The Combination of Bacillus Thuringiensis and Its Engineered Strain Expressing dsRNA Increases the Toxicity against Plutella Xylostella.

RNA interference (RNAi) has been developed and used as an emerging strategy for pest management. Here, an entomopathogen Bacillus thuringiensis (Bt) was used to express the dsRNA for the control of Plutella xylostella. A vector containing a 325-bp fragment of the conserved region of P. xylostella arginine kinase gene (PxAK) flanking in two ends with the promoter Pro3α was developed and transferred into Bt 8010 and BMB171, and consequently engineered Bt strains 8010AKi and BMB171AKi expressing dsRNA of PxAK were developed. The two engineered Bt strains were separately mixed with Bt 8010 in a series of ratios, and then fed to the P. xylostella larvae. We found that 8010:8010AKi of 9:1 and 8010:BMB171AKi of 7:3 caused a higher mortality than Bt 8010. PxAK expression levels in the individuals treated with the mixtures, 8010AKi and BMB171Aki, were lower than that in the control. The intrinsic rate of increase (r) and net reproductive rate (R0) of the population treated with 8010:8010AKi of 9:1 were lower than those of the population treated with Bt 8010 or 8010AKi. We developed a Bt-mediated insect RNAi for the control of P. xylostella and demonstrated a practical approach to integrating the entomopathogen with RNAi technique for the pest management.

Revealing the Cortical Glutamatergic Neural Activity During Burst Suppression by Simultaneous wide Field Calcium Imaging and Electroencephalography in Mice.

Burst suppression (BS) is an electroencephalogram (EEG) pattern in which signals alternates between high-amplitude slow waves (burst waves) and nearly flat low-amplitude waves (suppression waves). In this study, we used wide-field (8.32 mm × 8.32 mm) fluorescent calcium imaging to record the activity of glutamatergic neurons in the parietal and occipital cortex, in conjunction with EEG recordings under BS induced by different anesthetics (sevoflurane, isoflurane, and propofol), to investigate the spatiotemporal pattern of neural activity under BS. The calcium signal of all observed cortices was decreased during the phase of EEG suppression. However, during the phase of EEG burst, the calcium signal in areas of the medial cortex, such as the secondary motor and retrosplenial area, was excited, whereas the signal in areas of the lateral cortex, such as the hindlimb cortex, forelimb cortex, barrel field, and primary visual area, was still suppressed or only weakly excited. Correlation analysis showed a strong correlation between the EEG signal and the calcium signal in the medial cortex under BS (except for propofol induced signals). As the burst-suppression ratio (BSR) increased, the regions with strong correlation coefficients became smaller, but strong correlation coefficients were still noted in the medial cortex. Taken together, our results reveal the landscape of cortical activity underlying BS.

Double-Stranded RNA-Degrading Enzymes Reduce the Efficiency of RNA Interference in Plutella xylostella.

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.

Dilated Residual Learning With Skip Connections for Real-Time Denoising of Laser Speckle Imaging of Blood Flow in a Log-Transformed Domain.

Laser speckle contrast imaging (LSCI) is a wide-field and noncontact imaging technology for mapping blood flow. Although the denoising method based on block-matching and three-dimensional transform-domain collaborative filtering (BM3D) was proposed to improve its signal-to-noise ratio (SNR) significantly, the processing time makes it difficult to realize real-time denoising. Furthermore, it is still difficult to obtain an acceptable level of SNR with a few raw speckle images given the presence of significant noise and artifacts. A feed-forward denoising convolutional neural network (DnCNN) achieves state-of-the-art performance in denoising nature images and is efficiently accelerated by GPU. However, it performs poorly in learning with original speckle contrast images of LSCI owing to the inhomogeneous noise distribution. Therefore, we propose training DnCNN for LSCI in a log-transformed domain to improve training accuracy and it achieves an improvement of 5.13 dB in the peak signal-to-noise ratio (PSNR). To decrease the inference time and improve denoising performance, we further propose a dilated deep residual learning network with skip connections (DRSNet). The image-quality evaluations of DRSNet with five raw speckle images outperform that of spatially average denoising with 20 raw speckle images. DRSNet takes 35 ms (i.e., 28 frames per second) for denoising a blood flow image with 486×648 pixels on an NVIDIA 1070 GPU, which is approximately 2.5 times faster than DnCNN. In the test sets, DRSNet also improves 0.15 dB in the PSNR than that of DnCNN. The proposed network shows good potential in real-time monitoring of blood flow for biomedical applications.

Silencing arginine kinase/integrin ß1 subunit by transgenic plant expressing dsRNA inhibits the development and survival of Plutella xylostella.

BACKGROUND: Plutella xylostella is a devastating agricultural insect pest of cruciferous plants, including crops. Plant-mediated RNA interference (RNAi) is currently being developed for plant protection. In this study, we investigated the response of P. xylostella exposed to transgenic Arabidopsis thaliana plants that expressed double-stranded RNA (dsRNA) targeting P. xylostella genes of arginine kinase (PxAK) and integrin ß1 subunit (Pxß). RESULTS: Transgenic plants producing dsRNAs of the 384-bp fragment of PxAK (dsAK plants), the 497-bp fragment of Pxß (dsß plants), and the 881 bp of the combination of both genes (dsAK-ß plants) were generated and verified. Insect bioassay with these transgenic plants showed that the development of P. xylostella was affected, causing longer developmental time, and lower pupal weight and pupation rate. P. xylostella mortality rates were 25.0% when exposed to dsAK plants, 22.5% with dsß plants, and 30.0% with dsAK-ß plants, which were all higher than 7.5% for the wild-type plant. PxAK and Pxß in P. xylostella were suppressed by 26.6-79.7% at the transcription level by the transgenic plants. CONCLUSION: These results suggest that plant-mediated RNAi targeting single gene or both PxAK and Pxß may have the potential to control P. xylostella. © 2019 Society of Chemical Industry.

Density center-based fast clustering of widefield fluorescence imaging of cortical mesoscale functional connectivity and relation to structural connectivity.

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.

Manhattan Distance-Based Adaptive 3D Transform-Domain Collaborative Filtering for Laser Speckle Imaging of Blood Flow.

Laser speckle contrast imaging (LSCI) is a full-field, noncontact imaging technology for mapping blood flow with high spatio-temporal resolution, in which the speckle contrast can be estimated either in spatial domain or temporal domain. Temporal LSCI (tLSCI) provides higher spatial resolution than spatial domain does. However, when the number of sampling frames is limited, it is difficult to obtain accurate blood flow velocity owing to the significant statistical noise. The widely used spatially averaged tLSCI (savg-tLSCI) usually requires a large number of sampling frames to obtain acceptable denoising performance. Here, based on the nonlocal filtering strategy of block-matching and three-dimensional transform-domain collaborative filtering (BM3D), Manhattan distance-based adaptive BM3D (MD-ABM3D) is proposed to effectively manage the complicated inhomogeneous noise in tLSCI image and improve the signal-to-noise ratio. Manhattan distance improves the accuracy of the block matching in strong noise, and the adaptive algorithm adapts to the inhomogeneous noise and estimates suitable parameters for improved denoising. MD-ABM3D improves 4.91 dB in peak signal-to-noise ratio relative to savg-tLSCI. It achieves stability for denoising tLSCI image with different temporal windows. The image-quality evaluation of MD-ABM3D for tLSCI (t = 20 frames) equals that of savg-tLSCI (t = 60 frames). It achieves high signal-to-noise ratio with a reduced number of sampling frames. A reduced number of sampling frames are more practical for biomedical applications. It also offers higher temporal resolution and less disturbance from the motion of the moving object.