MiR-196a-5p facilitates progression of estrogen-dependent endometrial cancer by regulating FOXO1

Background and Purpose. Estrogen-dependent endometrial cancer mainly occurs in younger pre-menopausal and post-menopausal women and threatens their health. Recently, microRNAs (miRNAs) have been considered as novel targets in endometrial cancer treatment. Therefore, we aimed to explore the effect of miRNA (miR)-196a-5p in estrogen-dependent endometrial cancer. Methods. 17β-estradiol (E2; 2.5, 5, 10 and 20 nM) was used to treat RL95-2, HEC-1B and ECC-1 cells followed by cell viability assessment using 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT). The level of miR-196a-5p was measured by reverse transcription-quantitative PCR (RT-qPCR). We then transfected miR-196a-5p mimic/inhibitor and Forkhead box protein O1 (FOXO1) small interfering RNA (siRNA) into E2-treated cells. Apoptotic cells were measured by flow cytometry. Wound healing and Transwell assays were implemented to assess migration and invasion. Bioinformatics and luciferase reporter assays were applied to confirm the interaction between miR-196a-5p and FOXO1. Immunoblotting determined the levels of FOXO1, Bcl-2, Bax, Caspase 3. Results. E2 promoted cell viability and miR-196a-5p expression in RL95-2 and ECC-1 cells. miR-196a-5p mimic enhanced cell viability, migration and invasion but suppressed apoptosis and FOXO1, whilst miR-196a-5p inhibitor blocked these processes. In addition, miR-196a-5p upregulated Bcl-2, but down regulated Bax and Caspase 3 expression, an effect that was reversed by miR-196a-5p inhibitor. We determined that miR-196a-5p targeted FOXO1, and that si-FOXO1 blocked the effects of miR-196a-5p inhibitor on viability, apoptosis, migration and invasion of E2-treated RL95-2 and ECC-1 cells. Conclusions. Our findings suggested potential diagnostic and therapeutic applications for miR-196a-5p and its FOXO1 target in patients suffering from estrogen-dependent endometrial cancer

InSocialNet: Interactive visual analytics for role-event videos

Role–event videos are rich in information but challenging to be understood at the story level. The social roles and behavior patterns of characters largely depend on the interactions among characters and the background events. Understanding them requires analysis of the video contents for a long duration, which is beyond the ability of current algorithms designed for analyzing short-time dynamics. In this paper, we propose InSocialNet, an interactive video analytics tool for analyzing the contents of role–event videos. It automatically and dynamically constructs social networks from role–event videos making use of face and expression recognition, and provides a visual interface for interactive analysis of video contents. Together with social network analysis at the back end, InSocialNet supports users to investigate characters, their relationships, social roles, factions, and events in the input video. We conduct case studies to demonstrate the effectiveness of InSocialNet in assisting the harvest of rich information from role–event videos. We believe the current prototype implementation can be extended to applications beyond movie analysis, e.g., social psychology experiments to help understand crowd social behaviors

Dynamic Modelling of an Automated Vehicle Storage and Retrieval System and a Simulation Analysis of its Efficiency

In this paper operating-time models for single and multiple instructions are set up considering an AVS/RS (automated vehicle storage and retrieval system). The operation times of AVS/RS and AS/RS (automated storage and retrieval system) are simulated in different situations by changing the shelf structure and order density. The results show that the AVS/RS is more efficient than the AS/RS in all situations. Furthermore, the numbers of rows and columns of storage shelves greatly influence the operation time. The graph of operation-time compression ratio against number of columns shows an inverted U-type distribution, and the compression ratio decreases and ultimately tends to zero as the number of rows is increased. Also, the order density affects the efficiency difference between the two systems: the higher the order density, the higher the AVS/RS operating-time compression rate. Finally, compared with the AS/RS, the AVS/RS operating-time compression ratio improves greatly with increasing density and number of rows because of parallel operations, whereas with decreasing density and number of rows the AVS/RS advantages are gradually lost and the compression ratio decreases and eventually even reaches zero

Study of Deformation-Compensated Modeling for Flexible Material Path Processing Based on Fuzzy Neural Network and Fuzzy Clustering

In this paper, the Flexible Material Path Processing (FMPP) deformation compensation modeling method based on T-S fuzzy neural network is proposed. This method combined with T S fuzzy reasoning and fuzzy neural network.Firstly, fuzzy clustering is introduced to extract fuzzy membership functions and the fitness of fuzzy rules of T S fuzzy neural network antecedent from historical processing data; secondly, through back-propagation iteration to calculate connection weights of the network. Processing experiments shows that T S fuzzy neural network modeling in this paper is superior to typical T S model，the angle error and straightness error processing by NTS FNN is decreased than these of STS FNN

Deformation-compensated modeling of flexible material processing based on T-S fuzzy neural network and fuzzy clustering

According to the factors that influence flexible material processing (FMP), the deformation compensation modeling method based on T-S fuzzy neural network is proposed. This method combines T-S fuzzy reasoning with a fuzzy neural network. Firstly, fuzzy clustering is introduced to extract fuzzy membership functions and the fitness of fuzzy rules of T-S fuzzy neural network antecedent from the past processing data. Secondly, with the steepest descent method, back-propagation iteration is used to calculate the connection weights of the network. The processing of experiments shows that T-S fuzzy neural network modeling is superior to typical T-S model. The angle error and the straightness error processed by NTS-FNN is 40.4 %, 28.8 % lower than those of STS-FNN. The minimum processing time processed by NTS-FNN is lower by 46.1 % than that of STS-FNN

Modeling and Optimization of Inventory-Distribution Routing Problem for Agriculture Products Supply Chain

Mathematical models of inventory-distribution routing problem for two-echelon agriculture products distribution network are established, which are based on two management modes, franchise chain and regular chain, one-to-many, interval periodic order, demand depending on inventory, deteriorating treatment cost of agriculture products, start-up costs of vehicles and so forth. Then, a heuristic adaptive genetic algorithm is presented for the model of franchise chain. For the regular chain model, a two-layer genetic algorithm based on oddment modification is proposed, in which the upper layer is to determine the distribution period and quantity and the lower layer is to seek the optimal order cycle, quantity, distribution routes, and the rational oddment modification number for the distributor. By simulation experiments, the validity of the algorithms is demonstrated, and the two management modes are compared