Identification of the glycerol-3-phosphate dehydrogenase (GPDH) gene family in wheat and its expression profiling analysis under different stress treatments

Glycerol-3-phosphate dehydrogenase (GPDH) catalyses the interconversion of glycerol-3-phosphate (G3P) and dihydroxyacetone phosphate (DHAP), and plays key roles in different developmental processes and stress responses. GPDH family genes have been previously investigated in various plant species, such as Arabidopsis, maize, and soybean. However, very little is known in GPDH family genes in wheat. In this study, a total of 17 TaGPDH genes were identified from the wheat genome, including eight cytosolic GPDHs, six chloroplastic GPDHs and three mitochondrial GPDHs. Gene duplication analysis showed that segmental duplications contributed to the expansion of this gene family. Phylogenetic results showed that TaGPDHs were clustered into three groups with the same subcellular localization and domain distribution, and similar conserved motif arrangement and gene structure. Expression analysis based on the RNA-seq data showed that GPDH genes exhibited preferential expression in different tissues, and several genes displayed altered expression under various abiotic stresses. These findings provide the foundation for further research of wheat GPDH genes in plant growth, development and stress responses

Atomically Well-defined Nitrogen Doping in the Cross-plane Transport through Graphene Heterojunctions

The nitrogen doping of graphene leads to graphene heterojunctions with a tunable bandgap, suitable for electronics, electrochemical, and sensing applications. However, the microscopic nature and charge transport properties of atomic-level nitrogen-doped graphene are still unknown, mainly due to the multiple doping sites with topological diversities. In this work, we fabricated the atomically well-defined N-doped graphene heterojunctions and investigated the cross-plane transport through these heterojunctions to reveal the effects of doping on their electronic properties. We found that different doping number of nitrogen atoms leads to a conductance difference of up to ~288, and the conductance of graphene heterojunctions with nitrogen-doping at different positions in the conjugated framework can also lead to a conductance difference of ~170. Combined ultraviolet photoelectron spectroscopy measurements and theoretical calculations reveal that the insertion of nitrogen atoms into the conjugation framework significantly stabilizes the frontier molecular orbitals, leading to a change in the relative positions of HOMO and LUMO to the Fermi level of the electrodes. Our work provides a unique insight into the role of nitrogen doping on the charge transport through graphene heterojunctions and materials at the single atomic level

Optogenetic Control of Non-Apoptotic Cell Death

Herein, a set of optogenetic tools (designated LiPOP) that enable photoswitchable necroptosis and pyroptosis in live cells with varying kinetics, is introduced. The LiPOP tools allow reconstruction of the key molecular steps involved in these two non-apoptotic cell death pathways by harnessing the power of light. Further, the use of LiPOPs coupled with upconversion nanoparticles or bioluminescence is demonstrated to achieve wireless optogenetic or chemo-optogenetic killing of cancer cells in multiple mouse tumor models. LiPOPs can trigger necroptotic and pyroptotic cell death in cultured prokaryotic or eukaryotic cells and in living animals, and set the stage for studying the role of non-apoptotic cell death pathways during microbial infection and anti-tumor immunity

Evaluation of a computer-aided diagnostic model for corneal diseases by analyzing in vivo confocal microscopy images

ObjectiveIn order to automatically and rapidly recognize the layers of corneal images using in vivo confocal microscopy (IVCM) and classify them into normal and abnormal images, a computer-aided diagnostic model was developed and tested based on deep learning to reduce physicians’ workload.MethodsA total of 19,612 corneal images were retrospectively collected from 423 patients who underwent IVCM between January 2021 and August 2022 from Renmin Hospital of Wuhan University (Wuhan, China) and Zhongnan Hospital of Wuhan University (Wuhan, China). Images were then reviewed and categorized by three corneal specialists before training and testing the models, including the layer recognition model (epithelium, bowman’s membrane, stroma, and endothelium) and diagnostic model, to identify the layers of corneal images and distinguish normal images from abnormal images. Totally, 580 database-independent IVCM images were used in a human-machine competition to assess the speed and accuracy of image recognition by 4 ophthalmologists and artificial intelligence (AI). To evaluate the efficacy of the model, 8 trainees were employed to recognize these 580 images both with and without model assistance, and the results of the two evaluations were analyzed to explore the effects of model assistance.ResultsThe accuracy of the model reached 0.914, 0.957, 0.967, and 0.950 for the recognition of 4 layers of epithelium, bowman’s membrane, stroma, and endothelium in the internal test dataset, respectively, and it was 0.961, 0.932, 0.945, and 0.959 for the recognition of normal/abnormal images at each layer, respectively. In the external test dataset, the accuracy of the recognition of corneal layers was 0.960, 0.965, 0.966, and 0.964, respectively, and the accuracy of normal/abnormal image recognition was 0.983, 0.972, 0.940, and 0.982, respectively. In the human-machine competition, the model achieved an accuracy of 0.929, which was similar to that of specialists and higher than that of senior physicians, and the recognition speed was 237 times faster than that of specialists. With model assistance, the accuracy of trainees increased from 0.712 to 0.886.ConclusionA computer-aided diagnostic model was developed for IVCM images based on deep learning, which rapidly recognized the layers of corneal images and classified them as normal and abnormal. This model can increase the efficacy of clinical diagnosis and assist physicians in training and learning for clinical purposes