Identification of white degradable and non-degradable plastics in food field: A dynamic residual network coupled with hyperspectral technology.

In the food field, with the improvement of people's health and environmental protection awareness, degradable plastics have become a trend to replace non-degradable plastics. However, their appearance is very similar, making it difficult to distinguish them. This work proposed a rapid identification method for white non-degradable and degradable plastics. Firstly, a hyperspectral imaging system was used to collect the hyperspectral images of the plastics in visible and near-infrared bands (380-1038 nm). Secondly, a residual network (ResNet) was designed according to the characteristics of hyperspectral information. Finally, a dynamic convolution module was introduced into the ResNet to establish a dynamic residual network (Dy-ResNet) to adaptively mine the data features and realize the classification of the degradable and non-degradable plastics. Dy-ResNet had better classification performance than the other classical deep learning methods. The classification accuracy of the degradable and non-degradable plastics was 99.06%. In conclusion, hyperspectral imaging technology was combined with Dy-ResNet to identify the white non-degradable and degradable plastics effectively.

KLF4 upregulation is involved in alternative macrophage activation during secondary Echinococcus granulosus infection.

The objective of this study was to investigate macrophage polarization during the early stages of secondary Echinococcus granulosus sensu lato (E. granulosus s.l.) infection. We observed an early initial increase in inflammatory genes (peaking at 5-10 days) and a later rise in M (IL-4)-like genes (still rising by day 15). In addition, we showed that the induction of M (IL-4)-like genes was paralleled by an increase in expression of the transcription factor KLF4. Most of the changes observed in vivo were reproduced in vitro upon the culture of normal peritoneal macrophages with live E. granulosus s.l. protoscoleces (PSC), and that knockdown of KLF4 in this system attenuates M (IL-4) differentiation. Our results suggest that KLF4 pathway contributes to the differentiation of macrophages towards M (IL-4)-like phenotype during early stages of secondary E. granulosus s.l. infection.

Highly sensitive dual-core photonic crystal fiber based on a surface plasmon resonance sensor with a silver nano-continuous grating.

Two kinds of photonic crystal fiber (PCF) sensors based on surface plasmon resonance (SPR) with silver nano-continuous gratings (i) and (ii) are designed. The coupling characteristics and sensing properties are analyzed numerically by the finite element method (FEM). The results show that the proposed sensor based on silver nano-continuous grating (i) can achieve better performance than that of the sensors based on silver nano-continuous grating (ii) and plane silver film structures. When the segmented number is 50 and segmented angle is 0.5°, a wavelength sensitivity of the proposed sensor with silver nano-continuous grating (i) is obtained as high as 13,600 nm/RIU in the refractive index (RI) range from 1.330 to 1.365, corresponding to a maximum RI resolution of 7.35×10-6 RIU, which can have promising applications in medical and environmental monitoring and biochemical detection.

Odor Fingerprint Analysis Using Feature Mining Method Based on Olfactory Sensory Evaluation.

In this paper, we aim to use odor fingerprint analysis to identify and detect various odors. We obtained the olfactory sensory evaluation of eight different brands of Chinese liquor by a lab-developed intelligent nose. From the respective combination of the time domain and frequency domain, we extract features to reflect the samples comprehensively. However, the extracted feature combined time domain and frequency domain will bring redundant information that affects performance. Therefore, we proposed data by Principal Component Analysis (PCA) and Variable Importance Projection (VIP) to delete redundant information to construct a more precise odor fingerprint. Then, Random Forest (RF) and Probabilistic Neural Network (PNN) were built based on the above. Results showed that the VIP-based models achieved better classification performance than PCA-based models. In addition, the peak performance (92.5%) of the VIP-RF model had a higher classification rate than the VIP-PNN model (90%). In conclusion, odor fingerprint analysis using a feature mining method based on the olfactory sensory evaluation can be applied to monitor product quality in the actual process of industrialization.