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Background: A novel method to detect the text region from the natural image using the discriminative deep feature of text regions is presented with deep learning concept in this manuscript. Objective: Curve Text Detection (CTD) from the natural image is generally based on two different tasks: learning of text data and text region detection. In the learning of text data, the goal is to train the system with a sample of letters and natural images, while, in the text region detection, the aim is to confirm whether the detected regions are text region or not. The emphasis of this research is on the development of deep learning algorithm. Methods: A novel approach has been proposed to detect the text region from natural images which simultaneously tackles three combined challenges: 1) pre-processing of the image without losing text region; 2) appropriate segmentation of text region using their strokes, and 3) training of data. In pre-processing, image enhancement and binarization are done then morphological operations are defined with the Maximally Stable Extremal Region (MSER) based segmentation technique which operates on the basis of stroke region of text and then finds out the (Speed Up Robust Feature) SURF key point from those regions. Results: Based on the SURF feature, text region is detected from the images using a trained structure of Artificial Neural Network (ANN) which is based on deep learning mechanism. Conclusion: CTW-1500 dataset is used to simulate the proposed work and the parameters like Precision, Recall, F-Measure (H-mean), Execution time, Accuracy and Error Rate are computed and are compared with the existing work to depict the effectiveness of the work.

Objective: To study the MRI and CT characteristics of different periods of acute ischemic stroke and evaluate its diagnostic value by using semi-automatic mention segmentation method. Methods: CT, conventional MRI and DWI were performed in 64 patients with acute ischemic stroke. The average ADC value and average relative ADC (rADC) value of infarct lesions were measured and statistically analyzed. Results: There were no significant differences in CT, conventional MRI, and DWI signal characteristics between 1 and 7 days after the onset of acute ischemic stroke. The average ADC value and the average rADC value decreased, but the average rADC in the infarct area increased with time. The rADC value was statistically significant with the onset of 1d, 2d, 3d, and 4d (P < 0.05), but not statistically significant with the onset of 5d and 6d (P > 0.05). Conclusion: In the image processing method of semi-automatic segmentation method, the characteristics of CT, conventional MRI, and DWI signals combined with the evolution of rADC values over time can help to judge the pathophysiological changes of acute ischemic stroke, which is ischemic. Stroke staging and treatment guidance are provided.

As apex predators, marine mammals play a key role in maintaining community structure and functioning in marine ecosystems. Hence, a decline in marine mammal populations may have negative effects on...

Additional file 1: Table S1. International Classification of Disease (ICD) codes, ninth (ICD-9) and tenth (ICD-10) revisions for diagnoses used in this study. Table S2. The association between polygenic risk scores (PRSs, calculated using LASSO approach) for varied psychiatric disorders and its corresponding psychiatric disorders. Table S3. Associations between polygenic risk scores (PRS, calculated using Clumping + Thresholding approach) for substance misuse and the risk of substance misuse at different p value thresholds. Table S4. Associations between polygenic risk scores (PRS, calculated using Clumping + Thresholding approach) for depression and the risk of depression at different p value thresholds. Table S5. Associations between polygenic risk scores (PRS, calculated using Clumping + Thresholding approach) for anxiety and the risk of anxiety at different p value thresholds. Table S6. Associations between polygenic risk scores (PRS, calculated using Clumping + Thresholding approach) for psychotic disorder and the risk of psychotic disorder at different p value thresholds. Table S7. Associations between polygenic risk scores (PRS, calculated using Clumping + Thresholding approach) for stress-related disorder and the risk of stress-related disorder at different p value thresholds. Table S8. The association between polygenic risk scores (PRSs, calculated using Clumping + Thresholding approach) for psychiatric disorders and COVID-19. Table S9. The association between polygenic risk scores (PRSs, calculated using Clumping + Thresholding approach) for psychiatric disorders under different p value thresholds and COVID-19. Figure S1. Flowchart of the entire GWAS quality control process. Figure S2. Manhattan of the SNP-Based GWAS of psychiatric disorders, using UK Biobank base data (SNP=single-nucleotide polymorphism). Figure S3. The association between categorized polygenic risk scores (PRSs, calculated using Clumping + Thresholding approach) for psychiatric disorders and COVID-19 riska.

Additional file 1 Supplementary Fig. 1. Risk of COVID-19 among individuals with any or specific neurodegenerative disease, compared to matched individuals without such a condition. Supplementary Fig. 2. Changes of odds ratios with 95% confidence intervalsa, by assuming 5���100% underestimation of the studied outcomes among individuals without neurodegenerative diseases. Supplementary Table 1 A Summary of previous studies addressing the association between neurodegenerative diseases and COVID-19. Supplementary Table 2. International Classification of Disease (ICD) codes, ninth (ICD-9) and tenth (ICD-10) revisions for diagnoses used in this study.

Additional file 1: Figure S1. Age-varying associations between childhood asthma and individual height deviation (%) (Panel A), and the presence of height deficit (yes or no) (Panel B). Table S1. Agreement of self-reported data on asthma diagnosis between baseline and repeat measurements. Table S2. Associations between height PRSs and the height at different p value thresholds. Table S3. Baseline characteristic of study participants, by age at the index date. Table S4. The associations between asthma and attained adult height / height change (height deviation and deficit) at individual. Table S5. Associations between asthma and attained adult height /individual height change (height deviation and deficit) stratified by different characteristics. Table S6. Sensitivity analysis for the association between asthma and attained adult height / height change (height deviation and deficit) by removing unexposed individuals with asthma diagnosed after age of 18. Table S7. Associations between asthma and height by use of inhaled glucocorticoids (ICS), after multiple imputation for the missing values of ICS use. Table S8. The associations between asthma and attained adult height / height change (height deviation and deficit), based on analysis of a full cohort design.