Single Extracellular Vesicle Imaging and Computational Analysis Identifies Inherent Architectural Heterogeneity.

Evaluating the heterogeneity of extracellular vesicles (EVs) is crucial for unraveling their complex actions and biodistribution. Here, we identify consistent architectural heterogeneity of EVs using cryogenic transmission electron microscopy (cryo-TEM), which has an inherent ability to image biological samples without harsh labeling methods while preserving their native conformation. Imaging EVs isolated using different methodologies from distinct sources, such as cancer cells, normal cells, immortalized cells, and body fluids, we identify a structural atlas of their dominantly consistent shapes. We identify EV architectural attributes by utilizing a segmentation neural network model. In total, 7,576 individual EVs were imaged and quantified by our computational pipeline. Across all 7,576 independent EVs, the average eccentricity was 0.5366 ± 0.2, and the average equivalent diameter was 132.43 ± 67 nm. The architectural heterogeneity was consistent across all sources of EVs, independent of purification techniques, and compromised of single spherical, rod-like or tubular, and double shapes. This study will serve as a reference foundation for high-resolution images of EVs and offer insights into their potential biological impact.

Melt electrowriting enabled 3D liquid crystal elastomer structures for cross-scale actuators and temperature field sensors.

Liquid crystal elastomers (LCEs) have garnered attention for their remarkable reversible strains under various stimuli. Early studies on LCEs mainly focused on basic dimensional changes in macrostructures or quasi-three-dimensional (3D) microstructures. However, fabricating complex 3D microstructures and cross-scale LCE-based structures has remained challenging. In this study, we report a compatible method named melt electrowriting (MEW) to fabricate LCE-based microfiber actuators and various 3D actuators on the micrometer to centimeter scales. By controlling printing parameters, these actuators were fabricated with high resolutions (4.5 to 60 µm), actuation strains (10 to 55%), and a maximum work density of 160 J/kg. In addition, through the integration of a deep learning-based model, we demonstrated the application of LCE materials in temperature field sensing. Large-scale, real-time, LCE grid-based spatial temperature field sensors have been designed, exhibiting a low response time of less than 42 ms and a high precision of 94.79%.

PgDDS Changes the Plant Growth of Transgenic Aralia elata and Improves the Production of Re and Rg3 in Its Leaves.

Aralia elata (Miq.) Seem is a medicinal plant that shares a common pathway for the biosynthesis of triterpenoid saponins with Panax ginseng. Here, we transferred the dammarenediol-II synthase gene from P. ginseng (PgDDS; GenBank: AB122080.1) to A. elata. The growth of 2-year-old transgenic plants (L27; 9.63 cm) was significantly decreased compared with wild-type plants (WT; 74.97 cm), and the leaflet shapes and sizes of the transgenic plants differed from those of the WT plants. Based on a terpene metabolome analysis of leaf extracts from WT, L13, and L27 plants, a new structural skeleton for ursane-type triterpenoid saponins was identified. Six upregulated differentially accumulated metabolites (DAMs) were detected, and the average levels of Rg3 and Re in the leaves of the L27 plants were 42.64 and 386.81 µg/g, respectively, increased significantly compared with the WT plants (15.48 and 316.96 µg/g, respectively). Thus, the expression of PgDDS in A. elata improved its medicinal value.

Expression of PnSS Promotes Squalene and Oleanolic Acid (OA) Accumulation in Aralia elata via Methyl Jasmonate (MeJA) Induction.

Aralia elata is an important herb due to the abundance of pentacyclic triterpenoid saponins whose important precursors are squalene and OA. Here, we found that MeJA treatment promoted both precursors accumulation, especially the latter, in transgenic A. elata, overexpressing a squalene synthase gene from Panax notoginseng(PnSS). In this study, Rhizobium-mediated transformation was used to express the PnSS gene. Gene expression analysis and high-performance liquid chromatography (HPLC) were used to identify the effect of MeJA on squalene and OA accumulation. The PnSS gene was isolated and expressed in A. elata. Transgenic lines showed a very high expression of the PnSS gene and farnesyl diphosphate synthase gene (AeFPS) and a slightly higher squalene content than the wild-type, but endogenous squalene synthase (AeSS), squalene epoxidase (AeSE), and ß-amyrin synthase (Aeß-AS) gene were decreased as well as OA content. Following one day of MeJA treatment, the expression levels of PeSS, AeSS, and AeSE genes increased significantly. On day 3, the maximum content of both products reached 17.34 and 0.70 mg·g-1, which increased 1.39- and 4.90-fold than in the same lines without treatment. Transgenic lines expressing PnSS gene had a limited capability to promote squalene and OA accumulation. MeJA strongly activated their biosynthesis pathways, leading to enhance yield.

