Abstract: Objective　To investigate the composition and diversity of midgut microbial community of Haemaphysalis longicornis infected with severe fever with thrombocytopenia syndrome virus (SFTSV)． Methods　The midgut DNA of three group Haemaphysalis longicornis infected with SFTSV was extracted, and the 16S rDNA gene of the sample was sequenced by HiSeq platform. The composition and diversity of endosymbiotic microbial community were clarified by OTU cluster analysis and alpha diversity analysis. Results　The midgut microbial clusters of the three groups infected with SFTSV were 143, 113, 163 OTUs respectively; the sparsity curve and abundance grade curve showed that the data had sufficient sequencing depth, and the midgut of Haemaphysalis longicornis infected with SFTSV was rich in microbial composition, but the species distribution was uneven. The analysis of microbial community composition showed that Proteobacteria, Firmicutes and Actinobacteria were the main dominant bacteria at the phyla level. At the class level, Gammaproteobacteria, Bacilli, Betaproteobacteria and Actinomycetia were the main dominant bacteria. At the order level, Legionellales, Bacillales, Burkholderiales and Actinomycetales were the main dominant orders. At the family level, Coxiellaceae, Bacillaceae, Moraxellaceae and Rhodococcaceae were the main dominant families. At the genus level, the relative abundance of Coxiella was the highest, followed by Aeribaillus and Azonexus. Alpha diversity analysis showed that the average Shannon index was 139.67, the average Simpson index was 0.48, the average Chao index was 145.06, and the average ACE index was 147.11. Conclusions　The species diversity of intestinal microorganisms in Haemaphysalis longicornis infected with SFTSV is rich. The results provide a basis for further exploring the interaction between intestinal microbes of Haemaphysalis longicornis and SFTSV and developing new ideas for the prevention and control of ticks and tick-borne diseases.

This paper proposes a tree-structured wavelet algorithm for brain CT texture feature extraction. This algorithm is used to analyse subimages automatically and to choose the optimum subimage for feature extraction. Experimental results show that this algorithm can improve the performance of brain CT texture feature extraction.
Algorithms ; Brain ; diagnostic imaging ; Humans ; Image Processing, Computer-Assisted ; Tomography, X-Ray Computed ; methods

This paper presents a computer-aided diagnosis method for prostate cancer detection using Trans-rectal ultrasound(TRUS) images. Firstly, statistical texture analysis is implemented in every ROI in segmented prostate images. From each ROI, grey level difference vector features, edge-frequency features and texture features in frequency domain are constructed. Then, the number of features is reduced using ANOVA statistics to select the optimal feature subset. Finally, SVM is applied to the selected subset for detecting the cancer regions. Experimental results show that the proposed algorithm can recognize and detect the cancer images effectively so as to supply essential information for a diagnosis.
Algorithms ; Artificial Intelligence ; Humans ; Image Interpretation, Computer-Assisted ; methods ; Male ; Neural Networks (Computer) ; Prostatic Neoplasms ; diagnostic imaging ; Signal Processing, Computer-Assisted ; Ultrasonography

A filtering algorithm is proposed to deal with the medical ultrasonic image series in video format, which uses the relativity in spatial domain, gray value domain and temporal domain simultaneously. For each frame image, the relativity in spatial domain and gray value domain is utilized to construct the adaptive neighborhood first. Then the spatial weighted and gray value weighted filtering is performed in this neighborhood. Finally, the temporal relativity between the adjacent frames is used to perform the temporal weighted filtering. All the weighted filtering in the three domains uses Gaussian kernel, thus the filtering sensitivity resulting from the threshold selection is reduced, and the stability of the algorithm is enhanced. As it can be seen from the experimental results, the three-domains filtering algorithm can suppress the noise effectively, and the edge details can be reserved well. So it is useful for the feature extraction, recognition and analysis.
Algorithms ; Image Interpretation, Computer-Assisted ; methods ; Ultrasonography ; methods

In this paper, we propose that, the need of the costly re-initialization procedure can be completely eliminated by using the variation formulation, thus increasing the speed of computing operations. The edge detecting function in the geodesic active contour model is improved by incorporating a prior knowledge. The accuracy of the segmentation algorithm can be enhanced by using the minimal variance. Experimental results show that the proposed algorithm can segment the prostate ultrasound images effectively and avoid the problem of contour leakage.
Algorithms ; Humans ; Image Interpretation, Computer-Assisted ; methods ; Male ; Pattern Recognition, Automated ; methods ; Prostate ; diagnostic imaging ; Ultrasonography ; instrumentation ; methods

