Precise coordination of high-loading Fe single atoms with sulfur boosts selective generation of nonradicals.

Nonradicals are effective in selectively degrading electron-rich organic contaminants, which unfortunately suffer from unsatisfactory yield and uncontrollable composition due to the competitive generation of radicals. Herein, we precisely construct a local microenvironment of the carbon nitride-supported high-loading (~9 wt.%) Fe single-atom catalyst (Fe SAC) with sulfur via a facile supermolecular self-assembly strategy. Short-distance S coordination boosts the peroxymonosulfate (PMS) activation and selectively generates high-valent iron-oxo species (FeIV=O) along with singlet oxygen (1O2), significantly increasing the 1O2 yield, PMS utilization, and p-chlorophenol reactivity by 6.0, 3.0, and 8.4 times, respectively. The composition of nonradicals is controllable by simply changing the S content. In contrast, long-distance S coordination generates both radicals and nonradicals, and could not promote reactivity. Experimental and theoretical analyses suggest that the short-distance S upshifts the d-band center of the Fe atom, i.e., being close to the Fermi level, which changes the binding mode between the Fe atom and O site of PMS to selectively generate 1O2 and FeIV=O with a high yield. The short-distance S-coordinated Fe SAC exhibits excellent application potential in various water matrices. These findings can guide the rational design of robust SACs toward a selective and controllable generation of nonradicals with high yield and PMS utilization.

Cancer Cell Membrane Nanodiscs for Antitumor Vaccination.

Cell membrane-based nanovaccines have demonstrated attractive features due to their inherently multiantigenic nature and ability to be formulated with adjuvants. Here, we report on cellular nanodiscs fabricated from cancer cell membranes and incorporated with a lipid-based adjuvant for antitumor vaccination. The cellular nanodiscs, with their small size and discoidal shape, are readily taken up by antigen-presenting cells and drain efficiently to the lymph nodes. Due to its highly immunostimulatory properties, the nanodisc vaccine effectively stimulates the immune system and promotes tumor-specific immunity. Using a murine colorectal cancer model, strong control of tumor growth is achieved in both prophylactic and therapeutic settings, particularly in combination with checkpoint blockades. Considerable therapeutic efficacy is also observed in treating a weakly immunogenic metastatic melanoma model. This work presents a new paradigm for the design of multiantigenic nanovaccines that can effectively activate antitumor immune responses and may be applicable to a wide range of cancers.

Deciphering Structure-Activity Relationship Towards CO2 Electroreduction over SnO2 by A Standard Research Paradigm.

Authentic surface structures under reaction conditions determine the activity and selectivity of electrocatalysts, therefore, the knowledge of the structure-activity relationship can facilitate the design of efficient catalyst structures for specific reactivity requirements. However, understanding the relationship between a more realistic active surface and its performance is challenging due to the complicated interface microenvironment in electrocatalysis. Herein, we proposed a standard research paradigm to effectively decipher the structure-activity relationship in electrocatalysis, which is exemplified in the CO2 electroreduction over SnO2 . The proposed practice has aided in discovering authentic/resting surface states (Sn layer) of SnO2 accountable for the electrochemical CO2 reduction reaction (CO2 RR) performance under electrocatalytic conditions, which then is corroborated in the subsequent CO2 RR experiments over SnO2 with different morphologies (nanorods, nanoparticles, and nanosheets) in combination with in situ characterizations. This proposed methodology is further extended to the SnO electrocatalysts, providing helpful insights into catalytic structures. It is believed that our proposed standard research paradigm is also applicable to other electrocatalytic systems, in the meantime, decreases the discrepancy between theory and experiments, and accelerates the design of catalyst structures that achieve sustainable performance for energy conversion.

Efficient asymmetrical silicon-metal dimer electrocatalysts for the nitrogen reduction reaction.

