Multi-Label Continual Learning using Augmented Graph Convolutional Network

Multi-Label Continual Learning (MLCL) builds a class-incremental framework in a sequential multi-label image recognition data stream. The critical challenges of MLCL are the construction of label relationships on past-missing and future-missing partial labels of training data and the catastrophic forgetting on old classes, resulting in poor generalization. To solve the problems, the study proposes an Augmented Graph Convolutional Network (AGCN++) that can construct the cross-task label relationships in MLCL and sustain catastrophic forgetting. First, we build an Augmented Correlation Matrix (ACM) across all seen classes, where the intra-task relationships derive from the hard label statistics. In contrast, the inter-task relationships leverage hard and soft labels from data and a constructed expert network. Then, we propose a novel partial label encoder (PLE) for MLCL, which can extract dynamic class representation for each partial label image as graph nodes and help generate soft labels to create a more convincing ACM and suppress forgetting. Last, to suppress the forgetting of label dependencies across old tasks, we propose a relationship-preserving constrainter to construct label relationships. The inter-class topology can be augmented automatically, which also yields effective class representations. The proposed method is evaluated using two multi-label image benchmarks. The experimental results show that the proposed way is effective for MLCL image recognition and can build convincing correlations across tasks even if the labels of previous tasks are missing

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

Region Collaborative Network for Detection-Based Vision-Language Understanding

Given a query language, a Detection-based Vision-Language Understanding (DVLU) system needs to respond based on the detected regions (i.e.,bounding boxes). With the significant advancement in object detection, DVLU has witnessed great improvements in recent years, such as Visual Question Answering (VQA) and Visual Grounding (VG). However, existing DVLU methods always process each detected image region separately but ignore that they were an integral whole. Without the full consideration of each region’s context, the image’s understanding may contain more bias. In this paper, to solve the problem, a simple yet effective Region Collaborative Network (RCN) block is proposed to bridge the gap between independent regions and the integrative DVLU task. Specifically, the Intra-Region Relations (IntraRR) inside each detected region are computed by a position-wise and channel-wise joint non-local model. Then, the Inter-Region Relations (InterRR) across all the detected regions are computed by pooling and sharing parameters with IntraRR. The proposed RCN can enhance the features of each region by using information from all other regions and guarantees the dimension consistency between input and output. The RCN is evaluated on VQA and VG, and the experimental results show that our method can significantly improve the performance of existing DVLU models

The role of acetyl-coA carboxylase2 in head and neck squamous cell carcinoma

Background Acetyl-CoA carboxylase (ACC) plays an important role in the metabolism of various cancer cells, but its role in head and neck squamous cell carcinoma (HNSCC) is uncertain. Therefore, in the present study, we explored the role of ACC2 in HNSCC. Methods Western blot and immunohistochemistry assays were used to determine ACC2 protein expression levels in laryngocarcinoma and adjacent normal tissues derived from patients with laryngocarcinoma. ACC2 expression was knocked down in the hypopharyngeal cancer cell line FaDu to determine its effect on apoptosis. Lipid oil red staining was used to test the change of intracellular lipid. Results The results showed that the ACC2 protein was highly expressed in laryngocarcinoma and that the ACC2 expression level was positively associated with the clinical cancer stage and negatively associated with the degree of laryngocarcinoma cell differentiation. Kaplan–Meier analyses indicated that compared with patients having low levels of ACC2, those with high ACC2 levels had a decreased 5-year survival rate. The results of western blot and terminal deoxynucleotidyl transferase dUTP nick-end labeling assays showed that knockdown of ACC2 accelerated apoptosis in FaDu cells. Furthermore, knockdown of ACC2 significantly reduced the intracellular lipid levels in FaDu cells. Conclusion These findings suggest that ACC2 may be an important prognostic marker for patients with HNSCC and that ACC2 may be a potential target in the treatment of HNSCC

TRPP2 Enhances Metastasis by Regulating Epithelial-Mesenchymal Transition in Laryngeal Squamous Cell Carcinoma

