Uptake and Cytotoxicity of Ce(IV) Doped TiO 2

Ce(IV) doped anatase TiO2 nanoparticles (CDTs) were prepared and the underlying mechanism by which CDT nanoparticle enters into cell and its cytotoxicity were investigated in a human hepatocellular line L02 cell. The results showed that CDTs can enter into cytoplasm of L02 cell via endocytosis and nonendocytic ways. Large aggregation of CDTs went into cell by endocytosis and finally formed an endocytic vesicle with membrane boundary. Tiny aggregation of CDTs entered into cell cytoplasm via channels similar to that for lung-blood substance exchange in the alveolar-airway barrier. In addition, tiny aggregation of CDTs was observed in nucleus, and maybe CDTs could pass through the nucleus envelope via the channels provided by nuclear pore complexes (NPCs). Results from MTT assay, fluorescence microscope, and TEM observations showed that the cell viability, cell morphology, cell growth, and cell division periods could not be obviously impaired when cells were exposed to CDTs of different concentration from 30 to 150 μg mL−1 without UV irradiation. However, large vacuoles containing CDTs were found in cytoplasm, some structure changes were observed in mitochondria, and smooth envelope around the nucleus was shrank and deformed

Continuous Intermediate Token Learning with Implicit Motion Manifold for Keyframe Based Motion Interpolation

Deriving sophisticated 3D motions from sparse keyframes is a particularly challenging problem, due to continuity and exceptionally skeletal precision. The action features are often derivable accurately from the full series of keyframes, and thus, leveraging the global context with transformers has been a promising data-driven embedding approach. However, existing methods are often with inputs of interpolated intermediate frame for continuity using basic interpolation methods with keyframes, which result in a trivial local minimum during training. In this paper, we propose a novel framework to formulate latent motion manifolds with keyframe-based constraints, from which the continuous nature of intermediate token representations is considered. Particularly, our proposed framework consists of two stages for identifying a latent motion subspace, i.e., a keyframe encoding stage and an intermediate token generation stage, and a subsequent motion synthesis stage to extrapolate and compose motion data from manifolds. Through our extensive experiments conducted on both the LaFAN1 and CMU Mocap datasets, our proposed method demonstrates both superior interpolation accuracy and high visual similarity to ground truth motions.Comment: Accepted by CVPR 202

No-reference Point Cloud Geometry Quality Assessment Based on Pairwise Rank Learning

Objective geometry quality assessment of point clouds is essential to evaluate the performance of a wide range of point cloud-based solutions, such as denoising, simplification, reconstruction, and watermarking. Existing point cloud quality assessment (PCQA) methods dedicate to assigning absolute quality scores to distorted point clouds. Their performance is strongly reliant on the quality and quantity of subjective ground-truth scores for training, which are challenging to gather and have been shown to be imprecise, biased, and inconsistent. Furthermore, the majority of existing objective geometry quality assessment approaches are carried out by full-reference traditional metrics. So far, point-based no-reference geometry-only quality assessment techniques have not yet been investigated. This paper presents PRL-GQA, the first pairwise learning framework for no-reference geometry-only quality assessment of point clouds, to the best of our knowledge. The proposed PRL-GQA framework employs a siamese deep architecture, which takes as input a pair of point clouds and outputs their rank order. Each siamese architecture branch is a geometry quality assessment network (GQANet), which is designed to extract multi-scale quality-aware geometric features and output a quality index for the input point cloud. Then, based on the predicted quality indexes, a pairwise rank learning module is introduced to rank the relative quality of a pair of degraded point clouds.Extensive experiments demonstrate the effectiveness of the proposed PRL-GQA framework. Furthermore, the results also show that the fine-tuned no-reference GQANet performs competitively when compared to existing full-reference geometry quality assessment metrics

The co-transfer of plasmid-borne colistin-resistant genes mcr-1 and mcr-3.5, the carbapenemase gene blaNDM-5 and the 16S methylase gene rmtB from Escherichia coli

