MOF-derived xPd-NPs@ZnO porous nanocomposites for ultrasensitive ppb-level gas detection with photoexcitation: Design, diverse-scenario characterization, and mechanism.

Metal-organic frameworks (MOFs) have been regarded as a potential candidate with great application prospects in the field of gas sensing. Although plenty of previous efforts have been made to improve the sensitivity of MOF-based nanocomposites, it is still a great challenge to realize ultrafast and high selectivity to typical flammable gases in a wide range. Herein, porous xPd-NPs@ZnO were prepared by optimized heat treatment, which maintained the controllable morphology and high specific surface area of 471.08 m2g-1. The coupling effects of photoexcitation and thermal excitation on the gas-sensing properties of nanocomposites were systematically studied. An ultrafast high response of 88.37 % towards 200 ppm H2 was realized within 1.2 s by 5.0Pd-NPs@ZnO under UV photoexcitation. All xPd-NPs@ZnO exhibited favorable linearity over an extremely wide range (0.2-4000 ppm H2) of experimental tests, indicating the great potential in quantitative detection. The photoexcited carriers enabled the nanocomposites a considerable response at lower operating temperatures, which made diverse applications of the sensors. The mechanisms of high sensing performances and the photoexcitation enhancement were systematically explained by DFT calculations. This work provides a solid experimental foundation and theoretical basis for the design of controllable porous materials and novel photoexcited gas detection.

RAPID BRAIN MENINGES SURFACE RECONSTRUCTION WITH LAYER TOPOLOGY GUARANTEE.

The meninges, located between the skull and brain, are composed of three membrane layers: the pia, the arachnoid, and the dura. Reconstruction of these layers can aid in studying volume differences between patients with neurodegenerative diseases and normal aging subjects. In this work, we use convolutional neural networks (CNNs) to reconstruct surfaces representing meningeal layer boundaries from magnetic resonance (MR) images. We first use the CNNs to predict the signed distance functions (SDFs) representing these surfaces while preserving their anatomical ordering. The marching cubes algorithm is then used to generate continuous surface representations; both the subarachnoid space (SAS) and the intracranial volume (ICV) are computed from these surfaces. The proposed method is compared to a state-of-the-art deformable model-based reconstruction method, and we show that our method can reconstruct smoother and more accurate surfaces using less computation time. Finally, we conduct experiments with volumetric analysis on both subjects with multiple sclerosis and healthy controls. For healthy and MS subjects, ICVs and SAS volumes are found to be significantly correlated to sex (p<0.01) and age (p ≤ 0.03) changes, respectively.

Cranial Meninges Reconstruction Based on Convolutional Networks and Deformable Models: Applications to Longitudinal Study of Normal Aging.

The cranial meninges are membranes enveloping the brain. The space between these membranes contains mainly cerebrospinal fluid. It is of interest to study how the volumes of this space change with respect to normal aging. In this work, we propose to combine convolutional neural networks (CNNs) with nested topology-preserving geometric deformable models (NTGDMs) to reconstruct meningeal surfaces from magnetic resonance (MR) images. We first use CNNs to predict implicit representations of these surfaces then refine them with NTGDMs to achieve sub-voxel accuracy while maintaining spherical topology and the correct anatomical ordering. MR contrast harmonization is used to match the contrasts between training and testing images. We applied our algorithm to a subset of healthy subjects from the Baltimore Longitudinal Study of Aging for demonstration purposes and conducted longitudinal statistical analysis of the intracranial volume (ICV) and subarachnoid space (SAS) volume. We found a statistically significant decrease in the ICV and an increase in the SAS volume with respect to normal aging.

Bi-directional Synthesis of Pre- and Post-contrast MRI via Guided Feature Disentanglement.

Magnetic resonance imaging (MRI) with gadolinium contrast is widely used for tissue enhancement and better identification of active lesions and tumors. Recent studies have shown that gadolinium deposition can accumulate in tissues including the brain, which raises safety concerns. Prior works have tried to synthesize post-contrast T1-weighted MRIs from pre-contrast MRIs to avoid the use of gadolinium. However, contrast and image representations are often entangled during the synthesis process, resulting in synthetic post-contrast MRIs with undesirable contrast enhancements. Moreover, the synthesis of pre-contrast MRIs from post-contrast MRIs which can be useful for volumetric analysis is rarely investigated in the literature. To tackle pre- and post- contrast MRI synthesis, we propose a BI-directional Contrast Enhancement Prediction and Synthesis (BICEPS) network that enables disentanglement of contrast and image representations via a bi-directional image-to-image translation(I2I)model. Our proposed model can perform both pre-to-post and post-to-pre contrast synthesis, and provides an interpretable synthesis process by predicting contrast enhancement maps from the learned contrast embedding. Extensive experiments on a multiple sclerosis dataset demonstrate the feasibility of applying our bidirectional synthesis and show that BICEPS outperforms current methods.