Dynamic Serpentine Convolution with Attention Mechanism Enhancement for Beef Cattle Behavior Recognition.

Behavior recognition in beef cattle is a crucial component of beef cattle behavior warning and intelligent farming. Traditional beef cattle behavior recognition faces challenges in both difficulty in identification and low accuracy. In this study, the YOLOv8n_BiF_DSC (Fusion of Dynamic Snake Convolution and BiFormer Attention) algorithm was employed for the non-intrusive recognition of beef cattle behavior. The specific steps are as follows: 45 beef cattle were observed using a fixed camera (A LINE OF DEFENSE) and a mobile phone (Huawei Mate20Pro) to collect and filter posture data, yielding usable videos ranging from 1 to 30 min in length. These videos cover nine different behaviors in various scenarios, including standing, lying, mounting, fighting, licking, eating, drinking, walking, and searching. After data augmentation, the dataset comprised 34,560 samples. The convolutional layer (CONV) was improved by introducing variable convolution and dynamic snake-like convolution modules. The dynamic snake-like convolution, which yielded the best results, expanded the model's receptive field, dynamically perceived key features of beef cattle behavior, and enhanced the algorithm's feature extraction capability. Attention mechanism modules, including SE (Squeeze-and-Excitation Networks), CBAM (Convolutional Block Attention Module), CA (Coordinate Attention), and BiFormer (Vision Transformer with Bi-Level Routing Attention), were introduced. The BiFormer attention mechanism, selected for its optimal performance, improved the algorithm's ability to capture long-distance context dependencies. The model's computational efficiency was enhanced through dynamic and query-aware perception. Experimental results indicated that YOLOv8n_BiF_DSC achieved the best results among all improved algorithms in terms of accuracy, average precision at IoU 50, and average precision at IoU 50:95. The accuracy of beef cattle behavior recognition reached 93.6%, with the average precision at IoU 50 and IoU 50:95 being 96.5% and 71.5%, respectively. This represents a 5.3%, 5.2%, and 7.1% improvement over the original YOLOv8n. Notably, the average accuracy of recognizing the lying posture of beef cattle reached 98.9%. In conclusion, the YOLOv8n_BiF_DSC algorithm demonstrates excellent performance in feature extraction and high-level data fusion, displaying high robustness and adaptability. It provides theoretical and practical support for the intelligent recognition and management of beef cattle.

YOLOv5-KCB: A New Method for Individual Pig Detection Using Optimized K-Means, CA Attention Mechanism and a Bi-Directional Feature Pyramid Network.

Individual identification of pigs is a critical component of intelligent pig farming. Traditional pig ear-tagging requires significant human resources and suffers from issues such as difficulty in recognition and low accuracy. This paper proposes the YOLOv5-KCB algorithm for non-invasive identification of individual pigs. Specifically, the algorithm utilizes two datasets-pig faces and pig necks-which are divided into nine categories. Following data augmentation, the total sample size was augmented to 19,680. The distance metric used for K-means clustering is changed from the original algorithm to 1-IOU, which improves the adaptability of the model's target anchor boxes. Furthermore, the algorithm introduces SE, CBAM, and CA attention mechanisms, with the CA attention mechanism being selected for its superior performance in feature extraction. Finally, CARAFE, ASFF, and BiFPN are used for feature fusion, with BiFPN selected for its superior performance in improving the detection ability of the algorithm. The experimental results indicate that the YOLOv5-KCB algorithm achieved the highest accuracy rates in pig individual recognition, surpassing all other improved algorithms in average accuracy rate (IOU = 0.5). The accuracy rate of pig head and neck recognition was 98.4%, while the accuracy rate for pig face recognition was 95.1%, representing an improvement of 4.8% and 13.8% over the original YOLOv5 algorithm. Notably, the average accuracy rate of identifying pig head and neck was consistently higher than pig face recognition across all algorithms, with YOLOv5-KCB demonstrating an impressive 2.9% improvement. These results emphasize the potential for utilizing the YOLOv5-KCB algorithm for precise individual pig identification, facilitating subsequent intelligent management practices.

