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Dislocations in single-phase concentrated random alloys, including high-entropy alloys (HEAs), repeatedly encounter pinning during glide, resulting in jerky dislocation motion. While solute-dislocation interaction is well understood in conventional alloys, the origin of individual pinning points in concentrated random alloys is a matter of debate. In this work, we investigate the origin of dislocation pinning in the CoCrFeMnNi HEA. In-situ transmission electron microscopy studies reveal wavy dislocation lines and a jagged glide motion under external loading, even though no segregation or clustering is found around Shockley partial dislocations. Atomistic simulations reproduce the jerky dislocation motion and link the repeated pinning to local fluctuations in the Peierls friction. We demonstrate that the density of high local Peierls friction is proportional to the critical stress required for dislocation glide and the dislocation mobility. Dislocations in high-entropy alloys encounter pinning during glide resulting in jerky motion. Here the authors demonstrate that the density of high local Peierls force is proportional to the critical stress required for their glide and mobility.

Hint-based image colorization is an image-to-image translation task that aims at creating a full-color image from an input luminance image when a small set of color values for some pixels are given as hints. Though traditional deep-learning-based methods have been proposed in the literature, they are based on convolution neural networks (CNNs) that have strong spatial locality due to the convolution operations. This often causes non-trivial visual artifacts in the colorization results, such as false color and color bleeding artifacts. To overcome this limitation, this study proposes a vision transformer-based colorization network. The proposed hint-based colorization network has a hierarchical vision transformer architecture in the form of an encoder-decoder structure based on transformer blocks. As the proposed method uses the transformer blocks that can learn rich long-range dependency, it can achieve visually plausible colorization results, even with a small number of color hints. Through the verification experiments, the results reveal that the proposed transformer model outperforms the conventional CNN-based models. In addition, we qualitatively analyze the effect of the long-range dependency of the transformer model on hint-based image colorization.

Early identification of people at risk of imminent progression to dementia due to Alzheimer disease is crucial for timely intervention and treatment. We investigated whether the texture of MRI brain scans could predict the progression of mild cognitive impairment (MCI) to Alzheimer disease earlier than volume. We constructed a development data set (121 people who were cognitively normal and 145 who had mild Alzheimer disease) and a validation data set (113 patients with stable MCI who did not progress to Alzheimer disease for 3 years; 40 with early MCI who progressed to Alzheimer disease after 12–36 months; and 41 with late MCI who progressed to Alzheimer disease within 12 months) from the Alzheimer’s Disease Neuroimaging Initiative. We analyzed the texture of the hippocampus, precuneus and posterior cingulate cortex using a grey-level co-occurrence matrix. We constructed texture and volume indices from the development data set using logistic regression. Using area under the curve (AUC) of receiver operator characteristics, we compared the accuracy of hippocampal volume, hippocampal texture and the composite texture of the hippocampus, precuneus and posterior cingulate cortex in predicting conversion from MCI to Alzheimer disease in the validation data set. Compared with hippocampal volume, hippocampal texture (0.790 v. 0.739, p = 0.047) and composite texture (0.811 v. 0.739, p = 0.007) showed larger AUCs for conversion to Alzheimer disease from both early and late MCI. Hippocampal texture showed a marginally larger AUC than hippocampal volume in early MCI (0.795 v. 0.726, p = 0.060). Composite texture showed a larger AUC for conversion to Alzheimer disease than hippocampal volume in both early (0.817 v. 0.726, p = 0.027) and late MCI (0.805 v. 0.753, p = 0.019). This study was limited by the absence of histological data, and the pathology reflected by the texture measures remains to be validated. Textures of the hippocampus, precuneus and posterior cingulate cortex predicted conversion from MCI to Alzheimer disease at an earlier time point and with higher accuracy than hippocampal volume.

