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Aim: As the completed studies have small sample sizes and different algorithms, a meta-analysis was conducted to assess the accuracy of WCE in identifying polyps using deep learning., Method: Two independent reviewers searched PubMed, Embase, the Web of Science, and the Cochrane Library for potentially eligible studies published up to December 8, 2021, which were analysed on a per-image basis. STATA RevMan and Meta-DiSc were used to conduct this meta-analysis. A random effects model was used, and a subgroup and regression analysis was performed to explore sources of heterogeneity., Results: Eight studies published between 2017 and 2021 included 819 patients, and 18,414 frames were eventually included in the meta-analysis. The summary estimates for the WCE in identifying polyps by deep learning were sensitivity 0.97 (95% confidence interval (CI), 0.95-0.98); specificity 0.97 (95% CI, 0.94-0.98); positive likelihood ratio 27.19 (95% CI, 15.32-50.42); negative likelihood ratio 0.03 (95% CI 0.02-0.05); diagnostic odds ratio 873.69 (95% CI, 387.34-1970.74); and the area under the sROC curve 0.99., Conclusion: WCE uses deep learning to identify polyps with high accuracy, but multicentre prospective randomized controlled studies are needed in the future., Competing Interests: The authors declare that there are no conflicts of interest in this study., (Copyright © 2022 Junjie Mi et al.)

N4-methylcytosine (4mC) is a critical epigenetic modification that plays a pivotal role in the regulation of a multitude of biological processes, including gene expression, DNA replication, and cellular differentiation. Traditional experimental methods for detecting DNA N4-methylcytosine sites are time-consuming, labor-intensive, and costly, making them unsuitable for large-scale or high-throughput research. Computational methods for identifying DNA N4-methylcytosine sites enable the rapid and cost-effective analysis of DNA 4mC sites across entire genomes. In this study, we focus on the identification of DNA 4mC sites in the mouse genome. Although there are already some computational methods that can predict DNA 4mC sites in the mouse genome, there is still significant room for improvement in accurately predicting them due to their inability to fully capture the multifaceted characteristics of DNA sequences. To address this issue, we propose a new deep learning predictor called Mus4mCPred, which utilizes multi-view feature learning and deep hybrid networks for accurately predicting DNA 4mC sites in the mouse genome. The predictor Mus4mCPred firstly employed different encoding methods to extract the feature vectors of DNA sequences, then input these features generated by different encoding methods into various hybrid deep learning models for the learning and extraction of more sophisticated representations of these features, and finally fused the extracted multi-view features to serve as the final features for DNA 4mC site prediction in the mouse genome. Multi-view features enabled the more comprehensive capture of data characteristics, enhancing the feature representation of DNA sequences. The independent test results showed that the sensitivity (Sn), specificity (Sp), accuracy (Acc), and Matthews' correlation coefficient (MCC) were 0.7688, 0.9375, 0.8531, and 0.7165, respectively. The predictor Mus4mCPred outperformed other state-of-the-art methods, achieving the accurate identification of 4mC sites in the mouse genome. [ABSTRACT FROM AUTHOR]

With the decline in the protective function for agricultural ecosystems of farmland shelterbelts due to tree withering and dying caused by pest and disease, quickly and accurately identifying the distribution of canopy damage is of great significance for forestry management departments to implement dynamic monitoring. This study focused on Populus bolleana and utilized an unmanned aerial vehicle (UAV) multispectral camera to acquire red–green–blue (RGB) images and multispectral images (MSIs), which were fused with a digital surface model (DSM) generated by UAV LiDAR for feature fusion to obtain DSM + RGB and DSM + MSI images, and random forest (RF), support vector machine (SVM), maximum likelihood classification (MLC), and a deep learning U-Net model were employed to build classification models for forest stand canopy recognition for the four image types. The model results indicate that the recognition performance of RF is superior to that of U-Net, and U-Net performs better overall than SVM and MLC. The classification accuracy of different feature fusion images shows a trend of DSM + MSI images (Kappa = 0.8656, OA = 91.55%) > MSI images > DSM + RGB images > RGB images. DSM + MSI images exhibit the highest producer's accuracy for identifying healthy and withered canopies, with values of 95.91% and 91.15%, respectively, while RGB images show the lowest accuracy, with producer's accuracy values of 79.3% and 78.91% for healthy and withered canopies, respectively. This study presents a method for identifying the distribution of Populus bolleana canopies damaged by Anoplophora glabripennis and healthy canopies using the feature fusion of multi-source remote sensing data, providing a valuable data reference for the precise monitoring and management of farmland shelterbelts. [ABSTRACT FROM AUTHOR]

