Solid-state NMR Studies of Organotin compounds and of titania pigments

Not Availabl

Geometry-aware Line Graph Transformer Pre-training for Molecular Property Prediction

Molecular property prediction with deep learning has gained much attention over the past years. Owing to the scarcity of labeled molecules, there has been growing interest in self-supervised learning methods that learn generalizable molecular representations from unlabeled data. Molecules are typically treated as 2D topological graphs in modeling, but it has been discovered that their 3D geometry is of great importance in determining molecular functionalities. In this paper, we propose the Geometry-aware line graph transformer (Galformer) pre-training, a novel self-supervised learning framework that aims to enhance molecular representation learning with 2D and 3D modalities. Specifically, we first design a dual-modality line graph transformer backbone to encode the topological and geometric information of a molecule. The designed backbone incorporates effective structural encodings to capture graph structures from both modalities. Then we devise two complementary pre-training tasks at the inter and intra-modality levels. These tasks provide properly supervised information and extract discriminative 2D and 3D knowledge from unlabeled molecules. Finally, we evaluate Galformer against six state-of-the-art baselines on twelve property prediction benchmarks via downstream fine-tuning. Experimental results show that Galformer consistently outperforms all baselines on both classification and regression tasks, demonstrating its effectiveness.Comment: 9 pages, 5 figure

Interpretable bilinear attention network with domain adaptation improves drug-target prediction

Predicting drug-target interaction is key for drug discovery. Recent deep learning-based methods show promising performance but two challenges remain: (i) how to explicitly model and learn local interactions between drugs and targets for better prediction and interpretation; (ii) how to generalize prediction performance on novel drug-target pairs from different distribution. In this work, we propose DrugBAN, a deep bilinear attention network (BAN) framework with domain adaptation to explicitly learn pair-wise local interactions between drugs and targets, and adapt on out-of-distribution data. DrugBAN works on drug molecular graphs and target protein sequences to perform prediction, with conditional domain adversarial learning to align learned interaction representations across different distributions for better generalization on novel drug-target pairs. Experiments on three benchmark datasets under both in-domain and cross-domain settings show that DrugBAN achieves the best overall performance against five state-of-the-art baselines. Moreover, visualizing the learned bilinear attention map provides interpretable insights from prediction results.Comment: 16 pages, 6 figure

Design and optimization of dispersion-flattened microarray-core fiber with ultralow loss for terahertz transmission

The paper establishes a late-model of microarray-core based polymer optical fiber with flattened dispersion and ultra-low losses. Its transmission properties are calculated by virtue of the beam propagation approach. From the simulation results, it finds that the modelled fiber has a near-zero dispersion property of 0.29 ± 0.16 ps/THz/cm in a frequency area of 1.05 THz to 1.78 THz, a high birefringence of 1.6 × 10-3, an ultra-low confinement loss of 3.78 × 10-10 dB/m, an effective mode field zone of 4.6 × 105 μm2, and a nonlinear coefficient of 1.2 km-1·W−1. With these good properties, the modelled fiber could be applied for ethanol detection and polarization maintaining THz applications

A CRM1 Inhibitor Alleviates Cardiac Hypertrophy and Increases the Nuclear Distribution of NT-PGC-1α in NRVMs

