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Aims The integration preferences of human papillomavirus (HPV) have been intensively studied and contested over recent years. To disclose the integration preferences of high-risk HPV in cervical cancer, HPV transcriptional sites and features in different cervical cancer cell lines were identified. Main methods In this study, three cervical cancer cell lines (CaSki, HeLa, and SiHa) were subjected for HPV genome status determination by amplification of papillomavirus oncogene transcripts (APOT) assay. The numbers of viral copies in human genomes and numbers of viral–human fusion mRNAs in three HPV-integrated cervical cancer cell lines were measured and analysed. Key findings The results revealed that the gene desert region 8q24 of the HPV type 18 integrated HeLa cell line and the 13q21–22 region of the HPV type 16 integrated CaSki and SiHa cell lines were hotspots for HPV integration, and the numbers of viral copies in the human genomes of the three cell lines that we detected were not in accordance with those reported in previous studies. Significance Integration of the HPV genome into the host cell chromosome suggests that persistent HPV infection is vital for malignant cell transformation and carcinogenesis. This study provides information to benefit health care professionals seeking more comprehensive and accurate diagnostics for HPV-related disease”? Please check, and amend as necessary.

The temperature distribution of a LiFePO4 battery was studied by using the finite difference method in this study. The heat generation considers both Bernardi expression and current heat generation in current collectors. A temperature measurement experiment at the geometry center inside battery at 2C discharge was done to validate the model temperature prediction results. The effects of electrode configuration such as placing and center distance of current collecting tabs on battery temperature distribution were analysed. The results showed that the placing and center distance of current collecting tabs have significant effects on temperature rising and uniformity. The results could contribute to the battery design.

Probabilistic extreme learning machine (PELM) is a binary classification method, which can improve the computational speed, generalization performance and computational cost. In this work we extend the binary PELM to resolve multi-class classification problems by using one-against-one (OAO) and winner-takes-all strategy. The strategy one-against-one (OAO) involves C(C-1)/2 binary PELM models. A reliability for each sample is calculated from each binary PELM model, and the sample is assigned to the class with the largest combined reliability by using the winner-takes-all strategy. The proposed method is verified with the operational conditions classification of an industrial wastewater treatment plant. Experimental results show the good performance on classification accuracy and computational expense.

Operating condition recognition of a ball mill is an important part in grinding process. The load status can only be determined according to expert's experiences. If common operating conditions (such as under-load, best-load, and over-load) are not monitored and handled promptly and accurately, the quality of the grinding product may deteriorate or even the grinding production may come to a stop. This paper estimates the power spectral density of the input vibration and acoustic signals using Welch's averaged modified periodogram method of spectral estimation. Unsupervised Fuzzy C-Means classification and Partial Least Squares for Discrimination (DPLS) are used to build operating status model for the ball mill. Experimental results shown recognition capability is enhanced, the false alarming rate is decreased.