Kernel principal component analysis and differential non-linear feature extraction of pesticide residues on fruit surface based on surface-enhanced Raman spectroscopy.

Surface-enhanced Raman spectroscopy (SERS) has attracted much attention because of its high sensitivity, high speed, and simple sample processing, and has great potential for application in the field of pesticide residue detection. However, SERS is susceptible to the influence of a complex detection environment in the detection of pesticide residues on the surface of fruits, facing problems such as interference from the spectral peaks of detected impurities, unclear dimension of effective correlation data, and poor linearity of sensing signals. In this work, the enhanced raw data of the pesticide thiram residues on the fruit surface using gold nanoparticle (Au-NPs) solution are formed into the raw data set of Raman signal in the IoT environment of Raman spectroscopy principal component detection. Considering the non-linear characteristics of sensing data, this work adopts kernel principal component analysis (KPCA) including radial basis function (RBF) to extract the main features for the spectra in the ranges of 653∼683 cm-1, 705∼728 cm-1, and 847∼872 cm-1, and discusses the effects of different kernel function widths (σ) to construct a qualitative analysis of pesticide residues based on SERS spectral data model, so that the SERS spectral data produce more useful dimensionality reduction with minimal loss, higher mean squared error for cross-validation in non-linear scenarios, and effectively weaken the interference features of detecting impurity spectral peaks, unclear dimensionality of effective correlation data, and poor linearity of sensing signals, reflecting better extraction effects than conventional principal component analysis (PCA) models.

Biomimetic Vision for Zoom Object Detection Based on Improved Vertical Grid Number YOLO Algorithm.

With the development of bionic computer vision for images processing, researchers have easily obtained high-resolution zoom sensing images. The development of drones equipped with high-definition cameras has greatly increased the sample size and image segmentation and target detection are important links during the process of image information. As biomimetic remote sensing images are usually prone to blur distortion and distortion in the imaging, transmission and processing stages, this paper improves the vertical grid number of the YOLO algorithm. Firstly, the light and shade of a high-resolution zoom sensing image were abstracted, and the grey-level cooccurrence matrix extracted feature parameters to quantitatively describe the texture characteristics of the zoom sensing image. The Simple Linear Iterative Clustering (SLIC) superpixel segmentation method was used to achieve the segmentation of light/dark scenes, and the saliency area was obtained. Secondly, a high-resolution zoom sensing image model for segmenting light and dark scenes was established to made the dataset meet the recognition standard. Due to the refraction of the light passing through the lens and other factors, the difference of the contour boundary light and dark value between the target pixel and the background pixel would make it difficult to detect the target, and the pixels of the main part of the separated image would be sharper for edge detection. Thirdly, a YOLO algorithm with an improved vertical grid number was proposed to detect the target in real time on the processed superpixel image array. The adjusted aspect ratio of the target in the remote sensing image modified the number of vertical grids in the YOLO network structure by using 20 convolutional layers and five maximum aggregation layers, which was more accurately adapted to "short and coarse" of the identified object in the information density. Finally, through comparison with the improved algorithm and other mainstream algorithms in different environments, the test results on the aid dataset showed that in the target detection of high spatial resolution zoom sensing images, the algorithm in this paper showed higher accuracy than the YOLO algorithm and had real-time performance and detection accuracy.

A virus-target host proteins recognition method based on integrated complexes data and seed extension.