To ensure the safety and durability of concrete structures, timely detection and classification of concrete cracks using a low-cost and high-efficiency method is necessary. In this study, a concrete surface crack damage detection method based on the ResNet-18 residual network was developed. This method was implemented by training a model with images to extract the cracks, where the image processing algorithms and deep learning were combined. The results show that the computational accuracy can meet the requirements by utilizing the established image dataset and appropriate model hyperparameters. The trained model had high recognition accuracy when the 256 × 256 resolution images were adopted, and the worst accuracy of crack recognition in the test set was over 90%. The average accuracy in the test set was 91.3% when considering environmental interference generated by processing the images with a brightness adjustment, salt-and-pepper noise, and localized interference. Then, it was demonstrated that the present model possesses good robustness for crack identification in different environments. The average recognition accuracy when dealing with images of a real bridge, which are outside the training dataset, was 99.7%. The residual network model developed in this study has the advantages of low cost, high efficiency, and practicality compared to traditional detection methods. Compared to the existing deep learning methods, the model created in this study requires less computational resources and storage space, and shows a faster training speed and higher accuracy. [ABSTRACT FROM AUTHOR]

Antimicrobial peptides (AMPs) are vital components of innate immunotherapy. Existing approaches mainly rely on either deep learning for the automatic extraction of sequence features or traditional manual amino acid features combined with machine learning. The peptide sequence contains symmetrical sequence motifs or repetitive amino acid patterns, which may be related to the function and structure of the peptide. Recently, the advent of large language models has significantly boosted the representational power of sequence pattern features. In light of this, we present a novel AMP predictor called UniproLcad, which integrates three prominent protein language models—ESM-2, ProtBert, and UniRep—to obtain a more comprehensive representation of protein features. UniproLcad utilizes deep learning networks, encompassing the bidirectional long and short memory network (Bi-LSTM) and one-dimensional convolutional neural networks (1D-CNN), while also integrating an attention mechanism to enhance its capabilities. These deep learning frameworks, coupled with pre-trained language models, efficiently extract multi-view features from antimicrobial peptide sequences and assign attention weights to them. Through ten-fold cross-validation and independent testing, UniproLcad demonstrates competitive performance in the field of antimicrobial peptide identification. This integration of diverse language models and deep learning architectures enhances the accuracy and reliability of predicting antimicrobial peptides, contributing to the advancement of computational methods in this field. [ABSTRACT FROM AUTHOR]

Underwater images commonly experience degradation caused by light absorption and scattering in water. Developing lightweight and efficient neural networks to restore degraded images is challenging because of the difficulty in obtaining high-quality paired images and the delicate trade-off between model performance and computational demands. To provide a lightweight and efficient solution for restoring images in terms of color, structure, texture details, etc., enabling the underwater image restoration task to be applied in real-world scenes, we propose an unsupervised lightweight multi-branch context network. Specifically, we design two lightweight multi-branch context subnetworks that enable multiple receptive field feature extraction and long-range dependency modeling to estimate scene radiance and transmission maps. Gaussian blur is adopted to approximate the global background light on the twice-downsampled degraded image. We design a comprehensive loss function that incorporates multiple components, including self-supervised consistency loss and reconstruction loss, to train the network using degraded images in an unsupervised learning manner. Experiments on several underwater image datasets demonstrate that our approach realizes good performance with very few model parameters (0.12 M), and is even comparable to state-of-the-art methods (up to 149 M) in color correction and contrast restoration. [ABSTRACT FROM AUTHOR]

Livestock's live body dimensions are a pivotal indicator of economic output. Manual measurement is labor-intensive and time-consuming, often eliciting stress responses in the livestock. With the advancement of computer technology, the techniques for livestock live body dimension measurement have progressed rapidly, yielding significant research achievements. This paper presents a comprehensive review of the recent advancements in livestock live body dimension measurement, emphasizing the crucial role of computer-vision-based sensors. The discussion covers three main aspects: sensing data acquisition, sensing data processing, and sensing data analysis. The common techniques and measurement procedures in, and the current research status of, live body dimension measurement are introduced, along with a comparative analysis of their respective merits and drawbacks. Livestock data acquisition is the initial phase of live body dimension measurement, where sensors are employed as data collection equipment to obtain information conducive to precise measurements. Subsequently, the acquired data undergo processing, leveraging techniques such as 3D vision technology, computer graphics, image processing, and deep learning to calculate the measurements accurately. Lastly, this paper addresses the existing challenges within the domain of livestock live body dimension measurement in the livestock industry, highlighting the potential contributions of computer-vision-based sensors. Moreover, it predicts the potential development trends in the realm of high-throughput live body dimension measurement techniques for livestock. [ABSTRACT FROM AUTHOR]

