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ObjectiveTo provide reference for guiding consumption and carrying out safety supervision， the exposure of chloramphenicol （CAP） residues in commercially available animal-origin foods collected from Shenzhen local trading markets and supermarkets were assessed.MethodsFive hundred and eleven samples were collected in Shenzhen City from 2018 to 2019. The CAP residues was determined by ultra-high performance liquid chromatography-tandem mass spectrometry， and dietary exposure was estimated by point assessment method. Exposure risk for different populations was characterized by the margin of exposure （MOE） values.ResultsNo chloramphenicol was detected in 160 livestock and 60 poultry meat samples， and the detection rate of CAP was 26.80% in 291 aquatic samples， including shellfish， shrimp and fish. The CAP concentration in shellfish was in the range of 0.05-205.00 µg/kg， the mean concentration was 13.71 µg/kg， and the detection rate was 37.21% （64/172）. The CAP concentration in shrimp was in the range of 0.05-2.20 µg/kg， the mean concentration was 0.20 µg/kg， and the detection rate was 13.64% （64/172）. The CAP concentration in fish was in the range of 0.05-1.90 µg/kg， the mean concentration was 0.11 µg/kg， and the detection rate was 11.34% （11/97）. The detection rate of CAP in all animal-origin samples was 15.26% （78/511）. The result of dietary exposure assessment showed that the Chronic Daily Intake （CDI） values for average population for Shenzhen residents was 2.06×10-3 μg/kg·BW， and the MOE value was 485； the CDI value for 5th percentile was 4.85×10-5 μg/kg·BW， and the MOE value for was above 10 000； the CDI values for 50th and 95th percentile were 6.72×10-4 μg/kg·BW and 9.25×10-3 μg/kg·BW， respectively， and the MOE values were both below 10 000， suggesting some health risks.ConclusionExcessive consumption of animal-origin foods might lead to some health risk. In order to reduce the exposure risk， the population with higher exposure should limit their consumption， especially aquatic products. Meanwhile， the relevant departments should strengthen supervision and management of animal-origin foods quality and safety， and pay close attention to this problem.

With the development of earth observation satellites, it is very meaningful to study methods for intelligently processing remote sensing images (RSIs). As an important pretreatment technique, salient object detection (SOD) can produce accurate saliency predictions in natural scenes. However, due to the complex object types, high noise levels, and different imaging angles in RSIs, it is still difficult to generate accurate saliency predictions in such images. This paper proposes a SOD method for RSIs, namely, the two‐stage local attention network (TLANet). It uses two‐stage attention (TSA) to capture the relationships between the pixels of features at different scales and uses an adjacent semantic refinement (ASR) module to enhance the flow of information across scales. In addition, a cascade structure is introduced to the decoder so that more semantic features can guide the feature decoding process. Moreover, a new loss function is proposed to improve the prediction effects obtained for salient edges. Through comprehensive experiments conducted on two RSIs datasets, it is demonstrated that the TLANet is superior to the state‐of‐the‐art methods in both qualitative and quantitative comparisons. [ABSTRACT FROM AUTHOR]