Apple leaf diseases seriously damage the yield and quality of apples. Current apple leaf disease diagnosis methods primarily rely on human visual inspection, which often results in low efficiency and insufficient accuracy. Many computer vision algorithms have been proposed to diagnose apple leaf diseases, but most of them are designed to run on high-performance GPUs. This potentially limits their application in the field, in which mobile devices are expected to be used to perform computer vision-based disease diagnosis on the spot. In this paper, we propose a lightweight one-stage network, called the Mobile Ghost Attention YOLO network (MGA-YOLO), which enables real-time diagnosis of apple leaf diseases on mobile devices. We also built a dataset, called the Apple Leaf Disease Object Detection dataset (ALDOD), that contains 8,838 images of healthy and infected apple leaves with complex backgrounds, collected from existing public datasets. In our proposed model, we replaced the ordinary convolution with the Ghost module to significantly reduce the number of parameters and floating point operations (FLOPs) due to cheap operations of the Ghost module. We then constructed the Mobile Inverted Residual Bottleneck Convolution and integrated the Convolutional Block Attention Module (CBAM) into the YOLO network to improve its performance on feature extraction. Finally, an extra prediction head was added to detect extra large objects. We tested our method on the ALDOD testing set. Results showed that our method outperformed other state-of-the-art methods with the highest mAP of 89.3%, the smallest model size of only 10.34 MB and the highest frames per second (FPS) of 84.1 on the GPU server. The proposed model was also tested on a mobile phone, which achieved 12.5 FPS. In addition, by applying image augmentation techniques on the dataset, mAP of our method was further improved to 94.0%. These results suggest that our model can accurately and efficiently detect apple leaf diseases and can be used for real-time detection of apple leaf diseases on mobile devices.

The colonization of burn wounds by Pseudomonas aeruginosa can lead to septic shock, organ injuries, and high mortality rates. We hypothesized that negative pressure wound therapy (NPWT) would decrease invasion and proliferation of P. aeruginosa within the burn wound and reduce mortality. Thermal injuries were induced in anesthetized mice, and P. aeruginosa was applied to the wound surface for 24 h. After removing the burn eschar and debridement, the animals were subjected to either NPWT or wet-to-dry (WTD) treatment protocols. The bacterial loads on the wound surface were assessed during 7 d of treatment, as were the concentrations of inflammatory cytokines in the peripheral blood samples. Survival was monitored daily for 14 d after burn induction. Finally, samples of wounded skin, lung, liver, and kidney were collected and subjected to histopathological examination. Applying P. aeruginosa to the burn wound surface led to sepsis. During early stages of treatment, NPWT reduced the mortality of the septic animals and levels of P. aeruginosa within the burn wound compared with WTD-treated animals. Circulating levels of cytokines and cytoarchitectural abnormalities were also significantly reduced via NPWT. Our data indicate that NPWT inhibits the invasion and proliferation of P. aeruginosa in burn-wounded tissue and decreases early mortality in a murine model of burn-wound sepsis. These therapeutic benefits likely result from the ability of NPWT to decrease bacterial proliferation on the wound surface, reduce cytokine serum concentrations, and prevent damage to internal organs.

To address the current situation, in which pear defect detection is still based on a workforce with low efficiency, we propose the use of the CNN model to detect pear defects. Since it is challenging to obtain defect images in the implementation process, a deep convolutional adversarial generation network was used to augment the defect images. As the experimental results indicated, the detection accuracy of the proposed method on the 3000 validation set was as high as 97.35%. Variant mainstream CNNs were compared to evaluate the model’s performance thoroughly, and the top performer was selected to conduct further comparative experiments with traditional machine learning methods, such as support vector machine algorithm, random forest algorithm, and k-nearest neighbor clustering algorithm. Moreover, the other two varieties of pears that have not been trained were chosen to validate the robustness and generalization capability of the model. The validation results illustrated that the proposed method is more accurate than the commonly used algorithms for pear defect detection. It is robust enough to be generalized well to other datasets. In order to allow the method proposed in this paper to be applied in agriculture, an intelligent pear defect detection system was built based on an iOS device.

Motivation: N-linked glycosylation is a frequently occurring post-translational protein modification that serves critical functions in protein folding, stability, trafficking, and recognition. Its involvement spans across multiple biological processes and alterations to this process can result in various diseases. Therefore, identifying N-linked glycosylation sites is imperative for comprehending the mechanisms and systems underlying glycosylation. Due to the inherent experimental complexities, machine learning and deep learning have become indispensable tools for predicting these sites. Results: In this context, a new approach called EMNGly has been proposed. The EMNGly approach utilizes pretrained protein language model (Evolutionary Scale Modeling) and pretrained protein structure model (Inverse Folding Model) for features extraction and support vector machine for classification. Ten-fold cross-validation and independent tests show that this approach has outperformed existing techniques. And it achieves Matthews Correlation Coefficient, sensitivity, specificity, and accuracy of 0.8282, 0.9343, 0.8934, and 0.9143, respectively on a benchmark independent test set.

Counting wheat heads is a time-consuming process in agricultural production, which is currently primarily carried out by humans. Manually identifying wheat heads and statistically analyzing the findings has a rigorous requirement for the workforce and is prone to error. With the advancement of machine vision technology, computer vision detection algorithms have made wheat head detection and counting feasible. To accomplish this traditional labor-intensive task and tackle various tricky matters in wheat images, a high-precision wheat head detection model with strong generalizability was presented based on a one-stage network structure. The model's structure was referred to as that of the YOLO network; meanwhile, several modules were added and adjusted in the backbone network. The one-stage backbone network received an attention module and a feature fusion module, and the Loss function was improved. When compared to various other mainstream object detection networks, our model outperforms them, with a mAP of 0.688. In addition, an iOS-based intelligent wheat head counting mobile app was created, which could calculate the number of wheat heads in images shot in an agricultural environment in less than a second.

For each industry in the era of big data, a steady stream of data is generated continually. Due to the enormous information contained in big data, how to maximally extract the value from big data with lower cost to provide decision-making, guide production and resource allocation for the enterprises has attracted more and more attention of most enterprises. This paper proposes a data analysis solution in the era of big data–data factory, and implements a software system to build a data factory for an enterprise promptly and efficiently. By processing, analyzing and modeling the data in a workshop-based production mode similar to the traditional factories using raw materials, our data factory will acquire the analysis results and prediction models and then realize the clustering, classification, evaluation, and prediction data analysis. Moreover, the application of deploying data factory for an enterprise reveals that data factory is an efficient solution for big data analysis, and it improves the efficiency of enterprise data analysis.