RDASNet: Image Denoising via a Residual Dense Attention Similarity Network.

In recent years, thanks to the performance advantages of convolutional neural networks (CNNs), CNNs have been widely used in image denoising. However, most of the CNN-based image-denoising models cannot make full use of the redundancy of image data, which limits the expressiveness of the model. We propose a new image-denoising model that aims to extract the local features of the image through CNN and focus on the global information of the image through the attention similarity module (ASM), especially the global similarity details of the image. Furthermore, dilation convolution is used to enlarge the receptive field to better focus on the global features. Moreover, avg-pooling is used to smooth and suppress noise in the ASM to further improve model performance. In addition, through global residual learning, the effect is enhanced from shallow to deep layers. A large number of experiments show that our proposed model has a better image-denoising effect, including quantitative and visual results. It is more suitable for complex blind noise and real images.

A Dilated Residual Network for Turbine Blade ICT Image Artifact Removal.

Artifacts are divergent strip artifacts or dark stripe artifacts in Industrial Computed Tomography (ICT) images due to large differences in density among the components of scanned objects, which can significantly distort the actual structure of scanned objects in ICT images. The presence of artifacts can seriously affect the practical application effectiveness of ICT in defect detection and dimensional measurement. In this paper, a series of convolution neural network models are designed and implemented based on preparing the ICT image artifact removal datasets. Our findings indicate that the RF (receptive field) and the spatial resolution of network can significantly impact the effectiveness of artifact removal. Therefore, we propose a dilated residual network for turbine blade ICT image artifact removal (DRAR), which enhances the RF of the network while maintaining spatial resolution with only a slight increase in computational load. Extensive experiments demonstrate that the DRAR achieves exceptional performance in artifact removal.

Vaccination with Span, an antigen guided by SARS-CoV-2 S protein evolution, protects against challenge with viral variants in mice.

SARS-CoV-2 continues to accumulate mutations to evade immunity, leading to breakthrough infections after vaccination. How researchers can anticipate the evolutionary trajectory of the virus in advance in the design of next-generation vaccines requires investigation. Here, we performed a comprehensive study of 11,650,487 SARS-CoV-2 sequences, which revealed that the SARS-CoV-2 spike (S) protein evolved not randomly but into directional paths of either high infectivity plus low immune resistance or low infectivity plus high immune resistance. The viral infectivity and immune resistance of variants are generally incompatible, except for limited variants such as Beta and Kappa. The Omicron variant has the highest immune resistance but showed high infectivity in only one of the tested cell lines. To provide cross-clade immunity against variants that undergo diverse evolutionary pathways, we designed a new pan-vaccine antigen (Span). Span was designed by analyzing the homology of 2675 SARS-CoV-2 S protein sequences from the NCBI database before the Delta variant emerged. The refined Span protein harbors high-frequency residues at given positions that reflect cross-clade generality in sequence evolution. Compared with a prototype wild-type (Swt) vaccine, which, when administered to mice, induced serum with decreased neutralization activity against emerging variants, Span vaccination of mice elicited broad immunity to a wide range of variants, including those that emerged after our design. Moreover, vaccinating mice with a heterologous Span booster conferred complete protection against lethal infection with the Omicron variant. Our results highlight the importance and feasibility of a universal vaccine to fight against SARS-CoV-2 antigenic drift.

Single extracellular vesicle imaging and computational analysis identifies inherent architectural heterogeneity.

Evaluating the heterogeneity of extracellular vesicles (EVs) is crucial for unraveling their complex actions and biodistribution. Here, we identify consistent architectural heterogeneity of EVs using cryogenic transmission electron microscopy (cryo-TEM) which has an inherent ability to image biological samples without harsh labeling methods and while preserving their native conformation. Imaging EVs isolated using different methodologies from distinct sources such as cancer cells, normal cells, and body fluids, we identify a structural atlas of their dominantly consistent shapes. We identify EV architectural attributes by utilizing a segmentation neural network model. In total, 7,576 individual EVs were imaged and quantified by our computational pipeline. Across all 7,576 independent EVs, the average eccentricity was 0.5366, and the average equivalent diameter was 132.43 nm. The architectural heterogeneity was consistent across all sources of EVs, independent of purification techniques, and compromised of single spherical (S. Spherical), rod-like or tubular, and double shapes. This study will serve as a reference foundation for high-resolution EV images and offer insights into their potential biological impact.

Potential mechanisms of Pyrrosiae Folium in treating prostate cancer based on network pharmacology and molecular docking.

