Tracking single adatoms in liquid in a transmission electron microscope.

Single atoms or ions on surfaces affect processes from nucleation1 to electrochemical reactions2 and heterogeneous catalysis3. Transmission electron microscopy is a leading approach for visualizing single atoms on a variety of substrates4,5. It conventionally requires high vacuum conditions, but has been developed for in situ imaging in liquid and gaseous environments6,7 with a combined spatial and temporal resolution that is unmatched by any other method-notwithstanding concerns about electron-beam effects on samples. When imaging in liquid using commercial technologies, electron scattering in the windows enclosing the sample and in the liquid generally limits the achievable resolution to a few nanometres6,8,9. Graphene liquid cells, on the other hand, have enabled atomic-resolution imaging of metal nanoparticles in liquids10. Here we show that a double graphene liquid cell, consisting of a central molybdenum disulfide monolayer separated by hexagonal boron nitride spacers from the two enclosing graphene windows, makes it possible to monitor, with atomic resolution, the dynamics of platinum adatoms on the monolayer in an aqueous salt solution. By imaging more than 70,000 single adatom adsorption sites, we compare the site preference and dynamic motion of the adatoms in both a fully hydrated and a vacuum state. We find a modified adsorption site distribution and higher diffusivities for the adatoms in the liquid phase compared with those in vacuum. This approach paves the way for in situ liquid-phase imaging of chemical processes with single-atom precision.

Mutations in BrABCG26, encoding an ATP-binding cassette transporter, are responsible for male sterility in Chinese cabbage (Brassica rapa L. ssp. pekinensis).

KEY MESSAGE: The gene BrABCG26 responsible for male sterility of Chinese cabbage was confirmed by two allelic mutants. Male-sterile lines are an important way of heterosis utilization in Chinese cabbage. In this study, two allelic male-sterile mutants msm3-1 and msm3-2 were obtained from a Chinese cabbage double haploid (DH) line 'FT' by using EMS-mutagenesis. Compared to the wild-type 'FT,' the stamens of mutants were completely degenerated and had no pollen, and other characters had no obvious differences. Cytological observation revealed that the failure of vacuolation of the mononuclear microspore, accompanied by abnormal tapetal degradation, resulted in anther abortion in mutants. Genetic analysis showed that a recessive gene controlled the mutant trait. MutMap combined with kompetitive allele specific PCR genotyping analyses showed that BraA01g038270.3C, encoding a transporter ABCG26 that played a vital role in pollen wall formation, was the candidate gene for msm3-1, named BrABCG26. Compared with wild-type 'FT,' the mutations existed on the second exon (C to T) and the sixth exon (C to T) of BrABCG26 gene in mutants msm3-1 and msm3-2, leading to the loss-of-function truncated protein, which verified the BrABCG26 function in stamen development. Subcellular localization and expression pattern analysis indicated that BrABCG26 was localized in the nucleus and was expressed in all organs, with the highest expression in flower buds. Compared to the wild-type 'FT,' the expressions of BrABCG26 were significantly reduced in flower buds and anthers of mutants. Promoter activity analysis showed that a strong GUS signal was detected in flower buds. These results indicated that BrABCG26 is responsible for the male sterility of msm3 mutants in Chinese cabbage.

Spin-labeling Insights into How Chemical Fixation Impacts Glycan Organization on Cells.

As new methods to interrogate glycan organization on cells develop, it is important to have a molecular level understanding of how chemical fixation can impact results and interpretations. Site-directed spin labeling technologies are well suited to study how the spin label mobility is impacted by local environmental conditions, such as those imposed by cross-linking effects of paraformaldehyde cell fixation methods. Here, we utilize three different azide-containing sugars for metabolic glycan engineering with HeLa cells to incorporate azido glycans that are modified with a DBCO-based nitroxide moiety via click reaction. Continuous wave X-band electron paramagnetic resonance spectroscopy is employed to characterize how the chronological sequence of chemical fixation and spin labeling impacts the local mobility and accessibility of the nitroxide-labeled glycans in the glycocalyx of HeLa cells. Results demonstrate that chemical fixation with paraformaldehyde can alter local glycan mobility and care should be taken in the analysis of data in any study where chemical fixation and cellular labeling occur.

Dendrimer-conjugated isotretinoin for controlled transdermal drug delivery.

