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Gold nanoparticles/785 porous silicon photonic crystals (Au NPs/785 PSi PhCs) were used as substrates in combination with a low-concentration serum surface-enhanced Raman spectroscopy (SERS) detection scheme to obtain spectral signals from healthy individuals and cervical cancer patients. The best principal component scores were selected by principal component analysis (PCA) combined with linear discriminant analysis (LDA) and support vector machine (SVM) to analyze the spectral differences distinguishing healthy and cervical cancer patients. The accuracy of the two models was 97.9% and 96.9%, respectively. SERS technique based on Au NPs/785 PSi PhCs has great potential to improve the screening of cervical cancer. [ABSTRACT FROM AUTHOR]

Early detection of cervical lesions, accurate diagnosis of cervical lesions, and timely and effective therapy can effectively avoid the occurrence of cervical cancer or improve the survival rate of patients. In this paper, the spectra of tissue sections of cervical inflammation (n = 60), CIN (cervical intraepithelial neoplasia) I (n = 30), CIN II (n = 30), CIN III (n = 30), cervical squamous cell carcinoma (n = 30), and cervical adenocarcinoma (n = 30) were collected by a confocal Raman micro-spectrometer (LabRAM HR Evolution, Horiba France SAS, Villeneuve d'Ascq, France). The Raman spectra of six kinds of cervical tissues were analyzed, the dominant Raman peaks of different kinds of tissues were summarized, and the differences in chemical composition between the six tissue samples were compared. An independent sample t test (p ≤ 0.05) was used to analyze the difference of average relative intensity of Raman spectra of six types of cervical tissues. The difference of relative intensity of Raman spectra of six kinds of tissues can reflect the difference of biochemical components in six kinds of tissues and the characteristic of biochemical components in different kinds of tissues. The classification models of cervical inflammation, CIN I, CIN II, CIN III, cervical squamous cell carcinoma, and cervical adenocarcinoma were established by using a support vector machine (SVM) algorithm. Six types of cervical tissues were classified and identified with an overall diagnostic accuracy of 85.7%. This study laid a foundation for the application of Raman spectroscopy in the clinical diagnosis of cervical precancerous lesions and cervical cancer. [ABSTRACT FROM AUTHOR]

The landslide caused a huge disaster to the living environment and seriously threatened the lives and property safety of nearby residents. Assess or predict landslide-susceptible the landslide displacement through monitoring are great beneficial to guide landslide control and mitigate these hazards by taking appropriate preparatory measures. In this paper, a new water cycle algorithm optimization BP neural network (BPNN) dynamic prediction model (WCA-BPNN) was established to make up for the shortcoming of BPNN convergence speed. A typical step-wise landslide——Langshuwan Landslide happened in the Three Gorges Reservoir area of China is taken as a case, and the displacement monitoring data of 4 years was used for time series analysis and modeling. The long-term creep effect of the landslide and the short-term acceleration effect of the climate are considered in the model, and the accumulative displacement is divided into two kinds of trend displacement and periodic displacement. The key influencing factors of landslide periodic displacement were screened by gray relational grade analysis method, and then used as learning data. In addition to comparing the predicted value of the model with the measured value, it also compares the accuracy of the three models of BPNN, support vector machine, extreme learning machine under the training conditions of the same learning data set. The results show that the WAC-BPNN model has faster convergence speed and higher prediction accuracy than the traditional BPNN model, and it is also the most accurate of the four models. [ABSTRACT FROM AUTHOR]

This paper presents a novel method that integrates the Algebraic Connectivity Strength of Point (ACSP) and Scoring Criteria to identify genes associated with tumor type. First, for each gene, the ACSP is used to identify reliable expression levels of the gene in all the samples. The informative genes are then selected using Scoring Criteria based on these reliable expression levels. Finally, the Support Vector Machine (SVM) classifier is used to classify the two datasets of gene expression profiling. The results show that the informative genes selected by the proposed method have higher credibility than those selected by Scoring Criteria alone. [ABSTRACT FROM AUTHOR]