Visualization of facet-dependent pseudo-photocatalytic behavior of TiO2 nanorods for water splitting using In situ liquid cell TEM

We report an investigation of the pseudo-photocatalytic behavior of rutile TiO2 nanorods for water splitting observed with liquid cell transmission electron microscopy (TEM). The electron beam serves as a “light” source to initiate the catalytic reaction and a “water-in-salt” aqueous solution is employed as the electrolyte. The observation reveals that bubbles are generated preferentially residing near the {110} facet of a rutile TiO2 nanorod under a low electron dose rate (9.3–18.6 e-/Å2·s). These bubbles are ascribed to hydrogen gas generated from the pseudo-photocatalytic water splitting. As the electron beam current density increases to 93 e-/Å2 ·s, bubbles are also found at the {001} and {111} facets as well as in the bulk liquid solution, demonstrating the dominant effects of water electrolysis by electron beam under higher dose rates. The facet-dependent pseudo-photocatalytic behavior of rutile TiO2 nanorods is further validated using density functional theory (DFT)calculation. Our work establishes a facile liquid cell TEM setup for the study of pseudo-photocatalytic water splitting and it may also be applied to investigation of other photo-activated phenomena occurring at the solid-liquid interfaces

Quantitative Analysis of Breast Shapes

Institute of Textiles and ClothingRefereed conference pape

Measuring optical activity in the far-field from a racemic nanomaterial: Diffraction spectroscopy from plasmonic nanogratings

Recent progress in nanofabrication has redrawn the boundaries of the applicability of chiroptical (chiral optical) effects. Chirality, often expressed as a twist in biomolecules, is crucial for pharmaceuticals, where it can result in extremely different chemical properties. Because chiroptical effects are typically very weak in molecules, plasmonic nanomaterials are often proposed as a promising platform to significantly enhance these effects. Unfortunately, the ideal plasmonic nanomaterial has conflicting requirements: Its chirality should enhance that of the chiral molecules and yet it should have no chiroptical response on its own. Here, we propose a unique reconciliation to satisfy the requirements: A racemic plasmonic nanomaterial, consisting of equal amounts of opposite chiral unit cells. We show how diffraction spectroscopy can be used to unveil the presence of chirality in such racemic nanogratings in the far-field. Our experiments are supported by numerical simulations and yield a circular intensity difference of up to 15%. The physical origin is demonstrated by full wave simulations in combination with a Green's function-group-theory-based analysis. Contributions from Circular Dichroism in the Angular Distribution of Photoelectrons (CDAD) and pseudo/extrinsic chirality are ruled out. Our findings enable the far-field measurement and tuning of racemic nanomaterials, which is crucial for hyper-sensitive chiral molecular characterization.V. K. V. acknowledges support from the Royal Society through the University Research Fellowships. We acknowledge Royal Society grants CHG\R1\170067, PEF1\170015 and RGF\EA\180228, as well as STFC grant ST/R005842/1 and EPSRC grant EP/L015544/1. C. W. acknowledges financial support from Cancer Research UK (CRUK) Pioneer Award (C55962/A24669) and Wolfson College, Cambridge, UK. C. W. further acknowledges research support from S. Bohndiek, T. Wilkinson and G. Gordon. X. Z. and G. A. E. V. are grateful for the financial support from the FWO (G090017N) and KU Leuven internal research funds (C24/15/015)

CLEC5A-mediated enhancement of the inflammatory response in myeloid cells contributes to influenza virus pathogenicity in vivo

Human infections with influenza viruses exhibit mild to severe clinical outcomes as a result of complex virus-host interactions. Induction of inflammatory mediators via pattern recognition receptors may dictate subsequent host responses for pathogen clearance and tissue damage. We identified that human C-type lectin domain family 5 member A (CLEC5A) interacts with the hemagglutinin protein of influenza viruses expressed on lentiviral pseudoparticles through lectin screening. Silencing CLEC5A gene expression, blocking influenza-CLEC5A interactions with anti-CLEC5A antibodies, or dampening CLEC5A-mediated signaling using a spleen tyrosine kinase inhibitor consistently reduced the levels of proinflammatory cytokines produced by human macrophages without affecting the replication of influenza A viruses of different subtypes. Infection of bone marrow-derived macrophages from CLEC5A-deficient mice showed reduced levels of tumor necrosis factor alpha (TNF-α) and IP-10 but elevated alpha interferon (IFN-α) compared to those of wild-type mice. The heightened type I IFN response in the macrophages of CLEC5A-deficient mice was associated with upregulated TLR3 mRNA after treatment with double-stranded RNA. Upon lethal challenges with a recombinant H5N1 virus, CLEC5A-deficient mice showed reduced levels of proinflammatory cytokines, decreased immune cell infiltration in the lungs, and improved survival compared to the wild-type mice, despite comparable viral loads noted throughout the course of infection. The survival difference was more prominent at a lower dose of inoculum. Our results suggest that CLEC5A-mediated enhancement of the inflammatory response in myeloid cells contributes to influenza pathogenicity in vivo and may be considered a therapeutic target in combination with effective antivirals. Well-orchestrated host responses together with effective viral clearance are critical for optimal clinical outcome after influenza infections.published_or_final_versio

GIVE: portable genome browsers for personal websites.

