Purpose The purpose of this paper is to test the moderating role of work-related stressors on the relationship between voice behavior and the voicer’s creative performance. Design/methodology/approach The sample comprised 781 full-time employees from 16 companies covering six industries in the central region of China. Hierarchical moderated regression analyses were used to test the hypotheses. Findings Results showed that voice behavior had significant positive effect on creative performance. The positive relationship between voice behavior and creative performance was stronger for employees with low challenge stressors as well as for employees with high hindrance stressors. Research limitations/implications This study employs a cross-sectional design with data collected from the same source. Practical implications The findings suggest that employees should be encouraged to voice out their opinions and ideas. Work-related stressors should be treated differently to expand the effects of voice behavior on creative performance. Originality/value This study is one of the few to establish boundary conditions from the contextual perspective on the effect of voice behavior on employee performance. Considering whether work-related stressor is a challenge or a hindrance could possibly result in a better understanding of the role of work-related stressors in the voice behavior-creative performance relationship. An empirical evidence is provided for the positive relationship between voice behavior and employee performance outcomes.

Globally, gastric cancer (GC) is one of the most common types of cancer and the third leading cause of cancer‑related death. In China, gastric and liver cancers have the highest mortality rates. Melatonin, also known as N-acetyl‑5-methoxytryptamine, is a hormone that is produced by the pineal gland in animals and regulates sleep and wakefulness. Melatonin has been shown to inhibit various carcinomas, including GC. There are many different hypotheses to explain the anticancer effects of melatonin, including stimulation of apoptosis, inhibition of cell growth, regulation of anticancer immunity, induction of free-radical scavenging, and the competitive inhibition of estrogen. However, the underlying mechanism by which these effects are elicited remains elusive. The aim of the present study was to investigate the effects of melatonin on human GC cells and determine the underlying molecular mechanism. We treated SGC-7901 GC cells with melatonin and analyzed the resulting protein changes using protein chip technology. Several proteins related to cell apoptosis and proliferation were identified and further tested in SGC-7901 GC cells. We found that melatonin induced cell cycle arrest and the downregulation of CDC25A, phospho-CDC25A (at Ser75), p21 (p21Cip1/p21Waf1) and phospho-p21 (at Thr145). Melatonin also induced upregulation of Bax, downregulation of Bcl-xL, an increase in cleaved caspase-9 level and activation of caspase-3, which confirmed the involvement of the mitochondria in melatonin‑induced apoptosis. Upstream regulators of the above proteins, MDM2, phospho-MDM2 (at Ser166) and AKT, phospho-AKT (at Thr308) were all attenuated by melatonin, which led to an increase in p53. The present study demonstrated that the oncostatic effects of melatonin on SGC-7901 GC cells are mediated via the blockade of the AKT/MDM2 intracellular pathway.

A novel high-capacity protocol for deterministic secure quantum communication with four-qubit symmetric W state is proposed. In the presented protocol, the secret messages can be encoded on the four-qubit symmetric W states by employing four two-particle unitary operations and directly decoded by utilizing the corresponding measurements in Bell basis or single particle basis. It has a high capacity as each W state can carry two bits of secret information, and has a high intrinsic efficiency because almost all the instances are useful. The security of this communication can be ensured by the decoy photon checking technique and the order rearrangement of particle pairs technique. Furthermore, this protocol is feasible with present-day technique.

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As an important and challenging problem in the multimedia area, multi-modal data understanding aims to explore the intrinsic semantic information across different modalities in a collaborative manner. To address this problem, a possible solution is to effectively and adaptively capture the common cross-modal semantic information by modeling the inherent correlations between the latent topics from different modalities. Motivated by this task, we propose a supervised multi-modal mutual topic reinforce modeling (M 3 R) approach, which seeks to build a joint cross-modal probabilistic graphical model for discovering the mutually consistent semantic topics via appropriate interactions between model factors (e.g., categories, latent topics and observed multi-modal data). In principle, M 3 R is capable of simultaneously accomplishing the following two learning tasks: 1) modality-specific (e.g., image-specific or text-specific ) latent topic learning; and 2) cross-modal mutual topic consistency learning. By investigating the cross-modal topic-related distribution information, M 3 R encourages to disentangle the semantically consistent cross-modal topics (containing some common semantic information across different modalities). In other words, the semantically co-occurring cross-modal topics are reinforced by M 3 R through adaptively passing the mutually reinforced messages to each other in the modellearning process. To further enhance the discriminative power of the learned latent topic representations, M 3 R incorporates the auxiliary information (i.e., categories or labels) into the process of Bayesian modeling, which boosts the modeling capability of capturing the inter-class discriminative information. Experimental results over two benchmark datasets demonstrate the effectiveness of the proposed M 3 R in crossmodal retrieval.

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In multimedia information retrieval, most classic approaches tend to represent different modalities of media in the same feature space. With the click data collected from the users' searching behavior, existing approaches take either one-to-one paired data (text-image pairs) or ranking examples (text-query-image and/or image-query-text ranking lists) as training examples, which do not make full use of the click data, particularly the implicit connections among the data objects. In this paper, we treat the click data as a large click graph, in which vertices are images/text queries and edges indicate the clicks between an image and a query. We consider learning a multimodal representation from the perspective of encoding the explicit/implicit relevance relationship between the vertices in the click graph. By minimizing both the truncated random walk loss as well as the distance between the learned representation of vertices and their corresponding deep neural network output, the proposed model which is named multimodal random walk neural network (MRW-NN) can be applied to not only learn robust representation of the existing multimodal data in the click graph, but also deal with the unseen queries and images to support cross-modal retrieval. We evaluate the latent representation learned by MRW-NN on a public large-scale click log data set Clickture and further show that MRW-NN achieves much better cross-modal retrieval performance on the unseen queries/images than the other state-of-the-art methods.

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