Automatic pancreas segmentation plays a pivotal role in assisting physicians with diagnosing pancreatic diseases,facilitating treatment evaluations,and designing surgical plans.Due to the pancreas’s tiny size,signifi...Automatic pancreas segmentation plays a pivotal role in assisting physicians with diagnosing pancreatic diseases,facilitating treatment evaluations,and designing surgical plans.Due to the pancreas’s tiny size,significant variability in shape and location,and low contrast with surrounding tissues,achieving high segmentation accuracy remains challenging.To improve segmentation precision,we propose a novel network utilizing EfficientNetV2 and multi-branch structures for automatically segmenting the pancreas fromCT images.Firstly,an EfficientNetV2 encoder is employed to extract complex and multi-level features,enhancing the model’s ability to capture the pancreas’s intricate morphology.Then,a residual multi-branch dilated attention(RMDA)module is designed to suppress irrelevant background noise and highlight useful pancreatic features.And re-parameterization Visual Geometry Group(RepVGG)blocks with amulti-branch structure are introduced in the decoder to effectively integrate deep features and low-level details,improving segmentation accuracy.Furthermore,we apply re-parameterization to the model,reducing computations and parameters while accelerating inference and reducing memory usage.Our approach achieves average dice similarity coefficient(DSC)of 85.59%,intersection over union(IoU)of 75.03%,precision of 85.09%,and recall of 86.57%on the NIH pancreas dataset.Compared with other methods,our model has fewer parameters and faster inference speed,demonstrating its enormous potential in practical applications of pancreatic segmentation.展开更多
The Daurian Partridge(Perdix dauuricae) is a kind of hunting bird with high economic value.Genetic diversity and structure in the Daurian Partridge were studied by analyzing eight microsatellite loci in 23 populations...The Daurian Partridge(Perdix dauuricae) is a kind of hunting bird with high economic value.Genetic diversity and structure in the Daurian Partridge were studied by analyzing eight microsatellite loci in 23 populations found throughout the range of the species in China.The objectives were to evaluate the consequences on genetic diversity and differentiation of Daurian Partridge populations and to obtain a profound genetic insight for future management decisions and for effective measures to protect and exploit Daurian Partridges.The results showed that microsatellites were polymorphic in all Daurian Partridge populations,with a high level of genetic diversity over all the loci,especially in the Qaidam Basin populations which have the highest level of diversity.Significant genetic divergence was observed among different groups as well as between populations within the same group;most pairwise FST values were highly significant.Both phylogenetic trees and Bayesian clustering analyses revealed clear differentiation among the 23 populations of the Daurian Partridge,which were classified into two genetically differentiated groups.A bottleneck analysis indicated that Daurian Partridge populations have experienced a recent bottleneck.Our study argues that the Qaidam populations,North China populations,JN population,ZJC population,and Liupan Mountain populations should be paid special attention in order to retain adequate population sizes for maintaining genetic diversity.展开更多
Accurate diagnosis of apple leaf diseases is crucial for improving the quality of apple production and promoting the development of the apple industry. However, apple leaf diseases do not differ significantly from ima...Accurate diagnosis of apple leaf diseases is crucial for improving the quality of apple production and promoting the development of the apple industry. However, apple leaf diseases do not differ significantly from image texture and structural information. The difficulties in disease feature extraction in complex backgrounds slow the related research progress. To address the problems, this paper proposes an improved multi-scale inverse bottleneck residual network model based on a triplet parallel attention mechanism, which is built upon ResNet-50, while improving and combining the inception module and ResNext inverse bottleneck blocks, to recognize seven types of apple leaf(including six diseases of alternaria leaf spot, brown spot, grey spot, mosaic, rust, scab, and one healthy). First, the 3×3 convolutions in some of the residual modules are replaced by multi-scale residual convolutions, the convolution kernels of different sizes contained in each branch of the multi-scale convolution are applied to extract feature maps of different sizes, and the outputs of these branches are multi-scale fused by summing to enrich the output features of the images. Second, the global layer-wise