BACKGROUND: In the present study, we aimed to develop a novel isotretinoin delivery model for treating skin diseases, revealing its potential advantages in drug delivery and targeted therapy. Using a self-assembly strategy, we grafted a dendrimer, based on a well-defined branched structure for nanomedical devices, with a well-defined nanoarchitecture, yielding spherical, highly homogeneous molecules with multiple surface functionalities. Accordingly, a self-assembled dendrimer-conjugated system was developed to achieve the transdermal delivery of isotretinoin (13cRA-D). RESULTS: Herein, 13cRA-D showed remarkable controlled release, characterized by slow release in normal tissues but accelerated release in tissues with low pH, such as sites of inflammation. These release characteristics could abrogate the nonteratogenic side effects of isotretinoin and allow efficient skin permeation. Moreover, 13cRA-D exhibited high therapeutic efficacy in acne models. Based on in vitro and in vivo experimental results, 13cRA-D afforded better skin penetration than isotretinoin and allowed lesion targeting. Additionally, 13cRA-D induced minimal skin irritation. CONCLUSION: Our findings suggest that 13cRA-D is a safe and effective isotretinoin formulation for treating patients with skin disorders.

Single-shot hyperspectral imaging based on dual attention neural network with multi-modal learning.

Hyperspectral imaging is being extensively investigated owing to its promising future in critical applications such as medical diagnostics, sensing, and surveillance. However, current techniques are complex with multiple alignment-sensitive components and spatiospectral parameters predetermined by manufacturers. In this paper, we demonstrate an end-to-end snapshot hyperspectral imaging technique and build a physics-informed dual attention neural network with multimodal learning. By modeling the 3D spectral cube reconstruction procedure and solving that compressive-imaging inverse problem, the hyperspectral volume can be directly recovered from only one scene RGB image. Spectra features and camera spectral sensitivity are jointly leveraged to retrieve the multiplexed spatiospectral correlations and realize hyperspectral imaging. With the help of integrated attention mechanism, useful information supplied by disparate modal components is adaptively learned and aggregated to make our network flexible for variable imaging systems. Results show that the proposed method is ultra-faster than the traditional scanning method, and 3.4 times more precise than the existing hyperspectral imaging convolutional neural network. We provide theory for network design, demonstrate training process, and present experimental results with high accuracy. Without bulky benchtop setups and strict experimental limitations, this simple and effective method offers great potential for future spectral imaging applications such as pathological digital stain, computational imaging and virtual/augmented reality display, etc.

Enzymatic glycoengineering-based spin labelling of cell surface sialoglycans to enable their analysis by electron paramagnetic resonance (EPR) spectroscopy.

A novel method for spin labelling of sialoglycans on the cell surface is described. C9-Azido sialic acid was linked to glycans on live cells via CSTII-catalysed α2,3-sialylation utilizing azido-sialic acid nucleotide as a sialyl donor, which was followed by attachment of a spin label to the azide via click reaction. It enables the study of cell surface sialoglycans by EPR spectroscopy.

Review on Chemical Constituents of Schizonepeta tenuifolia Briq. and Their Pharmacological Effects

Schizonepeta tenuifolia Briq. is a famous Chinese traditional medicine with antipyretic, anti-inflammatory, analgesic and hemostatic effects. Many chemical components can be isolated and detected by using various analysis methods, including monoterpenes, sesquiterpenes, aldehydes, ketones, quinones, alcohols, phenols, carboxylic acids and esters, etc., in which volatile oil was considered to be the main chemical component. In this paper, the chemical constituents and their pharmacological effects were reviewed by summarizing the recent literature, revealing the relationship between them.

CVNet: confidence voting convolutional neural network for camera spectral sensitivity estimation.

Spectral sensitivity, as one of the most important parameters of a digital camera, is playing a key role in many computer vision applications. In this paper, a confidence voting convolutional neural network (CVNet) is proposed to rebuild the spectral sensitivity function, modeled as the sum of weighted basis functions. By evaluating useful information supplied by different image segments, disparate confidence is calculated to automatically learn basis functions' weights, only using one image captured by the object camera. Three types of basis functions are made up and employed in the network, including Fourier basis function (FBF), singular value decomposition basis function (SVDBF), and radial basis function (RBF). Results show that the accuracy of the proposed method with FBF, SVDBF, and RBF is 97.92%, 98.69%, and 99.01%, respectively. We provide theory for network design, build a dataset, demonstrate training process, and present experimental results with high precision. Without bulky benchtop setups and strict experimental limitations, this proposed simple and effective method could be an alternative in the future for spectral sensitivity function estimation.

Scan-free end-to-end new approach for snapshot camera spectral sensitivity estimation.

