Human telomeres are maintained by the shelterin protein complex in which TRF1 and TRF2 bind directly to duplex telomeric DNA. How these proteins find telomeric sequences among a genome of billions of base pairs and how they find protein partners to form the shelterin complex remains uncertain. Using single-molecule fluorescence imaging of quantum dot-labeled TRF1 and TRF2, we study how these proteins locate TTAGGG repeats on DNA tightropes. By virtue of its basic domain TRF2 performs an extensive 1D search on nontelomeric DNA, whereas TRF1’s 1D search is limited. Unlike the stable and static associations observed for other proteins at specific binding sites, TRF proteins possess reduced binding stability marked by transient binding (∼9–17 s) and slow 1D diffusion on specific telomeric regions. These slow diffusion constants yield activation energy barriers to sliding ∼2.8–3.6 κBT greater than those for nontelomeric DNA. We propose that the TRF proteins use 1D sliding to find protein partners and assemble the shelterin complex, which in turn stabilizes the interaction with specific telomeric DNA. This ‘tag-team proofreading’ represents a more general mechanism to ensure a specific set of proteins interact with each other on long repetitive specific DNA sequences without requiring external energy sources.

Purpose To evaluate the efficacy of a deep‐learning model to segment the lung and thorax regions in pediatric chest X‐rays (CXRs). Validating the diagnosis of bacterial or viral pneumonia could be improved after lung segmentation. Materials and methods A clinical‐pediatric CXR set including 1351 patients was proposed to develop a deep‐learning model for the pulmonary‐thoracic segmentations. Model performance was evaluated by Jaccard's similarity coefficient (JSC) and Dice's coefficient (DC). Two adult CXR sets were used to assess the model's generalizability. According to the pulmonary‐thoracic ratio, Pearson's correlation coefficient and the Bland‐Altman plot were generated to demonstrate the correlation and agreement between manual and automatic segmentations. The receiver operating characteristic curves and areas under the curve (AUCs) were used to compare the pneumonia classification performance based on the lung‐extracted images with that based on the original images. Results The model achieved JSCs of 0.910 and 0.950, DCs of 0.948 and 0.974 for lung and thorax segmentations, respectively. Pearson's r = 0.96, P < .0001. In the Bland‐Altman plot, the mean difference was 0.0025 with a 95% confidence interval of (−0.0451, 0.0501). For testing with two adult CXR sets, the JSCs were 0.903 and 0.888, respectively, while the DCs were 0.948 and 0.937, respectively. After lung segmentation, the AUC of a classifier to identify bacterial or viral pneumonia increased from 0.815 to 0.879. Conclusion We built a pediatric CXR dataset and exploited a deep‐learning model for accurate pulmonary‐thoracic segmentations. Lung segmentation can notably improve the diagnosis of bacterial or viral pneumonia.

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Purpose Comparing the efficacy of a deep‐learning model in classifying the etiology of pneumonia on pediatric chest X‐rays (CXRs) with that of human readers. Methods We built a clinical‐pediatric CXR set containing 4035 patients to exploit a deep‐learning model called Resnet‐50 for differentiating viral from bacterial pneumonia. The dataset was split into training (80%) and validation (20%). Model performance was assessed by receiver operating characteristic curve and area under the curve (AUC) on the first test set of 400 CXRs collected from different studies. For the second test set composed of 100 independent examinations obtained from the daily clinical practice at our institution, the kappa coefficient was selected to measure the interrater agreement in a pairwise fashion for the reference standard, all reviewers, and the model. Gradient‐weighted class activation mapping was used to visualize the significant areas contributing to the model prediction. Results On the first test set, the best‐performing classifier achieved an AUC of 0.919 (p < .001), with a sensitivity of 79.0% and specificity of 88.9%. On the second test set, the classifier achieved performance similar to that of human experts, which resulted in a sensitivity of 74.3% and specificity of 90.8%, positive and negative likelihood ratios of 8.1 and 0.3, respectively. Contingence tables and kappa values further revealed that expert reviewers and model reached substantial agreements on differentiating the etiology of pediatric pneumonia. Conclusions This study demonstrated that the model performed similarly as human reviewers and recognized the regions of pathology on CXRs.