Rodents living in a subterranean ecotope face a unique combination of evolutionary and ecological pressures and while host species evolution may be driven by the selective pressure from the parasites they harbour, the parasites may be responding to the selective pressures of the host. Here, we obtained all available subterranean rodent host–parasite records from the literature and integrated these data by utilizing a bipartite network analysis to determine multiple critical parameters to quantify and measure the structure and interactions of the organisms present in host–parasite communities. A total of 163 species of subterranean rodent hosts, 174 parasite species and 282 interactions were used to create 4 networks with data well-represented from all habitable continents. The results show that there was no single species of parasite that infects subterranean rodents throughout all zoogeographical regions. Nevertheless, species representing the genera and were common across all communities of subterranean rodents studied. Based on our analysis of host–parasite interactions across all communities studied, the parasite linkages show that community connectance (due to climate change or other anthropogenic factors) appears to show degraded linkages in both the Nearctic and Ethiopian regions: in this case parasites are acting as bell-weather probes signalling the loss of biodiversity.
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http://dx.doi.org/10.1017/S0031182023000148 | DOI Listing |
Nat Commun
January 2025
Beijing Advanced Innovation Center for Integrated Circuits, School of Integrated Circuits, Peking University, Beijing, China.
Compute-in-memory based on resistive random-access memory has emerged as a promising technology for accelerating neural networks on edge devices. It can reduce frequent data transfers and improve energy efficiency. However, the nonvolatile nature of resistive memory raises concerns that stored weights can be easily extracted during computation.
View Article and Find Full Text PDFNeural Netw
January 2025
School of Cyber Science and Engineering, Xi'an Jiaotong University, China. Electronic address:
Detecting anomalies in attributed networks has become a subject of interest in both academia and industry due to its wide spectrum of applications. Although most existing methods achieve desirable performance by the merit of various graph neural networks, the way they bundle node-affiliated multidimensional attributes into a whole for embedding calculation hinders their ability to model and analyze anomalies at the fine-grained feature level. To characterize anomalies from each feature dimension, we propose Eagle, a deep framework based on bipartitE grAph learninG for anomaLy dEtection.
View Article and Find Full Text PDFBrief Bioinform
November 2024
Computational Biology Department, Carnegie Mellon University, Pittsburgh, PA, 15213, USA.
Cryo-electron tomography (cryo-ET) is confronted with the intricate task of unveiling novel structures. General class discovery (GCD) seeks to identify new classes by learning a model that can pseudo-label unannotated (novel) instances solely using supervision from labeled (base) classes. While 2D GCD for image data has made strides, its 3D counterpart remains unexplored.
View Article and Find Full Text PDFAnimals (Basel)
January 2025
Laboratory and Museum of Evolutionary Ecology, Department of Ecology, Faculty of Humanities and Natural Sciences, University of Prešov, 080 01 Prešov, Slovakia.
This article emphasises the importance of parasitological research in understanding ecological dynamics and biodiversity conservation through a global analysis of quill mites (Syringophilidae) parasitising Sunbirds (Nectariniidae). Data from 764 Sunbird individuals across seventy-six species revealed twelve quill mite species, including three newly described species: Sikora and Unsoeld sp. n.
View Article and Find Full Text PDFEntropy (Basel)
November 2024
Department of Mathematics and Statistics, Dalhousie University Halifax, Halifax, NS B3H 3J5, Canada.
We study the algebraic connectivity for several classes of random semi-regular graphs. For large random semi-regular bipartite graphs, we explicitly compute both their algebraic connectivity as well as the full spectrum distribution. For an integer d∈3,7, we find families of random semi-regular graphs that have higher algebraic connectivity than random -regular graphs with the same number of vertices and edges.
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