Loop PDE of viral capsid protein is involved in immune escape of the emerging novel variant infectious bursal disease virus.

Infectious bursa disease (IBD) is an acute, highly contactable, lethal, immunosuppressive infectious disease caused by the Infectious bursa disease virus (IBDV). Currently, the emerged novel variant IBDV (nVarIBDV) and the sustainedly prevalent very virulent IBDV (vvIBDV) are the two most prevalent strains of IBDV in China. The antigenic properties of the two prevalent strains differed significantly, which led to the escape of nVarIBDV from the immune protection provided by the existing vvIBDV vaccine. However, the molecular basis of the nVarIBDV immune escape remains unclear. In this study, we demonstrated, for the first time, that residues 252, 254, and 256 in the PDE of VP2 are involved in the immune escape of the emerging nVarIBDV. Firstly, the IFA-mediated antigen-antibody affinity assay showed that PBC and PDE of VP2 could affect the affinity of vvIBDV antiserum to VP2, of which PDE was more significant. The key amino acids of PDE influencing the antigen-antibody affinity were also identified, with G254N being the most significant, followed by V252I and I256V. Then the mutated virus with point or combined mutations was rescued by reverse genetics. it was further demonstrated that mutations of V252I, G254N, and I256V in PDE could individually or collaboratively reduce antigen-antibody affinity and interfere with antiserum neutralization, with G254N being the most significant. This study revealed the reasons for the widespread prevalence of nVarIBDV in immunized chicken flocks and provided innovative ideas for designing novel vaccines that match the antigen of the epidemic strain.

Effects of amygdalin on ferroptosis and oxidative stress in diabetic retinopathy progression via the NRF2/ARE signaling pathway.

Oxidative stress has been involved in the pathogenesis of diabetic retinopathy (DR). Amygdalin is an effective component of bitter almond that exhibits excellent antioxidant properties. We explored the effects of amygdalin on ferroptosis and oxidative stress in high-glucose (HG)-stimulated human retinal endothelial cells (HRECs) via the NRF2/ARE pathway. HG-stimulated HRECs were used to establish a DR model. Cell viability was evaluated using the MTT assay. The release of lactate dehydrogenase was used to evaluate cell toxicity. The protein levels of NRF2, NQO1, and HO-1 were detected using western blotting. The GSH, GSSG, GPX4, SOD, CAT, MDA, and Fe2+ levels in the HRECs were also detected. Flow cytometry was used to detect reactive oxygen species (ROS) using a fluorescent probe. Immunofluorescence staining was performed to detect NRF2 expression. The results revealed that HG stimulation decreased the levels of GSH, GPX4, SOD, and CAT but increased those of MDA, ROS, GSSG, and Fe2+ in HRECs. Ferrostatin-1 treatment reversed the effects of HG stimulation, whereas erastin aggravated these effects. Amygdalin treatment relieved HG-induced injury in HRECs. Amygdalin treatment promoted the nuclear transport of NRF2 in HG-stimulated HRECs. NQO1 and HO-1 levels were upregulated in HG-stimulated HRECs after amygdalin treatment. An inhibitor of NRF2 reversed the effects of amygdalin. Therefore, amygdalin treatment inhibited ferroptosis and oxidative stress in HG-stimulated HRECs by activating the NRF2/ARE signaling pathway.

Machine learning algorithms to automate differentiating cardiac amyloidosis from hypertrophic cardiomyopathy.

Cardiac amyloidosis has a poor prognosis, and high mortality and is often misdiagnosed as hypertrophic cardiomyopathy, leading to delayed diagnosis. Machine learning combined with speckle tracking echocardiography was proposed to automate differentiating two conditions. A total of 74 patients with pathologically confirmed monoclonal immunoglobulin light chain cardiac amyloidosis and 64 patients with hypertrophic cardiomyopathy were enrolled from June 2015 to November 2018. Machine learning models utilizing traditional and advanced algorithms were established and determined the most significant predictors. The performance was evaluated by the receiver operating characteristic curve (ROC) and the area under the curve (AUC). With clinical and echocardiography data, all models showed great discriminative performance (AUC > 0.9). Compared with logistic regression (AUC 0.91), machine learning such as support vector machine (AUC 0.95, p = 0.477), random forest (AUC 0.97, p = 0.301) and gradient boosting machine (AUC 0.98, p = 0.230) demonstrated similar capability to distinguish cardiac amyloidosis and hypertrophic cardiomyopathy. With speckle tracking echocardiography, the predictive performance of the voting model was similar to that of LightGBM (AUC was 0.86 for both), while the AUC of XGBoost was slightly lower (AUC 0.84). In fivefold cross-validation, the voting model was more robust globally and superior to the single model in some test sets. Data-driven machine learning had shown admirable performance in differentiating two conditions and could automatically integrate abundant variables to identify the most discriminating predictors without making preassumptions. In the era of big data, automated machine learning will help to identify patients with cardiac amyloidosis and timely and effectively intervene, thus improving the outcome.

