130,612 research outputs found
Top-down venomics of the East African green mamba, Dendroaspis angusticeps, and the black mamba, Dendroaspis polylepis, highlight the complexity of their toxin arsenals.
No full textWe report the characterization, by combination of high-resolution on-line molecular mass and disulfide bond profiling and top-down MS/MS analysis, of the venom proteomes of two congeneric African snake species of medical importance, Dendroaspis angusticeps (green mamba) and D. polylepis (black mamba). Each of these mamba venoms comprised more than two-hundred polypeptides belonging to just a few toxin families. Both venom proteomes are overwhelmingly composed of post-synaptically-acting short- and long-chain neurotoxins that potently inhibit muscle- and neuronal-type nicotinic acetylcholine receptors; muscarinic cardiotoxins; and dendrotoxins, that block some of the Kv1, n-class of K+ channels. However, the identity of the major proteins and their relative abundances exhibit marked interspecific variation. In addition, the greater resolution of the top-down venomic analytical approach revealed previously undetected protein species, isoforms and proteoforms, including the identification and precise location of modified lysine residues in a number of proteins in both venoms, but particularly in green mamba toxins. This comparative top-down venomic analysis unveiled the untapped complexity of Dendroaspis venoms and lays the foundations for rationalizing the notably different potency of green and black mamba lethal arsenals at locus resolution.Significance paragraphDendroaspis angusticeps (eastern green mamba) and D. polylepis (black mamba) are African snake species of medical concern. Their venoms comprise a high diversity of pharmacologically active peptides, including extremely rapid-acting neurotoxins. Studies on the venoms of D. polylepis and D. angusticeps have focused on the biochemical and pharmacological characterization of their most relevant toxins to rationalize the common neurological and neuromuscular symptoms of envenomings caused by these species. Only very recently an overview of the composition of the venom of a Dendroaspis species, D. polylepis, has been reported through a bottom-up venomics strategy. Peptide-centric approaches provide incomplete sequence coverage, and in general do not allow to distinguish between different proteoforms or closely related toxin isoforms. To overcome this shortcoming we have now applied top-down venomics to unveil the complexity of the toxin arsenals of the black mamba and the eastern green mamba at locus resolution. Our data show that the green and the black mamba venom contain, respectively, ≥ 232 and ≥ 268 protein species, highlighting that D. angusticeps and D. polylepis venom comprise a much higher complexity than the 20 and the 27 toxin sequences available, respectively for these snake species, in the non-redundant NCBI database. On the other hand, 36 (D. angusticeps) and 3 (D. polylepis) minor venom proteins showed mass shifts of + 42 Da modifications, suggesting the presence of monoacetyl lysine residues. Noteworthy, although both venoms have highly similar global molecular compositions, the identity of the major proteins and their relative abundances vary between D. angusticeps and D. polylepis. Our data lay the foundation for rationalizing the notably different venom toxicity profiles of the green and the black mamba.</p
The faunal assemblage from the Early Iron Age site of Mamba I in the Thukela valley, Natal
No full textMambaSOD: Dual Mamba-Driven Cross-Modal Fusion Network for RGB-D Salient Object Detection
No full textThe purpose of RGB-D Salient Object Detection (SOD) is to pinpoint the most visually conspicuous areas within images accurately. While conventional deep models heavily rely on CNN extractors and overlook the long-range contextual dependencies, subsequent transformer-based models have addressed the issue to some extent but introduce high computational complexity. Moreover, incorporating spatial information from depth maps has been proven effective for this task. A primary challenge of this issue is how to fuse the complementary information from RGB and depth effectively. In this paper, we propose a dual Mamba-driven cross-modal fusion network for RGB-D SOD, named MambaSOD. Specifically, we first employ a dual Mamba-driven feature extractor for both RGB and depth to model the long-range dependencies in multiple modality inputs with linear complexity. Then, we design a cross-modal fusion Mamba for the captured multi-modal features to fully utilize the complementary information between the RGB and depth features. To the best of our knowledge, this work is the first attempt to explore the potential of the Mamba in the RGB-D SOD task, offering a novel perspective. Numerous experiments conducted on six prevailing datasets demonstrate our method\u27s superiority over sixteen state-of-the-art RGB-D SOD models. The source code will be released at https://github.com/YueZhan721/MambaSOD
Crystal structure of toxin MT9 from mamba venom
No full textCrystal structure of toxin MT9 from mamba veno
Point Cloud Mamba: Point Cloud Learning via State Space Model
No full textRecently, state space models have exhibited strong global modeling capabilities and linear computational complexity in contrast to transformers. This research focuses on applying such architecture to more efficiently and effectively model point cloud data globally with linear computational complexity. In particular, for the first time, we demonstrate that Mamba-based point cloud methods can outperform previous methods based on transformer or multi-layer perceptrons (MLPs). To enable Mamba to process 3-D point cloud data more effectively, we propose a novel Consistent Traverse Serialization method to convert point clouds into 1-D point sequences while ensuring that neighboring points in the sequence are also spatially adjacent. Consistent Traverse Serialization yields six variants by permuting the order of \textit{x}, \textit{y}, and \textit{z} coordinates, and the synergistic use of these variants aids Mamba in comprehensively observing point cloud data. Furthermore, to assist Mamba in handling point sequences with different orders more effectively, we introduce point prompts to inform Mamba of the sequence\u27s arrangement rules. Finally, we propose positional encoding based on spatial coordinate mapping to inject positional information into point cloud sequences more effectively. Point Cloud Mamba surpasses the state-of-the-art (SOTA) point-based method PointNeXt and achieves new SOTA performance on the ScanObjectNN, ModelNet40, ShapeNetPart, and S3DIS datasets. It is worth mentioning that when using a more powerful local feature extraction module, our PCM achieves 79.6 mIoU on S3DIS, significantly surpassing the previous SOTA models, DeLA and PTv3, by 5.5 mIoU and 4.9 mIoU, respectively.Update more results on S3DIS datase
