Plant Protein-Based Nanocomposite Materials: Modification of Layered Nanoclay by Surface Coating and Enhanced Interactions by Enzymatic and Chemical Cross-linking

Highly intercalated or exfoliated nanoclay montmorillonite (MMT) has promises to improve mechanical and barrier properties of nanocomposite materials that may be further improved by strengthening interactions between matrix polymers and nanofillers. In this work, water-soluble proteins extracted from hominy feed and soy flour were utilized to modify the structures of MMT layers by surface-coating. Following coating at 60 °C using different MMT:protein mass ratios (49:1-2:1) and pH (2.0-10.0), the nanoclay was triple-washed for zeta potential analysis and lyophilized for X-ray diffraction and Fourier transform infrared spectroscopy analyses. Results showed that protein adsorption on MMT occurred at all pH conditions by coulumbic and/or non-coulumbic forces. With a sufficient amount of protein, highly intercalated or fully exfoliated MMT structures were achieved. MMT coated by soy protein was incorporated in soy protein dispersions for enzymatic and chemical cross-linking. Dynamic rheological tests were applied as a non-destructive method to illustrate the gel network formation and the interactions between protein-coated MMT and matrix proteins. Variables in enzymatic cross-linking included concentrations of NaCl and microbial transglutaminase (mTGase), and the absence or presence of protein-coated-MMT at pH 6.5. Without MMT, the maximum storage modulus was achieved at 100 mM NaCl and stronger gels with shorter gelling times were observed at higher mTGase concentrations. Conversely, the incorporation of coated-MMT inhibited the effect of ionic strength on soy protein gelation, and further shortened the gelation time and increased synergistic development of storage modulus with the combined treatments of 100 mM NaCl and mTGase. For chemical cross-linking, glutaradehyde was used as a cross-linker and studied for the impacts of pH, temperature and cross-linker concentration on dynamic rheological properties. Without MMT, storage moduli gradually increased with increasing glutaradehyde concentration; while the increase of storage modulus was in a higher order of magnitude in the presence of MMT. The practical approach and established parameters from this work can be used to manufacture nanocomposite materials with improved properties

Sustained release of lysozyme encapsulated in zein micro- and nanocapsules

A hydrophobic biopolymer, corn zein, was studied as a carrier for manufacturing particulate delivery systems of antimicrobials with sustained release. Three techniques, i.e., solvent attrition, supercritical anti-solvent and spray drying, were investigated to produce lysozyme-loaded zein micro- or nanocapsules. The work was focused on particle synthesis and in vitro release kinetics as affected by formulations and processes. The size (100-200 nm) and morphology (separated or connected) of the zein nanoparticles produced using solvent attrition were significantly affected by shear force, ethanol and zein concentrations in stock solutions during synthesis. Zein nanoparticles showed gradual release of lysozyme at pH 7 and 8 but no sustained release at lower pHs. Further, the impact of adding 1% zein nanoparticles in model carboxymethylcellulose solutions (adjusted to pH 3 to 9) was studied for viscosities that increased with pH. Microcapsules produced from supercritical anti-solvent showed a continuous matrix with internal voids. Sustained release of lysozyme at pH 2 to 8 was observed over 36 days at room temperature, with slower release at higher pH. At pH 4, release kinetics was further slowed by addition of sodium chloride. Spray drying was studied as one commercially feasible process. To further reduce the material cost, partial purification of lysozyme from hen egg white was studied using binary aqueous alcohol. Extraction with 50% ethanol at pH 3.5 for 6 h enabled high lysozyme activity and relatively high purity. Lysozyme precipitated after increasing the ethanol concentration from 50% to 90% in the extract. The precipitates were resolubilized by dilution to 50% ethanol. Slurries after increasing ethanol concentration from 50% to 60%-90%, with or without additives of Tween 40 or thymol, were spray dried. Capsules without additives were porous and did not show sustained release of lysozyme. The addition of Tween 40 changed the capsule microstructure to packed nanoparticles but did not achieve sustained release of lysozyme. Thymol facilitated the formation of a continuous capsule matrix and allowed sustained release of lysozyme at near neutral pH. Findings from this work demonstrated the possibility of using zein as a carrier biopolymer to deliver antimicrobials in food matrices for sustained release

