Spinal cord injury (SCI) is one of the most debilitating of all the traumatic conditions that afflict individuals. For a number of years, extensive studies have been conducted to clarify the molecular mechanisms of SCI. Experimental and clinical studies have indicated that two phases, primary damage and secondary damage, are involved in SCI. The initial mechanical damage is caused by local impairment of the spinal cord. In addition, the fundamental mechanisms are associated with hyperflexion, hyperextension, axial loading and rotation. By contrast, secondary injury mechanisms are led by systemic and cellular factors, which may also be initiated by the primary injury. Although significant advances in supportive care have improved clinical outcomes in recent years, a number of studies continue to explore specific pharmacological therapies to minimize SCI. The present review summarized some important pathophysiologic mechanisms that are involved in SCI and focused on several pharmacological and non-pharmacological therapies, which have either been previously investigated or have a potential in the management of this debilitating injury in the near future.
Unsupervised domain adaptation (UDA) assumes that source and target domain data are freely available and usually trained together to reduce the domain gap. However, considering the data privacy and the inefficiency of data transmission, it is impractical in real scenarios. Hence, it draws our eyes to optimize the network in the target domain without accessing labeled source data. To explore this direction in object detection, for the first time, we propose a source data-free domain adaptive object detection (SFOD) framework via modeling it into a problem of learning with noisy labels. Generally, a straightforward method is to leverage the pre-trained network from the source domain to generate the pseudo labels for target domain optimization. However, it is difficult to evaluate the quality of pseudo labels since no labels are available in target domain. In this paper, self-entropy descent (SED) is a metric proposed to search an appropriate confidence threshold for reliable pseudo label generation without using any handcrafted labels. Nonetheless, completely clean labels are still unattainable. After a thorough experimental analysis, false negatives are found to dominate in the generated noisy labels. Undoubtedly, false negatives mining is helpful for performance improvement, and we ease it to false negatives simulation through data augmentation like Mosaic. Extensive experiments conducted in four representative adaptation tasks have demonstrated that the proposed framework can easily achieve state-of-the-art performance. From another view, it also reminds the UDA community that the labeled source data are not fully exploited in the existing methods.
Safflower (Carthamus tinctorius L.) is one of the most important crop plants that has been utilized for production of oleosins. miRNAs (microRNAs) are a class of small and non-coding RNAs that negatively regulate gene expression at post-transcriptional level thus playing a role in plant growth, development, and stress response. In this study, high-throughput Illumina sequencing technology has been used to comprehensively investigate the small RNA transcriptomes of safflower seed, flower, and leaf. It is found that there are at least 236 known miRNAs expressed in safflower, of which 100 miRNAs with relatively high expression abundance exhibited evolutionary conservation across multiple plants. Comparison of their expression abundance among different tissues shows that a total of 116, 133, and 128 miRNAs are significantly differentially expressed with higher abundance or lower abundance between safflower seed/leaf, seed/petal, and leaf/petal. The majority of the most significant differences in miRNA abundance between tissues are tissue-specific miRNAs. In addition, 13 putative novel miRNAs have been identified in safflower. The small RNA transcriptomes obtained in this study provide a basis for further investigation of the physiological roles of identified miRNAs in safflower.
Deep region-based object detector consists of a region proposal step and a deep object recognition step. In this paper, we make significant improvements on both of the two steps. For region proposal we propose a novel lightweight cascade structure which can effectively improve RPN proposal quality. For object recognition we re-implement global context modeling with a few modifications and obtain a performance boost (4.2% mAP gain on the ILSVRC 2016 validation set). Besides, we apply the idea of pre-training extensively and show its importance in both steps. Together with common training and testing tricks, we improve Faster R-CNN baseline by a large margin. In particular, we obtain 87.9% mAP on the PASCAL VOC 2012 test set, 65.3% on the ILSVRC 2016 test set and 36.8% on the COCO test-std set.
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