The aim of the paper is to report a systematic methodology which is used to evaluate and improve the ride comfort. An accurate model is necessary for further investigation and optimization. The vehicle dynamics model of tractor with tandem suspension is modeled and simulated in dynamics software ADAMS, which is redeveloped to add a function of automatic parametric modeling and simulation. The modeling methods of nonlinear characteristic components and various road excitation inputs, which can be simply seen as the implementation means for the model solution, are introduced. A new index called annoyance rate is presented to indicate the quantitative correlation between objective method and subjective comment. The quantitative correlation between them, which is quite different from the qualitative ''comfortable'' or ''uncomfortable'' results attained by objective evaluation, can be defined by function and regarded as a basis to scientifically evaluate and improve the ride comfort. According to the request of performance-based design, the parameter sensitive analysis and structure optimization have been carried out to find the trade-off among ride comfort, maneuverability and safety. The approach has proved to be very effective for predicting and improving the ride comfort by experiment results. The methodology can be also used for any other specific category of vehicle.

BackgroundAberrant expression of long non-coding RNAs (lncRNAs) is associated with prognosis of gastric cancer, some of which could be further evaluated as potential biomarkers. In this study, we attempted to identify a specific lncRNA signature to predict the prognosis of gastric cancer.Material/MethodsThe genome-wide lncRNA expression in the high-throughput RNA-sequencing data was retrieved from the Cancer Genome Atlas (TCGA). Differential expression of lncRNAs was identified using the Limma package. Survival analysis was conducted by use of univariate and multivariate Cox regression models. Functional enrichment analysis of lncRNAs was based on co-expressed mRNAs. DAVID was used to perform gene ontology and KEGG pathway analysis.ResultsA total of 452 differentially expressed lncRNAs between gastric cancer and matched normal tissues were screened, of which 76 lncRNAs were identified to be gastric cancer-specific from a pan-cancer analysis of 12 types of human cancer. Among these 76 gastric cancer-specific lncRNAs, 5 lncRNAs (CTD-2616J11.14, RP1-90G24.10, RP11-150O12.3, RP11-1149O23.2, and MLK7-AS1) were significantly associated with the overall survival of patients with gastric cancer. A gastric cancer-specific 5-lncRNA signature was deduced to divide the patients into high- and low-risk groups with significantly different survival times (P<0.0001). Multivariate Cox regression analysis showed that this 5-lncRNA signature was an independent predictor of prognosis. Functional enrichment analysis of the 5 lncRNAs showed that they were mainly involved in DNA replication, mitotic cell cycle, programmed cell death, and RNA splicing.ConclusionsOur results suggest that this tumor-specific lncRNA signature may be clinically useful in the prediction of gastric cancer prognosis.

Primary osteosarcoma is the most frequent malignant bone cancer in children and teenagers. Genetic alterations at the retinoblastoma 1 (RB1) gene has been implicated in the development and progression of human osteosarcoma. Here, we performed a meta-analysis to examine the impact of RB1 mutations on the survival of osteosarcoma patients, the risk of metastasis and the histological response of osteosarcoma to chemotherapy. A systemic review of the Medline, Embase, Scopus and Cochrane Library yielded 12 eligible studies with 491 patients for this study. Forest plots resulting from our meta-analyses illustrate that loss of RB1 function results in a 1.62-fold increase in the mortality rate for osteosarcoma patients (RR = 1.62, 95% CI: 1.23-2.13; Z = 3.44, P = 0.0006), a significant increase in osteosarcoma metastasis (OR = 3.95, 95% CI: 1.86-8.38; Z = 3.57; P = 0.0004), and a significant reduction in the histological response of osteosarcoma to chemotherapy (OR = 0.35; 95% CI: 0.13-0.94; Z = -2.08; P = 0.038). Additionally, the nearly symmetrical funnel plot (Egger's test, t = 1.15, P = 0.288) indicates absence of publication bias regarding the meta-analysis that examined the correlation of RB1 alterations with the survival rate for osteosarcoma patients. Our findings suggest that RB1 alterations may serve as a prognostic marker for the management of osteosarcoma patients.