We found an unusual Escherichia coli strain with resistance to colistin, carbapenem and amikacin from sewage. We therefore characterized the strain and determined the co-transfer of the resistance determinants. Whole genome sequencing was performed using both Illumina HiSeq X10 and MinION sequencers. Short and long reads were subjected to de novo hybrid assembly. Sequence type, antimicrobial resistance genes and plasmid replicons were identified from the genome sequences. Phylogenetic analysis of all IncHI2 plasmids carrying mcr-1 available in GenBank was performed based on core genes. Conjugation experiments were performed. mcr-3.5 was cloned into E. coli DH5α. The strain belonged to ST410, a type with a global distribution. Two colistin-resistant genes, mcr-1.1 and mcr-3.5, a carbapenemase gene blaNDM-5, and a 16S methylase gene rmtB were identified on different plasmids of IncHI2(ST3)/IncN, IncP, IncX3 and IncFII, respectively. All of the four plasmids were self-transmissible and mcr-1.1, mcr-3.5, blaNDM-5 and rmtB were transferred together. mcr-1-carrying IncHI2 plasmids belonged to several sequence types with ST3 and ST4 being predominant. MIC of colistin (4 μg/ml) for DH5α containing mcr-3.5 was identical to that containing the original mcr-3 variant. In conclusion, carbapenem resistance, colistin resistance and high-level aminoglycoside resistance can be transferred together even when their encoding genes are not located on the same plasmid. The co-transfer of multiple clinically-important antimicrobial resistance represents a particular challenge for clinical treatment and infection control in healthcare settings. Isolates with resistance to both carbapenem and colistin are not restricted to a given sequence type but rather are diverse in clonal background, which warrants further surveillance. The amino acid substitutions of MCR-3.5 have not altered its activity against colistin.</p

Investigation and validation of algorithms for estimating land surface temperature from Sentinel-3 SLSTR data

Land surface temperature (LST) is an important indicator of global ecological environment and climate change. The Sea and Land Surface Temperature Radiometer (SLSTR) onboard the recently launched Sentinel-3 satellites provides high-quality observations for estimating global LST. The algorithm of the official SLSTR LST product is a split-window algorithm (SWA) that implicitly assumes and utilizes knowledge of land surface emissivity (LSE). The main objective of this study is to investigate alternative SLSTR LST retrieval algorithms with an explicit use of LSE. Seventeen widely accepted SWAs, which explicitly utilize LSE, were selected as candidate algorithms. First, the SWAs were trained using a comprehensive global simulation dataset. Then, using simulation data as well as in-situ LST, the SWAs were evaluated according to their sensitivity and accuracy: eleven algorithms showed good training accuracy and nine of them exhibited low sensitivity to uncertainties in LSE and column water vapor content. Evaluation based on two global simulation datasets and a regional simulation dataset showed that these nine SWAs had similar accuracy with negligible systematic errors and RMSEs lower than 1.0 K. Validation based on in-situ LST obtained for six sites further confirmed the similar accuracies of the SWAs, with the lowest RMSE ranges of 1.57–1.62 K and 0.49−0.61 K for Gobabeb and Lake Constance, respectively. While the best two SWAs usually yielded good accuracy, the official SLSTR LST generally had lower accuracy. The SWAs identified and described in this study may serve as alternative algorithms for retrieving LST products from SLSTR data

Cross-Domain Personalized Learning Resources Recommendation Method

According to cross-domain personalized learning resources recommendation, a new personalized learning resources recommendation method is presented in this paper. Firstly, the cross-domain learning resources recommendation model is given. Then, a method of personalized information extraction from web logs is designed by making use of mixed interest measure which is presented in this paper. Finally, a learning resources recommendation algorithm based on transfer learning technology is presented. A time function and the weight constraint of wrong classified samples can be added to the classic TrAdaBoost algorithm. Through the time function, the importance of samples date can be distinguished. The weight constraint can be used to avoid the samples having too big or too small weight. So the Accuracy and the efficiency of algorithm are improved. Experiments on the real world dataset show that the proposed method could improve the quality and efficiency of learning resources recommendation services effectively

ST273 carbapenem-resistant <i>Klebsiella pneumoniae</i> carrying <i>bla</i>_NDM-1 and <i>bla</i>_IMP-4

ABSTRACT A carbapenem-resistant Klebsiella pneumoniae isolate was recovered from human blood. Its whole-genome sequence was obtained using Illumina and long-read MinION sequencing. The strain belongs to sequence type 273 (ST273), which was found recently and caused an outbreak in Southeast Asia. It has two carbapenemase genes, bla NDM-1 (carried by an ST7 IncN self-transmissible plasmid) and bla IMP-4 (located on a self-transmissible IncHI5 plasmid). Non-KPC-producing ST237 may represent a lineage of carbapenem-resistant K. pneumoniae , which warrants further monitoring. </jats:p