A Novel Image Recognition Method Based on DenseNet and DPRN

Image recognition is one of the important branches of computer vision, which has important theoretical and practical significance. For the insufficient use of features, the single type of convolution kernel and the incomplete network optimization problems in densely connected networks (DenseNet), a novel image recognition method based on DenseNet and deep pyramidal residual networks (DPRN) is proposed in this paper. In the proposed method, a new residual unit based on DPRN is designed, and the idea of a pyramid residual unit is introduced, which makes the input greater than the output. Then, a module based on dilated convolution is designed for parallel feature extraction. Finally, the designed module is fused with DenseNet in order to construct the image recognition model. This model not only overcomes some of the existing problems in DenseNet, but also has the same general applicability as DensenNet. The CIFAR10 and CIFAR100 are selected to prove the effectiveness of the proposed method. The experiment results show that the proposed method can effectively reuse features and has obtained accuracy rates of 83.98 and 51.19%, respectively. It is an effective method for dealing with images in different fields

A Novel Image Recognition Method Based on DenseNet and DPRN

Image recognition is one of the important branches of computer vision, which has important theoretical and practical significance. For the insufficient use of features, the single type of convolution kernel and the incomplete network optimization problems in densely connected networks (DenseNet), a novel image recognition method based on DenseNet and deep pyramidal residual networks (DPRN) is proposed in this paper. In the proposed method, a new residual unit based on DPRN is designed, and the idea of a pyramid residual unit is introduced, which makes the input greater than the output. Then, a module based on dilated convolution is designed for parallel feature extraction. Finally, the designed module is fused with DenseNet in order to construct the image recognition model. This model not only overcomes some of the existing problems in DenseNet, but also has the same general applicability as DensenNet. The CIFAR10 and CIFAR100 are selected to prove the effectiveness of the proposed method. The experiment results show that the proposed method can effectively reuse features and has obtained accuracy rates of 83.98 and 51.19%, respectively. It is an effective method for dealing with images in different fields

Elucidating the size-dependency of in vitro digested polystyrene microplastics on human intestinal cells health and function

The prevalence of microplastics (MPs) contamination in a broad spectrum of potable water sources has raised significant environmental and public health concerns. While evidence of ingested MPs bioaccumulation in the gastrointestinal tract (GIT) of aquatic and terrestrial organisms is mounting, the understanding of the effects of MPs on human gastrointestinal health remains scant. Herein, the potential deleterious biological effects of pristine and in vitro digested polystyrene (PS) MPs of varying sizes (i.e., 0.1, 1, and 10 µm) are systematically examined over a wide concentration range of 25–400 µg mL−1 on two human intestinal cell lines, namely Caco-2 and NCM 460. Specifically, significant internalization of 0.1 and 1 µm PS -MPs have been observed in both cell types 24 h postexposure. However, multiparametric dose and time-dependent analysis encompassing cell viability, reactive oxygen species (ROS), and nutrient absorption/metabolism measurement revealed no significant adversarial outcomes. Interestingly, it is found that the 0.1 µm PS-MPs can perturb redox homeostasis in NCM460 but not in Caco-2 cells. Based on the in vitro experimental boundaries and findings, it is concluded that ingested PS-MPs pose little acute cytotoxic harm to human gastrointestinal health

Inflammation Increases Susceptibility of Human Small Airway Epithelial Cells to Pneumonic Nanotoxicity

Exposure to inhaled anthropogenic nanomaterials (NM) with dimension <100 nm has been implicated in numerous adverse respiratory outcomes. Although studies have identified key NM physiochemical determinants of pneumonic nanotoxicity, the complex interactive and cumulative effects of NM exposure, especially in individuals with preexisting inflammatory respiratory diseases, remain unclear. Herein, the susceptibility of primary human small airway epithelial cells (SAEC) exposed to a panel of reference NM, namely, CuO, ZnO, mild steel welding fume (MSWF), and nanofractions of copier center particles (Nano-CCP), is examined in normal and tumor necrosis factor alpha (TNF-α)-induced inflamed SAEC. Compared to normal SAEC, inflamed cells display an increased susceptibility to NM-induced cytotoxicity by 15-70% due to a higher basal level of intracellular reactive oxygen species (ROS). Among the NM screened, ZnO, CuO, and Nano-CCP are observed to trigger an overcompensatory response in normal SAEC, resulting in an increased tolerance against subsequent oxidative insults. However, the inflamed SAEC fails to adapt to the NM exposure due to an impaired nuclear factor erythroid 2-related factor 2 (Nrf2)-mediated cytoprotective response. The findings reveal that susceptibility to pulmonary nanotoxicity is highly dependent on the interplay between NM properties and inflammation of the alveolar milieu.Nanyang Technological UniversityAccepted versionZ.W. and P.S. contributed equally to this work. The authors gratefully acknowledge support by the Nanyang Technological University—Harvard School of Public Health Initiative for Sustainable Nanotechnology (NTU-Harvard SusNano; NTU-Harvard Initiative for Sustainable Nanotechnology seed grant, reference number NTU-HSPH 18002). Engineered nanomaterials used in the research presented in this publication were synthesized, characterized, and provided by the Engineered Nanomaterials Resource and Coordination Core established at Harvard T. H. Chan School of Public Health (NIH grant # U24ES026946) as part of the Nanotechnology Health Implications Research (NHIR) Consortium