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

Calibrating Label Distribution for Class-Imbalanced Barely-Supervised Knee Segmentation

Segmentation of 3D knee MR images is important for the assessment of osteoarthritis. Like other medical data, the volume-wise labeling of knee MR images is expertise-demanded and time-consuming; hence semi-supervised learning (SSL), particularly barely-supervised learning, is highly desirable for training with insufficient labeled data. We observed that the class imbalance problem is severe in the knee MR images as the cartilages only occupy 6% of foreground volumes, and the situation becomes worse without sufficient labeled data. To address the above problem, we present a novel framework for barely-supervised knee segmentation with noisy and imbalanced labels. Our framework leverages label distribution to encourage the network to put more effort into learning cartilage parts. Specifically, we utilize 1.) label quantity distribution for modifying the objective loss function to a class-aware weighted form and 2.) label position distribution for constructing a cropping probability mask to crop more sub-volumes in cartilage areas from both labeled and unlabeled inputs. In addition, we design dual uncertainty-aware sampling supervision to enhance the supervision of low-confident categories for efficient unsupervised learning. Experiments show that our proposed framework brings significant improvements by incorporating the unlabeled data and alleviating the problem of class imbalance. More importantly, our method outperforms the state-of-the-art SSL methods, demonstrating the potential of our framework for the more challenging SSL setting.Comment: Provisionally accepted to MICCAI 2022; 10 pages, 3 figure

UAV Remote Sensing Prediction Method of Winter Wheat Yield Based on the Fused Features of Crop and Soil

The early and accurate acquisition of crop yields is of great significance for maintaining food market stability and ensuring global food security. Unmanned aerial vehicle (UAV) remote sensing offers the possibility of predicting crop yields with its advantages of flexibility and high resolution. However, most of the existing remote sensing yield estimation studies focused solely on crops but did not fully consider the influence of soil on yield formation. As an integrated system, the status of crop and soil together determines the final yield. Compared to crop-only yield prediction, the approach that additionally considers soil background information will effectively improve the accuracy and reduce bias in the results. In this study, a novel method for segmenting crop and soil spectral images based on different vegetation coverage is first proposed, in which pixels of crop and soil can be accurately identified by determining the discriminant value Q. On the basis of extracting crop and soil waveband’s information by individual pixel, an innovative approach, projected non-negative matrix factorization based on good point set and matrix cross fusion (PNMF-MCF), was developed to effectively extract and fuse the yield-related features of crop and soil. The experimental results on winter wheat show that the proposed segmentation method can accurately distinguish crop and soil pixels under complex soil background of four different growth periods. Compared with the single reflectance of crop or soil and the simple combination of crop and soil reflectance, the fused yield features spectral matrix FP obtained with PNMF−MCF achieved the best performance in yield prediction at the flowering, flag leaf and pustulation stages, with R2 higher than 0.7 in these three stages. Especially at the flowering stage, the yield prediction model based on PNMF-MCF had the highest R2 with 0.8516 and the lowest RMSE with 0.0744 kg/m2. Correlation analysis with key biochemical parameters (nitrogen and carbon, pigments and biomass) of yield formation showed that the flowering stage was the most vigorous season for photosynthesis and the most critical stage for yield prediction. This study provides a new perspective and complete framework for high-precision crop yield forecasting using UAV remote sensing technology

UAV Remote Sensing Prediction Method of Winter Wheat Yield Based on the Fused Features of Crop and Soil

The early and accurate acquisition of crop yields is of great significance for maintaining food market stability and ensuring global food security. Unmanned aerial vehicle (UAV) remote sensing offers the possibility of predicting crop yields with its advantages of flexibility and high resolution. However, most of the existing remote sensing yield estimation studies focused solely on crops but did not fully consider the influence of soil on yield formation. As an integrated system, the status of crop and soil together determines the final yield. Compared to crop-only yield prediction, the approach that additionally considers soil background information will effectively improve the accuracy and reduce bias in the results. In this study, a novel method for segmenting crop and soil spectral images based on different vegetation coverage is first proposed, in which pixels of crop and soil can be accurately identified by determining the discriminant value Q. On the basis of extracting crop and soil waveband’s information by individual pixel, an innovative approach, projected non-negative matrix factorization based on good point set and matrix cross fusion (PNMF-MCF), was developed to effectively extract and fuse the yield-related features of crop and soil. The experimental results on winter wheat show that the proposed segmentation method can accurately distinguish crop and soil pixels under complex soil background of four different growth periods. Compared with the single reflectance of crop or soil and the simple combination of crop and soil reflectance, the fused yield features spectral matrix FP obtained with PNMF−MCF achieved the best performance in yield prediction at the flowering, flag leaf and pustulation stages, with R2 higher than 0.7 in these three stages. Especially at the flowering stage, the yield prediction model based on PNMF-MCF had the highest R2 with 0.8516 and the lowest RMSE with 0.0744 kg/m2. Correlation analysis with key biochemical parameters (nitrogen and carbon, pigments and biomass) of yield formation showed that the flowering stage was the most vigorous season for photosynthesis and the most critical stage for yield prediction. This study provides a new perspective and complete framework for high-precision crop yield forecasting using UAV remote sensing technology