The absorption and scattering of light in water usually result in the degradation of underwater image quality, such as color distortion and low contrast. Additionally, the performance of acquisition devices may limit the spatial resolution of underwater images, resulting in the loss of image details. Efficient modeling of long-range dependency is essential for understanding the global structure and local context of underwater images to enhance and restore details, which is a challenging task. In this paper, we propose an agent-guided non-local attention network using a multi-color space for underwater image enhancement and super-resolution. Specifically, local features with different receptive fields are first extracted simultaneously in the RGB, Lab, and HSI color spaces of underwater images. Then, the designed agent-guided non-local attention module with high expressiveness and lower computational complexity is utilized to model long-range dependency. Subsequently, the results from the multi-color space are adaptively fused with learned weights, and finally, the reconstruction block composed of deconvolution and the designed non-local attention module is used to output enhanced and super-resolution images. Experiments on multiple datasets demonstrated that our method significantly improves the visual perception of degraded underwater images and efficiently reconstructs missing details, and objective evaluations confirmed the superiority of our method over other state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Behavior is one of the important factors reflecting the health status of dairy cows, and when dairy cows encounter health problems, they exhibit different behavioral characteristics. Therefore, identifying dairy cow behavior not only helps in assessing their physiological health and disease treatment but also improves cow welfare, which is very important for the development of animal husbandry. The method of relying on human eyes to observe the behavior of dairy cows has problems such as high labor costs, high labor intensity, and high fatigue rates. Therefore, it is necessary to explore more effective technical means to identify cow behaviors more quickly and accurately and improve the intelligence level of dairy cow farming. Automatic recognition of dairy cow behavior has become a key technology for diagnosing dairy cow diseases, improving farm economic benefits and reducing animal elimination rates. Recently, deep learning for automated dairy cow behavior identification has become a research focus. However, in complex farming environments, dairy cow behaviors are characterized by multiscale features due to large scenes and long data collection distances. Traditional behavior recognition models cannot accurately recognize similar behavior features of dairy cows, such as those with similar visual characteristics, i.e., standing and walking. The behavior recognition method based on 3D convolution solves the problem of small visual feature differences in behavior recognition. However, due to the large number of model parameters, long inference time, and simple data background, it cannot meet the demand for real-time recognition of dairy cow behaviors in complex breeding environments. To address this, we developed an effective yet lightweight model for fast and accurate dairy cow behavior feature learning from video data. We focused on four common behaviors: standing, walking, lying, and mounting. We recorded videos of dairy cow behaviors at a dairy farm containing over one hundred cows using surveillance cameras. A robust model was built using a complex background dataset. We proposed a two-pathway X3DFast model based on spatiotemporal behavior features. The X3D and fast pathways were laterally connected to integrate spatial and temporal features. The X3D pathway extracted spatial features. The fast pathway with R(2 + 1)D convolution decomposed spatiotemporal features and transferred effective spatial features to the X3D pathway. An action model further enhanced X3D spatial modeling. Experiments showed that X3DFast achieved 98.49% top-1 accuracy, outperforming similar methods in identifying the four behaviors. The method we proposed can effectively identify similar dairy cow behaviors while improving inference speed, providing technical support for subsequent dairy cow behavior recognition and daily behavior statistics. [ABSTRACT FROM AUTHOR]

In this paper, a network consisting of an object localization module and a discriminative feature extraction module is designed for fine-grained image retrieval (FGIR). In order to reduce the interference of complex backgrounds, the object localization module is introduced into the network before feature extraction. By selecting the convolutional feature descriptors, the main object is separated from the background, and thus, most of the interference is filtered out. Further, in order to improve the overall performance of the network, a discriminative filter bank is introduced into the network as the local feature detector. Hence, the local discriminative features can be extracted directly from the original feature map. The experimental results based on the CUB-200-2011 and Cars-196 datasets demonstrate that the proposed method can improve the performance of FGIR. [ABSTRACT FROM AUTHOR]

