Development of a rapid, sensitive detection method for SARS-CoV-2 and influenza virus based on recombinase polymerase amplification combined with CRISPR-Cas12a assay.

Respiratory tract infections are associated with the most common diseases transmitted among people and remain a huge threat to global public health. Rapid and sensitive diagnosis of causative agents is critical for timely treatment and disease control. Here, we developed a novel method based on recombinase polymerase amplification (RPA) combined with CRISPR-Cas12a to detect three viral pathogens, including SARS-CoV-2, influenza A, and influenza B, which cause similar symptom complexes of flu cold in the respiratory tract. The detection method can be completed within 1 h, which is faster than other standard detection methods, and the limit of detection is approximately 102 copies/µL. Additionally, this detection system is highly specific and there is no cross-reactivity with other common respiratory tract pathogens. Based on this assay, we further developed a more simplified RPA/CRISPR-Cas12a system combined with lateral flow assay on a manual microfluidic chip, which can simultaneously detect these three viruses. This low-cost detection system is rapid and sensitive, which could be applied in the field and resource-limited areas without bulky and expensive instruments, providing powerful tools for the point-of-care diagnostic.

Direct observation of topological magnon polarons in a multiferroic material.

Magnon polarons are novel elementary excitations possessing hybrid magnonic and phononic signatures, and are responsible for many exotic spintronic and magnonic phenomena. Despite long-term sustained experimental efforts in chasing for magnon polarons, direct spectroscopic evidence of their existence is hardly observed. Here, we report the direct observation of magnon polarons using neutron spectroscopy on a multiferroic Fe2Mo3O8 possessing strong magnon-phonon coupling. Specifically, below the magnetic ordering temperature, a gap opens at the nominal intersection of the original magnon and phonon bands, leading to two separated magnon-polaron bands. Each of the bands undergoes mixing, interconverting and reversing between its magnonic and phononic components. We attribute the formation of magnon polarons to the strong magnon-phonon coupling induced by Dzyaloshinskii-Moriya interaction. Intriguingly, we find that the band-inverted magnon polarons are topologically nontrivial. These results uncover exotic elementary excitations arising from the magnon-phonon coupling, and offer a new route to topological states by considering hybridizations between different types of fundamental excitations.

CDBA: a novel multi-branch feature fusion model for EEG-based emotion recognition.

EEG-based emotion recognition through artificial intelligence is one of the major areas of biomedical and machine learning, which plays a key role in understanding brain activity and developing decision-making systems. However, the traditional EEG-based emotion recognition is a single feature input mode, which cannot obtain multiple feature information, and cannot meet the requirements of intelligent and high real-time brain computer interface. And because the EEG signal is nonlinear, the traditional methods of time domain or frequency domain are not suitable. In this paper, a CNN-DSC-Bi-LSTM-Attention (CDBA) model based on EEG signals for automatic emotion recognition is presented, which contains three feature-extracted channels. The normalized EEG signals are used as an input, the feature of which is extracted by multi-branching and then concatenated, and each channel feature weight is assigned through the attention mechanism layer. Finally, Softmax was used to classify EEG signals. To evaluate the performance of the proposed CDBA model, experiments were performed on SEED and DREAMER datasets, separately. The validation experimental results show that the proposed CDBA model is effective in classifying EEG emotions. For triple-category (positive, neutral and negative) and four-category (happiness, sadness, fear and neutrality), the classification accuracies were respectively 99.44% and 99.99% on SEED datasets. For five classification (Valence 1-Valence 5) on DREAMER datasets, the accuracy is 84.49%. To further verify and evaluate the model accuracy and credibility, the multi-classification experiments based on ten-fold cross-validation were conducted, the elevation indexes of which are all higher than other models. The results show that the multi-branch feature fusion deep learning model based on attention mechanism has strong fitting and generalization ability and can solve nonlinear modeling problems, so it is an effective emotion recognition method. Therefore, it is helpful to the diagnosis and treatment of nervous system diseases, and it is expected to be applied to emotion-based brain computer interface systems.

Electrical switching of ferro-rotational order in nanometre-thick 1T-TaS2 crystals.

