Hierarchically porous and single Zn atom-embedded carbon molecular sieves for H2 separations.

Hierarchically porous materials containing sub-nm ultramicropores with molecular sieving abilities and microcavities with high gas diffusivity may realize energy-efficient membranes for gas separations. However, rationally designing and constructing such pores into large-area membranes enabling efficient H2 separations remains challenging. Here, we report the synthesis and utilization of hybrid carbon molecular sieve membranes with well-controlled nano- and micro-pores and single zinc atoms and clusters well-dispersed inside the nanopores via the carbonization of supramolecular mixed matrix materials containing amorphous and crystalline zeolitic imidazolate frameworks. Carbonization temperature is used to fine-tune pore sizes, achieving ultrahigh selectivity for H2/CO2 (130), H2/CH4 (2900), H2/N2 (880), and H2/C2H6 (7900) with stability against water vapor and physical aging during a continuous 120-h test.

Changes to the shape, orientation and packing of red cells as a function of retinal capillary size.

The free diameter of a red blood cell exceeds the lumen diameter of capillaries in the central nervous system, requiring significant deformation of cells. However the deformations undertaken in vivo are not well established due to the difficulty in observing cellular capillary flow in living human tissue. Here, we used high resolution adaptive optics imaging to non-invasively track 17,842 red blood cells in transit through 121 unique capillary segments of diameter 8 µm or less in the retina of 3 healthy human subjects. Within each vessel, a 2D en face profile was generated for the "average cell", whose shape was then inferred in 3D based on the key assumption of a circular capillary cross-section. From this we estimated the average volume, surface area, orientation, and separation between red cells within each capillary tube. Our results showed a network filtration effect, whereby narrower vessels were more likely to contain smaller cells (defined by surface area, which is thought not to vary during a cell's passage through the vascular system). A bivariate linear model showed that for larger cells in narrower vessels: cells re-orient themselves to align with the flow axis, their shape becomes more elongated, there are longer gaps between successive cells, and remarkably, that cell volume is less which implies the ejection of water from cells to facilitate capillary transit. Taken together, these findings suggest that red cells pass through retinal capillaries with some reluctance. A biphasic distribution for cell orientation and separation was evident, indicating a "tipping point" for vessels narrower than approx. 5 µm. This corresponds closely to the typical capillary lumen diameter, and may maximize sensitivity of cellular flow to small changes in diameter. We suggest that the minimization of unnecessary oxygen exchange, and hence of damage via reactive oxygen pathways, may have provided evolutionary pressure to ensure that capillary lumens are generally narrower than red blood cells.

Deep learning in MRI-guided radiation therapy: A systematic review.

Recent advances in MRI-guided radiation therapy (MRgRT) and deep learning techniques encourage fully adaptive radiation therapy (ART), real-time MRI monitoring, and the MRI-only treatment planning workflow. Given the rapid growth and emergence of new state-of-the-art methods in these fields, we systematically review 197 studies written on or before December 31, 2022, and categorize the studies into the areas of image segmentation, image synthesis, radiomics, and real time MRI. Building from the underlying deep learning methods, we discuss their clinical importance and current challenges in facilitating small tumor segmentation, accurate x-ray attenuation information from MRI, tumor characterization and prognosis, and tumor motion tracking. In particular, we highlight the recent trends in deep learning such as the emergence of multi-modal, visual transformer, and diffusion models.

SREBP Regulation of Lipid Metabolism in Liver Disease, and Therapeutic Strategies.

Sterol regulatory element-binding proteins (SREBPs) are master transcription factors that play a crucial role in regulating genes involved in the biogenesis of cholesterol, fatty acids, and triglycerides. As such, they are implicated in several serious liver diseases, including non-alcoholic fatty liver disease (NAFLD), non-alcoholic steatohepatitis (NASH), fibrosis, and hepatocellular carcinoma (HCC). SREBPs are subject to regulation by multiple cofactors and critical signaling pathways, making them an important target for therapeutic interventions. In this review, we first introduce the structure and activation of SREBPs, before focusing on their function in liver disease. We examine the mechanisms by which SREBPs regulate lipogenesis, explore how alterations in these processes are associated with liver disease, and evaluate potential therapeutic strategies using small molecules, natural products, or herb extracts that target these pathways. Through this analysis, we provide new insights into the versatility and multitargets of SREBPs as factors in the modulation of different physiological stages of liver disease, highlighting their potential targets for therapeutic treatment.

