Acetic acid and furfural are known as prevalent inhibitors deriving from pretreatment during lignocellulosic ethanol production.They negatively impact cell growth,glucose uptake and ethanol biosynthesis of Saccharomyc...Acetic acid and furfural are known as prevalent inhibitors deriving from pretreatment during lignocellulosic ethanol production.They negatively impact cell growth,glucose uptake and ethanol biosynthesis of Saccharomyces cerevisiae strains.Development of industrial S.cerevisiae strains with high tolerance towards these inhibitors is thus critical for efficient lignocellulosic ethanol production.In this study,the acetic acid or furfural tolerance of different S.cerevisiae strains could be significantly enhanced after adaptive evolution via serial cultivation for 40 generations under stress conditions.The acetic acid-based adaptive strain SPSC01-TA9 produced 30.5 g·L^(-1)ethanol with a yield of 0.46 g·g^(-1)in the presence of 9 g·L^(-1)acetic acid,while the acetic acid/furfural-based adaptive strain SPSC01-TAF94 produced more ethanol of 36.2 g·L^(-1)with increased yield up to 0.49 g·g^(-1)in the presence of both 9 g·L^(-1)acetic acid and 4 g·L^(-1)furfural.Significant improvements were also observed during non-detoxified corn stover hydrolysate culture by SPSC01-TAF94,which achieved ethanol production and yield of 29.1 g·L^(-1)and 0.49 g·g^(-1),respectively,the growth and fermentation efficiency of acetic acid/furfural-based adaptive strain in hydrolysate was 95%higher than those of wildtype strains,indicating the acetic acid-and furfural-based adaptive evolution strategy could be an effective approach for improving lignocellulosic ethanol production.The adapted strains developed in this study with enhanced tolerance against acetic acid and furfural could be potentially contribute to economically feasible and sustainable lignocellulosic biorefinery.展开更多
Background Subacute ruminal acidosis(SARA)is a common metabolic disorder of high yielding dairy cows,and it is associated with dysbiosis of the rumen and gut microbiome and host inflammation.This study evaluated the i...Background Subacute ruminal acidosis(SARA)is a common metabolic disorder of high yielding dairy cows,and it is associated with dysbiosis of the rumen and gut microbiome and host inflammation.This study evaluated the impact of two postbiotics from Saccharomyces cerevisiae fermentation products(SCFP)on rumen liquid associated microbiota of lactating dairy cows subjected to repeated grain-based SARA challenges.A total of 32 rumen cannulated cows were randomly assigned to 4 treatments from 4 weeks before until 12 weeks after parturition.Treatment groups included a Control diet or diets supplemented with postbiotics(SCFPa,14 g/d Original XPC;SCFPb-1X,19 g/d Nutri Tek;SCFPb-2X,38 g/d Nutri Tek,Diamond V,Cedar Rapids,IA,USA).Grain-based SARA challenges were conducted during week 5(SARA1)and week 8(SARA2)after parturition by replacing 20%DM of the base total mixed ration(TMR)with pellets containing 50%ground barley and 50%ground wheat.Total DNA from rumen liquid samples was subjected to V3–V416S r RNA gene amplicon sequencing.Characteristics of rumen microbiota were compared among treatments and SARA stages.Results Both SARA challenges reduced the diversity and richness of rumen liquid microbiota,altered the overall composition(β-diversity),and its predicted functionality including carbohydrates and amino acids metabolic pathways.The SARA challenges also reduced the number of significant associations among different taxa,number of hub taxa and their composition in the microbial co-occurrence networks.Supplementation with SCFP postbiotics,in particular SCFPb-2X,enhanced the robustness of the rumen microbiota.The SCFP supplemented cows had less fluctuation in relative abundances of community members when exposed