Underwater wireless sensor network (UWSN) plays a vital role in the field of ocean development and exploration. Designing a routing protocol for UWSN is a great challenge due to the characteristics of short lifetime and high delay. This paper proposes a Q-learning based routing optimization algorithm for UWSN. Two reward functions are designed based on the average residual energy of network, integrating factors such as energy information, transmission delay and link success rate to better balance transmission quality and lifetime. In addition, a holding time mechanism for packet forwarding is developed according to the priority of nodes. The simulation results show that compared to DBR and QLFR algorithms, this algorithm can effectively reduce transmission delay and prolong network lifetime.

Resource allocation faces significant challenges due to the complexity of the underwater environment. To address the problem of bandwidth assignment and improve the underwater resource utilization efficiency, this article proposes a quantum-based deep Q-network resource allocation algorithm. First, this algorithm combines factors, such as signal-to-noise ratio and data amount to construct the state space, which can better disclose the interaction between learning and environment. It also designs a unique reward function, which can guide nodes to select appropriate bandwidth, thus improving the learning capability of the deep reinforcement learning model. Furthermore, this article constructs a hybrid network model based on trainable quantum circuits, which fully utilizes various quantum gate operations to process and analyze data, predict the corresponding Q-values for actions. Simulation results show that the algorithm can reduce packet loss ratio and blocking probability while improving network bandwidth utilization.

Liver cancer is the third leading cause of cancer-related death worldwide, and hepatocellular carcinoma (HCC) accounts for a relatively large proportion of all primary liver malignancies. Among the several known risk factors, hepatitis B virus (HBV) infection is one of the important causes of HCC. In this study, we demonstrated that the HBV-infected HCC patients could be robustly classified into three clinically relevant subgroups, i.e. Cluster1, Cluster2 and Cluster3, based on consistent differentially expressed mRNAs and proteins, which showed better generalization. The proposed three subgroups showed different molecular characteristics, immune microenvironment and prognostic survival characteristics. The Cluster1 subgroup had near-normal levels of metabolism-related proteins, low proliferation activity and good immune infiltration, which were associated with its good liver function, smaller tumor size, good prognosis, low alpha-fetoprotein (AFP) levels and lower clinical stage. In contrast, the Cluster3 subgroup had the lowest levels of metabolism-related proteins, which corresponded with its severe liver dysfunction. Also, high proliferation activity and poor immune microenvironment in Cluster3 subgroup were associated with its poor prognosis, larger tumor size, high AFP levels, high incidence of tumor thrombus and higher clinical stage. The characteristics of the Cluster2 subgroup were between the Cluster1 and Cluster3 groups. In addition, MCM2-7, RFC2-5, MSH2, MSH6, SMC2, SMC4, NCPAG and TOP2A proteins were significantly upregulated in the Cluster3 subgroup. Meanwhile, abnormally high phosphorylation levels of these proteins were associated with high levels of DNA repair, telomere maintenance and proliferative features. Therefore, these proteins could be identified as potential diagnostic and prognostic markers. In general, our research provided a novel analytical protocol and insights for the robust classification, treatment and prevention of HBV-infected HCC.

SUMMARY: The next-generation sequencing brought opportunities for the diagnosis of genetic disorders due to its high-throughput capabilities. However, the majority of existing methods were limited to only sequencing candidate variants, and the process of linking these variants to a diagnosis of genetic disorders still required medical professionals to consult databases. Therefore, we introduce diseaseGPS, an integrated platform for the diagnosis of genetic disorders that combines both phenotype and genotype data for analysis. It offers not only a user-friendly GUI web application for those without a programming background but also scripts that can be executed in batch mode for bioinformatics professionals. The genetic and phenotypic data are integrated using the ACMG-Bayes method and a novel phenotypic similarity method, to prioritize the results of genetic disorders. diseaseGPS was evaluated on 6085 cases from Deciphering Developmental Disorders project and 187 cases from Shanghai Children's hospital. The results demonstrated that diseaseGPS performed better than other commonly used methods. AVAILABILITY AND IMPLEMENTATION: diseaseGPS is available to freely accessed at https://diseasegps.sjtu.edu.cn with source code at https://github.com/BioHuangDY/diseaseGPS.

Based on various genomic information of chimeric transcript, recent studies used machine-learning methods to predict the oncogenic potentials for chimeric transcripts, however these works ignored transcriptional signature of those chimeric transcripts. Based on clonal evolution theory, we hypothesized that a chimeric transcript is more likely to be an oncogenic `driver' mutation, if the neoplastic cells harboring this chimeric mutation has larger clonal size than other neoplastic cells in a particular tumor. Here we proposed a novel method, called iFCR (internal Fusion Clone Ratio), to estimate the ratio of subclone carrying chimeric transcripts to the rest of neoplastic cells in transcriptome sequencing data. To evaluate our hypothesis, we applied iFCR method on two public cancer transcriptome sequencing datasets, one for breast cancer cell line and the other for prostate tumors with adjacent normal tissues. Our results demonstrated that the chimeric transcripts in tumor samples appear to have higher iFCR value than normal tissues, the most frequent prostate cancer fusion mutation, TMPRSS2- ERG, has remarkably higher iFCR value in all three independent patients. Our work providing a novel point of view for screening oncogenesis chimeric transcripts in cancer research.

Objective: Health Information Exchange (HIE) enables providers to share healthcare information electronically across different organizations to promote safer, more efficient, and less costly patient-centered care. This paper describes the development and implementation of a city-wide HIE system in Shanghai, China. Methods: In 2006, as a product of the Chinese healthcare reform, the Health Information Exchange and Sharing Platform was proposed as a means to facilitate HIE within Shanghai. In collaboration with the Shanghai Hospital Development Center, a state-run nonprofit corporate, the HIE project was implemented across multiple levels within the city. The HIE system is based on the Service-oriented Architecture and complies with industry standards. Results: On September 2010, the first and largest Chinese HIE system was established. As of 2016 the system includes all of Shanghai's 38 tertiary hospitals (highest level hospitals in China), plus 6 district hospitals, and 40 community health centers, with coverage for 39 million patients. The system currently provides a rich source of patient information including encounter history, medication history, laboratory results, radiology images and reports, and clinical notes. Initial outcomes indicate a significant reduction in medication errors and duplication of tests, saving at least 48 million RMB a year, with overall improvement in the quality of care following implementation. Conclusion: The adoption of HIE in Shanghai resulted in improved access to accurate, complete, and relevant clinical information in real time, thus facilitating delivery of high-quality, cost-effective, and efficient care.

BACKGROUND: Gene set analysis based on Gene Ontology (GO) can be a promising method for the analysis of differential expression patterns. However, current studies that focus on individual GO terms have limited analytical power, because the complex structure of GO introduces strong dependencies among the terms, and some genes that are annotated to a GO term cannot be found by statistically significant enrichment. RESULTS: We proposed a method for enriching clustered GO terms based on semantic similarity, namely cluster enrichment analysis based on GO (CeaGO), to extend the individual term analysis method. Using an Affymetrix HGU95aV2 chip dataset with simulated gene sets, we illustrated that CeaGO was sensitive enough to detect moderate expression changes. When compared to parent-based individual term analysis methods, the results showed that CeaGO may provide more accurate differentiation of gene expression results. When used with two acute leukemia (ALL and ALL/AML) microarray expression datasets, CeaGO correctly identified specifically enriched GO groups that were overlooked by other individual test methods. CONCLUSION: By applying CeaGO to both simulated and real microarray data, we showed that this approach could enhance the interpretation of microarray experiments. CeaGO is currently available at http://chgc.sh.cn/en/software/CeaGO/.