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Down-regulation of GhADF1 in cotton(Gossypium hirsutum)improves plant drought tolerance and increases fiber yield

The Crop Journal 2022年第4期10卷 1037-1048页

作者： Lixia Qin Huanyang Zhang Jing Li Yonghong Zhu Gaili Jiao Chuangyun Wang Shenjie Wu Shanxi Agricultural University Taiyuan 030031ShanxiChina

Fiber productivity and quality of cotton are severely affected by abiotic stresses.In this study,we identified the role of GhADF1,an actin depolymerizing factor,in cotton response to drought stress.GhADF1 expression in cotton could be induced by PEG6000.GhADF1-RNAi transgenic cotton showed increased tolerance to drought stress during seed germination and seedling development as well as at the reproductive stage.In contrast,overexpression of GhADF1 led to a drought-sensitive phenotype in transgenic plants.GhADF1-RNAi plants produced an enlarged root system with longer primary roots,more lateral roots,increased root dry biomass,and increased cell size.In leaves of GhADF1-RNAi cotton,proline content and activities of reactive oxygen species-scavenging enzymes were increased following drought stress compared with those in wild type.GhADF1-RNAi lines showed higher water-use efficiency than the wild type,accompanied by reduced leaf stomatal density and conductance.GhADF1-RNAi cotton produced higher fiber yield in the field under both normal and drought conditions.Transcriptomic analyses identified 124 differentially expressed genes in leaves of GhADF1-RNAi lines compared with the wild type following drought treatment.Upregulated genes included those encoding transcription factors,protein kinases,heat shock proteins,and other proteins known to be involved in stress responses.We conclude that GhADF1 reduces the expression of abiotic stress-associated genes in cotton response to drought stress and may be a promising candidate gene for crop improvement by genetic manipulation.

关键词： Cotton(Gossypium hirsutum) Actin depolymerizing factors Drought stress Fiber yield Transcriptomic analysis

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Concurrent channel and spatial attention in Fully Convolutional Network for individual pig image segmentation

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International Journal of agricultural and Biological Engineering 2023年第1期16卷 232-242页

作者： Zhiwei Hu Hua Yang Tiantian Lou Hongwen Yan College of Information Science and Engineer Shanxi Agricultural UniversityTaigu 030801ShanxiChina School of Computer and Information Technology(School of Big Data) Shanxi UniversityTaiyuan 03006China College of agricultural Economics&Management Shanxi Agricultural UniversityTaigu 030801ShanxiChina

The separation of individual pigs from the pigpen scenes is crucial for precision farming,and the technology based on convolutional neural networks can provide a low-cost,non-contact,non-invasive method of pig image segmentation.However,two factors limit the development of this field.On the one hand,the individual pigs are easy to stick together,and the occlusion of debris such as pigpens can easily make the model misjudgment.On the other hand,manual labeling of group-raised pig data is time-consuming and labor-intensive and is prone to labeling errors.Therefore,it is urgent for an individual pig image segmentation model that can perform well in individual scenarios and can be easily migrated to a group-raised environment.In order to solve the above problems,taking individual pigs as research objects,an individual pig image segmentation dataset containing 2066 images was constructed,and a series of algorithms based on fully convolutional networks were proposed to solve the pig image segmentation problem.In order to capture the long-range dependencies and weaken the background information such as pigpens while enhancing the information of individual parts of pigs,the channel and spatial attention blocks were introduced into the best-performing decoders UNet and LinkNet.Experiments show that using ResNext50 as the encoder and Unet as the decoder as the basic model,adding two attention blocks at the same time achieves 98.30%and 96.71%on the F1 and IOU metrics,respectively.Compared with the model adding channel attention block alone,the two metrics are improved by 0.13%and 0.22%,respectively.The experiment of introducing channel and spatial attention alone shows that spatial attention is more effective than channel attention.Taking VGG16-LinkNet as an example,compared with channel attention,spatial attention improves the F1 and IOU metrics by 0.16%and 0.30%,respectively.Furthermore,the heatmap of the feature of different layers of the decoder after adding different attention information proves that with the increase

