GNNEvaluator: Evaluating GNN Performance On Unseen Graphs Without Labels

Evaluating the performance of graph neural networks (GNNs) is an essential task for practical GNN model deployment and serving, as deployed GNNs face significant performance uncertainty when inferring on unseen and unlabeled test graphs, due to mismatched training-test graph distributions. In this paper, we study a new problem, GNN model evaluation, that aims to assess the performance of a specific GNN model trained on labeled and observed graphs, by precisely estimating its performance (e.g., node classification accuracy) on unseen graphs without labels. Concretely, we propose a two-stage GNN model evaluation framework, including (1) DiscGraph set construction and (2) GNNEvaluator training and inference. The DiscGraph set captures wide-range and diverse graph data distribution discrepancies through a discrepancy measurement function, which exploits the outputs of GNNs related to latent node embeddings and node class predictions. Under the effective training supervision from the DiscGraph set, GNNEvaluator learns to precisely estimate node classification accuracy of the to-be-evaluated GNN model and makes an accurate inference for evaluating GNN model performance. Extensive experiments on real-world unseen and unlabeled test graphs demonstrate the effectiveness of our proposed method for GNN model evaluation.Comment: Accepted by NeurIPS 202

Temporal Control of Leaf Complexity by miRNA-Regulated Licensing of Protein Complexes

The tremendous diversity of leaf shapes has caught the attention of naturalists for centuries. In addition to interspecific and intraspecific differences, leaf morphologies may differ in single plants according to age, a phenomenon known as heteroblasty. In Arabidopsis thaliana, the progression from the juvenile to the adult phase is characterized by increased leaf serration. A similar trend is seen in species with more complex leaves, such as the A. thaliana relative Cardamine hirsuta, in which the number of leaflets per leaf increases with age. Although the genetic changes that led to the overall simpler leaf architecture in A. thaliana are increasingly well understood, less is known about the events underlying age-dependent changes within single plants, in either A. thaliana or C. hirsuta. Here, we describe a conserved miRNA transcription factor regulon responsible for an age-dependent increase in leaf complexity. In early leaves, miR319-targeted TCP transcription factors interfere with the function of miR164-dependent and miR164-independent CUC proteins, preventing the formation of serrations in A. thaliana and of leaflets in C. hirsuta. As plants age, accumulation of miR156-regulated SPLs acts as a timing cue that destabilizes TCP-CUC interactions. The destabilization licenses activation of CUC protein complexes and thereby the gradual increase of leaf complexity in the newly formed organs. These findings point to posttranslational interaction between unrelated miRNA-targeted transcription factors as a core feature of these regulatory circuits.European Molecular Biology Organization (EMBO) fellowship; Fundação para a Ciência e a Tecnologia fellowships; EMBO Installation Grant; National Natural Science Foundation of China grants: (31222029, 912173023); State Key Basic Research Program of China grant: (2013CB127000); Deutsche Forschungsgemeinschaft grants: (SPP1530 and a Gottfried Wilhelm Leibniz Award); Max Planck Society

Distributed quantum computing over 7.0 km

Distributed quantum computing provides a viable approach towards scalable quantum computation, which relies on nonlocal quantum gates to connect distant quantum nodes, to overcome the limitation of a single device. However, such an approach has only been realized within single nodes or between nodes separated by a few tens of meters, preventing the target of harnessing computing resources in large-scale quantum networks. Here, we demonstrate distributed quantum computing between two nodes spatially separated by 7.0 km, using stationary qubits based on multiplexed quantum memories, flying qubits at telecom wavelengths, and active feedforward control based on field-deployed fiber. Specifically, we illustrate quantum parallelism by implementing Deutsch-Jozsa algorithm and quantum phase estimation algorithm between the two remote nodes. These results represent the first demonstration of distributed quantum computing over metropolitan-scale distances and lay the foundation for the construction of large-scale quantum computing networks relying on existing fiber channels.Comment: 6 pages, 3 figure

