Total flavones of Desmodium styracifolium antagonize calcium oxalate monohydrate-triggered IL-2Rβ expression in renal epithelial cells

Purpose: To explore the effect of total flavone of Desmodium styracifolium (TFDS) on calcium oxalate monohydrate (COM)-triggered IL-2Rβ expression in human kidney proximal tubular epithelial cells. Methods: Human kidney proximal tubular epithelial cell line HK-2 was treated with COM, TFDS or both. The expression of IL-2Rβ was evaluated by quantitative polymerase chain reaction (qPCR) or flow cytometry. The responsiveness of HK-2 cells to IL-2 was determined by enzyme-linked immunosorbent assay (ELISA), qPCR and western blot. The signaling mechanism underlying the effect of TFDS was studied using western blot and qPCR. The clinical relevance of IL-2Rβ to renal inflammation was investigated by re-analyzing a Gene Expression Omnibus (GEO) dataset. Results: Total flavones of Desmodium styracifolium (TFDS) antagonize COM-triggered IL-2Rβ expression in HK-2 cells, thus reducing the responsiveness of HK-2 cells to IL-2 stimulation. Mechanistically, TFDS dampens IL-2Rβ expression by preventing the activation of STAT3. The level of IL-2Rβ is positively correlated with the inflammatory status of the kidney. Conclusions: The total flavones of Desmodium styracifolium (TFDS) prevent the upregulation of IL2Rβ in renal epithelial cells upon COM stimulation in a STAT3-dependent manner

JNMR: Joint Non-linear Motion Regression for Video Frame Interpolation

Video frame interpolation (VFI) aims to generate predictive frames by warping learnable motions from the bidirectional historical references. Most existing works utilize spatio-temporal semantic information extractor to realize motion estimation and interpolation modeling. However, they insufficiently consider the real mechanistic rationality of generated middle motions. In this paper, we reformulate VFI as a Joint Non-linear Motion Regression (JNMR) strategy to model the complicated motions of inter-frame. Specifically, the motion trajectory between the target frame and the multiple reference frames is regressed by a temporal concatenation of multi-stage quadratic models. ConvLSTM is adopted to construct this joint distribution of complete motions in temporal dimension. Moreover, the feature learning network is designed to optimize for the joint regression modeling. A coarse-to-fine synthesis enhancement module is also conducted to learn visual dynamics at different resolutions through repetitive regression and interpolation. Experimental results on VFI show that the effectiveness and significant improvement of joint motion regression compared with the state-of-the-art methods. The code is available at https://github.com/ruhig6/JNMR.Comment: Accepted by IEEE Transactions on Image Processing (TIP

A surface-enhanced Raman scattering (SERS)-active optical fiber sensor based on a three-dimensional sensing layer

AbstractTo fabricate a new surface-enhanced Raman scattering (SERS)-active optical fiber sensor, the design and preparation of SERS-active sensing layer is one of important topics. In this study, we fabricated a highly sensitive three-dimensional (3D) SERS-active sensing layer on the optical fiber terminal via in situ polymerizing a porous polymer material on a flat optical fiber terminal through thermal-induced process, following with the photochemical silver nanoparticles growth. The polymerized polymer formed a 3D porous structure with the pore size of 0.29–0.81μm, which were afterward decorated with abundant silver nanoparticles with the size of about 100nm, allowing for higher SERS enhancement. This SERS-active optical fiber sensor was applied for the determination of 4-mercaptopyridine, crystal violet and maleic acid The enhancement factor of this SERS sensing layer can be reached as about 108. The optical fiber sensor with high sensitive SERS-active porous polymer is expected for online analysis and environment detection

OR-006 Effect of Moderate-intensity Exercise on the Expression of Hypothalamic KiSS-1 and GPR54 mRNA in Diet Induced Obesity Rats

Objective To observe the effect of high fat diet on the hypothalamic expression of KiSS-1and the G-protein coupled receptor (GPR) 54 mRNA and explore the modulatory role of moderate-intensity exercise in the diet induced obesity male rats. Methods After 8 weeks high fat feeding, 20 obesity 11-weeks SD rats were randomly assigned to high-fat diet sedentary (FS, n=8) and high-fat diet exercise (FE, n=8) groups, 20 normal diet 11-weeks SD rats also were randomly assigned to sedentary (SS, n=8) and exercise (SE, n=8) groups. During the following 8 weeks, obesity rats were continued expose to high-fat-diet. SE and FE groups did the 60%-70%V(•)O2max treadmill training (5 days/week, 1 hour/day). The V(•)O2 max of exercise groups were remeasured every two weeks. The hypothalamic expression of KiSS-1 and GPR54 mRNA were tested in each group. Results After the first 8-weeks high fat feeding, the obesity rats were heavier than normal diet group (491.74±26.19g vs. 410.05±45.77g, p<0.01). After 8-weeks training, the FE group was lighter than FS group (590.23±35.74g vs. 681±52.56, p<0.01). The FS group had higher hypothalamic expression of KiSS-1 mRNA (1.51±0.66 vs 0.75±0.27, p<0.05) and GPR54 mRNA (2.45±0.38 vs 0.61±0.15, p<0.01) than SS group. The FE group had lower hypothalamic expression of KiSS-1 mRNA (0.69±0.13, p>0.05) and GPR54 mRNA (0.58±0.10, p<0.01) than FS group. Conclusions There is stimulating effect of high-fat diet induced obesity on hypothalamic expression of KiSS-1and GPR54 mRNA. 8-weeks 60%-70%V (•) O2max treadmill training could cure this effect. &nbsp

Improving Robust Fairness via Balance Adversarial Training

Adversarial training (AT) methods are effective against adversarial attacks, yet they introduce severe disparity of accuracy and robustness between different classes, known as the robust fairness problem. Previously proposed Fair Robust Learning (FRL) adaptively reweights different classes to improve fairness. However, the performance of the better-performed classes decreases, leading to a strong performance drop. In this paper, we observed two unfair phenomena during adversarial training: different difficulties in generating adversarial examples from each class (source-class fairness) and disparate target class tendencies when generating adversarial examples (target-class fairness). From the observations, we propose Balance Adversarial Training (BAT) to address the robust fairness problem. Regarding source-class fairness, we adjust the attack strength and difficulties of each class to generate samples near the decision boundary for easier and fairer model learning; considering target-class fairness, by introducing a uniform distribution constraint, we encourage the adversarial example generation process for each class with a fair tendency. Extensive experiments conducted on multiple datasets (CIFAR-10, CIFAR-100, and ImageNette) demonstrate that our method can significantly outperform other baselines in mitigating the robust fairness problem (+5-10\% on the worst class accuracy