关键词： pig image segmentation Fully Convolutional Network(FCN) attention mechanism channel and spatial attention

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Parallel channel and position attention-guided feature pyramid for pig face posture detection

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International Journal of agricultural and Biological Engineering 2022年第6期15卷 222-234页

作者： Zhiwei Hu Hongwen Yan Tiantian Lou College of Information Science and Engineer Shanxi Agricultural UniversityJinzhong 030801ShanxiChina School of Computer and Information Technology(School of Big Data) Shanxi UniversityTaiyuan 03006China College of agricultural Economics and Management Shanxi Agricultural UniversityJinzhong 030801ShanxiChina

The area of the pig’s face contains rich biological information,such as eyes,nose,and ear.The high-precision detection of pig face postures is crucial to the identification of pigs,and it can also provide fundamental archival information for the study of abnormal behavioral characteristics and regularities.In this study,a series of attention blocks were embedded in Feature Pyramid Network(FPN)for automatic detection of the pig face posture in group-breeding environments.Firstly,the Channel Attention Block(CAB)and Position Attention Block(PAB)were proposed to capture the channel dependencies and the pixel-level long-range relationships,respectively.Secondly,a variety of attention modules are proposed to effectively combine the two kinds of attention information,specifically including Parallel Channel Position(PCP),Cascade Position Channel(CPC),and Cascade Channel Position(CCP),which fuse the channel and position attention information in both parallel or cascade ways.Finally,the verification experiments on three task networks with two backbone networks were conducted for different attention blocks or modules.A total of 45 pigs in 8 pigpens were used as the research objects.Experimental results show that attention-based models perform better.Especially,with Faster Region Convolutional Neural Network(Faster R-CNN)as the task network and ResNet101 as the backbone network,after the introduction of the PCP module,the Average Precision(AP)indicators of the face poses of Downward with head-on face(D-O),Downward with lateral face(D-L),Level with head-on face(L-O),Level with lateral face(L-L),Upward with head-on face(U-O),and Upward with lateral face(U-L)achieve 91.55%,90.36%,90.10%,90.05%,85.96%,and 87.92%,respectively.Ablation experiments show that the PAB attention block is not as effective as the CAB attention block,and the parallel combination method is better than the cascade manner.Taking Faster R-CNN as the task network and ResNet101 as the backbone network,the heatmap visualization of different layers of FPN before

关键词： objection detection attention mechanism feature pyramid network face posture detection pig

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Automatic lameness detection in dairy cows based on machine vision

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International Journal of agricultural and Biological Engineering 2023年第3期16卷 217-224页

作者： Zongwei Jia Xuhui Yang Zhi Wang Ruirui Yu Ruibin Wang College of Information Science and Engineering Shanxi Agricultural UniversityTaigu 030800ShanxiChina

This study proposed a method for detecting lameness in dairy cows based on machine vision,addressing the challenges associated with manual detection.Data from a dairy farm in Taigu,shanxi,China were collected and divided into two parts.The first part was utilized to precisely position the cow’s back by employing a dedicated deep learning model named GhostNet_YOLOv4,which can be implemented on mobile or embedded devices.The second part was used with the Visual Background Extractor(Vibe)algorithm,incorporating additional morphological processing techniques.Enhancing the Vibe algorithm,a widely used background subtraction algorithm for image sequences,achieved more accurate recognition of the specific pixel areas of cows.Subsequently,cow shape-related feature parameters were extracted from the back area using the combined approach.These parameters were used to calculate the average curvature,which describes the degree of curvature of the cow’s back contour during walking.The differences in curvature values were employed for classification to detect lameness.Through extensive experimentation,distinct average curvature ranges of[−0.025,−0.125],[−0.025,+∞],and[−∞,−0.125]were established for normal cows,early lameness,and moderate-severe lameness,respectively.The algorithm’s effectiveness was validated by processing 600 image sequences of dairy cows,resulting in a lameness detection accuracy of 91.67%.These findings can serve as a reference for the timely and accurate recognition of lameness in dairy cows.