Spectral sensitivity is largely related to sensor imaging, which has drawn widespread attention in computer vision. Accurate estimation becomes increasingly urgent because manufacturers rarely disclose it. In this Letter, we present a novel, compact, inexpensive, and real-time computational system for snapshot spectral sensitivity estimation. A multi-scale camera based on the multi-scale convolutional neural network is first proposed, to the best of our knowledge, to automatically extract multiplexing features of an input image by multiscale deep learning, which is vital to solving the inverse problem in sensitivity estimation. Our network is flexible and can be designed with different convolutional kernel sizes for a given application. We build a dataset with 10,500 raw images and generate an excellent pre-trained model. Commercial cameras are adopted to test model validity; the results show that our system can achieve estimation accuracy as high as 91.35%. We provide a method for system design, propose a deep learning network, build a dataset, demonstrate training process, and present experimental results with high precision. This simple and effective method provides an accurate approach for precise estimation of spectral sensitivity and is suitable for computational applications such as pathological digital stain, virtual/augmented reality display, and high-quality image acquisition.

Atomic Resolution Imaging of CrBr3 Using Adhesion-Enhanced Grids.

Suspended specimens of 2D crystals and their heterostructures are required for a range of studies including transmission electron microscopy (TEM), optical transmission experiments, and nanomechanical testing. However, investigating the properties of laterally small 2D crystal specimens, including twisted bilayers and air-sensitive materials, has been held back by the difficulty of fabricating the necessary clean suspended samples. Here we present a scalable solution that allows clean free-standing specimens to be realized with 100% yield by dry-stamping atomically thin 2D stacks onto a specially developed adhesion-enhanced support grid. Using this new capability, we demonstrate atomic resolution imaging of defect structures in atomically thin CrBr3, a novel magnetic material that degrades in ambient conditions.

Highly Sensitive, Breathable, and Flexible Pressure Sensor Based on Electrospun Membrane with Assistance of AgNW/TPU as Composite Dielectric Layer.

Pressure sensors have been widely used in electronic wearable devices and medical devices to detect tiny physical movements and mechanical deformation. However, it remains a challenge to fabricate desirable, comfortable wearing, and highly sensitive as well as fast responsive sensors to capture human body physiological signs. Here, a new capacitive flexible pressure sensor that is likely to solve this problem was constructed using thermoplastic polyurethane elastomer rubber (TPU) electrospinning nanofiber membranes as a stretchable substrate with the incorporation of silver nanowires (AgNWs) to build a composite dielectric layer. In addition, carbon nanotubes (CNTs) were painted on the TPU membranes as flexible electrodes by screen printing to maintain the flexibility and breathability of the sensors. The flexible pressure sensor could detect tiny body signs; fairly small physical presses and mechanical deformation based on the variation in capacitance due to the synergistic effects of microstructure and easily altered composite permittivity of AgNW/TPU composite dielectric layers. The resultant sensors exhibited high sensitivity (7.24 kPa-1 within the range of 9.0 × 10-3 ~ 0.98 kPa), low detection limit (9.24 Pa), and remarkable breathability as well as fast responsiveness (<55 ms). Moreover, both continuously pressing/releasing cycle over 1000 s and bending over 1000 times did not impair the sensitivity, stability, and durability of this flexible pressure sensor. This proposed strategy combining the elastomer nanofiber membrane and AgNW dopant demonstrates a cost-effective and scalable fabrication of capacitive pressure sensors as a promising application in electronic skins and wearable devices.

Nanometer Resolution Elemental Mapping in Graphene-Based TEM Liquid Cells.

We demonstrate a new design of graphene liquid cell consisting of a thin lithographically patterned hexagonal boron nitride crystal encapsulated on both sides with graphene windows. The ultrathin window liquid cells produced have precisely controlled volumes and thicknesses and are robust to repeated vacuum cycling. This technology enables exciting new opportunities for liquid cell studies, providing a reliable platform for high resolution transmission electron microscope imaging and spectral mapping. The presence of water was confirmed using electron energy loss spectroscopy (EELS) via the detection of the oxygen K-edge and measuring the thickness of full and empty cells. We demonstrate the imaging capabilities of these liquid cells by tracking the dynamic motion and interactions of small metal nanoparticles with diameters of 0.5-5 nm. We further present an order of magnitude improvement in the analytical capabilities compared to previous liquid cell data with 1 nm spatial resolution elemental mapping achievable for liquid encapsulated bimetallic nanoparticles using energy dispersive X-ray spectroscopy (EDXS).

Highly Chemo- and Regioselective Vinylation of N-Heteroarenes with Vinylsulfonium Salts.

