Alpha-fetoprotein(AFP) behavior in patients with hepatocellular carcinoma(HCC) waiting for liver transplant(LT) represents a perfect biological example of a fractal model in which its progressive modification and poss...Alpha-fetoprotein(AFP) behavior in patients with hepatocellular carcinoma(HCC) waiting for liver transplant(LT) represents a perfect biological example of a fractal model in which its progressive modification and possible future prediction of its values are very hard to capture. As a consequence, AFP represents a useful but poorly manageable tool to increase the ability to better select HCC patients waiting for LT. Trying to find a "filrouge" in the recent literature, no definitive answers can be done to several open questions:(1) the best AFP value to adopt;(2) the best cut-off measurement; and(3) the best way to comfortably capture the effective, time-related, fluctuations of this biological marker. More, structured and prospective, studies using serial determination of AFP values within and without the context of locoregional therapies are needed in order to find the "ideal"(static and dynamic) cut-off values allowing to respond to all the still open questions in this field of transplant oncology.展开更多
Even though much advancements have been achieved with regards to the recognition of handwritten characters,researchers still face difficulties with the handwritten character recognition problem,especially with the adv...Even though much advancements have been achieved with regards to the recognition of handwritten characters,researchers still face difficulties with the handwritten character recognition problem,especially with the advent of new datasets like the Extended Modified National Institute of Standards and Technology dataset(EMNIST).The EMNIST dataset represents a challenge for both machine-learning and deep-learning techniques due to inter-class similarity and intra-class variability.Inter-class similarity exists because of the similarity between the shapes of certain characters in the dataset.The presence of intra-class variability is mainly due to different shapes written by different writers for the same character.In this research,we have optimized a deep residual network to achieve higher accuracy vs.the published state-of-the-art results.This approach is mainly based on the prebuilt deep residual network model ResNet18,whose architecture has been enhanced by using the optimal number of residual blocks and the optimal size of the receptive field of the first convolutional filter,the replacement of the first max-pooling filter by an average pooling filter,and the addition of a drop-out layer before the fully connected layer.A distinctive modification has been introduced by replacing the final addition layer with a depth concatenation layer,which resulted in a novel deep architecture having higher accuracy vs.the pure residual architecture.Moreover,the dataset images’sizes have been adjusted to optimize their visibility in the network.Finally,by tuning the training hyperparameters and using rotation and shear augmentations,the proposed model outperformed the state-of-the-art models by achieving average accuracies of 95.91%and 90.90%for the Letters and Balanced dataset sections,respectively.Furthermore,the average accuracies were improved to 95.9%and 91.06%for the Letters and Balanced sections,respectively,by using a group of 5 instances of the trained models and averaging the output class probabilities.展开更多
Aim: To investigate the reasons for discontinuations of sildenafil after the successful restoration of erectile function. Methods: One hundred fifty six patients, whose scores of erectile function domain of the 15-ite...Aim: To investigate the reasons for discontinuations of sildenafil after the successful restoration of erectile function. Methods: One hundred fifty six patients, whose scores of erectile function domain of the 15-item International Index of Erectile Function (IIEF) increased to 26 or more after sildenafil medication, were included in this study. Six-months after the first sildenafil prescription, compliance to medication and the reason for discontinuity were reviewed by chart or surveyed by telephone. Results: Fifty-four (34.6%) of the 156 successfully treated patients discontinued sildenafil medication. The r easons for discontinuance were shortcomings in the partners' or patients' emotional readiness for the restoration of sexual life after long-term abstinence (37.0%), fear of possible side effects (18.5 %), recovery of spontaneous erection (14.8 %), postponement of ED treatment because of co morbid disease treatment (11.1%), unwillingness to accept drug-dependent erection (7.4%), high drug cost (3.7%), unacceptability of planned sexual activity (3.7%) and lack of sexual interesi (3.7%). Conclusion: The reasons for discontinuing sildenafil medication were primarily emotional or relationship-oriented, which indicates that simple recovery of a rigid erection is insufficient to restore sexual activity. More education about the effects of drug and the counseling of both partners is recommended to promote the successful recovery of sexual activity.展开更多
As a supplement to traditional education, online courses offer people, regardless of their age, gender, or profession, the chance to access state-of-the-art knowledge. Nonetheless, despite the large number of students...As a supplement to traditional education, online courses offer people, regardless of their age, gender, or profession, the chance to access state-of-the-art knowledge. Nonetheless, despite the large number of students who choose to begin online courses, it is easy to observe that quite a few of them drop out in the middle, and information on this is vital for course organizers to improve their curriculum outlines. In this work, in order to make a precise prediction of the drop-out rate, we propose a combined method MOOP, which consists of a global tensor and local tensor to express all available feature aspects. Specifically, the global tensor structure is proposed to model the data of the online courses, while a local tensor is clustered to capture the inner connection of courses. Consequently, drop-out prediction is achieved by adopting a high-accuracy low-rank tensor completion method, equipped with a pigeon-inspired algorithm to optimize the parameters. The proposed method is empirically evaluated on real-world Massive Open Online Courses(MOOC) data, and is demonstrated to offer remarkable superiority over alternatives in terms of efficiency and accuracy.展开更多
文摘Alpha-fetoprotein(AFP) behavior in patients with hepatocellular carcinoma(HCC) waiting for liver transplant(LT) represents a perfect biological example of a fractal model in which its progressive modification and possible future prediction of its values are very hard to capture. As a consequence, AFP represents a useful but poorly manageable tool to increase the ability to better select HCC patients waiting for LT. Trying to find a "filrouge" in the recent literature, no definitive answers can be done to several open questions:(1) the best AFP value to adopt;(2) the best cut-off measurement; and(3) the best way to comfortably capture the effective, time-related, fluctuations of this biological marker. More, structured and prospective, studies using serial determination of AFP values within and without the context of locoregional therapies are needed in order to find the "ideal"(static and dynamic) cut-off values allowing to respond to all the still open questions in this field of transplant oncology.
