DsRNA-degrading enzymes (dsRNases) have been recognized as important factors in reducing RNA interference (RNAi) efficiency in different insect species. However, dsRNases in Plutella xylostella are still unknown. We identified the full-length cDNAs of PxdsRNase1, PxdsRNase2, PxdsRNase3, and PxdsRNase4. Gene expression profile showed that PxdsRNase1 was mainly expressed in the hemolymph; and that PxdsRNase2 and PxdsRNase3 were mainly expressed in the intestinal tract. The expression of PxCht (Chitinase of P. xylostella) in P. xylostella larvae injected with the mixture of dsPxCht (dsRNA of PxCht) and dsPxdsRNase1 (dsRNA of PxdsRNase1), dsPxdsRNase2 (dsRNA of PxdsRNase2), or dsPxdsRNase3 (dsRNA of PxdsRNase3) was significantly higher than that in the larvae injected with the mixture of dsGFP (dsRNA of green fluorescent protein gene, GFP) and dsPxCht; the transcription level of PxCht in the larvae feeding on the mixture of dsPxCht and dsPxdsRNase1, dsPxdsRNase2, or dsPxdsRNase3 was significantly higher than that in the larvae feeding on the mixture of dsPxCht and dsGFP. The recombinant protein of PxdsRNase1 degraded dsRNA rapidly, PxdsRNase3 cleaved dsRNA without complete degradation, and PxdsRNase2 could not degrade dsRNA in vitro. These results suggested that PxdsRNases1, PxdsRNases2, and PxdsRNases3 were involved in the dsRNA degradation to reduce RNAi efficiency with different mechanisms.
Spontaneous resting-state neural activity or hemodynamics has been used to reveal functional connectivity in the brain. However, most of the commonly used clustering algorithms for functional parcellation are time-consuming, especially for high-resolution imaging data. We propose a density center-based fast clustering (DCBFC) method that can rapidly perform the functional parcellation of isocortex. DCBFC was validated using both simulation data and the spontaneous calcium signals from widefield fluorescence imaging of excitatory neuron-expressing transgenic mice (Vglut2-GCaMP6s). Compared to commonly used clustering methods such as k-means, hierarchical, and spectral, DCBFC showed a higher adjusted Rand index when the signal-to-noise ratio was greater than −8 dB for simulated data and higher silhouette coefficient for in vivo mouse data. The resting-state functional connectivity (RSFC) patterns obtained by DCBFC were compared with the anatomic axonal projection density (PDs) maps derived from the voxel-scale model. The results showed a high spatial correlation between RSFC patterns and PDs.
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