Hippocampus substructure segmentation using morphological vision transformer learning.

The hippocampus plays a crucial role in memory and cognition. Because of the associated toxicity from whole brain radiotherapy, more advanced treatment planning techniques prioritize hippocampal avoidance, which depends on an accurate segmentation of the small and complexly shaped hippocampus. To achieve accurate segmentation of the anterior and posterior regions of the hippocampus from T1 weighted (T1w) MR images, we developed a novel model, Hippo-Net, which uses a cascaded model strategy. The proposed model consists of two major parts: (1) a localization model is used to detect the volume-of-interest (VOI) of hippocampus. (2) An end-to-end morphological vision transformer network (Franchietal2020Pattern Recognit.102107246, Ranemetal2022 IEEE/CVF Conf. on Computer Vision and Pattern Recognition Workshops (CVPRW) pp 3710-3719) is used to perform substructures segmentation within the hippocampus VOI. The substructures include the anterior and posterior regions of the hippocampus, which are defined as the hippocampus proper and parts of the subiculum. The vision transformer incorporates the dominant features extracted from MR images, which are further improved by learning-based morphological operators. The integration of these morphological operators into the vision transformer increases the accuracy and ability to separate hippocampus structure into its two distinct substructures. A total of 260 T1w MRI datasets from medical segmentation decathlon dataset were used in this study. We conducted a five-fold cross-validation on the first 200 T1w MR images and then performed a hold-out test on the remaining 60 T1w MR images with the model trained on the first 200 images. In five-fold cross-validation, the Dice similarity coefficients were 0.900 ± 0.029 and 0.886 ± 0.031 for the hippocampus proper and parts of the subiculum, respectively. The mean surface distances (MSDs) were 0.426 ± 0.115 mm and 0.401 ± 0.100 mm for the hippocampus proper and parts of the subiculum, respectively. The proposed method showed great promise in automatically delineating hippocampus substructures on T1w MR images. It may facilitate the current clinical workflow and reduce the physicians' effort.

Hippocampus Substructure Segmentation Using Morphological Vision Transformer Learning.

Background: The hippocampus plays a crucial role in memory and cognition. Because of the associated toxicity from whole brain radiotherapy, more advanced treatment planning techniques prioritize hippocampal avoidance, which depends on an accurate segmentation of the small and complexly shaped hippocampus. Purpose: To achieve accurate segmentation of the anterior and posterior regions of the hippocampus from T1 weighted (T1w) MRI images, we developed a novel model, Hippo-Net, which uses a mutually enhanced strategy. Methods: The proposed model consists of two major parts: 1) a localization model is used to detect the volume-of-interest (VOI) of hippocampus. 2) An end-to-end morphological vision transformer network is used to perform substructures segmentation within the hippocampus VOI. The substructures include the anterior and posterior regions of the hippocampus, which are defined as the hippocampus proper and parts of the subiculum. The vision transformer incorporates the dominant features extracted from MRI images, which are further improved by learning-based morphological operators. The integration of these morphological operators into the vision transformer increases the accuracy and ability to separate hippocampus structure into its two distinct substructures.A total of 260 T1w MRI datasets from Medical Segmentation Decathlon dataset were used in this study. We conducted a five-fold cross-validation on the first 200 T1w MR images and then performed a hold-out test on the remaining 60 T1w MR images with the model trained on the first 200 images. The segmentations were evaluated with two indicators, 1) multiple metrics including the Dice similarity coefficient (DSC), 95th percentile Hausdorff distance (HD95), mean surface distance (MSD), volume difference (VD) and center-of-mass distance (COMD); 2) Volumetric Pearson correlation analysis. Results: In five-fold cross-validation, the DSCs were 0.900±0.029 and 0.886±0.031 for the hippocampus proper and parts of the subiculum, respectively. The MSD were 0.426±0.115mm and 0.401±0.100 mm for the hippocampus proper and parts of the subiculum, respectively. Conclusions: The proposed method showed great promise in automatically delineating hippocampus substructures on T1w MRI images. It may facilitate the current clinical workflow and reduce the physicians' effort.

