Analysis on the Business Model of Fresh E-commerce------Taking Hema Supermarket as an Example

Enterprises are beginning to involve the fresh produce industry, but most companies have withdrawn from the fresh produce industry due to poor performance. This shows that there are many problems with e-commerce of fresh produce. In particular, the business model of e-commerce for fresh produce is a major factor constraining its development. This article takes Hema Supermarket as an example to analyze its business model. It summarizes the areas that can be used for product control, power distribution system construction, platform operation, etc., and provides reference and reference for the operation of fresh agricultural products

Developmental Expression of Secretory β-1,4-endoglucanases in the Subventral Esophageal Glands of Heterodera glycines

Two β-1,4-endoglucanases (EGases), Hg-eng-1 and Hg-eng-2, were recently cloned from the soybean cyst nematode, Heterodera glycines, and their expression was shown in the subventral esophageal glands of hatched second-stage juveniles (J2). We examined the expression of these EGases in the subventral glands of all post-embryonic life stages of H. glycines by in situ hybridization and immunolocalization. The first detectable accumulation of EGase mRNAs occurred in the subventral glands of unhatched J2. EGase transcripts remained detectable in J2 after hatching and during subsequent root invasion. However, in late parasitic J2 and third-stage juveniles (J3), the percentage of individuals that showed EGase transcripts decreased. In female fourth-stage juveniles and adult females, EGase transcripts were no longer detected in the subventral glands. EGase hybridization signal reappeared in unhatched males coiled within the J3 cuticle, and transcripts were also present in the subventral glands of migratory adult males. Immunofluorescence labeling showed that EGase translation products are most abundantly present in the subventral glands of preparasitic J2, migratory parasitic J2, and adult males. The presence of EGases predominantly in the migratory stages suggests that the enzymes are used by the nematodes to soften the walls of root cells during penetration and intracellular migration

Deactivation of Akt by a Small Molecule Inhibitor Targeting Pleckstrin Homology Domain and Facilitating Akt Ubiquitination

The phosphatidylinositol-3,4,5-triphosphate (PIP3) binding function of pleckstrin homology (PH) domain is essential for the activation of oncogenic Akt/PKB kinase. Following the PIP3-mediated activation at the membrane, the activated Akt is subjected to other regulatory events, including ubiquitination-mediated deactivation. Here, by identifying and characterizing an allosteric inhibitor, SC66, we show that the facilitated ubiquitination effectively terminates Akt signaling. Mechanistically, SC66 manifests a dual inhibitory activity that directly interferes with the PH domain binding to PIP3 and facilitates Akt ubiquitination. A known PH domain-dependent allosteric inhibitor, which stabilizes Akt, prevents the SC66-induced Akt ubiquitination. A cancer-relevant Akt1 (e17k) mutant is unstable, making it intrinsically sensitive to functional inhibition by SC66 in cellular contexts in which the PI3K inhibition has little inhibitory effect. As a result of its dual inhibitory activity, SC66 manifests a more effective growth suppression of transformed cells that contain a high level of Akt signaling, compared with other inhibitors of PIP3/Akt pathway. Finally, we show the anticancer activity of SC66 by using a soft agar assay as well as a mouse xenograft tumor model. In conclusion, in this study, we not only identify a dual-function Akt inhibitor, but also demonstrate that Akt ubiquitination could be chemically exploited to effectively facilitate its deactivation, thus identifying an avenue for pharmacological intervention in Akt signaling

ZO-1 interactions with F-actin and occludin direct epithelial polarization and single lumen specification in 3D culture

