Search

BPTF, a subunit of NURF, is well known to be involved in the development of eukaryotic cell, but little is known about its roles in cancers, especially in non-small-cell lung cancer (NSCLC). Here we showed that BPTF was specifically overexpressed in NSCLC cell lines and lung adenocarcinoma tissues. Knockdown of BPTF by siRNA significantly inhibited cell proliferation, induced cell apoptosis and arrested cell cycle progress from G1 to S phase. We also found that BPTF knockdown downregulated the expression of the phosphorylated Erk1/2, PI3K and Akt proteins and induced the cleavage of caspase-8, caspase-7 and PARP proteins, thereby inhibiting the MAPK and PI3K/AKT signaling and activating apoptotic pathway. BPTF knockdown by siRNA also upregulated the cell cycle inhibitors such as p21 and p18 but inhibited the expression of cyclin D, phospho-Rb and phospho-cdc2 in lung cancer cells. Moreover, BPTF knockdown by its specific shRNA inhibited lung cancer growth in vivo in the xenografts of A549 cells accompanied by the suppression of VEGF, p-Erk and p-Akt expression. Immunohistochemical assay for tumor tissue microarrays of lung tumor tissues showed that BPTF overexpression predicted a poor prognosis in the patients with lung adenocarcinomas. Therefore, our data indicate that BPTF plays an essential role in cell growth and survival by targeting multiply signaling pathways in human lung cancers.

In view of problems of too much residual inventory and high production cost in material supply management system of a Group Company, the paper proposed a design scheme of material supermarket management system of coal enterprise based on Internet of Things. It introduced key technologies of the system and actual business needs of material management of the Group Company, gave system structure based on perception layer, network layer and application layer, and introduced realization scheme of each layer in details. The system reflects effective utilization of Internet of Things in material management of coal enterprises.

In view of problems of information island and high cost of goods storage on information platform of coal enterprises, the paper proposed a design scheme of new-type material supply supermarket management system for coal enterprises, introduced general flow, overall framework, main functions and outstanding characteristics of the system in details. Taking each production zone of coal enterprise as source of material management, the system reduces storage and improves response flexibility. With three-layer structure based on B/S mode, the system has high development efficiency and ensures its security and reliability. And using bar-code scan device to obtain management information in real time, the system is more flexible. It is beneficial to material storage cost reduction and rational material distribution for coal enterprises.

For problem that traditional coal mine safety monitoring system cannot predict underground accidents in advance, the paper proposed a coal mine safety monitoring and early warning system based on cloud computing．It introduced related technologies including cloud computing, SaaS and data mining, and described design of overall architecture and cloud data center of the system in details. The system can effectively forecast underground gas accident, mechanical and electrical accident, fire accident, water damage accident, so as to reduce risk of security incidents.

For problem that abnormal gas change below pre-set warning value cannot be found in time because existing safety monitoring and control systems of coal mine use overrun theory and take measured value by gas sensors as criterion, the paper proposed a pre-warning method for gas mutation below the pre-set limit value, and introduced two gas mutation pre-warning methods named static pre-warning and dynamic pre-warning. It did stratified sampling as well as distribution validation and fitting of static data and dynamic data of pre-warning basic data of gas mutation below the limit value according to probed gas data of N2202 working face of Yuwu Coal Mine, and got a conclusion that the basic data distribution of the static pre-warning and dynamic pre-warning is both in normal distribution. It also proposed a method to real-timely calculate upper limit value of the static pre-warning and dynamic pre-warning with hypothesis testing and interval estimation, which was verified by use of probed gas data of N2202 working face.

For the problem that coal sale data has large volume but little information content, the paper proposed a design scheme of OLAP analyzing system of coal sale data based on Hadoop platform, introduced design ideas and structure of the system, and described specific process of implementation of deep and fast mining and intuitive display of data taking statistics of sales data as an example. The system uses Hadoop cloud platform for ETL data processing, creates Hive distributed data warehouse, and uses HQL language of Hive for OLAP statistical analysis, which achieves deep data mining, statistics and analysis of sales information from multi-level and multi-angle quickly and accurately, meanwhile reflects the result of data analysis intuitively form multi-angle.