Anatomy-Aware Lymph Node Detection in Chest CT using Implicit Station Stratification

Finding abnormal lymph nodes in radiological images is highly important for various medical tasks such as cancer metastasis staging and radiotherapy planning. Lymph nodes (LNs) are small glands scattered throughout the body. They are grouped or defined to various LN stations according to their anatomical locations. The CT imaging appearance and context of LNs in different stations vary significantly, posing challenges for automated detection, especially for pathological LNs. Motivated by this observation, we propose a novel end-to-end framework to improve LN detection performance by leveraging their station information. We design a multi-head detector and make each head focus on differentiating the LN and non-LN structures of certain stations. Pseudo station labels are generated by an LN station classifier as a form of multi-task learning during training, so we do not need another explicit LN station prediction model during inference. Our algorithm is evaluated on 82 patients with lung cancer and 91 patients with esophageal cancer. The proposed implicit station stratification method improves the detection sensitivity of thoracic lymph nodes from 65.1% to 71.4% and from 80.3% to 85.5% at 2 false positives per patient on the two datasets, respectively, which significantly outperforms various existing state-of-the-art baseline techniques such as nnUNet, nnDetection and LENS

Continual Segment: Towards a Single, Unified and Accessible Continual Segmentation Model of 143 Whole-body Organs in CT Scans

Deep learning empowers the mainstream medical image segmentation methods. Nevertheless current deep segmentation approaches are not capable of efficiently and effectively adapting and updating the trained models when new incremental segmentation classes (along with new training datasets or not) are required to be added. In real clinical environment, it can be preferred that segmentation models could be dynamically extended to segment new organs/tumors without the (re-)access to previous training datasets due to obstacles of patient privacy and data storage. This process can be viewed as a continual semantic segmentation (CSS) problem, being understudied for multi-organ segmentation. In this work, we propose a new architectural CSS learning framework to learn a single deep segmentation model for segmenting a total of 143 whole-body organs. Using the encoder/decoder network structure, we demonstrate that a continually-trained then frozen encoder coupled with incrementally-added decoders can extract and preserve sufficiently representative image features for new classes to be subsequently and validly segmented. To maintain a single network model complexity, we trim each decoder progressively using neural architecture search and teacher-student based knowledge distillation. To incorporate with both healthy and pathological organs appearing in different datasets, a novel anomaly-aware and confidence learning module is proposed to merge the overlapped organ predictions, originated from different decoders. Trained and validated on 3D CT scans of 2500+ patients from four datasets, our single network can segment total 143 whole-body organs with very high accuracy, closely reaching the upper bound performance level by training four separate segmentation models (i.e., one model per dataset/task)

Matching in the Wild: Learning Anatomical Embeddings for Multi-Modality Images

Radiotherapists require accurate registration of MR/CT images to effectively use information from both modalities. In a typical registration pipeline, rigid or affine transformations are applied to roughly align the fixed and moving images before proceeding with the deformation step. While recent learning-based methods have shown promising results in the rigid/affine step, these methods often require images with similar field-of-view (FOV) for successful alignment. As a result, aligning images with different FOVs remains a challenging task. Self-supervised landmark detection methods like self-supervised Anatomical eMbedding (SAM) have emerged as a useful tool for mapping and cropping images to similar FOVs. However, these methods are currently limited to intra-modality use only. To address this limitation and enable cross-modality matching, we propose a new approach called Cross-SAM. Our approach utilizes a novel iterative process that alternates between embedding learning and CT-MRI registration. We start by applying aggressive contrast augmentation on both CT and MRI images to train a SAM model. We then use this SAM to identify corresponding regions on paired images using robust grid-points matching, followed by a point-set based affine/rigid registration, and a deformable fine-tuning step to produce registered paired images. We use these registered pairs to enhance the matching ability of SAM, which is then processed iteratively. We use the final model for cross-modality matching tasks. We evaluated our approach on two CT-MRI affine registration datasets and found that Cross-SAM achieved robust affine registration on both datasets, significantly outperforming other methods and achieving state-of-the-art performance

Comparative Analysis of the Genomes of Two Field Isolates of the Rice Blast Fungus Magnaporthe oryzae.

