An Enhanced Grey Wolf Optimization Based Feature Selection Wrapped Kernel Extreme Learning Machine for Medical Diagnosis

In this study, a new predictive framework is proposed by integrating an improved grey wolf optimization (IGWO) and kernel extreme learning machine (KELM), termed as IGWO-KELM, for medical diagnosis. The proposed IGWO feature selection approach is used for the purpose of finding the optimal feature subset for medical data. In the proposed approach, genetic algorithm (GA) was firstly adopted to generate the diversified initial positions, and then grey wolf optimization (GWO) was used to update the current positions of population in the discrete searching space, thus getting the optimal feature subset for the better classification purpose based on KELM. The proposed approach is compared against the original GA and GWO on the two common disease diagnosis problems in terms of a set of performance metrics, including classification accuracy, sensitivity, specificity, precision, G-mean, F-measure, and the size of selected features. The simulation results have proven the superiority of the proposed method over the other two competitive counterparts

A Framework for Bus Trajectory Extraction and Missing Data Recovery for Data Sampled from the Internet

This paper presents a novel framework for trajectories’ extraction and missing data recovery for bus traveling data sampled from the Internet. The trajectory extraction procedure is composed of three main parts: trajectory clustering, trajectory cleaning and trajectory connecting. In the clustering procedure, we focus on feature construction and parameter selection for the fuzzy C-means clustering method. Following the clustering procedure, the trajectory cleaning algorithm is implemented based on a new introduced fuzzy connecting matrix, which evaluates the possibility of data belonging to the same trajectory and helps detect the anomalies in a ranked context-related order. Finally, the trajectory connecting algorithm is proposed to solve the issue that occurs in some cases when a route trajectory is incorrectly partitioned into several clusters. In the missing data recovery procedure, we developed the contextual linear interpolation for the cases of missing data occurring inside the trajectory and the median value interpolation for the cases of missing data outside the trajectory. Extensive experiments are conducted to demonstrate that the proposed framework offers a powerful ability to extract and recovery bus trajectories sampled from the Internet

A Framework for Bus Trajectory Extraction and Missing Data Recovery for Data Sampled from the Internet

This paper presents a novel framework for trajectories’ extraction and missing data recovery for bus traveling data sampled from the Internet. The trajectory extraction procedure is composed of three main parts: trajectory clustering, trajectory cleaning and trajectory connecting. In the clustering procedure, we focus on feature construction and parameter selection for the fuzzy C-means clustering method. Following the clustering procedure, the trajectory cleaning algorithm is implemented based on a new introduced fuzzy connecting matrix, which evaluates the possibility of data belonging to the same trajectory and helps detect the anomalies in a ranked context-related order. Finally, the trajectory connecting algorithm is proposed to solve the issue that occurs in some cases when a route trajectory is incorrectly partitioned into several clusters. In the missing data recovery procedure, we developed the contextual linear interpolation for the cases of missing data occurring inside the trajectory and the median value interpolation for the cases of missing data outside the trajectory. Extensive experiments are conducted to demonstrate that the proposed framework offers a powerful ability to extract and recovery bus trajectories sampled from the Internet

Mir-144-3p Promotes Cell Proliferation, Metastasis, Sunitinib Resistance in Clear Cell Renal Cell Carcinoma by Downregulating ARID1A

Background/Aims: We previously performed microRNA (miRNA) microarray to identify effective indicators of clear cell renal cell carcinoma (ccRCC) tissue samples and preoperative/postoperative plasma in which we identified miR-144-3p as an oncomiRNA. However, the molecular mechanism of miR-144-3p remains unclear. This study aims to explore the roles of miR-144-3p in the invasion, migration and Sunitinib-resistance in ccRCC and to elucidate the underlying mechanisms. Methods: Gain and loss of function approaches were used to investigate the cell proliferation, cycle distribution, clonogenicity, migration, invasion, chemosensitivity of miR-144-3p in vitro. The xenograft model was used to assess the effects of miR-144-3p overexpression on tumorigenesis. Bioinformatics analysis and dual-luciferase reporter assay were used to indentify AT-rich interactive domain 1A (ARID1A) as a direct target gene of miR-144-3p. Quantitative RT-PCR, Western blotting, and immunohistochemical (IHC) staining were used to explore ARID1A expression level of the mRNA and protein. Results: We found that miR-144-3p overexpression enhanced cell proliferation, clonogenicity, migration, invasion, and chemoresistance in ccRCC cells. Notably, the oncotumor activities of miR-144-3p were mediated by repressing the expression of ARID1A. The downregulation of ARIDIA could promote the function of miR-144-3p in cell proliferation, metastasis and chemoresistance. Consistently, ARID1A mRNA and protein levels were decreased in ccRCC and in nude mice, and they negatively correlated with miR-144-3p. Conclusion: Higher miR-144-3p may enhance malignancy and resistance to Sunitinib in ccRCC by targeting ARID1A, the observations may uncover novel strategies of ccRCC treatment

Tumor Cell “Slimming” Regulates Tumor Progression through PLCL1/UCP1‐Mediated Lipid Browning

Abstract Emerging evidence has highlighted the important role of abnormal lipid accumulation in cancer development and progression, but the mechanism for this phenomenon remains unclear. Here, it is demonstrated that phospholipase C‐like 1/uncoupling protein 1 (PLCL1)/(UCP1)‐mediated lipid browning promotes tumor cell “slimming” and represses tumor progression. By screening three independent lipid metabolism‐related gene sets in clear cell renal cell carcinoma (ccRCC) and analyzing the TCGA database, it is found that PLCL1 predicted a poor prognosis and was downregulated in ccRCC. Restoration of PLCL1 expression in ccRCC cells significantly represses tumor progression and reduces abnormal lipid accumulation. Additionally, a phenomenon called tumor cell “slimming,” in which tumor cell volume is reduced and lipid droplets are transformed into tiny pieces, is observed. Further studies show that PLCL1 promotes tumor cell “slimming” and represses tumor progression through UCP1‐mediated lipid browning, which consumes lipids without producing ATP energy. Mechanistic investigations demonstrate that PLCL1 improves the protein stability of UCP1 by influencing the level of protein ubiquitination. Collectively, the data indicate that lipid browning mediated by PLCL1/UCP1 promotes tumor cell “slimming” and consumes abnormal lipid accumulation, which represses the progression of ccRCC. Tumor cell “slimming” offers a promising new concept and treatment modality against tumor development and progression