Recently,deep learning-based image inpainting methods have made great strides in reconstructing damaged regions.However,these methods often struggle to produce satisfactory results when dealing with missing images wit...Recently,deep learning-based image inpainting methods have made great strides in reconstructing damaged regions.However,these methods often struggle to produce satisfactory results when dealing with missing images with large holes,leading to distortions in the structure and blurring of textures.To address these problems,we combine the advantages of transformers and convolutions to propose an image inpainting method that incorporates edge priors and attention mechanisms.The proposed method aims to improve the results of inpainting large holes in images by enhancing the accuracy of structure restoration and the ability to recover texture details.This method divides the inpainting task into two phases:edge prediction and image inpainting.Specifically,in the edge prediction phase,a transformer architecture is designed to combine axial attention with standard self-attention.This design enhances the extraction capability of global structural features and location awareness.It also balances the complexity of self-attention operations,resulting in accurate prediction of the edge structure in the defective region.In the image inpainting phase,a multi-scale fusion attention module is introduced.This module makes full use of multi-level distant features and enhances local pixel continuity,thereby significantly improving the quality of image inpainting.To evaluate the performance of our method.comparative experiments are conducted on several datasets,including CelebA,Places2,and Facade.Quantitative experiments show that our method outperforms the other mainstream methods.Specifically,it improves Peak Signal-to-Noise Ratio(PSNR)and Structure Similarity Index Measure(SSIM)by 1.141~3.234 db and 0.083~0.235,respectively.Moreover,it reduces Learning Perceptual Image Patch Similarity(LPIPS)and Mean Absolute Error(MAE)by 0.0347~0.1753 and 0.0104~0.0402,respectively.Qualitative experiments reveal that our method excels at reconstructing images with complete structural information and clear texture details.Furthermore,our model exhibits impressive performance in terms of the number of parameters,memory cost,and testing time.展开更多
We propose a high-accuracy artifacts-free single-frame digital holographic phase demodulation scheme for relatively lowcarrier frequency holograms-deep learning assisted variational Hilbert quantitative phase imaging(...We propose a high-accuracy artifacts-free single-frame digital holographic phase demodulation scheme for relatively lowcarrier frequency holograms-deep learning assisted variational Hilbert quantitative phase imaging(DL-VHQPI).The method,incorporating a conventional deep neural network into a complete physical model utilizing the idea of residual compensation,reliably and robustly recovers the quantitative phase information of the test objects.It can significantly alleviate spectrum-overlapping-caused phase artifacts under the slightly off-axis digital holographic system.Compared to the conventional end-to-end networks(without a physical model),the proposed method can reduce the dataset size dramatically while maintaining the imaging quality and model generalization.The DL-VHQPI is quantitatively studied by numerical simulation.The live-cell experiment is designed to demonstrate the method's practicality in biological research.The proposed idea of the deep learning-assisted physical model might be extended to diverse computational imaging techniques.展开更多
Objective:The global aim to lower preterm birth rates has been hampered by the insufficient and incomplete understanding of its etiology,classification,and diagnosis.This study was designed to evaluate the association...Objective:The global aim to lower preterm birth rates has been hampered by the insufficient and incomplete understanding of its etiology,classification,and diagnosis.This study was designed to evaluate the association of phenotypically classified preterm syndromes with neonatal outcomes;to what extent would these outcomes be modified after the obstetric interventions,including use of glucocorticoid,magnesium sulfate,and progesterone.Methods:This was a retrospective cohort study conducted at Tongji Hospital(composed of Main Branch,Optical Valley Branch and Sino-French New City Branch)in Wuhan.A total