Experimental Study and Calculation Methods of Shear Capacity for High-Strength Reinforced Concrete Full-Scale Deep Beams.

The shear behavior of 8 high-strength concrete full-scale deep beams with high-strength steel bars was studied. The depth beam size was 100 mm × 900 mm × 2200 mm, the test parameters included the shear span-to-depth ratio (λ = 0.9, 0.6, 0.3), longitudinal reinforcement ratio (ρs=0.66%, 1.06%, 1.26%) and stirrup reinforcement ratio (ρsv = 0, 0.26%, 0.34%, 0.5%). The ratio of the cracking load of the inclined section to the ultimate load is between 30% and 50%, and the bending deformation of the deep beam is small, showing the characteristics of brittle failure for deep beams. Under the action of a concentrated load, the failure mode of deep beams with a small shear span ratio is the failure of the diagonal compression struts, which is very different from that of shallow beams with a large shear span ratio. With the increase of shear span ratio from 0.3 to 0.9, the ultimate shear capacity of deep beams decreases by 19.33%. With the increase of longitudinal reinforcement ratio from 0.67% to 1.27%, the ultimate shear capacity of deep beams increased by 45.02%. With the increase of vertical stirrup reinforcement ratio from 0% to 0.5%, the ultimate shear capacity of deep beams increased by 8.93%. Increasing the area of longitudinal bars or stirrups limited the transverse tensile strain of the compression struts, which is conducive to improving the compressive strength of the compression struts of deep beams and then improving the bearing capacity of deep beams. The strut-and-tie model (STM) is more suitable for analyzing the shear capacity of deep beams. The calculation methods for calculating the shear capacity of deep beams were compared with ACI 318-19, CSA A23 3-19, EN 1992-1-1:2004, Tan-Tan model, Tan-Cheng model, softened STM (SSTM) and simplified SSTM (SSSTM). The results showed that the shear capacity of deep beams could be well predicted by reasonably determining the STM parameters.

Electro-optic analyzer of angular momentum hyperentanglement.

Characterizing a high-dimensional entanglement is fundamental in quantum information applications. Here, we propose a theoretical scheme to analyze and characterize the angular momentum hyperentanglement that two photons are entangled simultaneously in spin and orbital angular momentum. Based on the electro-optic sampling with a proposed hyper-entanglement analyzer and the simple matrix operation using Cramer rule, our simulations show that it is possible to retrieve effectively both the information about the degree of polarization entanglement and the spiral spectrum of high-dimensional orbital angular momentum entanglement.

[Development of ultrasonic power meter].

This article describes the design and development of an ultrasonic power meter which is consist of an electronic balance, a practice target, an acoustic enclosures and a blocking. The electronic balance mounted on the blocking is linked with the practice target by connecting rod. By adjusting the blocking makes the practice target suspended above ultrasound probe, and then the ultrasonic power can be measured. After initial tests, the ultrasonic power meter performanced with good stability and high precision.

Polarization singularities and orbital angular momentum sidebands from rotational symmetry broken by the Pockels effect.

The law of angular momentum conservation is naturally linked to the rotational symmetry of the involved system. Here we demonstrate theoretically how to break the rotational symmetry of a uniaxial crystal via the electro-optic Pockels effect. By numerical method based on asymptotic expansion, we discover the 3D structure of polarization singularities in terms of C lines and L surfaces embedded in the emerging light. We visualize the controllable dynamics evolution of polarization singularities when undergoing the Pockels effect, which behaves just like the binary fission of a prokaryotic cell, i.e., the splitting of C points and fission of L lines are animated in analogy with the cleavage of nucleus and division of cytoplasm. We reveal the connection of polarization singularity dynamics with the accompanying generation of orbital angular momentum sidebands. It is unexpected that although the total angular momentum of light is not conserved, the total topological index of C points is conserved.