The antigenic proteins of the piroplasm stage of Theileria species (China), the causative agent of theilerosis of small ruminants in China, were analyzed by Western blot, revealing several specific immunoreactive proteins of different predicted molecular weights. Furthermore, sera from Theileria species (China)-infected animals were probed for reactivity with the TaSP protein of T. annulata, for which a homologue has been described in Theileria species (China). Affinity chromatography demonstrated the presence of TaSPreactive antibodies, and the majority of the sera showed reactivity with this protein both in Western blots and in ELISA. The identified parasite antigens and TaSP will be assessed for their suitability for developing diagnostic methods as well as evaluated for their capacity to stimulated host immune competent cells.
Previous studies have shown that there is a strong correlation between radiologists' diagnoses and their gaze when reading medical images. The extent to which gaze is attracted by content in a visual scene can be characterised as visual saliency. There is a potential for the use of visual saliency in computer-aided diagnosis in radiology. However, little is known about what methods are effective for diagnostic images, and how these methods could be adapted to address specific applications in diagnostic imaging. In this study, we investigate 20 state-of-the-art saliency models including 10 traditional models and 10 deep learning-based models in predicting radiologists' visual attention while reading 196 mammograms. We found that deep learning-based models represent the most effective type of methods for predicting radiologists' gaze in mammogram reading; and that the performance of these saliency models can be significantly improved by transfer learning. In particular, an enhanced model can be achieved by pre-training the model on a large-scale natural image saliency dataset and then finetuning it on the target medical image dataset. In addition, based on a systematic selection of backbone networks and network architectures, we proposed a parallel multi-stream encoded model which outperforms the state-of-the-art approaches for predicting saliency of mammograms.
Eye movements reflect the visual process of humans' perception and cognition. In the field of medical imaging, the diagnosis rendered by radiologists is closely related to their eye movements when reading radiological images. It is beneficial to study the eye movements of radiologists to improve the diagnostic performance. However, existing studies are mainly focused on the radiologists' fixations but rarely on their saccade patterns. Moreover, these studies are almost based on limited datasets. In this paper, we present a quantitative study of the gaze behavior of radiologists from the perspective of saccade patterns on a large-scale dataset. The dataset comprises of the eye-tracking data of 10 expert radiologists reading 196 mammograms. By analyzing the saccade amplitude, direction, and bias of radiologists, we found that radiologists have specific saccade patterns in image reading and the saccade patterns are significantly affected by the different reading phases, working experience, and orientations of the mammograms.
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