Data Service Outsourcing and Privacy Protection in Mobile Internet

Mobile Internet data have the characteristics of large scale, variety of patterns, and complex association. On the one hand, it needs efficient data processing model to provide support for data services, and on the other hand, it needs certain computing resources to provide data security services. Due to the limited resources of mobile terminals, it is impossible to complete large-scale data computation and storage. However, outsourcing to third parties may cause some risks in user privacy protection. This monography focuses on key technologies of data service outsourcing and privacy protection, including the existing methods of data analysis and processing, the fine-grained data access control through effective user privacy protection mechanism, and the data sharing in the mobile Internet

Stemness Analysis Uncovers That The Peroxisome Proliferator-Activated Receptor Signaling Pathway Can Mediate Fatty Acid Homeostasis In Sorafenib-Resistant Hepatocellular Carcinoma Cells

Hepatocellular carcinoma (HCC) stem cells are regarded as an important part of individualized HCC treatment and sorafenib resistance. However, there is lacking systematic assessment of stem-like indices and associations with a response of sorafenib in HCC. Our study thus aimed to evaluate the status of tumor dedifferentiation for HCC and further identify the regulatory mechanisms under the condition of resistance to sorafenib. Datasets of HCC, including messenger RNAs (mRNAs) expression, somatic mutation, and clinical information were collected. The mRNA expression-based stemness index (mRNAsi), which can represent degrees of dedifferentiation of HCC samples, was calculated to predict drug response of sorafenib therapy and prognosis. Next, unsupervised cluster analysis was conducted to distinguish mRNAsi-based subgroups, and gene/geneset functional enrichment analysis was employed to identify key sorafenib resistance-related pathways. In addition, we analyzed and confirmed the regulation of key genes discovered in this study by combining other omics data. Finally, Luciferase reporter assays were performed to validate their regulation. Our study demonstrated that the stemness index obtained from transcriptomic is a promising biomarker to predict the response of sorafenib therapy and the prognosis in HCC. We revealed the peroxisome proliferator-activated receptor signaling pathway (the PPAR signaling pathway), related to fatty acid biosynthesis, that was a potential sorafenib resistance pathway that had not been reported before. By analyzing the core regulatory genes of the PPAR signaling pathway, we identified four candidate target genes, retinoid X receptor beta (RXRB), nuclear receptor subfamily 1 group H member 3 (NR1H3), cytochrome P450 family 8 subfamily B member 1 (CYP8B1) and stearoyl-CoA desaturase (SCD), as a signature to distinguish the response of sorafenib. We proposed and validated that the RXRB and NR1H3 could directly regulate NR1H3 and SCD, respectively. Our results suggest that the combined use of SCD inhibitors and sorafenib may be a promising therapeutic approach

The Scenario-Oriented Method for Recording and Playing-Back Healthcare Information

This paper proposes a new method, called the scenario-oriented method, to support the idea of recording and replaying the healthcare information such that the reporting and decision-support capabilities can be enhanced. In order to play back the changing history of certain information units, the scenario- oriented method attempts to organize related information and knowledge elements as a context so that the history of real medical activity can be recorded, and then be queried as a continuous, on-the-fly, understandable and playing-back information scenario through replay operations

Geminal Dimethyl Substitution Enabled Controlled Ring-Opening Polymerization and Selective Depolymerization of Penicillamine-Derived β-Thiolactones

To access infinitely recyclable plastics, one key is to design thermodynamically neutral systems based on dynamic bonds for easy manipulation of the polymerization and the reverse depolymerization under low energy cost. Here, we present the controlled ring-opening polymerization of various penicillamine-derived β-thiolactones and the highly specific depolymerization of the resultant polythioesters (PNR-PenTE) for complete monomer recycling. The gem-dimethyl group confers better ROP control by reducing the activity of the chain-end thiolate groups and stabilizing the thioester linkages in the polymer backbone. High molar mass and narrow dispersity PNR-PenTE are conveniently accessible at room temperature bearing well-defined end groups and tunable side chains. PNR-PenTE can be tailored with water solubility, and/or be easily fabricated into persistent films or fibers with interesting thermal and mechanical properties. Most importantly, PNR-PenTE can be recycled to pristine enantiopure β-thiolactones at >95% conversion in a well-controlled unzipping fashion within min to hours at room temperature. Overall, this work may streamline the rapid development of a wide range of polythioesters with immense application potential as self-immolative building blocks, high value biomaterials, and sacrificial domain for nanolithography

MicrobiotaProcess: A comprehensive R package for deep mining microbiome

The data output from microbiome research is growing at an accelerating rate, yet mining the data quickly and efficiently remains difficult. There is still a lack of an effective data structure to represent and manage data, as well as flexible and composable analysis methods. In response to these two issues, we designed and developed the MicrobiotaProcess package. It provides a comprehensive data structure, MPSE, to better integrate the primary and intermediate data, which improves the integration and exploration of the downstream data. Around this data structure, the downstream analysis tasks are decomposed and a set of functions are designed under a tidy framework. These functions independently perform simple tasks and can be combined to perform complex tasks. This gives users the ability to explore data, conduct personalized analyses, and develop analysis workflows. Moreover, MicrobiotaProcess can interoperate with other packages in the R community, which further expands its analytical capabilities. This article demonstrates the MicrobiotaProcess for analyzing microbiome data as well as other ecological data through several examples. It connects upstream data, provides flexible downstream analysis components, and provides visualization methods to assist in presenting and interpreting results