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您的检索式：作者名="Jinmeng Jia"
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| 1 | Towards efficiency in rare disease research: what is distinctive and important?显示文摘Characterized by their low prevalence, rare diseases are often chronically debilitating or life threatening. Despite their low prevalence, the aggregate number of individuals suffering from a rare disease is estimated to be nearly 400 million worldwide.Over the past decades, efforts from researchers, clinicians, and pharmaceutical industries have been focused on both the diagnosis and therapy of rare diseases. However, because of the lack of data and medical records for individual rare diseases and the high cost of orphan drug development, only limited progress has been achieved. In recent years, the rapid development of next-generation sequencing(NGS)-based technologies, as well as the popularity of precision medicine has facilitated a better understanding of rare diseases and their molecular etiology. As a result, molecular subclassification can be identified within each disease more clearly, significantly improving diagnostic accuracy. However, providing appropriate care for patients with rare diseases is still an enormous challenge. In this review, we provide a brief introduction to the challenges of rare disease research and make suggestions on where and how our efforts should be focused. | Jinmeng Jia Tieliu Shi | 2017 | Science China(Life Sciences)2017，60，7： | 3 |
| 2 | Novel green-fruit detection algorithm based on D2D framework显示文摘In the complex orchard environment,the efficient and accurate detection of object fruit is the basic requirement to realize the orchard yield measurement and automatic harvesting.Sometimes it is hard to differentiate between the object fruits and the background because of the similar color,and it is challenging due to the ambient light and camera angle by which the photos have been taken.These problems make it hard to detect green fruits in orchard environments.In this study,a two-stage dense to detection framework(D2D)was proposed to detect green fruits in orchard environments.The proposed model was based on multi-scale feature extraction of target fruit by using feature pyramid networks MobileNetV2+FPN structure and generated region proposal of target fruit by using Region Proposal Network(RPN)structure.In the regression branch,the offset of each local feature was calculated,and the positive and negative samples of the region proposals were predicted by a binary mask prediction to reduce the interference of the background to the prediction box.In the classification branch,features were extracted from each sub-region of the region proposal,and features with distinguishing information were obtained through adaptive weighted pooling to achieve accurate classification.The new proposed model adopted an anchor-free frame design,which improves the generalization ability,makes the model more robust,and reduces the storage requirements.The experimental results of persimmon and green apple datasets show that the new model has the best detection performance,which can provide theoretical reference for other green object detection. | Jinmeng Wei Yanhui Ding Jie Liu Muhammad Zakir Ullah Xiang Yin Weikuan Jia | 2022 | International Journal of Agricultural and Biological Engineering2022，15，1： | 0 |
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