An efficient chemo- and regioselective N-vinylation of N-heteroarenes has been developed using vinylsulfonium salts. The reaction proceeded under mild and transition-metal-free conditions and consistently provided moderate to high yields of vinylation products with 100% E-stereoselectivity. This reaction is also highly chemoselective, and compatible with a variety of functional groups, such as -NHR, -NH2, -OH, -COOH, ester, etc.

Divergence of the freshwater sleeper, Neodontobutishainanensis (Chen, 1985) (Teleostei, Odontobutidae), in the Pearl River basin and on Hainan Island of southern China.

Study of divergence of freshwater fish populations between island and adjacent mainland areas can shed light on the phylogeographical relationships of these regions. Neodontobutishainanensis is a freshwater fish species restricted to Hainan Island and in Guangdong and Guangxi provinces in the southern mainland China. We examine the phylogenetic relationship and population structure of N.hainanensis based on 3,176 nuclear loci using a gene-capture method. STRUCTURE analysis and principal coordinate analyses (PCA) indicate that populations from Guangdong, Guangxi and Hainan are each distinct, except that some individuals of the Guangdong population share minor genetic components with individuals of the Guangxi population. In the concatenated gene tree, the Hainan population is grouped with the Guangdong population, but the coalescent tree groups the Hainan population as the sister to the Guangxi population. Finally, coalescent simulations confirmed the divergence pattern supported by the coalescent tree and revealed a one-way introgression from the Guangxi population to the Guangdong population, which can explain the discordant results supported by the concatenated and coalescent phylogenetic analyses. Due to recent decline of N.hainanensis populations and the genetic patterns in this species, as revealed in this study, the populations in the three areas should be treated as separate conservation units.

New Strategies for constructing and analyzing semiconductor photosynthetic biohybrid systems based on ensemble Machine learning Models: Visualizing complex mechanisms and yield prediction.

Photosynthetic biohybrid systems (PBSs) composed of semiconductor-microbial hybrids provide a novel approach for converting light into chemical energy. However, comprehending the intricate interactions between materials and microbes that lead to PBSs with high apparent quantum yields (AQY) is challenging. Machine learning holds promise in predicting these interactions. To address this issue, this study employs ensemble learning (ESL) based on Random Forest, Gradient Boosting Decision Tree, and eXtreme Gradient Boosting to predict AQY of PBSs utilizing a dataset comprising 15 influential factors. The ESL model demonstrates exceptional accuracy and interpretability (R2 value of 0.927), offering insights into the impact of these factors on AQY while facilitating the selection of efficient semiconductors. Furthermore, this research propose that efficient charge carrier separation and transfer at the bio-abiotic interface are crucial for achieving high AQY levels. This research provides guidance for selecting semiconductors suitable for productive PBSs while elucidating mechanisms underlying their enhanced efficiency.

The intersection of virus infection and liver disease: A comprehensive review of pathogenesis, diagnosis, and treatment.

Liver disease represents a significant global burden, placing individuals at a heightened risk of developing cirrhosis and liver cancer. Viral infections act as a primary cause of liver diseases on a worldwide scale. Infections involving hepatitis viruses, notably hepatitis B, C, and E viruses, stand out as the most prevalent contributors to acute and chronic intrahepatic adverse outcome, although the hepatitis C virus (HCV) can be effectively cured with antiviral drugs, but no preventative vaccination developed. Hepatitis B virus (HBV) and HCV can lead to both acute and chronic liver diseases, including liver cirrhosis and hepatocellular carcinoma (HCC), which are principal causes of worldwide morbidity and mortality. Other viruses, such as Epstein-Barr virus (EBV) and cytomegalovirus (CMV), are capable of causing liver damage. Therefore, it is essential to recognize that virus infections and liver diseases are intricate and interconnected processes. A profound understanding of the underlying relationship between virus infections and liver diseases proves pivotal in the effective prevention, diagnosis, and treatment of these conditions. In this review, we delve into the mechanisms by which virus infections induce liver diseases, as well as explore the pathogenesis, diagnosis, and treatment of liver diseases. This article is categorized under: Infectious Diseases > Biomedical Engineering.

Energy utilization of agricultural waste: Machine learning prediction and pyrolysis transformation.

