Anthocyanin pigmentation is an important consumption trait of apple (Malus domestica Borkh.). In this study, we focused on the identification of NAC (NAM, ATAF1/2 and CUC2) proteins involved in the regulation of anthocyanin accumulation in apple flesh. A group of MdNACs was selected for comparison of expression patterns between the white-fleshed cultivar ‘Granny Smith’ and red-fleshed ‘Redlove’. Among them, MdNAC42 was screened, which exhibited a higher expression level in red-fleshed than in white-fleshed fruit, and has a positive correlation with anthocyanin content as fruits ripened. Moreover, overexpression of MdNAC42 in apple calli resulted in the up-regulation of flavonoid pathway genes, including MdCHS, MdCHI, MdF3H, MdDFR, MdANS and MdUFGT, thereby increasing the accumulation of anthocyanins, which confirmed the roles of MdNAC42 in anthocyanin biosynthesis. Notably, MdNAC42 was demonstrated to have an obvious interaction with MdMYB10 either in vitro or in vivo by yeast two-hybrid combined with bimolecular fluorescence complementation, further suggesting that MdNAC42 is an important part of the regulatory network controlling the anthocyanin pigmentation of red-fleshed apples. To the best of our knowledge, this is the first report identifying the MdNAC gene as related to anthocyanin accumulation in red-fleshed apples. This study provides valuable information for improving the regulatory model of anthocyanin biosynthesis in apple fruit.

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During the past two decades, epileptic seizure detection and prediction algorithms have evolved rapidly. However, despite significant performance improvements, their hardware implementation using conventional technologies, such as Complementary Metal-Oxide-Semiconductor (CMOS), in power and area-constrained settings remains a challenging task; especially when many recording channels are used. In this paper, we propose a novel low-latency parallel Convolutional Neural Network (CNN) architecture that has between 2-2,800x fewer network parameters compared to State-Of-The-Art (SOTA) CNN architectures and achieves 5-fold cross validation accuracy of 99.84% for epileptic seizure detection, and 99.01% and 97.54% for epileptic seizure prediction, when evaluated using the University of Bonn Electroencephalogram (EEG), CHB-MIT and SWEC-ETHZ seizure datasets, respectively. We subsequently implement our network onto analog crossbar arrays comprising Resistive Random-Access Memory (RRAM) devices, and provide a comprehensive benchmark by simulating, laying out, and determining hardware requirements of the CNN component of our system. To the best of our knowledge, we are the first to parallelize the execution of convolution layer kernels on separate analog crossbars to enable 2 orders of magnitude reduction in latency compared to SOTA hybrid Memristive-CMOS Deep Learning (DL) accelerators. Furthermore, we investigate the effects of non-idealities on our system and investigate Quantization Aware Training (QAT) to mitigate the performance degradation due to low Analog-to-Digital Converter (ADC)/Digital-to-Analog Converter (DAC) resolution. Finally, we propose a stuck weight offsetting methodology to mitigate performance degradation due to stuck R ON /R OFF memristor weights, recovering up to 32% accuracy, without requiring retraining. The CNN component of our platform is estimated to consume approximately 2.791W of power while occupying an area of 31.255mm2 in a 22nm FDSOI CMOS process.

Purpose To explore the differences between prolonged continuous Pringle maneuver (CPM) and prolonged intermittent Pringle maneuver (IPM) in patients with hepatocellular carcinoma (HCC), who underwent complex hepatectomy. Methods This retrospective cohort study performed between June 2014 and May 2016 included 142 patients who underwent complex hepatectomy for HCC and concomitant chronic liver disease but with good liver function. Patients were categorized into CPM (n = 69) and IPM groups (n = 73). The differences in these aspects were compared between the two groups which include operation time, intraoperative bleeding, perioperative transfusion, postoperative complications, liver function injury, postoperative overall survival (OS), and tumor recurrence. Results The cumulative clamping time, operation time, intraoperative bleeding, and perioperative transfusion rates were 38.0, 132 min, 300 ml, and 17.4% in CPM and 40.0, 145 min, 400 ml, and 32.9% in IPM, respectively. There were significant intergroup differences in operation time (p = 0.018), intraoperative bleeding (p < 0.001), and perioperative transfusion rates (p = 0.034). Besides, the postoperative complications and postoperative liver function injury of the CPM group were better than those of IPM. There was no significant intergroup difference in OS (p = 0.908) and tumor recurrence (p = 0.671) between two groups. Conclusion Compared with IPM, CPM with a cumulative clamping time between 30 and 50 min can shorten operation time, reduce intraoperative bleeding and perioperative transfusion, and reduce postoperative complications and postoperative liver function injury in patients who underwent complex hepatectomy for HCC and concomitant liver disease but with good liver function. There was no significant difference in OS and tumor recurrence between two groups.