The electrocatalytic nitrogen reduction reaction (ENRR) has been regarded as an eco-friendly and feasible substitute for the Haber-Bosch method. Identifying the effective catalysts for the ENRR is an extremely important prerequisite but challenging. Herein, asymmetrical silicon-metal dimer catalysts doped into g-C3N4 nanosheets with nitrogen vacancies (SiM@C3N4) were designed to address nitrogen activation and reduction. The concept catalysts of SiM@C3N4 can combine the advantages of silicon-based and metal-based catalysts during the ENRR. Among the catalysts investigated, SiMo@C3N4 and SiRu@C3N4 exhibited the highest activities towards the ENRR with ultra-low onset potentials of -0.20 and -0.39 V; meanwhile, they suppressed the competing hydrogen evolution reaction (HER) due to the relative difficulty in releasing hydrogen. Additionally, SiRu@C3N4 is demonstrated to possess strong hydrophobicity, which is greatly beneficial to the production of ammonia. This research provides insights into asymmetrical silicon-metal dimer catalysts and reveals a new method for developing dual-atom electrocatalysts. This asymmetrical dimer strategy can be applied in other electrocatalytic reactions for energy conversion.

Single Low-Dose Nanovaccine for Long-Term Protection against Anthrax Toxins.

Anthrax infections caused by Bacillus anthracis are an ongoing bioterrorism and livestock threat worldwide. Current approaches for management, including extended passive antibody transfusion, antibiotics, and prophylactic vaccination, are often cumbersome and associated with low patient compliance. Here, we report on the development of an adjuvanted nanotoxoid vaccine based on macrophage membrane-coated nanoparticles bound with anthrax toxins. This design leverages the natural binding interaction of protective antigen, a key anthrax toxin, with macrophages. In a murine model, a single low-dose vaccination with the nanotoxoids generates long-lasting immunity that protects against subsequent challenge with anthrax toxins. Overall, this work provides a new approach to address the ongoing threat of anthrax outbreaks and bioterrorism by taking advantage of an emerging biomimetic nanotechnology.

Quality Marker Discovery and Quality Evaluation of Eucommia ulmoides Pollen Using UPLC-QTOF-MS Combined with a DPPH-HPLC Antioxidant Activity Screening Method.

Pollen, as an important component of Eucommia ulmoides (EUP), is rich in nutrients and is receiving increasing attention. At present, there are no reports on research related to the chemical composition and quality standards of EUP, and there are significant quality differences and counterfeit phenomena in the market. This study used a UPLC-QTOF-MS system to identify 49 chemical components in EUP for the first time. In the second step, 2,2-diphenyl-1-picrylhydrazyl (DPPH)-HPLC antioxidant activity screening technology was used to identify the main active components of EUP, quercetin-3-O-sophoroside (QSH), quercetin-3-O-sambubioside (QSB), and quercetin 3-O-neohesperidoside (QNH), and their purification, preparation, and structure identification were carried out. Third, molecular docking was used to predict the activity of these components. Fourth, the intracellular ROS generation model of RAW264.7 induced by H2O2 was used to verify and evaluate the activity of candidate active ingredients to determine their feasibility as Q-markers. Finally, a quality control method for EUP was constructed using the three selected components as Q-markers. The identification of chemical components and the discovery, prediction, and confirmation of characteristic Q-markers in EUP provide important references for better research on EUP and the effective evaluation and control of its quality. This approach provides a new model for the quality control of novel foods or dietary supplements.

[Regulatory effect of eight Chinese herbal medicines on glucose and lipid metabolism and their potential active components based on HCBP6 target].

With the improvement of living standards and changes in working style, the prevalence of abnormal glucose and lipid metabolism in humans is increasing in modern society. Clinically, the related indicators are often improved by changing the lifestyle and/or taking hypoglycemic and lipid-lowering drugs, but there are no therapeutic drugs for disorders of glucose and lipid metabolism at present. Hepatitis C virus core protein binding protein 6(HCBP6) is a newly discovered target that can regulate triglyceride and cholesterol content according to level oscillations in the body, thereby regulating abnormal glucose and lipid metabolism. Relevant studies have shown that ginsenoside Rh_2 can significantly up-regulate the expression of HCBP6, but there are few studies on the effect of Chinese herbal medicines on HCBP6. Moreover, the three-dimensional structural information of HCBP6 has not been determined and the discovery of potential active components acting on HCBP6 is not rapidly advanced. Therefore, the total saponins of eight Chinese herbal medicines commonly used to regulate abnormal glucose and lipid metabolism were selected as the research objects to observe their effect on the expression of HCBP6. Then, the three-dimensional structure of HCBP6 was predicted, followed by molecular docking with saponins in eight Chinese herbal medicines to quickly find potential active components. The results showed that all total saponins tended to up-regulate HCBP6 mRNA and protein expression, where gypenosides showed the optimum effect on up-regulating HCBP6 mRNA and ginsenosides showed the optimum effect on up-regulating HCBP6 protein expression. Reliable protein structures were obtained after the prediction of protein structures using the Robetta website and the evaluation of the predicted structures by SAVES. The saponins from the website and literature were also collected and docked with the predicted protein, and the saponin components were found to have good binding activity to the HCBP6 protein. The results of the study are expected to provide ideas and methods for the discovery of new drugs from Chinese herbal medicines to regulate glucose and lipid metabolism.

