Optimization of Glycosaminoglycan Extraction on Patinopecten Yessoensis Waste

AbstractOn the basis of single factor experiment, the pH of enzymatic hydrolysis, enzymolysis temperature, enzymolysis time and solid-liquid ratio as independent variable, extraction rate as response value, extraction technology of glycosaminoglycan from Patinopecten yessoensis waste were optimized using response surface methodology. The order affecting glycosaminoglycan extraction rate was determined: the enzymatic pH > solid-liquid ratio > enzymatic time > enzymatic temperature. The optimal conditions of extraction were: the pH of enzymatic hydrolysis was 8.0, enzymolysis temperature was 40°C, enzymolysis time was 3.5h and solid-liquid ratio was 1:2. Click here and insert your abstract text

Coking-Resistant Sub-Nano Dehydrogenation Catalysts: PtnSnx/SiO2 (n=4, 7)

We present a combined experimental/theoretical study of Pt$_n$/SiO$_2$ and Pt$_n$Sn$_x$/SiO$_2$ (n = 4, 7) model catalysts for the endothermic dehydrogenation of hydrocarbons, using the ethylene intermediate as a model reactant. Supported pure Ptn clusters are found to be highly active toward dehydrogenation of C2D4, quickly deactivating due to a combination of carbon deposition and sintering, resulting in loss of accessible Pt sites. Addition of Sn to Ptn clusters results in the complete suppression of C2D4 dehydrogenation and carbon deposition, and also stabilizes the clusters against thermal sintering. Theory shows that both systems have thermal access to a multitude of cluster structures and adsorbate configurations that form a statistical ensemble. While Pt4/SiO2 clusters bind ethylene in both di-sigma and pi-bonded configurations, Pt$_4$Sn$_3$/SiO$_2$ binds C2H4 only in the pi-mode, with di-sigma bonding suppressed by a combination of electronic and geometric features of the PtSn clusters. Dehydrogenation reaction profiles on the accessible cluster isomers were calculated using the climbing image nudged elastic band (CI-NEB) method

Sn-modification of Pt7/alumina model catalysts: Suppression of carbon deposition and enhanced thermal stability.

An atomic layer deposition process is used to modify size-selected Pt7/alumina model catalysts by Sn addition, both before and after Pt7 cluster deposition. Surface science methods are used to probe the effects of Sn-modification on the electronic properties, reactivity, and morphology of the clusters. Sn addition, either before or after cluster deposition, is found to strongly affect the binding properties of a model alkene, ethylene, changing the number and type of binding sites, and suppressing decomposition leading to carbon deposition and poisoning of the catalyst. Density functional theory on a model system, Pt4Sn3/alumina, shows that the Sn and Pt atoms are mixed, forming alloy clusters with substantial electron transfer from Sn to Pt. The presence of Sn also makes all the thermally accessible structures closed shell, such that ethylene binds only by π-bonding to a single Pt atom. The Sn-modified catalysts are quite stable in repeated ethylene temperature programmed reaction experiments, suggesting that the presence of Sn also reduces the tendency of the sub-nano-clusters to undergo thermal sintering

PhantomSound: Black-Box, Query-Efficient Audio Adversarial Attack via Split-Second Phoneme Injection

In this paper, we propose PhantomSound, a query-efficient black-box attack toward voice assistants. Existing black-box adversarial attacks on voice assistants either apply substitution models or leverage the intermediate model output to estimate the gradients for crafting adversarial audio samples. However, these attack approaches require a significant amount of queries with a lengthy training stage. PhantomSound leverages the decision-based attack to produce effective adversarial audios, and reduces the number of queries by optimizing the gradient estimation. In the experiments, we perform our attack against 4 different speech-to-text APIs under 3 real-world scenarios to demonstrate the real-time attack impact. The results show that PhantomSound is practical and robust in attacking 5 popular commercial voice controllable devices over the air, and is able to bypass 3 liveness detection mechanisms with >95% success rate. The benchmark result shows that PhantomSound can generate adversarial examples and launch the attack in a few minutes. We significantly enhance the query efficiency and reduce the cost of a successful untargeted and targeted adversarial attack by 93.1% and 65.5% compared with the state-of-the-art black-box attacks, using merely ~300 queries (~5 minutes) and ~1,500 queries (~25 minutes), respectively.Comment: RAID 202

MicroRNA-206: Effective Inhibition of Gastric Cancer Progression through the c-Met Pathway

MicroRNAs are endogenous short chain nucleotide RNAs that regulate gene function by direct binding of target mRNAs. In this study, we investigated the effects of microRNA-206 (miR-206) on the development of gastric cancer. miR-206 was first confirmed to be downregulated in gastric cancer specimens. Conversely, upregulation of c-Met was confirmed in tissue samples of human gastric cancer, with its level inversely correlated with miR-206 expression. Introduction of miR-206 inhibited cellular proliferation by inducing G1 cell cycle arrest, as well as migration and invasion. Moreover, important proliferation and/or migration related molecules such as c-Met, CDK4, p-Rb, p-Akt and p-ERK were confirmed to be downregulated by Western blot analysis. Targeting of c-Met also directly affected AGS cell proliferation, migration and invasion. In vivo, miR-206 expressing tumor cells also displayed growth delay in comparison to unaffected tumor cells. Our results demonstrated that miR-206 suppressed c-Met expression in gastric cancer and could function as a potent tumor suppressor in c-Met overexpressing tumors. Inhibition of miR-206 function could contribute to aberrant cell proliferation and migration, leading to gastric cancer development

Automatic detection technology of sports athletes based on image recognition technology

Abstract In order to improve the motion recognition effect of sports athletes based on image recognition technology, this study takes the current common diving athletes as the research material in the actual research, and combines the research status of image recognition to study the athlete’s motion recognition from image processing. Simultaneously, in this study, the gradient segmentation method is used to segment the image, the research object is segmented from the video image, the traditional image grayscale method is improved, and the image segmentation algorithm adapted to the diving motion is obtained. On this basis, this study combines Gaussian mixture background modeling and background subtraction to achieve the detection and extraction of target human body regions, and uses morphological operators to deal with noise and void phenomena in foreground images. The example analysis shows that the proposed method has certain practicality and can provide theoretical reference for subsequent related research