Search

Pleomorphic adenoma (PA), Warthin's tumor (WT) and mucoepidermoid carcinoma (MEC) are three common subtypes of salivary gland tumors, for which the occurrence site is located in the parotid gland. Accurately diagnosing the subtypes of parotid tumors plays a vital role in the surgical treatment. Unfortunately, the current studies mainly focus on the binary classification of parotid tumors. The preoperative multi-classification of them has still been underexplored. For the purpose of broadening the application area of the predictive models and facilitating the clinical preoperative diagnosis, we suggested a multi-classification model, which was constructed by combining the variable combination population analysis (VCPA) algorithm with the partial least squares regression (PLSR), to simultaneously discriminate the three subtypes of parotid tumors as well as the normal parotid gland tissue based on the Raman spectra of the tissue samples. In addition, we investigated the impact of generating Raman spectra from different sampling locations on the reliability of the predictive models. For the validation set, the overall accuracy in predicting the subtypes of parotid tumors and the normal parotid gland tissue was 0.867. Similarly, the accuracies achieved by the models constructed with the Raman spectra from two different sampling locations were 0.877 and 0.883, respectively, indicating the minor influence of the sampling locations on the predictive models. Our findings can be helpful for establishing the method of rapidly diagnosing the salivary gland tumors preoperatively in clinics. Moreover, the characteristic wavenumbers used in model construction were highly associated with the variations of the structures and contents of nucleic acids, collagen, proteins, lipids and DNA/RNA in gland tissue, which revealed the mainly difference among three types of parotid tumors and can be conductive to better understanding the molecular mechanisms of them.

In clinical research, DNA microarrays are widely applied in the identification of the oncogenes, which are differentially expressed between two clinical states and considered as predictors for the cancer prognosis. Due to the heterogeneity of clinical samples, the differentially expressed genes (DEGs) discovered by current statistical methods or machine learning algorithms involve a number of genes unrelated to the phenotypic differences between the compared samples and, consequently, will impact on the reliability of the predictive models in the cancer prognosis. In our study, we proposed Bayesian nonparametric variable selection algorithm, a stochastic random and hierarchical search method, to separate out the cancer-related genes from the DEG lists. The importance of the genes in the DEG lists can be inferred from the posterior distribution of the predicted clinical endpoints, which can be simulated by the Markov Chain Monte Carlo (MCMC) algorithm. The cancer-related genes were identified according to their importance and used to construct models for the prediction of three clinical endpoints, namely the estrogen receptor status (ER status) of the breast cancer patient, the preoperative treatment response of breast cancer and the overall survival milestone outcome of acute myeloma leukemia (OS of AML). The prediction accuracies of preoperative treatment response, ER status and OS of AML were 86%, 89% and 58%, and the Mathew’s correlation coefficients were 0.42, 0.77 and 0.33, which were higher than those reported in previous studies. Furthermore, most of the genes identified by our method were reported as oncogenes in previous literatures. Our results demonstrated that the Bayesian nonparametric variable selection algorithm proposed in current study can efficiently identify the oncogenes for cancer prognosis and enhance the performance of the predictive models.

The correct diagnosis and the prompt treatment of oral leukoplakia (OLK) can efficiently prevent OLK from undergoing malignant transformation to oral squamous cell carcinoma (OSCC). However, the diagnostic model for distinguishing normal mucosa from low-grade dysplasia, as well as high-grade dysplasia from OSCC was not well established in a previous study. In this study, the characteristic wavenumbers in the Raman spectra were first identified by the variable selection methods. Then, the intensities at these wavenumbers were used to classify the biopsies. As a result, the accuracies achieved by the intensities at the characteristic wavenumbers were 70.5% and 94.0% for the classification of normal vs. low-grade dysplasia and high-grade dysplasia vs. OSCC, respectively, which were greater than those (accuracy of 65.4% and 88.0%, respectively) using all the intensities in the Raman spectra. Our results suggested that constructing a diagnostic model with the intensities at the characteristic wavenumbers can improve the identification of the different lesions of oral mucosa. Moreover, most of the Raman intensities for predicting normal vs. low-grade dysplasia indicated that the transformation from normal mucosa to low-grade dysplasia was associated with the changes in the contents of lipids, while most of the intensities for predicting high-grade dysplasia vs. OSCC indicated that the transformation from high-grade dysplasia to OSCC was associated with changes in the contents of proteins and nucleic acids. Our findings can be helpful for diagnosing the various grades of OLK with dysplasia and understanding the molecular mechanisms of potential malignant transformation of oral leukoplakia.

