Nitrogen(N)and potassium(K)are two key mineral nutrient elements involved in rice growth.Accurate diagnosis of N and K status is very important for the rational application of fertilizers at a specific rice growth sta...Nitrogen(N)and potassium(K)are two key mineral nutrient elements involved in rice growth.Accurate diagnosis of N and K status is very important for the rational application of fertilizers at a specific rice growth stage.Therefore,we propose a hybrid model for diagnosing rice nutrient levels at the early panicle initiation stage(EPIS),which combines a convolutional neural network(CNN)with an attention mechanism and a long short-term memory network(LSTM).The model was validated on a large set of sequential images collected by an unmanned aerial vehicle(UAV)from rice canopies at different growth stages during a two-year experiment.Compared with VGG16,AlexNet,GoogleNet,DenseNet,and inceptionV3,ResNet101 combined with LSTM obtained the highest average accuracy of 83.81%on the dataset of Huanghuazhan(HHZ,an indica cultivar).When tested on the datasets of HHZ and Xiushui 134(XS134,a japonica rice variety)in 2021,the ResNet101-LSTM model enhanced with the squeeze-and-excitation(SE)block achieved the highest accuracies of 85.38 and 88.38%,respectively.Through the cross-dataset method,the average accuracies on the HHZ and XS134 datasets tested in 2022 were 81.25 and 82.50%,respectively,showing a good generalization.Our proposed model works with the dynamic information of different rice growth stages and can efficiently diagnose different rice nutrient status levels at EPIS,which are helpful for making practical decisions regarding rational fertilization treatments at the panicle initiation stage.展开更多
OBJECTIVE: To study the value of serum insulin-like growth factor binding protein-3 (IGFBP-3) levels in differential diagnosis of growth hormone deficiency (GHD). METHODS: To measure serum IGFBP-3 levels by RIA in nor...OBJECTIVE: To study the value of serum insulin-like growth factor binding protein-3 (IGFBP-3) levels in differential diagnosis of growth hormone deficiency (GHD). METHODS: To measure serum IGFBP-3 levels by RIA in normal children and adolescents, GHD children and short-stature children without GHD. RESULTS: Serum level of IGFBP-3 in 129 children with untreated GHD and with no pubertal development was 1.6 +/- 0.9 mg/L, which was less than that in normal group of the same age, but overlapped with the normal children in Tanner stage I. After six-month treatment with recombinant human growth hormone (rhGH), serum level of IGFBP-3 in 59 GHD significantly increased from 1.3 +/- 0.7 mg/L to 2.7 +/- 0.9 mg/L, accompanied by an increase of body heights, growth velocities and serum level of IGF-1. Serum level of IGFBP-3 in 55 short-stature children without GHD was 3.3 +/- 2.2 mg/L, which was not significantly different from that in normal group. CONCLUSION: Serum IGFBP-3 level can reflect the status of GH secretion in children with GHD and is a useful marker for differential diagnosis of GHD.展开更多
基金supported by the National Key Research and Development Program of China(2022YFD2300700)the Open Project Program of State Key Laboratory of Rice Biology,China National Rice Research Institute(20210403)the Zhejiang“Ten Thousand Talents”Plan Science and Technology Innovation Leading Talent Project,China(2020R52035)。
文摘Nitrogen(N)and potassium(K)are two key mineral nutrient elements involved in rice growth.Accurate diagnosis of N and K status is very important for the rational application of fertilizers at a specific rice growth stage.Therefore,we propose a hybrid model for diagnosing rice nutrient levels at the early panicle initiation stage(EPIS),which combines a convolutional neural network(CNN)with an attention mechanism and a long short-term memory network(LSTM).The model was validated on a large set of sequential images collected by an unmanned aerial vehicle(UAV)from rice canopies at different growth stages during a two-year experiment.Compared with VGG16,AlexNet,GoogleNet,DenseNet,and inceptionV3,ResNet101 combined with LSTM obtained the highest average accuracy of 83.81%on the dataset of Huanghuazhan(HHZ,an indica cultivar).When tested on the datasets of HHZ and Xiushui 134(XS134,a japonica rice variety)in 2021,the ResNet101-LSTM model enhanced with the squeeze-and-excitation(SE)block achieved the highest accuracies of 85.38 and 88.38%,respectively.Through the cross-dataset method,the average accuracies on the HHZ and XS134 datasets tested in 2022 were 81.25 and 82.50%,respectively,showing a good generalization.Our proposed model works with the dynamic information of different rice growth stages and can efficiently diagnose different rice nutrient status levels at EPIS,which are helpful for making practical decisions regarding rational fertilization treatments at the panicle initiation stage.
文摘OBJECTIVE: To study the value of serum insulin-like growth factor binding protein-3 (IGFBP-3) levels in differential diagnosis of growth hormone deficiency (GHD). METHODS: To measure serum IGFBP-3 levels by RIA in normal children and adolescents, GHD children and short-stature children without GHD. RESULTS: Serum level of IGFBP-3 in 129 children with untreated GHD and with no pubertal development was 1.6 +/- 0.9 mg/L, which was less than that in normal group of the same age, but overlapped with the normal children in Tanner stage I. After six-month treatment with recombinant human growth hormone (rhGH), serum level of IGFBP-3 in 59 GHD significantly increased from 1.3 +/- 0.7 mg/L to 2.7 +/- 0.9 mg/L, accompanied by an increase of body heights, growth velocities and serum level of IGF-1. Serum level of IGFBP-3 in 55 short-stature children without GHD was 3.3 +/- 2.2 mg/L, which was not significantly different from that in normal group. CONCLUSION: Serum IGFBP-3 level can reflect the status of GH secretion in children with GHD and is a useful marker for differential diagnosis of GHD.