Transformer is extensively employed in natural language processing, and computer vision (CV), with the self-attention structure. Due to its outstanding long-range dependency modeling and parallel computing capability, some leading researchers have recently attempted to apply Transformer to intelligent fault diagnosis tasks for mechanical equipment, and have achieved remarkable results. Physical phenomena such as changes in vibration, sound, and heat play a crucial role in the research of mechanical equipment fault diagnosis, which directly reflects the operational status and potential faults of mechanical equipment. Currently, intelligent fault diagnosis of mechanical equipment based on monitoring signals such as vibration, sound, and temperature using Transformer-based models remains a popular research topic. While some review literature has explored the related principles and application scenarios of Transformer, there is still a lack of research on its application in intelligent fault diagnosis tasks for mechanical equipment. Therefore, this work begins by examining the current research status of fault diagnosis methods for mechanical equipment. This study first provides a brief overview of the development history of Transformer, outlines its basic structure and principles, and analyzes the characteristics and advantages of its model structure. Next it focuses on three model variants of Transformer that have generated a significant impact in the field of CV. Following that, the research progress and current challenges of Transformer-based intelligent fault diagnosis methods for mechanical equipment are discussed. Finally, the future development direction of Transformer in the field of mechanical equipment fault diagnosis is proposed.

Lysine glutarylation (Kglu) is a newly discovered post-translational modification of proteins with important roles in mitochondrial functions, oxidative damage, etc. The established biological experimental methods to identify glutarylation sites are often time-consuming and costly. Therefore, there is an urgent need to develop computational methods for efficient and accurate identification of glutarylation sites. Most of the existing computational methods only utilize handcrafted features to construct the prediction model and do not consider the positive impact of the pre-trained protein language model on the prediction performance. Based on this, we develop an ensemble deep-learning predictor Deepro-Glu that combines convolutional neural network and bidirectional long short-term memory network using the deep learning features and traditional handcrafted features to predict lysine glutaryation sites. The deep learning features are generated from the pre-trained protein language model called ProtBert, and the handcrafted features consist of sequence-based features, physicochemical property-based features and evolution information-based features. Furthermore, the attention mechanism is used to efficiently integrate the deep learning features and the handcrafted features by learning the appropriate attention weights. 10-fold cross-validation and independent tests demonstrate that Deepro-Glu achieves competitive or superior performance than the state-of-the-art methods. The source codes and data are publicly available at https://github.com/xwanggroup/Deepro-Glu. [ABSTRACT FROM AUTHOR]

Accurately segmenting an insect from its original ecological image is the core technology restricting the accuracy and efficiency of automatic recognition. However, the performance of existing segmentation methods is unsatisfactory in insect images shot in wild backgrounds on account of challenges: various sizes, similar colors or textures to the surroundings, transparent body parts and vague outlines. These challenges of image segmentation are accentuated when dealing with camouflaged insects. Here, we developed an insect image segmentation method based on deep learning termed the progressive refinement network (PRNet), especially for camouflaged insects. Unlike existing insect segmentation methods, PRNet captures the possible scale and location of insects by extracting the contextual information of the image, and fuses comprehensive features to suppress distractors, thereby clearly segmenting insect outlines. Experimental results based on 1900 camouflaged insect images demonstrated that PRNet could effectively segment the camouflaged insects and achieved superior detection performance, with a mean absolute error of 3.2%, pixel-matching degree of 89.7%, structural similarity of 83.6%, and precision and recall error of 72%, which achieved improvements of 8.1%, 25.9%, 19.5%, and 35.8%, respectively, when compared to the recent salient object detection methods. As a foundational technology for insect detection, PRNet provides new opportunities for understanding insect camouflage, and also has the potential to lead to a step progress in the accuracy of the intelligent identification of general insects, and even being an ultimate insect detector. [ABSTRACT FROM AUTHOR]

The complete traffic network data is crucial for accurate traffic data prediction in intelligent transportation systems. Inspired by the success of Generative Adversarial Networks (GANs) in image restoration, this study proposes a traffic flow imputation method by employing image restoration techniques. First, we propose a trajectory data representation method called Trajectory2Matrix, that converts the trajectory data into a two-dimensional spatiotemporal relation feature map. Consequently, the data imputation scale and scope are increased. Second, a spatiotemporal feature map generation module based on a graph convolutional network is designed to optimize the GAN generator, thus utilizing its advantages for non-Euclidean data and dynamic spatiotemporal correlation. Finally, a heterogeneous multisource data fusion module based on a channel attention mechanism is proposed to merge dynamic/static external attributes and multimode characteristics in time. The proposed method improved imputation accuracy and robustness for multitype missing data patterns, especially in high missing rate situations. [ABSTRACT FROM AUTHOR]