The rapid screening of agricultural waste materials for capacitor preparation holds significant importance in comprehending the relationship between material properties and enhancing experimental efficiency. In this study, we developed two machine learning models to predict electrode material characteristics using 2997 data points extracted from 235 articles. The identification and influence of key features on prediction indices provide a theoretical foundation for subsequent practical preparation. Through regression analysis and index evaluation, corn straw emerged as the optimal material for capacitor preparation, leading us to propose a one-step activation and two-step modification approach to convert corn straw into porous biochar. By modifying biochar with Co(NO3)2·6H2O, the maximum electrode capacitance of porous carbon reached 732.6 F/g. Furthermore, the electrode exhibited exceptional cycle stability with a remaining capacitance of 96 % after 5000 cycles. The prepared symmetric capacitor demonstrated pseudocapacitance behavior with a capacitance of 183.15 F/g at a current density of 1.0 A/g, power density of 22 kW/kg, and energy density of 9.03 Wh/kg. Considering the increasing annual output of corn straw and its superior industrial application prospects compared to acid-, base-, or precious metal-based alternatives due to their cost-effectiveness and environmental friendliness, these findings highlight the potential practical value in utilizing modified corn straw biochar as an efficient energy storage electrode material.

Designing surface exposed sites on Bacillus subtilis lipase A for spin-labeling and hydration studies.

Spin-labeling with electron paramagnetic resonance spectroscopy (EPR) is a facile method for interrogating macromolecular flexibility, conformational changes, accessibility, and hydration. Within we present a computationally based approach for the rational selection of reporter sites in Bacillus subtilis lipase A (BSLA) for substitution to cysteine residues with subsequent modification with a spin-label that are expected to not significantly perturb the wild-type structure, dynamics, or enzymatic function. Experimental circular dichroism spectroscopy, Michaelis-Menten kinetic parameters and EPR spectroscopy data validate the success of this approach to computationally select reporter sites for future magnetic resonance investigations of hydration and hydration changes induced by polymer conjugation, tethering, immobilization, or amino acid substitution in BSLA. Analysis of molecular dynamic simulations of the impact of substitutions on the secondary structure agree well with experimental findings. We propose that this computationally guided approach for choosing spin-labeled EPR reporter sites, which evaluates relative surface accessibility coupled with hydrogen bonding occupancy of amino acids to the catalytic pocket via atomistic simulations, should be readily transferable to other macromolecular systems of interest including selecting sites for paramagnetic relaxation enhancement NMR studies, other spin-labeling EPR studies or any method requiring a tagging method where it is desirable to not alter enzyme stability or activity.

MRI-guided Near-infrared Spectroscopic Tomography (MRg-NIRST) Imaging System with Wearable Breast Optical Interface for Breast Cancer Imaging.

OBJECTIVE: To develop a novel Magnetic Resonance Imaging (MRI)-guided Near-Infrared Spectroscopic Tomography (MRg-NIRST) imaging system with an MRI-compatible breast optical interface for breast imaging.

Establishment and application of TSDPSO-SVM model combined with multi-dimensional feature fusion method in the identification of fracture-related infection.

Fracture-related infection (FRI) is one of the most common and intractable complications in orthopedic trauma surgery. This complication can impose severe psychological burdens and socio-economic impacts on patients. Although the definition of FRI has been proposed recently by an expert group, the diagnostic criteria for FRI are not yet standardized. A total of 4761 FRI patients and 4761 fracture patients (Non-FRI) were included in the study. The feature set of patients included imaging characteristics, demographic information, clinical symptoms, microbiological findings, and serum inflammatory markers, which were reduced by the Principal Component Analysis. To optimize the Support Vector Machine (SVM) model, the Traction Switching Delay Particle Swarm Optimization (TSDPSO) algorithm, a recognition method was proposed. Moreover, five machine learning models, including TSDPSO-SVM, were employed to distinguish FRI from Non-FRI. The Area under the Curve of TSDPSO-SVM was 0.91, at least 5% higher than that of other models. Compared with the Random Forest, Backpropagation Neural Network (BP), SVM and eXtreme Gradient Boosting (XGBoost), TSDPSO-SVM demonstrated remarkable accuracy in the test set ([Formula: see text]). The recall of TSDPSO-SVM was 98.32%, indicating a significant improvement ([Formula: see text]). Compared with BP and SVM, TSDPSO-SVM exhibited significantly superior specificity, false positive rate and precision ([Formula: see text]. The five models yielded consistent results in the training and testing of FRI patients across different age groups. TSDPSO-SVM is validated to have the maximum overall prediction ability and can effectively distinguish between FRI and Non-FRI. For the early diagnosis of FRI, TSDPSO-SVM may provide a reference basis for clinicians, especially those with insufficient experience. These results also lay a foundation for the intelligent diagnosis of FRI. Furthermore, these findings exhibit the application potential of this model in the diagnosis and classification of other diseases.