Wireless localization with ultra-wideband technology or 5G/6G cellular networks is a major contributor to addressing satellite signal-denied positioning. The accuracy of localization, however, suffers from non-line-of-sight (NLOS) propagation. Literature has highlighted several approaches to improve precision, such as channel-based, map-based, and statistical feature-based. More attention has focused on the linear growth trend of NLOS error with distance at lower prior information acquisition costs. However, no previous study explores the uncertainty in the trend arising from a dynamic NLOS environment. Accordingly, this article intends to comprehensively utilize the linear trend and uncertainty to find the optimal location solution. First, we propose a distance-dependent uncertain model for NLOS error and construct an uncertain second-order cone programming. We then convert the nonconvex problem into a solvable positive semidefinite programming by Schur complement, Cauchy–Schwarz inequality, S-lemma, and constraint relaxation/tightening techniques. Simulations and experiments show that the resulting optimal and robust solution can obtain remarkable positioning accuracy in densely cluttered environments. The importance and originality of this study are that it fully uses the linear growth trend of NLOS error while incorporating the uncertainty for the trend.

MOTIVATION: Immune cells are important components of the immune system and are crucial for disease initiation, progression, prognosis and survival. Although several computational methods have been designed for predicting the abundance of immune cells, very few tools are applicable to mouse. Given that, mouse is the most widely used animal model in biomedical research, there is an urgent need to develop a precise algorithm for predicting mouse immune cells. RESULTS: We developed a tool named Immune Cell Abundance Identifier for mouse (ImmuCellAI-mouse), for estimating the abundance of 36 immune cell (sub)types from gene expression data in a hierarchical strategy of three layers. Reference expression profiles and robust marker gene sets of immune cell types were curated. The abundance of cells in three layers was predicted separately by calculating the ssGSEA enrichment score of the expression deviation profile per cell type. Benchmark results showed high accuracy of ImmuCellAI-mouse in predicting most immune cell types, with correlation coefficients between predicted value and real cell proportion of most cell types being larger than 0.8. We applied ImmuCellAI-mouse to a mouse breast tumor dataset and revealed the dynamic change of immune cell infiltration during treatment, which is consistent with the findings of the original study but with more details. We also constructed an online server for ImmuCellAI-mouse, on which users can upload expression matrices for analysis. ImmuCellAI-mouse will be a useful tool for studying the immune microenvironment, cancer immunology and immunotherapy in mouse models, providing an indispensable supplement for human disease studies. AVAILABILITY AND IMPLEMENTATION: Software is available at http://bioinfo.life.hust.edu.cn/ImmuCellAI-mouse/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Cancer cell lines (CCLs) as important model systems play critical roles in cancer research. The misidentification and contamination of CCLs are serious problems, leading to unreliable results and waste of resources. Current methods for CCL authentication are mainly based on the CCL-specific genetic polymorphism, whereas no method is available for CCL authentication using gene expression profiles. Here, we developed a novel method and homonymic web server (CCLA, Cancer Cell Line Authentication, http://bioinfo.life.hust.edu.cn/web/CCLA/) to authenticate 1291 human CCLs of 28 tissues using gene expression profiles. CCLA showed an excellent speed advantage and high accuracy for CCL authentication, a top 1 accuracy of 96.58 or 92.15% (top 3 accuracy of 100 or 95.11%) for microarray or RNA-Seq validation data (719 samples, 461 CCLs), respectively. To the best of our knowledge, CCLA is the first approach to authenticate CCLs using gene expression data. Users can freely and conveniently authenticate CCLs using gene expression profiles or NCBI GEO accession on CCLA website.

SUMMARY: Transcription factors (TFs) and microRNAs (miRNAs) are two kinds of important regulators for transcriptional and post-transcriptional regulations. Understanding cross-talks between the two regulators and their targets is critical to reveal complex molecular regulatory mechanisms. Here, we developed FFLtool, a web server for detecting potential feed forward loop (FFL) of TF-miRNA-target regulation in human. In FFLtool, we integrated comprehensive regulations of TF-target and miRNA-target, and developed two functional modules: (i) The 'FFL Analysis' module can detect potential FFLs and internal regulatory networks in a user-defined gene set. FFLtool also provides three levels of evidence to illustrate the reliability for each FFL and enrichment functions for co-target genes of the same TF and miRNA; (ii) The 'Browse FFLs' module displays FFLs comprised of differentially or specifically expressed TFs and miRNAs and their target genes in cancers. FFLtool is a valuable resource for investigating gene expression regulation and mechanism study in biological processes and diseases. AVAILABILITY AND IMPLEMENTATION: FFLtool is available on http://bioinfo.life.hust.edu.cn/FFLtool/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

According to the requirements of informational weapon equipment, the application of polymorphic real-time computation technology in complex embedded system is studied in this paper. The polymorphic real-time computation model for unmanned aerial vehicle(UAV) is designed and verified from the aspects of modal management and polymorphic tasks. The simulation results show that the proposed model can provide modal decision and ensure its real-time calculation to adapt the demand change of UAV system.