AN EMPIRICAL STUDY ON MEDIATED MULTI-ROUTES TR MODEL BASED ON SC PLATFORM

With the new era of intellectual economic, intellectual capital became the critical components of wealth creation. Core employees with higher organizational performance characteristics are often entitled Talent for their key networking status in creating the organizational intelligent capital values. They can also be competed hotly by other competitor in human capitall market. In the field of talents’ retention (TC), the empirical study of relationship-oriented between talents’ performance and voluntary turnover by modeling is taking lead way in highlighting the talents’ turnover mechanism. This paper, after survey in the cross- industries samples, developed talents’ performance characters- withdraw tendency model by introducing social capital (SC) construction and way of combination of the literature methodology and the empirical study.Talent retention, Social capital, Performance character, Withdraw tendency

A study of the interaction between inverted cucurbit[7]uril and symmetric viologens

The interaction between inverted cucuribit[7]uril (iQ[7]) and a series of symmetric viologen derivatives bearing aliphatic substituents of variable length, namely dicationic dialkyl-4,4′-bipyridinium guests where the alkyl is CH₃(CH₂)n with n = 0 to 6, has been studied in aqueous solution by ¹H NMR spectroscopy, electronic absorption spectroscopy, isothermal titration calorimetry and mass spectrometry. In the case of both n = 5 (HV ²⁺) and 6 (SV²⁺), single crystal X-ray diffraction revealed the composition to be [(iQ[7])₂(HV)₂][CdCl₃Br][H₃O+]₂[H₂O]₁₂.₅ and (iQ[7])₂(C7-SV)₁.₅[CdCl₄]₄(H₃O⁺)₅(H₂O)₈, respectively, with both adopting an external B-type structure (the alkyl chains of the viologen reside within the iQ[7])

AdaptDHM: Adaptive Distribution Hierarchical Model for Multi-Domain CTR Prediction

Large-scale commercial platforms usually involve numerous business domains for diverse business strategies and expect their recommendation systems to provide click-through rate (CTR) predictions for multiple domains simultaneously. Existing promising and widely-used multi-domain models discover domain relationships by explicitly constructing domain-specific networks, but the computation and memory boost significantly with the increase of domains. To reduce computational complexity, manually grouping domains with particular business strategies is common in industrial applications. However, this pre-defined data partitioning way heavily relies on prior knowledge, and it may neglect the underlying data distribution of each domain, hence limiting the model's representation capability. Regarding the above issues, we propose an elegant and flexible multi-distribution modeling paradigm, named Adaptive Distribution Hierarchical Model (AdaptDHM), which is an end-to-end optimization hierarchical structure consisting of a clustering process and classification process. Specifically, we design a distribution adaptation module with a customized dynamic routing mechanism. Instead of introducing prior knowledge for pre-defined data allocation, this routing algorithm adaptively provides a distribution coefficient for each sample to determine which cluster it belongs to. Each cluster corresponds to a particular distribution so that the model can sufficiently capture the commonalities and distinctions between these distinct clusters. Extensive experiments on both public and large-scale Alibaba industrial datasets verify the effectiveness and efficiency of AdaptDHM: Our model achieves impressive prediction accuracy and its time cost during the training stage is more than 50% less than that of other models

Establishment and characterization of three new human breast cancer cell lines derived from Chinese breast cancer tissues

<p>Abstract</p> <p>Background</p> <p>Breast cancer is a major malignancy affecting females worldwide. It is the most common cause of death from cancer in women. Cell lines are widely used in laboratory research and particularly as <it>in vitro </it>models in cancer research. But we found that the routinely used breast cancer cell lines were mostly derived from Caucasians or African-Americans. There were few standard models to study the pathogenic mechanism at molecular level and cell signaling pathway of breast cancer for Asian patients. It is quite necessary to establish new breast cancer cell lines from xanthoderm to study the pathogenic mechanism and therapeutic methods.</p> <p>Results</p> <p>Three new breast cancer cell lines, designated BC-019, BC-020 and BC-021, were successfully established and characterized from breast invasive ductal carcinoma tissues of three Chinese female patients. These new cell lines growing as adherent monolayer with characteristic epithelial morphology could be maintained continuously <it>in vitro</it>, and they were ER-, PR- and C-erbB-2-positive. Their chromosomes showed high hyperdiploidy and complex rearrangements, and they displayed aggressive tumorigencity in tumorigenesis test.</p> <p>Conclusion</p> <p>The three newly established breast cancer cell lines from Chinese patients were tested for a number of, and the results indicate that the cell lines were in good quality and could be served as new cell models in breast cancer study.</p

Metoprolol, N-Acetylcysteine, and Escitalopram Prevents Chronic Unpredictable Mild Stress-Induced Depression by Inhibition of Endoplasmic Reticulum Stress

