Silica particles convert thiol-containing molecules to disulfides

Synthetic amorphous silica is a common food additive and a popular cosmetic ingredient. Mesoporous silica particles are also widely studied for their potential use in drug delivery and imaging applications because of their unique properties, such as tunable pore sizes, large surfaces areas, and assumed biocompatibility. Such a nanomaterial, when consisting of pure silicon dioxide, is generally considered to be chemically inert, but in this study, we showed that oxidation yields for different compounds were facilitated by simply incubating aqueous solutions with pure silica particles. Three thiol-containing molecules, L-cysteine, glutathione, and D-penicillamine, were studied separately, and it was found that more than 95% of oxidation happened after incubating any of these compounds with mesoporous silica particles in the dark for a day at room temperature. Oxidation increased over incubation time, and more oxidation was found for particles having larger surface areas. For nonporous silica particles at submicron ranges, yields of oxidation were different based on the structures of molecules, correlating with steric hindrance while accessing surfaces. We propose that the silyloxy radical (SiO•) on silica surfaces is what facilitates oxidation. Density functional theory calculations were conducted for total energy changes for reactions between different aqueous species and silicon dioxide surfaces. These calculations identified two most plausible pathways of the lowest energy to generate SiO• radicals from water radical cations H2O•+ and hydroxyl radicals •OH, previously known to exist at water interfaces

AdaptGear: Accelerating GNN Training via Adaptive Subgraph-Level Kernels on GPUs

Graph neural networks (GNNs) are powerful tools for exploring and learning from graph structures and features. As such, achieving high-performance execution for GNNs becomes crucially important. Prior works have proposed to explore the sparsity (i.e., low density) in the input graph to accelerate GNNs, which uses the full-graph-level or block-level sparsity format. We show that they fail to balance the sparsity benefit and kernel execution efficiency. In this paper, we propose a novel system, referred to as AdaptGear, that addresses the challenge of optimizing GNNs performance by leveraging kernels tailored to the density characteristics at the subgraph level. Meanwhile, we also propose a method that dynamically chooses the optimal set of kernels for a given input graph. Our evaluation shows that AdaptGear can achieve a significant performance improvement, up to $6.49 \times$ ($1.87 \times$ on average), over the state-of-the-art works on two mainstream NVIDIA GPUs across various datasets

Efficient Adaptive Activation Rounding for Post-Training Quantization

Post-training quantization attracts increasing attention due to its convenience in deploying quantized neural networks. Although rounding-to-nearest remains the prevailing method for DNN quantization, prior research has demonstrated its suboptimal nature when applied to weight quantization. They propose optimizing weight rounding schemes by leveraging output error rather than the traditional weight quantization error. Our study reveals that similar rounding challenges also extend to activation quantization. Despite the easy generalization, the challenges lie in the dynamic nature of activation. Adaptive rounding is expected for varying activations and the method is subjected to runtime overhead. To tackle this, we propose the AQuant quantization framework with a novel perspective to reduce output error by adjusting rounding schemes of activations. Instead of using the constant rounding border 0.5 of the rounding-to-nearest operation, we make the border become a function w.r.t. the activation value to change the activation rounding by the adaptive border. To deal with the runtime overhead, we use a coarse-grained version of the border function. Finally, we introduce our framework to optimize the border function. Extensive experiments show that AQuant achieves notable improvements compared to state-of-the-art works and pushes the accuracy of ResNet-18 up to 60.31% under the 2-bit weight and activation quantization

Haemophilus parasuis Infection Disrupts Adherens Junctions and Initializes EMT Dependent on Canonical Wnt/β-Catenin Signaling Pathway

