Molecular Determinants of PI(4,5)P2 and PI(3,4,5)P3 Regulation of the Epithelial Na+ Channel

Phosphatidylinositol 4,5-bisphosphate (PI(4,5)P2) and phosphatidylinositol 3,4,5-trisphosphate (PI(3,4,5)P3) are physiologically important second messengers. These molecules bind effector proteins to modulate activity. Several types of ion channels, including the epithelial Na+ channel (ENaC), are phosphoinositide effectors capable of directly interacting with these signaling molecules. Little, however, is known of the regions within ENaC and other ion channels important to phosphoinositide binding and modulation. Moreover, the molecular mechanism of this regulation, in many instances, remains obscure. Here, we investigate modulation of ENaC by PI(3,4,5)P3 and PI(4,5)P2 to begin identifying the molecular determinants of this regulation. We identify intracellular regions near the inner membrane interface just following the second transmembrane domains in β- and γ- but not α-ENaC as necessary for PI(3,4,5)P2 but not PI(4,5)P2 modulation. Charge neutralization of conserved basic amino acids within these regions demonstrated that these polar residues are critical to phosphoinositide regulation. Single channel analysis, moreover, reveals that the regions just following the second transmembrane domains in β- and γ-ENaC are critical to PI(3,4,5)P3 augmentation of ENaC open probability, thus, defining mechanism. Unexpectedly, intracellular domains within the extreme N terminus of β- and γ-ENaC were identified as being critical to down-regulation of ENaC activity and Po in response to depletion of membrane PI(4,5)P2. These regions of the channel played no identifiable role in a PI(3,4,5)P3 response. Again, conserved positive-charged residues within these domains were particularly important, being necessary for exogenous PI(4,5)P2 to increase open probability. We conclude that β and γ subunits bestow phosphoinositide sensitivity to ENaC with distinct regions of the channel being critical to regulation by PI(3,4,5)P3 and PI(4,5)P2. This argues that these phosphoinositides occupy distinct ligand-binding sites within ENaC to modulate open probability

Blind Thrusting, Surface Folding and the Development of Geological Structure in the Mw 6.3 2015 Pishan (China) Earthquake

The relationship between individual earthquakes and the longer-term growth of topography and of geological structures is not fully understood, but is key to our ability to make use of topographic and geological datasets in the contexts of seismic hazard and wider-scale tectonics. Here we investigate those relationships at an active fold-and-thrust belt in the southwest Tarim Basin, Central Asia. We use seismic waveforms and interferometric synthetic aperture radar (InSAR) to determine the fault parameters and slip distribution of the 2015 Mw 6.3 Pishan earthquake - a blind, reverse-faulting event dipping towards the Tibetan Plateau. Our earthquake mechanism and location correspond closely to a fault mapped independently by seismic reflection, indicating that the earthquake was on a pre-existing ramp fault over a depth range of ˜9–13 km. However, the geometry of folding in the overlying fluvial terraces cannot be fully explained by repeated coseismic slip in events such as the 2015 earthquake nor by the early postseismic motion shown in our interferograms; a key role in growth of the topography must be played by other mechanisms. The earthquake occurred at the Tarim-Tibet boundary, with the unusually low dip of 21° . We use our source models from Pishan and a 2012 event to argue that the Tarim Basin crust deforms only by brittle failure on faults whose effective coefficient of friction is ≤0.05±0.025. In contrast, most of the Tibetan crust undergoes ductile deformation, with a viscosity of order 10²⁰–10²² Pa s. This contrast in rheologies provides an explanation for the low dip of the earthquake fault plane

Face Swapping: Realistic Image Synthesis Based on Facial Landmarks Alignment

We propose an image-based face swapping algorithm, which can be used to replace the face in the reference image with the same facial shape and features as the input face. First, a face alignment is made based on a group of detected facial landmarks, so that the aligned input face and the reference face are consistent in size and posture. Secondly, an image warping algorithm based on triangulation is presented to adjust the reference face and its background according to the aligned input faces. In order to achieve more accurate face swapping, a face parsing algorithm is introduced to realize the accurate detection of the face-ROIs, and then the face-ROI in the reference image is replaced with the input face-ROI. Finally, a Poisson image editing algorithm is adopted to realize the boundary processing and color correction between the replacement region and the original background, and then the final face swapping result is obtained. In the experiments, we compare our method with other face swapping algorithms and make a qualitative and quantitative analysis to evaluate the reality and the fidelity of the replaced face. The analysis results show that our method has some advantages in the overall performance of swapping effect

Laser stripe image denoising using convolutional autoencoder

Convolutional autoencoders are making a significant impact on computer vision and signal processing communities. In this work, a convolutional autoencoder denoising method is proposed to restore the corrupted laser stripe images of the depth sensor, which directly reduces the external noise of the depth sensor so as to increase its accuracy. To reduce the amount of training data and avoid overfitting, a patch size of the laser stripe image is determined, on the basis of which a small-scale dataset called Laser Stripe Image Patch (LSIP) is created. Also, a 14-layers convolutional autoencoder is constructed to reduce the noise of the image patches, which can learn the most salient features on the LSIP dataset. Moreover, the trained convolutional autoencoder is applied to an omnidirectional structured light system. Experimental results demonstrate that the proposed method obtains useful features and superior performance both visually and quantitatively on denoising tasks, and significantly improves the accuracy of the structured light system. Keywords: Convolutional autoencoder, Deep learning, Depth perception, Image denoising, Laser stripe, Structured ligh

Comparison of Satellite Reflectance Algorithms for Estimating Phycocyanin Values and Cyanobacterial Total Biovolume in a Temperate Reservoir Using Coincident Hyperspectral Aircraft Imagery and Dense Coincident Surface Observations

We analyzed 27 established and new simple and therefore perhaps portable satellite phycocyanin pigment reflectance algorithms for estimating cyanobacterial values in a temperate 8.9 km2 reservoir in southwest Ohio using coincident hyperspectral aircraft imagery and dense coincident water surface observations collected from 44 sites within 1 h of image acquisition. The algorithms were adapted to real Compact Airborne Spectrographic Imager (CASI), synthetic WorldView-2, Sentinel-2, Landsat-8, MODIS and Sentinel-3/MERIS/OLCI imagery resulting in 184 variants and corresponding image products. Image products were compared to the cyanobacterial coincident surface observation measurements to identify groups of promising algorithms for operational algal bloom monitoring. Several of the algorithms were found useful for estimating phycocyanin values with each sensor type except MODIS in this small lake. In situ phycocyanin measurements correlated strongly (r2 = 0.757) with cyanobacterial sum of total biovolume (CSTB) allowing us to estimate both phycocyanin values and CSTB for all of the satellites considered except MODIS in this situation