Detection of Carbamazepine and Its Metabolites in Blood Samples by LC-MS/MS.

OBJECTIVES: To establish a method for the detection of carbamazepine and its metabolites 10,11-dihydro-10,11-epoxycarbamazepine and 10,11-dihydro-10-hydroxycarbamazepine in blood samples by liquid chromatography-tandem mass spectrometry (LC-MS/MS). METHODS: The blood samples were treated with 1-butyl-3-methylimidazolium hexafluorophosphate as an extraction solvent. The samples were extracted by ultrasound-assisted extraction and separated by ZORBAX Eclipse Plus C18, 95Å column. The mobile phase A aqueous solution containing 0.1% formic acid and 10 mmol/L ammonium acetate, and mobile phase B mixed organic solvent containing acetonitrile/methanol (Vacetonitrile∶Vmethanol=2∶3) were used for gradient elution at the flow rate of 1.00 mL/min. An electrospray ion source in positive mode was used for detection in the multiple reaction monitoring. RESULTS: The linearities of carbamazepine and its metabolites 10,11-dihydro-10,11-epoxycarbamazepine and 10,11-dihydro-10-hydroxycarbamazepine in blood samples were good within the corresponding range, with correlation coefficients (r) greater than 0.995 6. The limits of detection were 3.00, 0.40 and 1.30 ng/mL, respectively. The limit of quantitation were 8.00, 1.00 and 5.00 ng/mL, respectively. The extraction recoveries ranged from 76.00% to 106.44%. The relative standard deviations of the intra-day and inter-day precisions were less than 16%. Carbamazepine and its main metabolite 10,11-dihydro-10,11-epoxycarbamazepine were detected in blood samples of death cases with a mass concentration of 2.71 µg/mL and 252.14 ng/mL, respectively. CONCLUSIONS: This method has high sensitivity and good selectivity, which is suitable for the detection of carbamazepine and its metabolites in blood samples, and can be used for carbamazepine-related forensic identifications.

Using Cell Membranes as Recognition Layers to Construct Ultrasensitive and Selective Bioelectronic Affinity Sensors.

Conventional sandwich immunosensors rely on antibody recognition layers to selectively capture and detect target antigen analytes. However, the fabrication of these traditional affinity sensors is typically associated with lengthy and multistep surface modifications of electrodes and faces the challenge of nonspecific adsorption from complex sample matrices. Here, we report on a unique design of bioelectronic affinity sensors by using natural cell membranes as recognition layers for protein detection and prevention of biofouling. Specifically, we employ the human macrophage (MΦ) membrane together with the human red blood cell (RBC) membrane to coat electrochemical transducers through a one-step process. The natural protein receptors on the MΦ membrane are used to capture target antigens, while the RBC membrane effectively prevents nonspecific surface binding. In an attempt to detect tumor necrosis factor alpha (TNF-α) cytokine using the bioelectronic affinity sensor, it demonstrates a remarkable limit of detection of 150 pM. This new sensor design integrates natural cell membranes and electronic transduction, which offers synergistic functionalities toward a broad range of biosensing applications.

Prefiltered Single-Carrier Frequency-Domain Equalization for Binary CPM over Shallow Water Acoustic Channel.

