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| 1 | Light quality regulates plant biomass and fruit quality through a photoreceptor-dependent HY5-LHC/CYCB module in tomato显示文摘Increasing photosynthesis and light capture offers possibilities for improving crop yield and provides a sustainable way to meet the increasing global demand for food.However,the poor light transmittance of transparent plastic films and shade avoidance at high planting density seriously reduce photosynthesis and alter fruit quality in vegetable crops,and therefore it is important to investigate the mechanisms of light signaling regulation of photosynthesis and metabolism in tomato(Solanum lycopersicum).Here,a combination of red,blue,and white(R1W1B0.5)light promoted the accumulation of chlorophyll,carotenoid,and anthocyanin,and enhanced photosynthesis and electron transport rates by increasing the density of active reaction centers and the expression of the genes LIGHT-HARVESTING COMPLEX B(SlLHCB)and A(SlLHCA),resulting in increased plant biomass.In addition,R1W1B0.5 light induced carotenoid accumulation and fruit ripening by decreasing the expression of LYCOPENEβ-CYCLASE(SlCYCB).Disruption of SlCYCB largely induced fruit lycopene accumulation,and reduced chlorophyll content and photosynthesis in leaves under red,blue,and white light.Molecular studies showed that ELONGATED HYPOCOTYL 5(SlHY5)directly activated SlCYCB,SlLHCB,and SlLHCA expression to enhance chlorophyll accumulation and photosynthesis.Furthermore,R1W1B0.5 light-induced chlorophyll accumulation,photosynthesis,and SlHY5 expression were largely decreased in the slphyb1cry1 mutant.Collectively,R1W1B0.5 light noticeably promoted photosynthesis,biomass,and fruit quality through the photoreceptor(SlPHYB1 and SlCRY1)-SlHY5-SlLHCA/B/SlCYCB module in tomato.Thus,the manipulation of light environments in protected agriculture is a crucial tool to regulate the two vital agronomic traits related to crop production efficiency and fruit nutritional quality in tomato. | Jiarong Yan Juan Liu Shengdie Yang Chenghao Jiang Yanan Liu Nan Zhang Xin Sun Ying Zhang Kangyou Zhu Yinxia Peng Xin Bu Xiujie Wang Golam Jalal Ahammed Sida Meng Changhua Tan Yufeng Liu Zhouping Sun Mingfang Qi Feng Wang Tianlai Li | 2023 | Horticulture Research2023，10，12： | 0 |
| 2 | Circular RNAs: Biomarkers of cancer显示文摘Circular RNAs(circRNAs)are a class of single‐stranded closed RNAs that are produced by the back splicing of precursor mRNAs.The formation of circRNAs mainly involves intron‐pairing‐driven circularization,RNA‐binding protein(RBP)‐driven circularization,and lariat‐driven circularization.The vast majority of circRNAs are found in the cytoplasm,and some intron‐containing circRNAs are localized in the nucleus.CircRNAs have been found to function as microRNA(miRNA)sponges,interact with RBPs and translate proteins,and play an important regulatory role in the development and progression of cancer.CircRNAs exhibit tissue‐and developmental stage–specific expression and are stable,with longer half‐lives than linear RNAs.CircRNAs have great potential as biomarkers for cancer diagnosis and prognosis,which is highlighted by their detectability in tissues,especially in fluid biopsy samples such as plasma,saliva,and urine.Here,we review the current studies on the properties and functions of circRNAs and their clinical application value. | Jingyi Cui Meng Chen Lanxin Zhang Sida Huang Fei Xiao Lihui Zou | 2022 | Cancer Innovation2022，1，3： | 0 |
| 3 | Machine learning-based prognostic and metastasis models of kidney cancer显示文摘Background:Kidney cancer originates from the urinary tubule epithelial system of the renal parenchyma,accounting for 20% of all urinary system tumors.Approximately 70% of cases are localized at diagnosis,and 30%are metastatic.Most localized kidney cancers can be cured by surgery,but most metastatic patients relapse after surgery and eventually die of kidney cancer.Therefore,accurately predicting patient survival and identifying high-risk metastatic patients will effectively guide interventions and improve prognosis.Methods:This study used the data of 12,394 kidney cancer patients from the surveillance,epidemiology,and end results database to construct a research cohort related to kidney cancer survival and metastasis.Eight machine learning models(including support vector machines,logistic regression,decision tree,random forest,XGBoost,AdaBoost,K-nearest neighbors,and multilayer perceptron)were developed to predict the survival and metastasis of kidney cancer and six evaluation indicators(accuracy,precision,sensitivity,specificity,F1 score,and area under the receiver operating characteristic[AUROC])were used to verify,evaluate,and optimize the models.Results:Among the eight machine learning models,Logistic Regression has the highest AUROC in both prediction scenarios.For 3-year survival prediction,the Logistic Regression model had an accuracy of 0.684,a sensitivity of 0.702,a specificity of 0.670,a precision of 0.686,an F1 score of 0.683,and an AUROC of 0.741.For tumor metastasis prediction,the Logistic Regression model had an accuracy of 0.800,a sensitivity of 0.540,a specificity of 0.830,a precision of 0.769,an F1 score of 0.772,and an AUROC of 0.804.Conclusion:In this study,we selected appropriate variables from both statistical and clinical significance and developed and compared eight machine learning models for predicting 3-year survival and metastasis of kidney cancer.The prediction results and evaluation results demonstrated that our model could provide decision support for early intervention for kidney cancer patients. | Yuxiang Zhang Na Hong Sida Huang Jie Wu Jianwei Gao Zheng Xu Fubo Zhang Shaohui Ma Ye Liu Peiyuan Sun Yanping Tang Chun Liu Jianzhong Shou Meng Chen | 2022 | Cancer Innovation2022，1，2： | 0 |
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