Decreased microRNA-224 and its clinical significance in non-small cell lung cancer patients

BACKGROUND: MicroRNA-224 has been proven dysregulated in some human malignancies and correlated with tumor progression. However, its expression and clinical significance in non–small cell lung cancer (NSCLC) is still unclear. Thus, the aim of this study was to explore the effects of miR-224 in NSCLC tumorigenesis and development. METHODS: Using real-time quantitative RT-PCR, we detected miR-224 expression in NSCLC cell lines and primary tumor tissues. The association of miR-224 expression with clinicopathological factors and prognosis was also statistically analyzed. MTT, flow cytometric, Transwell invasion and migration assays, and scratch migration assay were used to test the proliferation, apoptosis, invasion, and migration of NSCLC cells after miR-224 mimics transfection. RESULTS: MiR-224 expression levels were significantly down-regulated in NSCLC compared to the corresponding noncancerous lung tissues (P <0.001). In addition, decreased miR-224 expression was significantly associated with lymph node metastasis (P = 0.002), advanced TNM stage (P <0.001), and shorter overall survival (P <0.001). Multivariate regression analysis corroborated that down-regulation of miR-224 was an independent unfavourable prognostic factor for patients with NSCLC. Furthermore, transfection of miR-224 mimics in NSCLC A549 cells was able to reduce cell proliferation, invasion, and migration, and promote cell apoptosis. CONCLUSIONS: These findings indicate that miR-224 may act not only as a novel diagnostic and prognostic marker, but also as a potential target for miR-based therapy of NSCLC. VIRTUAL SLIDES: The virtual slide(s) for this article can be found here: http://www.diagnosticpathology.diagnomx.eu/vs/13000_2014_19

Decipher the sensitivity of urban canopy air temperature to anthropogenic heat flux with a forcing-feedback framework

The sensitivity of urban canopy air temperature (Ta) to anthropogenic heat flux (QAH) is known to vary with space and time, but the key factors controlling such spatiotemporal variabilities remain elusive. To quantify the contributions of different physical processes to the magnitude and variability of ∆Ta/∆QAH (where ∆ represents a change), we develop a forcing-feedback framework based on the energy budget of air within the urban canopy layer and apply it to diagnosing ∆Ta/∆QAH simulated by the Community Land Model Urban (CLMU) over the contiguous United States (CONUS). In summer, the median ∆Ta/∆QAH is around 0.01 K (W m-2)-1over CONUS. Besides the direct effect of QAH on Ta, there are important feedbacks through changes in the surface temperature, the atmosphere-canopy air heat conductance (ca), and the surface-canopy air heat conductance. The positive and negative feedbacks nearly cancel each other and ∆Ta/∆QAH is mostly controlled by the direct effect in summer. In winter, ∆Ta/∆QAH becomes stronger, with the median value increased by about 20% due to weakened negative feedback associated with ca. The spatial and temporal (both seasonal and diurnal) of ∆Ta/∆QAH and the nonlinear response of ∆Ta to ∆QAH are strongly related to the variability of ca, highlighting the importance of correctly parameterizing convective heat transfer in urban canopy models

Adjusting for indirectly measured confounding using large-scale propensity scores

Confounding remains one of the major challenges to causal inference with observational data. This problem is paramount in medicine, where we would like to answer causal questions from large observational datasets like electronic health records (EHRs). Modern medical data (such as EHRs) typically contain tens of thousands of covariates. Such a large set carries hope that many of the confounders are directly measured, and further hope that others are indirectly measured through their correlation with measured covariates. How can we exploit these large sets of covariates for causal inference? To help answer this question, this paper examines the performance of the large-scale propensity score (LSPS) approach on causal analysis of medical data. We demonstrate that LSPS may adjust for indirectly measured confounders by including tens of thousands of covariates that may be correlated with them. We present conditions under which LSPS removes bias due to indirectly measured confounders, and we show that LSPS may avoid bias when inadvertently adjusting for variables (like colliders) that otherwise can induce bias. We demonstrate the performance of LSPS with both simulated medical data and real medical data.Comment: 12 pages, 6 figure

Automatic driving lane change safety prediction model based on LSTM

Autonomous driving technology can improve traffic safety and reduce traffic accidents. In addition, it improves traffic flow, reduces congestion, saves energy and increases travel efficiency. In the relatively mature automatic driving technology, the automatic driving function is divided into several modules: perception, decision-making, planning and control, and a reasonable division of labor can improve the stability of the system. Therefore, autonomous vehicles need to have the ability to predict the trajectory of surrounding vehicles in order to make reasonable decision planning and safety measures to improve driving safety. By using deep learning method, a safety-sensitive deep learning model based on short term memory (LSTM) network is proposed. This model can alleviate the shortcomings of current automatic driving trajectory planning, and the output trajectory not only ensures high accuracy but also improves safety. The cell state simulation algorithm simulates the trackability of the trajectory generated by this model. The research results show that compared with the traditional model-based method, the trajectory prediction method based on LSTM network has obvious advantages in predicting the trajectory in the long time domain. The intention recognition module considering interactive information has higher prediction and accuracy, and the algorithm results show that the trajectory is very smooth based on the premise of safe prediction and efficient lane change. And autonomous vehicles can efficiently and safely complete lane changes