•χ-separation, a magnetic susceptibility source separation method, is applied to explore iron and myelin profiles across layers of cortex and white matter.•The depth-wise profiles of χpos and χneg were consistent with the profiles of iron and myelin from literatures.•The depth-wise profiles of χpos and χneg were different from those of QSM or R2*, carrying different information. The in-vivo profiling of iron and myelin across cortical depths and underlying white matter has important implications for advancing knowledge about their roles in brain development and degeneration. Here, we utilize χ-separation, a recently-proposed advanced susceptibility mapping that creates positive (χpos) and negative (χneg) susceptibility maps, to generate the depth-wise profiles of χpos and χneg as surrogate biomarkers for iron and myelin, respectively. Two regional sulcal fundi of precentral and middle frontal areas are profiled and compared with findings from previous studies. The results show that the χpos profiles peak at superificial white matter (SWM), which is an area beneath cortical gray matter known to have the highest accumulation of iron within the cortex and white matter. On the other hand, the χneg profiles increase in SWM toward deeper white matter. These characteristics in the two profiles are in agreement with histological findings of iron and myelin. Furthermore, the χneg profiles report regional differences that agree with well-known distributions of myelin concentration. When the two profiles are compared with those of QSM and R2*, different shapes and peak locations are observed. This preliminary study offers an insight into one of the possible applications of χ-separation for exploring microstructural information of the human brain, as well as clinical applications in monitoring changes of iron and myelin in related diseases.

The classification of Alzheimer’s disease (AD) using deep learning methods has shown promising results, but successful application in clinical settings requires a combination of high accuracy, short processing time, and generalizability to various populations. In this study, we developed a convolutional neural network (CNN)-based AD classification algorithm using magnetic resonance imaging (MRI) scans from AD patients and age/gender-matched cognitively normal controls from two populations that differ in ethnicity and education level. These populations come from the Seoul National University Bundang Hospital (SNUBH) and Alzheimer’s Disease Neuroimaging Initiative (ADNI). For each population, we trained CNNs on five subsets using coronal slices of T1-weighted images that cover the medial temporal lobe. We evaluated the models on validation subsets from both the same population (within-dataset validation) and other population (between-dataset validation). Our models achieved average areas under the curves of 0.91–0.94 for within-dataset validation and 0.88–0.89 for between-dataset validation. The mean processing time per person was 23–24 s. The within-dataset and between-dataset performances were comparable between the ADNI-derived and SNUBH-derived models. These results demonstrate the generalizability of our models to different patients with different ethnicities and education levels, as well as their potential for deployment as fast and accurate diagnostic support tools for AD.

Stridor is a high-pitched airway sound that can be produced from static or dynamic lesions along the upper airway. Airway fluoroscopy (AF) has been utilized as a diagnostic test to evaluate pediatric stridor, but prior studies have shown limitations. We aim to assess the reliability of airway fluoroscopy as compared to direct laryngoscopy and bronchoscopy (DLB) in evaluating pediatric stridor, with stratification of airway subsite. A retrospective chart review was performed. 184 patients were evaluated who had undergone both AF and DLB within one year of each other. Sensitivity and specificity were calculated at each airway subsite, including supraglottis, glottis, subglottis, trachea, and bronchi. Receiver operating characteristics (ROC) area under the curve (AUC) testing was performed at each subsite. Sensitivity and specificity were 12%/99%, 12%/98%, 44%/96%, and 39%/100% respectively for pathology at the level of the supraglottis, subglottis, trachea, and bronchi. ROC AUC was 0.558 ( p  < 0.05) for supraglottic pathology, 0.695 ( p  < 0.001) for tracheal pathology, and 0.676 ( p  < 0.001) for bronchial pathology. Airway fluoroscopy has poor sensitivity, but excellent specificity as a diagnostic test. Based on low ROC AUC values, airway fluoroscopy may not be a reliable screening test for the majority of stridulous patients; however, given its high specificity, low cost, and low risk profile it may be useful as an adjunct test in higher anesthetic risk populations or in patients where there is specific concern for tracheal or bronchial pathology.