OBJECTIVE: The network pharmacology approach and molecular docking were employed to explore the mechanism of Pyrrosiae Folium (PF) against prostate cancer (PCa). METHODS: The active compounds and their corresponding putative targets of PF were identified by the Traditional Chinese Medicine Systems Pharmacology (TCMSP), the gene names of the targets were obtained from the UniProt database. The collection of genes associated with PCa was obtained from GeneCards and DisGeNET database. We merged the drug targets and disease targets by online software, Draw Venn Diagram. The resulting gene list was imported into R software (v3.6.3) for GO and KEGG function enrichment analysis. The STRING database was utilized for protein-protein interaction (PPI) network construction. The cytoHubba plugin of Cytoscape was used to identify core genes. Further, molecular docking analysis of the hub targets was carried out using AutoDock Vina software (v1.5.6). RESULTS: A total of six active components were screened by PF, with 167 corresponding putative targets, 1395 related targets for PCa, and 113 targets for drugs and diseases. The 'drug-component-disease-target' network was constructed by Cytoscape software and the target genes mainly involved in the complex treating effects associated with response to oxidative stress, cytokine activity, pathways in cancer, PCa pathway, and tumor necrosis factor (TNF) signaling pathway. Core genes in the PPI network were TNF, JUN, IL6, IL1B, CXCL8, RELA, CCL2, TP53, IL10, and FOS. The molecular docking results reveal the better binding affinity of six active components to the core targets. CONCLUSION: The results of this study indicated that PF may be have a certain anti-PCa effect by regulating related target genes, affecting pathways in cancer, TNF signaling pathway, and hepatitis B signaling pathway.

Antibacterial effect and mechanism against Escherichia coli of polysaccharides from Armillariella tabescens mycelia.

The objectives of this study were (1) to prepare Armillariella tabescens mycelia polysaccharides (PAT) with remarkably growth inhibitory effect on typical food-borne pathogenic bacteria using a green and efficient polyamide method and (2) to explore the antibacterial mechanism of PAT for use as a natural antibacterial agent. The sugar and uronic acid contents of PAT were 93.41% and 12.24%, respectively. PAT could inhibit the growth of Escherichia coli, Proteus vulgaris, Bacillus subtilis, and Staphylococcus aureus cells, with minimum inhibitory concentrations of 0.5, 1.0, 4.0, and 4.0 mg/mL, respectively. Ultra-high-resolution field emission scanning electron microscopy and high-resolution transmission electron microscopy analysis revealed cell wall and membrane rupture of E. coli treated with PAT. Further, 0.5-4.0 mg/mL PAT was found to significantly (P < 0.01) and concentration-dependently increase the conductivity of the broth, exudation of the intracellular protein, and alkaline phosphatase and ß-galactosidase activities. Confocal laser scanning microscopy revealed morphological changes in E. coli DNA after PAT treatment and intracellular reactive oxygen species accumulation; flow cytometry revealed E. coli cell apoptosis. Our findings provide a theoretical basis and technical support for the development of PAT as a natural antibacterial product.

A High-Quality Reference Genome Sequence and Genetic Transformation System of Aralia elata.

Aralia elata is a perennial woody plant of the genus Aralia in the family Araliaceae. It is rich in saponins and therefore has a wide range of pharmacological effects. Here, we report a high-quality reference genome of A. elata, with a genome size of 1.21 Gb and a contig N50 of 51.34 Mb, produced by PacBio HiFi sequencing technology. This is the first genome assembly for the genus Aralia. Through genome evolutionary analysis, we explored the phylogeny and whole genome duplication (WGD) events in the A. elata genome. The results indicated that a recent WGD event occurred in the A. elata genome. Estimation of the divergence times indicated that the WGD may be shared by Araliaceae. By analyzing the genome sequence of A. elata and combining the transcriptome data from three tissues, we discovered important genes related to triterpene saponins biosynthesis. Furthermore, based on the embryonic callus induction system of A. elata established in our laboratory, we set up the genetic transformation system of this plant. The genomic resources and genetic transformation system obtained in this study provide insights into A. elata and lays the foundation for further exploration of the A. elata regulatory mechanism.

SAFS: Object Tracking Algorithm Based on Self-Adaptive Feature Selection.