dynamic coordinated inverse bottleneck structure is used to reduce the network feature loss. The inverse bottleneck structure makes the image information less lossy when transforming from different dimensional feature spaces. The fusion of multi-scale and layer-wise dynamic coordinated inverse bottlenecks makes the model effectively balances computational efficiency and feature representation capability, and more robust with a combination of horizontal and vertical features in the fine identification of apple leaf diseases. Finally, after each improved module, a triplet parallel attention module is integrated with cross-dimensional interactions among channels through rotations and residual transformations, which improves the parallel search efficiency of important features and the recognition rate of the network with relatively small computational costs while the dimensional dependencies are improved. To verify the validity of the model in this paper, we uniformly enhance apple leaf disease images screened from the public data sets of Plant Village, Baidu Flying Paddle, and the Internet. The final processed image count is 14,000. The ablation study, pre-processing comparison, and method comparison are conducted on the processed datasets. The experimental results demonstrate that the proposed method reaches 98.73% accuracy on the adopted datasets, which is 1.82% higher than the classical ResNet-50 model, and 0.29% better than the apple leaf disease datasets before preprocessing. It also achieves competitive results in apple leaf disease identification compared to some state-ofthe-art methods.展开更多
In order to investigate the effects of the structure of branches on the TPA properties for multi-branched molecules, the TPA cross section is calculated by using ZINDO/SOS method. The investigated mole- cules have dif...In order to investigate the effects of the structure of branches on the TPA properties for multi-branched molecules, the TPA cross section is calculated by using ZINDO/SOS method. The investigated mole- cules have different branches (chomorfores based on stilbene, dithienothiophene and flourene) with nitrogen(N) as coupling center. The results show that the cooperative enhancement in multi-branched molecules depends on the structures of the branches and the structures of branches play an important role in the enhancement of the TPA cross section. The designed molecules with stilbene and dithie- nothiophene as branched possess relatively larger two-photon absorption cross sections.展开更多
本研究应用18个微卫星分子标记,对白斑狗鱼(Esox lucius L.)3个中国新疆群体(乌伦古湖、吉力湖和6号湖)和1个匈牙利巴拉顿湖群体的遗传结构进行了分析。结果表明,3个中国白斑狗鱼群体的平均等位基因丰富度(AR)、平均观测杂合度(HO)和平...本研究应用18个微卫星分子标记,对白斑狗鱼(Esox lucius L.)3个中国新疆群体(乌伦古湖、吉力湖和6号湖)和1个匈牙利巴拉顿湖群体的遗传结构进行了分析。结果表明,3个中国白斑狗鱼群体的平均等位基因丰富度(AR)、平均观测杂合度(HO)和平均期望杂合度(HE)均显著低于匈牙利巴拉顿湖群体(P<0.05),而匈牙利群体的平均近交系数(FIS)高于中国群体;经SMM和TPM模型检测,匈牙利白斑狗鱼群体存在显著的遗传瓶颈信号(P<0.001);AMOVA和群体两两比较的FST值表明,中国与匈牙利白斑狗鱼的遗传分化十分显著(P<0.01);NJ树、主成分分析(PCA)进一步证实中国白斑狗鱼与匈牙利白斑狗鱼群体间存在显著的遗传差异和分化。此外,贝叶斯遗传聚类结果表明,中国新疆6号湖白斑狗鱼群体极可能来源于乌伦古湖,而非吉力湖。展开更多
基金supported by the Science and Technology Innovation Programof Hunan Province(Grant No.2022RC1021)the Hunan Provincial Natural Science Foundation Project(Grant No.2023JJ60124)+1 种基金the Changsha Natural Science Foundation Project(Grant No.kq2202265)the key project of the Hunan Provincial of Education(Grant No.22A0255).
文摘Automatic pancreas segmentation plays a pivotal role in assisting physicians with diagnosing pancreatic diseases,facilitating treatment evaluations,and designing surgical plans.Due to the pancreas’s tiny size,significant variability in shape and location,and low contrast with surrounding tissues,achieving high segmentation accuracy remains challenging.To improve segmentation precision,we propose a novel network utilizing EfficientNetV2 and multi-branch structures for automatically segmenting the pancreas fromCT images.Firstly,an EfficientNetV2 encoder is employed to extract complex and multi-level features,enhancing the model’s ability to capture the pancreas’s intricate morphology.Then,a residual multi-branch dilated attention(RMDA)module is designed to suppress irrelevant background noise and highlight useful pancreatic features.And re-parameterization Visual Geometry Group(RepVGG)blocks with amulti-branch structure are introduced in the decoder to effectively integrate deep features and low-level details,improving segmentation accuracy.Furthermore,we apply re-parameterization to the model,reducing computations and parameters while accelerating inference and reducing memory usage.Our approach achieves average dice similarity coefficient(DSC)of 85.59%,intersection over union(IoU)of 75.03%,precision of 85.09%,and recall of 86.57%on the NIH pancreas dataset.Compared with other methods,our model has fewer parameters and faster inference speed,demonstrating its enormous potential in practical applications of pancreatic segmentation.
