NODE-ImgNet : a PDE-informed effective and robust model for image denoising

Inspired by the traditional partial differential equation (PDE) approach for image denoising, we propose a novel neural network architecture, referred as NODE-ImgNet, that combines neural ordinary differential equations (NODEs) with convolutional neural network (CNN) blocks. NODE-ImgNet is intrinsically a PDE model, where the dynamic system is learned implicitly without the explicit specification of the PDE. This naturally circumvents the typical issues associated with introducing artifacts during the learning process. By invoking such a NODE structure, which can also be viewed as a continuous variant of a residual network (ResNet) and inherits its advantage in image denoising, our model achieves enhanced accuracy and parameter efficiency. In particular, our model exhibits consistent effectiveness in different scenarios, including denoising gray and color images perturbed by Gaussian noise, as well as real-noisy images, and demonstrates superiority in learning from small image datasets

An ensemble of VisNet, Transformer-M, and pretraining models for molecular property prediction in OGB Large-Scale Challenge @ NeurIPS 2022

In the technical report, we provide our solution for OGB-LSC 2022 Graph Regression Task. The target of this task is to predict the quantum chemical property, HOMO-LUMO gap for a given molecule on PCQM4Mv2 dataset. In the competition, we designed two kinds of models: Transformer-M-ViSNet which is an geometry-enhanced graph neural network for fully connected molecular graphs and Pretrained-3D-ViSNet which is a pretrained ViSNet by distilling geomeotric information from optimized structures. With an ensemble of 22 models, ViSNet Team achieved the MAE of 0.0723 eV on the test-challenge set, dramatically reducing the error by 39.75% compared with the best method in the last year competition

ViSNet: an equivariant geometry-enhanced graph neural network with vector-scalar interactive message passing for molecules

Geometric deep learning has been revolutionizing the molecular modeling field. Despite the state-of-the-art neural network models are approaching ab initio accuracy for molecular property prediction, their applications, such as drug discovery and molecular dynamics (MD) simulation, have been hindered by insufficient utilization of geometric information and high computational costs. Here we propose an equivariant geometry-enhanced graph neural network called ViSNet, which elegantly extracts geometric features and efficiently models molecular structures with low computational costs. Our proposed ViSNet outperforms state-of-the-art approaches on multiple MD benchmarks, including MD17, revised MD17 and MD22, and achieves excellent chemical property prediction on QM9 and Molecule3D datasets. Additionally, ViSNet achieved the top winners of PCQM4Mv2 track in the OGB-LCS@NeurIPS2022 competition. Furthermore, through a series of simulations and case studies, ViSNet can efficiently explore the conformational space and provide reasonable interpretability to map geometric representations to molecular structures

Molecular basis of ligand recognition and activation of human V2 vasopressin receptor.

Vasopressin type 2 receptor (V2R) belongs to the vasopressin (VP)/oxytocin (OT) receptor subfamily of G protein-coupled receptors (GPCRs), which comprises at least four closely related receptor subtypes: V1aR, V1bR, V2R, and OTR. These receptors are activated by arginine vasopressin (AVP) and OT, two endogenous nine-amino acid neurohypophysial hormones, which are thought to mediate a biologically conserved role in social behavior and sexual reproduction. V2R is mainly expressed in the renal collecting duct principal cells and mediates the antidiuretic action of AVP by accelerating water reabsorption, thereby playing a vital role in controlling water homeostasis. Moreover, numerous gain-of-function and loss-of-function mutations of V2R have been identified and are closely associated with human diseases, including nephrogenic syndrome of inappropriate diuresis (NSIAD) and X-linked congenital nephrogenic diabetes insipidus (NDI). Thus, V2R has attracted intense interest as a drug target. However, due to a lack of structural information, how AVP recognizes and activates V2R remains elusive, which hampers the V2R-targeted drug design. Here, we determined a 2.6 Å resolution cryo-EM structure of the full-length, G s -coupled human V2R bound to AVP (Fig. 1a; Supplementary information, Table S1). The G s protein was engineered based on mini-G s that was used in the crystal structure determination of the G s -coupled adenosine A 2A receptor (A 2A R) to stabilize the V2R–G s protein complex (Supplementary information, Data S1). The final structure of the AVP–V2R–G s complex contains all residues of AVP (residues 1–9), the Gα s Ras-like domain, Gβγ subunits, Nb35, scFv16, and the V2R residues from T31 to L339 8.57 (superscripts refer to Ballesteros–Weinstein numbering). The majority of amino acid side chains, including AVP, transmembrane domain (TMD), all flexible intracellular loops (ICLs) and extracellular loops (ECLs) except for ICL3 and G185–G188 in ECL2, were well resolved in the model, refined against the EM density map (Fig. 1a; Supplementary information, Figs. S1–3). The complex structure can provide detailed information on the binding interface between AVP and helix bundle of the receptor, as well as the receptor–G s interface

Ligand recognition and G-protein coupling selectivity of cholecystokinin A receptor.

