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Additional file 1: Table. S1. The details of Clinical specimen; Table. S2. Antibodies used in the experiment; Table. S3. Sequences of the primers for Real-time qPCR.

The origin and accession number of 25 R. anatipestifer genomes. Table S2. The CRISPR loci in R. anatipestifer genomes. Table S3. Cas9-related information for 39 Gram-negative type II system-containing bacteria. Figure S1. Multiple sequence alignment of Cas2 amino acid sequences. The asterisk represents the conserved base in all of the sequences for alignment. Figure S2. Multiple sequence alignment of Cas1 amino acid sequences. The asterisk represents the conserved base in all of the sequences for alignment. Figure S3. Multiple sequence alignment of Cas9 amino acid sequences. The asterisk represents the conserved base in all of the sequences for alignment. Based on previous report (Chylinski K, et al., [47]), we defined that the amino acid sequence located between 1 and 60 aa, 718â 775 aa and 910â 1131 aa are RuvC-like domains (black boxes). The amino acid sequence located between 776 and 909 aa is considered as HNH domain (red box). The yellow shadows indicate catalytic sites. Figure S4. Multiple sequence alignment for Cas9 from 40 Gram-negative type II system-containing bacteria. ClustalW (in MEGA software) was utilized to perform multiple sequence alignment for Cas9 from 40 Gram-negative type II system-containing bacteria. The default parameter values were chosen when computed. (DOCX 2633Â kb)

There are many studies on the application of boosting in image processing, such as face recognition, face detection, image retrieval, and so on. By using an appropriate classifier, the accuracy of classification and computation in time are improved by applying boosting algorithm. In this dissertation, I have investigated the AdaBoost algorithm and conducted the experiments for feature selection and classification. It has be demonstrated that during the feature selection process by using AdaBoost algorithm, the strong classifier can be formed in linear combination of weak classifiers. The experimental results show that Adaboost algorithm is effective in feature selection. It also shows that features selected with lowest error of misclassification will be different if we choose different positive samples and negative samples. The importance of each feature for the sample images will be different too if the selected samples are different. I have studied the face recognition and image retrieval. The application of Ad-aBoost algorithm on feature selection for face recognition and image retrieval have been discussed too. Master of Science (Signal Processing)