Application of the improved dynamical–Statistical–Analog ensemble forecast model for landfalling typhoon precipitation in Fujian province

The forecasting performance of the Dynamical–Statistical–Analog Ensemble Forecast (DSAEF) model for Landfalling Typhoon [or tropical cyclone (TC)] Precipitation (DSAEF_LTP), with new values of two parameters (i.e., similarity region and ensemble method) for landfalling TC precipitation over Fujian Province, is tested in four experiments. Forty-two TCs with precipitation over 100 mm in Fujian Province during 2004–2020 are chosen as experimental samples. Thirty of them are training samples and twelve are independent samples. First, simulation experiments for the training samples are used to determine the best scheme of the DSAEF_LTP model. Then, the forecasting performance of this best scheme is evaluated through forecast experiments. In the forecast experiments, the TSsum (the sum of threat scores for predicting TC accumulated rainfall of ≥250 mm and ≥100 mm) of experiments DSAEF_A, B, C, D is 0.0974, 0.2615, 0.2496, and 0.4153, respectively. The results show that the DSAEF_LTP model performs best when both adding new values of the similarity region and ensemble method (DSAEF_D). At the same time, the TSsum of the best performer of numerical weather prediction (NWP) models is only 0.2403. The improved DSAEF_LTP model shows advantages compared to the NWP models. It is an important method to improve the predictability of the DSAEF_LTP model by adopting different schemes in different regions

Sparse4D: Multi-view 3D Object Detection with Sparse Spatial-Temporal Fusion

Bird-eye-view (BEV) based methods have made great progress recently in multi-view 3D detection task. Comparing with BEV based methods, sparse based methods lag behind in performance, but still have lots of non-negligible merits. To push sparse 3D detection further, in this work, we introduce a novel method, named Sparse4D, which does the iterative refinement of anchor boxes via sparsely sampling and fusing spatial-temporal features. (1) Sparse 4D Sampling: for each 3D anchor, we assign multiple 4D keypoints, which are then projected to multi-view/scale/timestamp image features to sample corresponding features; (2) Hierarchy Feature Fusion: we hierarchically fuse sampled features of different view/scale, different timestamp and different keypoints to generate high-quality instance feature. In this way, Sparse4D can efficiently and effectively achieve 3D detection without relying on dense view transformation nor global attention, and is more friendly to edge devices deployment. Furthermore, we introduce an instance-level depth reweight module to alleviate the ill-posed issue in 3D-to-2D projection. In experiment, our method outperforms all sparse based methods and most BEV based methods on detection task in the nuScenes dataset

Conditional Random Fields as Recurrent Neural Networks

Pixel-level labelling tasks, such as semantic segmentation, play a central role in image understanding. Recent approaches have attempted to harness the capabilities of deep learning techniques for image recognition to tackle pixel-level labelling tasks. One central issue in this methodology is the limited capacity of deep learning techniques to delineate visual objects. To solve this problem, we introduce a new form of convolutional neural network that combines the strengths of Convolutional Neural Networks (CNNs) and Conditional Random Fields (CRFs)-based probabilistic graphical modelling. To this end, we formulate mean-field approximate inference for the Conditional Random Fields with Gaussian pairwise potentials as Recurrent Neural Networks. This network, called CRF-RNN, is then plugged in as a part of a CNN to obtain a deep network that has desirable properties of both CNNs and CRFs. Importantly, our system fully integrates CRF modelling with CNNs, making it possible to train the whole deep network end-to-end with the usual back-propagation algorithm, avoiding offline post-processing methods for object delineation. We apply the proposed method to the problem of semantic image segmentation, obtaining top results on the challenging Pascal VOC 2012 segmentation benchmark.Comment: This paper is published in IEEE ICCV 201

Deletion of the meq gene significantly decreases immunosuppression in chickens caused by pathogenic marek's disease virus

