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Background and aims Accurate evaluation of intraductal papillary mucinous neoplasm (IPMN) is necessary to inform clinical decision-making. But it is still difficult to distinguish benign and malignant IPMN preoperatively. This study aims to evaluate the utility of EUS to predict the pathology of IPMN. Methods Patients with IPMN who underwent endoscopic ultrasound within 3 months before surgery were collected from six centers. Logistic regression model and random forest model were used to determine risk factors associated with malignant IPMN. In both models, 70% and 30% of patients were randomly assigned to the exploratory group and validation group, respectively. Sensitivity, specificity, and ROC were used in model assessment. Results Of the 115 patients, 56 (48.7%) had low-grade dysplasia (LGD), 25 (21.7%) had high-grade dysplasia (HGD), and 34 (29.6%) had invasive cancer (IC). Smoking history (OR = 6.95, 95%CI: 1.98–24.44, p = 0.002), lymphadenopathy (OR = 7.91, 95%CI: 1.60–39.07, p = 0.011), MPD > 7 mm (OR = 4.75, 95%CI: 1.56–14.47, p = 0.006) and mural nodules > 5 mm (OR = 8.79, 95%CI: 2.40–32.24, p = 0.001) were independent risk factors predicting malignant IPMN according to the logistic regression model. The sensitivity, specificity, and AUC were 0.895, 0.571, and 0.795 in the validation group. In the random forest model, the sensitivity, specificity, and AUC were 0.722, 0.823, and 0.773, respectively. In patients with mural nodules, random forest model could reach a sensitivity of 0.905 and a specificity of 0.900. Conclusions Using random forest model based on EUS data is effective to differentiate benign and malignant IPMN in this cohort, especially in patients with mural nodules.

Additional file 1: Table S1. The site and distribution of the MEN1 mutation; Table S2. First manifestation of MEN1 patients with insulinoma.

In this study, the effects of various parameters such as power-law index of fluid (with the value of n 0.3–0.9), flow rate (0.1–2.0 m/s), pipe inner diameter (0.01–0.05 m) and operating pressure (0.1–40 MPa) on apparent viscosity of crude oil and emulsion during vertical pipe flow were investigated based on experimental friction pressure loss data and Hagen-Poiseuille equation. After that a new pressure model for calculating apparent viscosity was proposed. The experimental results obtained in this study showed that the apparent viscosity of crude oils and emulsions are significantly velocity dependent and they decrease when velocity increases. It was also found that the apparent viscosity increases with increasing in operating pressure. The pressure loss data obtained under high pressure in this study was used to develop new apparent viscosity model by modifying the original Metzner-Reed model. When at atmospheric pressure, the comparative evaluation of the new model with the original Metzner-Reed model and Newtonian approximation model indicated significant improvement in friction pressure loss estimation. A high goodness of fitting (R2, 0.9987) can be achieved when used the modified model to calculate friction pressure loss based on Hagen-Poiseuille equation. This value of R2 is significantly higher than the 0.8445 R2 calculated by Metzner-Reed model, as well as 0.3523 R2 calculated by Newtonian approximation model. In addition, the accuracy of newly developed model was tested against four sets of experimental data obtained from literatures. Results of this comparison also showed that friction pressure loss predicted by new modified apparent viscosity model is in better with experimental data compared to those predicted by Metzner-Reed model. Finally, the pressure model was proposed based on the modified model and shows a good agreement with the experimental data.

The recovery of heavy oil from ultradeep wells is a technical challenge faced by the petroleum industry. One of the key issues is the flow assurance of heavy oil inside the wellbore and through the pipelines, i.e., effectively lifting heavy oil from the wellbore and subsequently transporting it smoothly through the pipelines. Traditional chemical methods for reducing the viscosity of heavy petroleum fluids adopt oil-soluble and water-soluble viscosity reducers, but these methods have their limitations. In this work, we develop a new composite viscosity reducer called SDG-2, which combines the advantages of oil-soluble and water-soluble viscosity reducers. The molecular chain of this viscosity reducer is grafted with a high-carbon lipophilic polar group and a salt-tolerant hydrophilic group, providing the reducer with both dispersion and emulsification capabilities. An experimental test shows that, without water, the viscosity reducer SDG-2 can achieve a 50% degree of viscosity reduction (DVR) for the ultr...