Epithelia within tubular organs form and expand lumens. Failure of these processes can result in serious developmental anomalies. Although tight junction assembly is crucial to epithelial polarization, the contribution of specific tight junction proteins to lumenogenesis is undefined. Here, we show that ZO-1 (also known as TJP1) is necessary for the formation of single lumens. Epithelia lacking this tight junction scaffolding protein form cysts with multiple lumens and are defective in the earliest phases of polarization, both in two and three dimensions. Expression of ZO-1 domain-deletion mutants demonstrated that the actin-binding region and U5-GuK domain are crucial to single lumen development. For actin-binding region, but not U5-GuK domain, mutants, this could be overcome by strong polarization cues from the extracellular matrix. Analysis of the U5-GuK binding partners shroom2, α-catenin and occludin showed that only occludin deletion led to multi-lumen cysts. Like ZO-1-deficiency, occludin deletion led to mitotic spindle orientation defects. Single lumen formation required the occludin OCEL domain, which binds to ZO-1. We conclude that ZO-1–occludin interactions regulate multiple phases of epithelial polarization by providing cell-intrinsic signals that are required for single lumen formation

Industrial Data Denoising via Low-Rank and Sparse Representations and Its Application in Tunnel Boring Machine

The operation data of a tunnel boring machine (TBM) reflects its geological conditions and working status, which can provide critical references and essential information for TBM designers and operators. However, in practice, operation data may get corrupted due to equipment failures or data management errors. Moreover, the working state of a TBM system usually changes, which results in patterns of operation data that vary comparatively. This paper proposes a denoising approach to process the corrupted data. This approach is combined with low-rank matrix recovery (LRMR) and sparse representation (SR) theory. The classical LRMR model requires that the noise must be sparse, but the sparsity of noise cannot be fully guaranteed. In the proposed model, a weighted nuclear norm is utilized to enhance the sparsity of sparse components, and a constraint of condition number is applied to ensure the stability of the model solution. The approach is coupled with a fuzzy c-means algorithm (FCM) to find the natural partitioning using the TBM operation data as input. The performances of the proposed approach are illustrated through an application to the Shenzhen metro. Experimental results show that the proposed approach performs well in corrupted TBM data denoising. The different excavation status of the TBM recognition accuracy is improved remarkably after denoising

Industrial Data Denoising via Low-Rank and Sparse Representations and Its Application in Tunnel Boring Machine

The operation data of a tunnel boring machine (TBM) reflects its geological conditions and working status, which can provide critical references and essential information for TBM designers and operators. However, in practice, operation data may get corrupted due to equipment failures or data management errors. Moreover, the working state of a TBM system usually changes, which results in patterns of operation data that vary comparatively. This paper proposes a denoising approach to process the corrupted data. This approach is combined with low-rank matrix recovery (LRMR) and sparse representation (SR) theory. The classical LRMR model requires that the noise must be sparse, but the sparsity of noise cannot be fully guaranteed. In the proposed model, a weighted nuclear norm is utilized to enhance the sparsity of sparse components, and a constraint of condition number is applied to ensure the stability of the model solution. The approach is coupled with a fuzzy c-means algorithm (FCM) to find the natural partitioning using the TBM operation data as input. The performances of the proposed approach are illustrated through an application to the Shenzhen metro. Experimental results show that the proposed approach performs well in corrupted TBM data denoising. The different excavation status of the TBM recognition accuracy is improved remarkably after denoising

Data-Driven Approach for Passenger Mobility Pattern Recognition Using Spatiotemporal Embedding

Urban mobility pattern recognition has great potential in revealing human travel mechanism, discovering passenger travel purpose, and predicting and managing traffic demand. This paper aims to propose a data-driven method to identify metro passenger mobility patterns based on Automatic Fare Collection (AFC) data and geo-based data. First, Point of Information (POI) data within 500 meters of the metro stations are captured to characterize the spatial attributes of the stations. Especially, a fusion method of multisource geo-based data is proposed to convert raw POI data into weighted POI data considering service capabilities. Second, an unsupervised learning framework based on stacked auto-encoder (SAE) is designed to embed the spatiotemporal information of trips into low-dimensional dense trip vectors. In detail, the embedded spatiotemporal information includes spatial features (POI categories around the origin station and that around the destination station) and temporal features (start time, day of the week, and travel time). Third, a density-based clustering algorithm is introduced to identify passenger mobility patterns based on the embedded dense trip vectors. Finally, a case of Beijing metro network is used to verify the feasibility of the above methodology. The results show that the proposed method performs well in recognizing mobility patterns and outperforms the existing methods