<p>Abstract</p> <p>Background</p> <p>Marek's disease virus (MDV) causes an acute lymphoproliferative disease in chickens, resulting in immunosuppression, which is considered to be an integral aspect of the pathogenesis of Marek's disease (MD). A recent study showed that deletion of the Meq gene resulted in loss of transformation of T-cells in chickens and a Meq-null virus, rMd5ΔMeq, could provide protection superior to CVI988/Rispens.</p> <p>Results</p> <p>In the present study, to investigate whether the Meq-null virus could be a safe vaccine candidate, we constructed a Meq deletion strain, GX0101ΔMeq, by deleting both copies of the Meq gene from a pathogenic MDV, GX0101 strain, which was isolated in China. Pathogenesis experiments showed that the GX0101ΔMeq virus was fully attenuated in specific pathogen-free chickens because none of the infected chickens developed Marek's disease-associated lymphomas. The study also evaluated the effects of GX0101ΔMeq on the immune system in chickens after infection with GX0101ΔMeq virus. Immune system variables, including relative lymphoid organ weight, blood lymphocytes and antibody production following vaccination against AIV and NDV were used to assess the immune status of chickens. Experimental infection with GX0101ΔMeq showed that deletion of the Meq gene significantly decreased immunosuppression in chickens caused by pathogenic MDV.</p> <p>Conclusion</p> <p>These findings suggested that the Meq gene played an important role not only in tumor formation but also in inducing immunosuppressive effects in MDV-infected chickens.</p

Deletion of 1.8-kb mRNA of Marek's disease virus decreases its replication ability but not oncogenicity

<p>Abstract</p> <p>Background</p> <p>The 1.8-kb mRNA was reported as one of the oncogenesis-related genes of Marek's disease virus (MDV). In this study, the bacterial artificial chromosome (BAC) clone of a MDV field strain GX0101 was used as the platform to generate mutant MDV to examine the functional roles of 1.8-kb mRNA.</p> <p>Results</p> <p>Based on the BAC clone of GX0101, the 1.8-kb mRNA deletion mutant GX0101Δ(A+C) was constructed. The present experiments indicated that GX0101Δ(A+C) retained a low level of oncogenicity, and it showed a decreased replication capacity in vitro and in vivo when compared with its parent virus, GX0101. Further studies in vitro demonstrated that deletion of 1.8-kb mRNA significantly decreased the transcriptional activity of the bi-directional promoter between 1.8-kb mRNA and pp38 genes of MDV.</p> <p>Conclusion</p> <p>These results suggested that the 1.8-kb mRNA did not directly influence the oncogenesis but related to the replication ability of MDV.</p

Molecular Mechanisms of Same TCM Syndrome for Different Diseases and Different TCM Syndrome for Same Disease in Chronic Hepatitis B and Liver Cirrhosis

Traditional Chinese medicine (TCM) treatment is based on the traditional diagnose method to distinguish the TCM syndrome, not the disease. So there is a phenomenon in the relationship between TCM syndrome and disease, called Same TCM Syndrome for Different Diseases and Different TCM Syndrome for Same Disease. In this study, we demonstrated the molecular mechanisms of this phenomenon using the microarray samples of liver-gallbladder dampness-heat syndrome (LGDHS) and liver depression and spleen deficiency syndrome (LDSDS) in the chronic hepatitis B (CHB) and liver cirrhosis (LC). The results showed that the difference between CHB and LC was gene expression level and the difference between LGDHS and LDSDS was gene coexpression in the G-protein-coupled receptor protein-signaling pathway. Therein genes GPER, PTHR1, GPR173, and SSTR1 were coexpressed in LDSDS, but not in LGDHS. Either CHB or LC was divided into the alternative LGDHS and LDSDS by the gene correlation, which reveals the molecular feature of Different TCM Syndrome for Same Disease. The alternatives LGDHS and LDSDS were divided into either CHB or LC by the gene expression level, which reveals the molecular feature of Same TCM Syndrome for Different Diseases

A gate-variable spin current demultiplexer based on graphene

Spintronics, which utilizes spin as information carrier, is a promising solution for nonvolatile memory and low-power computing in the post-Moore era. An important challenge is to realize long distance spin transport, together with efficient manipulation of spin current for novel logic-processing applications. Here, we describe a gate-variable spin current demultiplexer (GSDM) based on graphene, serving as a fundamental building block of reconfigurable spin current logic circuits. The concept relies on electrical gating of carrier density dependent conductivity and spin diffusion length in graphene. As a demo, GSDM is realized for both single-layer and bilayer graphene. The distribution and propagation of spin current in the two branches of GSDM depend on spin relaxation characteristics of graphene. Compared with Elliot-Yafet spin relaxation mechanism, D'yakonov-Perel mechanism results in more appreciable gate-tuning performance. These unique features of GSDM would give rise to abundant spin logic applications, such as on-chip spin current modulators and reconfigurable spin logic circuits.Comment: 18 pages,3 figures,1 tabl

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