MADS1 maintains barley spike morphology at high ambient temperatures

Temperature stresses affect plant phenotypic diversity. The developmental stability of the inflorescence, required for reproductive success, is tightly regulated by the interplay of genetic and environmental factors. However, the mechanisms underpinning how plant inflorescence architecture responds to temperature are largely unknown. We demonstrate that the barley SEPALLATA MADS-box protein HvMADS1 is responsible for maintaining an unbranched spike architecture at high temperatures, while the loss-of-function mutant forms a branched inflorescence-like structure. HvMADS1 exhibits increased binding to target promoters via A-tract CArG-box motifs, which change conformation with temperature. Target genes for high-temperature-dependent HvMADS1 activation are predominantly associated with inflorescence differentiation and phytohormone signalling. HvMADS1 directly regulates the cytokinin-degrading enzyme HvCKX3 to integrate temperature response and cytokinin homeostasis, which is required to repress meristem cell cycle/division. Our findings reveal a mechanism by which genetic factors direct plant thermomorphogenesis, extending the recognized role of plant MADS-box proteins in floral development.Gang Li, Hendrik N. J. Kuijer, Xiujuan Yang, Huiran Liu, Chaoqun Shen, Jin Shi ... et al

Multi-Scale Measurement of Regional Inequality in Mainland China during 2005–2010 Using DMSP/OLS Night Light Imagery and Population Density Grid Data

This study used the Night Light Development Index (NLDI) to measure the regional inequality of public services in Mainland China at multiple scales. The NLDI was extracted based on a Gini Coefficient approach to measure the spatial differences of population distribution and night light distribution. Population data were derived from the dataset of China’s population density grid, and night light data were acquired from satellite imagery. In the multi-scale analysis, we calculated the NLDI for China as a whole, eight economic regions, 31 provincial regions, and 354 prefectural cities for the two years of 2005 and 2010. The results indicate that Southwest China and Northwest China are the regions with the most unequal public services, with NLDI values of 0.7116 and 0.7251 for 2005, respectively, and 0.6678 and 0.6304 for 2010, respectively. In contrast, Northern Coastal China had the lowest NLDI values of 0.4775 and 0.4312 for 2005 and 2010, respectively, indicating that this region had the most equal public services. Also, the regional inequality of Mainland China in terms of NLDI has been reduced from 0.6161 to 0.5743 during 2005–2010. The same pattern was observed from the provincial and prefectural analysis, suggesting that public services in Mainland China became more equal within the five-year period. A regression analysis indicated that provincial and prefectural regions with more public services per capita and higher population density had more equal public services

Development of S-NPP VIIRS global surface type classification map using support vector machines

With the launch of the Joint Polar Satellite System (JPSS)/Suomi National Polar-orbiting Partnership (S-NPP) satellite in October 2011, many of the terrestrial remote sensing products generated from Moderate Resolution Imaging Spectroradiometer (MODIS), such as the global land cover map, have been inherited and expanded into the JPSS/S-NPP mission using the new Visible Infrared Imaging Radiometer Suite (VIIRS) data. In this study, an improved algorithm including the use of a new classifier support vector machines (SVM) classifier was proposed to produce VIIRS surface type maps. In addition to the new classification algorithm, a new post-processing strategy involving the use of new ancillary data to refine the classification output is implemented. As a result, the new global International Geosphere-Biosphere Programme (IGBP) map based on the 2014 VIIRS surface reflectance data was generated with a 78.5 ± 0.6% overall classification accuracy. The new map was compared to a previously delivered VIIRS surface type map, and to the MODIS land cover product. Validation results including the error matrix, overall accuracy, and the user’s and producer’s accuracy suggest the new global surface type map provides similar classification accuracy compared to the old VIIRS surface type map, with higher accuracy achieved in agricultural types

Spatiotemporal Heterogeneity and the Key Influencing Factors of PM2.5 and PM10 in Heilongjiang, China from 2014 to 2018

Particulate matter (PM) degrades air quality and negatively impacts human health. The spatial???temporal heterogeneity of PM (PM2.5 and PM10) concentration in Heilongjiang Province during 2014???2018 and the key impacting factors were investigated based on principal component analysis-based ordinary least square regression (PCA-OLS), PCA-based geographically weighted regression (PCA-GWR), PCA-based temporally weighted regression (PCA-TWR), and PCA-based geographically and temporally weighted regression (PCA-GTWR). Results showed that six principal components represented the temperature, wind speed, air pressure, atmospheric pollution, humidity, and vegetation cover factor, respectively, contributing 87% of original variables. All the local models (PCA-GWR, PCA-TWR, and PCA-GTWR) were superior to the global model (PCA-OLS), and PCA-GTWR has the best performance. PM had greater temporal than spatial heterogeneity due to seasonal periodicity. Air pollutants (i.e., SO2, NO2, and CO) and pressure were promoted whereas temperature, wind speed, and vegetation cover inhibited the PM concentration. The downward trend of annual PM concentration is obvious, especially after 2017, and the hot spot gradually changed from southwestern to southeastern cities. This study laid the foundation for precise local government prevention and control by addressing both excessive effect factors (i.e., meteorological factors, air pollutants, vegetation cover) and spatial-temporal heterogeneity of PM. ?? 2022 by the authors

Improving Super-Resolution Mapping by Combining Multiple Realizations Obtained Using the Indicator-Geostatistics Based Method

Indicator-geostatistics based super-resolution mapping (IGSRM) is a popular super-resolution mapping (SRM) method. Unlike most existing SRM methods that produce only one SRM result each, IGSRM generates multiple equally plausible super-resolution realizations (i.e., SRM results). However, multiple super-resolution realizations are not desirable in many applications, where only one SRM result is usually required. These super-resolution realizations may have different strengths and weaknesses. This paper proposes a novel two-step combination method of generating a single SRM result from multiple super-resolution realizations obtained by IGSRM. In the first step of the method, a constrained majority rule is proposed to combine multiple super-resolution realizations generated by IGSRM into a single SRM result under the class proportion constraint. In the second step, partial pixel swapping is proposed to further improve the SRM result obtained in the previous step. The proposed combination method was evaluated for two study areas. The proposed method was quantitatively compared with IGSRM and Multiple SRM (M-SRM), an existing multiple SRM result combination method, in terms of thematic accuracy and geometric accuracy. Experimental results show that the proposed method produces SRM results that are better than those of IGSRM and M-SRM. For example, in the first example, the overall accuracy of the proposed method is 7.43–10.96% higher than that of the IGSRM method for different scale factors, and 1.09–3.44% higher than that of the M-SRM, while, in the second example, the improvement in overall accuracy is 2.42–4.92%, and 0.08–0.90%, respectively. The proposed method provides a general framework for combining multiple results from different SRM methods