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Pumpkin seeds are nutritious and have some medicinal value. However, the mold and sprouting are produced during the storage of pumpkin seeds. Food safety and quality problems may be caused if they are not removed in time for processing. The traditional testing methods are cumbersome to operate, complex, and destructive in sample preparation. Therefore, terahertz time‐domain spectroscopy (THz‐TDS) technology was proposed to achieve the detection of the internal quality of pumpkin seeds. Firstly, samples of pumpkin seeds of different qualities were crafted, and they were moldy for 3 days, moldy for 6 days, sprouted and moldy, sprouted and normal pumpkin seeds, respectively. Then, the pumpkin seeds of different qualities were dried, ground, and pressed, and their spectral data were collected. The terahertz spectra of the five types of samples were significantly different. The support vector machine (SVM), random forest (RF), and convolutional neural network (CNN) qualitative discriminant models were established with the raw absorbance spectral data, the preprocessed absorbance spectral data, and the preprocessed and band‐screened absorbance spectral data, respectively, where the CNN model based on the raw spectral data has the highest classification accuracy of 96%. The CNN models do not require advance spectral data processing, simplifying the spectral analysis process. And it achieves best classification results in the accuracy of detection compared to traditional chemometric models. The CNN combined with THz‐TDS method has great potential for application in the detection of agricultural products. It provides a new detection method for the field of quality detection of agricultural products. In this paper, a terahertz time‐domain spectroscopy system is utilized to detect pumpkin seeds. Firstly, the collected terahertz spectral data are preprocessed and band filtered. Then, traditional machine learning methods SVM, RF and deep learning method CNN are used to build the model respectively. Among them, the CNN model built based on the original data has the best classification effect, with a classification accuracy of 96%. The CNN model achieves better classification results compared with the traditional chemometrics model. [ABSTRACT FROM AUTHOR]

In order to reduce the low accuracy of the prediction model of soluble solid content caused by the size difference of gongpear, a general size model was proposed. Near-infrared diffuse reflection spectroscopy and partial least squares regression algorithm modeling method of the near-infrared spectrum were adopted, and the theoretical analysis and experimental verification were respectively carried out. Gongpear soluble solids content of data were obtained by a local size prediction model of the small fruit, middle fruit and big fruit size grades and a universal size model with different size grade. The results show that, the local size model is good at predicting the soluble solid content of gongpears with its own grade, but poor at predicting other grades. The correlation coefficient were 0.892, 0.937, 0.889 and root mean square error of small fruit, medium fruit and large fruit predicted by the general model were 0.524, 0.417, 0.551. The general size model could reduce the adverse effects of size difference and was suitable for the determination of soluble solid content of gongpear with different size grades. [ABSTRACT FROM AUTHOR]

Xeroderma pigmentosum complementation group C (XPC) protein recognizes bulky DNA adducts to initiate global genomic nucleotide excision repair (GG-NER). Humans carrying germline mutations in the XPC gene display strong susceptibility to skin and certain internal cancers. In addition to its role in NER, recent studies have indicated that XPC is also involved in other DNA damage repair pathways and transcription regulation. In this report, we generated a zebrafish xpc knockout mutant. Zebrafish xpc−/− mutant fish develop relative normally and are fertile. However, the mutant embryos were more sensitive to ultraviolet (UV) irradiation. Upon UV irradiation, compared with the wild type embryos, mutant embryos accumulated significantly higher levels of unrepaired DNA damages and apoptotic cells, which led to more severe abnormal development. Transcriptome analysis showed that the p53 signal pathway and apoptosis were enriched in the over upregulated genes in UV-irradiated mutant embryos, suggesting that high levels of unrepaired DNA lesions activated p53 to trigger apoptotic activity in mutant embryos. More interestingly, up to 972 genes in the untreated mutant embryos were differentially expressed, compared with those in the untreated WT. Among these differentially expressed genes (DEGs), 379 genes did not respond to UV irradiation, indicating that Xpc plays a role in addition of DNA damage repair. Our results demonstrate that Xpc is an evolutionally conserved factor in NER repair. Zebrafish xpc−/− mutant also provides a platform to study other functions of Xpc beyond the DNA damage repair. [ABSTRACT FROM AUTHOR]

