Salt stress is one of the major factors affecting plant growth and yield in soybean under saline soil condition.  Despite many studies on salinity tolerance of soybean during the past few decades, the detailed signaling pathways and the signaling molecules for salinity tolerance regulation have not been clarified.  In this study, a proteomic technology based on two-dimensional gel electrophoresis (2-DE) and mass spectrometry (MS) were used to identify proteins responsible for salinity tolerance in soybean plant.  Real-time quantitative PCR (qRT-PCR) and Western blotting (WB) were used to verify the results of 2-DE/MS.  Based on the results of 2-DE and MS, we selected glucosyltransferase (GsGT4), 4-coumarate, coenzyme A ligase (Gs4CL1), mitogen-activated protein kinase 4 (GsMAPK4), dehydration responsive element binding protein (GsDREB1), and soybean cold-regulated gene (GsSRC1) in the salinity tolerant soybean variety, and GsMAPK4 for subsequent research.  We transformed soybean plants with mitogen-activated-protein kinase 4 (GsMAPK4) and screened the resulting transgenics soybean plants using PCR and WB, which confirmed the expression of GsMAPK4 in transgenic soybean.  GsMAPK4-overexpressed transgenic plants showed significantly increased tolerance to salt stress, suggesting that GsMAPK4 played a pivotal role in salinity tolerance.  Our research will provide new insights for better understanding the salinity tolerance regulation at molecular level.

China has the largest apple planting area and total yield in the world, and the Fuji apple is the major cultivar, accounting for more than 70% of apple planting acreage in China.  Apple qualities are affected by meteorological conditions, soil types, nutrient content of soil, and management practices.  Meteorological factors, such as light, temperature and moisture are key environmental conditions affecting apple quality that are difficult to regulate and control.  This study was performed to determine the effect of meteorological factors on the qualities of Fuji apple and to provide evidence for a reasonable regional layout and planting of Fuji apple in China.  Fruit samples of Fuji apple and meteorological data were investigated from 153 commercial Fuji apple orchards located in 51 counties of 11 regions in China from 2010 to 2011.  Partial least-squares regression and linear programming were used to analyze the effect model and impact weight of meteorological factors on fruit quality, to determine the major meteorological factors influencing fruit quality attributes, and to establish a regression equation to optimize meteorological factors for high-quality Fuji apples.  Results showed relationships between fruit quality attributes and meteorological factors among the various apple producing counties in China.  The mean, minimum, and maximum temperatures from April to October had the highest positive effects on fruit qualities in model effect loadings and weights, followed by the mean annual temperature and the sunshine percentage, the temperature difference between day and night, and the total precipitation for the same period.  In contrast, annual total precipitation and relative humidity from April to October had negative effects on fruit quality.  The meteorological factors exhibited distinct effects on the different fruit quality attributes.  Soluble solid content was affected from the high to the low row preface by annual total precipitation, the minimum temperature from April to October, the mean temperature from April to October, the temperature difference between day and night, and the mean annual temperature.  The regression equation showed that the optimum meteorological factors on fruit quality were the mean annual temperature of 5.5–18°C and the annual total precipitation of 602–1 121 mm for the whole year, and the mean temperature of 13.3–19.6°C, the minimum temperature of 7.8–18.5°C, the maximum temperature of 19.5°C, the temperature difference of 13.7°C between day and night, the total precipitation of 227 mm, the relative humidity of 57.5–84.0%, and the sunshine percentage of 36.5–70.0% during the growing period (from April to October).