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Ship tracking technology is crucial for emergency rescue in the event of a disaster. Quickly identifying the position and status of vessels is vital for rescue teams to be able to deploy efficiently in disaster areas. When responding to emergencies or natural disasters, ship tracking technology plays a critical role in supporting emergency rescue operations and resource allocation, improving the overall resilience of the maritime transportation system. However, the research on multi-object tracking (MOT) algorithms has primarily focused on optical image datasets. In contrast, image data from synthetic aperture radar (SAR) presents unique challenges, such as defocus interference, a high false alarm rate, and a lack of prior samples. To overcome these particular challenges, we propose a robust MOT algorithm developed for SAR images to achieve effective multi-vessel tracking under difficult imaging conditions. In particular, we optimize the YOLOv8 detection network by introducing a diffusion model-based training method for data augmentation. This method improves the robustness of the network to scaling, rotational and translational deformations. Moreover, an enhanced swin transformer is proposed as a feature extraction network, which strengthens the representation capability of the detection network. Furthermore, the state parameters within the KF technique are enhanced by directly capturing the details of the height and width of the tracking rectangle box. This refinement of the ByteTrack algorithm aims to achieve a more precise and accurate fit of the tracking rectangle to the ship, further improving the overall tracking performance. The experimental results from the ship detection and multiple objects tracking datasets show the impressive performance of the proposed model. With a precision of 97.60%, a recall of 96.36%, and an average precision of 96.72%, the model achieves exceptional detection accuracy with an 18% reduction in model parameters. Furthermore, significant improvements can be observed in key tracking metrics such as HOTA, MOTA and IDF1, with improvements of 4.8%, 8.5% and 6.8% respectively compared to the baseline algorithm, alongside a remarkable 37.5% reduction in IDS. It is noteworthy that the tracker works in real time, achieving an average analysis speed of 47 frames per second. The proposed MOT algorithm achieves state-of-art tracking performance on a SAR image dataset with short time sequences. Therefore, the proposed approach is a compelling solution for ship tracking in SAR imagery.

At present, many infectious pathogens, especially emerging/re-emerging pathogens, exist in the blood of voluntary blood donors and may be transmitted through blood transfusions. However, most of Chinese blood centers only routinely screen for HBV, HCV, HIV, and syphilis. We employed metagenomic next-generation sequencing (mNGS) to investigate the microbiome in healthy voluntary blood donors to help assess blood safety in China by identifying infectious pathogens presented in donations that could lead to transfusion-acquired infections. We collected 10,720 plasma samples from voluntary blood donors from seven blood centers in different cities during 2012–2018 in China. A total of 562 GB of clean data was obtained. By analyzing the sequencing data, it was found that the most commonly identified bacteria found in the healthy blood were Serratia spp. (5.0176%), Pseudomonas spp. (0.6637%), and Burkholderia spp. (0.5544%). The principal eukaryote were Leishmania spp (1.3723%), Toxoplasma gondii (0.6352%), and Candida dubliniensis (0.1848%). Among viruses, Human Parvovirus B19 (B19V) accounts for the highest proportion (0.1490%), followed by Torque teno midi virus (0.0032%) and Torque teno virus (0.0015%). Since that B19V is a non-negligible threat to blood safety, we evaluated the positive samples for B19V tested by mNGS using quantitative polymerase chain reaction, Sanger sequencing, and phylogenetic analysis to achieve a better understanding of B19V in Chinese blood donors. Subsequently, 9 (0.07%) donations were positive for B19V DNA. The quantitative DNA levels ranged from 5.58 × 102 to 7.24 × 104 IU/ml. The phylogenic analyses showed that prevalent genotypes belonged to the B19-1A subtype, which disclosed previously unknown regional variability in the B19V positivity rate. The investigation revealed that many microbes dwell in the blood of healthy donors, including some pathogens that may be dormant in the blood and only cause disease under specific conditions. Thus, investigating the range and nature of potential pathogens in the qualified donations provided a framework for targeted interventions to help prevent emerging and re-emerging infectious diseases.