Arterial hypotension is associated with incidence of postoperative complications, such as myocardial infarction or acute kidney injury. Little research has been conducted for the real-time prediction of hypotension, even though many studies have been performed to investigate the factors which affect hypotension events. This forecasting problem is quite challenging compared to diagnosis that detects high-risk patients at current. The forecasting problem that specifies when events occur is more challenging than the forecasting problem that does not specify the event time. In this work, we challenge the forecasting problem in 5 min advance. For that, we aim to build a systematic feature engineering method that is applicable regardless of vital sign species, as well as a machine learning model based on these features for real-time predictions 5 min before hypotension. The proposed feature extraction model includes statistical analysis, peak analysis, change analysis, and frequency analysis. After applying feature engineering on invasive blood pressure (IBP), we build a random forest model to differentiate a hypotension event from other normal samples. Our model yields an accuracy of 0.974, a precision of 0.904, and a recall of 0.511 for predicting hypotensive events.

The elastic-to-plastic transition during the deformation of a dislocation-free nanoscale volume is accompanied by displacement bursts associated with dislocation nucleation. The dislocations that nucleate during the so-called “pop-in” burst take the form of prismatic dislocation loops (PDLs) and exhibit characteristic burst-like emission and plastic recovery. Here, we report the in-situ transmission electron microscopy (TEM) observation of the initial plasticity ensued by burst-like emission of PDLs on nanoindentation of dislocation-free Au nanowires. The in-situ TEM nanoindentation showed that the nucleation and subsequent cross slip of shear loop(s) are the rate-limiting steps. As the indentation size increases, the cross slip of shear loop becomes favored, resulting in a transition from PDLs to open half-loops to helical dislocations. In the present case of nanoindentation of dislocation-free volumes, the PDLs glide out of the indentation stress field while spreading the plastic zone, as opposed to the underlying assumption of the Nix-Gao model. Prismatic dislocation loops (PDLs) form during the elastic-to-plastic transition of a dislocation-free volume under nanoindentation. Here the authors observe the initial plasticity and burst-like emission of PDLs in Au nanowires by in-situ transmission electron microscopy, elucidating fundamental aspects of the formation process.

As a natural biocomposite, Strombus gigas , commonly known as the giant pink queen conch shell, exhibits outstanding mechanical properties, especially a high fracture toughness. It is known that the basic building block of conch shell contains a high density of growth twins with average thickness of several nanometres, but their effects on the mechanical properties of the shell remain mysterious. Here we reveal a toughening mechanism governed by nanoscale twins in the conch shell. A combination of in situ fracture experiments inside a transmission electron microscope, large-scale atomistic simulations and finite element modelling show that the twin boundaries can effectively block crack propagation by inducing phase transformation and delocalization of deformation around the crack tip. This mechanism leads to an increase in fracture energy of the basic building block by one order of magnitude, and contributes significantly to that of the overall structure via structural hierarchy. As a natural biocomposite, Strombus gigas , commonly known as the giant pink queen conch shell, exhibits outstanding mechanical properties such as fracture toughness. Here, the authors show that these properties can be partially attributed to nanoscale twin boundaries in the basic building block of the shell.

Mechanical response of metal nanowires has recently attracted a lot of interest due to their ultra-high strengths and unique deformation behaviours. Atomistic simulations have predicted that face-centered cubic metal nanowires deform in different modes depending on the orientation between wire axis and loading direction. Here we report, by combination of in situ transmission electron microscopy and molecular dynamic simulation, the conditions under which particular deformation mechanisms take place during the uniaxial loading of [110]-oriented Au nanowires. Furthermore, by performing cyclic uniaxial loading, we show reversible plastic deformation by twinning and consecutive detwinning in tension and compression, respectively. Molecular dynamics simulations rationalize the observed behaviours in terms of the orientation-dependent resolved shear stress on the leading and trailing partial dislocations, their potential nucleation sites and energy barriers. This reversible twinning–detwinning process accommodates large strains that can be beneficially utilized in applications requiring high ductility in addition to ultra-high strength. In situ studies of deformation in metal nanowires have yielded interesting results. Here, the authors perform cyclic loading on gold nanowires and observe twinning and detwinning phenomena, respectively caused by tensile and compressive loading, and elucidate the underpinning mechanism by molecular dynamics simulations.