Point Cloud Mamba: Point Cloud Learning via State Space Model
No full textRecently, state space models have exhibited strong global modeling capabilities and linear computational complexity in contrast to transformers. This research focuses on applying such architecture to more efficiently and effectively model point cloud data globally with linear computational complexity. In particular, for the first time, we demonstrate that Mamba-based point cloud methods can outperform previous methods based on transformer or multi-layer perceptrons (MLPs). To enable Mamba to process 3-D point cloud data more effectively, we propose a novel Consistent Traverse Serialization method to convert point clouds into 1-D point sequences while ensuring that neighboring points in the sequence are also spatially adjacent. Consistent Traverse Serialization yields six variants by permuting the order of x, y, and z coordinates, and the synergistic use of these variants aids Mamba in comprehensively observing point cloud data. Furthermore, to assist Mamba in handling point sequences with different orders more effectively, we introduce point prompts to inform Mamba of the sequence’s arrangement rules. Finally, we propose positional encoding based on spatial coordinate mapping to inject positional information into point cloud sequences more effectively. Point Cloud Mamba surpasses the state-of-the-art (SOTA) point-based method PointNeXt and achieves new SOTA performance on the ScanObjectNN, ModelNet40, ShapeNetPart, and S3DIS datasets. It is worth mentioning that when using a more powerful local feature extraction module, our PCM achieves 79.6 mIoU on S3DIS, significantly surpassing the previous SOTA models, DeLA and PTv3, by 5.5 mIoU and 4.9 mIoU, respectively
Doug Rose and the Green Mamba, Bonneville Drag Strip
No full textColor photograph of a scene at Bonneville Drag Strip in Salt Lake City, Utah, in 1981. Doug Rose works on his "Green Mamba" race car
Discovery of Peptide-based Inhibitors against Dendrotoxin B from Black Mamba through Phage Display Screening
No full textThe black mamba (Dendroaspis polylepis) is Africa’s most feared snake due to its potent, rapidacting venom and its speed of attack. The most abundant toxins in D. polylepis venom are the Kunitz-type proteinase inhibitors, dendrotoxins, which are unique for mamba. Dendrotoxinsare poorly neutralized by current antivenoms, and they are almost impossible to raise an immune response against due to their similarity to homologous mammalian, non-toxic proteins. Here,we report the discovery of peptide-based antitoxins against dendrotoxin B from D. polylepis through phage display screening
MeSH term explosion and author rank improve expert recommendations
No full textInformation overload is an often-cited phenomenon that reduces the productivity, efficiency and efficacy of scientists. One challenge for scientists is to find appropriate collaborators in their research. The literature describes various solutions to the problem of expertise location, but most current approaches do not appear to be very suitable for expert recommendations in biomedical research. In this study, we present the development and initial evaluation of a vector space model-based algorithm to calculate researcher similarity using four inputs: 1) MeSH terms of publications; 2) MeSH terms and author rank; 3) exploded MeSH terms; and 4) exploded MeSH terms and author rank. We developed and evaluated the algorithm using a data set of 17,525 authors and their 22,542 papers. On average, our algorithms correctly predicted 2.5 of the top 5/10 coauthors of individual scientists. Exploded MeSH and author rank outperformed all other algorithms in accuracy, followed closely by MeSH and author rank. Our results show that the accuracy of MeSH term-based matching can be enhanced with other metadata such as author rank
The medical threat of mamba envenoming in sub-Saharan Africa revealed by genus-wide analysis of venom composition, toxicity and antivenomics profiling of available antivenoms
No full textMambas (genus Dendroaspis) are among the most feared of the medically important elapid snakes found in sub-Saharan Africa, but many facets of their biology, including the diversity of venom composition, remain relatively understudied. Here, we present a reconstruction of mamba phylogeny, alongside genus-wide venom gland transcriptomic and high-resolution top-down venomic analyses. Whereas the green mambas, D. viridis, D. angusticeps, D. j. jamesoni and D. j. kaimosae, express 3FTx-predominant venoms, black mamba (D. polylepis) venom is dominated by dendrotoxins I and K. The divergent terrestrial ecology of D. polylepis compared to the arboreal niche occupied by all other mambas makes it plausible that this major difference in venom composition is due to dietary variation. The pattern of intrageneric venom variability across Dendroaspis represented a valuable opportunity to investigate, in a genus-wide context, the variant toxicity of the venom, and the degree of paraspecific cross-reactivity between antivenoms and mamba venoms. To this end, the immunological profiles of the five mamba venoms were assessed against a panel of commercial antivenoms generated for the sub-Saharan Africa market. This study provides a genus-wide overview of which available antivenoms may be more efficacious in neutralising human envenomings caused by mambas, irrespective of the species responsible. The information gathered in this study lays the foundations for rationalising the notably different potency and pharmacological profiles of Dendroaspis venoms at locus resolution. This understanding will allow selection and design of toxin immunogens with a view to generating a safer and more efficacious pan-specific antivenom against any mamba envenomation
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