Object tracking is one of the most challenging problems in the field of computer vision. In challenging object tracking scenarios such as illumination variation, occlusion, motion blur and fast motion, existing algorithms can present decreased performances. To make better use of the various features of the image, we propose an object tracking method based on the self-adaptive feature selection (SAFS) algorithm, which can select the most distinguishable feature sub-template to guide the tracking task. The similarity of each feature sub-template can be calculated by the histogram of the features. Then, the distinguishability of the feature sub-template can be measured by their similarity matrix based on the maximum a posteriori (MAP). The selection task of the feature sub-template is transformed into the classification task between feature vectors by the above process and adopt modified Jeffreys' entropy as the discriminant metric for classification, which can complete the update of the sub-template. Experiments with the eight video sequences in the Visual Tracker Benchmark dataset evaluate the comprehensive performance of SAFS and compare them with five baselines. Experimental results demonstrate that SAFS can overcome the difficulties caused by scene changes and achieve robust object tracking.

Libraries of Uniform Magnetic Multicore Nanoparticles with Tunable Dimensions for Biomedical and Photonic Applications.

Multicore iron oxide nanoparticles, also known as colloidal nanocrystal clusters, are magnetic materials with diverse applications in biomedicine and photonics. Here, we examine how both of their characteristic dimensional features, the primary particle and sub-micron colloid diameters, influence their magnetic properties and performance in two different applications. The characterization of these basic size-dependent properties is enabled by a synthetic strategy that provides independent control over both the primary nanocrystal and cluster dimensions. Over a wide range of conditions, electron microscopy and X-ray diffraction reveal that the oriented attachment of smaller nanocrystals results in their crystallographic alignment throughout the entire superstructure. We apply a sulfonated polymer with high charge density to prevent cluster aggregation and conjugate molecular dyes to particle surfaces so as to visualize their collection using handheld magnets. These libraries of colloidal clusters, indexed both by primary nanocrystal dimension (dp) and overall cluster diameter (Dc), form magnetic photonic crystals with relatively weak size-dependent properties. In contrast, their performance as MRI T2 contrast agents is highly sensitive to cluster diameter, not primary particle size, and is optimized for materials of 50 nm diameter (r2 = 364 mM-1 s-1). These results exemplify the relevance of dimensional control in developing applications for these versatile materials.

Evaluating physicochemical properties of crude oil as indicators of low-salinity-induced wettability alteration in carbonate minerals.

The injection of low-salinity brine enhances oil recovery by altering the mineral wettability in carbonate reservoirs. However, the reported effectiveness of low-salinity water varies significantly in the literature, and the underlying mechanism of wettability alteration is controversial. In this work, we investigate the relationships between characteristics of crude oils and the oils' response to low-salinity water in a spontaneous imbibition test, aiming (1) to identify suitable indicators of the effectiveness of low-salinity water and (2) to evaluate possible mechanisms of low-salinity-induced wettability alteration, including rock/oil charge repulsion and microdispersion formation. Seven oils are tested by spontaneous imbibition and fully characterized in terms of their acidity, zeta potential, interfacial tension, microdispersion propensity, water-soluble organics content and saturate-aromatic-resin-asphaltene fractionation. For the first time, the effectiveness of low-salinity water is found to positively correlate with the oil interfacial tension in low-salinity water. Oils with higher interfacial activity are found to respond more positively to low-salinity water. Moreover, cryogenic transmission electron microscopy images suggest that microdispersion is essentially macroemulsion, and its formation is an effective indicator - but not the root cause - of wettability alteration. The repulsive zeta potential for the rock and the oil in low-salinity water is found to be an insufficient condition for wettability alteration in carbonate minerals.

Enhancing Gaussian Estimation of Distribution Algorithm by Exploiting Evolution Direction With Archive.

As a typical model-based evolutionary algorithm, estimation of distribution algorithm (EDA) possesses unique characteristics and has been widely applied in global optimization. However, the commonly used Gaussian EDA (GEDA) usually suffers from premature convergence, which severely limits its search efficiency. This paper first systematically analyzes the reasons for the deficiency of traditional GEDA, then tries to enhance its performance by exploiting the evolution direction, and finally develops a new GEDA variant named EDA2. Instead of only utilizing some good solutions produced in the current generation to estimate the Gaussian model, EDA2 preserves a certain number of high-quality solutions generated in the previous generations into an archive and employs these historical solutions to assist estimating the covariance matrix of Gaussian model. By this means, the evolution direction information hidden in the archive is naturally integrated into the estimated model, which in turn can guide EDA2 toward more promising solution regions. Moreover, the new estimation method significantly reduces the population size of EDA2 since it needs fewer individuals in the current population for model estimation. As a result, a fast convergence can be achieved. To verify the efficiency of EDA2, we tested it on a variety of benchmark functions and compared it with several state-of-the-art EAs. The experimental results demonstrate that EDA2 is efficient and competitive.

Nigegladines A-C, Three Thymoquinone Dimers from Nigella glandulifera.