Study on the Effect of Calcium Alloy on Arsenic Removal from Scrap-Based Steel Production

Scrap steel is a kind of resource that can be recycled indefinitely. However, the enrichment of arsenic in the recycling process will seriously affect the performance of the product, making the recycling process unsustainable. In this study, the removal of arsenic from molten steel using calcium alloys was investigated experimentally, and the underlying mechanism was explored based on thermodynamic principles. The results show that the addition of calcium alloy is an effective means of reducing the arsenic content in molten steel, with the highest removal percentage of 56.36% observed with calcium aluminum alloy. A thermodynamic analysis revealed that the critical calcium content required for arsenic removal reaction is 0.0037%. Moreover, ultra-low levels of oxygen and sulfur were found to be crucial in achieving a good arsenic removal effect. When the arsenic removal reaction occurs in molten steel, the oxygen and sulfur concentrations in equilibrium with calcium were wO=0.0012% and wS=0.00548%, respectively. After successful arsenic removal, the arsenic removal product of the calcium alloy is Ca3As2, which usually does not appear alone. Instead, it is prone to combining with alumina, calcium oxide, and other inclusions to form composite inclusions, which is beneficial for the floating removal of inclusions and the purification of scrap steel in molten steel

Integrating bioinformatic analysis and detailed experiments reveal an EMT‐related biomarker for clear cell renal cell carcinoma

Abstract Background Epithelial–mesenchymal transition (EMT) is associated with early recurrence and a poor prognosis in clear cell renal cell carcinoma (ccRCC). Studies have shown that EMT‐related genes play an important regulatory role in tumor invasion, metastasis, and drug resistance, but the biological functions of EMT‐related genes in ccRCC have not been specifically described. Methods The mRNA and clinicopathological data of 532 ccRCC and 72 normal samples were downloaded from The Cancer Genome Atlas as a training set. The gene expression matrix and survival data of 91 and 101 ccRCC samples were obtained from the International Cancer Genome Consortium and the ArrayExpress databases as validation sets, respectively. Univariate Cox analysis was used to identify and cluster prognostic genes, and multivariate Cox was performed to construct a prognostic signature. Moreover, CIBERSORT and CellMiner were used to assess immune cell infiltration and prognostic gene‐drug sensitivity of the signature, respectively. Most importantly, we performed detailed experiments to verify the oncogenic function of a significant gene, OLFML2B, in vitro and in vivo. Results We constructed a prognostic signature including seven genes and divided patients into high‐risk and low‐risk groups. The prognosis of the high‐risk group was significantly worse than that of the low‐risk group through Kaplan–Meier survival analysis. Interestingly, significant differences were observed in clinical characteristics and immune cell infiltration between the two groups. In addition, a significant correlation was found between the expression of prognostic genes and the sensitivity of tumor cells to chemotherapeutics. Most importantly, OLFML2B was proved to contribute to the proliferation and metastasis of ccRCC through detailed functional experiments in vitro and in vivo, and its prognostic efficacy for ccRCC patients was affirmed. Conclusion We identified the prognostic signature of seven genes based on EMT‐related genes as prognostic biomarkers for ccRCC. Besides, OLFML2B was validated as a potential diagnostic and therapeutic target for ccRCC by our detailed experiments

A deep learning-based drug repurposing screening and validation for anti-SARS-CoV-2 compounds by targeting the cell entry mechanism

The recent outbreak of Corona Virus Disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been a severe threat to the global public health and economy, however, effective drugs to treat COVID-19 are still lacking. Here, we employ a deep learning-based drug repositioning strategy to systematically screen potential anti-SARS-CoV-2 drug candidates that target the cell entry mechanism of SARS-CoV-2 virus from 2635 FDA-approved drugs and 1062 active ingredients from Traditional Chinese Medicine herbs. In silico molecular docking analysis validates the interactions between the top compounds and host receptors or viral spike proteins. Using a SARS-CoV-2 pseudovirus system, we further identify several drug candidates including Fostamatinib, Linagliptin, Lysergol and Sophoridine that can effectively block the cell entry of SARS-CoV-2 variants into human lung cells even at a nanomolar scale. These efforts not only illuminate the feasibility of applying deep learning-based drug repositioning for antiviral agents by targeting a specified mechanism, but also provide a valuable resource of promising drug candidates or lead compounds to treat COVID-19