SenseCare: A Research Platform for Medical Image Informatics and Interactive 3D Visualization

Clinical research on smart healthcare has an increasing demand for intelligent and clinic-oriented medical image computing algorithms and platforms that support various applications. To this end, we have developed SenseCare research platform for smart healthcare, which is designed to boost translational research on intelligent diagnosis and treatment planning in various clinical scenarios. To facilitate clinical research with Artificial Intelligence (AI), SenseCare provides a range of AI toolkits for different tasks, including image segmentation, registration, lesion and landmark detection from various image modalities ranging from radiology to pathology. In addition, SenseCare is clinic-oriented and supports a wide range of clinical applications such as diagnosis and surgical planning for lung cancer, pelvic tumor, coronary artery disease, etc. SenseCare provides several appealing functions and features such as advanced 3D visualization, concurrent and efficient web-based access, fast data synchronization and high data security, multi-center deployment, support for collaborative research, etc. In this paper, we will present an overview of SenseCare as an efficient platform providing comprehensive toolkits and high extensibility for intelligent image analysis and clinical research in different application scenarios.Comment: 11 pages, 10 figure

Nonlinear Magneto-Electro-Mechanical Response of Physical Cross-Linked Magneto-Electric Polymer Gel

This work reports on a novel magnetorheological polymer gel with carbon nanotubes and carbonyl iron particles mixed into the physical cross-linked polymer gel matrix. The resulting composites show unusual nonlinear magneto-electro-mechanical responses. Because of the low matrix viscosity, effective conductive paths formed by the CNTs were mobile and high-performance sensing characteristics were observed. In particular, due to the transient and mutable physical cross-linked bonds in the polymer gel, the electromechanical behavior acted in a rate-dependent manner. External stimulus at a high rate significantly enhanced the electrical resistance response during mechanical deformation. Meanwhile, the rheological properties were regulated by the external magnetic field when magnetic particles were added. This dual enhancement mechanism further contributes to the active control of electromechanical performance. These polymer composites could be adopted as electromechanical sensitive sensors to measure impact and vibration under different frequencies. There is great potential for this magnetorheological polymer gel in the application of intelligent vibration controls

Molecular epizootiology of porcine reproductive and respiratory syndrome virus in the Xinjiang Uygur Autonomous Region of China

Rapid evolution of porcine reproductive and respiratory syndrome virus (PRRSV) is the bottleneck for effective prevention and control of PRRS. Thus, understanding the prevalence and genetic background of PRRSV strains in swine-producing regions is important for disease prevention and control. However, there is only limited information about the epizootiological situation of PRRS in the Xinjiang Uygur Autonomous Region, China. In this study, blood or lung tissue samples were collected from 1,411 PRRS-suspected weaned pigs from 9 pig farms in Changji, Shihezi, and Wujiaqu cities between 2020 and 2022. The samples were first tested by RT-quantitative PCR, yielding a PRRSV-2 positive rate of 53.6%. Subsequently, 36 PRRSV strains were isolated through initial adaptation in bone marrow-derived macrophages followed by propagation in grivet monkey Marc-145 cells. Furthermore, 28 PRRSV-positive samples and 20 cell-adapted viruses were selected for high-throughput sequencing (HTS) to obtain the entire PRRSV genome sequences. Phylogenetic analysis based on the nucleotide sequences of the ORF5 gene of the PRRSV strains identified in this study grouped into sub-lineages 1.8 and 8.7 the former being the dominant strain currently circulating in Xinjiang. However, the NSP2 proteins of the Xinjiang PRRSV strains shared the same deletion patterns as sub-lineage 1.8 prototype strain NADC30 with the exception of 4 strains carrying 2-3 additional amino acid deletions. Further analysis confirmed that recombination events had occurred in 27 of 37 PRRSVs obtained here with the parental strains belonging to sub-lineages 1.8 and 8.7, lineages 3 and 5, with the recombination events having occurred most frequently in the 5' and 3' termini of ORF1a and 5' terminus of ORF1b

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

Study on the Spatial and Temporal Characteristics of Mesoscale Drought in China under Future Climate Change Scenarios

In this study, precipitation, and temperature data from HadGEM2-ES under Representative Concentration Pathways (RCPs) 4.5 and 8.5 were used to evaluate drought in China in the 21st century. The K-means clustering algorithm was used to analyze the regional characteristics of the dry hazard index (DHI) in China, and the impact of climate change on the variation trend and periodicity of regional drought in China was explored. The results show that the temperature and potential evapotranspiration (PET) of all clusters have an increasing trend under the two RCPs, and the precipitation of most clusters shows a significantly increasing trend. The drought index calculated by the standardized precipitation-evapotranspiration index (SPEI) is higher than those calculated by the standardized precipitation index (SPI) and standardized effective precipitation evapotranspiration index (SP*ETI). The variation trends of drought intensity and frequency in China are not significant in the 21st century; however, the local variation trends are significant. The droughts in most parts of the Xinjiang Province, northern Tibet and western Qinghai Province show significantly increasing trends. According to the DHI analyses and the variations in the drought area ratio, with increases in greenhouse gas concentrations, the droughts in central and western China will become more severe, and drought will spread to the eastern areas of China. In the case that both precipitation and temperature may increase in the future, the increase in evapotranspiration caused by temperature rise will greatly affect drought dynamics. The main drought periodicity in China in the 21st century is 1~3.6 years. Drought is affected by climate change but not significantly