Harnessing dislocation motion using an electric field

Dislocations, line defects in crystalline materials, play an essential role in the mechanical[1,2], electrical[3], optical[4], thermal[5], and phase transition[6] properties of these materials. Dislocation motion, an important mechanism underlying crystal plasticity, is critical for the hardening, processing, and application of a wide range of structural and functional materials[1,7,8]. For decades, the movement of dislocations has been widely observed in crystalline solids under mechanical loading[9-11]. However, the goal of manipulating dislocation motion via a non-mechanical field alone remains elusive. Here, we present real-time observations of dislocation motion controlled solely by an external electric field in single-crystalline zinc sulfide (ZnS). We find that 30{\deg} partial dislocations can move back and forth depending on the direction of the electric field, while 90{\deg} partial dislocations are motionless. We reveal the nonstoichiometric nature of dislocation cores using atomistic imaging and determine their charge characteristics by density functional theory calculations. The glide barriers of charged 30{\deg} partial dislocations, which are lower than those of 90{\deg} partial dislocations, further decrease under an electric field, explaining the experimental observations. This study provides direct evidence of dislocation dynamics under a non-mechanical stimulus and opens up the possibility of modulating dislocation-related properties

Uncertainty-informed Mutual Learning for Joint Medical Image Classification and Segmentation

Classification and segmentation are crucial in medical image analysis as they enable accurate diagnosis and disease monitoring. However, current methods often prioritize the mutual learning features and shared model parameters, while neglecting the reliability of features and performances. In this paper, we propose a novel Uncertainty-informed Mutual Learning (UML) framework for reliable and interpretable medical image analysis. Our UML introduces reliability to joint classification and segmentation tasks, leveraging mutual learning with uncertainty to improve performance. To achieve this, we first use evidential deep learning to provide image-level and pixel-wise confidences. Then, an Uncertainty Navigator Decoder is constructed for better using mutual features and generating segmentation results. Besides, an Uncertainty Instructor is proposed to screen reliable masks for classification. Overall, UML could produce confidence estimation in features and performance for each link (classification and segmentation). The experiments on the public datasets demonstrate that our UML outperforms existing methods in terms of both accuracy and robustness. Our UML has the potential to explore the development of more reliable and explainable medical image analysis models. We will release the codes for reproduction after acceptance.Comment: 13 page

QM-Mechanism-Based Hierarchical High-Throughput in silico Screening Catalyst Design for Ammonia Synthesis

We propose and test a hierarchical high-throughput screening (HHTS) approach to catalyst design for complex catalytic reaction systems that is based on quantum mechanics (QM) derived full reaction networks with QM rate constants but simplified to examine only the reaction steps likely to be rate determining. We illustrate this approach by applying it to determine the optimum dopants (our of 35 candidates) to improve the turnover frequency (TOF) for the Fe-based Haber–Bosch ammonia synthesis process. We start from the QM-based free-energy reaction network for this reaction over Fe(111), which contains the 26 most important surface configurations and 17 transition states at operating conditions of temperature and pressure, from which we select the key reaction steps that might become rate determining for the alloy. These are arranged hierarchically by decreasing free-energy reaction barriers. We then extract from the full reaction network, a reduced set of reaction rates required to quickly predict the effect of the catalyst changes on each barrier. This allows us to test new candidates with only 1% of the effort for a full calculation. Thus, we were able to quickly screen 34 candidate dopants to select a small subset (Rh, Pt, Pd, Cu) that satisfy all criteria, including stability. Then from these four candidates expected to increase the TOF for NH3 production, we selected the best candidate (Rh) for a more complete free-energy and kinetic analysis (10 times the effort for HHTS but still 10% of the effort for a complete analysis of the full reaction network). We predict that Rh doping of Fe will increase the TOF for NH_3 synthesis by a factor of ∼3.3 times compared to Fe(111), in excellent agreement with our HHTS predictions, validating this approach

FaceScape: 3D Facial Dataset and Benchmark for Single-View 3D Face Reconstruction

In this paper, we present a large-scale detailed 3D face dataset, FaceScape, and the corresponding benchmark to evaluate single-view facial 3D reconstruction. By training on FaceScape data, a novel algorithm is proposed to predict elaborate riggable 3D face models from a single image input. FaceScape dataset provides 18,760 textured 3D faces, captured from 938 subjects and each with 20 specific expressions. The 3D models contain the pore-level facial geometry that is also processed to be topologically uniformed. These fine 3D facial models can be represented as a 3D morphable model for rough shapes and displacement maps for detailed geometry. Taking advantage of the large-scale and high-accuracy dataset, a novel algorithm is further proposed to learn the expression-specific dynamic details using a deep neural network. The learned relationship serves as the foundation of our 3D face prediction system from a single image input. Different than the previous methods, our predicted 3D models are riggable with highly detailed geometry under different expressions. We also use FaceScape data to generate the in-the-wild and in-the-lab benchmark to evaluate recent methods of single-view face reconstruction. The accuracy is reported and analyzed on the dimensions of camera pose and focal length, which provides a faithful and comprehensive evaluation and reveals new challenges. The unprecedented dataset, benchmark, and code have been released to the public for research purpose.Comment: 14 pages, 13 figures, journal extension of FaceScape(CVPR 2020). arXiv admin note: substantial text overlap with arXiv:2003.1398

