Classification of lung nodules based on deep residual networks and migration learning

The classification process of lung nodule detection in a traditional computer-aided detection (CAD) system is complex, and the classification result is heavily dependent on the performance of each step in lung nodule detection, causing low classification accuracy and high false positive rate. In order to alleviate these issues, a lung nodule classification method based on a deep residual network is proposed. Abandoning traditional image processing methods and taking the 50-layer ResNet network structure as the initial model, the deep residual network is constructed by combining residual learning and migration learning. The proposed approach is verified by conducting experiments on the lung computed tomography (CT) images from the publicly available LIDC-IDRI database. An average accuracy of 98.23% and a false positive rate of 1.65% are obtained based on the ten-fold cross-validation method. Compared with the conventional support vector machine (SVM)-based CAD system, the accuracy of our method improved by 9.96% and the false positive rate decreased by 6.95%, while the accuracy improved by 1.75% and 2.42%, respectively, and the false positive rate decreased by 2.07% and 2.22%, respectively, in contrast to the VGG19 model and InceptionV3 convolutional neural networks. The experimental results demonstrate the effectiveness of our proposed method in lung nodule classification for CT images

Tetrahydrofolate Modulates Floral Transition through Epigenetic Silencing.

Folates, termed from tetrahydrofolate (THF) and its derivatives, function as coenzymes in one-carbon transfer reactions and play a central role in synthesis of nucleotides and amino acids. Dysfunction of cellular folate metabolism leads to serious defects in plant development; however, the molecular mechanisms of folate-mediated cellular modifications and physiological responses in plants are still largely unclear. Here, we reported that THF controls flowering time by adjusting DNA methylation-regulated gene expression in Arabidopsis (Arabidopsis thaliana). Wild-type seedlings supplied with THF as well as the high endogenous THF content mutant dihydrofolate synthetase folypoly-Glu synthetase homolog B exhibited significant up-regulation of the flowering repressor of Flowering Wageningen and thereby delaying floral transition in a dose-dependent manner. Genome-wide transcripts and DNA methylation profiling revealed that THF reduces DNA methylation so as to manipulate gene expression activity. Moreover, in accompaniment with elevated cellular ratios between monoglutamylated and polyglutamylated folates under increased THF levels, the content of S-adenosylhomo-Cys, a competitive inhibitor of methyltransferases, was obviously higher, indicating that enhanced THF accumulation may disturb cellular homeostasis of the concerted reactions between folate polyglutamylation and folate-dependent DNA methylation. In addition, we found that the loss-of-function mutant of CG DNA methyltransferase MET1 displayed much less responsiveness to THF-associated flowering time alteration. Taken together, our studies revealed a novel regulatory role of THF on epigenetic silencing, which will shed lights on the understanding of interrelations in folate homeostasis, epigenetic variation, and flowering control in plants