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| 1 | A deep learning-integrated micro-CT image analysis pipeline for quantifying rice lodging resistance-related traits显示文摘Lodging is a common problemin rice,reducing its yield andmechanical harvesting efficiency.Rice architecture is a key aspect of its domestication and a major factor that limits its high productivity.The ideal rice culm structure,includingmajor_axis_culm,minor axis_culm,andwall thickness_culm,is critical for improving lodging resistance.However,the traditionalmethod ofmeasuring rice culms is destructive,time consuming,and labor intensive.In this study,we used a high-throughput micro-CT-RGB imaging system and deep learning(SegNet)todevelopa high-throughputmicro-CTimageanalysis pipelinethatcanextract 24 riceculmmorphological traits and lodging resistance-related traits.When manual and automatic measurements were compared at themature stage,the mean absolute percentage errors for major_axis_culm,minor_axis_culm,andwall_thickness_culmin 104 indica rice accessionswere 6.03%,5.60%,and 9.85%,respectively,and the R^(2) valueswere 0.799,0.818,and 0.623.We also builtmodels of bending stress using culmtraits at the mature and tillering stages,and the R^(2) values were 0.722 and 0.544,respectively.The modeling results indicated that this method can quantify lodging resistance nondestructively,even at an early growth stage.In addition,we also evaluated the relationships of bending stress toshoot dryweight,culm density,and drought-related traits and found that plants with greater resistance to bending stress had slightly higher biomass,culm density,and culm area but poorer drought resistance.In conclusion,we developed a deep learning-integrated micro-CT image analysis pipeline to accurately quantify the phenotypic traits of rice culms in4.6 min per plant;this pipeline will assist in future high-throughput screening of large rice populations for lodging resistance. | Di Wu Dan Wu Hui Feng Lingfeng Duan Guoxing Dai Xiao Liu Kang Wang Peng Yang Guoxing Chen Alan P.Gay John H.Doonan Zhiyou Niu Lizhong Xiong Wanneng Yang | 2021 | Plant Communications2021，2，2： | 7 |
| 2 | Facile construction of highly efficient MOF-based Pd@UiO-66-NH_(2)@ZnIn_(2)S_(4)flower-like nanocomposites for visible-light-driven photocatalytic hydrogen production显示文摘Construction of metal-organic-frame works-based composite photocatalysts has attracted much attention for the reasonable band gap and high surface areas to improve the photocatalytic activity.In this study,the ternary heterojunction Pd@UiO-66-NH_(2)@ZnIn_(2)S_(4)nanocomposites were facilely prepared for the first time by a two-step method.The visible-light-promoted hydrogen production rate of 0.3%Pd@UiO-66-NH_(2)@ZnIn_(2)S_(4)reaches up to 5.26 mmol g^(-1)h^(-1),which is evidently much higher than pure UiO-66-NH_(2),ZnIn_(2)S_(4)and binary UiO-66-NH_(2)/ZnIn_(2)S_(4)composites.Such a huge improvement in the photocatalytic performance is mainly attributed to the matched band gap of ZnIn_(2)S_(4)and UiO-66-NH_(2),and the introduction of Pd NPs into photocatalysts that broaden spectral response range and promote the photon induced charge carrier separation.This work may provide a feasible approach for the design and construction of metal-organic-frameworks-based photocatalytic materials. | Mengting Cao Fengli Yang Quan Zhang Juhua Zhang Lu Zhang Lingfeng Li Xiaohao Wang Wei-Lin Dai | 2021 | Journal of Materials Science & Technology2021，，17： | 3 |
| 3 | Determination of trophic levels of marine fish in the Yellow Sea and northern East China Sea using nitrogen stable isotope (δ^(15)N) analysis of otoliths显示文摘Fish otolithδ^(15) N(δ^(15) N_(oto))is a demonstrated source of information of dietary history for marine fi sh as it is available iN_(oto)lith archives and sedimentary deposits unlike white muscle tissue(WMT).WMT and stomach content data are insufficient for trophic level(TL)data of past fi shes which is important for the changes of marine fi shery resources over long time scales.To determine the correlation betweenδ^(15) N_(oto) and fi sh WMTδ^(15) N(δ^(15) N_(wmt))and the feasibility of usingδ^(15) N_(oto) in characterizing the TLs of marine fi shes,we conducted nitrogen stable isotope analysis(SIA)in the otolith and WMT of 36 marine fi sh species sampled from the Yellow Sea and northern East China Sea in 2011-2014.Bothδ^(15) N_(oto) andδ^(15) N_(wmt) were analyzed using an elemental analyzer coupled with an isotope ratio mass spectrometer(EA-IRMS).Multiple otoliths were combined to make each otolith measurement and were analyzed as-is without a carbonate dissolution pre-processing step.