Apical meristem transcriptome analysis identifies a role for the blue light receptor gene GhFKF1 in cotton architecture development

Cotton architecture is determined by the differentiation fate transition of axillary meristem(AM),and influences cotton yield and the efficiency of mechanized harvesting.We observed that the initiation of flowering primordium was earlier in early-maturing than that in late-maturing cultivars during the differentiation and development of AM.The RNA-Seq and expression level analyses showed that genes FLAVIN BINDING,KELCH REPEAT,F-BOX1(GhFKF1),and GIGANTEA(GhGI)were in response to circadian rhythms,and involved in the regulation of cotton flowering.The gene structure,predicted protein structure,and motif content analyses showed that in Arabidopsis,cotton,rapseed,and soybean,proteins GhFKF1 and GhGI were functionally conserved and share evolutionary origins.Compared to the wild type,in GhFKF1 mutants that were created by the CRISPR/Cas9 system,the initiation of branch primordium was inhibited.Conversely,the knocking out of GhGI increased the number of AM differentiating into flower primordium,and there were much more lateral branch differentiation and development.Besides,we investigated that proteins GhFKF1 and GhGI can interact with each other.These results suggest that GhFKF1 and GhGI are key regulators of cotton architecture development,and may collaborate to regulate the differentiation fate transition of AM,ultimately influencing plant architecture.We describe a strategy for using the CRISPR/Cas9 system to increase cotton adaptation and productivity by optimizing plant architecture.

Transcriptome and QTL analyses reveal candidate genes for fiber quality in Upland cotton

With increasing demand for high-quality cotton,it is desirable to identify genes involved in fiber development for molecular improvement of cotton.In this study,780 differentially expressed genes(DEGs)were identified in developing fibers at 10 days post-anthesis(DPA)in Gossypium hirsutum acc.DH962 and G.hirsutum cv.Jimian 5 using RNA-seq.Of 15 stable QTL for fiber quality identified in the same two parents in previous studies,4,3,6,1,and 1 QTL were associated with fiber length(FL),fiber strength(FS),micronaire(MIC),fiber elongation(FE)and fiber length uniformity ratio(FU),respectively.Integration of DEGs and QTL allowed the identification of 31 genes in 9 QTL regions,of which 25 were highly expressed in fibers based on the transcriptome datasets and 9 were preferentially expressed in different stages of fiber development.Gh_A01G0453(GhDTX19),Gh_D07G1581 and Gh_D04G0942 were expressed specifically in 5 and 10 DPA fibers,with Gh_D04G0942 showing low expression in other tissues except pistil.Gh_D07G1799(GhGAUT9),Gh_D11G0326(GhVPS29),Gh_D11G0333(GhTCP14),and Gh_D11G0334(GhNRP2)were preferentially expressed in 5 or 10 DPA fibers;Gh_A01G0397(GhABCG10)and Gh_D07G1744 were expressed specifically in 20 and 25 DPA fibers.These results suggest candidate genes for molecular improvement of cotton fiber quality.

肝神经内分泌肿瘤和肝细胞癌超声影像学特征对比

目的对比肝神经内分泌肿瘤(HNEN)和肝细胞癌(HCC)的超声影像学特征,探讨超声在二者鉴别诊断中的临床应用价值。方法回顾性分析2009年9月至2020年12月于复旦大学附属中山医院病理诊断为HNEN的40例患者的常规超声及超声造影声像图特征,并随机选取同时期40例病理诊断为HCC患者的常规超声及超声造影声像图作为对照;常规超声的观察指标包括病灶的数目、边界、回声水平、动脉血流信号检出率等;超声造影的观察指标包括病灶的增强时间、增强方式、峰值表现、门脉期及延迟期表现等。分析比较2组病灶上述超声影像学特征:采用独立样本t检验或Mann-Whitney U检验比较2组间超声造影的增强开始时间、达峰时间及消退时间的差异;采用χ^(2)检验或精确概率检验比较2组间病灶数目、边界、回声水平、动脉血流信号检出率的差异。结果肿瘤数目、增强的消退时间、增强方式、峰值表现、门脉期及延迟期表现在HNEN和HCC之间差异具有统计学意义(P<0.05)。与HCC相比,HNEN更多表现为多发病灶(27/40 vs 5/40,P<0.001)。超声造影上,HNEN增强消退时间早于HCC[40.0(33.0,49.3)s vs 67.0(48.8,120.0)s],差异具有统计学意义(Z=4.592,P=0.001);HNEN以向心性增强方式增强、峰值时表现为环形强化、门脉期及延迟期表现为明显减退的比例高于HCC(8/40 vs 0/40,P=0.005;5/40 vs 0/40,P=0.048;29/40 vs 9/40,P<0.001;36/40 vs 22/40,P=0.002)。结论HNEN和HCC具有不同的超声影像学特征,超声对二者的鉴别诊断有一定帮助。

Identifying the Correlation Between Ambient Temperature and Gas Consumption in a Local Energy System

In order to understand energy consumption and ensure precise load prediction,it is essential to identify the variation of gas consumption in response to ambient temperature change outdoor.In this paper,the relationship is identified by using Empirical Mode Decomposition(EMD)and linear regression analysis together with outlier detection.EMD is a data processing tool that can divide original data into several Intrinsic Mode Functions(IMFs)with a lower frequency residue.By applying the data mining technique-Mahalanobis distance measurement,some outliers from real-time gas consumption and temperature data points are detected,which are excluded from the data sets to ensure accuracy.Correlation coefficients between the gas load and ambient temperature are calculated and denoted as an important index to quantify their relationship through regression analysis.By comparing such indices on realtime data and EMD processed data,the weather-sensitive part of gas demand is identified.The methods are implemented on a local energy system and the results reveal that the outcome after EMD presents a higher level of correlation between the gas load and ambient temperature,compared to the results from directly using the real-time gas load and temperature data.