MicroRNA 98-5p Overexpression Contributes to Delayed Fracture Healing via Targeting BMP-2.

MicroRNAs (miRNAs) are related to the regulation of bone metabolism. Delayed fracture healing (DFH) is a common complication after fracture surgery. The study attempted to examine the role of miR-98-5p and bone morphogenetic protein (BMP)-2 with the onset of DFH. A total of 140 patients with femoral neck fracture were recruited, including 80 cases with normal fracture healing (NFH) and 60 cases with DFH. MC3T3-E1 cells were induced cell differentiation for cell function experiments. Real-time quantitative polymerase chain reaction (RT-qPCR) was carried out to test mRNA levels. Cell proliferation and apoptosis were determined via CCK-8 and flow cytometry assay. Luciferase reporter assay was done to verify the targeted regulatory relationship of miR-98-5p with BMP-2. In comparison with NFH cases, DFH patients owned high levels of serum miR-98-5p and low concentration of BMP-2, and the levels of the two indexes are significantly negatively correlated. Both miR-98-5p and BMP-2 had the ability to predict DFH, while their combined diagnostic value is the highest. BMP-2 was demonstrated to be the target gene of miR-98-5p. Overexpression of BMP-2 reversed the role of miR-98-5p in MC3T3-E1 cell proliferation, apoptosis and differentiation. Increased miR-98-5p and decreased BMP-2 serve as potential biomarkers for the diagnosis of DFH. MiR-98-5p overexpression inhibits osteoblast proliferation and differentiation via targeting BMP-2.

Research on a Rice Counting Algorithm Based on an Improved MCNN and a Density Map.

The thousand grain weight is an index of size, fullness and quality in crop seed detection and is an important basis for field yield prediction. To detect the thousand grain weight of rice requires the accurate counting of rice. We collected a total of 5670 images of three different types of rice seeds with different qualities to construct a model. Considering the different shapes of different types of rice, this study used an adaptive Gaussian kernel to convolve with the rice coordinate function to obtain a more accurate density map, which was used as an important basis for determining the results of subsequent experiments. A Multi-Column Convolutional Neural Network was used to extract the features of different sizes of rice, and the features were fused by the fusion network to learn the mapping relationship from the original map features to the density map features. An advanced prior step was added to the original algorithm to estimate the density level of the image, which weakened the effect of the rice adhesion condition on the counting results. Extensive comparison experiments show that the proposed method is more accurate than the original MCNN algorithm.

Estimation of the Gender Ratio of Chickens Based on Computer Vision: Dataset and Exploration.

The gender ratio of free-range chickens is considered as a major animal welfare problem in commercial broiler farming. Free-range chicken producers need to identify chicken gender to estimate the economic value of their flock. However, it is challenging for farmers to estimate the gender ratio of chickens efficiently and accurately, since the environmental background is complicated and the chicken number is dynamic. Moreover, manual estimation is likely double counts or missed count and thus is inaccurate and time consuming. Hence, automated methods that can lead to results efficiently and accurately replace the identification abilities of a chicken gender expert, working in a farm environment, are beneficial to the industry. The contributions in this paper include: (1) Building the world's first chicken gender classification database annotated manually, which comprises 800 chicken flock images captured on a farm and 1000 single chicken images separated from the flock images by an object detection network, labelled with gender information. (2) Training a rooster and hen classifier using a deep neural network and cross entropy in information theory to achieve an average accuracy of 96.85%. The evaluation of the algorithm performance indicates that the proposed automated method is practical for the gender classification of chickens on the farm environment and provides a feasible way of thinking for the estimation of the gender ratio.

Hyperspectral and Fluorescence Imaging Approaches for Nondestructive Detection of Rice Chlorophyll.

Estimating and monitoring chlorophyll content is a critical step in crop spectral image analysis. The quick, non-destructive assessment of chlorophyll content in rice leaves can optimize nitrogen fertilization, benefit the environment and economy, and improve rice production management and quality. In this research, spectral analysis of rice leaves is performed using hyperspectral and fluorescence spectroscopy for the detection of chlorophyll content in rice leaves. This study generated ninety experimental spectral datasets by collecting rice leaf samples from a farm in Sichuan Province, China. By implementing a feature extraction algorithm, this study compresses redundant spectral bands and subsequently constructs machine learning models to reveal latent correlations among the extracted features. The prediction capabilities of six feature extraction methods and four machine learning algorithms in two types of spectral data are examined, and an accurate method of predicting chlorophyll concentration in rice leaves was devised. The IVSO-IVISSA (Iteratively Variable Subset Optimization-Interval Variable Iterative Space Shrinkage Approach) quadratic feature combination approach, based on fluorescence spectrum data, has the best prediction performance among the CNN+LSTM (Convolutional Neural Network Long Short-Term Memory) algorithms, with corresponding RMSE-Train (Root Mean Squared Error), RMSE-Test, and RPD (Ratio of standard deviation of the validation set to standard error of prediction) indexes of 0.26, 0.29, and 2.64, respectively. We demonstrated in this study that hyperspectral and fluorescence spectroscopy, when analyzed with feature extraction and machine learning methods, provide a new avenue for rapid and non-destructive crop health monitoring, which is critical to the advancement of smart and precision agriculture.

