The influences of biotic and abiotic factors on the occurrence and severity of poplar canker disease in Qingfeng County, China and the management implications

Landscape pathology is a research approach that can provide validation of the effectiveness of regional controls of forest disease at a landscape scale. In this paper, we analyzed the effects of stand features, management approaches, and geographical locations on poplar canker disease incidence （DI） and disease severity index （DSI） of individual trees at a 10 km x 10 km mesoscale landscape in Qingfeng County, China. DI varied significantly with stand age, tree densities, and the degree of canopy closure. DI in stands younger than 4 years old was significantly lower than that in the stands over 6 years old and reached the highest value at a stand age of 8-10 years. Overall, DI was positively correlated with stand age, stand density, andthe degree of canopy closure. DI was significantly lower in agro-forest stand patches than in other three patch types, i.e. isolated patch, pure stand patch, and mixed stand patch. Poplar plantations distributed around and near to villages exhibited significantly higher DI mainly due to human activities and herbivores. Fragmentation or connectivity in this mesoscale landscape seemed not impact disease occurrence. DSI was not significantly correlated with stand density, but varied significantly with tree varieties and trees ages. DSI was highest in stands of 10-12 year trees for all poplar varieties we studied here. Plantation density and plantation age were thus critical factors in determining DI and DSI. A logistic predictive model of disease occurrence was developed for the study area, considering varieties, age, height, density, canopy cover, stand types, patch types, management status, and stand geographical locations. Our study here shows that adjustment of stand density by thinning at different plantation ages is an effective approach controlling the occurrence canker disease in short-rotation poplar plantations at the landscape scale.

An analysis of clinical features of celiac disease patients in different ethnic

Objective To summarize the clinical of different racial patients with celiac disease(CD)and analyze the disease prevalence,diagnosis and treatment in Chinese population.Methods All the patients were diagnosed as CD and enrolled in Beijing United Family Hospital between January 2005 and July 2015.Clinical data including nationality,age,symptoms,endoscopic and patho-

Intelligent approach of score-based artificial fish swarm algorithm(SAFSA)for Parkinson’s disease diagnosis

Purpose-Conventional diagnostic techniques,on the other hand,may be prone to subjectivity since they depend on assessment of motions that are often subtle to individual eyes and hence hard to classify,potentially resulting in misdiagnosis.Meanwhile,early nonmotor signs of Parkinson’s disease(PD)can be mild and may be due to variety of other conditions.As a result,these signs are usually ignored,making early PD diagnosis difficult.Machine learning approaches for PD classification and healthy controls or individuals with similar medical symptoms have been introduced to solve these problems and to enhance the diagnostic and assessment processes of PD(like,movement disorders or other Parkinsonian syndromes).Design/methodology/approach-Medical observations and evaluation of medical symptoms,including characterization of a wide range of motor indications,are commonly used to diagnose PD.The quantity of the data being processed has grown in the last five years;feature selection has become a prerequisite before any classification.This study introduces a feature selection method based on the score-based artificial fish swarm algorithm(SAFSA)to overcome this issue.Findings-This study adds to the accuracy of PD identification by reducing the amount of chosen vocal features while to use the most recent and largest publicly accessible database.Feature subset selection in PD detection techniques starts by eliminating features that are not relevant or redundant.According to a few objective functions,features subset chosen should provide the best performance.Research limitations/implications-In many situations,this is an Nondeterministic Polynomial Time(NPHard)issue.This method enhances the PD detection rate by selecting the most essential features from the database.To begin,the data set’s dimensionality is reduced using Singular Value Decomposition dimensionality technique.Next,Biogeography-Based Optimization(BBO)for feature selection;the weight value is a vital parameter for finding the best features in PD classification.Originality/value-PD classification is done by using ensemble learning classification approaches such as hybrid classifier of fuzzy K-nearest neighbor,kernel support vector machines,fuzzy convolutional neural network and random forest.The suggested classifiers are trained using data from UCIMLrepository,and their results are verified using leave-one-person-out cross validation.The measures employed to assess the classifier efficiency include accuracy,F-measure,Matthews correlation coefficient.