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With a view to using solar energy, the exploitation of near‐infrared (NIR) light, which constitutes about 50 % of solar energy, in photocatalytic H2O2 synthesis remains challenging. In this study, resorcinol−formaldehyde (RF), which has a relatively low bandgap and high conductivity, is introduced for photothermal catalytic generation of H2O2 under ambient conditions. Owing to the promoted surface charge transfer rate under high temperature, the photosynthetic yield reaches roughly 2000 μm within 40 min under 400 mW cm−2 irradiation with a solar‐to‐chemical conversion (SCC) efficiency of up to 0.19 % at 338 K under ambient conditions, exceeding the rate of photocatalysis with a cooling system by a factor of about 2.5. Notably, the H2O2 produced by RF during photothermal process was formed via a two‐channel pathway, leading to the overall promotion of H2O2 formation. The resultant H2O2 can be applied in situ for pollutant removal. This work offers a sustainable and economical route for the efficient formation of H2O2. [ABSTRACT FROM AUTHOR]

Background: Breast cancer (BC), the most common cause of cancer death in women, overtook lung cancer as the leading cause of cancer worldwide in 2020. Although many studies have proposed KIN17 as a biomarker of tumorigenesis in different cancer types, its role in tumor metastasis, particularly in BC metastasis, has been underexplored. This study aimed to explore the role of KIN17 in BC metastasis. Methods: Survival analyses was performed to identify the association between KIN17 expression and BC patient survival in silico. Using lentivirus constructs, we developed bidirectional KIN17 expression (KD, knockdown; OE, overexpression) cellular models of luminal‐A (Lum‐A) breast cancer MCF‐7 cells. We performed in vitro wound healing, transwell with and without Matrigel assays, and in vivo tail‐vein metastasis assay to evaluate the migration and invasion abilities of MCF‐7 with stable KIN17 knockdown or overexpression. Western blotting was performed to compare the changes in protein expression. Results: We found that KIN17 expression was associated with poor overall survival (OS), relapse‐free survival (RFS), distant metastasis‐free survival (DMFS) and post‐progression survival (PPS), particularly in Lum‐A breast cancer patients. Later, we found that KIN17 knockdown inhibited migration and invasion of MCF‐7 cells via regulating EMT‐associated signaling pathways in vitro and decreases metastatic spread of the disease in vivo. In contrast, KIN17 overexpression promoted migration and invasion of MCF‐7 cells in vitro and increased the metastatic spread of the disease in vivo. Conclusions: Overall, our findings provide preliminary data which suggests KIN17 of importance to target in metastatic Lum‐A patients. [ABSTRACT FROM AUTHOR]

Software stability means the resistance to the amplification of changes in software. It has become one of the most important attributes that affect maintenance cost. To control the maintenance cost, many approaches have been proposed to measure software stability. However, it is still a very difficult task to evaluate the software stability especially when software becomes very large and complex. In this paper, we propose to characterize software stability via change propagation simulation. First, we propose a class coupling network (CCN) to model software structure at the class level. Then, we analyze the change propagation process in the CCN by using a simulation way, and by doing so, we develop a novel metric, SS (software stability), to measure software stability. Our SS metric is validated theoretically using the widely accepted Weyuker's properties and empirically using a set of open source Java software systems. The theoretical results show that our SS metric satisfies most of Weyuker's properties with only two exceptions, and the empirical results show that our metric is an effective indicator for software quality improvement and class importance. Empirical results also show that our approach has the ability to be applied to large software systems. [ABSTRACT FROM AUTHOR]

Investigates the response of the growing season of local plant communities to climate change and the linkage of satellite sensor-derived greenness to the surface growing season using phenological, meteorological, and time-integrated normalized difference vegetation index (TI NDVI) data from 1982 to 1993. Results suggesting that mean air temperature and growing degree days (GDDs) above 5 degrees Celsius during late winter and spring, and precipitation in autumn, are the most important controls on the beginning and end dates of the growing season (BGS and EGS); Developed models using correlation and regression analysis; Impact on beginning and end dates of mean air temperature increases in late winter and spring; Impact on end date of increase in autumn precipitation; Data indicating mean air temperature in late winter and spring, annual mean air temperature, and annual GDD totals have significant positive correlations with growing season duration, whereas growing season TI NDVI has a negative relationship.