Leaf venation networks evolved along several functional axes, including resource transport, damage resistance, mechanical strength, and construction cost. Because functions may depend on architectural features at different scales, network architecture may vary across spatial scales to satisfy functional tradeoffs. We develop a framework for quantifying network architecture with multiscale statistics describing elongation ratios, circularity ratios, vein density, and minimum spanning tree ratios. We quantify vein networks for leaves of 260 southeast Asian tree species in samples of up to 2 cm , pairing multiscale statistics with traits representing axes of resource transport, damage resistance, mechanical strength, and cost. We show that these multiscale statistics clearly differentiate species' architecture and delineate a phenotype space that shifts at larger scales; functional linkages vary with scale and are weak, with vein density, minimum spanning tree ratio, and circularity ratio linked to mechanical strength (measured by force to punch) and elongation ratio and circularity ratio linked to damage resistance (measured by tannins); and phylogenetic conservatism of network architecture is low but scale-dependent. This work provides tools to quantify the function and evolution of venation networks. Future studies including primary and secondary veins may uncover additional insights.
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http://dx.doi.org/10.1111/nph.16830 | DOI Listing |
Heliyon
December 2024
School of Civil and Hydraulic Engineering, Huazhong University of Science and Technology, Wuhan, 430074, China.
Basalt, which is a geological medium used for engineering construction in Southwest China, contains defect structures at various scales. In particular, the widespread presence of mesoscale hidden joints significantly affects the mechanical properties of basalt and the stability of engineering structures. However, research in this specific subject has been limited.
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School of Mathematics and Statistics, The University of Melbourne, Parkville, Australia.
The epidemiological behavior of Plasmodium vivax malaria occurs across spatial scales including within-host, population, and metapopulation levels. On the within-host scale, P. vivax sporozoites inoculated in a host may form latent hypnozoites, the activation of which drives secondary infections and accounts for a large proportion of P.
View Article and Find Full Text PDFHum Brain Mapp
December 2024
Department of Neurosciences and Mental Health, Fondazione IRCS Cà Granda Ospedale Policlinico, Milano, Italy.
Data aggregation across multiple research centers is gaining importance in the context of MRI research, driving diverse high-dimensional datasets to form large-scale heterogeneous sample, increasing statistical power and relevance of machine learning and deep learning algorithm. Site-related effects have been demonstrated to introduce bias in MRI features and confound subsequent analyses. Although Combating Batch (ComBat) technique has been recently reported to successfully harmonize multi-scale neuroimaging features, its performance assessments are still limited and largely based on qualitative visualizations and statistical analyses.
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Department of Environmental Engineering, Ondokuz Mayıs Üniversitesi, Samsun, Turkey.
This study aims to develop a stable and efficient magnetic nanocomposite hydrogel (MNCH) for selective removal of methylene blue (MB) and crystal violet (CV). MNCHs with different FeO contents (0-9 wt%) were synthesized following graft co-polymerization method using sodium alginate, acrylamide, itaconic acid, ammonium persulfate and N,N-methylene bisacrylamide. Among them, MNCH, with 5 wt% FeO, showed highest removal efficiency (>95 %).
View Article and Find Full Text PDFSensors (Basel)
December 2024
Beijing Urban Construction Survey and Design Institute, Beijing 100101, China.
Interferometric Synthetic Aperture Radar (InSAR) is a widely used remote sensing technology for Earth observation, enabling the detection and measurement of ground deformation through the generation of interferograms. However, phase noise remains a critical factor that degrades interferogram quality. To address this issue, this study proposes MOMFNet, a deep learning approach for InSAR phase filtering based on multi-objective multi-kernel feature extraction that leverages multi-objective multi-kernel feature extraction.
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