Esophagus squamous cell carcinoma (ESCC) is one of the most deadly malignances because of its high frequency of metastasis. Given the associations of MUC1 with ESCC and tumor metastasis, we explored a potential role of MUC1 in ESCC metastasis. Among 40 ESCC and 20 paired normal tissue specimens examined, we found a significant increase of MUC1 expression in ESCC and more importantly, that expression of MUC1 and MMP13 are strongly correlated in patients who had lymph node metastasis. Studies with cell models indicated that overexpression of MUC1 upregulates the expression of MMP13, leading to increased cell migration. In support of a mode of transcriptional regulation, promoter analysis revealed that MUC1 stimulates MMP13 expression through the Runx-2-binding site. The link of MUC1 to cell motility was further confirmed by the finding that depletion of MUC1 resulted in reduced expression of MMP13 and cell migration, invasion and adhesion. Moreover, the loss of cell metastatic potential was rescued by overexpression of MMP13 completely. Collectively, our findings indicate that MUC1 contributes to ESCC metastasis by stimulating MMP13 expression, suggesting MUC1 as a novel diagnostic biomarker and therapeutic target in ESCC. Esophageal squamous cell carcinoma (ESCC) frequently exhibits extensive local invasion or regional lymph node metastasis at the time of initial diagnosis; therefore, it is one of the most common aggressive diseases with poor outcome. 1 Tumor invasion and metastasis involve degradation of different components of the extracellular matrix and require the actions of proteolytic enzymes, such as matrix metalloproteinases (MMPs), which are produced either by the tumor cells or surrounding stromal cells.2,3 MMP13 is a highly regulated zinc-dependent endopeptidase and has been reported to be associated with vascular invasion and lymph node metastasis in ESCC. 4 Mechanisms involved in regulation of MMP13 in ESCC are likely complex and poorly understood.Mucins are high-molecular-weight glycoproteins that have been identified as markers of adverse prognosis and as attractive therapeutic targets.5 MUC1, one of transmembrane mucins, is normally expressed in esophageal epithelium.Patients with MUC1 high expression often appear with advanced stage or lymph node metastasis suggesting correlation of the MUC1 expression and the invasion or metastasis of ESCC.6 In this study, we investigated the expression of MUC1 and MMP13 in ESCC patients and the potential functional relationship in tumor metastasis and prognosis. MATERIALS AND METHODS Tissue Sample CollectionA total of 40 paraffin-embedded archival specimens of primary ESCC cases were enrolled in this study: 20 with lymph node metastasis and 20 without lymph node metastasis. A total of 20 paired normal esophageal tissue specimens distant from the cancerous lesion in patients without lymph node metastasis were used as control. These patients did not receive any preoperative adjuvant radiation or chemotherapy.

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Recent years have witnessed a drastic increase in the number of urban metro passengers, which inevitably causes the overcrowdedness in the metro systems of many cities. Clearly, an accurate prediction of passenger flows at metro stations is critical for a variety of metro system management operations, such as line scheduling and staff preallocation, that help alleviate such overcrowdedness. Thus, in this paper, we aim to address the problem of accurately predicting metro station passenger (MSP) flows. Similar to other traffic data, such as road traffic volume and highway speed, MSP flows are also spatial-temporal in nature. However, existing methods for other traffic prediction tasks are usually suboptimal to predict MSP flows due to MSP flows' unique spatial-temporal characteristics. As a result, we propose a novel deep learning framework STP-TrellisNets, which for the first time augments the newly-emerged temporal convolutional framework TrellisNet for spatial-temporal prediction. The temporal module of STP-TrellisNets (named CP-TrellisNets) employs two TrellisNets in serial to jointly capture the short-and long-term temporal correlation of MSP flows. In parallel to CP-TrellisNets, its spatial module (named GC-TrellisNet) adopts a novel transfer flow-based metric to characterize the spatial correlation among MSP flows, and implements multiple diffusion graph convolutional networks (DGCNs) in time-series order with their outputs connected to a TrellisNet to capture the dynamics of such spatial correlation. Clearly, GC-TrellisNet essentially integrates TrellisNet with graph convolution, and empowers TrellisNet with the ability to capture dynamic graph-structured correlation. We conduct extensive experiments with two large-scale real-world automated fare collection datasets, which contain respectively about 1.5 billion records in Shenzhen, China and 70 million records in Hangzhou, China. The experimental results demonstrate that STP-TrellisNets outperforms the state-of-the-art baselines. CCS CONCEPTS • Information systems → Spatial-temporal systems; Data mining; • Computing methodologies → Neural networks.

Aimed at solving the problems of poor stability and easily falling into the local optimal solution in the grey wolf optimizer (GWO) algorithm, an improved GWO algorithm based on the differential evolution (DE) algorithm and the OTSU algorithm is proposed (DE-OTSU-GWO). The multithreshold OTSU, Tsallis entropy, and DE algorithm are combined with the GWO algorithm. The multithreshold OTSU algorithm is used to calculate the fitness of the initial population. The population is updated using the GWO algorithm and the DE algorithm through the Tsallis entropy algorithm for crossover steps. Multithreshold OTSU calculates the fitness in the initial population and makes the initial stage basically stable. Tsallis entropy calculates the fitness quickly. The DE algorithm can solve the local optimal solution of GWO. The performance of the DE-OTSU-GWO algorithm was tested using a CEC2005 benchmark function (23 test functions). Compared with existing particle swarm optimizer (PSO) and GWO algorithms, the experimental results showed that the DE-OTSU-GWO algorithm is more stable and accurate in solving functions. In addition, compared with other algorithms, a convergence behavior analysis proved the high quality of the DE-OTSU-GWO algorithm. In the results of classical agricultural image recognition problems, compared with GWO, PSO, DE-GWO, and 2D-OTSU-FA, the DE-OTSU-GWO algorithm had accuracy in straw image recognition and is applicable to practical problems. The OTSU algorithm improves the accuracy of the overall algorithm while increasing the running time. After adding the DE algorithm, the time complexity will increase, but the solution time can be shortened. Compared with GWO, DE-GWO, PSO, and 2D-OTSU-FA, the DE-OTSU-GWO algorithm has better results in segmentation assessment.

Human effective intestinal membrane permeability (Peff) is one of the two important indicators for drug classification according to the Biopharmaceutical Classification System (BCS), and contributes greatly to the performance of oral drug absorption. Here, a structure-based in silico predictive model of Peff was developed successfully to facilitate in silico BCS classification in the early stage of drug discovery, even before the compound was synthesized. The quantitative structure-Peff relationship for 30 drugs was constructed based on seven structural parameters. Then the model was built by the multiple linear regression method and internally validated by the residual analysis, the normal probability-probability plot and the Williams plot. For the entire data set, the R² and adjusted R² values were 0.782 and 0.712, respectively. The results indicated that the fitted model was robust, stable and satisfied all the prerequisites of the regression models. As for the 102 tested drugs, the predicted Peff values had a good correlation with the experimental human absorbed fraction (Fa). This model was also used to perform high/low Peff classification for 57 drugs that have been classified according to the BCS, and 72% of drugs could be classified correctly, indicating that the developed model can be used for rapid BCS classification in the early stages of drug discovery.