Rice blast caused by Magnaporthe oryzae is one of the most destructive diseases of rice worldwide. The fungal pathogen is notorious for its ability to overcome host resistance. To better understand its genetic variation in nature, we sequenced the genomes of two field isolates, Y34 and P131. In comparison with the previously sequenced laboratory strain 70-15, both field isolates had a similar genome size but slightly more genes. Sequences from the field isolates were used to improve genome assembly and gene prediction of 70-15. Although the overall genome structure is similar, a number of gene families that are likely involved in plant-fungal interactions are expanded in the field isolates. Genome-wide analysis on asynonymous to synonymous nucleotide substitution rates revealed that many infection-related genes underwent diversifying selection. The field isolates also have hundreds of isolate-specific genes and a number of isolate-specific gene duplication events. Functional characterization of randomly selected isolate-specific genes revealed that they play diverse roles, some of which affect virulence. Furthermore, each genome contains thousands of loci of transposon-like elements, but less than 30% of them are conserved among different isolates, suggesting active transposition events in M. oryzae. A total of approximately 200 genes were disrupted in these three strains by transposable elements. Interestingly, transposon-like elements tend to be associated with isolate-specific or duplicated sequences. Overall, our results indicate that gain or loss of unique genes, DNA duplication, gene family expansion, and frequent translocation of transposon-like elements are important factors in genome variation of the rice blast fungus

The DNA Methylome of Human Peripheral Blood Mononuclear Cells

Analysis across the genome of patterns of DNA methylation reveals a rich landscape of allele-specific epigenetic modification and consequent effects on allele-specific gene expression

A Universal Power-law Prescription for Variability from Synthetic Images of Black Hole Accretion Flows

We present a framework for characterizing the spatiotemporal power spectrum of the variability expected from the horizon-scale emission structure around supermassive black holes, and we apply this framework to a library of general relativistic magnetohydrodynamic (GRMHD) simulations and associated general relativistic ray-traced images relevant for Event Horizon Telescope (EHT) observations of Sgr A*. We find that the variability power spectrum is generically a red-noise process in both the temporal and spatial dimensions, with the peak in power occurring on the longest timescales and largest spatial scales. When both the time-averaged source structure and the spatially integrated light-curve variability are removed, the residual power spectrum exhibits a universal broken power-law behavior. On small spatial frequencies, the residual power spectrum rises as the square of the spatial frequency and is proportional to the variance in the centroid of emission. Beyond some peak in variability power, the residual power spectrum falls as that of the time-averaged source structure, which is similar across simulations; this behavior can be naturally explained if the variability arises from a multiplicative random field that has a steeper high-frequency power-law index than that of the time-averaged source structure. We briefly explore the ability of power spectral variability studies to constrain physical parameters relevant for the GRMHD simulations, which can be scaled to provide predictions for black holes in a range of systems in the optically thin regime. We present specific expectations for the behavior of the M87* and Sgr A* accretion flows as observed by the EHT

Broadband multi-wavelength properties of M87 during the 2017 Event Horizon Telescope campaign

In 2017, the Event Horizon Telescope (EHT) Collaboration succeeded in capturing the first direct image of the center of the M87 galaxy. The asymmetric ring morphology and size are consistent with theoretical expectations for a weakly accreting supermassive black hole of mass ∼6.5 × 109Me. The EHTC also partnered with several international facilities in space and on the ground, to arrange an extensive, quasi-simultaneous multi-wavelength campaign. This Letter presents the results and analysis of this campaign, as well as the multi-wavelength data as a legacy data repository. We captured M87 in a historically low state, and the core flux dominates over HST-1 at high energies, making it possible to combine core flux constraints with the more spatially precise very long baseline interferometry data. We present the most complete simultaneous multi-wavelength spectrum of the active nucleus to date, and discuss the complexity and caveats of combining data from different spatial scales into one broadband spectrum. We apply two heuristic, isotropic leptonic single-zone models to provide insight into the basic source properties, but conclude that a structured jet is necessary to explain M87’s spectrum. We can exclude that the simultaneous γ-ray emission is produced via inverse Compton emission in the same region producing the EHT mm-band emission, and further conclude that the γ-rays can only be produced in the inner jets (inward of HST-1) if there are strongly particle-dominated regions. Direct synchrotron emission from accelerated protons and secondaries cannot yet be excluded.http://iopscience.iop.org/2041-8205am2022Physic