Amphiphilic block copolymer prodrugs, which can self-assemble into stable core-shell micelles, have been widely used for anticancer drug delivery. However, a major challenge remains to design drug-conjugating linkers stable in...
Network Intrusion leaks the personal information of network users on a large scale, causing serious security risks. It is of great significance to the Intrusion Detection Systems (IDS) to find abnormal traffic from a huge database in time. Traditional machine learning methods to detect abnormal network traffic usually need to manually extract features from the dataset, which is time-consuming and has low accuracy. This paper proposes a deep learning-based abnormal traffic detection method based on an Improved One-Dimensional Convolutional Neural Networks (ICNN-1D) to detect abnormal network traffic, which greatly improves the extraction accuracy of abnormal traffic features and improves the identification of attack traffic. CNN applies multiple filters (convolution kernels) to the raw pixel data of an image to extract and learn higher-level features. After multiple convolutions, the characteristic graph with the same number of categories as the number of samples is obtained. The experimental results on the dataset CIC-IDS2017 show that the accuracy of the hybrid algorithm is 99.8%. Compared with other learning algorithms, the accuracy of our method greatly improves, and the operation time has been reduced.
Background Human pluripotent stem cells (hPSCs) have started to emerge as a potential tool with application in fields of drug discovery, disease modelling and cell therapy. A variety of protocols for culturing and differentiating pluripotent stem cells into pancreatic β like cells have been published. However, small-scale dynamic suspension culture systems, which could be applied toward systematically optimizing production strategies for cell replacement therapies to accelerate the pace of their discovery and development toward the clinic, are overlooked. Methods Human embryonic stem cell (hESC) line H9 was used to establish the novel 48-well dynamic suspension culture system. The effects of various rotational speeds and culture medium volumes on cell morphology, cell proliferation, cell viability and cell phenotype were evaluated. Effect of cell density on the pancreatic differentiation efficiency from H9 cells in 48-well plates was further investigated. In vitro the function of pancreatic β like cells was assessed by measuring glucosestimulated insulin secretion. Main Results A 48-well dynamic suspension culture system for hESC expansion as cell aggregates was developed. With optimized rotational speed and culture medium volume, hESCs maintained normal karyotype, viability and pluripotency. Furthermore, the system can also support the hESC aggregates subsequent differentiation into functional pancreatic β like cells after optimizing initial cell seeding density. Conclusion A controllable 48-well suspension culture system in microplates for hESCs maintenance, expansion and pancreatic differentiation was developed, which may provide an efficient platform for high-throughput drug screening.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.