Dairy farm management is crucial to maintain the longevity of the farm,and poor dairy youngstock or calf management could lead to gradually deteriorating calf health,which often causes premature death.This was found t...Dairy farm management is crucial to maintain the longevity of the farm,and poor dairy youngstock or calf management could lead to gradually deteriorating calf health,which often causes premature death.This was found to be the most neglected part among the management workflows in Malaysia and has caused continuous loss over the recent years.Calf posture recognition is one of the effective methods to monitor calf behaviour and health state,which can be achieved by monitoring the calf behaviours of standing and lying where the former depicts active calf,and the latter,passive calf.Calf posture recognition module is an important component of some automated calf monitoring systems,as the system requires the calf to be in a standing posture before proceeding to the next stage of monitoring,or at the very least,to monitor the activeness of the calves.Calf posture such as standing or resting can easily be distinguished by human eye,however,to be recognized by a machine,it will require more complicated frameworks,particularly one that involves a deep learning neural networks model.Large number of highquality images are required to train a deep learning model for such tasks.In this paper,multiple ConvolutionalNeuralNetwork(CNN)architectures were compared,and the residual network(ResNet)model(specifically,ResNet-50)was ultimately chosen due to its simplicity,great performance,and decent inference time.Two ResNet-50 models having the exact same architecture and configuration have been trained on two different image datasets respectively sourced by separate cameras placed at different angle.There were two camera placements to use for comparison because camera placements can significantly impact the quality of the images,which is highly correlated to the deep learning model performance.After model training,the performance for both CNN models were 99.7%and 99.99%accuracies,respectively,and is adequate for a real-time calf monitoring system.展开更多
为鉴定冀北地区养殖场中引起犊牛腹泻的病原菌,本实验于2021年~2023年采集冀北地区养殖场中患腹泻病奶肉犊牛的肛拭子、粪便及肝脏等病料样品247份,采用细菌分离培养、形态学观察、生化鉴定和ureR基因的PCR扩增等方法进行细菌的分离鉴定...为鉴定冀北地区养殖场中引起犊牛腹泻的病原菌,本实验于2021年~2023年采集冀北地区养殖场中患腹泻病奶肉犊牛的肛拭子、粪便及肝脏等病料样品247份,采用细菌分离培养、形态学观察、生化鉴定和ureR基因的PCR扩增等方法进行细菌的分离鉴定,结果显示,从采集的247份患腹泻病的奶肉犊牛病料样品中分离并鉴定到102株奇异变形杆菌(PM)。将分离菌人工感染小鼠(0.20 m L/只,含菌量为108cfu/mL),检测PM对小鼠的致病性。结果显示其中72株PM引起小鼠发病与死亡,为致病性PM,小鼠的死亡率在40%~100%。采用PCR方法及K-B药敏纸片法分别检测分离菌的毒力基因、相关耐药基因及耐药性,采用SPSS19软件中的Fisher确切概率法分析PM耐药表型与相关耐药基因之间的相关性。结果显示,毒力基因fliL、zapA、pmf A、atfA、rsb A、ureC、atfC在72株致病性PM中的检出率在63.4%~100%,其他毒力基因的检出率在2.7%~8.1%;72株致病性PM对阿莫西林、氨苄西林、安普霉素等12种药物耐药的菌株占51.4%以上,对其他药物耐药的菌株占6.9%~33.3%,均呈多重耐药性(MDR),且至少耐3类药物;72株致病性PM的β-内酰胺类耐药基因blaOXA、blaCTX-M、blaTEM,磺胺类耐药基因Sul1、Sul2、Sul3,氨基糖苷类耐药基因adA、aac(6’)-Ib,四环素类耐药基因TetA、TetM的检出率在43.2%~98.6%,其他耐药基因检出率在4.2%~33.3%,其耐药表型与耐药基因型之间基本呈正相关(除多粘菌素类和大环内酯类药物外)。本研究为致奶肉犊牛腹泻的奇异变形杆菌病的防控提供了参考。展开更多
基金funded under the Malaysian Young Researchers grant scheme(MRUN-MYRGS)Vote number:5539500(Universiti Putra Malaysia)Title:Precision surveillance system to support dairy young stock rearing decisions(NMN).
文摘Dairy farm management is crucial to maintain the longevity of the farm,and poor dairy youngstock or calf management could lead to gradually deteriorating calf health,which often causes premature death.This was found to be the most neglected part among the management workflows in Malaysia and has caused continuous loss over the recent years.Calf posture recognition is one of the effective methods to monitor calf behaviour and health state,which can be achieved by monitoring the calf behaviours of standing and lying where the former depicts active calf,and the latter,passive calf.Calf posture recognition module is an important component of some automated calf monitoring systems,as the system requires the calf to be in a standing posture before proceeding to the next stage of monitoring,or at the very least,to monitor the activeness of the calves.Calf posture such as standing or resting can easily be distinguished by human eye,however,to be recognized by a machine,it will require more complicated frameworks,particularly one that involves a deep learning neural networks model.Large number of highquality images are required to train a deep learning model for such tasks.In this paper,multiple ConvolutionalNeuralNetwork(CNN)architectures were compared,and the residual network(ResNet)model(specifically,ResNet-50)was ultimately chosen due to its simplicity,great performance,and decent inference time.Two ResNet-50 models having the exact same architecture and configuration have been trained on two different image datasets respectively sourced by separate cameras placed at different angle.There were two camera placements to use for comparison because camera placements can significantly impact the quality of the images,which is highly correlated to the deep learning model performance.After model training,the performance for both CNN models were 99.7%and 99.99%accuracies,respectively,and is adequate for a real-time calf monitoring system.
文摘为鉴定冀北地区养殖场中引起犊牛腹泻的病原菌,本实验于2021年~2023年采集冀北地区养殖场中患腹泻病奶肉犊牛的肛拭子、粪便及肝脏等病料样品247份,采用细菌分离培养、形态学观察、生化鉴定和ureR基因的PCR扩增等方法进行细菌的分离鉴定,结果显示,从采集的247份患腹泻病的奶肉犊牛病料样品中分离并鉴定到102株奇异变形杆菌(PM)。将分离菌人工感染小鼠(0.20 m L/只,含菌量为108cfu/mL),检测PM对小鼠的致病性。结果显示其中72株PM引起小鼠发病与死亡,为致病性PM,小鼠的死亡率在40%~100%。采用PCR方法及K-B药敏纸片法分别检测分离菌的毒力基因、相关耐药基因及耐药性,采用SPSS19软件中的Fisher确切概率法分析PM耐药表型与相关耐药基因之间的相关性。结果显示,毒力基因fliL、zapA、pmf A、atfA、rsb A、ureC、atfC在72株致病性PM中的检出率在63.4%~100%,其他毒力基因的检出率在2.7%~8.1%;72株致病性PM对阿莫西林、氨苄西林、安普霉素等12种药物耐药的菌株占51.4%以上,对其他药物耐药的菌株占6.9%~33.3%,均呈多重耐药性(MDR),且至少耐3类药物;72株致病性PM的β-内酰胺类耐药基因blaOXA、blaCTX-M、blaTEM,磺胺类耐药基因Sul1、Sul2、Sul3,氨基糖苷类耐药基因adA、aac(6’)-Ib,四环素类耐药基因TetA、TetM的检出率在43.2%~98.6%,其他耐药基因检出率在4.2%~33.3%,其耐药表型与耐药基因型之间基本呈正相关(除多粘菌素类和大环内酯类药物外)。本研究为致奶肉犊牛腹泻的奇异变形杆菌病的防控提供了参考。