关键词： dairy cow lameness detection machine vision object detection deep learning

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Detection navigation baseline in row-following operation of maize weeder based on axis extraction

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International Journal of agricultural and Biological Engineering 2020年第5期13卷 181-186页

作者： Junhui Feng Zhiwei Li Wei Yang Xiaoping Han Xueli Zhang College of agricultural Engineering Shanxi Agricultural UniversityJinzhong 030801ShanxiChina

Detection navigation baseline is primary for the automation of maize weeder in seedling.In the navigation technology based on machine vision,maize seeding or weed near the camera is photographed as a discrete area,while a plant far away from the camera is photographed as a strip area along with other plants in the same row.The two problems cannot be solved by one method.However,in this paper,an algorithm of detection navigation baseline in the row-following operation of maize weeder based on axis extraction was proposed to solve the both problems.Firstly,plants are distinguished from the background based on color feature,and the binary image is acquired.Secondly,plants are described as a set of connected components with numbers after connected components labeling and noise clearing.Thirdly,the axes of all connected components are extracted according to the calculation method of rotary inertia in physics.Next,the abnormal connected components with axes are deleted because the angles between the axes and X-axis are above angle threshold.Then,the judgment model is built based on angle tolerance and distance tolerance,the connected components in a same row based on this model through two-step traversal are merged,and a new axis is re-extracted as the axis of the plant row.Finally,the navigation baselines are detected based on the axes of the plant row.The experimental results show that the accuracy of this algorithm is more than 93%,and the computing time is less than 1.6 s,which can meet the accuracy and real-time performance requirements of maize weeder.

关键词： detection navigation baseline maize weeder machine vision extraction axis

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Influences of open-central canopy on photosynthetic parameters and fruit quality of apples(Malus×domestica)in the Loess Plateau of China

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Horticultural Plant Journal 2022年第2期8卷 133-142页

作者： Lu Lin Zimian Niu Chuangdao Jiang Lu Yu Hongning Wang Meiyu Qiao College of Horticulture Shanxi Agricultural UniversityTaiyuanShanxi 030031China Key Laboratory of Plant Resources Institute of BotanyChinese Academy of SciencesBeijing 100093China Institute of Pomology Shanxi Agricultural UniversityTaiyuanShanxi 030031China

Although open-central canopy(OCC)is popular in apple(Malus×domestica)orchards in Loess Plateau of China,its relevant photosynthetic mechanisms have not been elucidated.In this study,changes in photosynthetically active radiation(PAR),gas exchange,chlorophyll fluorescence quenching and fruit quality in apple trees were measured in OCC and compared with those in the conventional condensed round and large canopy(RLC).Results showed that light intercepted at different orientations was 44%higher by OCC than that by RLC.The improved light environment within OCC remarkably increased leaf maximum net photosynthetic rate(Pnmax)and significantly decreased stomatal limitation.Under high light,the ratio of photorespiratory rate to gross photosynthetic rate(Pr/Pg)in OCC was higher than that in RLC.Moreover,reversible component in non-photochemical quenching(r(qE))was increased,while irreversible component(r(qI))was decreased in OCC than in RLC.As a result,the fruit quality in OCC was greatly boosted as evidenced by the significantly increased single fruit weight,fruit flesh firmness and fruit soluble solid contents and the sharply decreased fruit titratable acid contents.PAR intercepted by the canopy and the fruit soluble solid contents,leaf Pnmaxor single fruit weight were positively correlated,while PAR or Pnmaxwas negatively correlated with the fruit titratable acid contents.Accordingly,the improved crown light environment and the enhanced leaf photosynthetic performance and photoprotective capacity in OCC led to the boosted fruit quality.