Hysteretic switching of domain states is a salient characteristic of all ferroic materials and the foundation for their multifunctional applications. Ferro-rotational order is emerging as a type of ferroic order that features structural rotations, but control over state switching remains elusive due to its invariance under both time reversal and spatial inversion. Here we demonstrate electrical switching of ferro-rotational domain states in the charge-density-wave phases of nanometre-thick 1T-TaS2 crystals. Cooling from the high-symmetry phase to the ferro-rotational phase under an external electric field induces domain state switching and domain wall formation, which is realized in a simple two-terminal configuration using a volt-scale bias. Although the electric field does not couple with the order due to symmetry mismatch, it drives domain wall propagation to give rise to reversible, durable and non-volatile isothermal state switching at room temperature. These results offer a route to the manipulation of ferro-rotational order and its nanoelectronic applications.

A fast vanishing point detection method based on row space features suitable for real driving scenarios.

The vanishing point (VP) is particularly important road information, which provides an important judgment criterion for the autonomous driving system. Existing vanishing point detection methods lack speed and accuracy when dealing with real road environments. This paper proposes a fast vanishing point detection method based on row space features. By analyzing the row space features, clustering candidates for similar vanishing points in the row space are performed, and then motion vectors are screened for the vanishing points in the candidate lines. The experimental results show that the average error of the normalized Euclidean distance is 0.0023716 in driving scenes under various lighting conditions. The unique candidate row space greatly reduces the amount of calculation, making the real-time FPS up to 86. It can be concluded that the fast vanishing point detection proposed in this paper would be suitable for high-speed driving scenarios.

DSCNN-LSTMs: A Lightweight and Efficient Model for Epilepsy Recognition.

Epilepsy is the second most common disease of the nervous system. Because of its high disability rate and the long course of the disease, it is a worldwide medical problem and social public health problem. Therefore, the timely detection and treatment of epilepsy are very important. Currently, medical professionals use their own diagnostic experience to identify seizures by visual inspection of the electroencephalogram (EEG). Not only does it require a lot of time and effort, but the process is also very cumbersome. Machine learning-based methods have recently been proposed for epilepsy detection, which can help clinicians make rapid and correct diagnoses. However, these methods often require extracting the features of EEG signals before using the data. In addition, the selection of features often requires domain knowledge, and feature types also have a significant impact on the performance of the classifier. In this paper, a one-dimensional depthwise separable convolutional neural network and long short-term memory networks (1D DSCNN-LSTMs) model is proposed to identify epileptic seizures by autonomously extracting the features of raw EEG. On the UCI dataset, the performance of the proposed 1D DSCNN-LSTMs model is verified by cross-validation and time complexity comparison. Compared with other previous models, the experimental results show that the highest recognition rates of binary and quintuple classification are 99.57% and 81.30%, respectively. It can be concluded that the 1D DSCNN-LSTMs model proposed in this paper is an effective method to identify seizures based on EEG signals.

Modulating the catalytic activity of gold nanoparticles using amine-terminated ligands.

Nanozymes have broad applications in theranostics and point-of-care tests. To enhance the catalytic activity of nanozymes, the conventional strategy is doping metals to form highly active nanoalloys. However, high-quality and stable nanoalloys are hard to synthesize. Ligand modification is a powerful strategy to achieve chemoselectivity or bioactivity by changing the surface chemistry. Here, we explore different ligands to enhance the catalytic activity of nanozymes, e.g., gold nanoparticles (AuNPs). We systematically studied the impacts on the enzymatic activity of AuNPs by ligand engineering of surface chemistry (charge, group, and surface distance). Our work established critical guidelines for surface modification of nanozymes. The amine group favors higher activity of AuNPs than other groups. The flexible amine-rich ligand enhances the catalytic activity of AuNPs in contrast to other ligands and unmodified AuNPs. Using a proof-of-concept model, we screened many candidate ligands to obtain polyamine-AuNPs, which have strongly enhanced peroxidase-like activity and 100 times enhanced sensitivity compared to unmodified AuNPs. The strategy of enhancing the catalytic activity of AuNPs using ligands will facilitate the catalysis-related applications of nanozymes in biology and diagnostics.