The continuous phase modulation (CPM) technique is an excellent solution for underwater acoustic (UWA) channels with limited bandwidth and high propagation attenuation. However, the severe intersymbol interference is a big problem for the algorithm applying in shallow water. To solve this problem, an algorithm for prefiltered single-carrier frequency-domain equalization (PF-SCFDE) is presented in this paper. The regular whitening filter is replaced by a prefilter in the proposed algorithm. The output information sequence of this prefilter contains the forward information. To improve the performance, the output of the equalizer, combined with the forward information, is used to make the maximum likelihood estimation. The simulation results with minimum-shift keying and Gaussian-filtered minimum-shift keying signals over shallow water acoustic channels with low root mean square delay spread demonstrate that PF-SCFDE outperformed the traditional single-carrier frequency-domain equalization (SCFDE) by approximately 1 dB under a bit error rate (BER) of 10-4. A shallow sea trial has demonstrated the effectiveness of PF-SCFDE; PF-SCFDE had a reduction in BER of 18.35% as compared to the traditional SCFDE.

A Novel Steganography-Based Pattern for Print Matter Anti-Counterfeiting by Smartphone Cameras.

Print matter authentication based on anti-counterfeiting techniques has received continuously increasing concern from academia and industry. However, the existing printing anti-counterfeiting solutions often have the defects of poor identification experience, high cost, or weak anti-counterfeiting ability, and cannot achieve pre-sale anti-counterfeiting. Therefore, a novel steganography-based pattern for print matter anti-counterfeiting by smartphone cameras is proposed in this study. Firstly, every pixel in the original binary message image (such as QR code) is replaced by a square pixel block with the same binary gray value of 0 or 255 (the first-level expansion). Secondly, the obtained image is encrypted based on the logistic chaotic sequence, and then scrambled by Arnold transform. Lastly, once again every pixel in the generated image is replaced with a square pixel block (the second-level expansion), the size and gray value of which can be set to control the semi-fragile ability to distinguish an originally printed pattern from its illegitimate copy. If the message extracted from the printed pattern through the inverse procedure is complete enough to decode and read, the pattern is assumed to be an original print. Experimental results verify the advancement and effectiveness of the proposed scheme in distinguishing the copied pattern.

Sound Localization and Speech Enhancement Algorithm Based on Dual-Microphone.

In order to simplify the complexity and reduce the cost of the microphone array, this paper proposes a dual-microphone based sound localization and speech enhancement algorithm. Based on the time delay estimation of the signal received by the dual microphones, this paper combines energy difference estimation and controllable beam response power to realize the 3D coordinate calculation of the acoustic source and dual-microphone sound localization. Based on the azimuth angle of the acoustic source and the analysis of the independent quantity of the speech signal, the separation of the speaker signal of the acoustic source is realized. On this basis, post-wiener filtering is used to amplify and suppress the voice signal of the speaker, which can help to achieve speech enhancement. Experimental results show that the dual-microphone sound localization algorithm proposed in this paper can accurately identify the sound location, and the speech enhancement algorithm is more robust and adaptable than the original algorithm.

MSF-Net: Multi-Scale Feature Learning Network for Classification of Surface Defects of Multifarious Sizes.

In the field of surface defect detection, the scale difference of product surface defects is often huge. The existing defect detection methods based on Convolutional Neural Networks (CNNs) are more inclined to express macro and abstract features, and the ability to express local and small defects is insufficient, resulting in an imbalance of feature expression capabilities. In this paper, a Multi-Scale Feature Learning Network (MSF-Net) based on Dual Module Feature (DMF) extractor is proposed. DMF extractor is mainly composed of optimized Concatenated Rectified Linear Units (CReLUs) and optimized Inception feature extraction modules, which increases the diversity of feature receptive fields while reducing the amount of calculation; the feature maps of the middle layer with different sizes of receptive fields are merged to increase the richness of the receptive fields of the last layer of feature maps; the residual shortcut connections, batch normalization layer and average pooling layer are used to replace the fully connected layer to improve training efficiency, and make the multi-scale feature learning ability more balanced at the same time. Two representative multi-scale defect data sets are used for experiments, and the experimental results verify the advancement and effectiveness of the proposed MSF-Net in the detection of surface defects with multi-scale features.

FAR-Net: Feature-Wise Attention-Based Relation Network for Multilabel Jujube Defect Classification.

