Secondary soil salinization causes plant stress, which can be relieved by different ratios of red to far-red light (R:FR). Our study aimed to elucidate the role of low R:FR ratios treatments on photosynthesis and growth of tomato seedlings in salinized soils. Tomato seedlings were treated under three R:FR ratios and calcium nitrate was applied simultaneously. The results showed that the treatments under low R:FR ratios stimulated growth parameters of tomato seedlings under calcium nitrate stress, the best impact being achieved at the R:FR ratio of 0.7 in this experiment. Low R:FR ratios treatments increased proline content as well as PSII maximum efficiency, actual electron transport operating efficiency, and photochemical quenching of tomato seedlings under calcium nitrate stress but decreased the value of nonphotochemical quenching. Moreover, low R:FR ratios treatments promoted net photosynthetic rate and increased the expression of a Rubisco gene. In conclusion, low R:FR ratios treatments could improve the salt resistance of greenhouse tomato plants.
Microblogging filtering is intended to filter out irrelevant content, and select useful, new, and timely content from microblogs. However, microblogging filtering suffers from the problem of insufficient samples which renders the probabilistic models unreliable. To mitigate this problem, a novel method is proposed in this study. It is believed that an explicit brief query is only an abstract of the user's information needs, and its difficult to infer users' actual searching intents and interests. Based on this belief, a filtering model is built where the multi-sources query expansion in microblogging filtering is exploited and expanded query is submitted as users interest. To manage the external expansion risk, a user filter graph inference method is proposed, which is characterized by combination of external multi-sources information, and a risk minimization filtering model is introduced to achieve the best reasoning through the multi-sources expansion. A series of experiments are conducted to evaluate the effectiveness of proposed framework on an annotated tweets corpus. The results of these experiments show that our method is effective in tweets retrieval as compared with the baseline standards.