Binder-free layered graphene/polyaniline composite film was prepared by an environmentally friendly and facile two-step route for the first time. Firstly, a sandwich-structured PANI/graphene/PANI nanosheet was prepared in situ from aqueous solution, followed an electrophoretic deposition process. By observations of scanning electron microscopy (SEM), transmission electron microscopy (TEM) and atomic force microscopy (AFM), it conforms that the graphene sheet is uniformly covered by an ultralthin PANI film (3.7 nm). Raman spectra, Fourier transform infrared (FT-IR) spectra and X-ray photoelectron spectroscopy (XPS) are jointly used to confirm strong π-π electron and hydrogen bond interaction in the nanosheets. The layered PANI/graphene composite film exhibits an excellent gravimetric capacitance of 384 F g-1 at 0.5 A g-1 which is much higher than many other hybrid supercapacitors reported to date. It maintains its capacity up to 84% over 1000 cycles at a current density of 2 A g-1. This preparation method may provide a promising strategy for preparation of graphene-based composites with other conducting polymers and binder-free film electrodes.

The mechanical properties of three-dimensionally ordered macroporous (3DOM) nickel with pore sizes between 312 and 1200 nm are explored under nanoindentation and microhardness tester. This paper demonstrates that the hardness and elastic modulus of 3DOM Ni increase as the pore size decreases. The phenomenon that “smaller is stronger” is mainly due to the combined effects of sharing the load with more and smaller pore cells, the decrease of the moment upon the small pore wall and larger surface area to volume ratio. The deformation process of 3DOM Ni is pore wall “fracture-dominated” under nanoindentation while “bending-dominated” under microhardness tester. The results demonstrate how pore size control at the microstructural scale can tailor hardness and elastic modulus and hold great potential for the design and application of new 3DOM materials and devices.

Renqiu buried hill reservoir of electric submersible pump well in order to reduce the sewage invalid recycling and saving energy, using the electric submersible pump(ESP) well convert into pumping unit well to produce. The scheme design lifting process adjustment and optimization of 13 wells. Through the analysis of economic benefit, 13 ESP wells convert into the pumping unit wells will obtain the production economic benefit of 5.5809 million Yuan. The investment recovery period is 24 months. The field application results are obvious, reaching the purpose of energy saving. Keywords-Renqiu buried hill reservoir; electric submersible pump; pumping unit well; lift methods; economic benefit analysis.

For the purpose of improving the prediction of cancer prognosis in the clinical researches, various algorithms have been developed to construct the predictive models with the gene signatures detected by DNA microarrays. Due to the heterogeneity of the clinical samples, the list of differentially expressed genes (DEGs) generated by the statistical methods or the machine learning algorithms often involves a number of false positive genes, which are not associated with the phenotypic differences between the compared clinical conditions, and subsequently impacts the reliability of the predictive models. In this study, we proposed a strategy, which combined the statistical algorithm with the gene-pathway bipartite networks, to generate the reliable lists of cancer-related DEGs and constructed the models by using support vector machine for predicting the prognosis of three types of cancers, namely, breast cancer, acute myeloma leukemia, and glioblastoma. Our results demonstrated that, combined with the gene-pathway bipartite networks, our proposed strategy can efficiently generate the reliable cancer-related DEG lists for constructing the predictive models. In addition, the model performance in the swap analysis was similar to that in the original analysis, indicating the robustness of the models in predicting the cancer outcomes.