Measuring red blood cell shape in the human retina.

The free diameter of a red blood cell generally exceeds the lumen diameter of capillaries in the central nervous system, requiring significant cellular deformation. However, the deformations undertaken are not well established under natural conditions due to the difficulty in observing corpuscular flow in vivo. Here we describe a novel, to the best of our knowledge, method to noninvasively study the shape of red blood cells as they traverse the narrow capillary networks of the living human retina, using high-speed adaptive optics. One hundred and twenty-three capillary vessels were analyzed in three healthy subjects. For each capillary, image data were motion-compensated and then averaged over time to reveal the appearance of the blood column. Data from hundreds of red blood cells were used to profile the average cell in each vessel. Diverse cellular geometries were observed across lumens ranging from 3.2 to 8.4 µm in diameter. As capillaries narrowed, cells transitioned from rounder to more elongated shapes and from being counter-aligned to aligned with the axis of flow. Remarkably, in many vessels the red blood cells maintained an oblique orientation relative to the axis of flow.

Diverse Sample Generation: Pushing the Limit of Generative Data-Free Quantization.

Generative data-free quantization emerges as a practical compression approach that quantizes deep neural networks to low bit-width without accessing the real data. This approach generates data utilizing batch normalization (BN) statistics of the full-precision networks to quantize the networks. However, it always faces the serious challenges of accuracy degradation in practice. We first give a theoretical analysis that the diversity of synthetic samples is crucial for the data-free quantization, while in existing approaches, the synthetic data completely constrained by BN statistics experimentally exhibit severe homogenization at distribution and sample levels. This paper presents a generic Diverse Sample Generation (DSG) scheme for the generative data-free quantization, to mitigate detrimental homogenization. We first slack the statistics alignment for features in the BN layer to relax the distribution constraint. Then, we strengthen the loss impact of the specific BN layers for different samples and inhibit the correlation among samples in the generation process, to diversify samples from the statistical and spatial perspectives, respectively. Comprehensive experiments show that for large-scale image classification tasks, our DSG can consistently quantization performance on different neural architectures, especially under ultra-low bit-width. And data diversification caused by our DSG brings a general gain to various quantization-aware training and post-training quantization approaches, demonstrating its generality and effectiveness.

BiFSMNv2: Pushing Binary Neural Networks for Keyword Spotting to Real-Network Performance.

Deep neural networks, such as the deep-FSMN, have been widely studied for keyword spotting (KWS) applications while suffering expensive computation and storage. Therefore, network compression technologies such as binarization are studied to deploy KWS models on edge. In this article, we present a strong yet efficient binary neural network for KWS, namely, BiFSMNv2, pushing it to the real-network accuracy performance. First, we present a dual-scale thinnable 1-bit-architecture (DTA) to recover the representation capability of the binarized computation units by dual-scale activation binarization and liberate the speedup potential from an overall architecture perspective. Second, we also construct a frequency-independent distillation (FID) scheme for KWS binarization-aware training, which distills the high-and low-frequency components independently to mitigate the information mismatch between full-precision and binarized representations. Moreover, we propose the learning propagation binarizer (LPB), a general and efficient binarizer that enables the forward and backward propagation of binary KWS networks to be continuously improved through learning. We implement and deploy BiFSMNv2 on ARMv8 real-world hardware with a novel fast bitwise computation kernel (FBCK), which is proposed to fully use registers and increase instruction throughput. Comprehensive experiments show our BiFSMNv2 outperforms the existing binary networks for KWS by convincing margins across different datasets and achieves comparable accuracy with the full-precision networks (only a tiny 1.51% drop on Speech Commands V1-12). We highlight that benefiting from the compact architecture and optimized hardware kernel, BiFSMNv2 can achieve an impressive 25.1 × speedup and 20.2 × storage-saving on edge hardware.

