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Radar beam blockage is an important error source that affects the quality of weather radar data. The S-band dual-polarization radar in Guangzhou has multi-azimuth occlusion at low elevation and is partially occluded at high elevation. Based on deep learning methods such as convolutional neural network, two echo filling networks, i.e., VEF(vertical echo-filling) and HEF(horizontal echo-filling) are constructed. Based on this architecture, echoes from the unblocked area are used to construct training datasets and fill the reflectivity ZH and differential reflectivity ZDR in the occlusion area. For the area with only 0.5° elevation occlusion, multi-modal modeling is carried out based on VEF architecture by using 3D data from multiple upper elevations, radial directions and gates. Considering that the radar beam broadens with distance and to avoid the influence of the melting layer, the radar beam is divided into four sections according to the oblique distance of 0.5° elevation, and the vertical echo-filling model is trained respectively. For the area with high occlusion elevation, multi-mode modeling is carried out based on HEF architecture using the data of multiple adjacent radial directions and gates with the same elevation. According to the number of occlusion radial, two types of horizontal echo-filling models, three radials echo-filling model and five radials echo-filling model are constructed respectively. Finally, the models are evaluated by three cases and three indicators:Explained variance, mean absolute error and correlation coefficient. The maximum explained variance of ZH vertical echo-filling model is 0.91, the minimum mean absolute error is 1.72 dB, and the maximum correlation coefficient is 0.96. The maximum explained variance of ZDR vertical echo-filling model is 0.87, the minimum mean absolute error is 0.12 dB, and the maximum correlation coefficient is 0.92. The maximum explained variance of ZH horizontal fill model is 0.92, the minimum mean absolute error is 1.69 dB, and the maximum correlation coefficient is 0.96. The maximum explained variance of ZDR horizontal echo-filling model is 0.92, the minimum mean absolute error is 0.12 dB, and the maximum correlation coefficient is 0.96. The deep learning echo-filling model can be used to correct the echoes of Guangzhou S-band dual-polarization radar occlusion area effectively, and the quality of weather radar data is improved.

MYB-related transcription factors (TFs) subfamily is a subfamily of MYB TFs, which are mainly involved in plant secondary metabolism, growth and development, and stress response. To explore the function of MYB-related subfamily genes in Rosa chinensis, this study systematically analyzed characters of the MYB-related subfamily members in R. chinensis with bioinformatic analysis using the genomic data of R. chinensis and investigated their expression characteristics using quantitative real-time PCR (qRT-PCR). The results show that 100 MYB-related proteins were identified in R. chinensis. Proteins are mainly found in the nucleus. Chromosome localization revealed that all MYB-related genes are mapped to seven chromosomes and are distributed in clusters. Collinear analysis shows that 13 pairs of MYB-related genes had a collinear relationship, indicating R. chinensis may have evolved its MYB-related subfamily gene through fragment duplication. The analysis of motifs and conserved domains shows that Motif 3 is the most conserved motif. There are numerous ABA and MeJA response elements in MYB-related genes. ABA and MeJA treatments significantly shortened the vase life of R. chinensis, while the flower diameter on day 3 was the largest, suggesting that ABA and MeJA might induce MYB-related gene expression during cut flower senescence. The expression of MYB-related genes is tissue specific, most of which show the highest expression levels in petals. Notably, among six plant growth regulator treatments, ABA treatment significantly increased RcMYB002 expression in R. chinensis, suggesting that RcMYB002 may be a crucial gene for ABA response. This study provides a reference for further research on the function of MYB-related genes in R. chinensis. [ABSTRACT FROM AUTHOR]

Objective: Cardiovascular complications associated with COVID‐19 are being increasingly recognized. They include potential long‐term effects on the heart, although how these complications manifest in patients with stroke who are already vulnerable to cardiovascular issues is lacking. This study addressed this gap by investigating the influence of SARS‐CoV‐2 infection on cardiac hemodynamic changes in inpatients with stroke, thereby providing valuable insights into the management of cardiac complications in this population. Methods: This retrospective cohort study enrolled inpatients with stroke who did or did not experience SARS‐CoV‐2 infection in Shenzhen Second People's Hospital. Clinical information, hemodynamics data, serum myocardial enzyme levels, functional levels, including Barthel index, and Longshi scale assessment (bedridden, domestic, and community) were extracted from clinical records. An independent sample t‐test and the Mann–Whitney U test were used for comparison between groups. Multiple logistic regression analysis was used to identify indicators associated with decline in cardiac function in patients with stroke post‐COVID‐19. Results: Eighty‐three patients with stroke (average age of 66.0 ± 15.5 years) were recruited (42 patients in the study group and 41 in the control group). No significant difference in general characteristics was observed between the groups. The ejection fraction (EF, 56.45 ± 12.46 vs. 47.64 ± 14.76, p = 0.04) was decreased, and the end‐diastolic volume (EDV, 142.74 ± 80.00 vs. 193.34 ± 140.17, p = 0.001) was increased significantly in patients infected with SARS‐CoV‐2 compared with the noninfected participants. SARS‐CoV‐2 infection was an independent risk factor for EF (−10%) decrease (OR: 5.205, 95% CI: 1.621–16.720, p = 0.006). The risk of cardiac function decline among bedridden infected patients was 15.43 times higher than that of uninfected nonbedridden patients (OR: 15.430, 95% CI: 1.402–169.805, p = 0.025). Conclusion: Patients who are bedridden after stroke face a higher potential risk of cardiac hemodynamic impairment following SARS‐CoV‐2 infection, emphasizing the need for meticulous hemodynamic monitoring during treatment and rehabilitation of these patients to prevent any potential cardiovascular complications. Trial Registration: ClinicalTrials.gov identifier: ChiCTR2300071376. [ABSTRACT FROM AUTHOR]