of 900 pregnant women and 1064 neonates were retrospectively enrolled.The outcomes were the distribution of different phenotypes among parturition signs and pathway to delivery,the association of phenotypically classified clusters with short-term unfavorable neonatal outcomes,and to what extent these outcomes could be modified by obstetric interventions.Results:Eight clusters were identified using two-step cluster analysis,including premature rupture of fetal membranes(PPROM)phenotype,abnormal amniotic fluid(AF)phenotype,placenta previa phenotype,mixed condition phenotype,fetal distress phenotype,preeclampsia-eclampsia&hemolysis,elevated liver enzymes,and low platelets syndrome(PE-E&HELLP)phenotype,multiple fetus phenotype,and no main condition phenotype.Except for no main condition phenotype,the other phenotypes were associated with one or more complications,which conforms to the clinical practice.Compared with no main condition phenotype,some phenotypes were significantly associated with short-term adverse neonatal outcomes.Abnormal AF phenotype,mixed condition phenotype,PE-E&HELLP phenotype,and multiple fetus phenotype were risk factors for neonatal small-for gestation age(SGA);placenta previa phenotype was not associated with adverse outcomes except low APGAR score being 0-7 at one min;mixed condition phenotype was associated with low APGAR scores,SGA,mechanical ventilation,and gradeⅢ-Ⅳintraventricular hemorrhage(IVH);fetal distress phenotype was frequently associated with neonatal SGA and mechanical ventilation;PE-E&HELLP phenotype was correlated with low APGAR score being 0-7 at one min,SGA and neonatal intensive care unit(NICU)admission;multiple fetus phenotype was not a risk factor for the outcomes included except for SGA.Not all neonates benefited from obstetric interventions included in this study.Conclusion:Our research disclosed the independent risk of different preterm phenotypes for adverse pregnancy outcomes.This study is devoted to putting forward the paradigm of classifying preterm birth phenotypically,with the ultimate purpose of defining preterm phenotypes based on multi-center studies and diving into the underlying mechanisms.展开更多
BACKGROUND Persistent left superior vena cava(PLSVC)is the most common venous system variant.The clinical characteristics and amniotic fluid cytogenetics of fetuses with PLSVC remain to be further explored.AIM To deve...BACKGROUND Persistent left superior vena cava(PLSVC)is the most common venous system variant.The clinical characteristics and amniotic fluid cytogenetics of fetuses with PLSVC remain to be further explored.AIM To develop reliable prenatal diagnostic recommendations through integrated analysis of the clinical characteristics of fetuses with PLSVC.METHODS Cases of PLSVC diagnosed using prenatal ultrasonography between September 2019 and November 2022 were retrospectively studied.The clinical characteristics of the pregnant women,ultrasonic imaging information,gestational age at diagnosis,pregnancy outcomes,and amniocentesis results were summarized and analyzed using categorical statistics and the chi-square test or Fisher’s exact test.RESULTS Of the 97 cases diagnosed by prenatal ultrasound,49(50.5%)had isolated PLSVC and 48(49.5%)had other structural abnormalities.The differences in pregnancy outcomes and amniocentesis conditions between the two groups were statistically significant(P<0.05).No significant differences were identified between the two groups in terms of advanced maternal age and gestational age(P>0.05).According to the results of the classification statistics,the most common intrac-ardiac abnormality was a ventricular septal defect and the most common extrac-ardiac abnormality was a single umbilical artery.In the subgroup analysis,the concurrent combination of intra-and extracardiac structural abnormalities was a risk factor for adverse pregnancy outcomes(odds ratio>1,P<0.05).Additional-ly,all abnormal cytogenetic findings on amniocentesis were observed in the comorbidity group.One case was diagnosed with 21-trisomy and six cases was diagnosed with chromosome segment duplication.CONCLUSION Examination for other structural abnormalities is strongly recommended when PLSVC is diagnosed.Poorer pregnancy outcomes and increased amniocentesis were observed in PLSVC cases with other structural abnor-malities.Amniotic fluid cytogenetics of fetuses is recommended for PLSVC with other structural abnormalities.展开更多