BACKGROUND: Target drugs play an important role in the clinical treatment of virus diseases. Virus-encoded proteins are widely used as targets for target drugs. However, they cannot cope with the drug resistance caused by a mutated virus and ignore the importance of host proteins for virus replication. Some methods use interactions between viruses and their host proteins to predict potential virus-target host proteins, which are less susceptible to mutated viruses. However, these methods only consider the network topology between the virus and the host proteins, ignoring the influences of protein complexes. Therefore, we introduce protein complexes that are less susceptible to drug resistance of mutated viruses, which helps recognize the unknown virus-target host proteins and reduce the cost of disease treatment. RESULTS: Since protein complexes contain virus-target host proteins, it is reasonable to predict virus-target human proteins from the perspective of the protein complexes. We propose a coverage clustering-core-subsidiary protein complex recognition method named CCA-SE that integrates the known virus-target host proteins, the human protein-protein interaction network, and the known human protein complexes. The proposed method aims to obtain the potential unknown virus-target human host proteins. We list part of the targets after proving our results effectively in enrichment experiments. CONCLUSIONS: Our proposed CCA-SE method consists of two parts: one is CCA, which is to recognize protein complexes, and the other is SE, which is to select seed nodes as the core of protein complexes by using seed expansion. The experimental results validate that CCA-SE achieves efficient recognition of the virus-target host proteins.

A novel ß-1,3-glucanase Gns6 from rice possesses antifungal activity against Magnaporthe oryzae.

As members of the pathogenesis-related protein (PR)-2 family, ß-1,3-glucanases play pivotal roles in plant defense. Previous study showed that the rice genome contains 16 genes encoding putative ß-1,3-glucanases, and the ß-1,3-glucanases in subfamily A were deduced to be involved in plant defense. However, there was limited direct evidence. In this study, the expression of rice ß-1,3-glucanases Gns2-Gns6 belonging to subfamily A in rice plant infection with Magnaporthe oryzae was investigated, and the enhanced expression of Gns6 during infection confirmed its crucial role in the defense of rice seedlings. Enzymological characterization revealed that Gns6 preferentially hydrolyzed laminarin, pachymaran, and yeast glucan. The ß-1,3; 1,6-glucanase Gns6 exhibited a specific activity of 1.2 U/mg with laminarin as the substrate. In addition, Gns6 could hydrolyze laminarin via an endo-type mechanism, yielding a series of oligosaccharides with various degrees of polymerization that are known immune elicitors in plants. Moreover, Gns6 exhibited a significant inhibitory effect against the formation of the germ tubes and appressoria, with potential applications in plant protection. Taken together, this study shows that Gns6 is an essential effector in the defensive response of rice against pathogenic fungi.

Preparation of Active Chitooligosaccharides with a Novel Chitosanase AqCoA and Their Application in Fungal Disease Protection.

Enzymes that degrade fungal cell walls and the resulting oligosaccharides are promising weapons to combat plant fungal disease. In this study, we identified a novel endo-chitosanase, AqCoA, from Aquabacterium sp. A7-Y. The enzyme showed a specific activity of 18 U/mg toward 95% deacetylated chitosan at pH 5.0 and 40 °C. AqCoA also showed activity toward sodium carboxymethylcellulose, indicating substrate promiscuity. AqCoA hydrolyzed chitosan into chitooligosaccharides (CoA-COSs) with degrees of polymerization (DPs) of 3-5 but showed no activity toward CoA-COSs with DPs <6, indicating an endo-type activity. At 2.5 µg/mL, AqCoA inhibited appressorium formation of Magnaporthe oryzae; the produced CoA-COSs also inhibited the growth of M. oryzae and Fusarium oxysporum. Furthermore, CoA-COSs acted as immune elicitors in rice by inducing the reactive oxygen species burst and the expression of defense genes. These results demonstrated that AqCoA and its resulting CoA-COSs might be effective tools for protecting plants against pathogenic fungi.

Ethanol extract of Zhongtian hawthorn lowers serum cholesterol in mice by inhibiting transcription of 3-hydroxy-3-methylglutaryl-CoA reductase via nuclear factor-kappa B signal pathway.

