Learning-based deformable image registration for infant MR images in the first year of life

Many brain development studies have been devoted to investigate dynamic structural and functional changes in the first year of life. To quantitatively measure brain development in such a dynamic period, accurate image registration for different infant subjects with possible large age gap is of high demand. Although many state-of-the-art image registration methods have been proposed for young and elderly brain images, very few registration methods work for infant brain images acquired in the first year of life, because of (1) large anatomical changes due to fast brain development and (2) dynamic appearance changes due to white matter myelination

Intrinsic Piezoelectric Anisotropy of Tetragonal ABO3 Perovskites: A High-Throughput Study

A comprehensive understand of the intrinsic piezoelectric anisotropy stemming from diverse chemical and physical factors is a key step for the rational design of highly anisotropic materials. We performed high-throughput calculations on tetragonal ABO3 perovskites to investigate the piezoelectricity and the interplay between lattice, displacement, polarization and elasticity. Among the 123 types of perovskites, the structural tetragonality is naturally divided into two categories: normal tetragonal (c/a ratio < 1.1) and super-tetragonal (c/a ratio > 1.17), exhibiting distinct ferroelectric, elastic, and piezoelectric properties. Charge analysis revealed the mechanisms underlying polarization saturation and piezoelectricity suppression in the super-tetragonal region, which also produces an inherent contradiction between high d33 and large piezoelectric anisotropy ratio |d33/d31|. The polarization axis and elastic softness direction jointly determine the maximum longitudinal piezoelectric response d33 direction. The validity and deficiencies of the widely utilized |d33/d31| ratio for representing piezoelectric anisotropy were reevaluated

Evidences for pressure-induced two-phase superconductivity and mixed structures of NiTe₂ and NiTe in type-II Dirac semimetal NiTe_(2-x) (x = 0.38 ± 0.09) single crystals

Bulk NiTe₂ is a type-II Dirac semimetal with non-trivial Berry phases associated with the Dirac fermions. Theory suggests that monolayer NiTe₂ is a two-gap superconductor, whereas experimental investigation of bulk NiTe_(1.98) for pressures (P) up to 71.2 GPa do not reveal any superconductivity. Here we report experimental evidences for pressure-induced two-phase superconductivity as well as mixed structures of NiTe₂ and NiTe in Te-deficient NiTe_(2-x) (x = 0.38±0.09) single crystals. Hole-dominant multi-band superconductivity with the P3M1 hexagonal-symmetry structure of NiTe₂ appears at P ≥ 0.5 GPa, whereas electron-dominant single-band superconductivity with the P2/m monoclinic-symmetry structure of NiTe emerges at 14.5 GPa < P < 18.4 GPa. The coexistence of hexagonal and monoclinic structures and two-phase superconductivity is accompanied by a zero Hall coefficient up to ∼ 40 GPa, and the second superconducting phase prevails above 40 GPa, reaching a maximum T_c = 7.8 K and persisting up to 52.8 GPa. Our findings suggest the critical role of Te-vacancies in the occurrence of superconductivity and potentially nontrivial topological properties in NiTe_(2-x)

Denoising of 3D Brain MR Images with Parallel Residual Learning of Convolutional Neural Network Using Global and Local Feature Extraction

Magnetic resonance (MR) images often suffer from random noise pollution during image acquisition and transmission, which impairs disease diagnosis by doctors or automated systems. In recent years, many noise removal algorithms with impressive performances have been proposed. In this work, inspired by the idea of deep learning, we propose a denoising method named 3D-Parallel-RicianNet, which will combine global and local information to remove noise in MR images. Specifically, we introduce a powerful dilated convolution residual (DCR) module to expand the receptive field of the network and to avoid the loss of global features. Then, to extract more local information and reduce the computational complexity, we design the depthwise separable convolution residual (DSCR) module to learn the channel and position information in the image, which not only reduces parameters dramatically but also improves the local denoising performance. In addition, a parallel network is constructed by fusing the features extracted from each DCR module and DSCR module to improve the efficiency and reduce the complexity for training a denoising model. Finally, a reconstruction (REC) module aims to construct the clean image through the obtained noise deviation and the given noisy image. Due to the lack of ground-truth images in the real MR dataset, the performance of the proposed model was tested qualitatively and quantitatively on one simulated T1-weighted MR image dataset and then expanded to four real datasets. The experimental results show that the proposed 3D-Parallel-RicianNet network achieves performance superior to that of several state-of-the-art methods in terms of the peak signal-to-noise ratio, structural similarity index, and entropy metric. In particular, our method demonstrates powerful abilities in both noise suppression and structure preservation