Objective: To identify module genes that are closely related to clinical features of hepatocellular carcinoma (HCC) by weighted gene co‑expression network analysis, and to provide a reference for early clinical diagno...Objective: To identify module genes that are closely related to clinical features of hepatocellular carcinoma (HCC) by weighted gene co‑expression network analysis, and to provide a reference for early clinical diagnosis and treatment. Methods: GSE84598 chip data were downloaded from the GEO database, and module genes closely related to the clinical features of HCC were extracted by comprehensive weighted gene co‑expression network analysis. Hub genes were identified through protein interaction network analysis by the maximum clique centrality (MCC) algorithm;Finally, the expression of hub genes was validated by TCGA database and the Kaplan Meier plotter online database was used to evaluate the prognostic relationship between hub genes and HCC patients. Results: By comparing the gene expression data between HCC tissue samples and normal liver tissue samples, a total of 6 262 differentially expressed genes were obtained, of which 2 207 were upregulated and 4 055 were downregulated. Weighted gene co‑expression network analysis was applied to identify 120 genes of key modules. By intersecting with the differentially expressed genes, 115 candidate hub genes were obtained. The results of enrichment analysis showed that the candidate hub genes were closely related to cell mitosis, p53 signaling pathway and so on. Further application of the MCC algorithm to the protein interaction network of 115 candidate hub genes identified five hub genes, namely NUF2, RRM2, UBE2C, CDC20 and MAD2L1. Validation of hub genes by TCGA database revealed that all five hub genes were significantly upregulated in HCC tissues compared to normal liver tissues;Moreover, survival analysis revealed that high expression of hub genes was closely associated with poor prognosis in HCC patients. Conclusions: This study identifies five hub genes by combining multiple databases, which may provide directions for the clinical diagnosis and treatment of HCC.展开更多
基金supported in part by the National Natural Science Foundation of China under Grant 62062061/in part by the Major Project Cultivation Fund of Xizang Minzu University under Grant 324112300447.
文摘Recently,deep learning-based image inpainting methods have made great strides in reconstructing damaged regions.However,these methods often struggle to produce satisfactory results when dealing with missing images with large holes,leading to distortions in the structure and blurring of textures.To address these problems,we combine the advantages of transformers and convolutions to propose an image inpainting method that incorporates edge priors and attention mechanisms.The proposed method aims to improve the results of inpainting large holes in images by enhancing the accuracy of structure restoration and the ability to recover texture details.This method divides the inpainting task into two phases:edge prediction and image inpainting.Specifically,in the edge prediction phase,a transformer architecture is designed to combine axial attention with standard self-attention.This design enhances the extraction capability of global structural features and location awareness.It also balances the complexity of self-attention operations,resulting in accurate prediction of the edge structure in the defective region.In the image inpainting phase,a multi-scale fusion attention module is introduced.This module makes full use of multi-level distant features and enhances local pixel continuity,thereby significantly improving the quality of image inpainting.To evaluate the performance of our method.comparative experiments are conducted on several datasets,including CelebA,Places2,and Facade.Quantitative experiments show that our method outperforms the other mainstream methods.Specifically,it improves Peak Signal-to-Noise Ratio(PSNR)and Structure Similarity Index Measure(SSIM)by 1.141~3.234 db and 0.083~0.235,respectively.Moreover,it reduces Learning Perceptual Image Patch Similarity(LPIPS)and Mean Absolute Error(MAE)by 0.0347~0.1753 and 0.0104~0.0402,respectively.Qualitative experiments reveal that our method excels at reconstructing images with complete structural information and clear texture details.Furthermore,our model exhibits impressive performance in terms of the number of parameters,memory cost,and testing time.
