Understanding the Role of Streamer Emotion in E-Commerce Livestreaming

The combination of e-commerce and livestreaming video (e-commerce livestreaming) offers an unprecedented opportunity for streamers (salespeople) to show their emotional displays to viewers (consumers) in real-time. However, it remains unclear how and to what extent streamer emotion influences purchase intentions, especially in the context of different product types where consumers have different decision-making considerations. Based on the stereotype content model, which considers two basic dimensions of social judgments (i.e., warmth and competence), this study intends to explore the impact of the interaction effect of streamer emotion (happiness vs. neutrality) and product type (utilitarian vs. hedonic product) on consumers’ purchase intentions and behaviors. Both laboratory experiment and secondary data analysis will be conducted to test our hypotheses. We hope this study can not only extend the livestreaming and emotion-related literature but also provide suggestions on emotional expressions for streamers in their marketing campaigns

Prediction of complex super-secondary structure βαβ motifs based on combined features

AbstractPrediction of a complex super-secondary structure is a key step in the study of tertiary structures of proteins. The strand-loop-helix-loop-strand (βαβ) motif is an important complex super-secondary structure in proteins. Many functional sites and active sites often occur in polypeptides of βαβ motifs. Therefore, the accurate prediction of βαβ motifs is very important to recognizing protein tertiary structure and the study of protein function. In this study, the βαβ motif dataset was first constructed using the DSSP package. A statistical analysis was then performed on βαβ motifs and non-βαβ motifs. The target motif was selected, and the length of the loop-α-loop varies from 10 to 26 amino acids. The ideal fixed-length pattern comprised 32 amino acids. A Support Vector Machine algorithm was developed for predicting βαβ motifs by using the sequence information, the predicted structure and function information to express the sequence feature. The overall predictive accuracy of 5-fold cross-validation and independent test was 81.7% and 76.7%, respectively. The Matthew’s correlation coefficient of the 5-fold cross-validation and independent test are 0.63 and 0.53, respectively. Results demonstrate that the proposed method is an effective approach for predicting βαβ motifs and can be used for structure and function studies of proteins

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

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

The Herb Medicine Formula “Chong Lou Fu Fang” Increases the Cytotoxicity of Chemotherapeutic Agents and Down-Regulates the Expression of Chemotherapeutic Agent Resistance-Related Genes in Human Gastric Cancer Cells In Vitro

The herb medicine formula “Chong Lou Fu Fang” (CLFF) has efficacy in inhibiting the proliferation of human gastric cancer in vitro and in vivo. To explore the potentially useful combination of CLFF with chemotherapeutic agents commonly used in gastric cancer therapy, we assess the interaction between CLFF and these chemotherapeutic agents in both SGC-7901 cell lines and BGC-823 cell lines using a median effect analysis and apoptosis analysis, and we also investigate the influence of CLFF on chemotherapeutic agent-associated gene expression. The synergistic analysis indicated that CLFF had a synergistic effect on the cytotoxicity of 5-fluorouracil (5-FU) in a relative broad dose inhibition range (20–95% fraction affected in SGC-7901cell lines and 5–65% fraction affected in BGC-823 cell lines), while the synergistic interaction between CLFF and oxaliplatin or docetaxel only existed in a low dose inhibition range (≤50% fraction affected in both cell lines). Combination of CLFF and chemotherapeutic agents could also induce apoptosis in a synergistic manner. After 24 h, CLFF alone or CLFF combination with chemotherapeutic agents could significantly suppress the levels of expression of chemotherapeutic agent resistance related genes in gastric cancer cells. Our findings indicate that there are useful synergistic interactions between CLFF and chemotherapeutic agents in gastric cancer cells, and the possible mechanisms might be partially due to the down-regulation of chemotherapeutic agent resistance related genes and the synergistic apoptotic effect

Cloning of a gene encoding glycosyltransferase from Pueraria lobata (Wild.) Ohwi and its expression in Pichia pastoris

The key enzyme of puerarin biosynthesis in Pueraria lobata (Willd.) Ohwi was unclear but may involve glycosylation. To investigate the regulation of puerarin biosynthesis, a putative UDP-dependent glycosyltransferase (UGT) gene, PlUGT1 was isolated from P. lobata root, which contained abundant puerarin. PlUGT1 encoded 480 deduced amino acid residues with a conserved UDP-glucose-binding domain, which has 61 to 84% similarity to homologues from other plant species. SDS polyacrylamide gel electrophoresis and western blotting results showed that, fusion protein migrated as a single protein band with a molecular weight of 55 kDa. A yeast expression vector pPICZA-PlUGT1 was constructed and was transformed into Pichia pastoris strain GS115. Several recombinants containing multi-copy expression cassettes were obtained on the zeocin-YPD plate and confirmed by southern dot blotting. The yield of PlUGT1 attained 0.05 g/l when recombinant cells were cultured at pH 5.5, 30°C and induced with 0.5% methanol for 72 h. The expression of PlUGT1 protein correlates positively with the copy numbers of PlUGT1 in transformed yeast cells. These results suggest that, the PlUGT1 protein can be expressed efficiently in the P. pastoris expression system and may supply a new economic and convenient way for the production of PlUGT1 protein.Keywords: Pueraria lobata (Willd.) Ohwi, glycosyltransferase, cloning, expression, Pichia pastori