Background/Aim: Surgery and chemotherapy treatments of human laryngeal squamous cell carcinoma (HLSCC) may fail due to metastasis, in which epithelial-mesenchymal transition (EMT) plays an important role. TRPP2, a nonselective cation channel, is expressed in various cell types and participates in many biological processes. Here, we show that TRPP2 enhanced metastasis by regulating EMT. Methods: We used immunohistochemistry, western blotting, Ca2+ imaging, transwell and wound healing assays to investigate TRPP2 expression levels in HLSCC tissue, and the role of TRPP2 in invasion and metastasis of a human laryngocarcinoma cell line (Hep2 cell). Results: We found that TRPP2 protein expression levels were significantly increased in HLSCC tissue; higher TRPP2 levels were associated with decreased patient survival time and degree of differentiation and advanced clinical stage. Knockdown of TRPP2 by transfection with TRPP2 siRNA markedly suppressed ATP-induced Ca2+ release, wound healing, and cell invasion in Hep2 cells. Moreover, TRPP2 siRNA significantly decreased vimentin expression but increased E-cadherin expression in Hep2 cells. In the EMT signalling pathway, TRPP2 siRNA significantly decreased Smad4, STAT3, SNAIL, SLUG and TWIST expression in Hep2 cells. Conclusion: We revealed a previously unknown function of TRPP2 in cancer development and a TRPP2-dependent mechanism underlying laryngocarcinoma cell invasion and metastasis. Our results suggest that TRPP2 may be used as a biomarker for evaluating patient prognosis and as a novel therapeutic target in HLSCC

Melatonin Alleviates Copper Toxicity via Improving ROS Metabolism and Antioxidant Defense Response in Tomato Seedlings

The excessive accumulation of copper (Cu2+) has become a threat to worldwide crop production. Recently, it was revealed that melatonin (MT) could play a crucial role against heavy metal (HM) stresses in plants. However, the underlying mechanism of MT function acted upon by Cu2+ stress (CS) has not been substantiated in tomatoes. In the present work, we produced MT-rich tomato plants by foliar usage of MT, and MT-deficient tomato plants by employing a virus-induced gene silencing methodology and exogenous foliar application of MT synthesis inhibitor para-chlorophenylalanine (pCPA). The obtained results indicate that exogenous MT meaningfully alleviated the dwarf phenotype and impeded the reduction in plant growth caused by excess Cu2+. Furthermore, MT effectively restricted the generation of reactive oxygen species (ROS) and habilitated cellular integrity by triggering antioxidant enzyme activities, especially via CAT and APX, but not SOD and POD. In addition, MT increased nonenzymatic antioxidant activity, including FRAP and the GSH/GSSG and ASA/DHA ratios. MT usage improved the expression of several defense genes (CAT, APX, GR and MDHAR) and MT biosynthesis-related genes (TDC, SNAT and COMT). Taken together, our results preliminarily reveal that MT alleviates Cu2+ toxicity via ROS scavenging, enhancing antioxidant capacity when subjected to excessive Cu2+. These results build a solid foundation for developing new insights to solve problems related to CS

MOESM1 of Knockdown of CLIC4 enhances ATP-induced HN4 cell apoptosis through mitochondrial and endoplasmic reticulum pathways

Additional file 1: Figure S1. Effect of CLIC4 or scrambled siRNA on CLIC4 expression. Effect of CLIC4 or scrambled siRNA on CLIC4 expression. Summarized data showing the expression level of CLIC4. HN4 cells were transfected with CLIC4 or scrambled siRNA, and then were treated with (ATP-Con, ATP-CLIC4) or without (Con, Con-CLIC4) 100 μmol/L ATP for 3 h. β-Tubulin was used as a loading control. Values are shown as the mean ± SE. n = 3. *P < 0.05. vs. the control (Con) group,# P < 0.05 vs. the ATP control (ATP-Con) group