Red Mold Rice Mitigates Oral Carcinogenesis in 7,12-Dimethyl-1,2-Benz[a]anthracene-Induced Oral Carcinogenesis in Hamster

The prevalence of oral tumor has exponentially increased in recent years; however, the effective therapies or prevention strategies are not sufficient. Red mold rice is a traditional Chinese food, and several reports have demonstrated that red mold rice had an anti-tumor effect. However, the possible anti-tumor mechanisms of the red mold rice are unclear. In this study, we examined the anti-tumor effect of red mold rice on 7,12-dimethyl-1,2-benz[a]anthracene (DMBA)-induced oral tumor in hamster. The ethanol extract of red mold rice (RMRE) treatment significantly decreases the levels of DMBA-induced reactive oxygen species, nitro oxide and prostaglandin E2 than those of the lovastatin-treated group (P < .001). Moreover, RMRE decreases the formation of oral tumor induced by DMBA. Monacolin K, monascin, ankaflavin or other red mold rice metabolites had been reported to decrease inflammation and oxidative stress and exerted anti-tumor effects. Therefore, we evaluated the anti-inflammation and anti-oxidative stress effects of monacolin K, monascin, ankaflavin and citrinin in lipopolysaccharide-treated RAW264.7 cells. We found that RMRE reduced the LPS-induced nitrite levels in RAW264.7 cells better than monacolin K, monascin, ankaflavin or citrinin (P < .05)

Applying Theory of Planned Behavior to Predict Nurses' Intention and Volunteering to Care for Sars Patients in Southern Taiwan

Severe acute respiratory syndrome (SARS) spread worldwide after an outbreak in Guangdong Province, China, in mid-November 2002. Health care workers were at highest risk of infection. The purpose of this study, which was based on Ajzen's theory of planned behavior (TPB), was to determine the extent to which personal attitudes, subjective norms, and perceived control influence nurses' intention and volunteering to care for SARS patients. After the SARS outbreak, a total of 750 staff nurses (response rate 90%) at one hospital completed a questionnaire assessing their intention to provide care to SARS patients. Overall, 42.7% of nurses had a positive intention to provide care to SARS patients, and 25.4% of nurses would volunteer to care for SARS patients. Four factors explaining 35% of the variance in nurses' intention to care for SARS patients were self-efficacy (β = 0.39, p < 0.001), attitude (β = 0.25, p < 0.001), years of working in the study hospital (β = −0.15, p < 0.001), and receiving resources from the hospital (β = 0.13, p < 0.001). Two factors explaining 15% of the variance in nurses' volunteering to care for SARS patients were intention (β = 0.31, p < 0.001) and attitude (β = 0.15, p < 0.001). The final model shows that the variables of the TPB contributed significantly to the explanation of a portion of variance in nurses' intention and volunteering to care for SARS patients. The results are helpful for human resources managers facing a new contagious disease

Customer churn prediction for web browsers

In the competitive web browser market, identifying potential churners is critical to decreasing the loss of existing customers. Churn prediction based on customer behaviors plays a vital role in customer retention strategies. However, traditional churn prediction algorithms such as Tree-based models cannot exploit the temporal characteristics of browser customers behaviors, while sequence models cannot explicitly extract the information between multiple behaviors. To meet this challenge, we propose a novel model named Multivariate Behavior Sequence Transformer (MBST) with two complementary attention mechanisms to explore the temporal and behavioral information separately. Furthermore, a Tree-based classifier is attached for churn prediction instead of using the multilayer perceptron. Extensive experiments on a real-world Tencent QQ browser dataset with over 600,000 samples demonstrate that the proposed MBST achieves the F-score of 82.72% and the Area Under Curve (AUC) of 93.75%, which significantly outperforms state-of-the-art methods in terms of churn prediction

Fibroblast Growth Factor 2 Modulates Hippocampal Microglia Activation in a Neuroinflammation Induced Model of Depression

Recent studies indicate that disturbed structure and function of microglia can cause depression and associated neurogenesis impairments. Our previous work has demonstrated that exogenous fibroblast growth factor 2 (FGF2) reverses the depressive-like behaviors and the impaired hippocampal neurogenesis in a neuroinflammatory model of depression. However, whether and how the antidepressant effects of FGF2 involve the modulation of microglia activation has not been elucidated. In this study, to examine the effects of FGF2 on microglia activation, exogenous FGF2 was supplemented to the lateral ventricle of rats during the neuroinflammatory state induced by central lipopolysaccharides (LPS) administrations. It was found that FGF2 infusions reversed the LPS-induced depressive-like behaviors and inhibited the hippocampal microglia activation. In LPS-treated rats, FGF2 decreased the level of pro-inflammatory cytokines including interlukin-1β (IL-1β), IL-6 and tumor necrosis factor (TNF)-α, increased the level of IL-10, the anti-inflammatory cytokine and reversed the decreased expression of CX3CL1, a chemokine mainly expressed by neurons and keeping microglia in surveillance. Further, we examined the effects of inhibited FGF2 signaling by administration of SU5402, an FGFR inhibitor. It was found that SU5402 itself evoked depressive-like behaviors, induced microglia activation, increased production of pro-inflammatory cytokines including IL-1β, IL-6 and TNF-α, and decreased the expression of CX3CL1. Two lines of results that FGF2 signaling and FGFR inhibitor can effectively but oppositely modulate the regulation of microglia and the generation of depressive-like behavior, suggesting that microglia-regulated mechanisms may underlie the antidepressant role of FGF2. The present data provide novel insights into the understanding of mechanism of neuroinflammation-associated depression and may serve as a novel mechanism-based target for the treatment of inflammation-related depression

Synthesis of SnO 2

Zinc oxides deposited on Tin dioxide nanowires have been successfully synthesized by atomic layer deposition (ALD). The diameter of SnO2-ZnO core-shell nanowires is 100 nm by ALD 200 cycles. The result of electricity measurements shows that the resistance of SnO2-ZnO core-shell nanowires (ALD: 200 cycles) is 925 Ω, which is much lower than pure SnO2 nanowires (3.6 × 106 Ω). The result of UV light test shows that the recovery time of SnO2-ZnO core-shell nanowires (ALD: 200 cycles) is 328 seconds, which is lower than pure SnO2 nanowires (938 seconds). These results demonstrated that the SnO2-ZnO core-shell nanowires have potential application as UV photodetectors with high photon-sensing properties

Demonstration of Adiabatic Variational Quantum Computing with a Superconducting Quantum Coprocessor

Adiabatic quantum computing enables the preparation of many-body ground states. This is key for applications in chemistry, materials science, and beyond. Realisation poses major experimental challenges: Direct analog implementation requires complex Hamiltonian engineering, while the digitised version needs deep quantum gate circuits. To bypass these obstacles, we suggest an adiabatic variational hybrid algorithm, which employs short quantum circuits and provides a systematic quantum adiabatic optimisation of the circuit parameters. The quantum adiabatic theorem promises not only the ground state but also that the excited eigenstates can be found. We report the first experimental demonstration that many-body eigenstates can be efficiently prepared by an adiabatic variational algorithm assisted with a multi-qubit superconducting coprocessor. We track the real-time evolution of the ground and exited states of transverse-field Ising spins with a fidelity up that can reach about 99%.Comment: 12 pages, 4 figure