Seasonal nitrogen remobilization and the role of auxin transport in poplar trees

Seasonal nitrogen (N) cycling in Populus, involves bark storage proteins (BSPs) that accumulate in bark phloem parenchyma in the autumn and decline when shoot growth resumes in the spring. Little is known about the contribution of BSPs to growth or the signals regulating N remobilization from BSPs. Knockdown of BSP accumulation via RNAi and N sink manipulations were used to understand how BSP storage influences shoot growth. Reduced accumulation of BSPs delayed bud break and reduced shoot growth following dormancy. Further, 13N tracer studies also showed that BSP accumulation is an important factor in N partitioning from senescing leaves to bark. Thus, BSP accumulation has a role in N remobilization during N partitioning both from senescing leaves to bark and from bark to expanding shoots once growth commences following dormancy. The bark transcriptome during BSP catabolism and N remobilization was enriched in genes associated with auxin transport and signaling, and manipulation of the source of auxin or auxin transport revealed a role for auxin in regulating BSP catabolism and N remobilization. Therefore, N remobilization appears to be regulated by auxin produced in expanding buds and shoots that is transported to bark where it regulates protease gene expression and BSP catabolism

NTIRE 2022 Challenge on Efficient Super-Resolution: Methods and Results

This paper reviews the NTIRE 2022 challenge on efficient single image super-resolution with focus on the proposed solutions and results. The task of the challenge was to super-resolve an input image with a magnification factor of $\times$4 based on pairs of low and corresponding high resolution images. The aim was to design a network for single image super-resolution that achieved improvement of efficiency measured according to several metrics including runtime, parameters, FLOPs, activations, and memory consumption while at least maintaining the PSNR of 29.00dB on DIV2K validation set. IMDN is set as the baseline for efficiency measurement. The challenge had 3 tracks including the main track (runtime), sub-track one (model complexity), and sub-track two (overall performance). In the main track, the practical runtime performance of the submissions was evaluated. The rank of the teams were determined directly by the absolute value of the average runtime on the validation set and test set. In sub-track one, the number of parameters and FLOPs were considered. And the individual rankings of the two metrics were summed up to determine a final ranking in this track. In sub-track two, all of the five metrics mentioned in the description of the challenge including runtime, parameter count, FLOPs, activations, and memory consumption were considered. Similar to sub-track one, the rankings of five metrics were summed up to determine a final ranking. The challenge had 303 registered participants, and 43 teams made valid submissions. They gauge the state-of-the-art in efficient single image super-resolution.Comment: Validation code of the baseline model is available at https://github.com/ofsoundof/IMDN. Validation of all submitted models is available at https://github.com/ofsoundof/NTIRE2022_ES