δ^(15) N_(oto) andδ^(15) N_(wmt) comparisons for species in the Yellow Sea and northern East China Sea are currently lacking and would be helpful for both regional studies and for increasing the number of species for whichδ^(15) N_(oto) andδ^(15) N_(wmt) have been compared.Additionally,to determine the relative accuracy of trophic level calculated usingδ^(15) N_(oto),we compared TL calculated fromδ^(15) N_(oto) to traditional trophic level metrics calculated usingδ^(15) N_(wmt).The results showed a positive and highly signifi cant correlation(R=0.780,P<0.001)betweenδ^(15) N_(oto) andδ^(15) N_(wmt).Trophic level estimation using WMT(TL wmt)and otolith(TL oto)showed congruence in our study,which is not entirely surprising given thatδ^(15) N_(oto) was regressed againstδ^(15) N_(wmt) and the resulting regression coefficient was used to convertδ^(15) N_(oto) toδ^(15) N_(wmt) prior to calculating TL oto.This conversion was required in order to be consistent with previousδ^(15) N_(wmt)-based calculations of TL for comparison.TL oto calculations resulted in TL values that were largely within 5%-10%of TL values calculated withδ^(15) N_(wmt).Our fi ndings show thatδ^(15) N_(oto) is a feasible technique for characterizing the TLs of marine fi sh and can also assist in food web and marine ecosystem studies. | Huaiyu BAI Yukun WANG Tingting ZHANG Fangqun DAI Lingfeng HUANG Yao SUN | 2022 | Journal of Oceanology and Limnology2022，40，2： | 1 |
| 4 | AIPI suppresses atherosclerosis by limiting hyperlipidemia-induced inflammation and vascular endothelial dysfunction 显示文摘 | HUANG Qunhua QIN Lingfeng DAI Shengchuan | 2013 | Arterioscl Throm Vasculr Bio ( ATVB )2013，33，4： | 1 |
| 5 | Investigation on Agronomic Characters of Dwarf Mutant 778 in Broomcorn Millet(Panicum miliaceum L.)and Analysis of Its Sensitivity to GA显示文摘In order to investigate the differences between agronomic traits of dwarf mutant and original material 260 and whether the cause of dwarf is related to GA synthesis or signaling pathway,this experiment used dwarf mutant 778 and its original material 260 as experimental materials.Morphological observation and determination were performed for agronomic traits on plant height,ear length,internode length,internode number,seed length,seed width and number of seeds in different growth periods and different concentrations.The plants were treated by GA spraying,and the changes of plant height,root length,stem width,leaf length and leaf width were measured.The results are as follows:①The plant height of the dwarf mutant material was significantly different from that of the original high material,which was mainly caused by the difference between above-ground basal part and the length of the first and second elongation joints.②Comparing and analyzing the differences of traits between dwarf mutant material 778 and original high material 260,it was found that the plant height,ear length,internode number,grain number per ear and internode length of dwarf mutant 778 were significantly lower than that of high stalk 260(P<0.01),and the seed length of dwarf mutant 778 was significantly higher than that of high stalk 260(P<0.05).③Different concentrations of gibberellin(0,50,100,200 mg/L)had no significant effect on plant height and root length of dwarf mutant 778(P>0.05).Different concentrations of gibberellin had significant effects on plant height,root length and sensitivity coefficient of high stalk 260(P<0.05).And compared with the control group,all high materials 260 treated with different concentrations of gibberellin performed differently in plant morphology and growth potential.④Under the conditions of 100 and 200 mg/L GA,the difference of plant height between the dwarf mutant and the high stalk control decreased with time,and there was no difference at the end.There were no differences in sensitivity coefficient GRI between different concentrations of gibberellin treatment groups,indicating that the external gibberellin could restore the scorpion dwarf mutant to the original high stalk,and the gene that causes the mutation might be related with the gibberellin synthesis pathway. | Bo ZHANG Xiaojie LIU Yingjie GUO Xiaoping JIA Dezhi YANG Yuan ZHAO Lingfeng DAI Shujun KOU Xiaomei ZHANG Dianyun HOU Xuehai ZHU | 2020 | Agricultural Biotechnology2020，9，4： | 0 |
| 6 | Lightweight Network Ensemble Architecture for Environmental Perception on the Autonomous System显示文摘It is important for the autonomous system to understand environmental information.For the autonomous system,it is desirable to have a strong generalization ability to deal with different complex environmental information,as well as have high accuracy and quick inference speed.Network ensemble architecture is a good choice to improve network performance.However,it is unsuitable for real-time applications on the autonomous system.To tackle this problem,a new neural network ensemble named partial-shared ensemble network(PSENet)is presented.PSENet changes network ensemble architecture from parallel architecture to scatter architecture and merges multiple component networks together to accelerate the inference speed.To make component networks independent of each other,a training method is designed to train the network ensemble architecture.Experiments on Camvid and CIFAR-10 reveal that PSENet achieves quick inference speed while maintaining the ability of ensemble learning.In the real world,PSENet is deployed on the unmanned system and deals with vision tasks such as semantic segmentation and environmental prediction in different fields. | Yingpeng Dai Junzheng Wang Jing Li Lingfeng Meng Songfeng Wang | 2023 | Computer Modeling in Engineering & Sciences2023，，1： | 0 |
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