Graph-based compression of dynamic 3D point cloud sequences

This paper addresses the problem of compression of 3D point cloud sequences that are characterized by moving 3D positions and color attributes. As temporally successive point cloud frames are similar, motion estimation is key to effective compression of these sequences. It however remains a challenging problem as the point cloud frames have varying numbers of points without explicit correspondence information. We represent the time-varying geometry of these sequences with a set of graphs, and consider 3D positions and color attributes of the points clouds as signals on the vertices of the graphs. We then cast motion estimation as a feature matching problem between successive graphs. The motion is estimated on a sparse set of representative vertices using new spectral graph wavelet descriptors. A dense motion field is eventually interpolated by solving a graph-based regularization problem. The estimated motion is finally used for removing the temporal redundancy in the predictive coding of the 3D positions and the color characteristics of the point cloud sequences. Experimental results demonstrate that our method is able to accurately estimate the motion between consecutive frames. Moreover, motion estimation is shown to bring significant improvement in terms of the overall compression performance of the sequence. To the best of our knowledge, this is the first paper that exploits both the spatial correlation inside each frame (through the graph) and the temporal correlation between the frames (through the motion estimation) to compress the color and the geometry of 3D point cloud sequences in an efficient way

Uptake and transport of novel amphiphilic polyelectrolyte-insulin nanocomplexes by caco-2 cells - towards oral insulin

“The original publication is available at www.springerlink.com”. Copyright SpringerPurpose: The influence of polymer architecture on cellular uptake and transport across Caco-2 cells of novel amphiphilic polyelectrolyte-insulin nanocomplexes was investigated. Method: Polyallylamine (PAA) (15 kDa) was grafted with palmitoyl chains (Pa) and subsequently modified with quaternary ammonium moieties (QPa). These two amphiphilic polyelectrolytes (APs) were tagged with rhodamine and their uptake by Caco-2 cells or their polyelectrolyte complexes (PECs) with fluorescein isothiocyanate-insulin (FITC-insulin) uptake were investigated using fluorescence microscopy. The integrity of the monolayer was determined by measurement of transepithelial electrical resistance (TEER). Insulin transport through Caco-2 monolayers was determined during TEER experiments. Result: Pa and insulin were co-localised in the cell membranes while QPa complexes were found within the cytoplasm. QPa complex uptake was not affected by calcium, cytochalasin D or nocodazole. Uptake was reduced by co-incubation with sodium azide, an active transport inhibitor. Both polymers opened tight junctions reversibly where the TEER values fell by up to 35 % within 30 minutes incubation with Caco-2 cells. Insulin transport through monolayers increased when QPa was used (0.27 ngmL-1 of insulin in basal compartment) compared to Pa (0.14 ngmL-1 of insulin in basal compartment) after 2 hours. Conclusion: These APs have been shown to be taken up by Caco-2 cells and reversibly open tight cell junctions. Further work is required to optimise these formulations with a view to maximising their potential to facilitate oral delivery of insulin.Peer reviewe

Interface Engineering of Water-Dispersible Near-Infrared-Emitting CuInZnS/ZnSe/ZnS Quantum Dots

© 2024 The Authors. Published by American Chemical Society. This is an open access article distributed under the Creative Commons Attribution License, to view a copy of the license, see: https://creativecommons.org/licenses/by/4.0/We report the synthesis of near-infrared (IR)-emitting core/shell/shell quantum dots of CuInZnS/ZnSe/ZnS and their phase transfer to water. The intermediate ZnSe shell was added to inhibit the migration of ions from the standard ZnS shell into the emitting core, which often leads to a blue shift in the emission profile. By engineering the interface between the core and terminal shell layer, the optical properties can be controlled, and emission was maintained in the near-IR region, making the materials attractive for biological applications. In addition, the hydrodynamic diameter of the particle was controlled using amphiphilic polymers.Peer reviewe

Effect of Soluble ICAM-1 on a Sjögren's Syndrome-like Phenotype in NOD Mice Is Disease Stage Dependent