基金We are grateful for financial supports from the National Natural Science Foundation of China(61905115,62105151,62175109,U21B2033,62227818)Leading Technology of Jiangsu Basic Research Plan(BK20192003)+5 种基金Youth Foundation of Jiangsu Province(BK20190445,BK20210338)Biomedical Competition Foundation of Jiangsu Province(BE2022847)Key National Industrial Technology Cooperation Foundation of Jiangsu Province(BZ2022039)Fundamental Research Funds for the Central Universities(30920032101)Open Research Fund of Jiangsu Key Laboratory of Spectral Imaging&Intelligent Sense(JSGP202105,JSGP202201)National Science Center,Poland(2020/37/B/ST7/03629).The authors thank F.Sun for her contribution to this paper in terms of language expression and grammatical correction.
文摘We propose a high-accuracy artifacts-free single-frame digital holographic phase demodulation scheme for relatively lowcarrier frequency holograms-deep learning assisted variational Hilbert quantitative phase imaging(DL-VHQPI).The method,incorporating a conventional deep neural network into a complete physical model utilizing the idea of residual compensation,reliably and robustly recovers the quantitative phase information of the test objects.It can significantly alleviate spectrum-overlapping-caused phase artifacts under the slightly off-axis digital holographic system.Compared to the conventional end-to-end networks(without a physical model),the proposed method can reduce the dataset size dramatically while maintaining the imaging quality and model generalization.The DL-VHQPI is quantitatively studied by numerical simulation.The live-cell experiment is designed to demonstrate the method's practicality in biological research.The proposed idea of the deep learning-assisted physical model might be extended to diverse computational imaging techniques.
基金supported by the National Science Foundation of China(No.81873843)the National Science and Technology Pillar Program of China during the Twelfth Five-Year Plan Period(No.2014BAI05B05)the Fundamental Research Funds for the Central Universities(Nos.2017KFYXJJ102,2019KFYXKJC053).
文摘Objective:The global aim to lower preterm birth rates has been hampered by the insufficient and incomplete understanding of its etiology,classification,and diagnosis.This study was designed to evaluate the association of phenotypically classified preterm syndromes with neonatal outcomes;to what extent would these outcomes be modified after the obstetric interventions,including use of glucocorticoid,magnesium sulfate,and progesterone.Methods:This was a retrospective cohort study conducted at Tongji Hospital(composed of Main Branch,Optical Valley Branch and Sino-French New City Branch)in Wuhan.A total of 900 pregnant women and 1064 neonates were retrospectively enrolled.The outcomes were the distribution of different phenotypes among parturition signs and pathway to delivery,the association of phenotypically classified clusters with short-term unfavorable neonatal outcomes,and to what extent these outcomes could be modified by obstetric interventions.Results:Eight clusters were identified using two-step cluster analysis,including premature rupture of fetal membranes(PPROM)phenotype,abnormal amniotic fluid(AF)phenotype,placenta previa phenotype,mixed condition phenotype,fetal distress phenotype,preeclampsia-eclampsia&hemolysis,elevated liver enzymes,and low platelets syndrome(PE-E&HELLP)phenotype,multiple fetus phenotype,and no main condition phenotype.Except for no main condition phenotype,the other phenotypes were associated with one or more complications,which conforms to the clinical practice.Compared with no main condition phenotype,some phenotypes were significantly associated with short-term adverse neonatal outcomes.Abnormal AF phenotype,mixed condition phenotype,PE-E&HELLP phenotype,and multiple fetus phenotype were risk factors for neonatal small-for gestation age(SGA);placenta previa phenotype was not associated with adverse outcomes except low APGAR score being 0-7 at one min;mixed condition phenotype was associated with low APGAR scores,SGA,mechanical ventilation,and gradeⅢ-Ⅳintraventricular hemorrhage(IVH);fetal distress phenotype was frequently associated with neonatal SGA and mechanical ventilation;PE-E&HELLP phenotype was correlated with low APGAR score being 0-7 at one min,SGA and neonatal intensive care unit(NICU)admission;multiple fetus phenotype was not a risk factor for the outcomes included except for SGA.Not all neonates benefited from obstetric interventions included in this study.Conclusion:Our research disclosed the independent risk of different preterm phenotypes for adverse pregnancy outcomes.This study is devoted to putting forward the paradigm of classifying preterm birth phenotypically,with the ultimate purpose of defining preterm phenotypes based on multi-center studies and diving into the underlying mechanisms.