文摘Even though much advancements have been achieved with regards to the recognition of handwritten characters,researchers still face difficulties with the handwritten character recognition problem,especially with the advent of new datasets like the Extended Modified National Institute of Standards and Technology dataset(EMNIST).The EMNIST dataset represents a challenge for both machine-learning and deep-learning techniques due to inter-class similarity and intra-class variability.Inter-class similarity exists because of the similarity between the shapes of certain characters in the dataset.The presence of intra-class variability is mainly due to different shapes written by different writers for the same character.In this research,we have optimized a deep residual network to achieve higher accuracy vs.the published state-of-the-art results.This approach is mainly based on the prebuilt deep residual network model ResNet18,whose architecture has been enhanced by using the optimal number of residual blocks and the optimal size of the receptive field of the first convolutional filter,the replacement of the first max-pooling filter by an average pooling filter,and the addition of a drop-out layer before the fully connected layer.A distinctive modification has been introduced by replacing the final addition layer with a depth concatenation layer,which resulted in a novel deep architecture having higher accuracy vs.the pure residual architecture.Moreover,the dataset images’sizes have been adjusted to optimize their visibility in the network.Finally,by tuning the training hyperparameters and using rotation and shear augmentations,the proposed model outperformed the state-of-the-art models by achieving average accuracies of 95.91%and 90.90%for the Letters and Balanced dataset sections,respectively.Furthermore,the average accuracies were improved to 95.9%and 91.06%for the Letters and Balanced sections,respectively,by using a group of 5 instances of the trained models and averaging the output class probabilities.
文摘Aim: To investigate the reasons for discontinuations of sildenafil after the successful restoration of erectile function. Methods: One hundred fifty six patients, whose scores of erectile function domain of the 15-item International Index of Erectile Function (IIEF) increased to 26 or more after sildenafil medication, were included in this study. Six-months after the first sildenafil prescription, compliance to medication and the reason for discontinuity were reviewed by chart or surveyed by telephone. Results: Fifty-four (34.6%) of the 156 successfully treated patients discontinued sildenafil medication. The r easons for discontinuance were shortcomings in the partners' or patients' emotional readiness for the restoration of sexual life after long-term abstinence (37.0%), fear of possible side effects (18.5 %), recovery of spontaneous erection (14.8 %), postponement of ED treatment because of co morbid disease treatment (11.1%), unwillingness to accept drug-dependent erection (7.4%), high drug cost (3.7%), unacceptability of planned sexual activity (3.7%) and lack of sexual interesi (3.7%). Conclusion: The reasons for discontinuing sildenafil medication were primarily emotional or relationship-oriented, which indicates that simple recovery of a rigid erection is insufficient to restore sexual activity. More education about the effects of drug and the counseling of both partners is recommended to promote the successful recovery of sexual activity.
基金partially supported by the National Project of Educational Science Planning(No.ECA160409)the Hunan Provincial Project of Educational Science Planning(No.XJK016QXX001)the National Natural Science Foundation of China(Nos.71690233 and 71331008)
文摘As a supplement to traditional education, online courses offer people, regardless of their age, gender, or profession, the chance to access state-of-the-art knowledge. Nonetheless, despite the large number of students who choose to begin online courses, it is easy to observe that quite a few of them drop out in the middle, and information on this is vital for course organizers to improve their curriculum outlines. In this work, in order to make a precise prediction of the drop-out rate, we propose a combined method MOOP, which consists of a global tensor and local tensor to express all available feature aspects. Specifically, the global tensor structure is proposed to model the data of the online courses, while a local tensor is clustered to capture the inner connection of courses. Consequently, drop-out prediction is achieved by adopting a high-accuracy low-rank tensor completion method, equipped with a pigeon-inspired algorithm to optimize the parameters. The proposed method is empirically evaluated on real-world Massive Open Online Courses(MOOC) data, and is demonstrated to offer remarkable superiority over alternatives in terms of efficiency and accuracy.
