Aim The purpose of this study was to develop a mathe-matical model to quantitatively describe the passive trans-port of macromolecules within dental biofilms. Methodology Fluorescently labeled dextrans with different ...Aim The purpose of this study was to develop a mathe-matical model to quantitatively describe the passive trans-port of macromolecules within dental biofilms. Methodology Fluorescently labeled dextrans with different molecular mass （3 kD,10 kD,40 kD,70 kD,2 000 kD） were used as a series of diffusion probes. Streptococcus mutans,Streptococcus sanguinis,Actinomyces naeslundii and Fusobacterium nucleatum were used as inocula for biofilm formation. The diffusion processes of different probes through the in vitro biofilm were recorded with a confocal laser microscope. Results Mathematical function of biofilm penetration was constructed on the basis of the inverse problem method. Based on this function,not only the relationship between average concentration of steady-state and molecule weights can be analyzed,but also that between penetrative time and molecule weights. Conclusion This can be used to predict the effective concentration and the penetrative time of anti-biofilm medicines that can diffuse through oral biofilm. Further-more,an improved model for large molecule is proposed by considering the exchange time at the upper boundary of the dental biofilm.展开更多
目的视网膜眼底图像广泛用于临床筛查和诊断眼科疾病,但由于散焦、光线条件不佳等引起的眼底图像模糊,导致医生无法正确诊断,且现有图像增强方法恢复的图像仍存在模糊、高频信息缺失以及噪点增多问题。本文提出了一个卷积字典扩散模型,...目的视网膜眼底图像广泛用于临床筛查和诊断眼科疾病,但由于散焦、光线条件不佳等引起的眼底图像模糊,导致医生无法正确诊断,且现有图像增强方法恢复的图像仍存在模糊、高频信息缺失以及噪点增多问题。本文提出了一个卷积字典扩散模型,将卷积字典学习的去噪能力与条件扩散模型的灵活性相结合,从而解决了上述问题。方法算法主要包括两个过程:扩散过程和去噪过程。首先向输入图像中逐步添加随机噪声,得到趋于纯粹噪声的图像;然后训练一个神经网络逐渐将噪声从图像中移除,直到获得一幅清晰图像。本文利用卷积网络来实现卷积字典学习并获取图像稀疏表示,该算法充分利用图像的先验信息,有效避免重建图像高频信息缺失和噪点增多的问题。结果将本文模型在EyePACS数据集上进行训练,并分别在合成数据集DRIVE(dgital retinal images for vessel extraction)、CHASEDB1(child heart and health study in England)、ROC(retinopathy online challenge)和真实数据集RF(real fundus)、HRF(high-resolution fundus)上进行测试,验证了所提方法在图像增强任务上的性能及跨数据集的泛化能力,其评价指标峰值信噪比(peak signal-to-noise ratio,PSNR)和学习感知图像块相似度(learned perceptual image patch similarity,LPIPS)与原始扩散模型(learning enhancement from degradation,Led)相比平均分别提升了1.9929 dB和0.0289。此外,将本文方法用于真实眼科图像下游任务的前处理能够有效提升下游任务的表现,在含有分割标签的DRIVE数据集上进行的视网膜血管分割实验结果显示,相较于原始扩散模型,其分割指标对比其受试者工作特征曲线下面积(area under the curve,AUC),准确率(accuracy,Acc)和敏感性(sensitivity,Sen)平均分别提升0.0314,0.0030和0.0738。结论提出的方法能够在保留真实眼底特征的同时去除模糊、恢复更丰富的细节,从而有利于临床图像的分析和应用。展开更多
基金supported by a grant from the National Natural Science Foundation of China (NSFC) No. 81070826/30872886/30400497Sponsored by Shanghai Rising-Star Program No. 09QA1403700+1 种基金funded by Shanghai Leading Academic Discipline Project (Project Number: S30206)the Science and Technology Commission of Shanghai (08DZ2271100)
文摘Aim The purpose of this study was to develop a mathe-matical model to quantitatively describe the passive trans-port of macromolecules within dental biofilms. Methodology Fluorescently labeled dextrans with different molecular mass （3 kD,10 kD,40 kD,70 kD,2 000 kD） were used as a series of diffusion probes. Streptococcus mutans,Streptococcus sanguinis,Actinomyces naeslundii and Fusobacterium nucleatum were used as inocula for biofilm formation. The diffusion processes of different probes through the in vitro biofilm were recorded with a confocal laser microscope. Results Mathematical function of biofilm penetration was constructed on the basis of the inverse problem method. Based on this function,not only the relationship between average concentration of steady-state and molecule weights can be analyzed,but also that between penetrative time and molecule weights. Conclusion This can be used to predict the effective concentration and the penetrative time of anti-biofilm medicines that can diffuse through oral biofilm. Further-more,an improved model for large molecule is proposed by considering the exchange time at the upper boundary of the dental biofilm.
文摘目的视网膜眼底图像广泛用于临床筛查和诊断眼科疾病,但由于散焦、光线条件不佳等引起的眼底图像模糊,导致医生无法正确诊断,且现有图像增强方法恢复的图像仍存在模糊、高频信息缺失以及噪点增多问题。本文提出了一个卷积字典扩散模型,将卷积字典学习的去噪能力与条件扩散模型的灵活性相结合,从而解决了上述问题。方法算法主要包括两个过程:扩散过程和去噪过程。首先向输入图像中逐步添加随机噪声,得到趋于纯粹噪声的图像;然后训练一个神经网络逐渐将噪声从图像中移除,直到获得一幅清晰图像。本文利用卷积网络来实现卷积字典学习并获取图像稀疏表示,该算法充分利用图像的先验信息,有效避免重建图像高频信息缺失和噪点增多的问题。结果将本文模型在EyePACS数据集上进行训练,并分别在合成数据集DRIVE(dgital retinal images for vessel extraction)、CHASEDB1(child heart and health study in England)、ROC(retinopathy online challenge)和真实数据集RF(real fundus)、HRF(high-resolution fundus)上进行测试,验证了所提方法在图像增强任务上的性能及跨数据集的泛化能力,其评价指标峰值信噪比(peak signal-to-noise ratio,PSNR)和学习感知图像块相似度(learned perceptual image patch similarity,LPIPS)与原始扩散模型(learning enhancement from degradation,Led)相比平均分别提升了1.9929 dB和0.0289。此外,将本文方法用于真实眼科图像下游任务的前处理能够有效提升下游任务的表现,在含有分割标签的DRIVE数据集上进行的视网膜血管分割实验结果显示,相较于原始扩散模型,其分割指标对比其受试者工作特征曲线下面积(area under the curve,AUC),准确率(accuracy,Acc)和敏感性(sensitivity,Sen)平均分别提升0.0314,0.0030和0.0738。结论提出的方法能够在保留真实眼底特征的同时去除模糊、恢复更丰富的细节,从而有利于临床图像的分析和应用。