In production, due to natural conditions or process peculiarities, a single product often may exhibit more than one type of defect. The accurate identification of all defects has an important guiding significance and practical value to improve the planting and production processes. Concerning the surface defect classification task, convolutional neural networks can be implemented as a powerful instrument. However, a typical convolutional neural network tends to consider an image as an inseparable entity and a single instance when extracting features; moreover, it may overlook semantic correlations between different labels. To address these limitations, in the present paper, we proposed a feature-wise attention-based relation network (FAR-Net) for multilabel jujube defect classification. The network included four different modules designed for (1) image feature extraction, (2) label-wise feature aggregation, (3) feature activation and deactivation, and (4) correlation learning among labels. To evaluate the proposed method, a unique multilabel jujube defect dataset was constructed as a benchmark for the multilabel classification task of the jujube defect images. The results of experiments show that owing to the relation learning mechanism, the average precision of the three main composite defects in the dataset increases by 5.77%, 4.07%, and 3.50%, respectively, compared to the backbone of our network, namely Inception v3, which indicated that the proposed FAR-Net effectively facilitated the learning of correlation between labels and eventually, improved the multilabel classification accuracy.

[Rapid discovery of tyrosinase inhibitors from Sophora flavescens:significance for comprehensive utilization of its non-medicinal resources].

Sophorae Flavescentis Radix,derived from the root of Sophora flavescens in the Leguminosae family,has been widely used in the medicine,agriculture,animal husbandry,and daily chemical industry. A pharmacophore model-based method for rapid discovery of tyrosinase inhibitors( TIs) from S. flavescens was established by molecular docking under Lipinski rules,and verified by enzyme assays. Briefly,the chemical constituent database of S. flavescens( CDSF) was established based on the previous papers. Theoptimal pharmacophore model( OPM) was constructed by DS 2019 on the basis of known active TIs. Eighty-three hits predominated by flavonoids having higher fitting scores with OPM than the positive control were screened out,and subjected to molecular docking based on the three-dimensional structure of tyrosinase crystal protein. The potential TIs such as kurarinone and nor-kurarinone were rapidly discovered from the compounds with higher docking scores than the positive control under the Lipinski rules. The results were verified by the in vitro enzyme assays. The inhibition activities of tyrosinase from non-medicinal parts of S. flavescens were also tested to explore the relationship between the inhibition activity and chemical compositions. This study is expected to provide data support for the comprehensive application and development of S. flavescens and also a new method for the rapid discovery of active substances or functional constituents in the complex systems.

[Determination of ß-nicotinamide mononucleotide and nicotinamide adenine dinucleotide in Dendrobium officinale and congeneric species by UPLC-MS/MS].

As anti-aging ingredients, ß-nicotinamide mononucleotide(NMN) and nicotinamide adenine dinucleotide(NAD~+) have attracted worldwide attention in recent years. After oral administration, NMN can be converted into NAD~+ in vivo and the latter is the actual ingredient which exerts anti-aging effect. In order to explore the "rejuvenating and anti-aging" effect of Dendrobium officinale, which was firstly recorded in Shennong's Herbal Classic of Materia Medica, this study established the quantitative method of UPLC-MS/MS for simultaneous determination of NMN and NAD~+ in D. officinale and the congeneric species for the first time, and 34 batches of samples were detected. UPLC conditions are as follows: ACQUITY UPLC HSS T3 column(2.1 mm × 100 mm, 1.8 µm), gradient elution with acetonitrile-0.1% formic acid in water at the flow rate of 0.3 mL·min~(-1), and column temperature of 40 ℃. MS conditions were scanned electrospray ionization source and multiple reaction monitoring mode. The method was verified by systematic methodology. The mean recoveries of NMN and NAD~+ were 77.58% and 80.70%, respectively, with RSD of 3.6% and 4.3%, separately. All results showed that the content of NMN was higher in D. officinale than in the other congeneric species. Particularly, the content in fresh D. officinale stems was as high as 0.931 9 µg·g~(-1). NAD~+ was only found in D. officinale and the content was three times higher than that of NMN. This may be the reason that D. officinale topped the "nine famous anti-aging herbs". In addition, processing method influences the content of NMN and NAD~+ in Dendrobium. Specifically, the content of NMN and NAD~+ was in the order of fresh Dendrobium stems > dried Dendrobium stem segments > spiral or spring-like dried Dendrobium stems.

[Optimization of determination of astragaloside â £ in Astragali Radix by continuous single-factor method].

