Efficient residual network using hyperspectral images for corn variety identification

Corn seeds are an essential element in agricultural production, and accurate identification of their varieties and quality is crucial for planting management, variety improvement, and agricultural product quality control. However, more than traditional manual classification methods are needed to meet the needs of intelligent agriculture. With the rapid development of deep learning methods in the computer field, we propose an efficient residual network named ERNet to identify hyperspectral corn seeds. First, we use linear discriminant analysis to perform dimensionality reduction processing on hyperspectral corn seed images so that the images can be smoothly input into the network. Second, we use effective residual blocks to extract fine-grained features from images. Lastly, we detect and categorize the hyperspectral corn seed images using the classifier softmax. ERNet performs exceptionally well compared to other deep learning techniques and conventional methods. With 98.36% accuracy rate, the result is a valuable reference for classification studies, including hyperspectral corn seed pictures

Improved Glucose and Lipid Metabolism in the Early Life of Female Offspring by Maternal Dietary Genistein Is Associated With Alterations in the Gut Microbiota

Maternal over-nutrition can lead to metabolic disorders in offspring, whereas maternal dietary genistein may have beneficial effects on the metabolic health of offspring. Our objective was to determine whether maternal dietary genistein could attenuate the detrimental effects of a maternal high-fat diet on their offspring's metabolism and to explore the role of the gut microbiota on their offspring's glucose and lipid metabolism. C57BL/6 female mice were fed either a high-fat diet without genistein (HF), high-fat diet with low-dose genistein (0.25 g/kg diet) (HF.LG), high-fat diet with high-dose genistein (0.6 g/kg diet) (HF.HG) or normal control diet (Control) for 3 weeks prior to breeding and throughout gestation and lactation. The female offspring in the HF group had lower birth weights and glucose intolerance and higher serum insulin, triacylglycerol (TG) and total cholesterol (TC) levels at weaning compared with the Control group. Offspring from HF.LG dams had increased birth weight, improved glucose tolerance, and decreased fasting insulin, whereas the serum TG and TC levels were decreased in HF.HG offspring in comparison with HF offspring. The significant enrichment of Bacteroides and Akkermansia in offspring from genistein-fed dams might play vital roles in improving glucose homeostasis and insulin sensitivity, and the significantly increased abundance of Rikenella and Rikenellaceae_RC9_ gut_group in the HF.HG group may be associated with the decreased serum levels of TG and TC. In conclusion, maternal dietary genistein negates the harmful effects of a maternal high-fat diet on glucose and lipid metabolism in female offspring, in which the altered gut microbiota plays crucial roles. The ability of maternal genistein intake to improve offspring metabolism is important since this intervention could fight the transmission of diabetes to subsequent generations

The extended Heine-Stieltjes polynomials associated with a special LMG model

New polynomials associated with a special Lipkin-Meshkov-Glick (LMG) model corresponding to the standard two-site Bose-Hubbard model are derived based on the Stieltjes correspondence. It is shown that there is a one-to-one correspondence between zeros of this new polynomial and solutions of the Bethe ansatz equations for the LMG model.A one-dimensional classical electrostatic analogue corresponding to the special LMG model is established according to Stieltjes early work. It shows that any possible configuration of equilibrium positions of the charges in the electrostatic problem corresponds uniquely to one set of roots of the Bethe ansatz equations for the LMG model, and the number of possible configurations of equilibrium positions of the charges equals exactly to the number of energy levels in the LMG model. Some relations of sums of powers and inverse powers of zeros of the new polynomials related to the eigenenergies of the LMG model are derived.Comment: 11 pages, LaTe

IAGC: Interactive Attention Graph Convolution Network for Semantic Segmentation of Point Clouds in Building Indoor Environment