Background: Endoplasmic reticulum stress (ERS) has been recently suggested to be activated in the major depressive disorder (MDD). However, whether ERS is a potential therapeutic target for MDD is largely unknown. Here we attempted to assess the preventive effect of metoprolol (MET), N-acetylcysteine (NAC), and escitalopram (ESC) on chronic unpredictable mild stress (CUMS)-induced depression and investigate whether ERS mediates the antidepressant role of these drugs.Method: Forty-five sprague-dawley rats were randomly divided into five groups: control, CUMS, CUMS+ESC, CUMS+NAC, and CUMS+MET. Weight measurement, open field activity and sucrose preference were performed before and after stress. Hippocampal nerve cells and capillary ultrastructure were observed by transmission electron microscope, and hippocampal cells apoptosis were detected by flow cytometry. Furthermore, expression of ERS markers glucose-regulated protein 78 (GRP78), C/EBP-homologous protein (CHOP), and caspase-12 were measured by western blot and qRT-PCR.Results: The CUMS-induced rats showed significantly increased depressive-like behaviors including decreased open field activity and sucrose preference. Moreover, CUMS-exposed rats exhibited significantly increased hippocampal cell apoptosis, and showed damage in hippocampal nerve cells and capillary ultrastructure. Furthermore, ESC and NAC not only mitigated depressive-like behaviors, but also decreased apoptosis and pathologies, while MET fail to decrease apoptosis. Moreover, CUMS stimulation significantly elevated ERS by increasing the levels of GRP78, CHOP, and decreasing the level of caspase-12, while ESC, NAC, and MET significantly decreased the ERS.Conclusion: ESC, NAC, and MET might prevent the MDD partly through inactivating the ERS. These findings demonstrated ERS as a novel treatment target for depression

G2PTL: A Pre-trained Model for Delivery Address and its Applications in Logistics System

Text-based delivery addresses, as the data foundation for logistics systems, contain abundant and crucial location information. How to effectively encode the delivery address is a core task to boost the performance of downstream tasks in the logistics system. Pre-trained Models (PTMs) designed for Natural Language Process (NLP) have emerged as the dominant tools for encoding semantic information in text. Though promising, those NLP-based PTMs fall short of encoding geographic knowledge in the delivery address, which considerably trims down the performance of delivery-related tasks in logistic systems such as Cainiao. To tackle the above problem, we propose a domain-specific pre-trained model, named G2PTL, a Geography-Graph Pre-trained model for delivery address in Logistics field. G2PTL combines the semantic learning capabilities of text pre-training with the geographical-relationship encoding abilities of graph modeling. Specifically, we first utilize real-world logistics delivery data to construct a large-scale heterogeneous graph of delivery addresses, which contains abundant geographic knowledge and delivery information. Then, G2PTL is pre-trained with subgraphs sampled from the heterogeneous graph. Comprehensive experiments are conducted to demonstrate the effectiveness of G2PTL through four downstream tasks in logistics systems on real-world datasets. G2PTL has been deployed in production in Cainiao's logistics system, which significantly improves the performance of delivery-related tasks

Interferon-α regulates glutaminase 1 promoter through STAT1 phosphorylation: relevance to HIV-1 associated neurocognitive disorders.

HIV-1 associated neurocognitive disorders (HAND) develop during progressive HIV-1 infection and affect up to 50% of infected individuals. Activated microglia and macrophages are critical cell populations that are involved in the pathogenesis of HAND, which is specifically related to the production and release of various soluble neurotoxic factors including glutamate. In the central nervous system (CNS), glutamate is typically derived from glutamine by mitochondrial enzyme glutaminase. Our previous study has shown that glutaminase is upregulated in HIV-1 infected monocyte-derived-macrophages (MDM) and microglia. However, how HIV-1 leads to glutaminase upregulation, or how glutaminase expression is regulated in general, remains unclear. In this study, using a dual-luciferase reporter assay system, we demonstrated that interferon (IFN) α specifically activated the glutaminase 1 (GLS1) promoter. Furthermore, IFN-α treatment increased signal transducer and activator of transcription 1 (STAT1) phosphorylation and glutaminase mRNA and protein levels. IFN-α stimulation of GLS1 promoter activity correlated to STAT1 phosphorylation and was reduced by fludarabine, a chemical that inhibits STAT1 phosphorylation. Interestingly, STAT1 was found to directly bind to the GLS1 promoter in MDM, an effect that was dependent on STAT1 phosphorylation and significantly enhanced by IFN-α treatment. More importantly, HIV-1 infection increased STAT1 phosphorylation and STAT1 binding to the GLS1 promoter, which was associated with increased glutamate levels. The clinical relevance of these findings was further corroborated with investigation of post-mortem brain tissues. The glutaminase C (GAC, one isoform of GLS1) mRNA levels in HIV associated-dementia (HAD) individuals correlate with STAT1 (