Ca is absorbed by roots and transported upward through the xylem to the apoplastic space of the leaf, after which it is deposited into the leaf cell. In Arabidopsis (Arabidopsis thaliana), the tonoplast-localized Ca/H transporters CATION EXCHANGER1 (CAX1) and CAX3 sequester Ca from the cytosol into the vacuole, but it is not known what transporter mediates the initial Ca influx from the apoplast to the cytosol. Here, we report that Arabidopsis CYCLIC NUCLEOTIDE-GATED CHANNEL2 (CNGC2) encodes a protein with Ca influx channel activity and is expressed in the leaf areas surrounding the free endings of minor veins, which is the primary site for Ca unloading from the vasculature and influx into leaf cells. Under hydroponic growth conditions, with 0.1 mm Ca, both Arabidopsis cngc2 and cax1cax3 loss-of-function mutants grew normally. Increasing the Ca concentration to 10 mm induced HO accumulation, cell death, and leaf senescence and partially suppressed the hypersensitive response to avirulent pathogens in the mutants but not in the wild type. In vivo apoplastic Ca overaccumulation was found in the leaves of cngc2 and cax1cax3 but not the wild type under the 10 mm Ca condition, as monitored by Oregon Green BAPTA 488 5N, a low-affinity and membrane-impermeable Ca probe. Our results indicate that CNGC2 likely has no direct roles in leaf development or the hypersensitive response but, instead, that CNGC2 could mediate Ca influx into leaf cells. Finally, the in vivo extracellular Ca imaging method developed in this study provides a new tool for investigating Ca dynamics in plant cells.

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Effort-aware just-in-time (JIT) defect prediction is to rank source code changes based on the likelihood of detects as well as the effort to inspect such changes. Accurate defect prediction algorithms help to find more defects with limited effort. To improve the accuracy of defect prediction, in this paper, we propose a deep learning based approach for effort-aware just-in-time defect prediction. The key idea of the proposed approach is that neural network and deep learning could be exploited to select useful features for defect prediction because they have been proved excellent at selecting useful features for classification and regression. First, we preprocess ten numerical metrics of code changes, and then feed them to a neural network whose output indicates how likely the code change under test contains bugs. Second, we compute the benefit cost ratio for each code change by dividing the likelihood by its size. Finally, we rank code changes according to their benefit cost ratio. Evaluation results on a well-known data set suggest that the proposed approach outperforms the state-of-the-art approaches on each of the subject projects. It improves the average recall and popt by 15.6% and 8.1%, respectively.