Point-based networks have been widely used in the semantic segmentation of point clouds owing to the powerful 3D convolution neural network (CNN) baseline. Most of the current methods resort to intermediate regular representations for reorganizing the structure of point clouds for 3D CNN networks, but they may neglect the inherent contextual information. In our work, we focus on capturing discriminative features with the interactive attention mechanism and propose a novel method consisting of the regional simplified dual attention network and global graph convolution network. Firstly, we cluster homogeneous points into superpoints and construct a superpoint graph to effectively reduce the computation complexity and greatly maintain spatial topological relations among superpoints. Secondly, we integrate cross-position attention and cross-channel attention into a single head attention module and design a novel interactive attention gating (IAG)-based multilayer perceptron (MLP) network (IAG–MLP), which is utilized for the expansion of the receptive field and augmentation of discriminative features in local embeddings. Afterwards, the combination of stacked IAG–MLP blocks and the global graph convolution network, called IAGC, is proposed to learn high-dimensional local features in superpoints and progressively update these local embeddings with the recurrent neural network (RNN) network. Our proposed framework is evaluated on three indoor open benchmarks, and the 6-fold cross-validation results of the S3DIS dataset show that the local IAG–MLP network brings about 1% and 6.1% improvement in overall accuracy (OA) and mean class intersection-over-union (mIoU), respectively, compared with the PointNet local network. Furthermore, our IAGC network outperforms other CNN-based approaches in the ScanNet V2 dataset by at least 7.9% in mIoU. The experimental results indicate that the proposed method can better capture contextual information and achieve competitive overall performance in the semantic segmentation task

Estimating and mapping the dynamics of soil salinity under different crop types using Sentinel-2 satellite imagery

Soil salinization is one of the main factors contributing to land degradation, affecting ecological equilibrium, environmental health, and the sustainable development of agriculture. Due to the spatial and temporal heterogeneity of soil properties and environmental conditions in a large-scale region, the monitoring accuracy of soil salinization can be challenging. This study investigated whether the classification of diverse crop types on a time series can improve the prediction accuracy of regional soil salinity levels. Specifically, we evaluated the changes in soil salt content (SSC) under diverse vegetation cover over time in the Hetao Irrigation District (HID) using multi-phase Sentinel-2 imagery and ground-truth data collected from June to September 2021 and 2022. Focused on sunflower and maize fields, this study analyzed the impact of classifying these two crop types and examining four distinct time series on the accuracy of SSC estimation. Five indices were selected as characteristic parameters from a pool of 17 vegetation indices (VIs) and 13 soil salinity indices (SIs) derived from satellite images. Moreover, three machine learning algorithms were used to establish SSC estimation models. The findings underscored the efficacy of classifying crop types and considering different time series in enhancing the response sensitivity of spectral indices to SSC and improving modeling accuracy. Among the spectral indices, VIs made more contributions to the SSC estimation model than SIs, achieving the highest coefficient of determination (R2) of 0.71. The artificial neural networks algorithm outperformed the other two machine learning algorithms in terms of accuracy and stability, yielding an optimal R2 of 0.72 and a Root Mean Square Error (RMSE) of 0.15%. This study proposed a modeling and mapping approach that considers crop types and various time series, offering valuable insights for accurately assessing soil salinization, guiding strategies for its prevention and remediation

Evaluation of the Strain Bacillus amyloliquefaciens YP6 in Phoxim Degradation via Transcriptomic Data and Product Analysis