Dataset of tensile strength development of concrete with manufactured sand

This article presents 755 groups splitting tensile strength tests data of concrete with manufactured sand (MSC) in different curing age ranged from 1 day to 388 days related to the research article “Experimental study on tensile strength development of concrete with manufactured sand” (Zhao et al., 2017) [1]. These data were used to evaluate the precision of the prediction formulas of tensile strength of MSC, and can be applied as dataset for further studies

Efficient Knowledge Distillation for Brain Tumor Segmentation

Deep learning has allowed great progress to be made in obtaining more accurate prediction results for brain tumor segmentation. The current mainstream research approaches obtain segmentation accuracy improvements by modifying deep-learning model architectures, while ignoring the computational and storage efficiency issues of segmentation. In this paper, we proposed an improved knowledge distillation method: coordinate distillation (CD), which integrates channel and space information and completes brain tumor segmentation by training the student network with the teacher network without changing the original network architecture. Experimental results showed that the method was effective and that it could enhance the segmentation accuracy of brain tumors without changing the segmentation efficiency

Structural properties and strain engineering of a BeB2 monolayer from first-principles

Using crystal structure prediction and first-principles calculations, we investigated new phases of BeB2 monolayers and discussed their structural, electronic and strain effect properties of such boron-based 2D materials

Ferroelectric polarization of hydroxyapatite from density functional theory

The theoretical ferroelectric polarization of the low-temperature (monoclinic, P21) phase and the high-temperature (hexagonal, P63) phase of hydroxyapatite Ca10(PO4)6(OH)2 is calculated based on the density functional theory (DFT)

One-Dimensional Organic–Inorganic Hybrid Perovskite Incorporating Near-Infrared-Absorbing Cyanine Cations

Hybrid perovskite crystals with organic and inorganic structural components are able to combine desirable properties from both classes of materials. Electronic interactions between the anionic inorganic framework and functional organic cations (such as chromophores or semiconductors) can give rise to unusual photophysical properties. Cyanine dyes are a well known class of cationic organic dyes with high extinction coefficients and tunable absorption maxima all over the visible and near-infrared spectrum. Here we present the synthesis and characterization of an original 1D hybrid perovskite composed of NIR-absorbing cyanine cations and polyanionic lead halide chains. This first demonstration of a cyanine-perovskite hybrid material is paving the way to a new class of compounds with great potential for applications in photonic devices

Effect of swap disorder on the physical properties of the quaternary Heusler alloy PdMnTiAl: a first-principles study

Heusler alloys crystallize in a close-packed cubic structure, having a four-atom basis forming a face-centred cubic lattice. By selecting different composite elements, Heusler alloys provide a large family of members for frontier research of spintronics and magnetic materials and devices. In this paper, the structural, electronic and magnetic properties of a novel quaternary Heusler alloy, PdMnTiAl, have been investigated using a first-principles computational materials calculation. It was found that the stable ordered structure is a non-magnetic Y-type1, in good agreement with the Slater–Pauling rule. From the band structure and the density of states, it is predicted that this Y-type1 configuration is a new gapless semi-metal material. Furthermore, it was discovered that the Pd–Mn swap-disordered structure is more stable than the Y-type1 structure. The present work provides a guide for experiments to synthesize and characterize this Heusler alloy