关键词： Apple Open-central canopy Light environment Photosynthesis Thermal dissipation Photorespiration Fruit quality

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The leaf extract of crofton weed(Eupatorium adenophorum)inhibits primary root growth by inducing cell death in maize root border cells

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Plant Diversity 2020年第3期42卷 174-180页

作者： Jinhu Ma Xinxin Feng Xiaohuan Yang Yongheng Cao Weifeng Zhao Liangliang Sun College of Enginemng Shanxi Agricultural UniversityTaigu030807China College of Horticulture Shanxi Agricultural UniversityTaigu030801China College of Agriculture Shanxi Agricultural UniversityTaigu030801China Faculty of Tropical Crops Yunnan Agricultural UniversityPuer665000China

The extract of crofton weed(Eupatorium adenophorum) inhibits seed germination and weed growth;however,the physiological mechanisms underlying the effect of crofton weed extract on the modulation of seedling growth and root system development remain largely unclear.In this study,we investigated the effects of the leaf extract of crofton weed(LECW) on primary root(PR) growth in maize seedlings.Treatment with LECW markedly inhibited seed germination and seedling growth in a dose-dependent manner.Physiological analysis indicated that the LECW induced reactive oxygen species(ROS) accumulation in root tips,thereby leading to cell swelling and deformation both in the root cap and elongation zone of root tips,finally leading to cell death in root border cells(RBCs) and PR growth inhibition.The LECW also inhibited pectin methyl esterase(PME) activity,thereby decreasing the RBC number.Taken together,our results indicated that the LECW inhibited PR growth by inducing ROS accumulation and subsequent cell death in RBCs.The present study provides a better understanding of how the LECW modifies root system development and provides insight for evaluating the toxicity of crofton weed extracts in plants.

关键词： Leaf extract of crofton weed Maize Cell death Pectin methyl esterase Root border cell

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Fecal metabolomics reveals the positive effect of ethanol extract of propolis on T2DM mice

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Food Science and Human Wellness 2023年第1期12卷 161-172页

作者： Chunmei Wang Huiting Zhao Kai Xu Yali Du Jinjia Liu Jinfei Wang Yusuo Jiang College of Animal Science Shanxi Agricultural UniversityTaigu 030801China College of Life Sciences Shanxi Agricultural UniversityTaigu 030801China Jilin Province Institute of Apicultural Science Jilin 132108China College of Veterinary Medicine Shanxi Agricultural UniversityTaigu 030801China

A large number of studies have shown that propolis has positive effects in the treatment of type 2 diabetes mellitus(T2DM).However,there are have only been a few reports that are based on an ultra-high performance liquid chromatography-quadrupole-time-of-flight-mass spectrometry(UPLC-Q-TOF-MS)analysis of the fecal metabolomics of ethanol extract of propolis(EEP)in the treatment of T2DM.The present investigation was designed to screen potential biomarkers of T2DM by the metabonomic method and to explain the possible anti-diabetes mechanism of EEP according to the changes in the biomarkers.The results showed that EEP improved the body weight(BW)of T2DM mice,lowered blood sugar levels,and significantly restored blood biochemical indicators related to T2DM,such as fasting insulin(FINS),homeostasis model assessment of insulin resistance(HOMA-IR),aspartate transaminase(AST),and alanine aminotransferase(ALT).Liver pathology showed that EEP reversed liver damage caused by T2DM.Metabolomics data identified 27 potential biomarkers in fecal samples.EEP effectively regulated the dysfunction in the metabolic pathways of glycerophospholipids,sphingolipids,riboflavins,and sterol lipids caused by T2DM.In summary,our research results revealed positive effects of EEP in the treatment of T2DM and provided potential candidate markers for further research and in the clinical treatment of T2DM.

