Localization of BEN1-LIKE protein and nuclear degradation during development of metaphloem sieve elements in Triticum aestivum L.

Metaphloem sieve elements (MSEs) in the developing caryopsis of Triticum aestivum L. undergo a unique type of programmed cell death (PCD); cell organelles gradually degrade with the MSE differentiation while mature sieve elements keep active. This study focuses on locating BEN1-LIKE protein and nuclear degradation in differentiating MSEs of wheat. Transmission electron microscopy (TEM) showed that nuclei degraded in MSE development. First, the degradation started at 2–3 days after flowering (DAF). The degraded fragments were then swallowed by phagocytic vacuoles at 4 DAF. Finally, nuclei almost completely degraded at 5 DAF. We measured the BEN1-LIKE protein expression in differentiating MSEs. In situ hybridization showed that BEN1-LIKE mRNA was a more obvious hybridization signal at 3–4 DAF at the microscopic level. Immuno-electron microscopy further revealed that BEN1-LIKE protein was mainly localized in MSE nuclei. Furthermore, MSE differentiation was tested using a TSQ Zn2+ fluorescence probe which showed that the dynamic change of Zn2+ accumulation was similar to BEN1-LIKE protein expression. These results suggest that nucleus degradation in wheat MSEs is associated with BEN1-LIKE protein and that the expression of this protein may be regulated by Zn2+ accumulation variation

Clinical Efficacy of Temozolomide and Its Predictors in Aggressive Pituitary Tumors and Pituitary Carcinomas: A Systematic Review and Meta-Analysis

Background: A growing number of evidences suggest that TMZ applications can generate impressive benefits for APT and PC patients. However, the definite role of TMZ for individuals remains unclarified due to the variation between studies. And the predictive factors to alter its efficacy remain debatable.Objective: To evaluate the long-term effectiveness and safety profile of TMZ in the treatment of pituitary malignancies, and delineate the predictors during its clinical employment.Results: A literature retrieval was conducted from online databases for studies published up to December 31, 2020. Twenty one studies involving 429 patients were identified. TMZ exhibited 41% radiological overall response rate (rORR). The biochemical response rate was determinate in 53% of the functioning subset. Two-year and 4-year survival rate were 79 and 61%, respectively. TMZ prolonged the median PFS and OS as 20.18 and 40.24 months. TMZ-related adverse events occurred in 19% of patients. Regarding predictors of TMZ response, rORR was dramatically improved in patients with low/intermediate MGMT expression than those with high-MGMT (>50%) (p < 0.001). The benefit of TMZ varied according to functioning subtype of patients, with greater antitumor activities in functioning subgroups and fewer activities in non-functioning sets (p < 0.001). Notably, the concomitant therapy of radiotherapy and TMZ significantly increased the rORR (p = 0.007).Conclusion: TMZ elicits clinical benefits with moderate adverse events in APT and PC patients. MGMT expression and clinical subtype of secreting function might be vital predictors of TMZ efficacy. In the future, the combination of radiotherapy with TMZ may further improve the clinical outcomes than TMZ monotherapy

CAN DESTRUCTIVE NETWORK ACTIVITIES PUSH AN ECONOMY TO A LOWER LEVEL OF SPECIALIZATION? A THEORETICAL ILLUSTRATION

In the existing literature of infra-marginal analysis, there are many models describing how infrastructure investment can promote division of labour, trade interdependency, and income growth. However, detrimental effects from destructive network activities have not been formally studied. Destructive network activities can slow down both production and transaction activities. This paper develops a general equilibrium model to illustrate how destructive network activities can push a developed market economy to a lower level of division of labour, thereby causing losses in both productivity and real per capita income.Network frictions, productivity, transaction efficiency

An Adaptive Thresholding Approach toward Rapid Flood Coverage Extraction from Sentinel-1 SAR Imagery

Flood disasters have a huge effect on human life, the economy, and the ecosystem. Quickly extracting the spatial extent of flooding is necessary for disaster analysis and rescue planning. Thus, extensive studies have utilized optical or radar data for the extraction of water distribution and monitoring of flood events. As the quality of detected flood inundation coverage by optical images is degraded by cloud cover, the current data products derived from optical sensors cannot meet the needs of rapid flood-range monitoring. The presented study proposes an adaptive thresholding method for extracting water coverage (AT-EWC) regarding rapid flooding from Sentinel-1 synthetic aperture radar (SAR) data with the assistance of prior information from Landsat data. Our method follows three major steps. First, applying the dynamic surface water extent (DSWE) algorithm to Landsat data acquired from the year 2000 to 2016, the distribution probability of water and non-water is calculated through the Google Earth Engine platform. Then, current water coverage is extracted from Sentinel-1 data. Specifically, the persistent water and non-water datasets are used to automatically determine the type of image histogram. Finally, the inundated areas are calculated by combining the persistent water and non-water datasets and the current water coverage as derived from the above two steps. This approach is fast and fully automated for flood detection. In the classification results from the WeiFang and Ji’An sites, the overall classification accuracy of water and land detection reached 95–97%. Our approach is fully automatic. In particular, the proposed algorithm outperforms the traditional method over small water bodies (inland watersheds with few lakes) and makes up for the low temporal resolution of existing water products