Background and objectives Skin defects are one of the primary problems that occur in post-harvest grading and processing of loquats. Skin defects lead to the loquat being easily destroyed during transportation and storage, which causes the risk of other loquats being infected, affecting the selling price. Materials and Methods In this paper, a method combining band radio image with an improved three-phase level set segmentation algorithm (ITPLSSM) is proposed to achieve high accuracy, rapid, and non-destructive detection of skin defects of loquats. Principal component analysis (PCA) was used to find the characteristic wavelength and PC images to distinguish four types of skin defects. The best band ratio image based on characteristic wavelength was determined. Results The band ratio image (Q782/944) based on PC2 image is the best segmented image. Based on pseudo-color image enhancement, morphological processing, and local clustering criteria, the band ratio image (Q782/944) has better contrast between defective and normal areas in loquat. Finally, the ITPLSSM was used to segment the processing band ratio image (Q782/944), with an accuracy of 95.28%. Conclusions The proposed ITPLSSM method is effective in distinguishing four types of skin defects. Meanwhile, it also effectively segments images with intensity inhomogeneities. [ABSTRACT FROM AUTHOR]

The zebrafish intestine and liver, as in other vertebrates, are derived from the endoderm. Great effort has been devoted to deciphering the molecular mechanisms controlling the specification and development of the zebrafish intestine and liver; however, genome-wide comparison of the transcriptomes between these two organs at the larval stage remains unexplored. There is a lack of extensive identification of feature genes marking specific cell types in the zebrafish intestine and liver at 5 days post-fertilization, when the larval fish starts food intake. In this report, through RNA sequencing and single-cell RNA sequencing of intestines and livers separately dissected from wild-type zebrafish larvae at 5 days post-fertilization, together with the experimental validation of 47 genes through RNA whole-mount in situ hybridization, we identified not only distinctive transcriptomes for the larval intestine and liver, but also a considerable number of feature genes for marking the intestinal bulb, mid-intestine and hindgut, and for marking hepatocytes and cholangiocytes. Meanwhile, we identified 135 intestine- and 97 liver-enriched transcription factor genes in zebrafish larvae at 5 days post-fertilization. Our findings provide rich molecular and cellular resources for studying cell patterning and specification during the early development of the zebrafish intestine and liver. [ABSTRACT FROM AUTHOR]

Bruising is one of the main problems in the post-harvest grading and processing of 'Zaozhong 6' loquats, reducing the economic value of loquats, and even food quality and safety problems are caused by it. Therefore, one of the main tasks in the post-harvest processing of loquats is to detect whether loquats are bruised, as well as the degree of bruising of loquats, to reduce the loss by proper treatment. An appropriate dimensionality reduction method can be used to reduce the redundancy of variables and improve the detection speed. The multispectral analysis method (MAM) has the advantage of accurate, rapid, and nondestructive detection, which was proposed to identify the different bruising degrees of loquats in this study. Firstly, the visible and near-infrared region (Vis–NIR, 400–1000 nm), the visible region (Vis, 400–780 nm), and the near-infrared region (NIR, 781–1000 nm) were analyzed using principal component analysis (PCA) to obtain the spectral regions and PC vectors, which could be used to effectively distinguish bruised loquats from normal loquats. Then, based on the selected second PC (PC2) score images, a morphological segmentation method (MSM) was proposed to distinguish bruised loquats from normal loquats. Furthermore, the weight coefficients of corresponding wavelength points of different degrees of bruising of loquats were analyzed, and the local extreme points and both sides of the interval were selected as the characteristic wavelength points for multi-spectral image processing. A gray level co-occurrence matrix (GLCM) was used to extract texture features and gray information from two-band ratio images K782/999. Finally, the MAM was proposed to detect the degree of bruising of loquats, which included the spectral data of three characteristic wavelength points in the NIR region coupled with texture features of the two-band ratio images, and the classification accuracy was 91.3%. This study shows that the MAM can be used as an effective dimensionality reduction method. The method not only improves the effect of prediction but also simplifies the process of prediction and ensures the accuracy of classification. The MSM can be used for rapid detection of normal and bruised fruits, and the MAM can be used to classify the degree of bruising of bruised fruits. Consequently, the processed methods are effective and can be used for the rapid and nondestructive detection of the degree of bruising of fruit. [ABSTRACT FROM AUTHOR]