Fault Identification of Chemical Processes Based on k-NN Variable Contribution and CNN Data Reconstruction Methods.

Data-driven fault detection and identification methods are important in large-scale chemical processes. However, some traditional methods often fail to show superior performance owing to the self-limitations and the characteristics of process data, such as nonlinearity, non-Gaussian distribution, and multi-operating mode. To cope with these issues, the k-NN (k-Nearest Neighbor) fault detection method and extensions have been developed in recent years. Nevertheless, these methods are primarily used for fault detection, and few papers can be found that examine fault identification. In this paper, in order to extract effective fault information, the relationship between various faults and abnormal variables is studied, and an accurate "fault⁻symptom" table is presented. Then, a novel fault identification method based on k-NN variable contribution and CNN data reconstruction theories is proposed. When there is an abnormality, a variable contribution plot method based on k-NN is used to calculate the contribution index of each variable, and the feasibility of this method is verified by contribution decomposition theory, which includes a feasibility analysis of a single abnormal variable and multiple abnormal variables. Furthermore, to identify all the faulty variables, a CNN (Center-based Nearest Neighbor) data reconstruction method is proposed; the variables that have the larger contribution indices can be reconstructed using the CNN reconstruction method in turn. The proposed search strategy can guarantee that all faulty variables are found in each sample. The reliability and validity of the proposed method are verified by a numerical example and the Continuous Stirred Tank Reactor system.

[Polymorphism analysis of Plasmodium falciparum histidine-rich protein II and III].

OBJECTIVE: To analyze the polymorphism of Plasmodium falciparum histidine-rich protein (PfHRP) II and III. METHODS: Genomic DNA was isolated from blood samples of 20 patients infected with Plasmodium falciparum in Yunnan Province. Blood samples were tested by microscopy and RDTs. The Pfhrp2 and Pfhrp3 gene fragments were amplified by PCR and sequenced. The sequencing results were analyzed and compared using the bioinformatics software. RESULTS: 20 patients infected with Plasmodium falciparum tested by microscopy and RDTs. PCR showed that the Pfhrp2 gene was with 389~986 bp, and Pfhrp3 gene with 329-640 bp. All PfH-IRP II sequences started with type 1 repeat (AHHAHHVAD) and ended with the type 12 repeat (AHHAAAHHEAATH). The number of type 7 (AHHAAD), type 2 (AHHAHHAAD) and type 6 (AHHATD) within PfHRP II was more than the other types of repeats, as well as type 16 (AHHAAN) and type 17 (AHHDG) for PfHRP III. Type 11 repeat (AHN) was not found from the PfHRP II and PfHRP III sequences. CONCLUSION: There is an extensive diversity in Pfhrp2 and Pfhrp3 fragments in the individuals infected with P. falciparum in Yunnan. Some types of repeats are shared by PfHRP II and PfHRP III.

Construction of a prognostic model via WGCNA combined with the LASSO algorithm for stomach adenocarcinoma patients.

Objective: This study aimed to identify prognostic signatures to predict the prognosis of patients with stomach adenocarcinoma (STAD), which is necessary to improve poor prognosis and offer possible treatment strategies for STAD patients. Methods: The overlapping genes between the key model genes that were screened by the weighted gene co-expression network analysis (WGCNA) and differentially expressed genes (DEGs) whose expression was different with significance between normal and tumor tissues were extracted to serve as co-expression genes. Then, enrichment analysis was performed on these genes. Furthermore, the least absolute shrinkage and selection operator (LASSO) regression was performed to screen the hub genes among overlapping genes. Finally, we constructed a model to explore the influence of polygenic risk scores on the survival probability of patients with STAD, and interaction effect and mediating analyses were also performed. Results: DEGs included 2,899 upregulated genes and 2,896 downregulated genes. After crossing the DEGs and light-yellow module genes that were obtained by WGCNA, a total of 39 overlapping genes were extracted. The gene enrichment analysis revealed that these genes were enriched in the prion diseases, biosynthesis of unsaturated fatty acids, RNA metabolic process, hydrolase activity, etc. PIP5K1P1, PTTG3P, and SNORD15B were determined by LASSO-Cox. The prognostic prediction of the three-gene model was established. The Cox regression analysis showed that the comprehensive risk score for three genes was an independent prognosis factor. Conclusion: PIP5K1P1, PTTG3P, and SNORD15B are related to the prognosis and overall survival of patients. The three-gene risk model constructed has independent prognosis predictive ability for STAD.

Bone metastasis in a novel breast cancer mouse model containing human breast and human bone.

In practice, investigations for bone metastasis of breast cancer rely heavily on models in vivo. Lacking of such ideal model makes it difficult to study the whole process or accurate mechanism of each step of this metastatic disease. Development of xenograft mouse models has made great contributions in this area. Currently, the best animal model of breast cancer metastasizing to bone is NOD/SCID-hu models containing human bone, which makes it possible to let the breast cancer cells and the bone target of osteotropic metastasis be both of human origin. We have developed a novel mouse model containing both human bone and breast, and proved it functional and reliable. In this study, a set of human breast cancer cell line including MDA-MB-231, MDA-MB-231BO, MCF-7, ZR-75-1 and SUM1315 were characterized their osteotropism in this model. A specific cell line SUM1315 made species-specific bone metastasis, certifying the osteotropism-identification utility of the novel mouse model. Furthermore, gene expression and microRNA expression profiling analysis were done to the two SUM1315 derived sub lines isolated and purified from the orthotopic and metastatic xenograft. In addition, to demonstrate the disparity between the "spontaneous" and "forced" bone metastasis in mouse model, MDA-MB-231 cells were inoculated into both the human implants in this model simultaneously, and then primary cultured and profiling analyzed. Supported by overall results of profiling analyses, this study suggested the novel model was a useful tool for understanding, preventing and treating bone metastasis of breast cancer, meanwhile it had provided significant information for further investigations.

[Correlation research on the MRI quantity of lumbar modic changes and low back pain].

OBJECTIVE: To analyzed the relationship between lumbar endplate Modic area changes rate and low back pain by measuring MRI T2 sagittal image of lumbar endplate Modic area changes rate. METHODS: From December 2011 to June 2012,70 patients with low back pain in operation were evaluated on pain by VAS and function by JOA,and examined by MRI including 39 males and 31 females with an average age of (51.00 +/- 11.89) years ranging from 29 to 72 years old. Among them, 54 cases had lumbar endplate Modic changes involving 15 cases in types Modic I ,21 cases in type Modic II, 11 cases in type Modic III ,mixed type Modic in 7 cases (eliminated for too few cases). Modic area changes and corresponding vertebral area were measured on MRI T2 median sagittal. The areas of two ways were compared to yield the rate of changes for Modic, for multisegmental Modic changes to calculate the total ratios. A correlation was observed among JOA, VAS and the rate of Modic changes. RESULTS: The correlation coefficient of change rate of Modic I with JOA score was r = -0.308, P = 0.048 < 0.05, there was a negative correlation;the correlation coefficient of change rate of Modic I with VAS scores was r = 0.428,P = 0.021 < 0.05, there was a positive correlation. The correlation coefficient of change rate of Modic II with JOA score was r = -0.375, P = 0.043 < 0.05, there was a negative correlation;the correlation coefficient of change rate of Modic II with VAS score was r = 0.352, P = 0.041 < 0.05, there was a positive correlation. The area change rate of Modic III had no significant correlation with low back pain degree (P > 0.05). CONCLUSION: Modic I and II area changes rate of of patients with low back pain is closely related to the degree of pain low back pain, Modic III area changes rate is not significant correlated to the degree of lower back pain.