Synthetic aperture radar (SAR) imaging is used to identify ships, which is a vital task in the maritime industry for managing maritime fisheries, marine transit, and rescue operations. However, some problems, like complex background interferences, various size ship feature variations, and indistinct tiny ship characteristics, continue to be challenges that tend to defy accuracy improvements in SAR ship detection. This research study for multiscale SAR ships detection has developed an upgraded YOLOv5s technique to address these issues. Using the C3 and FPN + PAN structures and attention mechanism, the generic YOLOv5 model has been enhanced in the backbone and neck section to achieve high identification rates. The SAR ship detection datasets and AirSARship datasets, along with two SAR large scene images acquired from the Chinese GF-3 satellite, are utilized to determine the experimental results. This model’s applicability is assessed using a variety of validation metrics, including accuracy, different training and test sets, and TF values, as well as comparisons with other cutting-edge classification models (ARPN, DAPN, Quad-FPN, HR-SDNet, Grid R-CNN, Cascade R-CNN, Multi-Stage YOLOv4-LITE, EfficientDet, Free-Anchor, Lite-Yolov5). The performance values demonstrate that the suggested model performed superior to the benchmark model used in this study, with higher identification rates. Additionally, these excellent identification rates demonstrate the recommended model’s applicability for maritime surveillance.

Detailed management of the coastline is critical to the development of coastal states. However, the current classification of the coastline is relatively weak. This study proposed an automatic method to detect coastlines with category attributes based on multi-scale segmentation and multi-level inheritance classification. Fully integrating the advantages of multi-scale segmentation and multi-level classification, it solved the problems that traditional methods could not solve, such as extracting coastlines with categorical attributes, cultivation ponds that are easily affected by tidal flats, and complex coastal terrain. The Chinese GF-2 satellite images are used to extract various types of coastlines in Jiaozhou Bay and its surrounding areas such as the harbor-wharf coastline, silt coastline, pond coastline, rocky coastline, and sandy coastline. Compared with the human interpretation, it is found that the coastline extracted by our proposed method is different by 10.104 km in the harbor-wharf coastline, 0.099 km in the silt coastline, 2.677 km in the pond coastline, 8.831 km in the rocky coastline, and 0.218 km in the sandy coastline. Furthermore, compared to the object-based region growing integrating edge detection (OBRGIE) method, it is increased by 13.52%, 2.16%, 14.48%, 52.57%, and 22.97%, respectively. The results show that our proposed method is algorithmically more reasonable, accurate, and powerful. It can provide data support for refined coastline management.

Currently, an effective targeted therapy for pancreatitis is lacking. Hereditary pancreatitis (HP) is a heritable, autosomal-dominant disorder with recurrent acute pancreatitis (AP) progressing to chronic pancreatitis (CP) and a markedly increased risk of pancreatic cancer. In 1996, mutations in PRSS1 were linked to the development of HP. Here, we developed a mouse model by inserting a full-length human [PRSS1.sup.R122H] gene, the most commonly mutated gene in human HP, into mice. Expression of [PRSS1.sup.R122H] protein in the pancreas markedly increased stress signaling pathways and exacerbated AP. After the attack of AP, all [PRSS1.sup.R122H] mice had disease progression to CP, with similar histologic features as those observed in human HP. By comparing [PRSS1.sup.R122H] mice with [PRSS1.sup.WT] mice, as well as enzymatically inactivated Dead-[PRSS1.sup.R122H] mice, we unraveled that increased trypsin activity is the mechanism for R122H mutation to sensitize mice to the development of pancreatitis. We further discovered that trypsin inhibition, in combination with anticoagulation therapy, synergistically prevented progression to CP in [PRSS1.sup.R122H] mice. These animal models help us better understand the complex nature of this disease and provide powerful tools for developing and testing novel therapeutics for human pancreatitis., Introduction Pancreatitis, both acute and chronic, results in significant morbidity and mortality (1-3). With approximately 275,000 hospitalizations/year in the United States (4), acute pancreatitis (AP) is the most common gastrointestinal [...]

Monitoring marine oil pollution holds both practical and scientific significance. Synthetic Aperture Radar (SAR) is an active microwave remote sensing technique capable of all-weather and all-day with fine spatial resolution. However, under low wind conditions, rain cells and young ice are examples of look-alikes affect the accuracy of oil spill detection. Polarimetric SAR assumes a crucial role in this context, as it can extract abundant polarimetric features by polarimetric target decomposition. Drawing inspiration from this advancement, an improved polarimetric feature named relative feature based on Cloude-Pottier target decomposition was proposed. The Jeffries-Matusita distance indicates the substantial potential of the relative feature in detecting oil spills. The improved polarimetric feature within U-Net, FCN-8s, and DeepLabv3+ResNet-18 for oil spill detection using polarization SAR images. Experiment results demonstrated that the relative feature has superior performance compared to other polarimetric features and obtained the highest accuracy and dice within U-Net compared to the other two models. These findings introduce promising concepts for achieving rapid and precise detection of oil spills in future applications. [ABSTRACT FROM AUTHOR]