to SARA challenges.The SCFP supplementation promoted the populations of lactate utilizing and fibrolytic bacteria,including members of Ruminococcaceae and Lachnospiraceae,and also increased the numbers of hub taxa during non-SARA and SARA stages.Supplementation with SCFPb-2X prevented the fluctuations in the abundances of hub taxa that were positively correlated with the acetate concentration,andα-andβ-diversity metrics in rumen liquid digesta.Conclusions Induction of SARA challenges reduced microbiota richness and diversity and caused fluctuations in major bacterial phyla in rumen liquid microbiota in lactating dairy cows.Supplementation of SCFP postbiotics could attenuate adverse effects of SARA on rumen liquid microbiota.展开更多
[目的]本文旨在解决在自然环境下不同成熟度苹果目标检测精度较低的问题。[方法]提出了一种改进的YOLOv5s模型SODSTR-YOLOv5s(YOLOv5s with small detection layer and omni-dimensional dynamic convolution and swin transformer bloc...[目的]本文旨在解决在自然环境下不同成熟度苹果目标检测精度较低的问题。[方法]提出了一种改进的YOLOv5s模型SODSTR-YOLOv5s(YOLOv5s with small detection layer and omni-dimensional dynamic convolution and swin transformer block),用于不同成熟度苹果检测。首先改进YOLOv5s的多尺度目标检测层,在Prediction中构建检测160×160特征图的检测头,提高小尺寸的不同成熟度苹果的检测精度;其次在Backbone结构中融合Swin Transformer Block,加强同级成熟度的苹果纹理特征融合,弱化纹理特征分布差异带来的消极影响,提高模型泛化能力;最后将Neck结构的Conv模块替换为动态卷积模块ODConv,细化局部特征映射,实现局部苹果细粒度特征的充分提取。基于不同成熟度苹果数据集进行试验,验证改进模型的性能。[结果]改进模型SODSTR-YOLOv5s检测的精确率、召回率、平均精度均值分别为89.1%、95.5%、93.6%,高、中、低成熟度苹果平均精度均值分别为94.1%、93.1%、93.7%,平均检测时间为16 ms,参数量为7.34 M。相比于YOLOv5s模型,改进模型SODSTR-YOLOv5s精确率、召回率、平均精度均值分别提高了3.8%、5.0%、2.9%,参数量和平均检测时间分别增加了0.32 M和5 ms。[结论]改进模型SODSTR-YOLOv5s提升了在自然环境下对不同成熟度苹果的检测能力,能较好地满足实际采摘苹果的检测要求。展开更多
针对煤矿高噪声、低照度、运动模糊与大批量煤矸混杂等复杂工况环境因素导致煤矸识别存在误检、漏检以及检测精度低的问题,提出一种基于CFS-YOLO算法的煤矸智能识别模型。采用ConvNeXt V2(Convolutional Neural Network with NeXt Units...针对煤矿高噪声、低照度、运动模糊与大批量煤矸混杂等复杂工况环境因素导致煤矸识别存在误检、漏检以及检测精度低的问题,提出一种基于CFS-YOLO算法的煤矸智能识别模型。采用ConvNeXt V2(Convolutional Neural Network with NeXt Units Version 2)特征提取模块替换主干网络末端的2个C3(Cross Stage Partial Bottle Neck Mudule)模块,通过将掩码自动编码器(Masked Autoencoders,MAE)和全局响应归一化(Global Response Normalization,GRN)层添加到ConvNeXt架构中,有效缓解特征崩溃问题以及保持特征在网络传递过程中的多样性;采用Focal-EIOU(Focal and Efficient Intersection Over Union)损失函数替换原CIOU(Computer Intersection Over Union)损失函数,通过其Focal-Loss机制和调整样本权重的方式优化边界框回归任务中的样本不平衡问题,提高模型的收敛速度和定位精度;添加无参注意力机制(Simple Attention Mechanism,SimAM)于主干网络每个C3模块的后端,凭借其注意力权重自适应调整策略,提升模型对尺度变化较大或低分辨率煤矸目标关键特征的提取能力。通过消融试验和对比试验验证所提CFS-YOLO模型的有效性与优越性。试验结果表明:CFS-YOLO模型对于煤矸在煤矿高噪声、低照度、运动模糊与大批量煤矸混杂等复杂环境下的检测效果均得到有效提高,模型的平均精度均值达到90.2%,相较于原YOLOv5s模型的平均精度均值提高了3.7%,平均检测速度达到90.09 FPS,可充分满足煤矸实时检测的需求。同时与YOLOv5s、YOLOv7-tiny与YOLOv8n等6种YOLO系列算法相比,CFS-YOLO模型对煤矿复杂环境的适应性最强且综合检测性能最佳,可为煤矸的智能高效分选提供技术支持。展开更多
基金supported by the National Key Research and Development Program of China(2021YFC2101303)the National Natural Science Foundation of China(U22A20424 and 22378048)+6 种基金the Major Scientific and Technological Projects of Sinopecthe Dalian Technology Talents Project for Distinguished Young Scholars(2021RJ03)the Yunnan Provincial Rural Energy Engineering Key Laboratory(2022KF003)the National Natural Science Foundation of Liaoning Province(2023-MS-110)the Liaoning Revitalization Talents Program(XLYC2202049)the Fundamental Research Funds for the Central Universities(DUT22LK22)the CAS Key Laboratory of Renewable Energy,Guangzhou Institute of Energy Conversion(E229kf0401)。