Hawthorn is a berry-like fruit from the species of Crataegus. In China, it has another more famous name, Shan-Zha, which has been used to improve digestion as a traditional Chinese medicine or food for thousands of years. Moreover, during the last decades, hawthorn has received more attention because of its potential to treat cardiovascular diseases. However, currently, only fruits of C. pinnatifida and C. pinnatifida var. major are included as Shan-Zha in the Chinese Pharmacopoeia. In this study, our results showed that the ethanol extract of Zhongtian hawthorn, a novel grafted cultivar of C. cuneata (wild Shan-Zha), could markedly reduce body weight and levels of serum total cholesterol, triglyceride, low-density lipoprotein cholesterol, and liver cholesterol of hyperlipidemia mice. It could suppress the stimulation effect of high-fat diet on the transcription of 3-hydroxy-3-methylglutaryl-CoA reductase (HMGCR) and p65, and counteract the downregulation of CYP7A1 and LDLR. In addition, the results of luciferase reporter assay and Western blot showed that the transcriptional activity of HMGCR promoter was inhibited by Zhongtian hawthorn ethanol extract in a dose-dependent manner, while overexpression of p65 could reverse this transcriptional repression effect. These results suggested that Zhongtian hawthorn could provide health benefits by counteracting the high-fat diet-induced hypercholesteolemic and hyperlipidemic effects in vivo, and the mechanism underlying this event was mainly dependent on the suppressive effect of Zhongtian hawthorn ethanol extract on the transcription of HMGCR via nuclear factor-kappa B (NF-κB) signal pathway. Therefore, this novel cultivar of hawthorn cultivar which has much bigger fruits, early bearing, high yield, cold resistance, and drought resistance, might be considered as a good alternative to Shan-Zha and has great value in the food and medicine industry. In addition, to our best knowledge, this is also the first report that the extract of Crataegus could suppress the transcription of HMGCR via NF-κB signal pathway.

Sensitive and selective cataluminescence-based sensor system for acetone and diethyl ether determination.

A three-dimensional hierarchical CdO nanostructure with a novel bio-inspired morphology is reported. The field emission scanning electronic microscopy, transmission electron microscopy and X-ray diffractometer were employed to characterize the as-prepared samples. In gas-sensing measurements, acetone and diethyl ether were employed as target gases to investigate cataluminescence (CTL) sensing properties of the CdO nanostructure. The results show that the as-fabricated CdO nanostructure exhibited outstanding CTL properties such as stable intensity, high signal/noise values, short response and recovery time. The limit of detection of acetone and diethyl ether was ca. 6.5 ppm and 6.7 ppm, respectively, which was below the standard permitted concentrations. Additionally, a principal components analysis method was used to investigate the recognizable ability of the CTL sensor, and it was found that acetone and diethyl ether can be distinguished clearly. The performance of the bio-inspired CdO nanostructure-based sensor system suggested the promising application of the CdO nanostructure as a novel highly efficient CTL sensing material.

The analysis of time-resolved optical waveguide absorption spectroscopy based on positive matrix factorization.

Time-resolved optical waveguide absorption spectroscopy (OWAS) makes use of an evanescent field to detect the polarized absorption spectra of sub-monomolecular adlayers. This technique is suitable for the investigation of kinetics at the solid/liquid interface of dyes, pigments, fluorescent molecules, quantum dots, metallic nanoparticles, and proteins with chromophores. In this work, we demonstrate the application of positive matrix factorization (PMF) to analyze time-resolved OWAS for the first time. Meanwhile, PCA is researched to compare with PMF. The absorption/desorption kinetics of Rhodamine 6G (R6G) onto a hydrophilic glass surface and the dynamic process of Meisenheimer complex between Cysteine and TNT are selected as samples to verify experimental system and analytical methods. The results are shown that time-resolved OWAS can well record the absorption/desorption of R6G onto a hydrophilic glass surface and the dynamic formation process of Meisenheimer complexes. The feature of OWAS extracted by PMF is dynamic and consistent with the results analyzed by the traditional function of time/wavelength-absorbance. Moreover, PMF prevents the negative factors from occurring, avoids contradicting physical reality, and makes factors more easily interpretable. Therefore, we believe that PMF will provide a valuable analysis route to allow processing of increasingly large and complex data sets.