In searching of differentially expressed genes in human uterine leiomyomas, differential display was used with twelve pairs of primers to compare human uterine leiomyomas with matched myometrium. False positives were ...In searching of differentially expressed genes in human uterine leiomyomas, differential display was used with twelve pairs of primers to compare human uterine leiomyomas with matched myometrium. False positives were eliminated by reverse Northern analysis. Positives were confirmed by Northern blot analysis. RESULTS: Four of 69 cDNA fragments (3 up-regulated named L1, L2 and L3 and 1 down-regulated named M1 in leiomyoma) were confirmed by Northern analysis. Sequence comparison and Northern analysis proved that L1 is exactly the human ribosomal protein S19. It was present ubiquitously in 13 tissues tested but in various levels and even in different size. L1 was highly expressed in parotidean cystadenocarcinoma, pancreatic cancer and breast cancer examined. No mutations have been found in human uterine leiomyomas (n=6). CONCLUSIONS: hRPS19 overexpression might be a universal signal in rapid cell growth tissues.展开更多
Currently,deep learning is widely used in medical image segmentation and has achieved good results.However,3D medical image segmentation tasks with diverse lesion characters,blurred edges,and unstable positions requir...Currently,deep learning is widely used in medical image segmentation and has achieved good results.However,3D medical image segmentation tasks with diverse lesion characters,blurred edges,and unstable positions require complex networks with a large number of parameters.It is computationally expensive and results in high requirements on equipment,making it hard to deploy the network in hospitals.In this work,we propose a method for network lightweighting and applied it to a 3D CNN based network.We experimented on a COVID-19 lesion segmentation dataset.Specifically,we use three cascaded one-dimensional convolutions to replace a 3D convolution,and integrate instance normalization with the previous layer of one-dimensional convolutions to accelerate network inference.In addition,we simplify test-time augmentation and deep supervision of the network.Experiments show that the lightweight network can reduce the prediction time of each sample and the memory usage by 50%and reduce the number of parameters by 60%compared with the original network.The training time of one epoch is also reduced by 50%with the segmentation accuracy dropped within the acceptable range.展开更多
文摘In searching of differentially expressed genes in human uterine leiomyomas, differential display was used with twelve pairs of primers to compare human uterine leiomyomas with matched myometrium. False positives were eliminated by reverse Northern analysis. Positives were confirmed by Northern blot analysis. RESULTS: Four of 69 cDNA fragments (3 up-regulated named L1, L2 and L3 and 1 down-regulated named M1 in leiomyoma) were confirmed by Northern analysis. Sequence comparison and Northern analysis proved that L1 is exactly the human ribosomal protein S19. It was present ubiquitously in 13 tissues tested but in various levels and even in different size. L1 was highly expressed in parotidean cystadenocarcinoma, pancreatic cancer and breast cancer examined. No mutations have been found in human uterine leiomyomas (n=6). CONCLUSIONS: hRPS19 overexpression might be a universal signal in rapid cell growth tissues.
文摘Currently,deep learning is widely used in medical image segmentation and has achieved good results.However,3D medical image segmentation tasks with diverse lesion characters,blurred edges,and unstable positions require complex networks with a large number of parameters.It is computationally expensive and results in high requirements on equipment,making it hard to deploy the network in hospitals.In this work,we propose a method for network lightweighting and applied it to a 3D CNN based network.We experimented on a COVID-19 lesion segmentation dataset.Specifically,we use three cascaded one-dimensional convolutions to replace a 3D convolution,and integrate instance normalization with the previous layer of one-dimensional convolutions to accelerate network inference.In addition,we simplify test-time augmentation and deep supervision of the network.Experiments show that the lightweight network can reduce the prediction time of each sample and the memory usage by 50%and reduce the number of parameters by 60%compared with the original network.The training time of one epoch is also reduced by 50%with the segmentation accuracy dropped within the acceptable range.
