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Minimum distance to a solid wall is a primary parameter in turbulence models and overset grid assembly for computational fluid dynamics. In present work, a parallel advancing front method is proposed based on partitioned unstructured grids for the sake of further efficient wall distance computation. In order to overcome the inherent problem of conventional advancing front method in parallel environment, a novel "advancing twice and rippling once" strategy is developed to compute wall distance efficiently and further recover the accuracy. Significantly, the established framework is of modular nature to be easily extended to overset grid system for complex multi‐body configurations. The performance of the developed techniques is evidenced by comparison with the existing alternative ways in terms of computing efficiency and accuracy. Subsequently, further case studies are performed to examine its capability to deal with complex engineering applications such as a full transportation, a wing‐store configuration, a helicopter and a F‐16 fighter with onboard payloads. Results show that the proposed advancing front methodology is in practice able to solve extremely complex geometries with both convex and concave shapes with high efficiency and robustness. [ABSTRACT FROM AUTHOR]

Dysfunction of fronto-striato-thalamic (FST) circuits is thought to contribute to dopaminergic dysfunction and symptom onset in psychosis, but it remains unclear whether this dysfunction is driven by aberrant bottom-up subcortical signalling or impaired top-down cortical regulation. We used spectral dynamic causal modelling of resting-state functional MRI to characterize the effective connectivity of dorsal and ventral FST circuits in a sample of 46 antipsychotic-naïve first-episode psychosis patients and 23 controls and an independent sample of 36 patients with established schizophrenia and 100 controls. We also investigated the association between FST effective connectivity and striatal 18F-DOPA uptake in an independent healthy cohort of 33 individuals who underwent concurrent functional MRI and PET. Using a posterior probability threshold of 0.95, we found that midbrain and thalamic connectivity were implicated as dysfunctional across both patient groups. Dysconnectivity in first-episode psychosis patients was mainly restricted to the subcortex, with positive symptom severity being associated with midbrain connectivity. Dysconnectivity between the cortex and subcortical systems was only apparent in established schizophrenia patients. In the healthy 18F-DOPA cohort, we found that striatal dopamine synthesis capacity was associated with the effective connectivity of nigrostriatal and striatothalamic pathways, implicating similar circuits to those associated with psychotic symptom severity in patients. Overall, our findings indicate that subcortical dysconnectivity is evident in the early stages of psychosis, that cortical dysfunction may emerge later in the illness, and that nigrostriatal and striatothalamic signalling are closely related to striatal dopamine synthesis capacity, which is a robust marker for psychosis. [ABSTRACT FROM AUTHOR]

In vivo H2O2 visualization is crucial for disease diagnosis. Catalytic reaction‐based probes show potential in H2O2 detection, yet their in vivo application remains challenging because catalysts always require a specific pH to function and cellular glutathione (GSH) may suppress signaling by depletion of hydroxyl radicals and oxidized substrates. Here, a microenvironment‐tailored catalytic nanoprobe (MTCN) comprising Fe2+, citric acid (CA), 2,2′‐azino‐bis(3‐ethylbenzothiazoline‐6‐sulfonic acid) diammonium salt (ABTS), and downconversion nanoparticles in the liposomal cavity as well as a reference dye in the lipid membrane is reported, which utilizes the selective permeability of the liposomal membrane to offer a favorable pH for Fe2+ catalyst with the aid of CA and to avoid GSH‐triggered signal loss by preventing entry of GSH into the cavity. The MTCN displays a large NIR‐II fluorescence (FL) ratio between 1550 and 1080 nm (FL1550Em,808Ex/FL1080Em,980Ex), but a small photoacoustic (PA) ratio between 808 and 1048 nm (PA808/PA1048). Upon exposure of MTCN to H2O2, catalytic conversion of ABTS into its oxidized form ABTS·+ with 808 nm absorption causes a noticeable increment in PA808/PA1048 accompanied by an apparent decrement in FL1550Em,808Ex/FL1080Em,980Ex, enabling bimodal ratiometric imaging of H2O2 in the tumor and lymphatic metastasis. [ABSTRACT FROM AUTHOR]

Level of Evidence: 5 Technical Efficacy Stage: 3 [ABSTRACT FROM AUTHOR]

Amyotrophic lateral sclerosis (ALS) results in progressive impairment of upper and lower motor neurons. Increasing evidence from both in vivo and ex vivo studies suggest that iron accumulation in the motor cortex is a neuropathological hallmark in ALS. An in vivo neuroimaging marker of iron dysregulation in ALS would be useful in disease diagnosis and prognosis. Magnetic resonance imaging (MRI), with its unique capability to generate a variety of soft tissue contrasts, provides opportunities to image iron distribution in the human brain with millimeter to sub‐millimeter anatomical resolution. Conventionally, MRI T1‐weighted, T2‐weighted, and T2*‐weighted images have been used to investigate iron dysregulation in the brain in vivo. Susceptibility weighted imaging has enhanced contrast for para‐magnetic materials that provides superior sensitivity to iron in vivo. Recently, the development of quantitative susceptibility mapping (QSM) has realized the possibility of using quantitative assessments of magnetic susceptibility measures in brain tissues as a surrogate measurement of in vivo brain iron. In this review, we provide an overview of MRI techniques that have been used to investigate iron dysregulation in ALS in vivo. The potential uses, strengths, and limitations of these techniques in clinical trials, disease diagnosis, and prognosis are presented and discussed. We recommend further longitudinal studies with appropriate cohort characterization to validate the efficacy of these techniques. We conclude that quantitative iron assessment using recent advances in MRI including QSM holds great potential to be a sensitive diagnostic and prognostic marker in ALS. The use of multimodal neuroimaging markers in combination with iron imaging may also offer improved sensitivity in ALS diagnosis and prognosis that could make a major contribution to clinical care and treatment trials. Level of Evidence: 2 Technical Efficacy: Stage 3 [ABSTRACT FROM AUTHOR]

The suppression of motion artefacts from MR images is a challenging task. The purpose of this paper was to develop a standalone novel technique to suppress motion artefacts in MR images using a data‐driven deep learning approach. A simulation framework was developed to generate motion‐corrupted images from motion‐free images using randomly generated motion profiles. An Inception‐ResNet deep learning network architecture was used as the encoder and was augmented with a stack of convolution and upsampling layers to form an encoder‐decoder network. The network was trained on simulated motion‐corrupted images to identify and suppress those artefacts attributable to motion. The network was validated on unseen simulated datasets and real‐world experimental motion‐corrupted in vivo brain datasets. The trained network was able to suppress the motion artefacts in the reconstructed images, and the mean structural similarity (SSIM) increased from 0.9058 to 0.9338. The network was also able to suppress the motion artefacts from the real‐world experimental dataset, and the mean SSIM increased from 0.8671 to 0.9145. The motion correction of the experimental datasets demonstrated the effectiveness of the motion simulation generation process. The proposed method successfully removed motion artefacts and outperformed an iterative entropy minimization method in terms of the SSIM index and normalized root mean squared error, which were 5–10% better for the proposed method. In conclusion, a novel, data‐driven motion correction technique has been developed that can suppress motion artefacts from motion‐corrupted MR images. The proposed technique is a standalone, post‐processing method that does not interfere with data acquisition or reconstruction parameters, thus making it suitable for routine clinical practice. [ABSTRACT FROM AUTHOR]