文摘Acetic acid and furfural are known as prevalent inhibitors deriving from pretreatment during lignocellulosic ethanol production.They negatively impact cell growth,glucose uptake and ethanol biosynthesis of Saccharomyces cerevisiae strains.Development of industrial S.cerevisiae strains with high tolerance towards these inhibitors is thus critical for efficient lignocellulosic ethanol production.In this study,the acetic acid or furfural tolerance of different S.cerevisiae strains could be significantly enhanced after adaptive evolution via serial cultivation for 40 generations under stress conditions.The acetic acid-based adaptive strain SPSC01-TA9 produced 30.5 g·L^(-1)ethanol with a yield of 0.46 g·g^(-1)in the presence of 9 g·L^(-1)acetic acid,while the acetic acid/furfural-based adaptive strain SPSC01-TAF94 produced more ethanol of 36.2 g·L^(-1)with increased yield up to 0.49 g·g^(-1)in the presence of both 9 g·L^(-1)acetic acid and 4 g·L^(-1)furfural.Significant improvements were also observed during non-detoxified corn stover hydrolysate culture by SPSC01-TAF94,which achieved ethanol production and yield of 29.1 g·L^(-1)and 0.49 g·g^(-1),respectively,the growth and fermentation efficiency of acetic acid/furfural-based adaptive strain in hydrolysate was 95%higher than those of wildtype strains,indicating the acetic acid-and furfural-based adaptive evolution strategy could be an effective approach for improving lignocellulosic ethanol production.The adapted strains developed in this study with enhanced tolerance against acetic acid and furfural could be potentially contribute to economically feasible and sustainable lignocellulosic biorefinery.
基金supported by grants from Natural Science and Engineering Research Council(NSERC)of Canada Collaborative Research and Development(CRD)programNSERC Discovery program,Dairy Farmers of Manitoba,and Diamond V,USA,to EK and JCP。
文摘Background Subacute ruminal acidosis(SARA)is a common metabolic disorder of high yielding dairy cows,and it is associated with dysbiosis of the rumen and gut microbiome and host inflammation.This study evaluated the impact of two postbiotics from Saccharomyces cerevisiae fermentation products(SCFP)on rumen liquid associated microbiota of lactating dairy cows subjected to repeated grain-based SARA challenges.A total of 32 rumen cannulated cows were randomly assigned to 4 treatments from 4 weeks before until 12 weeks after parturition.Treatment groups included a Control diet or diets supplemented with postbiotics(SCFPa,14 g/d Original XPC;SCFPb-1X,19 g/d Nutri Tek;SCFPb-2X,38 g/d Nutri Tek,Diamond V,Cedar Rapids,IA,USA).Grain-based SARA challenges were conducted during week 5(SARA1)and week 8(SARA2)after parturition by replacing 20%DM of the base total mixed ration(TMR)with pellets containing 50%ground barley and 50%ground wheat.Total DNA from rumen liquid samples was subjected to V3–V416S r RNA gene amplicon sequencing.Characteristics of rumen microbiota were compared among treatments and SARA stages.Results Both SARA challenges reduced the diversity and richness of rumen liquid microbiota,altered the overall composition(β-diversity),and its predicted functionality including carbohydrates and amino acids metabolic pathways.The SARA challenges also reduced the number of significant associations among different taxa,number of hub taxa and their composition in the microbial co-occurrence networks.Supplementation with SCFP postbiotics,in particular SCFPb-2X,enhanced the robustness of the rumen microbiota.The SCFP supplemented cows had less fluctuation in relative abundances of community members when exposed to SARA challenges.The SCFP supplementation promoted the populations of lactate utilizing and fibrolytic bacteria,including members of Ruminococcaceae and Lachnospiraceae,and also increased the numbers of hub taxa during non-SARA and SARA stages.Supplementation with SCFPb-2X prevented the fluctuations in the abundances of hub taxa that were positively correlated with the acetate concentration,andα-andβ-diversity metrics in rumen liquid digesta.Conclusions Induction of SARA challenges reduced microbiota richness and diversity and caused fluctuations in major bacterial phyla in rumen liquid microbiota in lactating dairy cows.Supplementation of SCFP postbiotics could attenuate adverse effects of SARA on rumen liquid microbiota.
