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With the fast development of globalization, more and more attentions has been paid for foreign language study especially during higher education stage. Japan as the biggest developed country in Asia has friendly communication with China, while the education of Japanese in China is not match its importance. Combined with the teaching practice experience, the computer aided language learning strategy could be applied widely in Japanese education. The following paper will begin with the current situation of Japanese teaching in university and provide reasonable recommence. The poor ability of Japanese application is mainly caused by the lack of actual situation in learning process, which could be remedied by computer technology. The computer aided language learning platform could provide various learning material by multimedia software, which is the breakthrough points of specific measurements in university Japanese learning as well. [ABSTRACT FROM AUTHOR]

The dynamic reaction pathways after passing the initial barrier for the reaction of atomic oxygen radical anion (O−) with ethylene (CH2=CH2) have been investigated with Born–Oppenheimer molecular dynamics (BOMD) simulations. The BOMD simulations initiated at this [O⋯H⋯CH=CH2]− barrier on the exit-channel potential energy surface (PES) reveal several different types of dynamic reaction pathways leading to various anionic products. In particular, as the energy added on the transition vector of the [O⋯H⋯CH=CH2]− transition state increases remarkably, the OH− and CH2CH become the dominant products instead of the CH2=CHO− and H. As a result, animated images are displayed and more extensive reaction mechanisms are illuminated for the title reaction from the perspective of the dynamic reaction pathways. [Copyright &y& Elsevier]

In intelligent logistics systems, predicting the Estimated Time of Pick-up Arrival (ETPA) of packages is a crucial task, which aims to predict the courier's arrival time to all the unpicked-up packages at any time. Accurate prediction of ETPA can help systems alleviate customers' waiting anxiety and improve their experience. We identify three main challenges of this problem. First, unlike the travel time estimation problem in other fields like ride-hailing, the ETPA task is distinctively a multi-destination and path-free prediction problem. Second, an intuitive idea for solving ETPA is to predict the pick-up route and then the time in two stages. However, it is difficult to accurately and efficiently predict couriers' future routes in the route prediction step since their behaviors are affected by multiple complex factors. Third, furthermore, in the time prediction step, the requirement for providing a courier's all unpicked-up packages' ETPA at once in real time makes the problem even more challenging. To tackle the preceding challenges, we propose RankETPA, which integrates the route inference into the ETPA prediction. First, a learning-based pick-up route predictor is designed to learn the route-ranking strategies of couriers from their massive spatial-temporal behaviors. Then, a spatial-temporal attention-based arrival time predictor is designed for real-time ETPA inference via capturing the spatial-temporal correlations between the unpicked-up packages. Extensive experiments on two real-world datasets and a synthetic dataset demonstrate that RankETPA achieves significant performance improvement against the baseline models. [ABSTRACT FROM AUTHOR]

Over 10 billion packages are picked up every day in China. A fundamental task raised in the emerging intelligent logistics systems is the couriers' package pick-up route prediction, which is beneficial for package dispatching, arrival-time estimation and overdue-risk evaluation, by leveraging the predicted routes to improve those downstream tasks. In the package pick-up scene, the decision-making of a courier is affected by strict spatial-temporal constraints (e.g., package location, promised pick-up time, current time, and courier's current location). Furthermore, couriers have different decision preferences on various factors (e.g., time factor, distance factor, and balance of both), based on their own perception of the environments and work experience. In this article, we propose a novel model, named DeepRoute+, to predict couriers' future package pick-up routes according to the couriers' decision experience and preference learned from the historical behaviors. Specifically, DeepRoute+ consists of three layers: (1) The representation layer produces experience- and preference-aware representations for the unpicked-up packages, in which a decision preference module can dynamically adjust the importance of factors that affects the courier's decision under the current situation. (2) The transformer encoder layer encodes the representations of packages while considering the spatial-temporal correlations among them. (3) The attention-based decoder layer uses the attention mechanism to generate the whole pick-up route recurrently. Experiments on a real-world logistics dataset demonstrate the state-of-the-art performance of our model. [ABSTRACT FROM AUTHOR]

Compelling evidence suggests that phosphoprotein phosphatases (PPPs) are involved in a large spectrum of physiological and pathological processes, but little is known about their roles in pancreatic cancer. We investigated the expression level, prognostic value, and potential function of PPPs with data from Oncomine, GEPIA, THPA, and TCGA databases and an independent cohort of patients with pancreatic cancer. Among all the PPP catalytic subunits (PPPcs), the transcription levels of PPP1CA, PPP1CB, PPP3CA, PPP3CB, and PPP4C were higher in pancreatic cancer than in normal pancreas (P<0.01, fold change > 2). Kaplan-Meier analysis showed that high transcription levels of PPP1CA, PPP1CB, PPP2CA, PPP2CB, PPP3CA, and PPP4C correlated with poorer survival. In contrast, patients with high levels of PPP3CB, PPP3CC, PPP5C, PPP6C, and PPEF2 had much better prognoses. Data from THPA and patients with pancreatic cancer enrolled in our hospital also confirmed the prognostic value of PPP1CA, PPP1CB, PPP2CA, PPP2CB, PPP3CA, PPP3CB, and PPP6C at the protein level. In addition, the Pearson Chi-square test showed that PPP3CB level was significantly correlated with T and N stages. GO and KEGG analyses showed that the genes and pathways related to the pathogenesis and progression of pancreatic cancer were greatly affected by alterations in PPPcs. Results of the present study suggest that PPP1CA, PPP1CB, PPP2CA, PPP2CB, and PPP3CA have deleterious effects but PPP3CB, PPP5C, and PPP6C have beneficial effects on pancreatic cancer. [ABSTRACT FROM AUTHOR]

Traditionally, it was presumed that gastric cancer was derived from tumor cells with stem-like properties. In the present study, stem-like cells from the gastric cancer cell lines MKN45 and SGC7901 were enriched by growing them as spheres in a defined serum-free medium. Following enrichment for stem-like cells, cluster of differentiation (CD)24 and CD44 were applied as candidate stem cell markers to examine the expression profile. It was revealed that the sphere-derived cells contained a higher proportion of cells expressing the stem cell surface markers CD24 and CD44 when compared with the parental cells. It was also identified that the expression of cytokeratin 18 in sphere-derived cells was decreased and the expression of vimentin and aldehyde dehydrogenase 1 (ALDH1) was increased compared with the parental cells. This finding supports the existence of a population of tumor sphere-forming cells with stem cell properties in the MKN45 and SGC7901 cell lines. Furthermore, the stem cell population was enriched in cells expressing CD24, CD44, vimentin and ALDH1 cell surface markers. These results support the existence of gastric cancer stem cells and provide an alternative approach to the diagnosis and treatment of gastric cancer. [ABSTRACT FROM AUTHOR]