Nigegladines A-C (1-3), three thymoquinone dimers, were isolated from the seeds of Nigella glandulifera. Racemic 1 possesses a unique tricyclo[5.4.0.12,6]dodecane carbon skeleton, and compounds 2 and 3 are two unusual diterpenoid alkaloids with indole cores. Their structures were determined by extensive spectroscopic analyses, and that of 1 was confirmed by single-crystal X-ray diffraction. Both (+)-1 and (-)-1 exhibited significant protective effects against hypoxia/reoxygenation-induced H9c2 myocardial cell injury.

Object Tracking Using Local Multiple Features and a Posterior Probability Measure.

Object tracking has remained a challenging problem in recent years. Most of the trackers can not work well, especially when dealing with problems such as similarly colored backgrounds, object occlusions, low illumination, or sudden illumination changes in real scenes. A centroid iteration algorithm using multiple features and a posterior probability criterion is presented to solve these problems. The model representation of the object and the similarity measure are two key factors that greatly influence the performance of the tracker. Firstly, this paper propose using a local texture feature which is a generalization of the local binary pattern (LBP) descriptor, which we call the double center-symmetric local binary pattern (DCS-LBP). This feature shows great discrimination between similar regions and high robustness to noise. By analyzing DCS-LBP patterns, a simplified DCS-LBP is used to improve the object texture model called the SDCS-LBP. The SDCS-LBP is able to describe the primitive structural information of the local image such as edges and corners. Then, the SDCS-LBP and the color are combined to generate the multiple features as the target model. Secondly, a posterior probability measure is introduced to reduce the rate of matching mistakes. Three strategies of target model update are employed. Experimental results show that our proposed algorithm is effective in improving tracking performance in complicated real scenarios compared with some state-of-the-art methods.

Three new alkaloids from the seeds of Nigella glandulifera.

Three new alkaloids namely 8-(4-hydroxyphenyl)-6-methoxy-3,4-dihydroisoquinolin-1(2H)-one (1), 4-aminonigellidine (2), and N-[(4-hydroxy-2-isopropyl-5-methyl)]phenylurea (3), along with six known ones (4-9), were isolated from the seeds of Nigella glandulifera. The structures of 1-3 were determined through spectroscopic analyses (HRESIMS, 1D/2D NMR). Compound 1 was a rare isoquinolinone alkaloid with phenyl substituted at C-8.

Suppressive effects of total alkaloids of Lycopodiastrum casuarinoides on adjuvant-induced arthritis in rats.

ETHNOPHARMACOLOGICAL RELEVANCE: Lycopodiastrum casuarinoides is a folk medicine used to treat inflammation-associated diseases including rheumatoid arthritis in South China. Since the major secondary metabolites in Lycopodiastrum casuarinoides are alkaloids, the present study aims to investigate the suppressive effects of total alkaloids of Lycopodiastrum casuarinoides (ALC) on adjuvant-induced arthritis (AA) in rats. MATERIALS AND METHODS: AA was induced (day 0) in male Sprague-Dawley rats by intradermal injection of complete Freund׳s adjuvant (CFA) in right hind footpad. Diclofenac sodium (SD) was chosen as the positive drug. SD (10mg/kg) and ALC (20 and 40 mg/kg) administration started from day 1 and continued for 28 days. Paw swelling, arthritis scores, and histopathological changes were evaluated. In addition, the serum levels of tumor necrosis factor alpha (TNF-α), interleukin-6 (IL-6), and prostaglandin E2 (PGE2), as well as cyclooxygenase-2 (COX-2) and nuclear factor (NF)-κB expressions in joint synovial tissues were detected. RESULTS: ALC administration significantly suppressed the inflammatory responses in the joints of AA rats. It also decreased the serum levels of TNF-α, IL-6 and PGE2. Moreover, Western blot analysis showed that COX-2 and NF-κB expressions in synovial tissues of AA rats were significantly reduced. CONCLUSION: These results indicated that ALC prevented the pathological development of AA in rats. ALC may be a potential candidate for the treatment of inflammation and arthritis.

Synthesis and Crystal Structure of Gold Nanobelts.

Gold nanobelts were synthesized by the reduction of tetrachloroauric acid with ascorbic acid in the presence of the surfactants cetyltrimethylammonium bromide and sodium dodecylsulfate. The resulting structures have rectangular cross sectional dimensions that are tens of nanometers and lengths that are tens to hundreds of micrometers. We find that the nanobelt yield and resulting structures are very sensitive to temperature which is likely due to the transition of the surfactant solution from wormlike micelles to spherical micelles. The nanobelt crystal structure contains a mixture of face centered cubic and hexagonally close packed lattice phases that can be isolated and examined individually due to the unique nanobelt size and shape.