Enhanced In Vitro Anti-Photoaging Effect of Degraded Seaweed Polysaccharides by UV/H2O2 Treatment.

The high molecular weight and poor solubility of seaweed polysaccharides have limited their function and application. In this study, ultraviolet/hydrogen peroxide (UV/H2O2) treatment was used to prepare low-molecular-weight seaweed polysaccharides from Sargassum fusiforme. The effects of UV/H2O2 treatment on the physicochemical properties and anti-photoaging activity of S. fusiforme polysaccharides were studied. UV/H2O2 treatment effectively degraded polysaccharides from S. fusiforme (DSFPs), reducing their molecular weight from 271 kDa to 26 kDa after 2 h treatment. The treatment did not affect the functional groups in DSFPs but changed their molar percentage of monosaccharide composition and morphology. The effects of the treatment on the anti-photoaging function of S. fusiforme polysaccharides were investigated using human epidermal HaCaT cells in vitro. DFSPs significantly improved the cell viability and hydroxyproline secretion of UVB-irradiated HaCaT cells. In particular, DSFP-45 obtained from UV/H2O2 treatment for 45 min showed the best anti-photoaging effect. Moreover, DSFP-45 significantly increased the content and expression of collagen I and decreased those of pro-inflammatory cytokines, including interleukin-1ß, interleukin-6, and tumor necrosis factor-α. Thus, UV/H2O2 treatment could effectively improve the anti-photoaging activity of S. fusiforme polysaccharides. These results provide some insights for developing novel and efficient anti-photoaging drugs or functional foods from seaweed polysaccharides.

Sargassum fusiforme Polysaccharide-Based Hydrogel Microspheres Enhance Crystal Violet Dye Adsorption Properties.

In this study, a polysaccharide-based hydrogel microsphere (SFP/SA) was prepared using S. fusiforme polysaccharide (SFP) and sodium alginate (SA). Fourier transform infrared spectroscopy (FT-IR) demonstrated that SFP was effectively loaded onto the hydrogel microsphere. Texture profile analysis (TPA) and differential scanning calorimetry (DSC) showed that, with the increase of SFP concentration, the hardness of SFP/SA decreased, while the springiness and cohesiveness of SFP/SA increased, and the thermal stability of SFP/SA improved. The equilibrium adsorption capacity of SFP/SA increased from 8.20 mg/g (without SFP) to 67.95 mg/g (SFP accounted 80%) without swelling, and from 35.05 mg/g (without SFP) to 81.98 mg/g (SFP accounted 80%) after 24 h swelling. The adsorption of crystal violet (CV) dye by SFP/SA followed pseudo-first order and pseudo-second order kinetics (both with R2 > 0.99). The diffusion of intraparticle in CV dye was not the only influencing factor. Moreover, the adsorption of CV dye for SFP/SA (SFP accounted 60%) fit the Langmuir and Temkin isotherm models. SFP/SA exhibited good regenerative adsorption capacity. Its adsorption rate remained at > 97% at the 10th consecutive cycle while SFP accounted for 80%. The results showed that the addition of Sargassum fusiforme polysaccharide could increase the springiness, cohesiveness and thermal stability of the hydrogel microsphere, as well as improve the adsorption capacity of crystal violet dye.

An end-to-end stereo matching algorithm based on improved convolutional neural network.

Deep end-to-end learning based stereo matching methods have achieved great success as witnessed by the leaderboards across different benchmarking datasets. Depth information in stereo vision systems are obtained by a dense and accurate disparity map, which is computed by a robust stereo matching algorithm. However, previous works adopt network layer with the same size to train the feature parameters and get an unsatisfactory efficiency, which cannot be satisfied for the real scenarios by existing methods. In this paper, we present an end-to-end stereo matching algorithm based on "downsize" convolutional neural network (CNN) for autonomous driving scenarios. Firstly, the road images are feed into the designed CNN to get the depth information. And then the "downsize" full-connection layer combined with subsequent network optimization is employed to improve the accuracy of the algorithm. Finally, the improved loss function is utilized to approximate the similarity of positive and negative samples in a more relaxed constraint to improve the matching effect of the output. The loss function error of the proposed method for KITTI 2012 and KITTI 2015 datasets are reduced to 2.62 and 3.26% respectively, which also reduces the runtime of the proposed algorithm. Experimental results illustrate that the proposed end-to-end algorithm can obtain a dense disparity map and the corresponding depth information can be used for the binocular vision system in autonomous driving scenarios. In addition， our method also achieves better performance when the size of the network is compressed compared with previous methods.