Growing popularity and diversity of genomic data demand portable and versatile genome browsers. Here, we present an open source programming library called GIVE that facilitates the creation of personalized genome browsers without requiring a system administrator. By inserting HTML tags, one can add to a personal webpage interactive visualization of multiple types of genomics data, including genome annotation, "linear" quantitative data, and genome interaction data. GIVE includes a graphical interface called HUG (HTML Universal Generator) that automatically generates HTML code for displaying user chosen data, which can be copy-pasted into user's personal website or saved and shared with collaborators. GIVE is available at: https://www.givengine.org/

Bone Marrow Stem Cell Treatment for Ischemic Heart Disease in Patients with No Option of Revascularization: A Systematic Review and Meta-Analysis

PMCID: PMC3686792This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited

Deep generative modeling for single-cell transcriptomics.

Single-cell transcriptome measurements can reveal unexplored biological diversity, but they suffer from technical noise and bias that must be modeled to account for the resulting uncertainty in downstream analyses. Here we introduce single-cell variational inference (scVI), a ready-to-use scalable framework for the probabilistic representation and analysis of gene expression in single cells ( https://github.com/YosefLab/scVI ). scVI uses stochastic optimization and deep neural networks to aggregate information across similar cells and genes and to approximate the distributions that underlie observed expression values, while accounting for batch effects and limited sensitivity. We used scVI for a range of fundamental analysis tasks including batch correction, visualization, clustering, and differential expression, and achieved high accuracy for each task

Transparency in health economic modeling : options, issues and potential solutions

Economic models are increasingly being used by health economists to assess the value of health technologies and inform healthcare decision making. However, most published economic models represent a kind of black box, with known inputs and outputs but undisclosed internal calculations and assumptions. This lack of transparency makes the evaluation of the model results challenging, complicates comparisons between models, and limits the reproducibility of the models. Here, we aim to provide an overview of the possible steps that could be undertaken to make economic models more transparent and encourage model developers to share more detailed calculations and assumptions with their peers. Scenarios with different levels of transparency (i.e., how much information is disclosed) and reach of transparency (i.e., who has access to the disclosed information) are discussed, and five key concerns (copyrights, model misuse, confidential data, software, and time/resources) pertaining to model transparency are presented, along with possible solutions. While a shift toward open-source models is underway in health economics, as has happened before in other research fields, the challenges ahead should not be underestimated. Importantly, there is a pressing need to find an acceptable trade-off between the added value of model transparency and the time and resources needed to achieve such transparency. To this end, it will be crucial to set incentives at different stakeholder levels. Despite the many challenges, the many benefits of publicly sharing economic models make increased transparency a goal worth pursuing

Demon-like Algorithmic Quantum Cooling and its Realization with Quantum Optics

The simulation of low-temperature properties of many-body systems remains one of the major challenges in theoretical and experimental quantum information science. We present, and demonstrate experimentally, a universal cooling method which is applicable to any physical system that can be simulated by a quantum computer. This method allows us to distill and eliminate hot components of quantum states, i.e., a quantum Maxwell's demon. The experimental implementation is realized with a quantum-optical network, and the results are in full agreement with theoretical predictions (with fidelity higher than 0.978). These results open a new path for simulating low-temperature properties of physical and chemical systems that are intractable with classical methods.Comment: 7 pages, 5 figures, plus supplementarity material

recount3: summaries and queries for large-scale RNA-seq expression and splicing

We present recount3, a resource consisting of over 750,000 publicly available human and mouse RNA sequencing (RNA-seq) samples uniformly processed by our new Monorail analysis pipeline. To facilitate access to the data, we provide the recount3 and snapcount R/Bioconductor packages as well as complementary web resources. Using these tools, data can be downloaded as study-level summaries or queried for specific exon-exon junctions, genes, samples, or other features. Monorail can be used to process local and/or private data, allowing results to be directly compared to any study in recount3. Taken together, our tools help biologists maximize the utility of publicly available RNA-seq data, especially to improve their understanding of newly collected data. recount3 is available from http://rna.recount.bio