This paper aims to solve the problems of complicated-unstable test solution preparation process and insufficient extraction of the active ingredient astragaloside Ⅳ in the legal method for the determination of astragaloside Ⅳ in Astragali Radix. The continuous single-factor analysis of seven main factors affecting the content of astragaloside Ⅳ was carried out by HPLC-ELSD, and then the pre-paration method of test solution was optimized. This optimized method exhibited excellent performance in precision, repeatability and stability. The average recovery rate of astragaloside Ⅳ was 99.65% with RSD 2.2%. Astragaloside Ⅳ showed a good linearity between the logarithm of peak area and the logarithm of injection quantity in the range of 0.46-9.1 µg(r=0.999 6). The contents of astragaloside Ⅳ in 29 batches of Astragali Radix were determined by the new and the legal methods. The results showed that the average content of astragaloside Ⅳ in these Astragali Radix samples determined by the former method was 1.458 times than that of the latter one, indicating the new method was simple, reliable and more adequate to extract target compound. According to the results, it is suggested to improve the content standard of astragaloside Ⅳ in Astragali Radix in the new edition of Chinese Pharmacopeia.

TiO2 Nanoparticles and Commensal Bacteria Alter Mucus Layer Thickness and Composition in a Gastrointestinal Tract Model.

Nanoparticles (NPs) are used in food packaging and processing and have become an integral part of many commonly ingested products. There are few studies that have focused on the interaction between ingested NPs, gut function, the mucus layer, and the gut microbiota. In this work, an in vitro model of gastrointestinal (GI) tract is used to determine whether, and how, the mucus layer is affected by the presence of Gram-positive, commensal Lactobacillus rhamnosus; Gram-negative, opportunistic Escherichia coli; and/or exposure to physiologically relevant doses of pristine or digested TiO2 NPs. Caco-2/HT29-MTX-E12 cell monolayers are exposed to physiological concentrations of bacteria (expressing fluorescent proteins) and/or TiO2 nanoparticles for a period of 4 h. To determine mucus thickness and composition, cell monolayers are stained with alcian blue, periodic acid schiff, or an Alexa Fluor 488 conjugate of wheat germ agglutinin. It is found that the presence of both bacteria and nanoparticles alter the thickness and composition of the mucus layer. Changes in the distribution or pattern of mucins can be indicative of pathological conditions, and this model provides a platform for understanding how bacteria and/or NPs may interact with and alter the mucus layer.

Electrocatalytic dinitrogen reduction reaction on silicon carbide: a density functional theory study.

It is challenging to identify effective electrocatalysts for nitrogen reduction in order to advance electrochemical nitrogen fixation under ambient conditions using methods that are powered by renewable energy. Silicon carbide was investigated computationally as a metal-free, surface-derived catalyst for the electrocatalytic nitrogen reduction reaction. As demonstrated by first-principle calculations, Si-terminated and C-terminated surfaces, with the Si and C as active sites, are all reactive for dinitrogen capture and activation, resembling the catalytic behavior of popular B-based electrocatalysts, but the latter (C-terminated) offers an ultralow over-potential of 0.39 V, which is lower than most metals and alloys, while retarding hydrogen evolution. This research enriches the design of catalysts for dinitrogen fixation under ambient conditions, and also highlights a new direction for Si-based materials for nitrogen reduction.

Digital Forensics of Scanned QR Code Images for Printer Source Identification Using Bottleneck Residual Block.

With the rapid development of information technology and the widespread use of the Internet, QR codes are widely used in all walks of life and have a profound impact on people's work and life. However, the QR code itself is likely to be printed and forged, which will cause serious economic losses and criminal offenses. Therefore, it is of great significance to identify the printer source of QR code. A method of printer source identification for scanned QR Code image blocks based on convolutional neural network (PSINet) is proposed, which innovatively introduces a bottleneck residual block (BRB). We give a detailed theoretical discussion and experimental analysis of PSINet in terms of network input, the first convolution layer design based on residual structure, and the overall architecture of the proposed convolution neural network (CNN). Experimental results show that the proposed PSINet in this paper can obtain extremely excellent printer source identification performance, the accuracy of printer source identification of QR code on eight printers can reach 99.82%, which is not only better than LeNet and AlexNet widely used in the field of digital image forensics, but also exceeds state-of-the-art deep learning methods in the field of printer source identification.