Both tumor and adjacent normal tissues are valuable in cancer research. Transcriptional response profiles represent the changes of gene expression levels between paired tumor and adjacent normal tissues. In this study, we performed a pan-cancer analysis based on the transcriptional response profiles from 633 samples across 13 cancer types. We obtained two interesting results. Using consensus clustering method, we characterized ten clusters with distinct transcriptional response patterns and enriched pathways. Notably, head and neck squamous cell carcinoma was divided in two subtypes, enriched in cell cycle-related pathways and cell adhesion-related pathways respectively. The other interesting result is that we identified 92 potential pan-cancer genes that were consistently upregulated across multiple cancer types. Knockdown of FAM64A or TROAP inhibited the growth of cancer cells, suggesting that these genes may promote tumor development and are worthy of further validations. Our results suggest that transcriptional response profiles of paired tumor-normal tissues can provide novel perspectives in pan-cancer analysis.

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Protein-coding genes and non-coding RNAs cooperate mutually in cells. Integrative analysis of protein-coding and non-coding RNAs may facilitate characterizing tumor heterogeneity. We introduced integrated consensus clustering (ICC) method to integrate mRNA, miRNA and lncRNA expression profiles of 431 primary clear cell renal cell carcinomas (ccRCCs). We identified one RCC subgroup easily misdiagnosed as ccRCC in clinic and four robust ccRCC subtypes associated with distinct clinicopathologic and molecular features. In subtype R1, AMPK signaling pathway is significantly upregulated, which may improve the oncologic-metabolic shift and partially account for its best prognosis. Subtype R2 has more chromosomal abnormities, higher expression of cell cycle genes and less expression of genes in various metabolism pathways, which may explain its more aggressive characteristic and the worst prognosis. Moreover, much more miRNAs and lncRNAs are significantly upregulated in R2 and R4 respectively, suggesting more important roles of miRNAs in R2 and lncRNAs in R4. Triple-color co-expression network analysis identified 28 differentially expressed modules, indicating the importance of cooperative regulation of mRNAs, miRNAs and lncRNAs in ccRCC. This study establishes an integrated transcriptomic classification which may contribute to understanding the heterogeneity and implicating the treatment of ccRCC.

Background Increasing evidence indicates that site-distant metastases are associated with survival outcomes in patients with epithelial ovarian cancer. This study aimed to investigate the prognostic values of site-distant metastases and clinical factors and develop a prognostic nomogram score individually predicting overall survival (OS, equivalent to all-cause mortality) and cancer specific survival (CSS, equivalent to cancer-specific mortality) in patients with epithelial ovarian cancer. Methods We retrospectively collected data on patients with epithelial ovarian cancer from the Surveillance, Epidemiology, and End Results (SEER) database between 1975 and 2016. Multivariate Cox regression was performed to identify survival trajectories. A nomogram score was used to predict long-term survival probability. A comparison between the nomogram and the International Federation of Gynecology and Obstetrics (FIGO 2018) staging system was conducted using time-dependent receiver operating characteristic (tROC) curve. Results A total of 131,050 patients were included, 18.2, 7.8 and 66.1% had localized, regional and distant metastases, respectively. Multivariate analysis identified several prognostic factors for OS including race, grade, histology, FIGO staging, surgery, bone metastasis, liver metastasis, lung metastasis, and lymphatic metastasis. Prognostic factors for CSS included grade, site, FIGO staging, surgery, bone metastasis, brain metastasis, lung metastasis, lymphatic metastasis, and insurance. Following bootstrap correction, the C-index of OS and CSS was 0.791 and 0.752, respectively. These nomograms showed superior performance compared with the FIGO 2018 staging criteria (P < 0.05). Conclusions A novel prognostic nomogram score provides better prognostic performance than the FIGO 2018 staging system. These nomograms contribute to directing clinical treatment and prognosis assessment in patients harboring site-distant metastases.