Insects are the most speciose group of animals, but the phylogenetic relationships of many major lineages remain unresolved. We inferred the phylogeny of insects from 1478 protein-coding genes. Phylogenomic analyses of nucleotide and amino acid sequences, with site-specific nucleotide or domain-specific amino acid substitution models, produced statistically robust and congruent results resolving previously controversial phylogenetic relations hips. We dated the origin of insects to the Early Ordovician [~479 million years ago (Ma)], of insect flight to the Early Devonian (~406 Ma), of major extant lineages to the Mississippian (~345 Ma), and the major diversification of holometabolous insects to the Early Cretaceous. Our phylogenomic study provides a comprehensive reliable scaffold for future comparative analyses of evolutionary innovations among insects.
METTL3 catalyzes the formation of N6 -methyl-adenosine (m 6 A) which has important roles in regulating various biological processes. However, the in vivo function of Mettl3 remains largely unknown in mammals. Here we generated germ cell-specific Mettl3 knockout mice and demonstrated that Mettl3 was essential for male fertility and spermatogenesis. The ablation of Mettl3 in germ cells severely inhibited spermatogonial differentiation and blocked the initiation of meiosis. Transcriptome and m 6 A profiling analysis revealed that genes functioning in spermatogenesis had altered profiles of expression and alternative splicing. Our findings provide novel insights into the function and regulatory mechanisms of Mettl3-mediated m 6 A modification in spermatogenesis and reproduction in mammals.
The goal of this paper is to contribute to the field of nondestructive testing by eddy currents. We provide a mathematical analysis and a numerical framework for simulating the imaging of arbitrarily shaped small volume conductive inclusions from electromagnetic induction data. We derive, with proof, a small-volume expansion of the eddy current data measured away from the conductive inclusion. The formula involves two polarization tensors: one associated with the magnetic contrast and the second with the conductivity of the inclusion. Based on this new formula, we design a location search algorithm. We include in this paper a discussion on data sampling, noise reduction, and on probability of detection. We provide numerical examples that support our findings.
Mathematics Subject Classification (MSC2000): 35R30, 35B30
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