additively. Curiously, this affinity dependence on synergy was absent for 5-Helix-type FIs. We linked this complex behavior to the CoRA dependence of Env deactivation following FI binding. For both FI classes, reducing chemokine receptor levels on target cells or eliminating competent chemokine receptor-binding sites on Env trimers resulted in a loss of synergistic activity. These data imply that the stoichiometry required for CoRA/FI synergy exceeds that required for HIV-1 entry. Our analysis suggests two distinct roles for chemokine receptor binding, one to trigger formation of the FI-sensitive intermediate state and another to facilitate subsequent conformational transitions. Together, our results could explain the wide variety of previously reported activities for CoRA/FI combinations. These findings also have implications for the combined use of CoRAs and FIs in antiviral therapies and point to a multifaceted role for chemokine receptor binding in promoting HIV-1 entry

Minimum Weight Perfect Matching via Blossom Belief Propagation

Max-product Belief Propagation (BP) is a popular message-passing algorithm for computing a Maximum-A-Posteriori (MAP) assignment over a distribution represented by a Graphical Model (GM). It has been shown that BP can solve a number of combinatorial optimization problems including minimum weight matching, shortest path, network flow and vertex cover under the following common assumption: the respective Linear Programming (LP) relaxation is tight, i.e., no integrality gap is present. However, when LP shows an integrality gap, no model has been known which can be solved systematically via sequential applications of BP. In this paper, we develop the first such algorithm, coined Blossom-BP, for solving the minimum weight matching problem over arbitrary graphs. Each step of the sequential algorithm requires applying BP over a modified graph constructed by contractions and expansions of blossoms, i.e., odd sets of vertices. Our scheme guarantees termination in O(n^2) of BP runs, where n is the number of vertices in the original graph. In essence, the Blossom-BP offers a distributed version of the celebrated Edmonds' Blossom algorithm by jumping at once over many sub-steps with a single BP. Moreover, our result provides an interpretation of the Edmonds' algorithm as a sequence of LPs

Diversity in Public Administration and Public Management: Representation and Public Service for a Changing Country

Public managers and agencies in the U.S. face the challenge of serving communities that differ by race, gender, ethnicity, cultural backgrounds, sexual orientation and other characteristics. This project addresses the changes to the diversity in the workforce and the challenges that people may face

Diversity in Public Administration and Public Management: Representation and Public Service for a Changing Country

Public managers and agencies in the U.S. face the challenge of serving communities that differ by race, gender, ethnicity, cultural backgrounds, sexual orientation and other characteristics. This project addresses the changes to the diversity in the workforce and the challenges that people may face

Convolutional Sparse Kernel Network for Unsupervised Medical Image Analysis

The availability of large-scale annotated image datasets and recent advances in supervised deep learning methods enable the end-to-end derivation of representative image features that can impact a variety of image analysis problems. Such supervised approaches, however, are difficult to implement in the medical domain where large volumes of labelled data are difficult to obtain due to the complexity of manual annotation and inter- and intra-observer variability in label assignment. We propose a new convolutional sparse kernel network (CSKN), which is a hierarchical unsupervised feature learning framework that addresses the challenge of learning representative visual features in medical image analysis domains where there is a lack of annotated training data. Our framework has three contributions: (i) We extend kernel learning to identify and represent invariant features across image sub-patches in an unsupervised manner. (ii) We initialise our kernel learning with a layer-wise pre-training scheme that leverages the sparsity inherent in medical images to extract initial discriminative features. (iii) We adapt a multi-scale spatial pyramid pooling (SPP) framework to capture subtle geometric differences between learned visual features. We evaluated our framework in medical image retrieval and classification on three public datasets. Our results show that our CSKN had better accuracy when compared to other conventional unsupervised methods and comparable accuracy to methods that used state-of-the-art supervised convolutional neural networks (CNNs). Our findings indicate that our unsupervised CSKN provides an opportunity to leverage unannotated big data in medical imaging repositories.Comment: Accepted by Medical Image Analysis (with a new title 'Convolutional Sparse Kernel Network for Unsupervised Medical Image Analysis'). The manuscript is available from following link (https://doi.org/10.1016/j.media.2019.06.005

Addition polymers from 1,4,5,8-tetrahydro-1,4;5,8-diepoxyanthracene and Bis-dienes. 2: Evidence for thermal dehydration occurring in the cure process

Diels-Alder cycloaddition copolymers from 1,4,5,8-tetrahydro-1,4;5,8-diepoxyanthracene and anthracene end-capped polyimide oligomers appear, by thermogravimetric analysis (TGA), to undergo dehydration at elevated temperatures. This would produce thermally stable pentiptycene units along the polymer backbone, and render the polymers incapable of unzipping through a retro-Diels-Alder pathway. High resolution solid 13C nuclear magnetic resonance (NMR) of one formulation of the polymer system before and after heating at elevated temperatures, shows this to indeed be the case. NMR spectra of solid samples of the polymer before and after heating correlated well with those of the parent pentiptycene model compound before and after acid-catalyzed dehydration. Isothermal gravimetric analyses and viscosities of the polymer before and after heat treatment support dehydration as a mechanism for the cure reaction