Diel Feeding Rhythm and Grazing Selectivity of Small-Sized Copepods in a Subtropical Embayment, the Northern South China Sea

Small marine copepods are key components of the pelagic food webs in Chinese coastal waters, but very few studies have addressed their trophodynamics, with even fewer studies addressing their diel feeding rhythms. In this study, the diel feeding rhythm and grazing selectivity of the copepod assemblage in Daya Bay during September 30 to October 2, 2014, were studied based on gut pigment analysis. Small copepods (body length < 1.5 mm) including Paracalanus parvus, Temora turbinata, Acrocalanus gibber, Temora stylifera, Euterpe acutifrons, and Acrocalanus gracilis, accounted for 73.9–100% of the total copepod abundance. The copepod assemblage generally exhibited a diurnal feeding pattern, characterized by a higher gut pigment content and ingestion rate during the daytime, consistent with variation in the ambient Chl α concentration. Fifty-five percent of the phytoplankton standing stock per day was consumed by the copepod assemblage, wherein diatoms, prymnesiophytes, and cyanobacteria were the main prey items with average contributions of 19.4–32.9% to the gut pigment contents. The copepod assemblage showed a strong feeding preference for prymnesiophytes, a weak feeding preference for diatoms, and avoidance of cyanobacteria. These results suggest a strong top-down control on phytoplankton community, especially on small groups from small copepods in the Daya Bay ecosystem

Predicting nosocomial lower respiratory tract infections by a risk index based system

Although belonging to one of the most common type of nosocomial infection, there was currently no simple prediction model for lower respiratory tract infections (LRTIs). This study aims to develop a risk index based system for predicting nosocomial LRTIs based on data from a large point-prevalence survey. Among the 49328 patients included, the prevalence of nosocomial LRTIs was 1.70% (95% confidence interval [CI], 1.64% to 1.76%). The areas under the receiver operating characteristic (ROC) curve for logistic regression and fisher discriminant analysis were 0.907 (95% CI, 0.897 to 0.917) and 0.902 (95% CI, 0.892 to 0.912), respectively. The constructed risk index based system also displayed excellent discrimination (area under the ROC curve: 0.905 [95% CI, 0.895 to 0.915]) to identify LRTI in internal validation. Six risk levels were generated according to the risk score distribution of study population, ranging from 0 to 5, the corresponding prevalence of nosocomial LRTIs were 0.00%, 0.39%, 3.86%, 12.38%, 28.79% and 44.83%, respectively. The sensitivity and specificity of prediction were 0.87 and 0.79, respectively, when the best cut-off point of risk score was set to 14. Our study suggested that this newly constructed risk index based system might be applied to boost more rational infection control programs in clinical settings

Margin-aware rectified augmentation for long-tailed recognition

The long-tailed data distribution is prevalent in real world and it poses great challenge on deep neural network training. In this paper, we propose Margin-aware Rectified Augmentation (MRA) to tackle this problem. Specifically, the MRA consists of two parts. From the data perspective, we analyze that data imbalance will cause the decision boundary be biased, and we propose a novel Margin-aware Rectified mixup (MR-mixup) that adaptively rectifies the biased decision boundary. Furthermore, from the model perspective, we analyze that the imbalance will also lead to consistent ‘gradient suppression’ on minority class logits. Then we propose Reweighted Mutual Learning (RML) that provides extra ‘soft target’ as supervision signal and augments the ‘encouraging gradients’ on the minority classes. We conduct extensive experiments on benchmark datasets CIFAR-LT, ImageNet-LT and iNaturalist18. The results demonstrate that the proposed MRA not only achieves state-of-the-art performance, but also yields a better-calibrated prediction.</p

Diel Feeding Rhythm and Grazing Selectivity of Small-Sized Copepods in a Subtropical Embayment, the Northern South China Sea

Small marine copepods are key components of the pelagic food webs in Chinese coastal waters, but very few studies have addressed their trophodynamics, with even fewer studies addressing their diel feeding rhythms. In this study, the diel feeding rhythm and grazing selectivity of the copepod assemblage in Daya Bay during September 30 to October 2, 2014, were studied based on gut pigment analysis. Small copepods (body length &amp;lt; 1.5 mm) including Paracalanus parvus, Temora turbinata, Acrocalanus gibber, Temora stylifera, Euterpe acutifrons, and Acrocalanus gracilis, accounted for 73.9–100% of the total copepod abundance. The copepod assemblage generally exhibited a diurnal feeding pattern, characterized by a higher gut pigment content and ingestion rate during the daytime, consistent with variation in the ambient Chl α concentration. Fifty-five percent of the phytoplankton standing stock per day was consumed by the copepod assemblage, wherein diatoms, prymnesiophytes, and cyanobacteria were the main prey items with average contributions of 19.4–32.9% to the gut pigment contents. The copepod assemblage showed a strong feeding preference for prymnesiophytes, a weak feeding preference for diatoms, and avoidance of cyanobacteria. These results suggest a strong top-down control on phytoplankton community, especially on small groups from small copepods in the Daya Bay ecosystem.</jats:p

A New Deformation Enhancement Method Based on Multitemporal InSAR for Landslide Surface Stability Assessment

The complex terrain and abundant ravines in the western mountainous areas of Beijing have led to dramatic changes in the geological environment. Monitoring and assessing the stability of landslide surfaces is of great significance for disaster prevention and ensuring the safety of the capital city. According to the spatial similarity characteristics of landslide surfaces, we propose a new interferometric synthetic aperture radar (InSAR) deformation enhancement method by taking into account the time-series deformation information of spatially adjacent homogeneous monitoring points. Taking Dongjiang Gully, Beijing as a typical study area, using multitemporal InSAR technology, 80 scenes of RADARSAT-2 data from September 2016 to September 2022 were processed to obtain their time-series surface deformation to verify the advantages of the proposed method. The results show that the standard deviation of the deformation difference of all monitoring points is generally reduced after the deformation enhancement, and the mean value is reduced from 5.1 to 3.3, which is 35.2&#x0025; lower in comparison. Then this study assesses the stability of the landslide surface based on the deformation enhancement results. First, the optical image interpretation was combined with the angular distortions derived from deformation gradients to analyze the spatial location and boundaries of the landslide, and then to identify the infrastructure that is more susceptible to landslide impact. Second, through the principal component analysis method, the correlation between each component and the distribution characteristics of surface deformation was analyzed. Finally, starting from geological factors and triggering conditions, the driving force for landslide surface deformation was discussed, and it was drawn that seasonal precipitation is a major influencing factor. The proposed method can provide a reference for landslide monitoring and assessment in similar areas