文摘[目的]本文旨在解决在自然环境下不同成熟度苹果目标检测精度较低的问题。[方法]提出了一种改进的YOLOv5s模型SODSTR-YOLOv5s(YOLOv5s with small detection layer and omni-dimensional dynamic convolution and swin transformer block),用于不同成熟度苹果检测。首先改进YOLOv5s的多尺度目标检测层,在Prediction中构建检测160×160特征图的检测头,提高小尺寸的不同成熟度苹果的检测精度;其次在Backbone结构中融合Swin Transformer Block,加强同级成熟度的苹果纹理特征融合,弱化纹理特征分布差异带来的消极影响,提高模型泛化能力;最后将Neck结构的Conv模块替换为动态卷积模块ODConv,细化局部特征映射,实现局部苹果细粒度特征的充分提取。基于不同成熟度苹果数据集进行试验,验证改进模型的性能。[结果]改进模型SODSTR-YOLOv5s检测的精确率、召回率、平均精度均值分别为89.1%、95.5%、93.6%,高、中、低成熟度苹果平均精度均值分别为94.1%、93.1%、93.7%,平均检测时间为16 ms,参数量为7.34 M。相比于YOLOv5s模型,改进模型SODSTR-YOLOv5s精确率、召回率、平均精度均值分别提高了3.8%、5.0%、2.9%,参数量和平均检测时间分别增加了0.32 M和5 ms。[结论]改进模型SODSTR-YOLOv5s提升了在自然环境下对不同成熟度苹果的检测能力,能较好地满足实际采摘苹果的检测要求。
文摘针对煤矿高噪声、低照度、运动模糊与大批量煤矸混杂等复杂工况环境因素导致煤矸识别存在误检、漏检以及检测精度低的问题,提出一种基于CFS-YOLO算法的煤矸智能识别模型。采用ConvNeXt V2(Convolutional Neural Network with NeXt Units Version 2)特征提取模块替换主干网络末端的2个C3(Cross Stage Partial Bottle Neck Mudule)模块,通过将掩码自动编码器(Masked Autoencoders,MAE)和全局响应归一化(Global Response Normalization,GRN)层添加到ConvNeXt架构中,有效缓解特征崩溃问题以及保持特征在网络传递过程中的多样性;采用Focal-EIOU(Focal and Efficient Intersection Over Union)损失函数替换原CIOU(Computer Intersection Over Union)损失函数,通过其Focal-Loss机制和调整样本权重的方式优化边界框回归任务中的样本不平衡问题,提高模型的收敛速度和定位精度;添加无参注意力机制(Simple Attention Mechanism,SimAM)于主干网络每个C3模块的后端,凭借其注意力权重自适应调整策略,提升模型对尺度变化较大或低分辨率煤矸目标关键特征的提取能力。通过消融试验和对比试验验证所提CFS-YOLO模型的有效性与优越性。试验结果表明:CFS-YOLO模型对于煤矸在煤矿高噪声、低照度、运动模糊与大批量煤矸混杂等复杂环境下的检测效果均得到有效提高,模型的平均精度均值达到90.2%,相较于原YOLOv5s模型的平均精度均值提高了3.7%,平均检测速度达到90.09 FPS,可充分满足煤矸实时检测的需求。同时与YOLOv5s、YOLOv7-tiny与YOLOv8n等6种YOLO系列算法相比,CFS-YOLO模型对煤矿复杂环境的适应性最强且综合检测性能最佳,可为煤矸的智能高效分选提供技术支持。