Emerging studies have focused on ways to treat cancers by modulating T cell activation. However, whether B cell receptor signaling in the tumor microenvironment (TME) can be harnessed for immunotherapy is unclear. Here, we report that an Asia-specific variant of human IgG1 containing a Gly396 to Arg396 substitution (hIgG1-G396R) conferred improved survival of patients with colorectal cancer (CRC). Mice with knockin of the murine functional homolog mIgG2c-G400R recapitulated the alleviated tumorigenesis and progression in murine colon carcinoma models. Immune profiling of the TME revealed broad mobilizations of [IgG1.sup.+] plasma cells, [CD8.sup.+] T cells, [CD103.sup.+] DCs, and active tertiary lymphoid structure formation, suggesting an effective antitumor microenvironment in hIgG1-G396R CRC patients. Mechanistically, this variant potentiated tumorassociated antigen-specific (TAA-specific) plasma cell differentiation and thus antibody production. These elevated TAA-specific IgG2c antibodies in turn efficiently boosted the antibody-dependent tumor cell phagocytosis and TAA presentation to effector [CD8.sup.+] T cells. Notably, adoptive transfer of TAA-specific class-switched memory B cells harboring this variant exhibited therapeutic efficacy in murine tumor models, indicating their clinical potential. All these results prompted a prospective investigation of hIgG1-G396R in patients with CRC as a biomarker for clinical prognosis and demonstrated that manipulating the functionality of [IgG1.sup.+] memory B cells in tumors could improve immunotherapy outcomes., Introduction Effective immune responses are indispensable for optimal cancer treatment; however, tumor cells often evade immune clearance by inhibiting neoantigen presentation and inducing immune silencing in the tumor microenvironment (TME) [...]

CAR-T cell immunotherapy has achieved remarkable success in malignant B-cell malignancies, but progress in solid tumors is slow, and one of the key reasons is the lack of ideal targets. Cancer-specific extra domain B of fibronectin (EDB-FN) is widely upregulated in solid tumors and expressed at low levels in normal tissues. Many imaging and targeted cancer therapies based on EDB-FN targets have been developed and tested in clinical trials, making EDB-FN an ideal target for immunotherapy. We constructed two EDB-FN-targeted CAR-Ts based on the peptide APT0 and the single-chain antibody CGS2 in a lentiviral infection manner for the first time. Luciferase cytotoxicity assay to assess CAR-T killing of tumor cells. An enzyme-linked immunosorbent assay was used to detect the release of the cytokine IFN-γ. Fluorescence imaging to evaluate the dynamics of CAR-T cell and tumor cell coculture. Knockdown assays were used to validate the target specificity of CAR-T cells. In this research, two CAR-Ts targeting EDB-FN, APT0 CAR-T, and CGS2 CAR-T, were constructed. In vitro, both CAR-T cells produced broad-spectrum killing of multiple EDB-FN-positive solid tumor cell lines and were accompanied by cytokine IFN-γ release. Regarding safety, the two CAR-T cells did not affect T cells' normal growth and proliferation and were not toxic to HEK-293T human embryonic kidney epithelial cells. APT0 CAR-T and CGS2 CAR-T cells are two new CAR-Ts targeting EDB-FN. Both CAR-T cells can successfully identify and specifically kill various EDB-FN-positive solid tumor cells with potential clinical applications. [ABSTRACT FROM AUTHOR]

