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Ebola disease is a lethal viral hemorrhagic fever caused by ebolaviruses within the Filoviridae family with mortality rates of up to 90%. Monoclonal antibody (mAb) based therapies have shown great potential for the treatment of EVD. However, the potential emerging ebolavirus isolates and the negative effect of decoy protein on the therapeutic efficacy of antibodies highlight the necessity of developing novel antibodies to counter the threat of Ebola. Here, 11 fully human mAbs were isolated from transgenic mice immunized with GP protein and recombinant vesicular stomatitis virus-bearing GP (rVSV-EBOV GP). These mAbs were divided into five groups according to their germline genes and exhibited differential binding activities and neutralization capabilities. In particular, mAbs 8G6, 2A4, and 5H4 were cross-reactive and bound at least three ebolavirus glycoproteins. mAb 4C1 not only exhibited neutralizing activity but no cross-reaction with sGP. mAb 7D8 exhibited the strongest neutralizing capacity. Further analysis on the critical residues for the bindings of 4C1 and 8G6 to GPs was conducted using antibodies complementarity-determining regions (CDRs) alanine scanning. It has been shown that light chain CDR3 played a crucial role in binding and neutralization and that any mutation in CDRs could not improve the binding of 4C1 to sGP. Importantly, mAbs 7D8, 8G6, and 4C1 provided complete protections against EBOV infection in a hamster lethal challenge model when administered 12 h post-infection. These results support mAbs 7D8, 8G6, and 4C1 as potent antibody candidates for further investigations and pave the way for further developments of therapies and vaccines.

Although progress has been made in multisource data scene parsing of natural scene images, extracting complex backgrounds from aerial images of various types and presenting the image at different scales remain challenging. Various factors in high-resolution aerial images (HRAIs), such as imaging blur, background clutter, object shadow, and high resolution, substantially reduce the integrity and accuracy of object segmentation. By applying multisource data fusion, as in scene parsing of natural scene images, we can solve the aforementioned problems through the integration of auxiliary data into HRAIs. To this end, we propose a multiscale cross-layer interactive and similarity refinement network (MISNet) for scene parsing of HRAIs. First, in a feature fusion optimization module, we extract, filter, and optimize multisource features and further guide and optimize the features using a feature guidance module. Second, a multiscale context aggregation module increases the receptive field, captures semantic information, and extracts rich multiscale background features. Third, a dense decoding module fuses the global guidance information and high-level fused features. We also propose a joint learning method based on feature similarity and a joint learning module to obtain deep multilevel information, enhance feature generation, and fuse multiscale and global features to enhance network representation for accurate scene parsing of HRAIs. Comprehensive experiments on two benchmark HRAIs datasets indicate that our proposed MISNet is qualitatively and quantitatively superior to similar state-of-the-art models.

Anomaly detection is a crucial research field in computer vision with diverse applications in practical scenarios. The common anomaly detection methods employed currently consist of autoencoders, generative adversarial networks, and knowledge distillation (KD) models. However, the teacher and student models in KD might not always yield distinct representations to signify anomalies due to their similar model structure and data flow. This study proposes a novel autoencoder-like KD model based on the attention mechanism for anomaly detection. The pre-trained teacher model incorporates a dual attention module as the encoder, while the student model integrates the same dual attention module as the decoder. The teacher guides the student to learn the feature knowledge of the input image. To connect the teacher-student model, a BottleNeck module is employed, converting the features extracted from the teacher model into more compact latent codes for precise restoration by the student model, thereby achieving anomaly detection. In general, the proposed model exhibits superior performance compared to other existing anomaly detection models on specific datasets. Experimental results demonstrate that the proposed model attains the state-of-the-art (SOTA) performance in anomaly detection on the public dataset MVTec. It achieves an average AUC of 98.2% and 98.0% at sample and pixel levels, respectively.

Although significant progress has been made in scene classification of high-resolution remote-sensing images (HRRSIs), dual-modal HRRSI scene classification is still an active and challenging issue. In this study, we introduce an end-to-end dense-attention–similarity-fusion network (DASFNet) for dual-modal HRRSIs. Specifically, we propose a dense-attention map module based on graph convolution, which adaptively captures long-range semantic cues and further directs shallow-attention cues to the deep level to guide the generation of high-level feature attention cues. At the encoder stage, DASFNet uses feature similarity to explore the correlation between dual-modal features; a similarity-fusion module extracts complementary information by fusing features from different modalities. A multiscale context-feature-aggregation module is used to strengthen the feature embedding of any two spatial scales; this solves the of scale change problem. A large number of experiments on two HRRSI benchmark datasets for scene classification indicate that the proposed DASFNet outperforms the outstanding scene classification approaches.

The success of deep learning and the segmentation of remote sensing images (RSIs) has improved semantic segmentation in recent years. However, existing RSI segmentation methods have two inherent problems: (1) detecting objects of various scales in RSIs of complex scenes is challenging, and (2) feature reconstruction for accurate segmentation is difficult. To solve these problems, we propose a deep-separation-guided progressive reconstruction network that achieves accurate RSI segmentation. First, we design a decoder comprising progressive reconstruction blocks capturing detailed features at various resolutions through multi-scale features obtained from various receptive fields to preserve accuracy during reconstruction. Subsequently, we propose a deep separation module that distinguishes various classes based on semantic features to use deep features to detect objects of different scales. Moreover, adjacent middle features are complemented during decoding to improve the segmentation performance. Extensive experimental results on two optical RSI datasets show that the proposed network outperforms 11 state-of-the-art methods.