Intercellular adhesion molecule-1 (ICAM-1) is involved in migration and co-stimulation of T and B cells. Membrane bound ICAM-1 is over expressed in the salivary glands (SG) of Sjögren's syndrome (SS) patients and has therefore been proposed as a potential therapeutic target. To test the utility of ICAM-1 as a therapeutic target, we used local gene therapy in Non Obese Diabetic (NOD) mice to express soluble (s)ICAM-1 to compete with membrane bound ICAM-1 for binding with its receptor. Therapy was given prior to and just after the influx of immune cells into the SG.A recombinant serotype 2 adeno associated virus (rAAV2) encoding ICAM-1/Fc was constructed and its efficacy tested in the female NOD mice after retrograde instillation in SG at eight (early treatment) and ten (late treatment) weeks of age. SG inflammation was evaluated by focus score and immunohistochemical quantification of infiltrating cell types. Serum and SG tissue were analyzed for immunoglobulins (Ig).Early treatment with ICAM-1/Fc resulted in decreased average number of inflammatory foci without changes in T and B cell composition. In contrast, late treated mice did not show any change in focus scores, but immunohistochemical staining showed an increase in the overall number of CD4+ and CD8+ T cells. Moreover, early treated mice showed decreased IgM within the SGs, whereas late treated mice had increased IgM levels, and on average higher IgG and IgA.Blocking the ICAM-1/LFA-1 interaction with sICAM-1/Fc may result in worsening of a SS like phenotype when infiltrates have already formed within the SG. As a treatment for human SS, caution should be taken targeting the ICAM-1 axis since most patients are diagnosed when inflammation is clearly present within the SG

Fabrication, Modeling and Characterization of Multi-Crosslinked Methacrylate Copolymeric Nanoparticles for Oral Drug Delivery

Nanotechnology remains the field to explore in the quest to enhance therapeutic efficacies of existing drugs. Fabrication of a methacrylate copolymer-lipid nanoparticulate (MCN) system was explored in this study for oral drug delivery of levodopa. The nanoparticles were fabricated employing multicrosslinking technology and characterized for particle size, zeta potential, morphology, structural modification, drug entrapment efficiency and in vitro drug release. Chemometric Computational (CC) modeling was conducted to deduce the mechanism of nanoparticle synthesis as well as to corroborate the experimental findings. The CC modeling deduced that the nanoparticles synthesis may have followed the mixed triangular formations or the mixed patterns. They were found to be hollow nanocapsules with a size ranging from 152 nm (methacrylate copolymer) to 321 nm (methacrylate copolymer blend) and a zeta potential range of 15.8–43.3 mV. The nanoparticles were directly compressible and it was found that the desired rate of drug release could be achieved by formulating the nanoparticles as a nanosuspension, and then directly compressing them into tablet matrices or incorporating the nanoparticles directly into polymer tablet matrices. However, sustained release of MCNs was achieved only when it was incorporated into a polymer matrix. The experimental results were well corroborated by the CC modeling. The developed technology may be potentially useful for the fabrication of multi-crosslinked polymer blend nanoparticles for oral drug delivery

What Does It Take to Synergistically Combine Sub-Potent Natural Products into Drug-Level Potent Combinations?

10.1371/journal.pone.0049969PLoS ONE711

The United States COVID-19 Forecast Hub dataset

Academic researchers, government agencies, industry groups, and individuals have produced forecasts at an unprecedented scale during the COVID-19 pandemic. To leverage these forecasts, the United States Centers for Disease Control and Prevention (CDC) partnered with an academic research lab at the University of Massachusetts Amherst to create the US COVID-19 Forecast Hub. Launched in April 2020, the Forecast Hub is a dataset with point and probabilistic forecasts of incident cases, incident hospitalizations, incident deaths, and cumulative deaths due to COVID-19 at county, state, and national, levels in the United States. Included forecasts represent a variety of modeling approaches, data sources, and assumptions regarding the spread of COVID-19. The goal of this dataset is to establish a standardized and comparable set of short-term forecasts from modeling teams. These data can be used to develop ensemble models, communicate forecasts to the public, create visualizations, compare models, and inform policies regarding COVID-19 mitigation. These open-source data are available via download from GitHub, through an online API, and through R packages