Correlation Analysis of China’s Urban Rail Transit Industry

Urban rail transit has provided safe, convenient, fast and comfortable transport services. Its development and construction not only require a huge investment but also have a long industry chain and involve industry sectors of different types. This paper studies backward linkage and forward linkage industries of urban rail transit industry according to input-output tables for urban rail transit of subdivided sectors in 2007 and 2012

Internet Financial Credit Risk Assessment with Sliding Window and Attention Mechanism LSTM Model

With the accelerated pace of market-oriented reform, Internet finance has gained a broad and healthy development environment. Existing studies lack consideration of time trends in financial risk, and treating all features equally may lead to inaccurate predictions. To address the above problems, we propose an LSTM model based on sliding window and attention mechanism. The model uses sliding windows to enable the model to effectively exploit the contextual relevance of loan data. And we introduce the attention mechanism into the model, which enables the model to focus on important information. The result on the Lending Club public desensitization dataset shows that our model outperforms ARIMA, SVM, ANN, LSTM, and GRU models

Internet Financial Credit Risk Assessment with Sliding Window and Attention Mechanism LSTM Model

With the accelerated pace of market-oriented reform, Internet finance has gained a broad and healthy development environment. Existing studies lack consideration of time trends in financial risk, and treating all features equally may lead to inaccurate predictions. To address the above problems, we propose an LSTM model based on sliding window and attention mechanism. The model uses sliding windows to enable the model to effectively exploit the contextual relevance of loan data. And we introduce the attention mechanism into the model, which enables the model to focus on important information. The result on the Lending Club public desensitization dataset shows that our model outperforms ARIMA, SVM, ANN, LSTM, and GRU models

Oncogenic Mutations in Armadillo Repeats 5 and 6 of β-Catenin Reduce Binding to APC, Increasing Signaling and Transcription of Target Genes

Background & Aims: The β-catenin signaling pathway is one of the most commonly deregulated pathways in cancer cells. Amino acid substitutions within armadillo repeats 5 and 6 (K335, W383, and N387) of β-catenin are found in several tumor types, including liver tumors. We investigated the mechanisms by which these substitutions increase signaling and the effects on liver carcinogenesis in mice. Methods: Plasmids encoding tagged full-length β-catenin (CTNNB1) or β-catenin with the K335I or N387K substitutions, along with MET, were injected into tails of FVB/N mice. Tumor growth was monitored, and livers were collected and analyzed by histology, immunohistochemistry, and quantitative reverse-transcription polymerase chain reaction. Tagged full-length and mutant forms of β-catenin were expressed in HEK293, HCT116, and SNU449 cells, which were analyzed by immunoblots and immunoprecipitation. A panel of β-catenin variants and cell lines with knock-in mutations were analyzed for differences in N-terminal phosphorylation, half-life, and association with other proteins in the signaling pathway. Results: Mice injected with plasmids encoding K335I or N387K β-catenin and MET developed larger, more advanced tumors than mice injected with plasmids encoding WT β-catenin and MET. K335I and N387K β-catenin bound APC with lower affinity than WT β-catenin but still interacted with scaffold protein AXIN1 and in the nucleus with TCF7L2. This interaction resulted in increased transcription of genes regulated by β-catenin. Studies of protein structures supported the observed changes in relative binding affinities. Conclusion: Expression of β-catenin with mutations in armadillo repeats 5 and 6, along with MET, promotes formation of liver tumors in mice. In contrast to N-terminal mutations in β-catenin that directly impair its phosphorylation by GSK3 or binding to BTRC, the K335I or N387K substitutions increase signaling via reduced binding to APC. However, these mutant forms of β-catenin still interact with the TCF family of transcription factors in the nucleus. These findings show how these amino acid substitutions increase β-catenin signaling in cancer

Nf1-Dependent Tumors Require a Microenvironment Containing Nf1+/−- and c-kit-Dependent Bone Marrow

SummaryInteractions between tumorigenic cells and their surrounding microenvironment are critical for tumor progression yet remain incompletely understood. Germline mutations in the NF1 tumor suppressor gene cause neurofibromatosis type 1 (NF1), a common genetic disorder characterized by complex tumors called neurofibromas. Genetic studies indicate that biallelic loss of Nf1 is required in the tumorigenic cell of origin in the embryonic Schwann cell lineage. However, in the physiologic state, Schwann cell loss of heterozygosity is not sufficient for neurofibroma formation and Nf1 haploinsufficiency in at least one additional nonneoplastic lineage is required for tumor progression. Here, we establish that Nf1 heterozygosity of bone marrow-derived cells in the tumor microenvironment is sufficient to allow neurofibroma progression in the context of Schwann cell Nf1 deficiency. Further, genetic or pharmacologic attenuation of c-kit signaling in Nf1+/− hematopoietic cells diminishes neurofibroma initiation and progression. Finally, these studies implicate mast cells as critical mediators of tumor initiation

Applying a Probabilistic Network Method to Solve Business-Related Few-Shot Classification Problems

It can be challenging to learn algorithms due to the research of business-related few-shot classification problems. Therefore, in this paper, we evaluate the classification of few-shot learning in the commercial field. To accurately identify the categories of few-shot learning problems, we proposed a probabilistic network (PN) method based on few-shot and one-shot learning problems. The enhancement of the original data was followed by the subsequent development of the PN method based on feature extraction, category comparison, and loss function analysis. The effectiveness of the method was validated using two examples (absenteeism at work and Las Vegas Strip hotels). Experimental results demonstrate the ability of the PN method to effectively identify the categories of commercial few-shot learning problems. Therefore, the proposed method can be applied to business-related few-shot classification problems