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Rivers are among the most diverse, dynamic, and productive ecosystems on Earth. River flow regimes are constantly changing, but characterizing and understanding such changes have been challenging from a long-term and global perspective. By analyzing water extent variations observed from four-decade Landsat imagery, we here provide a global attribution of the recent changes in river regime to morphological dynamics (e.g., channel shifting and anabranching), expansion induced by new dams, and hydrological signals of widening and narrowing. Morphological dynamics prevailed in ~20% of the global river area. Booming reservoir constructions, mostly skewed in Asia and South America, contributed to ~32% of the river widening. The remaining hydrological signals were characterized by contrasting hotspots, including prominent river widening in alpine and pan-Arctic regions and narrowing in the arid/semi-arid continental interiors, driven by varying trends in climate forcing, cryospheric response to warming, and human water management. Our findings suggest that the recent river extent dynamics diverge based on hydroclimate and socio-economic conditions, and besides reflecting ongoing morphodynamical processes, river extent changes show close connections with external forcings, including climate change and anthropogenic interference.

With the continuous development of social software and multimedia technology, images have become a kind of important carrier for spreading information and socializing. How to evaluate an image comprehensively has become the focus of recent researches. The traditional image aesthetic assessment methods often adopt single numerical overall assessment scores, which has certain subjectivity and can no longer meet the higher aesthetic requirements. In this paper, we construct an new image attribute dataset called aesthetic mixed dataset with attributes(AMD-A) and design external attribute features for fusion. Besides, we propose a efficient method for image aesthetic attribute assessment on mixed multi-attribute dataset and construct a multitasking network architecture by using the EfficientNet-B0 as the backbone network. Our model can achieve aesthetic classification, overall scoring and attribute scoring. In each sub-network, we improve the feature extraction through ECA channel attention module. As for the final overall scoring, we adopt the idea of the teacher-student network and use the classification sub-network to guide the aesthetic overall fine-grain regression. Experimental results, using the MindSpore, show that our proposed method can effectively improve the performance of the aesthetic overall and attribute assessment., Comment: 7 pages, 9figures, to appear: ACM Transactions on Multimedia Computing Communications and Applications (TOMM)

Shuangjun He,* Jun Ma,* Chenyu Fan, Chao Tang, Yi Chen, Cuiying Xie Department of Emergency, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, People’s Republic of China*These authors contributed equally to this workCorrespondence: Yi Chen; Cuiying XieDepartment of Emergency, Shanghai Jiao Tong University School of Medicine Affiliated Renji Hospital, 2000 Jiangyue Road, Minhang District, Shanghai, 200025, People’s Republic of ChinaEmail chenyirenji@163.com; xiecuiyingrenji@163.comPurpose: To evaluate the performance of serum procalcitonin (PCT) concentrations to diagnose fungal infection in patients with systemic lupus erythematosus (SLE).Patients and Methods: From January 2017 to October 2020, SLE patients hospitalized for serious infection with an identified single bacterial or fungal pathogen, as well as PCT measured within 24h after admission were included. The diagnostic performance of PCT was evaluated independently and in combination with the white blood cell (WBC) count, C-reactive protein (CRP) level, and erythrocyte sedimentation rate (ESR). The analysis included the sensitivity, specificity, positive and negative predictive values, positive and negative likelihood ratios, and the crude and adjusted area under the receiver operating characteristic curve (AUROC).Results: Sixty-nine patients were included; 26 had a fungal infection (38%) and 43 had a bacterial infection (22 gram-positive and 21 gram-negative). Fungal infection patients were mainly distributed in the respiratory group (88.5%), and bacterial infection distribution were more prevalent in respiratory group (44.2%) and abdominal/urinary group (23.3%). The PCT concentration was significantly lower in fungal infections than bacterial infections (fungal: 0.22 ng/mL, interquartile range [IQR], 0.09– 0.44 vs bacterial: 0.60 ng/mL, IQR, 0.16– 5.74; p = 0.016) and differed significantly between different infection sites (p = 0.022). PCT had bett