Surface PEGylation is essential for preventing non-specific binding of biomolecules when silica nanoparticles are utilized for in vivo applications. Methods for installing poly(ethylene glycol) on a silica surface have been widely explored but varies from study to study. Because there is a lack of a satisfactory method for evaluating the properties of silica surface after PEGylation, the prepared nanoparticles are not fully characterized before use. In some cases, even non-PEGylated silica nanoparticles were produced, which is unfortunately not recognized by the end-user. In this work, a fluorescent protein was employed, which acts as a sensitive material for evaluating the surface protein adsorption properties of silica nanoparticles. Eleven different methods were systematically investigated for their reaction efficiency towards surface PEGylation. Results showed that both reaction conditions (including pH, catalyst) and surface functional groups of parent silica nanoparticles play critical roles in producing fully PEGylated silica nanoparticles. Great care needs to be taken in choosing the proper coupling chemistry for surface PEGylation. The data and method shown here will guarantee high-quality PEGylated silica nanoparticles to be produced and guide their applications in biology, chemistry, industry and medicine.

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Language detection models based on system calls suffer from certain false negatives and detection blind spots. Hence, the normal behavior sequences of some malware applications for a short period can become malicious behavior within a certain time window. To detect such behaviors, we extract a multidimensional time distribution feature matrix on the basis of statistical analysis. This matrix mainly includes multidimensional time distribution features, multidimensional word pair correlation features, and multidimensional word frequency distribution features. A multidimensional time distribution model based on neural networks is built to detect the overall abnormal behavior within a given time window. Experimental evaluation is conducted using the ADFA-LD dataset. Accuracy, precision, and recall are used as the measurement indicators of the model. An accuracy rate of 95.26% and a recall rate of 96.11% are achieved.

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In this study Ginkgo biloba leaves (GBL) decoction and commercial capsules were digested using an in vitro model. Thirty-six active compounds were identified and quantified by HPLC-ESI-MS analysis based on the MS/MS patterns (precursor ions and product ions) and retention times, in comparison with reference standards. Most compounds in GBL showed a significant decrease during intestinal digestion, with an exception of vanillic acid and biflavonoids. Bioaccessibility values of chemical compositions varied between decoction and capsules samples. Also, significant reductions of total flavonoids and total phenolic content was observed after in vitro digestion. Both, 2,2-diphenyl-1-picrylhydrazyl (DPPH) and 2,2′-azino-bis (3-ethylbenzothiazo-line-6-sulfonic acid (ABTS) scavenging capacity decreased after gastric digestion, but increased during intestinal digestion. Nevertheless, different behaviour was observed in reducing antioxidant power (FRAP) assay. Compared to the pH of digestion, the influence of digestive enzymes on the chemical composition and antioxidant activity of GBL was relatively minor. Overall, these results may help provide a valid foundation for further investigations on bioactive compounds and the pharmacodynamics of GBL.