Chromosomal maintenance 1 (CRM1) inhibitors display antihypertrophic effects and control protein trafficking between the nucleus and the cytoplasm. PGC-1α (peroxisome proliferator-activated receptor gamma coactivator-1alpha) is a type of transcriptional coactivator that predominantly resides in the nucleus and is downregulated during heart failure. NT-PGC-1α is an alternative splicing variant of PGC-1α that is primarily distributed in the cytoplasm. We hypothesized that the use of a CRM1 inhibitor could shuttle NT-PGC-1α into the nucleus and activate PGC-1α target genes to potentially improve cardiac function in a mouse model of myocardial infarction (MI). We showed that PGC-1α and NT-PGC-1α were decreased in MI-induced heart failure mice. Phenylephrine and angiotensin II were applied to induce hypertrophy in neonatal rat ventricular myocytes (NRVMs). The antihypertrophic effects of the CRM1-inhibitor Selinexor was verified through profiling the expression of β-MHC and through visualizing the cell cross-sectional area. NRVMs were transfected with adenovirus-NT-PGC-1α or adenovirus-NLS (nucleus localization sequence)-NT-PGC-1α and then exposed to Selinexor. Confocal microscopy was then used to observe the shuttling of NT-PGC-1α. After NT-PGC-1α was shuttled into the nucleus, there was increased expression of its related genes, including PPAR-α, Tfam, ERR-γ, CPT1b, PDK4, and Nrf2. The effects of Selinexor on post-MI C57BL/6j mice were determined by echocardiography and qPCR. We found that Selinexor showed antihypertrophic effects but did not influence the ejection fraction of MI-mice. Interestingly, the antihypertrophic effects of Selinexor might be independent of NT-PGC-1α transportation

High-temperature and stress response behavior of femtosecond laser pulses inscribed eccentric fiber Bragg gratings

Eccentric fiber Bragg grating (EFBG) is inscribed in standard communication single-mode fiber using femtosecond laser pulses, and the temperature and strain sensing characteristics are experimentally demonstrated and analyzed. The EFBG exhibits strong thermal stability and good robustness in high-temperature measurement up to 1000 °C, and undergoes different thermal sensitivities during Bragg peak and the strong resonance coupled cladding spectral comb. The temperature sensitivity linearly increases with respect to the effective index of the resonant modes. Such a situation also occurs in axial strain measurement. These characteristics are of high interest for multiparametric sensing at high temperatures

Electric-Field-Induced Connectivity Switching in Single-Molecule Junctions

Summary(#br)The manipulation of molecule-electrode interaction is essential for the fabrication of molecular devices and determines the connectivity from electrodes to molecular components. Although the connectivity of molecular devices could be controlled by molecular design to place anchor groups in different positions of molecule backbones, the reversible switching of such connectivities remains challenging. Here, we develop an electric-field-induced strategy to switch the connectivity of single-molecule junctions reversibly, leading to the manipulation of different connectivities in the same molecular backbone. Our results offer a new concept of single-molecule manipulation and provide a feasible strategy to regulate molecule-electrode interaction

IL-17A Synergizes with IFN-γ to Upregulate iNOS and NO Production and Inhibit Chlamydial Growth

IFN-γ-mediated inducible nitric oxide synthase (iNOS) expression is critical for controlling chlamydial infection through microbicidal nitric oxide (NO) production. Interleukin-17A (IL-17A), as a new proinflammatory cytokine, has been shown to play a protective role in host defense against Chlamydia muridarum (Cm) infection. To define the related mechanism, we investigated, in the present study, the effect of IL-17A on IFN-γ induced iNOS expression and NO production during Cm infection in vitro and in vivo. Our data showed that IL-17A significantly enhanced IFN-γ-induced iNOS expression and NO production and inhibited Cm growth in Cm-infected murine lung epithelial (TC-1) cells. The synergistic effect of IL-17A and IFN-γ on Chlamydia clearance from TC-1 cells correlated with iNOS induction. Since one of the main antimicrobial mechanisms of activated macrophages is the release of NO, we also examined the inhibitory effect of IL-17A and IFN-γ on Cm growth in peritoneal macrophages. IL-17A (10 ng/ml) synergizes with IFN-γ (200 U/ml) in macrophages to inhibit Cm growth. This effect was largely reversed by aminoguanidine (AG), an iNOS inhibitor. Finally, neutralization of IL-17A in Cm infected mice resulted in reduced iNOS expression in the lung and higher Cm growth. Taken together, the results indicate that IL-17A and IFN-γ play a synergistic role in inhibiting chlamydial lung infection, at least partially through enhancing iNOS expression and NO production in epithelial cells and macrophages