Methane activation by nickel cluster cations, Nin+ (n=2-16): reaction mechanisms and thermochemistry of cluster-CHx (x=0-3) complexes

Journal ArticleThe kinetic energy dependences of the reactions of Nin+ (n=2-16) with CD4 are studied in a guided ion beam tandem mass spectrometer over the energy range of 0-10 eV. The main products are hydride formation NinD1, dehydrogenation to form NinCD2 1 , and double dehydrogenation yielding NinC1

Guided ion-beam studies of the kinetic-energy-dependent reactions of Con+ (n=2-16) with D2: cobalt cluster-deuteride bond energies

Journal ArticleThe kinetic-energy-dependent cross sections for the reactions of Con + (n=2-16) with D2 are measured as a function of kinetic energy over a range of 0-8 eV in a guided ion-beam tandem mass spectrometer. The observed products are ConD+ for all clusters and ConD2+ for n=4,5,9-16

Guided ion-beam studies of the reactions of Con + (n=2-20) with O2: cobalt cluster-oxide and -dioxide bond energies

Journal ArticleThe kinetic-energy dependence for the reactions of Con + (n=2-20) with O2 is measured as a function of kinetic energy over a range of 0 to 10 eV in a guided ion-beam tandem mass spectrometer. A variety of Com+ , ComO+, and ComO2 + (m<n) product ions is observed, with the dioxide cluster ions dominating the products for all larger clusters. Reaction efficiencies of Con+ cations with O2 are near unity for all but the dimer

Guided ion beam studies of the reaction of Nin+ (n=2-16) with D2: nickel cluster-deuteride bond energies

Journal ArticleThe kinetic-energy dependences of the reactions of Nin+ (n=2-16) with D2 are studied in a guided ion beam tandem mass spectrometer. The products observed are NinD+ for all clusters and NinD2 + for n=5-16. Reactions for formation of NinD+ are observed to exhibit thresholds, whereas cross sections for formation of NinD2+ (n=5-16) exhibit no obvious barriers to reaction

Plasmonic Rectenna for Efficient Conversion of Light into Electricity

Medical genetic

Regression analysis for covariate-adaptive randomization: A robust and efficient inference perspective

Linear regression is arguably the most fundamental statistical model; however, the validity of its use in randomized clinical trials, despite being common practice, has never been crystal clear, particularly when stratified or covariate-adaptive randomization is used. In this paper, we investigate several of the most intuitive and commonly used regression models for estimating and inferring the treatment effect in randomized clinical trials. By allowing the regression model to be arbitrarily misspecified, we demonstrate that all these regression-based estimators robustly estimate the treatment effect, albeit with possibly different efficiency. We also propose consistent non-parametric variance estimators and compare their performances to those of the model-based variance estimators that are readily available in standard statistical software. Based on the results and taking into account both theoretical efficiency and practical feasibility, we make recommendations for the effective use of regression under various scenarios. For equal allocation, it suffices to use the regression adjustment for the stratum covariates and additional baseline covariates, if available, with the usual ordinary-least-squares variance estimator. For unequal allocation, regression with treatment-by-covariate interactions should be used, together with our proposed variance estimators. These recommendations apply to simple and stratified randomization, and minimization, among others. We hope this work helps to clarify and promote the usage of regression in randomized clinical trials

Deciphering of interactions between platinated DNA and HMGB1 by hydrogen/deuterium exchange mass spectrometry

A high mobility group box 1 (HMGB1) protein has been reported to recognize both 1,2-intrastrand crosslinked DNA by cisplatin (1,2-cis-Pt-DNA) and monofunctional platinated DNA using trans-[PtCl2(NH3)(thiazole)] (1-trans-PtTz-DNA). However, the molecular basis of recognition between the trans-PtTz-DNA and HMGB1 remains unclear. In the present work, we described a hydrogen/deuterium exchange mass spectrometry (HDX-MS) method in combination with docking simulation to decipher the interactions of platinated DNA with domain A of HMGB1. The global deuterium uptake results indicated that 1-trans-PtTz-DNA bound to HMGB1a slightly tighter than the 1,2-cis-Pt-DNA. The local deuterium uptake at the peptide level revealed that the helices I and II, and loop 1 of HMGB1a were involved in the interactions with both platinated DNA adducts. However, docking simulation disclosed different H-bonding networks and distinct DNA-backbone orientations in the two Pt-DNA-HMGB1a complexes. Moreover, the Phe37 residue of HMGB1a was shown to play a key role in the recognition between HMGB1a and the platinated DNAs. In the cis-Pt-DNA-HMGB1a complex, the phenyl ring of Phe37 intercalates into a hydrophobic notch created by the two platinated guanines, while in the trans-PtTz-DNA-HMGB1a complex the phenyl ring appears to intercalate into a hydrophobic crevice formed by the platinated guanine and the opposite adenine in the complementary strand, forming a penta-layer π–π stacking associated with the adjacent thymine and the thiazole ligand. This work demonstrates that HDX-MS associated with docking simulation is a powerful tool to elucidate the interactions between platinated DNAs and proteins

Towards Self-Interpretable Graph-Level Anomaly Detection

Graph-level anomaly detection (GLAD) aims to identify graphs that exhibit notable dissimilarity compared to the majority in a collection. However, current works primarily focus on evaluating graph-level abnormality while failing to provide meaningful explanations for the predictions, which largely limits their reliability and application scope. In this paper, we investigate a new challenging problem, explainable GLAD, where the learning objective is to predict the abnormality of each graph sample with corresponding explanations, i.e., the vital subgraph that leads to the predictions. To address this challenging problem, we propose a Self-Interpretable Graph aNomaly dETection model (SIGNET for short) that detects anomalous graphs as well as generates informative explanations simultaneously. Specifically, we first introduce the multi-view subgraph information bottleneck (MSIB) framework, serving as the design basis of our self-interpretable GLAD approach. This way SIGNET is able to not only measure the abnormality of each graph based on cross-view mutual information but also provide informative graph rationales by extracting bottleneck subgraphs from the input graph and its dual hypergraph in a self-supervised way. Extensive experiments on 16 datasets demonstrate the anomaly detection capability and self-interpretability of SIGNET.Comment: 23 pages; accepted to NeurIPS 202

Bifunctional Electrocatalysts for Oxygen Reduction and Borohydride Oxidation Reactions Using Ag3Sn Nanointermetallic for the Ensemble Effect

2017-2018 > Academic research: refereed > Publication in refereed journal201805 bcrcAccepted ManuscriptOthersNational Natural Science Foundation of China; the Research Fund of State Key Laboratory of Solidification Processing in China; the Aeronautic Science Foundation Program of China; the Science and Technology Innovation Fund of Western Metal Materials; the Doctoral Fund of Ministry of Education of ChinaPublishe