Cholecystokinin A receptor (CCKAR) belongs to family A G-protein-coupled receptors and regulates nutrient homeostasis upon stimulation by cholecystokinin (CCK). It is an attractive drug target for gastrointestinal and metabolic diseases. One distinguishing feature of CCKAR is its ability to interact with a sulfated ligand and to couple with divergent G-protein subtypes, including Gs, Gi and Gq. However, the basis for G-protein coupling promiscuity and ligand recognition by CCKAR remains unknown. Here, we present three cryo-electron microscopy structures of sulfated CCK-8-activated CCKAR in complex with Gs, Gi and Gq heterotrimers, respectively. CCKAR presents a similar conformation in the three structures, whereas conformational differences in the 'wavy hook' of the Gα subunits and ICL3 of the receptor serve as determinants in G-protein coupling selectivity. Our findings provide a framework for understanding G-protein coupling promiscuity by CCKAR and uncover the mechanism of receptor recognition by sulfated CCK-8

Potential of Core-Collapse Supernova Neutrino Detection at JUNO

JUNO is an underground neutrino observatory under construction in Jiangmen, China. It uses 20kton liquid scintillator as target, which enables it to detect supernova burst neutrinos of a large statistics for the next galactic core-collapse supernova (CCSN) and also pre-supernova neutrinos from the nearby CCSN progenitors. All flavors of supernova burst neutrinos can be detected by JUNO via several interaction channels, including inverse beta decay, elastic scattering on electron and proton, interactions on C12 nuclei, etc. This retains the possibility for JUNO to reconstruct the energy spectra of supernova burst neutrinos of all flavors. The real time monitoring systems based on FPGA and DAQ are under development in JUNO, which allow prompt alert and trigger-less data acquisition of CCSN events. The alert performances of both monitoring systems have been thoroughly studied using simulations. Moreover, once a CCSN is tagged, the system can give fast characterizations, such as directionality and light curve

Detection of the Diffuse Supernova Neutrino Background with JUNO

As an underground multi-purpose neutrino detector with 20 kton liquid scintillator, Jiangmen Underground Neutrino Observatory (JUNO) is competitive with and complementary to the water-Cherenkov detectors on the search for the diffuse supernova neutrino background (DSNB). Typical supernova models predict 2-4 events per year within the optimal observation window in the JUNO detector. The dominant background is from the neutral-current (NC) interaction of atmospheric neutrinos with 12C nuclei, which surpasses the DSNB by more than one order of magnitude. We evaluated the systematic uncertainty of NC background from the spread of a variety of data-driven models and further developed a method to determine NC background within 15\% with {\it{in}} {\it{situ}} measurements after ten years of running. Besides, the NC-like backgrounds can be effectively suppressed by the intrinsic pulse-shape discrimination (PSD) capabilities of liquid scintillators. In this talk, I will present in detail the improvements on NC background uncertainty evaluation, PSD discriminator development, and finally, the potential of DSNB sensitivity in JUNO

Real-time Monitoring for the Next Core-Collapse Supernova in JUNO

Core-collapse supernova (CCSN) is one of the most energetic astrophysical events in the Universe. The early and prompt detection of neutrinos before (pre-SN) and during the SN burst is a unique opportunity to realize the multi-messenger observation of the CCSN events. In this work, we describe the monitoring concept and present the sensitivity of the system to the pre-SN and SN neutrinos at the Jiangmen Underground Neutrino Observatory (JUNO), which is a 20 kton liquid scintillator detector under construction in South China. The real-time monitoring system is designed with both the prompt monitors on the electronic board and online monitors at the data acquisition stage, in order to ensure both the alert speed and alert coverage of progenitor stars. By assuming a false alert rate of 1 per year, this monitoring system can be sensitive to the pre-SN neutrinos up to the distance of about 1.6 (0.9) kpc and SN neutrinos up to about 370 (360) kpc for a progenitor mass of 30$M_{\odot}$ for the case of normal (inverted) mass ordering. The pointing ability of the CCSN is evaluated by using the accumulated event anisotropy of the inverse beta decay interactions from pre-SN or SN neutrinos, which, along with the early alert, can play important roles for the followup multi-messenger observations of the next Galactic or nearby extragalactic CCSN.Comment: 24 pages, 9 figure

NODE-ImgNet : a PDE-informed effective and robust model for image denoising

Inspired by the traditional partial differential equation (PDE) approach for image denoising, we propose a novel neural network architecture, referred as NODE-ImgNet, that combines neural ordinary differential equations (NODEs) with convolutional neural network (CNN) blocks. NODE-ImgNet is intrinsically a PDE model, where the dynamic system is learned implicitly without the explicit specification of the PDE. This naturally circumvents the typical issues associated with introducing artifacts during the learning process. By invoking such a NODE structure, which can also be viewed as a continuous variant of a residual network (ResNet) and inherits its advantage in image denoising, our model achieves enhanced accuracy and parameter efficiency. In particular, our model exhibits consistent effectiveness in different scenarios, including denoising gray and color images perturbed by Gaussian noise, as well as real-noisy images, and demonstrates superiority in learning from small image datasets

The Investigation on Static Stability Analysis for Reticulated Shell with Initial Defect Value Using Stochastic Defect Mode Method

Regarding the effect of the initial geometric defect (IGD) on the static stability of single-layer reticulated shells, its distribution pattern and magnitude are the main concerns to researchers. However, the suitable selection of the initial geometric defect magnitude (IGDM) is still a controversial topic. Therefore, it is intended to study the determination of the proper IGDM based on the structure force state (SFS) and the defect coefficient. In order to find out a qualified IGDM, more than 5200 numerical cases are carried out for four types of commonly used single-layer reticulated shells with the span ranging from 40 to 70 m and the rise–span ratio from 1/4 to 1/7, within the random defect mode method, by taking both geometric and material nonlinearity into account. The results show that it is more feasible to set the L/500 as IGDM when evaluating the stability of the single-layer reticulated shell. In addition, an updated criterion to identify the SFS at the stability critical state (SCS) is developed