The interactions between tumor immune microenvironment (TIME) and pancreatic cancer cells can affect chemotherapeutic efficacy; however, the mechanisms still remain largely unknown. Thirty items in TIME were comprehensively screened by using tissue microarray from pancreatic cancer patients. Their expressions, interconnections and predictive roles for survival were analyzed. Twenty-one of 30 items could stratify the survival of the patients; however, multivariate analysis found that only 5 independent risk factors could predict worse survival (M2-polarized tumor-associated macrophages (TAMs), IgG4 positive cells, TGF-β1, GM-CSF and lymphangiogenesis). They had a much higher expression levels in tumoral tissue, compared to peritumoral tissue. The Spearman analysis showed that M2-polarized TAM, TGF-β1 and GM-CSF were positively correlated with pancreatic cancer stem cells (PCSC), angiogenesis and lymphangiogenesis. Both human and murine pancreatic cancer cells could induce M2-polarized TAM, which showed substantial roles to decease chemotherapeutic effects. After treated by gemcitabine, both human and murine pancreatic cancer cell lines expressed higher level of immune check points, PCSC markers and varieties of immunosuppressive factors; however, TGF-β1 and GM-CSF had the highest increase. Based on the above results, TGF-β1 and GM-CSF were proposed to be the optimal potential targets to improve chemotherapeutic effects. In immunocompetent murine models, we demonstrated that combined blockade of TGF-β1 and GM-CSF improved the chemotherapeutic effects by inhibition of M2-polarized TAM and induction of CD8 positive T cells. This study presents a novel promising combined strategy to improve the chemotherapeutic effects for pancreatic cancer.

We study the joint spectrum of projections in a Hilbert space, and calculate the joint spectrum of a pair of projections. In particular, by calculating the joint spectrum for a tuple $[I,P,Q]$ in which $I$ is the identity and $P$, $Q$ is a regular pair of projections,we get specific characterizations of the spectrums of $P+Q$ and $P-Q$, respectively. Conversely, we prove that two closed subsets of $\\mathbb~C$ in particular forms are respectively the spectrums of the sum and the difference of a regular pair of projections. We also calculate the joint spectrum of a $3$-tuple of projections $[P,Q,R]$ for $P$, $Q$ and $R$ satisfying some specific conditions.

Sparse representation classification achieves good results by addressing recognition problem with sufficient training samples per subject. However, SRC performs not very well for small sample data. In this paper, an inverse-projection group sparse representation model is presented for breast tumor classification, which is based on constructing low-rank variation dictionary. The proposed low-rank variation dictionary tackles tumor recognition problem from the viewpoint of detecting and using variations in gene expression profiles of normal and patients, rather than directly using these samples. The inverse projection group sparsity representation model is constructed based on taking full using of exist samples and group effect of microarray gene data. Extensive experiments on public breast tumor microarray gene expression datasets demonstrate the proposed technique is competitive with state-of-the-art methods. The results of Breast-1, Breast-2 and Breast-3 databases are 80.81%, 89.10% and 100% respectively, which are better than the latest literature., Comment: 31 pages, 14 figures, 12 tables. arXiv admin note: text overlap with arXiv:1803.03562

The research focus of the CBIR is to improving the accuracy of the image retrieval with the feedback technology. But this technology can not solve the problem between the low-level feature and high-level semantic , which is the difficulty of the CBIR and the difficulty of the image compressing and video retrieval. This paper shows the basic method of image retrieval in the compressed domain and the developing situation of the feedback. One complicated feedback framework at the CBIR are proposed.