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Healthcare is a big application scenario of blockchain, and blockchains used in healthcare are called health blockchain. In general, blockchain blocks are open and the transactions in them are public. If some privacy data are involved in these transactions, they will be leaked. Owing to healthcare system involving a great deal of privacy data, certain security mechanisms must be built to protect these privacy data in health blockchain. Furthermore, because the core of security mechanisms is the key management schemes, the appropriate key management schemes should be designed before blockchains can be used in healthcare system. Here, according to the features of health blockchain, the authors use a body sensor network to design a lightweight backup and efficient recovery scheme for keys of health blockchain. The authors' analyses show that the scheme has high security and performance, and it can be used to protect privacy messages on health blockchain effectively and to promote the application of health blockchain.

In this study, the associations of novel LRP5 variants with BMD variation were detected and some replicated in the two ethnic groups of Chinese and white origins, respectively. These data support the concept that LRP5 variation can contribute to minor and major variation in bone structure.Introduction: Mutations in the low-density lipoprotein receptor-related protein 5 (LRP5) gene have been shown to cause both high and low bone mass. However, it is still controversial whether LRP5 is associated with normal BMD variation. This study explored the association of LRP5 with BMD phenotypes at three clinically important skeletal sites-the spine, hip, and ultradistal radius (UD)-in two independent populations of Chinese and white ethnicities, respectively. Materials and Methods:The Chinese sample consisted of 733 unrelated subjects. The white sample was made up of 1873 subjects from 405 nuclear families. High-density single nucleotide polymorphisms (SNPs) across the whole LRP5 gene were genotyped and analyzed in both samples. Results: Linkage disequilibrium (LD) analyses showed that the haplotype structures of LRP5 between Chinese and whites were in good agreement. Association tests showed that polymorphisms in block 5 spanning intron 7 to intron 19 of LRP5 significantly associated with spine BMD variation in both samples. Particularly, the significant association of SNP rs491347 in intron 7 with spine BMD in the Chinese sample (p ‫ס‬ 0.002) was replicated in whites, even after adjusting for multiple testing (p ‫ס‬ 0.005). Its strongly associated SNP rs1784235 could cause the loss of an estrogen receptor ␣ (ER␣) binding site in LRP5, which could partially explain the above replicated association. However, we did not observe any significant replication with BMD variation at the hip and UD. After accounting for multiple testing, associations with BMD variation at these two sites were mainly found in Chinese. Sex-stratified analyses further revealed that the LRP5 associations with BMD in Chinese and whites were driven by male and female subjects, respectively. Conclusions: Our work supported LRP5 genetic variants as possible susceptibility factors for osteoporosis and fractures in humans. Especially, the SNP rs491347 and its strongly associated SNPs (e.g., rs1784235) could be important to human osteoporosis phenotypes.

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Background Tyrosine protein tyrosine kinase binding protein (TYROBP) binds non-covalently to activated receptors on the surface of various immune cells, and mediates signal transduction and cellular activation. It is dysregulated in various malignancies, although little is known regarding its role in low-grade glioma. The aim of this study is to explore the clinicopathological significance, prognostic value and immune signature of TYROBP expression in low-grade glioma (LGG). Methods The differentially expressed genes (DEGs) between glioma samples and normal tissues were identified from two GEO microarray datasets using the limma package. The DEGs overlapping across both datasets were functionally annotated by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses. STRING database was used to establish the protein-protein interaction (PPI) of the DEGs. The PPI network was visualized by Cytoscape and cytoHubba, and the core module and hub genes were identified. The expression profile of TYROBP and patient survival were validated in the Oncomine, GEPIA2 and CGGA databases. The correlation between TYROBP expression and the clinicopathologic characteristics were evaluated. Gene Set Enrichment Analysis (GSEA) and single-sample GSEA (ssGSEA) were performed by R based on the LGG data from TCGA. The TIMER2.0 database was used to determine the correlation between TYROBP expression and tumor immune infiltrating cells in the LGG patients. Univariate and multivariate Cox regression analyses were performed to determine the prognostic impact of clinicopathological factors via TCGA database. Results Sixty-two overlapping DEGs were identified in the 2 datasets, and were mainly enriched in the response to wounding, focal adhesion, GTPase activity and Parkinson disease pathways. TYROBP was identified through the PPI network and cytoHubba. TYROBP expression levels were significantly higher in the LGG tissues compared to the normal tissues, and was associated with worse prognosis and poor clinicopathological parameters. In addition, GSEA showed that TYROBP was positively correlated to neutrophil chemotaxis, macrophage activation, chemokine signaling pathway, JAK-STAT signaling pathway, and negatively associated with gamma aminobutyric acid signaling pathway, neurotransmitter transport, neuroactive ligand receptor intersection etc. TIMER2.0 and ssGSEA showed that TYROBP expression was significantly associated with the infiltration of neutrophils, macrophages, myeloid dendritic cells and monocytes. The infiltration of the M2 phenotype macrophages, cancer-associated fibroblasts and myeloid dendritic cells correlated to worse prognosis in LGG patients. Finally, multivariate analysis showed that elevated TYROBP expression is an independent risk factor for LGG. Conclusion TYROBP is dysregulated in LGG and correlates with immune infiltration. It is a potential therapeutic target and prognostic marker for LGG.

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In this work, the combination of cucurbit[6]uril (Q[6]) with varied naphthalene disulfonates and alkaline-earth metal ions allows for successful isolation of 9 novel Q[6]-based coordination complexes with formulas of [Ca(H2O)4(Q[6])0.5(1,5-NDA)0.5]·(1,5-NDA)0.5·3H2O...

Graphs have been widely used to represent complex data in many applications, such as e-commerce, social networks, and bioinformatics. Efficient and effective analysis of graph data is important for graph-based applications. However, most graph analysis tasks are combinatorial optimization (CO) problems, which are NP-hard. Recent studies have focused a lot on the potential of using machine learning (ML) to solve graph-based CO problems. Most recent methods follow the two-stage framework. The first stage is graph representation learning, which embeds the graphs into low-dimension vectors. The second stage uses machine learning to solve the CO problems using the embeddings of the graphs learned in the first stage. The works for the first stage can be classified into two categories, graph embedding methods and end-to-end learning methods. For graph embedding methods, the learning of the the embeddings of the graphs has its own objective, which may not rely on the CO problems to be solved. The CO problems are solved by independent downstream tasks. For end-to-end learning methods, the learning of the embeddings of the graphs does not have its own objective and is an intermediate step of the learning procedure of solving the CO problems. The works for the second stage can also be classified into two categories, non-autoregressive methods and autoregressive methods. Non-autoregressive methods predict a solution for a CO problem in one shot. A non-autoregressive method predicts a matrix that denotes the probability of each node/edge being a part of a solution of the CO problem. The solution can be computed from the matrix using search heuristics such as beam search. Autoregressive methods iteratively extend a partial solution step by step. At each step, an autoregressive method predicts a node/edge conditioned to current partial solution, which is used to its extension. In this survey, we provide a thorough overview of recent studies of the graph learning-based CO methods. The survey ends with several remarks on future research directions.