Palladium-Percolated Networks Enabled by Low Loadings of Branched Nanorods for Enhanced H2 Separations.

Nanoparticles (NPs) at high loadings are often used in mixed matrix membranes (MMMs) to improve gas separation properties, but they can lead to defects and poor processability that impede membrane fabrication. Herein, it is demonstrated that branched nanorods (NRs) with controlled aspect ratios can significantly reduce the required loading to achieve superior gas separation properties while maintaining excellent processability, as demonstrated by the dispersion of palladium (Pd) NRs in polybenzimidazole for H2 /CO2 separation. Increasing the aspect ratio from 1 for NPs to 40 for NRs decreases the percolation threshold volume fraction by a factor of 30, from 0.35 to 0.011. An MMM with percolated networks formed by Pd NRs at a volume fraction of 0.039 exhibits H2 permeability of 110 Barrer and H2 /CO2 selectivity of 31 when challenged with simulated syngas at 200 °C, surpassing Robeson's upper bound. This work highlights the advantage of NRs over NPs and nanowires and shows that right-sizing nanofillers in MMMs is critical to construct highly sieving pathways at minimal loadings. This work paves the way for this general feature to be applied across materials systems for a variety of chemical separations.

Deep Learning in MRI-guided Radiation Therapy: A Systematic Review.

MRI-guided radiation therapy (MRgRT) offers a precise and adaptive approach to treatment planning. Deep learning applications which augment the capabilities of MRgRT are systematically reviewed. MRI-guided radiation therapy offers a precise, adaptive approach to treatment planning. Deep learning applications which augment the capabilities of MRgRT are systematically reviewed with emphasis placed on underlying methods. Studies are further categorized into the areas of segmentation, synthesis, radiomics, and real time MRI. Finally, clinical implications, current challenges, and future directions are discussed.

Structural basis for proteolytic processing of Aspergillus sojae α-glucosidase L with strong transglucosylation activity.

An α-glucosidase from Aspergillus sojae, AsojAgdL, exhibits strong transglucosylation activity to produce α-1,6-glucosidic linkages. The most remarkable structural feature of AsojAgdL is that residues 457-560 of AsojAgdL (designated the NC sequence) is not conserved in other glycoside hydrolase family 31 enzymes, and part of this NC sequence is proteolytically cleaved during its maturation. In this study, the enzyme was expressed in Pichia pastoris, and electrophoretic analysis indicated that the recombinant enzyme, rAsojAgdL, consisted of two polypeptide chains, as observed in the case of the enzyme produced in an Aspergillus strain. The crystal structure of rAsojAgdL was determined in complex with the substrate analog trehalose. Electron density corresponding to residues 496-515 of the NC sequence was not seen, and there were no α-helices or ß-strands except for a short α-helix in the structures of residues 457-495 and residues 516-560, both of which belong to the NC sequence. The residues 457-495 and the residues 516-560 both formed extra components of the catalytic domain. The residues 457-495 constituted the entrance of the catalytic pocket of rAsojAgdL, and Gly467, Asp468, Pro469, and Pro470 in the NC sequence were located within 4 Å of Trp400, a key residue involved in binding of the substrate. The results suggest that the proteolytic processing of the NC sequence is related to the formation of the catalytic pocket of AsojAgdL.

In Situ Growth of Crystalline and Polymer-Incorporated Amorphous ZIFs in Polybenzimidazole Achieving Hierarchical Nanostructures for Carbon Capture.

Mixed matrix materials (MMMs) hold great potential for membrane gas separations by merging nanofillers with unique nanostructures and polymers with excellent processability. In situ growth of the nanofillers is adapted to mitigate interfacial incompatibility to avoid the selectivity loss. Surprisingly, functional polymers have not been exploited to co-grow the nanofillers for membrane applications. Herein, in situ synergistic growth of crystalline zeolite imidazole framework-8 (ZIF-8) in polybenzimidazole (PBI), creating highly porous structures with high gas permeability, is demonstrated. More importantly, PBI contains benzimidazole groups (similar to the precursor for ZIF-8, i.e., 2-methylimidazole) and induces the formation of amorphous ZIFs, enhancing interfacial compatibility and creating highly size-discriminating bottlenecks. For instance, the formation of 15 mass% ZIF-8 in PBI improves H2 permeability and H2 /CO2 selectivity by ≈100% at 35 °C, breaking the permeability/selectivity tradeoff. This work unveils a new platform of MMMs comprising functional polymer-incorporated amorphous ZIFs with hierarchical nanostructures for various applications.