With the advent of convolutional neural networks, research progress in visual saliency prediction has been impressive. While integrating features at different stages from the backbone network is important, feature extraction itself is equally relevant. A network may lose representative information during feature extraction. We address the loss of spatial information and perform a fusion of features extracted from RGB and depth data for eye-fixation prediction. Specifically, we propose an asymmetric feature extraction network comprising an edge guidance module (EGM) and a feature integration module (FIM) that processes RGB-D images. Edge guidance supports the extraction of spatial information, while feature integration merges features from RGB images and the corresponding depth maps. We obtain the eye-fixation prediction maps by linearly fusing the features from the backbone network with those optimized using the two modules. Experimental results on NCTU and NUS, two benchmark datasets for RGB-D saliency prediction, verify the effectiveness and high-performance of the proposed network compared with similar methods.

Indoor scene segmentation is a difficult task in computer vision. We propose an indoor scene segmentation framework, called DFMNet, incorporating RGB and complementary depth information to establish indoor scene segmentation. We use the squeeze-and-excitation residual network as encoder to simultaneously extract features from RGB and depth data and fuse them in the decoder. Multiple average pooling layers and transposed convolution layers are used to process the encoded outputs and fuse their outputs over several decoder layers. To optimize the network parameters, we use a pyramid supervision training scheme, which applies supervised learning over different layers in the decoder to prevent vanishing gradients. We evaluated the proposed DFMNet on the NYU Depth V2 dataset, which consists of 1449 cluttered indoor scenes, achieving competitive results compared to state-of-the-art methods.

Depth information has been widely used to improve RGB-D salient object detection by extracting attention maps to determine the position information of objects in an image. However, non-salient objects may be close to the depth sensor and present high pixel intensities in the depth maps. This situation in depth maps inevitably leads to erroneously emphasize non-salient areas and may have a negative impact on the saliency results. To mitigate this problem, we propose a hybrid attention neural network that fuses middle- and high-level RGB features with depth features to generate a hybrid attention map to remove background information. The proposed network extracts multilevel features from RGB images using the Res2Net architecture and then integrates high-level features from depth maps using the Inception-v4-ResNet2 architecture. The mixed high-level RGB features and depth features generate the hybrid attention map, which is then multiplied to the low-level RGB features. After decoding by several convolutions and upsampling, we obtain the final saliency prediction, achieving state-of-the-art performance on the NJUD and NLPR datasets. Moreover, the proposed network has good generalization ability compared with other methods. An ablation study demonstrates that the proposed network effectively performs saliency prediction even when non-salient objects interfere detection. In fact, after removing the branch with high-level RGB features, the RGB attention map that guides the network for saliency prediction is lost, and all the performance measures decline. The resulting prediction map from the ablation study shows the effect of non-salient objects close to the depth sensor. This effect is not present when using the complete hybrid attention network. Therefore, RGB information can correct and supplement depth information, and the corresponding hybrid attention map is more robust than using a conventional attention map constructed only with depth information.

In order to obtain the distances between the surrounding objects and the vehicle in the traffic scene in front of the vehicle, a monocular visual depth estimation method based on the depthwise separable convolutional neural network is proposed in this study. First, features containing shallow depth information were extracted from the RGB images using the convolution layers and maximum pooling layers. Subsampling operations were also performed on these images. Subsequently, features containing advanced depth information were extracted using a block based on an ensemble of convolution layers and a block based on depth separable convolution layers. The output from all different blocks is combined afterwards. Finally, transposed convolution layers were used for upsampling the feature maps to the same size with the original RGB image. During the upsampling process, skip connections were used to merge the features containing shallow depth information that was obtained from the convolution operation through the depthwise separable convolution layers. The depthwise separable convolution layers can provide more accurate depth information features for estimating the monocular visual depth. At the same time, they require reduced computational cost and fewer parameter numbers while providing a similar level (or slightly better) computing performance. Integrating multiple simple convolutions into a block not only increases the overall depth of the neural network but also enables a more accurate extraction of the advanced features in the neural network. Combining the output from multiple blocks can prevent the loss of features containing important depth information. The testing results show that the depthwise separable convolutional neural network provides a superior performance than the other monocular visual depth estimation methods. Therefore, applying depthwise separable convolution layers in the neural network is a more effective and accurate approach for estimating the visual depth.

Blind perceptual quality measurement of stereoscopic 3D (S3D) images has become an important and challenging issue in the research field of S3D imaging. In this paper, a blind S3D image quality measurement (IQM) method that does not depend on examples of distorted S3D images and corresponding subjective scores is proposed. As the main contribution of this work, we replace human subjective scores with a quality codebook of binocular rivalry responses (BRRs); this allows blind S3D-IQM methods to be learned without evaluation performance loss. Our results, using the publicly accessible LIVE S3D dataset, confirm that our method is highly robust and efficient.

Marburg virus disease (MVD) is a severe infectious disease caused by the Marburg virus (MARV), posing a significant threat to humans. MARV needs to be operated under strict biosafety Level 4 (BSL-4) laboratory conditions. Therefore, accessible and practical animal models are urgently needed to advance prophylactic and therapeutic strategies for MARV. In this study, we constructed a recombinant vesicular stomatitis virus (VSV) expressing the Marburg virus glycoprotein (VSV-MARV/GP). Syrian hamsters infected with VSV-MARV/GP presented symptoms such as thrombocytopenia, lymphopenia, haemophilia, and multiorgan failure, developing a severe systemic disease akin to that observed in human MARV patients. Notably, the pathogenicity was found to be species-specific, age-related, sex-associated, and challenge route-dependent. Subsequently, the therapeutic efficacy of the MR191 monoclonal antibody was validated in this model. In summary, this alternative model is an effective tool for rapidly screening medical countermeasures against MARV GP in vivo under BSL-2 conditions.