Prediction of Progression to Severe Stroke in Initially Diagnosed Anterior Circulation Ischemic Cerebral Infarction

Purpose: Accurate prediction of the progression to severe stroke in initially diagnosed nonsevere patients with acute–subacute anterior circulation nonlacuna ischemic infarction (ASACNLII) is important in making clinical decision. This study aimed to apply a machine learning method to predict if the initially diagnosed nonsevere patients with ASACNLII would progress to severe stroke by using diffusion-weighted images and clinical information on admission.Methods: This retrospective study enrolled 344 patients with ASACNLII from June 2017 to August 2020 on admission, and 108 cases progressed to severe stroke during hospitalization within 3–21 days. The entire data were randomized into a training set (n = 271) and an independent test set (n = 73). A U-Net neural network was employed for automatic segmentation and volume measurement of the ischemic lesions. Predictive models were developed and used for evaluating the progression to severe stroke using different feature sets (the volume data, the clinical data, and the combination) and machine learning methods (random forest, support vector machine, and logistic regression).Results: The U-Net showed high correlation with manual segmentation in terms of Dice coefficient of 0.806 and R2 value of the volume measurements of 0.960 in the test set. The random forest classifier of the volume + clinical combination achieved the best area under the receiver operating characteristic curve of 0.8358 (95% CI 0.7321–0.9269), and the accuracy, sensitivity, and specificity were 0.7780 (0.7397–0.7945), 0.7695 (0.6102–0.9074), and 0.8686 (0.6923–1.0), respectively. The Shapley additive explanation diagram showed the volume variable as the most important predictor.Conclusion: The U-Net was fully automatic and showed a high correlation with manual segmentation. An integrated approach combining clinical variables and stroke lesion volumes that were derived from the advanced machine learning algorithms had high accuracy in predicting the progression to severe stroke in ASACNLII patients

Disruption of splicing-regulatory elements using CRISPR/Cas9 to rescue spinal muscular atrophy in human iPSCs and mice

We here report a genome-editing strategy to correct spinal muscular atrophy (SMA). Rather than directly targeting the pathogenic exonic mutations, our strategy employed Cas9 and guide-sgRNA for the targeted disruption of intronic splicing-regulatory elements. We disrupted intronic splicing silencers (ISSs, including ISS-N1 and ISS + 100) of survival motor neuron (SMN) 2, a key modifier gene of SMA, to enhance exon 7 inclusion and full-length SMN expression in SMA iPSCs. Survival of splicing-corrected iPSC-derived motor neurons was rescued with SMN restoration. Furthermore, co-injection of Cas9 mRNA from Streptococcus pyogenes (SpCas9) or Cas9 from Staphylococcus aureus (SaCas9) alongside their corresponding sgRNAs targeting ISS-N1 into zygotes rescued 56% and 100% of severe SMA transgenic mice (Smn , SMN2 ). The median survival of the resulting mice was extended to >400 days. Collectively, our study provides proof-of-principle for a new strategy to therapeutically intervene in SMA and other RNA-splicing-related diseases. -/- tg/

Brittle failure of orthorhombic borides from first-principles simulations

The orthorhombic boride family X M B14 , where X and M are metal atoms, have been of great interest in hard coating applications because of such novel properties as high thermal stability, low density, chemical stability, and a low friction coefficient. However, the brittle failure of orthorhombic borides limits their mechanical stability under working environments and prevents their extended engineering applications. To provide guidelines of improving their stability, we employed density functional theory (DFT) to examine the bonding character and mechanical response of X M B14 under pure shear, biaxial shear, and tensile loading conditions. Two typical X M B14 compounds, AlLiB14 and A l0.75M g0.78B14 , were examined to illustrate the effects of intrinsic metal vacancies. We find that the ideal strength for AlLiB14 is higher than that for A l0.75M g0.75B14 , suggesting that AlLiB14 is intrinsically stronger than A l0.75M g0.75B14 . The failure mechanism of both AlLiB14 and A l0.75M g0.78B14 arises from deconstructing B12 icosahedra under pure shear and biaxial shear conditions, while the structural failure under tensile deformation arises from breaking interlayer bonds between icosahedral layers