Tailoring sub-3.3 Å ultramicropores in advanced carbon molecular sieve membranes for blue hydrogen production.

Carbon molecular sieve (CMS) membranes prepared by carbonization of polymers containing strongly size-sieving ultramicropores are attractive for high-temperature gas separations. However, polymers need to be carbonized at extremely high temperatures (900° to 1200°C) to achieve sub-3.3 Å ultramicroporous channels for H2/CO2 separation, which makes them brittle and impractical for industrial applications. Here, we demonstrate that polymers can be first doped with thermolabile cross-linkers before low-temperature carbonization to retain the polymer processability and achieve superior H2/CO2 separation properties. Specifically, polybenzimidazole (PBI) is cross-linked with pyrophosphoric acid (PPA) via H bonding and proton transfer before carbonization at ≤600°C. The synergistic PPA doping and subsequent carbonization of PBI increase H2 permeability from 27 to 140 Barrer and H2/CO2 selectivity from 15 to 58 at 150°C, superior to state-of-the-art polymeric materials and surpassing Robeson's upper bound. This study provides a facile and effective way to tailor subnanopore size and porosity in CMS membranes with desirable molecular sieving ability.

A wide-field micro-computed tomography detector: micron resolution at half-centimetre scale.

Ideal three-dimensional imaging of complex samples made up of micron-scale structures extending over mm to cm, such as biological tissues, requires both wide field of view and high resolution. For existing optics and detectors used for micro-CT (computed tomography) imaging, sub-micron pixel resolution can only be achieved for fields of view of <2 mm. This article presents a unique detector system with a 6 mm field-of-view image circle and 0.5 µm pixel size that can be used in micro-CT units utilizing both synchrotron and commercial X-ray sources. A resolution-test pattern with linear microstructures and whole adult Daphnia magna were imaged at beamline 8.3.2 of the Berkeley Advanced Light Source. Volumes of 10000 × 10000 × 7096 isotropic 0.5 µm voxels were reconstructed over a 5.0 mm × 3.5 mm field of view. Measurements in the projection domain confirmed a 0.90 µm measured spatial resolution that is largely Nyquist-limited. This unprecedented combination of field of view and resolution dramatically reduces the need for sectional scans and computational stitching for large samples, ultimately offering the means to elucidate changes in tissue and cellular morphology in the context of larger, whole, intact model organisms and specimens. This system is also anticipated to benefit micro-CT imaging in materials science, microelectronics, agricultural science and biomedical engineering.

Particulate ECM biomaterial ink is 3D printed and naturally crosslinked to form structurally-layered and lubricated cartilage tissue mimics.

Articular cartilage is a layered tissue with a complex, heterogeneous structure and lubricated surface which is challenging to reproduce using traditional tissue engineering methods. Three-dimensional printing techniques have enabled engineering of complex scaffolds for cartilage regeneration, but constructs fail to replicate the unique zonal layers, and limited cytocompatible crosslinkers exist. To address the need for mechanically robust, layered scaffolds, we developed an extracellular matrix particle-based biomaterial ink (pECM biomaterial ink) which can be extruded, polymerizes via disulfide bonding, and restores layered tissue structure and surface lubrication. Our cartilage pECM biomaterial ink utilizes functionalized hyaluronan (HA), a naturally occurring glycosaminoglycan, crosslinked directly to decellularized tissue particles (ø40-100µm). We experimentally determined that HA functionalized with thiol groups (t-HA) forms disulfide bonds with the ECM particles to form a 3D network. We show that two inks can be co-printed to create a layered cartilage scaffold with bulk compressive and surface (friction coefficient, adhesion, and roughness) mechanics approaching values measured on native cartilage. We demonstrate that our printing process enables the addition of macropores throughout the construct, increasing the viability of introduced cells by 10%. The delivery of these 3D printed scaffolds to a defect is straightforward, customizable to any shape, and adheres to surrounding tissue.