LSD-YOLO: Enhanced YOLOv8n Algorithm for Efficient Detection of Lemon Surface Diseases.

Lemon, as an important cash crop with rich nutritional value, holds significant cultivation importance and market demand worldwide. However, lemon diseases seriously impact the quality and yield of lemons, necessitating their early detection for effective control. This paper addresses this need by collecting a dataset of lemon diseases, consisting of 726 images captured under varying light levels, growth stages, shooting distances and disease conditions. Through cropping high-resolution images, the dataset is expanded to 2022 images, comprising 4441 healthy lemons and 718 diseased lemons, with approximately 1-6 targets per image. Then, we propose a novel model lemon surface disease YOLO (LSD-YOLO), which integrates Switchable Atrous Convolution (SAConv) and Convolutional Block Attention Module (CBAM), along with the design of C2f-SAC and the addition of a small-target detection layer to enhance the extraction of key features and the fusion of features at different scales. The experimental results demonstrate that the proposed LSD-YOLO achieves an accuracy of 90.62% on the collected datasets, with mAP@50-95 reaching 80.84%. Compared with the original YOLOv8n model, both mAP@50 and mAP@50-95 metrics are enhanced. Therefore, the LSD-YOLO model proposed in this study provides a more accurate recognition of healthy and diseased lemons, contributing effectively to solving the lemon disease detection problem.

An Efficient Method for Monitoring Birds Based on Object Detection and Multi-Object Tracking Networks.

To protect birds, it is crucial to identify their species and determine their population across different regions. However, currently, bird monitoring methods mainly rely on manual techniques, such as point counts conducted by researchers and ornithologists in the field. This method can sometimes be inefficient, prone to errors, and have limitations, which may not always be conducive to bird conservation efforts. In this paper, we propose an efficient method for wetland bird monitoring based on object detection and multi-object tracking networks. First, we construct a manually annotated dataset for bird species detection, annotating the entire body and head of each bird separately, comprising 3737 bird images. We also built a new dataset containing 11,139 complete, individual bird images for the multi-object tracking task. Second, we perform comparative experiments using a state-of-the-art batch of object detection networks, and the results demonstrated that the YOLOv7 network, trained with a dataset labeling the entire body of the bird, was the most effective method. To enhance YOLOv7 performance, we added three GAM modules on the head side of the YOLOv7 to minimize information diffusion and amplify global interaction representations and utilized Alpha-IoU loss to achieve more accurate bounding box regression. The experimental results revealed that the improved method offers greater accuracy, with mAP@0.5 improving to 0.951 and mAP@0.5:0.95 improving to 0.815. Then, we send the detection information to DeepSORT for bird tracking and classification counting. Finally, we use the area counting method to count according to the species of birds to obtain information about flock distribution. The method described in this paper effectively addresses the monitoring challenges in bird conservation.

Combined spectral and speech features for pig speech recognition.

The sound of the pig is one of its important signs, which can reflect various states such as hunger, pain or emotional state, and directly indicates the growth and health status of the pig. Existing speech recognition methods usually start with spectral features. The use of spectrograms to achieve classification of different speech sounds, while working well, may not be the best approach for solving such tasks with single-dimensional feature input. Based on the above assumptions, in order to more accurately grasp the situation of pigs and take timely measures to ensure the health status of pigs, this paper proposes a pig sound classification method based on the dual role of signal spectrum and speech. Spectrograms can visualize information about the characteristics of the sound under different time periods. The audio data are introduced, and the spectrogram features of the model input as well as the audio time-domain features are complemented with each other and passed into a pre-designed parallel network structure. The network model with the best results and the classifier were selected for combination. An accuracy of 93.39% was achieved on the pig speech classification task, while the AUC also reached 0.99163, demonstrating the superiority of the method. This study contributes to the direction of computer vision and acoustics by recognizing the sound of pigs. In addition, a total of 4,000 pig sound datasets in four categories are established in this paper to provide a research basis for later research scholars.

New deep learning method for efficient extraction of small water from remote sensing images.

