The Predicting Model of E-commerce Site Based on the Ideas of Curve Fitting

AbstractOn the basis of the idea of the second multiplication curve fitting, the number and scale of Chinese E-commerce site is analyzed. A preventing increase model is introduced in this paper, and the model parameters are solved by the software of Matlab. The validity of the preventing increase model is confirmed though the numerical experiment. The experimental results show that the precision of preventing increase model is ideal

CXCL13/CXCR5 Axis Predicts Poor Prognosis and Promotes Progression Through PI3K/AKT/mTOR Pathway in Clear Cell Renal Cell Carcinoma

The chemokine ligands and their receptors play critical roles in cancer progression and patients outcomes. We found that CXCL13 was significantly upregulated in ccRCC tissues compared with normal tissues in both The Cancer Genome Atlas (TCGA) cohort and a validated cohort of 90 pairs ccRCC tissues. Statistical analysis showed that high CXCL13 expression related to advanced disease stage and poor prognosis in ccRCC. We also revealed that serum CXCL13 levels in ccRCC patients (n = 50) were significantly higher than in healthy controls (n = 40). Receiver operating characteristic (ROC) curve revealed that tissue and serum CXCL13 expression might be a diagnostic biomarker for ccRCC with an area under curve (AUC) of 0.809 and 0.704, respectively. CXCL13 was significantly associated with its receptor, CXCR5, in ccRCC tissues, and ccRCC patients in high CXCL13 high CXCR5 expression group have a worst prognosis. Functional and mechanistic study revealed that CXCL13 promoted the proliferation and migration of ccRCC cells by binding to CXCR5 and activated PI3K/AKT/mTOR signaling pathway. These results suggested that CXCL13/CXCR5 axis played a significant role in ccRCC and might be a therapeutic target and prognostic biomarker

High-Speed Train Stop-Schedule Optimization Based on Passenger Travel Convenience

The stop-schedules for passenger trains are important to the operation planning of high-speed trains, and they decide the quality of passenger service and the transportation efficiency. This paper analyzes the specific manifestation of passenger travel convenience and proposes the concepts of interstation accessibility and degree of accessibility. In consideration of both the economic benefits of railway corporations and the travel convenience of passengers, a multitarget optimization model is established. The model aims at minimizing stop cost and maximizing passenger travel convenience. Several constraints are applied to the model establishment, including the number of stops made by individual trains, the frequency of train service received by each station, the operation section, and the 0-1 variable. A hybrid genetic algorithm is designed to solve the model. Both the model and the algorithm are validated through case study

A Soft Rough-Fuzzy Preference Set-Based Evaluation Method for High-Speed Train Operation Diagrams

This paper proposes a method of high-speed railway train operation diagram evaluation based on preferences of locomotive operation, track maintenance, S & C, vehicles and other railway departments, and customer preferences. The application of rough set-based attribute reduction obtains the important relative indicators by eliminating excessive and redundant evaluation indicators. Soft fuzzy set theory is introduced for the overall evaluation of train operation diagrams. Each expert utilizes a set of indicators during evaluation based on personal preference. In addition, soft fuzzy set theory is applied to integrate the information obtained via expert evaluation in order to obtain an overall evaluation. The proposed method was validated by a case study. Results demonstrate that the proposed method flexibly expresses the subjective judgments of experts while effectively and reasonably handling the uncertainty of information, which is consistent with the judgment process of humans. The proposed method is also applicable to the evaluation of train operation schemes which consist of multiple diagrams

Long Non-Coding RNA LUCAT1 Promotes Proliferation and Invasion in Clear Cell Renal Cell Carcinoma Through AKT/GSK-3β Signaling Pathway

Background/Aims: Long non-coding RNAs (lncRNAs) have emerged as new regulators and biomarkers in several cancers. However, few lncRNAs have been well characterized in clear cell renal cell carcinoma (ccRCC). Methods: We investigated the lncRNA expression profile by microarray analysis in 5 corresponding ccRCC tissues and adjacent normal tissues. Lung cancer–associated transcript 1 (LUCAT1) expression was examined in 90 paired ccRCC tissues by real-time PCR and validated by The Cancer Genome Atlas (TCGA) database. Kaplan-Meier analysis was used to examine the prognostic value of LUCAT1 and CXCL2 in ccRCC patients. Loss and gain of function were performed to explore the effect of LUCAT1 on proliferation and invasion in ccRCC cells. Western blotting was performed to evaluate the underlying mechanisms of LUCAT1 in ccRCC progression. Chemokine stimulation assay was performed to investigate possible mechanisms controlling LUCAT1 expression in ccRCC cells. Enzyme-linked immunosorbent assays were performed to determine serum CXCL2 in ccRCC patients and healthy volunteers. Receiver operating characteristic curve analysis was performed to examine the clinical diagnostic value of serum CXCL2 in ccRCC. Results: We found that LUCAT1 was significantly upregulated in both clinical ccRCC tissues (n = 90) and TCGA ccRCC tissues (n = 448) compared with normal tissues. Statistical analysis revealed that the LUCAT1 expression level positively correlated with tumor T stage (P < 0.01), M stage (P < 0.01), and TNM stage (P < 0.01). Overall survival and disease-free survival time were significantly shorter in the high-LUCAT1-expression group than in the low-LUCAT1-expression group (log-rank P < 0.01). LUCAT1 knockdown inhibited ccRCC cell proliferation and colony formation, induced cell cycle arrest at G1 phase, and inhibited cell migration and invasion. Overexpression of LUCAT1 promoted proliferation, migration, and invasion of ccRCC cells. Mechanistic investigations showed that LUCAT1 induced cell cycle G1 arrest by regulating the expression of cyclin D1, cyclin-dependent kinase 4, and phosphorylated retinoblastoma transcriptional corepressor 1. Moreover, LUCAT1 promoted proliferation and invasion in ccRCC cells partly through inducing the phosphorylation of AKT and suppressing the phosphorylation of GSK-3β. We also revealed that chemokine CXCL2, upregulated in ccRCC, induced LUCAT1 expression and might be a diagnostic and prognostic biomarker in ccRCC. Conclusions: LUCAT1 was upregulated in ccRCC tissues and renal cancer cell lines, and significantly correlated with malignant stage and poor prognosis in ccRCC. LUCAT1 promoted proliferation and invasion in ccRCC cells through the AKT/GSK-3β signaling pathway. We also revealed that LUCAT1 overexpression was induced by chemokine CXCL2. These findings indicate that the CXCL2/LUCAT1/AKT/GSK-3β axis is a potential therapeutic target and molecular biomarker for ccRCC