This paper proposes an algorithm of evaluating the compression depth, and then to extract four normalized mammary elasticity characteristic parameters with respect to the compression depth. The classification experiments show that these elasticity parameters have a good capability in determining whether the tumor is benign or malignant, and if combined with morphological parameters, the accuracy, sensitivity and specificity can be improved and increased to 95.19%, 98.82% and 92.16%, respectively.
Algorithms ; Breast Neoplasms ; diagnostic imaging ; Elasticity Imaging Techniques ; Female ; Humans ; Sensitivity and Specificity ; Ultrasonography, Mammary ; methods

In this paper, a new, fully-automatic method for the quantification of brain atrophy based on CT volume data is put forward by taking advantage of the characteristics of cerebral CT images in combination with the prior medical knowledge. This algorithm has been verified through the calculation of 2388 cases of normal and brain atrophy subjects.
Adolescent ; Adult ; Aged ; Aged, 80 and over ; Algorithms ; Autoanalysis ; methods ; Brain ; diagnostic imaging ; Child ; Child, Preschool ; Humans ; Infant ; Middle Aged ; Pick Disease of the Brain ; diagnostic imaging ; Tomography, X-Ray Computed ; methods ; Young Adult

Ultrasound image has a lot of speckle noise, which brings great difficulties to the feature extraction, recognition and analysis. Especially in the edge extraction, the conventional extraction algorithms are difficult to achieve the desired results because of the speckle noise. To solve this problem, an algorithm based on the anisotropic diffusion equation is presented. The new algorithm combines the robust estimation and considers the characteristic of the speckle noise, so it can suppress the speckle noise effectively and be more robust, thus the edge details of the ultrasound image can be preserved even enhanced, which can provide effective safeguard for the following edge extraction. Furthermore, the paper proposes a new method to compute speckle scale coefficients automatically, which reduces the influence of the human beings, and enhances the stability of the algorithm.
Algorithms ; Artifacts ; Image Interpretation, Computer-Assisted ; Ultrasonography ; methods

OBJECTIVETo investigate influential factors for the tendency to medicate and medication compliance in children with attention deficit hyperactivity disorder (ADHD).

METHODSA total of 188 children aged from 5 to 16 years, who were initially diagnosed with ADHD according to DSM-IV criteria, were included in the study. They underwent symptom assessment and cognitive function test. The compliance of methylphenidate treatment was evaluated.

RESULTSPatients with better emotional state, and fewer oppositional and hyperactive behaviors and those who had a family history of psychiatric diseases and who obtained lower scores in the number cancellation test (NCT), were more prone to medication and/or exhibited better medication compliance. Logistic regression analysis showed that fewer oppositional and hyperactive behaviors and lower NCT scores were the predictive factors for a higher tendency to medicate, and a better emotional state was the predictive factor for better medication compliance. Patients of predominantly inattentive type were more prone to medication and showed better medication compliance, as compared with those of combined type. Gender, age and symptom severity were not associated with the tendency to medicate and/or medication compliance.

CONCLUSIONSThere is a need to enhance medication compliance in children with ADHD who have hyperactive, impulsive and oppositional behaviors, and to improve their long-term social functions.

Adolescent ; Attention Deficit Disorder with Hyperactivity ; drug therapy ; psychology ; Central Nervous System Stimulants ; therapeutic use ; Child ; Child, Preschool ; Emotions ; Female ; Humans ; Logistic Models ; Male ; Medication Adherence ; Methylphenidate ; therapeutic use

This paper proposes an improved C-V model, which can avoid the step of re-initialization and simplify the formation of the initial level set function, thus the speed of segmentation can be accelerated greatly. Furthermore, based on the grayscale distribution characteristics of the breast tumor ultrasound images and on the hypothesis of piecewise constant in the C-V model, a semiautomatic segmentation flow has been presented, in which the rough contour is sketched first, and then a subimage would be obtained for the refined segmentation algorithm. This flow has improved not only the accuracy, but also the efficiency of the segmentation algorithm. The experiments show that the proposed algorithm could extract the contour of the breast tumor from the ultrasound images efficiently and accurately, which is fundamentally important for the following target feature extraction and analysis.
Algorithms ; Breast Neoplasms ; diagnostic imaging ; Female ; Humans ; Image Interpretation, Computer-Assisted ; methods ; Ultrasonography, Mammary ; methods