In this study, animal experimentation verified that the canonical Wnt/β-catenin signaling pathway was activated under a reduced activity of p-β-catenin (Ser33/37/Thr41) and an increased accumulation of β-catenin in the lungs and kidneys of pigs infected with a highly virulent strain of H. parasuis. In PK-15 and NPTr cells, it was also confirmed that infection with a high-virulence strain of H. parasuis induced cytoplasmic accumulation and nuclear translocation of β-catenin. H. parasuis infection caused a sharp degradation of E-cadherin and an increase of the epithelial cell monolayer permeability, as well as a broken interaction between β-catenin and E-cadherin dependent on Wnt/β-catenin signaling pathway. Moreover, Wnt/β-catenin signaling pathway also contributed to the initiation of epithelial-mesenchymal transition (EMT) during high-virulence strain of H. parasuis infection with expression changes of epithelial/mesenchymal markers, increased migratory capabilities as well as the morphologically spindle-like switch in PK-15 and NPTr cells. Therefore, we originally speculated that H. parasuis infection activates the canonical Wnt/β-catenin signaling pathway leading to a disruption of the epithelial barrier, altering cell structure and increasing cell migration, which results in severe acute systemic infection characterized by fibrinous polyserositis during H. parasuis infection

Optimizing interplanar spacing, oxygen vacancies and micromorphology via lithium-ion pre-insertion into ammonium vanadate nanosheets for advanced cathodes in aqueous zinc-ion batteries

Ammonium vanadates, featuring an N─H···O hydrogen bond network structure between NH4+ and V─O layers, have become popular cathode materials for aqueous zinc-ion batteries (AZIBs). Their appeal lies in their multi-electron transfer, high specific capacity, and facile synthesis. However, a major drawback arises as Zn2+ ions tend to form bonds with electronegative oxygen atoms between V─O layers during cycling, leading to irreversible structural collapse. Herein, Li+ pre-insertion into the intermediate layer of NH4V4O10 is proposed to enhance the electrochemical activity of ammonium vanadate cathodes for AZIBs, which extends the interlayer distance of NH4V4O10 to 9.8 Å and offers large interlaminar channels for Zn2+ (de)intercalation. Moreover, Li+ intercalation weakens the crystallinity, transforms the micromorphology from non-nanostructured strips to ultrathin nanosheets, and increases the level of oxygen defects, thus exposing more active sites for ion and electron transport, facilitating electrolyte penetration, and improving electrochemical kinetics of electrode. In addition, the introduction of Li+ significantly reduces the bandgap by 0.18 eV, enhancing electron transfer in redox reactions. Leveraging these unique advantages, the Li+ pre-intercalated NH4V4O10 cathode exhibits a high reversible capacity of 486.1 mAh g−1 at 0.5 A g−1 and an impressive capacity retention rate of 72% after 5,000 cycles at 5 A g−1

Study of the B−→Λc+Λˉc−K−B^{-} \to \Lambda_{c}^{+} \bar{\Lambda}_{c}^{-} K^{-} decay

The decay $B^{-} \to \Lambda_{c}^{+} \bar{\Lambda}_{c}^{-} K^{-}$ is studied in proton-proton collisions at a center-of-mass energy of $\sqrt{s}=13$ TeV using data corresponding to an integrated luminosity of 5 $\mathrm{fb}^{-1}$ collected by the LHCb experiment. In the $\Lambda_{c}^+ K^{-}$ system, the $\Xi_{c}(2930)^{0}$ state observed at the BaBar and Belle experiments is resolved into two narrower states, $\Xi_{c}(2923)^{0}$ and $\Xi_{c}(2939)^{0}$, whose masses and widths are measured to be $$m(\Xi_{c}(2923)^{0}) = 2924.5 \pm 0.4 \pm 1.1 \,\mathrm{MeV}, \\ m(\Xi_{c}(2939)^{0}) = 2938.5 \pm 0.9 \pm 2.3 \,\mathrm{MeV}, \\ \Gamma(\Xi_{c}(2923)^{0}) = \phantom{000}4.8 \pm 0.9 \pm 1.5 \,\mathrm{MeV},\\ \Gamma(\Xi_{c}(2939)^{0}) = \phantom{00}11.0 \pm 1.9 \pm 7.5 \,\mathrm{MeV},$$ where the first uncertainties are statistical and the second systematic. The results are consistent with a previous LHCb measurement using a prompt $\Lambda_{c}^{+} K^{-}$ sample. Evidence of a new $\Xi_{c}(2880)^{0}$ state is found with a local significance of $3.8\,\sigma$, whose mass and width are measured to be $2881.8 \pm 3.1 \pm 8.5\,\mathrm{MeV}$ and $12.4 \pm 5.3 \pm 5.8 \,\mathrm{MeV}$, respectively. In addition, evidence of a new decay mode $\Xi_{c}(2790)^{0} \to \Lambda_{c}^{+} K^{-}$ is found with a significance of $3.7\,\sigma$. The relative branching fraction of $B^{-} \to \Lambda_{c}^{+} \bar{\Lambda}_{c}^{-} K^{-}$ with respect to the $B^{-} \to D^{+} D^{-} K^{-}$ decay is measured to be $2.36 \pm 0.11 \pm 0.22 \pm 0.25$, where the first uncertainty is statistical, the second systematic and the third originates from the branching fractions of charm hadron decays.Comment: All figures and tables, along with any supplementary material and additional information, are available at https://cern.ch/lhcbproject/Publications/p/LHCb-PAPER-2022-028.html (LHCb public pages

