Search Results (5)

Pekar et al. (2022) propose that SARS-CoV-2 was a zoonotic spillover that first infected humans in the Huanan Seafood Market in Wuhan, China. They propose that there were two separate spillovers of the closely related lineages A and lineage B in a short period of time. The two lineages are differentiated by two SNVs; hence, a single-SNV A-B intermediate must have occurred in an unsampled animal host if the two-spillover hypothesis is correct. Consequently, confirmation of the existence of an intermediate A-B genome from humans would falsify their hypothesis of two spillovers. Pekar et al. identified and excluded 20 A-B intermediate genomes from their analysis. A variety of exclusion criteria were applied, including low read depth and the assertion of repeated erroneous base calls at lineage-defining positions 8782 and 28144. However, data from GISAID show that most of the genomes were sequenced to high average sequencing depth, appearing inconsistent with these criteria. The decision to exclude the majority of genomes was based on personal communications, with raw data unavailable for inspection. Multiple errors, biases, and inconsistencies were observed in the exclusion process. For example, 12 intermediate genomes from one study were excluded; however, 54 other genomes from the same study were included, indicating selection bias. Puzzlingly, two intermediate genomes from Beijing were discarded despite an average sequencing depth of 2175X; however, four genomes from the same sequencing study were included in the analysis. Lastly, we discuss 14 additional possible intermediate genomes not discussed by Pekar et al. and note that genome sequence filtration is inappropriate when considering the presence or absence of a specific SNV pair in an outbreak. Consequently, we find that the exclusion of many of the intermediate genomes is unfounded, leaving the conclusion of two natural zoonoses unsupported. Full article

Pangolins are the only animals other than bats proposed to have been infected with SARS-CoV-2 related coronaviruses (SARS2r-CoVs) prior to the COVID-19 pandemic. Here, we examine the novel SARS2r-CoV we previously identified in game animal metatranscriptomic datasets sequenced by the Nanjing Agricultural University in 2022, and find that sections of the partial genome phylogenetically group with Guangxi pangolin CoVs (GX PCoVs), while the full RdRp sequence groups with bat-SL-CoVZC45. While the novel SARS2r-CoV is found in 6 pangolin datasets, it is also found in 10 additional NGS datasets from 5 separate mammalian species and is likely related to contamination by a laboratory researched virus. Absence of bat mitochondrial sequences from the datasets, the fragmentary nature of the virus sequence and the presence of a partial sequence of a cloning vector attached to a SARS2r-CoV read suggests that it has been cloned. We find that NGS datasets containing the novel SARS2r-CoV are contaminated with significant Homo sapiens genetic material, and numerous viruses not associated with the host animals sampled. We further identify the dominant human haplogroup of the contaminating H. sapiens genetic material to be F1c1a1, which is of East Asian provenance. The association of this novel SARS2r-CoV with both bat CoV and the GX PCoV clades is an important step towards identifying the origin of the GX PCoVs. Full article

Deep learning-based methods succeed in remote sensing scene classification (RSSC). However, current methods require training on a large dataset, and if a class does not appear in the training set, it does not work well. Zero-shot classification methods are designed to address the classification for unseen category images in which the generative adversarial network (GAN) is a popular method. Thus, our approach aims to achieve the zero-shot RSSC based on GAN. We employed the conditional Wasserstein generative adversarial network (WGAN) to generate image features. Since remote sensing images have inter-class similarity and intra-class diversity, we introduced classification loss, semantic regression module, and class-prototype loss to constrain the generator. The classification loss was used to preserve inter-class discrimination. We used the semantic regression module to ensure that the image features generated by the generator can represent the semantic features. We introduced class-prototype loss to ensure the intra-class diversity of the synthesized image features and avoid generating too homogeneous image features. We studied the effect of different semantic embeddings for zero-shot RSSC. We performed experiments on three datasets, and the experimental results show that our method performs better than the state-of-the-art methods in zero-shot RSSC in most cases. Full article

Ego-network, which can describe relationships between a focus node (i.e., ego) and its neighbor nodes (i.e., alters), often changes over time. Exploring dynamic ego-networks can help users gain insight into how each ego interacts with and is influenced by the outside world. However, most of the existing methods do not fully consider the multilevel analysis of dynamic ego-networks, resulting in some evolution information at different granularities being ignored. In this paper, we present an interactive visualization system called DyEgoVis which allows users to explore the evolutions of dynamic ego-networks at global, local and individual levels. At the global level, DyEgoVis reduces dynamic ego-networks and their snapshots to 2D points to reveal global patterns such as clusters and outliers. At the local level, DyEgoVis projects all snapshots of the selected dynamic ego-networks onto a 2D space to identify similar or abnormal states. At the individual level, DyEgoVis utilizes a novel layout method to visualize the selected dynamic ego-network so that users can track, compare and analyze changes in the relationships between the ego and alters. Through two case studies on real datasets, we demonstrate the usability and effectiveness of DyEgoVis. Full article

In recent years, convolutional neural networks (CNNs) have shown great success in the scene classification of computer vision images. Although these CNNs can achieve excellent classification accuracy, the discriminative ability of feature representations extracted from CNNs is still limited in distinguishing more complex remote sensing images. Therefore, we propose a unified feature fusion framework based on attention mechanism in this paper, which is called Deep Discriminative Representation Learning with Attention Map (DDRL-AM). Firstly, by applying Gradient-weighted Class Activation Mapping (Grad-CAM) algorithm, attention maps associated with the predicted results are generated in order to make CNNs focus on the most salient parts of the image. Secondly, a spatial feature transformer (SFT) is designed to extract discriminative features from attention maps. Then an innovative two-channel CNN architecture is proposed by the fusion of features extracted from attention maps and the RGB (red green blue) stream. A new objective function that considers both center and cross-entropy loss are optimized to decrease the influence of inter-class dispersion and within-class variance. In order to show its effectiveness in classifying remote sensing images, the proposed DDRL-AM method is evaluated on four public benchmark datasets. The experimental results demonstrate the competitive scene classification performance of the DDRL-AM approach. Moreover, the visualization of features extracted by the proposed DDRL-AM method can prove that the discriminative ability of features has been increased. Full article