文摘BACKGROUND Persistent left superior vena cava(PLSVC)is the most common venous system variant.The clinical characteristics and amniotic fluid cytogenetics of fetuses with PLSVC remain to be further explored.AIM To develop reliable prenatal diagnostic recommendations through integrated analysis of the clinical characteristics of fetuses with PLSVC.METHODS Cases of PLSVC diagnosed using prenatal ultrasonography between September 2019 and November 2022 were retrospectively studied.The clinical characteristics of the pregnant women,ultrasonic imaging information,gestational age at diagnosis,pregnancy outcomes,and amniocentesis results were summarized and analyzed using categorical statistics and the chi-square test or Fisher’s exact test.RESULTS Of the 97 cases diagnosed by prenatal ultrasound,49(50.5%)had isolated PLSVC and 48(49.5%)had other structural abnormalities.The differences in pregnancy outcomes and amniocentesis conditions between the two groups were statistically significant(P<0.05).No significant differences were identified between the two groups in terms of advanced maternal age and gestational age(P>0.05).According to the results of the classification statistics,the most common intrac-ardiac abnormality was a ventricular septal defect and the most common extrac-ardiac abnormality was a single umbilical artery.In the subgroup analysis,the concurrent combination of intra-and extracardiac structural abnormalities was a risk factor for adverse pregnancy outcomes(odds ratio>1,P<0.05).Additional-ly,all abnormal cytogenetic findings on amniocentesis were observed in the comorbidity group.One case was diagnosed with 21-trisomy and six cases was diagnosed with chromosome segment duplication.CONCLUSION Examination for other structural abnormalities is strongly recommended when PLSVC is diagnosed.Poorer pregnancy outcomes and increased amniocentesis were observed in PLSVC cases with other structural abnor-malities.Amniotic fluid cytogenetics of fetuses is recommended for PLSVC with other structural abnormalities.
基金National Natural Science Foundation of China (No.81760851)Guangxi University Youth Promotion Program (No.2019KY0348)。
文摘Objective: To identify module genes that are closely related to clinical features of hepatocellular carcinoma (HCC) by weighted gene co‑expression network analysis, and to provide a reference for early clinical diagnosis and treatment. Methods: GSE84598 chip data were downloaded from the GEO database, and module genes closely related to the clinical features of HCC were extracted by comprehensive weighted gene co‑expression network analysis. Hub genes were identified through protein interaction network analysis by the maximum clique centrality (MCC) algorithm;Finally, the expression of hub genes was validated by TCGA database and the Kaplan Meier plotter online database was used to evaluate the prognostic relationship between hub genes and HCC patients. Results: By comparing the gene expression data between HCC tissue samples and normal liver tissue samples, a total of 6 262 differentially expressed genes were obtained, of which 2 207 were upregulated and 4 055 were downregulated. Weighted gene co‑expression network analysis was applied to identify 120 genes of key modules. By intersecting with the differentially expressed genes, 115 candidate hub genes were obtained. The results of enrichment analysis showed that the candidate hub genes were closely related to cell mitosis, p53 signaling pathway and so on. Further application of the MCC algorithm to the protein interaction network of 115 candidate hub genes identified five hub genes, namely NUF2, RRM2, UBE2C, CDC20 and MAD2L1. Validation of hub genes by TCGA database revealed that all five hub genes were significantly upregulated in HCC tissues compared to normal liver tissues;Moreover, survival analysis revealed that high expression of hub genes was closely associated with poor prognosis in HCC patients. Conclusions: This study identifies five hub genes by combining multiple databases, which may provide directions for the clinical diagnosis and treatment of HCC.