Fine-Grained Ship Recognition Based on Visible and Near-Infrared Multimodal Remote Sensing Images: Dataset,Methodology and Evaluation

Fine-grained recognition of ships based on remote sensing images is crucial to safeguarding maritime rights and interests and maintaining national security.Currently,with the emergence of massive high-resolution multi-modality images,the use of multi-modality images for fine-grained recognition has become a promising technology.Fine-grained recognition of multi-modality images imposes higher requirements on the dataset samples.The key to the problem is how to extract and fuse the complementary features of multi-modality images to obtain more discriminative fusion features.The attention mechanism helps the model to pinpoint the key information in the image,resulting in a significant improvement in the model’s performance.In this paper,a dataset for fine-grained recognition of ships based on visible and near-infrared multi-modality remote sensing images has been proposed first,named Dataset for Multimodal Fine-grained Recognition of Ships(DMFGRS).It includes 1,635 pairs of visible and near-infrared remote sensing images divided into 20 categories,collated from digital orthophotos model provided by commercial remote sensing satellites.DMFGRS provides two types of annotation format files,as well as segmentation mask images corresponding to the ship targets.Then,a Multimodal Information Cross-Enhancement Network(MICE-Net)fusing features of visible and near-infrared remote sensing images,has been proposed.In the network,a dual-branch feature extraction and fusion module has been designed to obtain more expressive features.The Feature Cross Enhancement Module(FCEM)achieves the fusion enhancement of the two modal features by making the channel attention and spatial attention work cross-functionally on the feature map.A benchmark is established by evaluating state-of-the-art object recognition algorithms on DMFGRS.MICE-Net conducted experiments on DMFGRS,and the precision,recall,mAP0.5 and mAP0.5:0.95 reached 87%,77.1%,83.8%and 63.9%,respectively.Extensive experiments demonstrate that the proposed MICE-Net has more excellent performance on DMFGRS.Built on lightweight network YOLO,the model has excellent generalizability,and thus has good potential for application in real-life scenarios.

Full-profile pharmacokinetics, anticancer activity and toxicity of an extended release trivalent PEGylated irinotecan prodrug

Irinotecan is an anticancer topoisomerase I inhibitor that acts as a prodrug of the active metabolite,SN-38.Unfortunately,the limited utility of irinotecan is attributed to its pH-dependent stability,short half-life and dose-limiting toxicity.To address this problem,a novel trivalent PEGylated prodrug(PEG-[Irinotecan]3)has been synthesized and its full-profile pharmacokinetics,antitumor activity and toxicity compared with those of irinotecan.The results show that after intravenous administration to rats,PEG-[Irinotecan]3 undergoes stepwise loss of irinotecan to form PEG-[Irinotecan]3-x(x=1,2)and PEG-[linker]during which time the released irinotecan undergoes conversion to SN-38.As compared with conventional irinotecan,PEG-[Irinotecan]3 displays extended release of irinotecan and efficient formation of SN-38 with significantly improved AUC and half-life.In a colorectal cancer-bearing model in nude mice,the tumor concentrations of irinotecan and SN-38 produced by PEG-[Irinotecan]3 were respectively 86.2 and 2293 times higher at 48 h than produced by irinotecan.In summary,PEG-[Irinotecan]3 displays superior pharmacokinetic characteristics and antitumor activity with lower toxicity than irinotecan.This supports the view that PEG-[Irinotecan]3 is a superior anticancer drug to irinotecan and it has entered the phaseⅡtrial stage.

美国临床病理学会全球实验室检验人员专业认证

美国临床病理学会（American Society for ClinicalPathology, ASCP）对实验室检验人员的专业认证是由非政府机构或协会承认个人己达到预定资格能力的过程。它帮助确定致力于实验室检验专业，并对本专业知识和经验寻求认可的个人。ASCP认证委员会（Board of Certification，BOC）对准备从事，或继续从事和以医学实验室检验为职业生涯的人才开发、建立并坚持标准和流程。