Extracting water bodies from remote sensing images is important in many fields, such as in water resources information acquisition and analysis. Conventional methods of water body extraction enhance the differences between water bodies and other interfering water bodies to improve the accuracy of water body boundary extraction. Multiple methods must be used alternately to extract water body boundaries more accurately. Water body extraction methods combined with neural networks struggle to improve the extraction accuracy of fine water bodies while ensuring an overall extraction effect. In this study, false color processing and a generative adversarial network (GAN) were added to reconstruct remote sensing images and enhance the features of tiny water bodies. In addition, a multi-scale input strategy was designed to reduce the training cost. We input the processed data into a new water body extraction method based on strip pooling for remote sensing images, which is an improvement of DeepLabv3+. Strip pooling was introduced in the DeepLabv3+ network to better extract water bodies with a discrete distribution at long distances using different strip kernels. The experiments and tests show that the proposed method can improve the accuracy of water body extraction and is effective in fine water body extraction. Compared with seven other traditional remote sensing water body extraction methods and deep learning semantic segmentation methods, the prediction accuracy of the proposed method reaches 94.72%. In summary, the proposed method performs water body extraction better than existing methods.

A novel method for credit scoring based on feature transformation and ensemble model.

Credit scoring is a very critical task for banks and other financial institutions, and it has become an important evaluation metric to distinguish potential defaulting users. In this paper, we propose a credit score prediction method based on feature transformation and ensemble model, which is essentially a cascade approach. The feature transformation process consisting of boosting trees (BT) and auto-encoders (AE) is employed to replace manual feature engineering and to solve the data imbalance problem. For the classification process, this paper designs a heterogeneous ensemble model by weighting the factorization machine (FM) and deep neural networks (DNN), which can efficiently extract low-order intersections and high-order intersections. Comprehensive experiments were conducted on two standard datasets and the results demonstrate that the proposed approach outperforms existing credit scoring models in accuracy.

Effects of dietary vitamin E on growth performance as well as intestinal structure and function of channel catfish (Ictalurus punctatus, Rafinesque 1818).

To evaluate the impact of dietary vitamin E supplementation on growth performance, the intestinal structure and function of channel catfish (Ictalurus punctatus, Rafinesque 1818) was investigated. A total of 900 healthy channel catfish (weight, 5.20±0.15 g) were divided into four groups, which received experimental diets with different vitamin E content (0, 50, 100 or 1,000 mg/kg). At the end of the feeding trial (after 15 weeks), the growth and gut performance of the animals was determined. The digestive enzyme activity in hepatopancreas and gut was also detected. In addition, the height of intestinal fold, the thickness of the mucous membrane and the number of somatostatin-positive cells was examined by histological analysis. Dietary vitamin E supplementation at 50 and 100 mg/kg significantly improved the growth and gut performance, which also increased the activity of several digestive enzymes compared to that in animals without vitamin E supplementation (P<0.05). In addition, vitamin E supplementation also significantly increased the height of intestinal fold and the thickness of the mucous membrane (P<0.05). Fish with dietary vitamin E supplementation at appropriate doses also had more somatostatin-positive cells in than those without vitamin E supplementation (P<0.05). In conclusion, dietary vitamin E supplementation at 50 and 100 mg/kg was shown to improve the growth performance as well as intestinal structure and function of channel catfish.

Morphology and tissue distribution of four kinds of endocrine cells in the digestive tract of the Chinese yellow quail (Coturnix japonica).

OBJECTIVE: To describe the tissue distribution, density, and the morphological characteristics of 4 kinds of endocrine cells in the digestive tract of the Chinese yellow quail (Coturnix japonica). STUDY DESIGN: The streptavidin-biotin-peroxidase complex immunohistochemical method was used to identify the distribution of somatostatin (SS), serotonin (5-HT), gastrin and neuropeptide Y (NPY) in digestive tracts including proventriculus, duodenum, jejunum, ileum, and rectum. SPSS 19.0 software was used to perform biological statistical analysis. RESULTS: The results showed that the SS and 5-HT secreting cells were mainly distributed in the proventriculus (19.2 +/- 6.9 and 16.1 +/- 3.4 cfu/mm2) and duodenum (2.9 +/- 2.0 and 1.9 +/- 0.6 cfu/mm2). Gastrin and NPY were not detected in each section of the digestive tract. Moreover, there was no significant difference in the quantitative distribution and morphological characteristics of SS and 5-HT secreting cells in the digestive tract between male and female quails. CONCLUSION: The distribution and morphological characteristics of endocrine cells were closely related to the physiological functions of different parts in the digestive tract. The preferential location of endocrine cells provides additional information for future studies on the physiological roles of gastrointestinal peptides in the gastrointestinal tract of the Chinese yellow quail.