关键词： Ethanol extract of propolis Type 2 diabetes mellitus Fecal metabolites UPLC-Q-TOF-MS Metabolomics

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Yolk free egg substitute improves the serum phospholipid profile of mice with metabolic syndrome based on lipidomic analysis

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Food Science and Human Wellness 2024年第1期13卷 482-493页

作者： Zhihui Yu Lingyu Fan Fei Tai Lixin Zhang Xiaoyu Zhang Yisheng Chen College of Food Science and Engineering Shanxi Agricultural UniversityTaigu 030801China Insitute of Food Nutrition and Safety Shanxi Agricultural UniversityTaiyuan 030031China shanxi Key Laboratory of Edible Fungi for Loess Plateau Taigu 030801China

In this study,the impacts of egg consumption on mice model of metabolic syndrome(Met S)were comparatively investigated.Mice were divided into five groups(n=8):normal diet group(ND),high-fat diet group(HFD),HFD with whole egg group(WE),HFD with free-yolk egg substitute group(YFES),and HFD with lovastatin group(Lov).Main biochemical indexes and a non-targeted lipidomic analysis were employed to insight the lipid profile changes in serum.It was revealed that WE could significantly improve serum biochemical indexes by reducing body weight,low-density lipoprotein cholesterol(LDL-C)and total cholesterol(TC),while increasing high-density lipoprotein cholesterol.YFES exhibited remarkably better performance in increasing phosphatidylglycerol and phosphatidic acids,while decreasing phosphatidylinositol than WE.A total of 50 differential lipids biomarkers tightly related to glycerophospholipids metabolism were screened out.Carnitine C18:2 and C12:1,SM(d18:0/12:0),and SM(d18:1/14:1)were significantly upregulated in YFES compared to WE.YFES reduced expression of SREBP-1c and Cpt1a,while did not affect the expression of PPAR-α.Sphingomyelin biomarkers were positively related to the TC(|r|>0.6),while PPAR-αwas negatively correlated with triglyceride and LDL-C levels.To sum up,YFES attenuated HFD-induced Met S by improving the serum phospholipids,which account for its modulation of glycerophospholipid metabolism.

关键词： Metabolic syndrome Whole egg Yolk free egg substitute Serum Lipidomics

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Optimization of sowing date and seeding rate for high winter wheat yield based on pre-winter plant development and soil water usage in the Loess Plateau,China

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Journal of Integrative Agriculture 2019年第1期18卷 33-42页

作者： REN Ai-xia SUN Min WANG Pei-ru XUE Ling-zhu LEI Miao-miao XUE Jian-fu GAO Zhi-qiang YANG Zhen-ping College of Agriculture Shanxi Agricultural University

Sowing date and seeding rate are critical for productivity of winter wheat(Triticum aestivum L.).A three-year field experiment was conducted with three sowing dates(20 September(SD1),1 October(SD2),and 10 October(SD3)) and three seeding rates(SR67.5,SR90,and SR112.5) to determine suitable sowing date and seeding rate for high wheat yield.A large seasonal variation in accumulated temperature from sowing to winter dormancy was observed among three growing seasons.Suitable sowing dates for strong seedlings before winter varied with the seasons,that was SD2 in 2012–2013,SD3 in 2013–2014,and SD2 as well as SD1 in 2014–2015.Seasonal variation in precipitation during summer fallow also had substantial effects on soil water storage,and consequently influenced grain yield through soil water consumption from winter dormancy to maturity stages.Lower consumption of soil water from winter dormancy to booting stages could make more water available for productive growth from anthesis to maturity stages,leading to higher grain yield.SD2 combined with SR90 had the lowest soil water consumption from winter dormancy to booting stages in 2012–2013 and 2014–2015; while in 2013–2014,it was close to that with SR67.5 or SR112.5.For productive growth from anthesis to maturity stages,SD2 with SR90 had the highest soil water consumption in all three seasons.The highest water consumption in the productive growth period resulted in the best grain yield in both low and high rainfall years.Ear number largely contributed to the seasonal variation in grain yield,while grain number per ear and 1 000-grain weight also contributed to grain yield,especially when soil water storage was high.Our results indicate that sowing date and seeding rate affect grain yield through seedling development before winter and also affect soil water consumption in different growth periods.By selecting the suitable sowing date(1 October) in combination with the proper seeding rate of 90 kg ha–1,the best yield was achieved.Based on these results,we recommend that the cu

关键词： accumulated temperature dry-land sowing date seeding rate soil water winter wheat yield components

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