Forest bathing trip is a short, leisurely visit to forest. In this study we determined the health effects of forest bathing trip on elderly patients with chronic obstructive pulmonary disease （COPD）. The patients we...Forest bathing trip is a short, leisurely visit to forest. In this study we determined the health effects of forest bathing trip on elderly patients with chronic obstructive pulmonary disease （COPD）. The patients were randomly divided into two groups. One group was sent to forest, and the other was sent to an urban area as control. Flow cytometry, ELISA, and profile of mood states （POMS） evaluation were performed. In the forest group,展开更多
Forest diseases and pests are perceived as a growing hazard to China economy. It is a common conclusion that the actualities of forest pests in china are no effective measures to the old important pests, some secondar...Forest diseases and pests are perceived as a growing hazard to China economy. It is a common conclusion that the actualities of forest pests in china are no effective measures to the old important pests, some secondary pests are ascending to chief pests, increasing devastation from exotic pests, frequent ecological pest eruption induced by environmental detriment and host-leading diseases to threaten the "Western Development Project "in China, which is the most important economical strategy to China; th...展开更多
Neuroinformatics is a fascinating research field that applies computational models and analytical tools to high dimensional experimental neuroscience data for a better understanding of how the brain functions or dysfu...Neuroinformatics is a fascinating research field that applies computational models and analytical tools to high dimensional experimental neuroscience data for a better understanding of how the brain functions or dysfunctions in brain diseases. Neuroinformaticians work in the intersection of neuroscience and informatics supporting the integration of various sub-disciplines（behavioural neuroscience, genetics, cognitive psychology, etc.） working on brain research. Neuroinformaticians are the pathway of information exchange between informaticians and clinicians for a better understanding of the outcome of computational models and the clinical interpretation of the analysis. Machine learning is one of the most significant computational developments in the last decade giving tools to neuroinformaticians and finally to radiologists and clinicians for an automatic and early diagnosis-prognosis of a brain disease. Random forest（RF） algorithm has been successfully applied to high-dimensional neuroimaging data for feature reduction and also has been applied to classify the clinical label of a subject using single or multi-modal neuroimaging datasets. Our aim was to review the studies where RF was applied to correctly predict the Alzheimer＇s disease（AD）, the conversion from mild cognitive impairment（MCI） and its robustness to overfitting, outliers and handling of non-linear data. Finally, we described our RF-based model that gave us the 1 ^（st） position in an international challenge for automated prediction of MCI from MRI data.展开更多
Identifying the association between metabolites and diseases will help us understand the pathogenesis of diseases,which has great significance in diagnosing and treating diseases.However,traditional biometric methods ...Identifying the association between metabolites and diseases will help us understand the pathogenesis of diseases,which has great significance in diagnosing and treating diseases.However,traditional biometric methods are time consuming and expensive.Accordingly,we propose a new metabolite-disease association prediction algorithm based on DeepWalk and random forest(DWRF),which consists of the following key steps:First,the semantic similarity and information entropy similarity of diseases are integrated as the final disease similarity.Similarly,molecular fingerprint similarity and information entropy similarity of metabolites are integrated as the final metabolite similarity.Then,DeepWalk is used to extract metabolite features based on the network of metabolite-gene associations.Finally,a random forest algorithm is employed to infer metabolite-disease associations.The experimental results show that DWRF has good performances in terms of the area under the curve value,leave-one-out cross-validation,and five-fold cross-validation.Case studies also indicate that DWRF has a reliable performance in metabolite-disease association prediction.展开更多
基金supported by funds from the National Natural Science Foundation of China(31301139&31201040)funds from Science Technology Department of Zhejiang Province(2012C24005&2014C33130)+2 种基金Health Bureau of Zhejiang Province(11-CX01&2013ZDA002)Zhejiang Provincial Key Disciplinary Fields of Geriatrics Program
文摘Forest bathing trip is a short, leisurely visit to forest. In this study we determined the health effects of forest bathing trip on elderly patients with chronic obstructive pulmonary disease （COPD）. The patients were randomly divided into two groups. One group was sent to forest, and the other was sent to an urban area as control. Flow cytometry, ELISA, and profile of mood states （POMS） evaluation were performed. In the forest group,
文摘Forest diseases and pests are perceived as a growing hazard to China economy. It is a common conclusion that the actualities of forest pests in china are no effective measures to the old important pests, some secondary pests are ascending to chief pests, increasing devastation from exotic pests, frequent ecological pest eruption induced by environmental detriment and host-leading diseases to threaten the "Western Development Project "in China, which is the most important economical strategy to China; th...
基金supported by Medical Research Council(MRC)grant MR/K004360/1 to SIDMARIE CURIE COFUND EU-UK Research Fellowship to SID
文摘Neuroinformatics is a fascinating research field that applies computational models and analytical tools to high dimensional experimental neuroscience data for a better understanding of how the brain functions or dysfunctions in brain diseases. Neuroinformaticians work in the intersection of neuroscience and informatics supporting the integration of various sub-disciplines（behavioural neuroscience, genetics, cognitive psychology, etc.） working on brain research. Neuroinformaticians are the pathway of information exchange between informaticians and clinicians for a better understanding of the outcome of computational models and the clinical interpretation of the analysis. Machine learning is one of the most significant computational developments in the last decade giving tools to neuroinformaticians and finally to radiologists and clinicians for an automatic and early diagnosis-prognosis of a brain disease. Random forest（RF） algorithm has been successfully applied to high-dimensional neuroimaging data for feature reduction and also has been applied to classify the clinical label of a subject using single or multi-modal neuroimaging datasets. Our aim was to review the studies where RF was applied to correctly predict the Alzheimer＇s disease（AD）, the conversion from mild cognitive impairment（MCI） and its robustness to overfitting, outliers and handling of non-linear data. Finally, we described our RF-based model that gave us the 1 ^（st） position in an international challenge for automated prediction of MCI from MRI data.
文摘Identifying the association between metabolites and diseases will help us understand the pathogenesis of diseases,which has great significance in diagnosing and treating diseases.However,traditional biometric methods are time consuming and expensive.Accordingly,we propose a new metabolite-disease association prediction algorithm based on DeepWalk and random forest(DWRF),which consists of the following key steps:First,the semantic similarity and information entropy similarity of diseases are integrated as the final disease similarity.Similarly,molecular fingerprint similarity and information entropy similarity of metabolites are integrated as the final metabolite similarity.Then,DeepWalk is used to extract metabolite features based on the network of metabolite-gene associations.Finally,a random forest algorithm is employed to infer metabolite-disease associations.The experimental results show that DWRF has good performances in terms of the area under the curve value,leave-one-out cross-validation,and five-fold cross-validation.Case studies also indicate that DWRF has a reliable performance in metabolite-disease association prediction.