Microscopic techniques for fabrication of polyethersulfone thin-film nanocomposite membranes intercalated with UiO-66-SO3 H for heavy metal ions removal from water.

Environmental remediation of heavy metals from wastewater is becoming popular area in the field of membrane technology. Heavy metals are toxic in nature and have ability to bioaccumulate in water bodies. In current study, zirconium-based metal organic frameworks (MOFs), that is, UiO-66 and UiO-66-SO3 H with a mean diameter of 200 nm were synthesized and intercalated into polyethersulfone (PES) substrate to fabricate thin-film nanocomposite (TFN) membranes via an interfacial polymerization (IP) method. TFN membranes exhibit higher selectivity and permeability as compared to thin-film composite (TFC) membranes for heavy metals, such as cadmium (Cd) and mercury (Hg). Zirconium-based MOFs are highly stable in water and due to smaller pore size enhanced hydrophilicity of TFN membranes. In addition, TFN membrane with functionalized MOF (UiO-66-SO3 H) performed best as compared to TFC and TFN with UiO-66 MOF. The effect of loading of different weight percentages (wt%) of both MOFs for TFN membranes was also investigated. The TFN membranes with loading (0.2 wt%) of UiO-66-SO3 H displayed highest permeability of 9.57 LMH/bar and notable rejections of 90% and 87.7% toward Cd and Hg, respectively. To our best understanding, it is the first study of intercalating functionalized UiO-66-SO3 H in TFC membranes by IP and their application on heavy metals especially Cd and Hg.

Whole-organism 3D quantitative characterization of zebrafish melanin by silver deposition micro-CT.

We previously described X-ray histotomography, a high-resolution, non-destructive form of X-ray microtomography (micro-CT) imaging customized for three-dimensional (3D), digital histology, allowing quantitative, volumetric tissue and organismal phenotyping (Ding et al., 2019). Here, we have combined micro-CT with a novel application of ionic silver staining to characterize melanin distribution in whole zebrafish larvae. The resulting images enabled whole-body, computational analyses of regional melanin content and morphology. Normalized micro-CT reconstructions of silver-stained fish consistently reproduced pigment patterns seen by light microscopy, and further allowed direct quantitative comparisons of melanin content across wild-type and mutant samples, including subtle phenotypes not previously noticed. Silver staining of melanin for micro-CT provides proof-of-principle for whole-body, 3D computational phenomic analysis of a specific cell type at cellular resolution, with potential applications in other model organisms and melanocytic neoplasms. Advances such as this in whole-organism, high-resolution phenotyping provide superior context for studying the phenotypic effects of genetic, disease, and environmental variables.

Scalable Polymeric Few-Nanometer Organosilica Membranes with Hydrothermal Stability for Selective Hydrogen Separation.

Nanoporous silica membranes exhibit excellent H2/CO2 separation properties for sustainable H2 production and CO2 capture but are prepared via complicated thermal processes above 400 °C, which prevent their scalable production at a low cost. Here, we demonstrate the rapid fabrication (within 2 min) of ultrathin silica-like membranes (∼3 nm) via an oxygen plasma treatment of polydimethylsiloxane-based thin-film composite membranes at 20 °C. The resulting organosilica membranes unexpectedly exhibit H2 permeance of 280-930 GPU (1 GPU = 3.347 × 10-10 mol m-2 s-1 Pa-1) and H2/CO2 selectivity of 93-32 at 200 °C, far surpassing state-of-the-art membranes and Robeson's upper bound for H2/CO2 separation. When challenged with a 3 d simulated syngas test containing water vapor at 200 °C and a 340 d stability test, the membrane shows durable separation performance and excellent hydrothermal stability. The robust H2/CO2 separation properties coupled with excellent scalability demonstrate the great potential of these organosilica membranes for economic H2 production with minimal carbon emissions.