Phoxim, a type of organophosphorus pesticide (OP), is widely used in both agriculture and fisheries. The persistence of phoxim has caused serious environmental pollution problems. In this study, Bacillus amyloliquefaciens YP6 (YP6), which is capable of promoting plant growth and degrading broad-spectrum OPs, was used to study phoxim degradation. Different culture media were applied to evaluate the growth and phoxim degradation of YP6. YP6 can grow rapidly and degrade phoxim efficiently in Luria–Bertani broth (LB broth) medium. Furthermore, it can also utilize phoxim as the sole phosphorus source in a mineral salt medium. Response surface methodology was performed to optimize the degradation conditions of phoxim by YP6 in LB broth medium. The optimum biodegradation conditions were 40 °C, pH 7.20, and an inoculum size of 4.17% (v/v). The phoxim metabolites, O,O-diethylthiophosphoric ester, phoxom, and α-cyanobenzylideneaminooxy phosphonic acid, were confirmed by liquid chromatography–mass spectrometry. Meanwhile, transcriptome analysis and qRT-PCR were performed to give insight into the phoxim-stress response at the transcriptome level. The hydrolase-, oxidase-, and NADPH-cytochrome P450 reductase-encoding genes were significantly upregulated for phoxim hydrolysis, sulfoxidation, and o-dealkylation. Furthermore, the phoxim biodegradation pathways by YP6 were proposed, for the first time, based on transcriptomic data and product analysis

The association between sleep duration and physical performance in Chinese community-dwelling elderly.

BACKGROUND:Physical performance is an important healthy factor in elder people. Good living habits, which include sleep, can maintain physical strength and physical performance. The aim of the present study was to conduct a cross-sectional study to determine the association between total sleep duration and physical performance. METHODS:Our study population comprised residents of the township central hospital in the suburban of Tianjin, China. We measured muscle strength, walk speed and balance function by grip, 4-m walk test and timed up and go test (TUGT). We divided sleep duration into four groups 8-9h, >9h. RESULTS:A total 898 participants had completed data (392 men and 506 women, mean age 67.71 years). In man, adjusted sleep duration was associated with lower grip in > 9 h group, the mean value (95% CI) was 0.429 (0.409, 0.448), and longer TUGT time was also associated with long sleep duration, 10.46s (9.97 s, 10.95 s). In women, adjusted slower 4-m walk speed present an inverse U-shaped relation with sleep duration, by 0.93 m/s (0.86 m/s, 0.98 m/s), 0.97 m/s (0.96 m/s, 1.00 m/s), 0.97 m/s (0.95 m/s, 0.99 m/s) and 0.92 m/s (0.89 m/s, 0.96 m/s); longer TUGT time were associated with long sleep duration (> 9 h), by 11.23 s (10.70 s, 11.77 s). CONCLUSION:In Chinese community-dwelling elderly, lower muscle strength and lower balance function were associated with long sleep duration in men. Slower walk speed and lower balance function were associated with long sleep duration in women

Dietary Genistein Could Modulate Hypothalamic Circadian Entrainment, Reduce Body Weight, and Improve Glucose and Lipid Metabolism in Female Mice

Genistein has beneficial effects on metabolic disorders. However, the specific mechanism is not clearly understood. In light of the significant role of the hypothalamus in energy and metabolic homeostasis, this study was designed to explore whether dietary genistein intake could mitigate the harmful effects of a high-fat diet on glucose and lipid metabolism and whether any alterations caused by dietary genistein were associated with hypothalamic gene expression profiles. C57BL/6 female mice were fed a high-fat diet without genistein (HF), a high-fat diet with genistein (HFG), or a normal control diet (CON) for 8 weeks. Body weight and energy intake were assessed. At the end of the study, glucose tolerance and serum levels of insulin and lipids were analyzed. Hypothalamic tissue was collected for whole transcriptome sequencing and reverse transcription quantitative PCR (RT-qPCR) validation. Energy intake and body weight were significantly reduced in the mice of the HFG group compared with those of the HF group. Mice fed the HFG diet had improved glucose tolerance and decreased serum triacylglycerol, free fatty acids, and low-density lipoprotein cholesterol compared with those fed the HF diet. The HFG diet also modulated gene expression in the hypothalamus; the most abundant genes were enriched in the circadian entrainment pathway. Dietary genistein intake could reduce body weight, improve glucose and lipid metabolism, and regulate hypothalamic circadian entrainment. The ability of genistein intake to influence regulation of the hypothalamic circadian rhythm is important since this could provide a novel target for the treatment of obesity and diabetes