Measurement of the ratios of branching fractions R(D∗)\mathcal{R}(D^{*}) and R(D0)\mathcal{R}(D^{0})

The ratios of branching fractions $\mathcal{R}(D^{*})\equiv\mathcal{B}(\bar{B}\to D^{*}\tau^{-}\bar{\nu}_{\tau})/\mathcal{B}(\bar{B}\to D^{*}\mu^{-}\bar{\nu}_{\mu})$ and $\mathcal{R}(D^{0})\equiv\mathcal{B}(B^{-}\to D^{0}\tau^{-}\bar{\nu}_{\tau})/\mathcal{B}(B^{-}\to D^{0}\mu^{-}\bar{\nu}_{\mu})$ are measured, assuming isospin symmetry, using a sample of proton-proton collision data corresponding to 3.0 fb${ }^{-1}$ of integrated luminosity recorded by the LHCb experiment during 2011 and 2012. The tau lepton is identified in the decay mode $\tau^{-}\to\mu^{-}\nu_{\tau}\bar{\nu}_{\mu}$. The measured values are $\mathcal{R}(D^{*})=0.281\pm0.018\pm0.024$ and $\mathcal{R}(D^{0})=0.441\pm0.060\pm0.066$, where the first uncertainty is statistical and the second is systematic. The correlation between these measurements is $\rho=-0.43$. Results are consistent with the current average of these quantities and are at a combined 1.9 standard deviations from the predictions based on lepton flavor universality in the Standard Model.Comment: All figures and tables, along with any supplementary material and additional information, are available at https://cern.ch/lhcbproject/Publications/p/LHCb-PAPER-2022-039.html (LHCb public pages

Difunctional Hydrogel Optical Fiber Fluorescence Sensor for Continuous and Simultaneous Monitoring of Glucose and pH

It is significant for people with diabetes to know their body’s real-time glucose level, which can guide the diagnosis and treatment. Therefore, it is necessary to research continuous glucose monitoring (CGM) as it gives us real-time information about our health condition and its dynamic changes. Here, we report a novel hydrogel optical fiber fluorescence sensor segmentally functionalized with fluorescein derivative and CdTe QDs/3-APBA, which can continuously monitor pH and glucose simultaneously. In the glucose detection section, the complexation of PBA and glucose will expand the local hydrogel and decrease the fluorescence of the quantum dots. The fluorescence can be transmitted to the detector by the hydrogel optical fiber in real time. As the complexation reaction and the swelling–deswelling of the hydrogel are all reversible, the dynamic change of glucose concentration can be monitored. For pH detection, the fluorescein attached to another segment of the hydrogel exhibits different protolytic forms when pH changes and the fluorescence changes correspondingly. The significance of pH detection is compensation for pH errors in glucose detection because the reaction between PBA and glucose is sensitive to pH. The emission peaks of the two detection units are 517 nm and 594 nm, respectively, so there is no signal interference between them. The sensor can continuously monitor glucose in 0–20 mM and pH in 5.4–7.8. The advantages of this sensor are multi-parameter simultaneous detection, transmission-detection integration, real-time dynamic detection, and good biocompatibility