In the era of the digital economy, traditional industries have begun to realize digital transformations. For commercial banks, digital transformation is a trend and a requirement and is the only way to achieve sustainable development. At the same time, at the helm of the enterprise, executives play an essential role in the development of commercial banks. This study explored the relationship between digital bank transformation and bank efficiency, environment, society, and corporate governance (ESG) through empirical analysis, and how executives' innovation awareness and executive technical background affect the relationships between digital bank transformation, bank efficiency, and ESG. This study used the regression method of fixed effects to conduct empirical research on the data of China's A-share listed banks from 2011 to 2021. The research results show that the digital transformation of banks has improved efficiency and promoted the ESG performance of commercial banks. At the same time, executives' innovation consciousness and technical background have played a positive regulatory role in banks' digital transformation to promote bank efficiency and ESG. The main research object of this study was Chinese commercial banks. The bank's digital transformation results were examined and the research was expanded to digital transformation and ESG. At the same time, this study has particular significance for investors who have a financial interest in banks. [ABSTRACT FROM AUTHOR]

Whether in natural scenes or laboratory environments, leaf instance segmentation is still a challenging task in high-throughput plant phenotypic research. Because compared with normal instance objects, leaves have more complex boundaries and severe inter-leaf occlusions. In this paper, we present an effective two-stage method called Bilayer Convolution Mask (BCMask) for high-quality leaf instance segmentation. BCMask consists of three main modules: (1) Bottom-up Path Augmentation (BPA) module is added after Feature Pyramid Network (FPN) in Faster R-CNN. BPA shortens the information path between lower layers and high-level layers, and helps accurate semantical features in lower layers to enhance the entire feature hierarchy; (2) Bilayer Occlusion Module. This module consists of two convolutional layers with a residual structure, which decouples the occluding leaves and the partially occluded target leaf during the mask regression; (3) Mask Refining Module. This module uses an iterative refinement method with adaptive selection to classify pixels, which effectively alleviates the problem of inaccurate leaf boundary segmentation. To validate BCMask, this paper takes the chrysanthemum seedling leaf dataset for experiment, which is collected in the natural environment with complex boundaries and severe occlusions. Two remarkable public datasets CVPPA and Komatsuna under laboratory environments are also added as supplements to validate the robustness of BCMask. The proposed method achieves the 60.42% average precision (AP) score outperforming state-of-the-art methods. [ABSTRACT FROM AUTHOR]

The development of financial technology has promoted the innovation and digital transformation of commercial banks. Through digital transformation, commercial banks can improve bank efficiency and operational capabilities. Through empirical analysis, this study explored the relationship between digital bank transformation and commercial bank operating capabilities and how COVID-19, bank categories, and enterprise life cycles affect the relationship between digital bank transformation and commercial bank operating capabilities. This study selected data from China's commercial banks from 2011 to 2021 and used the regression method of fixed effects to conduct an empirical analysis. The research results show that the digital transformation of banks has improved the operational capabilities of commercial banks. Further analysis showed that the emergence of COVID-19 has negatively affected their relationship. At the same time, compared with rural commercial banks and commercial banks in the recession and phase-out periods, non-rural commercial banks and commercial banks in the growth and maturity stages play a more vital moderating role in the impact of the digital transformation of banks on the financial performance of commercial banks. The main research object of this study is Chinese commercial banks, and this study examines the results of banks' digital transformation and enriches the research on digital transformation. At the same time, this study is helpful to investors who like investment banks and has good practical significance. [ABSTRACT FROM AUTHOR]

The transport of a non-uniform bed load in a river is a complicated process and has enormous implications on the sediment flux and anomalous riverbed evolution. To investigate the transport characteristics of the non-uniform bed load and the related particle interactions, a real-time monitoring system of the bed load transport was developed to determine the instant transport rate and grain composition of the bed load. Doppler Velocimetry was used to synchronously measure the fluctuating velocity in high frequency. A total of 211 cases of flume experiments were conducted, focusing on non-uniform sediment with a bimodal pattern. The experimental results indicate that the random fluctuation of the bed load transport amount closely depends on the flow-intensity fluctuation caused by the turbulence burst near the bed. When the value of the flow-fluctuation peak is bigger than 2.5 σ, the coarse sands tend to incipient motion in high probability but are mostly fine sand transport when the peak is less than 1.5 σ. The transport rate of fine particles remains continuous throughout the process, while that of coarse particles is intermittent because the incipient motion mechanism of bed load sands mainly follows three modes. If the difference in diameter between the coarse and fine particles is large, the transport of coarse particles may undergo supernormal transport because of the effect of the fine particles on the coarse particles. The bed load flux of total, fine, and coarse sand present different trends with changes in the bed material composition, in which the transport rate of coarse sands and total bed load sands presents a humped curve in terms of the mixture ratio, and the optimal corresponding mixture ratio ηc is about 3:7. The optimal mixture ratio is not fixed, and it depends on the grain composition and size differential of bed material. With a proper mixture ratio, the transport rate of a non-uniform bed load is higher than the uniform bed load of related size. These findings might provide valuable support for predicting bed load transport and bed evolution in rivers. [ABSTRACT FROM AUTHOR]