The Impact of Green Disclosure Nudging in Online Reuse Markets: Evidence from a Natural Experiment

As the environment continues to deteriorate, for managers, the means of shifting consumer behavior to green is urgently needed. Information disclosure as a tactic to promote green consumption has been widely studied, which typically stimulates demand for new green products through cost disclosure. In contrast, the impact of environmental benefit information disclosure on green consumption, especially on online reuse platforms, remains to be ascertained. In this study, we examine the economic effects of a green disclosure nudge through a natural experiment. Drawing on daily sales data, we find that the green disclosure nudge can stimulate consumer demand and generate economic benefits. We provide suggestive evidence that this positive effect stems from an increase in consumers’ perceptions of the functional and symbolic value of used products, respectively. In addition, exploratory analysis shows that the nudge may have potential social benefits. The findings provide practical and theoretical implications for promoting green consumption

Expression and role of fibroblast activation protein-alpha in microinvasive breast carcinoma

<p>Abstract</p> <p>Background</p> <p>Diagnosis of ductal carcinoma in situ (DCIS) in breast cancer cases is challenging for pathologist due to a variety of in situ patterns and artefacts, which could be misinterpreted as stromal invasion. Microinvasion is detected by the presence of cytologically malignant cells outside the confines of the basement membrane and myoepithelium. When malignant cells invade the stroma, there is tissue remodeling induced by perturbed stromal-epithelial interactions. Carcinoma-associated fibroblasts (CAFs) are main cells in the microenvironment of the remodeled tumor-host interface. They are characterized by the expression of the specific fibroblast activation protein-alpha (FAP-α), and differ from that of normal fibroblasts exhibiting an immunophenotype of CD34. We hypothesized that staining for FAP-α may be helpful in determining whether DCIS has microinvasion.</p> <p>Methods</p> <p>349 excised breast specimens were immunostained for smooth muscle actin SMA, CD34, FAP-α, and Calponin. Study material was divided into 5 groups: group 1: normal mammary tissues of healthy women after plastic surgery; group 2: usual ductal hyperplasia (UDH); group 3: DCIS without microinvasion on H & E stain; group 4: DCIS with microinvasion on H & E stain (DCIS-MI), and group 5: invasive ductal carcinoma (IDC). A comparative evaluation of the four immunostains was conducted.</p> <p>Results</p> <p>Our results demonstrated that using FAP-α and Calponin adjunctively improved the sensitivity of pathological diagnosis of DCIS-MI by 11.29%, whereas the adjunctive use of FAP-α and Calponin improved the sensitivity of pathological diagnosis of DCIS by 13.6%.</p> <p>Conclusions</p> <p>This study provides the first evidence that immunostaining with FAP-α and Calponin can serve as a novel marker for pathologically diagnosing whether DCIS has microinvasion.</p

Advances in Acute Myeloid Leukemia Stem Cells

As a common hematological malignant tumor, acute leukemia is believed to originate from a subpopulation of special cancer cells, named cancer stem cells. Cancer stem cells are recognized to be the main source of tumor origin, multidrug resistance, metastasis, and recurrence. Leukemic stem cells (LSCs) were first identified and confirmed to play an important role in the occurrence and development of leukemia. In this article, we summarize the following content: special markers and sorting methods for acute myeloid leukemia stem cells and the role of cancer stem cells in treatment resistance, metastasis and invasion, recurrence, and target treatment of acute leukemia

Antimicrobial photodynamic inactivation as an alternative approach to inhibit the growth of Cronobacter sakazakii by fine-tuning the activity of CpxRA two-component system

Cronobacter sakazakii is an opportunistic foodborne pathogen primarily found in powdered infant formula (PIF). To date, it remains challenging to control the growth of this ubiquitous bacterium. Herein, antimicrobial photodynamic inactivation (aPDI) was first employed to inactivate C. sakazakii. Through 460 nm light irradiation coupled with hypocrellin B, the survival rate of C. sakazakii was diminished by 3~4 log. The photokilling effect was mediated by the attenuated membrane integrity, as evidenced by PI staining. Besides, scanning electron microscopy showed the deformed and aggregated cell cluster, and intracellular ROS was augmented by 2~3 folds when light doses increase. In addition to planktonic cells, the biofilm formation of C. sakazakii was also affected, showing an OD590nm decline from 0.85 to 0.25. In terms of molecular aspects, a two-component system called CpxRA, along with their target genes, was deregulated during illumination. Using the knock-out strain of ΔCpxA, the bacterial viability was reduced by 2 log under aPDI, a wider gap than the wildtype strain. Based on the promoted expression of CpxR and OmpC, aPDI is likely to play its part through attenuating the function of CpxRA-OmpC pathway. Finally, the aPDI system was applied to PIF, and C. sakazakii was inactivated under various desiccated or heated storage conditions. Collectively, aPDI serves as an alternative approach to decontaminate C. sakazakii, providing a new strategy to reduce the health risks caused by this prevalent foodborne pathogen

Authenticity and Loyalty at Heritage Sites: The Moderation Effect of Postmodern Authenticity

Authenticity and postmodern authenticity are often regarded as contradictory, and the two are rarely considered simultaneously by researchers. Taking as research cases two Chinese World Heritage Sites, Kaiping watchtowers in Guangdong province and Yongding earth building in Fujian province, this study constructed a relationship model of perceived authenticity, existential authenticity, and loyalty by examining the effects of tourists\u27 perceptions of the authenticity of tangible and intangible heritage on tourists\u27 existential authenticity and destination loyalty, as well as the relationship between existential authenticity and destination loyalty. Building upon the relationship model, this study further examined the moderating role of postmodern authenticity on the relationship between perceived authenticity and existential authenticity. Results indicated that postmodern authenticity moderates the influence of architectural heritage on existential authenticity: the higher the level of postmodern authenticity, the lower the effect. Theoretical and management implications are discussed

Photovoltaic Array Fault Diagnosis Based on Gaussian Kernel Fuzzy C-Means Clustering Algorithm

In the fault diagnosis process of a photovoltaic (PV) array, it is difficult to discriminate single faults and compound faults with similar signatures. Furthermore, the data collected in the actual field experiment also contains strong noise, which leads to the decline of diagnostic accuracy. In order to solve these problems, a new eigenvector composed of the normalized PV voltage, the normalized PV current and the fill factor is constructed and proposed to characterize the common faults, such as open circuit, short circuit and compound faults in the PV array. The combination of these three feature characteristics can reduce the interference of external meteorological conditions in the fault identification. In order to obtain the new eigenvectors, a multi-sensory system for fault diagnosis in a PV array, combined with a data-mining solution for the classification of the operational state of the PV array, is needed. The selected sensors are temperature sensors, irradiance sensors, voltage sensors and current sensors. Taking account of the complexity of the fault data in the PV array, the Kernel Fuzzy C-means clustering method is adopted to identify these fault types. Gaussian Kernel Fuzzy C-means clustering method (GKFCM) shows good clustering performance for classifying the complex datasets, thus the classification accuracy can be effectively improved in the recognition process. This algorithm is divided into the training and testing phases. In the training phase, the feature vectors of 8 different fault types are clustered to obtain the training core points. According to the minimum Euclidean Distances between the training core points and new fault data, the new fault datasets can be identified into the corresponding classes in the fault classification stage. This strategy can not only diagnose single faults, but also identify compound fault conditions. Finally, the simulation and field experiment demonstrated that the algorithm can effectively diagnose the 8 common faults in photovoltaic arrays