Identification of Defective Maize Seeds Using Hyperspectral Imaging Combined with Deep Learning.

Seed quality affects crop yield and the quality of agricultural products, and traditional identification methods are time-consuming, complex, and irreversibly destructive. This study aims to establish a fast, non-destructive, and effective approach for defect detection in maize seeds based on hyperspectral imaging (HSI) technology combined with deep learning. Raw spectra collected from maize seeds (200 each healthy and worm-eaten) were pre-processed using detrending (DE) and multiple scattering correction (MSC) to highlight the spectral differences between samples. A convolutional neural network architecture (CNN-FES) based on a feature selection mechanism was proposed according to the importance of wavelength in the target classification task. The results show that the subset of 24 feature wavelengths selected by the proposed CNN-FES can capture important feature information in the spectral data more effectively than the conventional successive projections algorithm (SPA) and competitive adaptive reweighted sampling (CARS) algorithms. In addition, a convolutional neural network architecture (CNN-ATM) based on an attentional classification mechanism was designed for one-dimensional spectral data classification and compared with three commonly used machine learning methods, linear discriminant analysis (LDA), random forest (RF), and support vector machine (SVM). The results show that the classification performance of the designed CNN-ATM on the full wavelength does not differ much from the above three methods, and the classification accuracy is above 90% on both the training and test sets. Meanwhile, the accuracy, sensitivity, and specificity of CNN-ATM based on feature wavelength modeling can reach up to 97.50%, 98.28%, and 96.77% at the highest, respectively. The study shows that hyperspectral imaging-based defect detection of maize seed is feasible and effective, and the proposed method has great potential for the processing and analysis of complex hyperspectral data.

The Role of Tet2-mediated Hydroxymethylation in Poststroke Depression.

Poststroke depression (PSD) is a common complication of stroke and has long been a serious threat to human health. PSD greatly affects neurological recovery, quality of life and mortality. Recent studies have shown that 5-hydroxymethylcytosine (5hmC), an important epigenetic modification, is enriched in the brain and associated with many neurological diseases. However, its role in PSD is still unclear. In this study, middle cerebral artery occlusion (MCAO) and spatial restraint stress were used to successfully induce a PSD mouse model and resulted in reduced 5hmC levels, which were caused by Tet2. Furthermore, genome-wide analysis of 5hmC revealed that differentially hydroxymethylated regions (DhMRs) were associated with PSD. DhMRs were enriched among genes involved in the Wnt signaling pathway, neuron development and learning or memory. In particular,DhMRs were strongly enriched in genes with lymphoid enhancer factor 1 (LEF1) binding motifs. Finally, we demonstrated that decreases in TET2 expression in the brain caused PSD by decreasing Wnt/ß-catenin/LEF1 pathway signaling to promote inflammatory factor IL-18 expression. In conclusion, our data highlight the potential for 5hmC modification as a therapeutic target for PSD.