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| 1 | Enhanced production of seed oil with improved fatty acid composition by overexpressing NAD^(+)-dependent glycerol-3-phosphate dehydrogenase in soybean显示文摘There is growing interest in expanding the production of soybean oils(mainly triacylglycerol, or TAG) to meet rising feed demand and address global energy concerns. We report that a plastidlocalized glycerol-3-phosphate dehydrogenase(GPDH), encoded by GmGPDHp1 gene, catalyzes the formation of glycerol-3-phosphate(G3 P), an obligate substrate required for TAG biosynthesis.Overexpression of GmGPDHp1 increases soybean seed oil content with high levels of unsaturated fatty acids(FAs), especially oleic acid(C18:1), without detectably affecting growth or seed protein content or seed weight. Based on the lipidomic analyses, we found that the increase in G3 P content led to an elevated diacylglycerol(DAG) pool, in which the Kennedy pathwayderived DAG was mostly increased, followed by PC-derived DAG, thereby promoting the synthesis of TAG containing relatively high proportion of C18:1. The increased G3 P levels induced several transcriptional alterations of genes involved in the glycerolipid pathways. In particular, genes encoding the enzymes responsible for de novo glycerolipid synthesis were largely upregulated in the transgenic lines, in-line with the identified biochemical phenotype. These results reveal a key role for GmGPDHp1-mediated G3 P metabolism in enhancing TAG synthesis and demonstrate a strategy to modify the FA compositions of soybean oils for improved nutrition and biofuel. | Ying Zhao Pan Cao Yifan Cui Dongxu Liu Jiapeng Li Yabin Zhao Siqi Yang Bo Zhang Runnan Zhou Minghao Sun Xuetian Guo Mingliang Yang Dawei Xin Zhanguo Zhang Xin Li Chen Lv Chunyan Liu Zhaoming Qi Jingyu Xu Xiaoxia Wu Qingshan Chen | 2021 | Journal of Integrative Plant Biology2021，63，6： | 4 |
| 2 | Artificial intelligence in gastric cancer:applications and challenges显示文摘Gastric cancer(GC)is one of the most common malignant tumors with high mortality.Accurate diagnosis and treatment decisions for GC rely heavily on human experts’careful judgments on medical images.However,the improvement of the accuracy is hindered by imaging conditions,limited experience,objective criteria,and inter-observer discrepancies.Recently,the developments of machine learning,especially deep-learning algorithms,have been facilitating computers to extract more information from data automatically.Researchers are exploring the far-reaching applications of artificial intelligence(AI)in various clinical practices,including GC.Herein,we aim to provide a broad framework to summarize current research on AI in GC.In the screening of GC,AI can identify precancerous diseases and assist in early cancer detection with endoscopic examination and pathological confirmation.In the diagnosis of GC,AI can support tumor-node-metastasis(TNM)staging and subtype classification.For treatment decisions,AI can help with surgical margin determination and prognosis prediction.Meanwhile,current approaches are challenged by data scarcity and poor interpretability.To tackle these problems,more regulated data,unified processing procedures,and advanced algorithms are urgently needed to build more accurate and robust AI models for GC. | Runnan Cao Lei Tang Mengjie Fang Lianzhen Zhong Siwen Wang Lixin Gong Jiazheng Li Di Dong Jie Tian | 2022 | Gastroenterology Report2022，10，1： | 3 |
| 3 | Aqueous Two-Phase Interfacial Assembly of COF Membranes for Water Desalination显示文摘Aqueous two-phase system features with ultralow interfacial tension and thick interfacial region,affording unique confined space for membrane assembly.Here,for the first time,an aqueous two-phase interfacial assembly method is proposed to fabricate covalent organic framework(COF)membranes.The aqueous solution containing polyethylene glycol and dextran undergoes segregated phase separation into two water-rich phases.By respectively distributing aldehyde and amine monomers into two aqueous phases,a series of COF membranes are fabricated at water-water interface.The resultant membranes exhibit high NaCl rejection of 93.0-93.6% and water permeance reaching 1.7-3.7 L m^(−2) h^(−1) bar^(−1),superior to most water desalination membranes.Interestingly,the interfacial tension is found to have pronounced effect on membrane structures.The appropriate interfacial tension range(0.1-1.0 mN m^(−1))leads to the tight and intact COF membranes.Furthermore,the method is extended to the fabrication of other COF and metal-organic polymer membranes.This work is the first exploitation of fabricating membranes in all-aqueous system,confering a green and generic method for advanced membrane manufacturing. | Hongjian Wang Jiashuai Zhao Yang Li Yu Cao Ziting Zhu Meidi Wang Runnan Zhang Fusheng Pan Zhongyi Jiang | 2022 | Nano-Micro Letters2022，14，12： | 1 |
| 4 | Pomegranate-like C_(60)@cobalt/nitrogen-codoped porous carbon for high-performance oxygen reduction reaction and lithium-sulfur battery显示文摘Porous carbon materials play essential roles in electrocatalysis and electrochemical energy storage.It is of significant importance to rationally design and tune their porous structure and active sites for achieving high electrochemical activity and stability.Herein,we develop a novel approach to tune the morphology of porous carbon materials(PCM)by embedding fullerene C_(60),achieving improved performance of oxygen reduction reaction(ORR)and lithium-sulfur(Li-S)battery.Owing to the strong interaction between C_(60)and imidazole moieties,pomegranate-like hybrid of Ow-embedded zeolitic imidazolate framework(ZIF-67)precursor is synthesized,which is further pyrolyzed to form C_(60)-embedded cobalt/nitrogen-codoped porous carbon materials(abbreviated as C_(60)@Co-N-PCM).Remarkably,the unique structure of C_(60)@Co-N-PCM offers excellent ORR electrocatalytic activity and stability in alkaline solutions,outperforming the commercial Pt/C(20 wt.%)catalyst.Besides,C_(60)@Co-N-PCM as a novel cathode delivers a high specific capacity of-900 mAh·g^(-1) at 0.2 C rate in Li-S batteries,which is superior to the pristine ZIF-67-derived PCM without embedding C_(60). | Jianhua Wu Shiyang Wang Zhanwu Lei Runnan Guan Muqing Chen Pingwu Du Yalin Lu Ruiguo Cao Shangfeng Yang | 2021 | Nano Research2021，14，8： | 0 |
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