The final model is generated by integrating all local segments into a global volumetric representation under the constraint of Manhattan frame-based registration across segments. Our algorithm outperforms others that use depth data only in terms of both the mean distance error and the absolute trajectory error, and it is also very competitive compared with RGB-D based reconstruction algorithms. Moreover, our algorithm outperforms the state-of-the-art in terms of the surface area coverage by 10% 40%, largely due to the usefulness and effectiveness of the Manhattan assumption through the reconstruction pipeline.High frame rate (HFR) speckle tracking echocardiography (STE) assesses myocardial function by quantifying motion and deformation at high temporal resolution. Among the proposed HFR techniques, Multi-Line Transmission (MLT) and Diverging Wave (DW) imaging have been used in this context both being characterized by specific advantages and disadvantages. Therefore, in this paper, we directly contrast both approaches in an in-vivo setting while operating at the same frame rate. First, images were recorded at baseline (resting condition) from healthy volunteers and patients. Next, additional acquisitions during stress echocardiography were performed on volunteers. Each scan was contoured and processed by a previously proposed 2D HFR STE algorithm based on cross-correlation. Then, strain curves and their end-systolic (ES) values were extracted for all myocardial segments for further statistical analysis. The baseline acquisitions did not reveal differences in estimated strain between the acquisition modes (p>0.35); myocardial segments (p>0.3) nor an interaction between imaging mode and depth (p>0.87). Similarly, during stress testing, no difference (p=0.7) was observed for the two scan sequences, stress levels nor an interaction sequence-stress level (p=0.94). Overall, our findings show that MLT and DW compounding give comparable HFR STE strain values and that the choice for using one method or the other may thus rather be based on other factors, e.g. system requirements or computational cost.High resolution magnetic resonance (MR) images are desired in many clinical and research applications. Acquiring such images with high signal-to-noise (SNR), however, can require a long scan duration, which is difficult for patient comfort, is more costly, and makes the images susceptible to motion artifacts. A very common practical compromise for both 2D and 3D MR imaging protocols is to acquire volumetric MR images with high in-plane resolution, but lower through-plane resolution. In addition to having poor resolution in one orientation, 2D MRI acquisitions will also have aliasing artifacts, which further degrade the appearance of these images. This paper presents an approach SMORE1 based on convolutional neural networks (CNNs) that restores image quality by improving resolution and reducing aliasing in MR images.2 This approach is self-supervised, which requires no external training data because the high-resolution and low-resolution data that are present in the image itself are used for training. For 3D MRI, the method consists of only one self-supervised super-resolution (SSR) deep CNN that is trained from the volumetric image data. For 2D MRI, there is a self-supervised anti-aliasing (SAA) deep CNN that precedes the SSR CNN, also trained from the volumetric image data. Both methods were evaluated on a broad collection of MR data, including filtered and downsampled images so that quantitative metrics could be computed and compared, and actual acquired low resolution images for which visual and sharpness measures could be computed and compared. The super-resolution method is shown to be visually and quantitatively superior to previously reported methods.In this paper, we study the formalism of unsupervised multi-class domain adaptation (multi-class UDA), which underlies a few recent algorithms whose learning objectives are only motivated empirically. Multi-Class Scoring Disagreement (MCSD) divergence is presented by aggregating the absolute margin violations in multi-class classification, and this proposed MCSD is able to fully characterize the relations between any pair of multi-class scoring hypotheses. By using MCSD as a measure of domain distance, we develop a new domain adaptation bound for multi-class UDA; its data-dependent, probably approximately correct bound is also developed that naturally suggests adversarial learning objectives to align conditional feature distributions across source and target domains. Consequently, an algorithmic framework of Multi-class Domain-adversarial learning Networks (McDalNets) is developed, and its different instantiations via surrogate learning objectives either coincide with or resemble a few recently popular methods, thus (partially) underscoring their practical effectiveness. Based on our identical theory for multi-class UDA, we also introduce a new algorithm of Domain-Symmetric Networks (SymmNets), which is featured by a novel adversarial strategy of domain confusion and discrimination. SymmNets affords simple extensions that work equally well under the problem settings of either closed set, partial, or open set UDA. We conduct careful empirical studies to compare different algorithms of McDalNets and our newly introduced SymmNets. Experiments verify our theoretical analysis and show the efficacy of our proposed SymmNets. In addition, we have made our implementation code publicly available.The Pro47Ser variant of p53 (S47) exists in African-descent populations and is associated with increased cancer risk in humans and mice.  https://www.selleckchem.com/products/pyrotinib.html  Due to impaired repression of the cystine importer Slc7a11, S47 cells show increased glutathione (GSH) accumulation compared to cells with wild -type p53. We show that mice containing the S47 variant display increased mTOR activity and oxidative metabolism, as well as larger size, improved metabolic efficiency, and signs of superior fitness. Mechanistically, we show that mTOR and its positive regulator Rheb display increased association in S47 cells; this is due to an altered redox state of GAPDH in S47 cells that inhibits its ability to bind and sequester Rheb. Compounds that decrease glutathione normalize GAPDH-Rheb complexes and mTOR activity in S47 cells. This study reveals a novel layer of regulation of mTOR by p53, and raises the possibility that this variant may have been selected for in early Africa.