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Objective To establish and evaluate a method for determining 3 α-thalassemia non-deletional mutations（αWS，αQS，αCS） and 8 β-thalassemia point mutations [CD26（G>A），CD41/42（-TCTT）， nt29（A>G） ， IVS-Ⅱ-654（C>T）， nt28（A>G） ， CD71/72（+A） ，CD17（A>T）， CD27/28（+C）] simultaneously using hetero-tail dual-labeled fluorescent probe hydridization. Methods Polymerase chain reaction（PCR） primers and hetero-tail dual-labeled fluorescent probes targeting 11 most common α-thalassemia non-deletional and β-thalassemia point mutations were designed. An optimized multiplex asymmetric PCR and hetero-tail dual-labeled fluorescent probe hydridization were established. Performance specifications（accuracy，specificity and sensitivity） were evaluated by the national reference materials for thalassemia. Totally，200 clinical samples were determined by PCR-reverse dot blot kit and hetero-tail dual-labeled fluorescent probe hydridization concurrently to assess the consistency. Results For 11 mutations within the determination range，hetero-tail dual-labeled fluorescent probe hydridization showed 100% consistency with the national reference materials for thalassemia，and none of the mutations outside the determination range were determined. For different concentrations of Hb CS，CD41/42 and IVS-Ⅱ-654 reference materials ranging from 1.0，2.5，5.0 to 10.0 ng/μL，the results were consistent. The consistency rate between PCR-reverse dot blot kit and hetero-tail dual-labeled fluorescent probe hydridization was 100%. Conclusions Hetero-tail dual-labeled fluorescent probe hydridization enables fast and accurate determination of 11 α-thalassemia non-deletional and β-thalassemia point mutations simultaneously by one-step real-time PCR，which reduces turn-around time and contamination risk and shows potential for future clinical application. ispartof: Laboratory Medicine (Shanghai Journal of Medical Laboratory) / Jianyan Yixue vol:37 issue:10 pages:955-962 status: published

Chiral lactic acids and their ester derivatives are crucial building blocks and intermediates for the synthesis of a great variety of valuable functional materials and pharmaceuticals. Before our s...

Optical Braille Recognition methods usually use many designed steps, such as image deskewing, Braille dots detection, Braille cell grids construction and Braille character recognition, which are less robust for complex Braille scenes. This paper proposes an optimal semantic segmentation framework BraUNet to directly detect and recognize Braille characters in the whole original Braille images. BraUNet adds extra auxiliary learning strategy to UNet network, which uses long-range connections of feature maps between encoder and decoder to get more low-level features. And auxiliary learning strategy can combine multi-class Braille characters segmentation with Braille foreground extraction, which can improve the feature learning ability and the Braille segmentation performance. Then morphological post-processing is used on semantic segmentation results to get the final individual Braille character regions. Experimental results show the proposed framework is robust, effective and fast for Braille characters segmentation and recognition on both complex double sided Braille image dataset and handwritten Braille image dataset.

This paper proposes a novel two-stage learning framework TS-OBR for double-sided Braille images recognition. In the first stage, a Haar cascaded classifier with the sliding window strategy is adopted to quickly detect Braille recto dots with high confidence. Then a coarse-to-fine de-skewing method is proposed to correct original skewed Braille images, which maximizes the variance of horizontal and vertical projection at different angles. And an adaptive Braille cells grid construction method based on statistical analysis is proposed, which can dynamically generate the Braille cells grid for each Braille image. In the second stage, a decision-level SVM classifier with four classifiers recognition results is used to get recto dots detection results only on intersections of the Braille cells grid. Experimental results on the public double-sided Braille dataset and our Braille exam answer paper dataset show the proposed framework TS-OBR is effective, robust and fast for Braille dots detection and Braille characters recognition.

Braille is an effective way for the visually impaired to learn knowledge and obtain information. Braille image recognition aims to automatically detect Braille dots in the whole Braille image. There is no available public datasets for Braille image recognition to push relevant research and evaluate algorithms. This paper constructs a large-scale Double-Sided Braille Image dataset DSBI with detailed Braille recto dots, verso dots and Braille cells annotation. To quickly annotate Braille images, an auxiliary annotation strategy is proposed, which adopts initial automatic detection of Braille dots and modifies annotation results by convenient human-computer interaction method. This labeling strategy can averagely increase label efficiency by six times for recto dots annotation in one Braille image. Braille dots detection is the core and basic step for Braille image recognition. This paper also evaluates some Braille dots detection methods on our dataset DSBI and gives the benchmark performance of recto dots detection. We have released our Braille images dataset on the GitHub website.