基金supported by the National Natural Science Foundation of China (Grant No. 30530130)
文摘The Daurian Partridge(Perdix dauuricae) is a kind of hunting bird with high economic value.Genetic diversity and structure in the Daurian Partridge were studied by analyzing eight microsatellite loci in 23 populations found throughout the range of the species in China.The objectives were to evaluate the consequences on genetic diversity and differentiation of Daurian Partridge populations and to obtain a profound genetic insight for future management decisions and for effective measures to protect and exploit Daurian Partridges.The results showed that microsatellites were polymorphic in all Daurian Partridge populations,with a high level of genetic diversity over all the loci,especially in the Qaidam Basin populations which have the highest level of diversity.Significant genetic divergence was observed among different groups as well as between populations within the same group;most pairwise FST values were highly significant.Both phylogenetic trees and Bayesian clustering analyses revealed clear differentiation among the 23 populations of the Daurian Partridge,which were classified into two genetically differentiated groups.A bottleneck analysis indicated that Daurian Partridge populations have experienced a recent bottleneck.Our study argues that the Qaidam populations,North China populations,JN population,ZJC population,and Liupan Mountain populations should be paid special attention in order to retain adequate population sizes for maintaining genetic diversity.
基金supported in part by the General Program Hunan Provincial Natural Science Foundation of 2022,China(2022JJ31022)the Undergraduate Education Reform Project of Hunan Province,China(HNJG-20210532)the National Natural Science Foundation of China(62276276)。
文摘Accurate diagnosis of apple leaf diseases is crucial for improving the quality of apple production and promoting the development of the apple industry. However, apple leaf diseases do not differ significantly from image texture and structural information. The difficulties in disease feature extraction in complex backgrounds slow the related research progress. To address the problems, this paper proposes an improved multi-scale inverse bottleneck residual network model based on a triplet parallel attention mechanism, which is built upon ResNet-50, while improving and combining the inception module and ResNext inverse bottleneck blocks, to recognize seven types of apple leaf(including six diseases of alternaria leaf spot, brown spot, grey spot, mosaic, rust, scab, and one healthy). First, the 3×3 convolutions in some of the residual modules are replaced by multi-scale residual convolutions, the convolution kernels of different sizes contained in each branch of the multi-scale convolution are applied to extract feature maps of different sizes, and the outputs of these branches are multi-scale fused by summing to enrich the output features of the images. Second, the global layer-wise dynamic coordinated inverse bottleneck structure is used to reduce the network feature loss. The inverse bottleneck structure makes the image information less lossy when transforming from different dimensional feature spaces. The fusion of multi-scale and layer-wise dynamic coordinated inverse bottlenecks makes the model effectively balances computational efficiency and feature representation capability, and more robust with a combination of horizontal and vertical features in the fine identification of apple leaf diseases. Finally, after each improved module, a triplet parallel attention module is integrated with cross-dimensional interactions among channels through rotations and residual transformations, which improves the parallel search efficiency of important features and the recognition rate of the network with relatively small computational costs while the dimensional dependencies are improved. To verify the validity of the model in this paper, we uniformly enhance apple leaf disease images screened from the public data sets of Plant Village, Baidu Flying Paddle, and the Internet. The final processed image count is 14,000. The ablation study, pre-processing comparison, and method comparison are conducted on the processed datasets. The experimental results demonstrate that the proposed method reaches 98.73% accuracy on the adopted datasets, which is 1.82% higher than the classical ResNet-50 model, and 0.29% better than the apple leaf disease datasets before preprocessing. It also achieves competitive results in apple leaf disease identification compared to some state-ofthe-art methods.
基金Supported by the National Nature Science Foundation of China (Grant Nos. 20473046, 50323006)the Education Foundation of China (Grant No. 305010)
文摘In order to investigate the effects of the structure of branches on the TPA properties for multi-branched molecules, the TPA cross section is calculated by using ZINDO/SOS method. The investigated mole- cules have different branches (chomorfores based on stilbene, dithienothiophene and flourene) with nitrogen(N) as coupling center. The results show that the cooperative enhancement in multi-branched molecules depends on the structures of the branches and the structures of branches play an important role in the enhancement of the TPA cross section. The designed molecules with stilbene and dithie- nothiophene as branched possess relatively larger two-photon absorption cross sections.
文摘本研究应用18个微卫星分子标记,对白斑狗鱼(Esox lucius L.)3个中国新疆群体(乌伦古湖、吉力湖和6号湖)和1个匈牙利巴拉顿湖群体的遗传结构进行了分析。结果表明,3个中国白斑狗鱼群体的平均等位基因丰富度(AR)、平均观测杂合度(HO)和平均期望杂合度(HE)均显著低于匈牙利巴拉顿湖群体(P<0.05),而匈牙利群体的平均近交系数(FIS)高于中国群体;经SMM和TPM模型检测,匈牙利白斑狗鱼群体存在显著的遗传瓶颈信号(P<0.001);AMOVA和群体两两比较的FST值表明,中国与匈牙利白斑狗鱼的遗传分化十分显著(P<0.01);NJ树、主成分分析(PCA)进一步证实中国白斑狗鱼与匈牙利白斑狗鱼群体间存在显著的遗传差异和分化。此外,贝叶斯遗传聚类结果表明,中国新疆6号湖白斑狗鱼群体极可能来源于乌伦古湖,而非吉力湖。
