Objective: To identify the influences of local and regional climate phenomena on dengue transmission in Lahore District of Pakistan, from 2006 to 2014. Methods: Time-series models were applied to analyze associations ...Objective: To identify the influences of local and regional climate phenomena on dengue transmission in Lahore District of Pakistan, from 2006 to 2014. Methods: Time-series models were applied to analyze associations between reported cases of dengue and climatic parameters. The coherence trend of regional climate phenomena(IOD and ENSO) was evaluated with wavelet analysis. Results: The minimum temperature 4 months before the dengue outbreak played the most important role in the Lahore District(P=0.03). A NINO 3.4 index 9 months before the outbreaks exhibited a significant negative effect on dengue transmission(P=0.02). The IOD exhibited a synchronized pattern with dengue outbreak from 2010 to 2012. The ENSO effect(NINO 3.4 index) might have played a more important role after 2012. Conclusions: This study provides preliminary results of climate influences on dengue transmission in the Lahore District of Pakistan. An increasing dengue transmission risk accompanied by frequent climate changes should be noted. Integrating the influences of climate variability into disease prevention strategies should be considered by public health authorities.展开更多
Machine learning based image analysis for predicting and diagnosing certain diseases has been entirely trustworthy and even as efficient as a domain expert’s inspection.However,the style of non-transparency functioni...Machine learning based image analysis for predicting and diagnosing certain diseases has been entirely trustworthy and even as efficient as a domain expert’s inspection.However,the style of non-transparency functioning by a trained machine learning system poses a more significant impediment for seamless knowledge trajectory,clinical mapping,and delusion tracing.In this proposed study,a deep learning based framework that employs deep convolution neural network(Deep-CNN),by utilizing both clinical presentations and conventional magnetic resonance imaging(MRI)investigations,for diagnosing tumors is explored.This research aims to develop a model that can be used for abnormality detection over MRI data quite efficiently with high accuracy.This research is based on deep learning and Deep-CNN was deployed to examine the MR brain image for tracing the tumor.The system runs on Tensor flow and uses a feature extraction module in DeepCNN to elicit the factors of that part of the image from where underlying issues are identified and subsequently succeeded in prediction of the disease in the MR image.The results of this study showed that our model did not have any adverse effect on classification,achieved higher accuracy than the peers in recent years,and attained good detection outcomes including case of abnormality.In the future work,further improvement can be made by designing models that can drastically reduce the parameter space without affecting classification accuracy.展开更多
基金funded by the Taiwan Ministry of Science and Technology(MOST 104-2119-M-038-002)the Taipei Medical University(TMU101-AE1-B62)
文摘Objective: To identify the influences of local and regional climate phenomena on dengue transmission in Lahore District of Pakistan, from 2006 to 2014. Methods: Time-series models were applied to analyze associations between reported cases of dengue and climatic parameters. The coherence trend of regional climate phenomena(IOD and ENSO) was evaluated with wavelet analysis. Results: The minimum temperature 4 months before the dengue outbreak played the most important role in the Lahore District(P=0.03). A NINO 3.4 index 9 months before the outbreaks exhibited a significant negative effect on dengue transmission(P=0.02). The IOD exhibited a synchronized pattern with dengue outbreak from 2010 to 2012. The ENSO effect(NINO 3.4 index) might have played a more important role after 2012. Conclusions: This study provides preliminary results of climate influences on dengue transmission in the Lahore District of Pakistan. An increasing dengue transmission risk accompanied by frequent climate changes should be noted. Integrating the influences of climate variability into disease prevention strategies should be considered by public health authorities.
基金supported by the Ministry of Science and Technology,Taiwan,under Grant:MOST 103-2221-E-224-016-MY3y funded by the“Intelligent Recognition Industry Service Research Center”from“The Featured Areas Research Center Program within the framework”of the“Higher Education Sprout Project”by the Ministry of Education(MOE)in Taiwan and the APC was funded by the aforementioned Project.
文摘Machine learning based image analysis for predicting and diagnosing certain diseases has been entirely trustworthy and even as efficient as a domain expert’s inspection.However,the style of non-transparency functioning by a trained machine learning system poses a more significant impediment for seamless knowledge trajectory,clinical mapping,and delusion tracing.In this proposed study,a deep learning based framework that employs deep convolution neural network(Deep-CNN),by utilizing both clinical presentations and conventional magnetic resonance imaging(MRI)investigations,for diagnosing tumors is explored.This research aims to develop a model that can be used for abnormality detection over MRI data quite efficiently with high accuracy.This research is based on deep learning and Deep-CNN was deployed to examine the MR brain image for tracing the tumor.The system runs on Tensor flow and uses a feature extraction module in DeepCNN to elicit the factors of that part of the image from where underlying issues are identified and subsequently succeeded in prediction of the disease in the MR image.The results of this study showed that our model did not have any adverse effect on classification,achieved higher accuracy than the peers in recent years,and attained good detection outcomes including case of abnormality.In the future work,further improvement can be made by designing models that can drastically reduce the parameter space without affecting classification accuracy.