The potential mechanisms and application prospects of non-coding RNAs in intervertebral disc degeneration

Low back pain (LBP) is one of the most common musculoskeletal symptoms and severely affects patient quality of life. The majority of people may suffer from LBP during their life-span, which leading to huge economic burdens to family and society. According to the series of the previous studies, intervertebral disc degeneration (IDD) is considered as the major contributor resulting in LBP. Furthermore, non-coding RNAs (ncRNAs), mainly including microRNAs (miRNAs), long noncoding RNAs (lncRNAs) and circular RNAs (circRNAs), can regulate diverse cellular processes, which have been found to play pivotal roles in the development of IDD. However, the potential mechanisms of action for ncRNAs in the processes of IDD are still completely unrevealed. Therefore, it is challenging to consider ncRNAs to be used as the potential therapeutic targets for IDD. In this paper, we reviewed the current research progress and findings on ncRNAs in IDD: i). ncRNAs mainly participate in the process of IDD through regulating apoptosis of nucleus pulposus (NP) cells, metabolism of extracellular matrix (ECM) and inflammatory response; ii). the roles of miRNAs/lncRNAs/circRNAs are cross-talk in IDD development, which is similar to the network and can modulate each other; iii). ncRNAs have been attempted to combat the degenerative processes and may be promising as an efficient bio-therapeutic strategy in the future. Hence, this review systematically summarizes the principal pathomechanisms of IDD and shed light on the therapeutic potentials of ncRNAs in IDD

In Vivo Outer Hair Cell Length Changes Expose the Active Process in the Cochlea

BACKGROUND: Mammalian hearing is refined by amplification of the sound-evoked vibration of the cochlear partition. This amplification is at least partly due to forces produced by protein motors residing in the cylindrical body of the outer hair cell. To transmit power to the cochlear partition, it is required that the outer hair cells dynamically change their length, in addition to generating force. These length changes, which have not previously been measured in vivo, must be correctly timed with the acoustic stimulus to produce amplification. METHODOLOGY/PRINCIPAL FINDINGS: Using in vivo optical coherence tomography, we demonstrate that outer hair cells in living guinea pigs have length changes with unexpected timing and magnitudes that depend on the stimulus level in the sensitive cochlea. CONCLUSIONS/SIGNIFICANCE: The level-dependent length change is a necessary condition for directly validating that power is expended by the active process presumed to underlie normal hearing

Modelling and impact analysis of interdependent characteristics on cascading overload failure of syncretic railway networks.

To study the performance and mutual influence of a syncretic railway network (SRN) that comprises high-speed railway, regional railway, and urban rail transit under the condition of traffic overload during peak hours, we discuss the interdependent characteristics on cascading overload failure of SRNs under the cooperative organization from the perspective of an interdependent network. However, most existing research on cascading failure in interdependent network ignores the inconsistency between the physical structure and transportation organization of the subnetwork in an actual network, in addition to the restrictions on the load redistribution strategy of stations and sections in the load-capacity model of the interdependent network; especially, the influence of transfer behavior on the load redistribution inter subnetwork. In this study, we investigate the robustness of an interdependent SRN under overload and risk propagation. We propose a partially interdependent network model of a multimode rail transit, develop a novel cascading overload failure model with tunable parameters of load redistribution inter subnetwork, and analyze interdependent characteristics, cascade failure process, and robustness of an SRN under multiscene conditions, i.e., different attack and load distribution strategies, via simulations. A case study of an SRN in the metropolitan area of Chengdu, China is presented; the results indicate that, when the reserve coefficient of the metro subnetwork is 0.4 and the overload coefficient of the regional railway subnetwork is greater than 1.2, the station reserve capacity and overload capacity of the SRN is appropriately improved. When passenger load increases to a certain range, the reserve and overload capacities of stations in the regional railway subnetwork do not considerably contribute to robustness. Thus, a surplus load distribution strategy is recommended to improve robustness. The results of this paper have considerable significance for the planning, structural optimization, and operation safety of SRNs

Real-Time and Accurate Indoor Localization with Fusion Model of Wi-Fi Fingerprint and Motion Particle Filter

As the development of Indoor Location Based Service (Indoor LBS), a timely localization and smooth tracking with high accuracy are desperately needed. Unfortunately, any single method cannot meet the requirement of both high accuracy and real-time ability at the same time. In this paper, we propose a fusion location framework with Particle Filter using Wi-Fi signals and motion sensors. In this framework, we use Extreme Learning Machine (ELM) regression algorithm to predict position based on motion sensors and use Wi-Fi fingerprint location result to solve the error accumulation of motion sensors based location occasionally with Particle Filter. The experiments show that the trajectory is smoother as the real one than the traditional Wi-Fi fingerprint method