Optimal cutoff scores of the Chinese version of 15-item negative symptom assessment that indicate prominent negative symptoms of schizophrenia

ObjectiveThe Chinese version of 15-item negative symptom assessment (NSA-15) is an instrument with a three-factor structure specifically validated for assessing negative symptoms of schizophrenia. To provide a reference for future practical applications in the recognition of schizophrenia patients with negative symptoms, this study aimed to determine an appropriate NSA-15 cutoff score regarding negative symptoms to identify prominent negative symptoms (PNS).MethodsA total of 199 participants with schizophrenia were recruited and divided into the PNS group (n = 79) and non-PNS group (n = 120) according to scale for assessment of negative symptoms (SANS) scores. Receiver-operating characteristic (ROC) curve analysis was used to determine the optimal NSA-15 cutoff score for identifying PNS.ResultsThe optimal cutoff NSA-15 score for identifying PNS was 40. Communication, emotion and motivation factors in the NSA-15 had cutoffs of 13, 6, and 16, respectively. The communication factor score had slightly better discrimination than scores on the other two factors. The discriminant ability of the global rating of the NSA-15 was not as good as that of the NSA-15 total score (area under the curve (AUC): 0.873 vs. 0.944).ConclusionThe optimal NSA-15 cutoff scores for identifying PNS in schizophrenia were determined in this study. The NSA-15 provides a convenient and easy-to-use assessment for identifying patients with PNS in Chinese clinical situations. The communication factor of the NSA-15 also has excellent discrimination

A Novel Inhibitor of Homodimerization Targeting MyD88 Ameliorates Renal Interstitial Fibrosis by Counteracting TGF-β1-Induced EMT in Vivo and in Vitro

Background/Aims: The TLR/MyD88/NF-κB signaling pathway has been successfully used to treat renal interstitial fibrosis (RIF). However, the exact therapeutic mechanism is still unknown. Here, we assessed the therapeutic efficacy of TJ-M2010-2, a small molecular compound that inhibits MyD88 homodimerization, in RIF induced by ischemia reperfusion injury (IRI). Methods: In vivo, RIF was induced in mice by IRI, and the mice were prophylactically treated with TJ-M2010-2. In vitro, HK-2 cells were incubated with TGF-β1 to induce EMT, and the cells were pretreated with TJ-M2010-2. Results: We found that, compared with the IRI group, the TJ-M2010-2 group showed marked attenuation of RIF and renal function injury; decreased expression of TGF-β1, α-SMA, vimentin, MMP2 and MMP9; and increased E-cadherin expression. Furthermore, TGF-β1-induced EMT was blocked by TJ-M2010-2 in HK-2 cells, as evidenced by blocked morphologic transformation, restored E-cadherin expression and inhibited α-SMA expression. In addition, compared to the TGF-β1 group, the TJ-M2010-2 group showed profound inhibition of the expression of TRAF6, p65 and Snail and upregulation of the expression of IκBα. Conclusion: This MyD88 inhibitor may be a potential therapeutic agent to ameliorate RIF

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

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

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

Finishing the euchromatic sequence of the human genome

The sequence of the human genome encodes the genetic instructions for human physiology, as well as rich information about human evolution. In 2001, the International Human Genome Sequencing Consortium reported a draft sequence of the euchromatic portion of the human genome. Since then, the international collaboration has worked to convert this draft into a genome sequence with high accuracy and nearly complete coverage. Here, we report the result of this finishing process. The current genome sequence (Build 35) contains 2.85 billion nucleotides interrupted by only 341 gaps. It covers ∼99% of the euchromatic genome and is accurate to an error rate of ∼1 event per 100,000 bases. Many of the remaining euchromatic gaps are associated with segmental duplications and will require focused work with new methods. The near-complete sequence, the first for a vertebrate, greatly improves the precision of biological analyses of the human genome including studies of gene number, birth and death. Notably, the human enome seems to encode only 20,000-25,000 protein-coding genes. The genome sequence reported here should serve as a firm foundation for biomedical research in the decades ahead