Gag Protein Oriented Supramolecular Nets as Potential HIV Traps.

For HIV/AIDS treatment, the cocktail therapy which uses a combination of anti-retroviral drugs remains the most widely accepted practice. However, the potential drug toxicity, patient tolerability, and emerging drug resistance have limited its long-term efficiency. Here, we design a HIV Gag protein-targeting redox supramolecular assembly (ROSA) system for potential HIV inhibition. An assembling precursor was constructed through conjugation of an oxidation-activatable fluorogenic compound BQA with a selected tetrapeptide GGFF. Since BQA shares a similar structure with the known Gag inhibitor, the precursor could bind to HIV Gag protein with moderate affinity. Moreover, after oxidation, the corresponding nanofibers could bind to Gag protein and trap HIV to realize virus control, thus providing a potential anti-HIV strategy.

Spatiotemporal variations in the incidence of bacillary dysentery and long-term effects associated with meteorological and socioeconomic factors in China from 2013 to 2017.

Bacillary dysentery is a global public health problem that exhibits manifest spatiotemporal heterogeneity. However, long-term variations and regional determinant factors remain unclear. In this study, the Bayesian space-time hierarchy model was used to identify the long-term spatiotemporal heterogeneity of the incidence of bacillary dysentery and quantify the associations of meteorological factors with the incidence of bacillary dysentery in northern and southern China from 2013 to 2017. GeoDetector was used to quantify the determinant powers of socioeconomic factors in the two regions. The results showed that the incidence of bacillary dysentery peaked in summer (June to August), indicating temporal seasonality. Geographically, the hot spots (high-risk areas) were distributed in northwestern China (Xinjiang, Gansu, and Ningxia) and northern China (including Beijing, Tianjin, and Hebei), whereas the cold spots (low-risk areas) were concentrated in southeastern China (Jiangsu, Zhejiang, Fujian, and Guangdong). Moreover, significant regional differences were found among the meteorological and socioeconomic factors. Average temperature was the dominant meteorological factor in both northern and southern China. In northern and southern China, a 1 °C increase in the average temperature led to an increase of 1.01% and 4.26% in bacillary dysentery risk, respectively. The dominant socioeconomic factors in northern and southern China were per capita gross domestic product and the number of health technicians, with q statistic values of 0.81 and 0.49, respectively. These findings suggest that hot, moist, and overcrowded environments or poor health conditions increase the risk of bacillary dysentery. This study provides suggestions and serves as a basis for surveillance efforts. Further, the suggestions may aid in the control of bacillary dysentery and in the implementation of disease prevention policies.

Evidence of the Berezinskii-Kosterlitz-Thouless phase in a frustrated magnet.

The Berezinskii-Kosterlitz-Thouless (BKT) mechanism, building upon proliferation of topological defects in 2D systems, is the first example of phase transition beyond the Landau-Ginzburg paradigm of symmetry breaking. Such a topological phase transition has long been sought yet undiscovered directly in magnetic materials. Here, we pin down two transitions that bound a BKT phase in an ideal 2D frustrated magnet TmMgGaO4, via nuclear magnetic resonance under in-plane magnetic fields, which do not disturb the low-energy electronic states and allow BKT fluctuations to be detected sensitively. Moreover, by applying out-of-plane fields, we find a critical scaling behavior of the magnetic susceptibility expected for the BKT transition. The experimental findings can be explained by quantum Monte Carlo simulations applied on an accurate triangular-lattice Ising model of the compound which hosts a BKT phase. These results provide a concrete example for the BKT phase and offer an ideal platform for future investigations on the BKT physics in magnetic materials.

Supramolecular assemblies mimicking neutrophil extracellular traps for MRSE infection control.

Neutrophil extracellular traps (NETs) stick to bacteria and prevent infections in vivo, whose activation is upon inflammatory stimuli along with the sudden increase of reactive oxygen species (ROS). Nevertheless, the risky over activation in NETosis may result in deleterious outcome. A big challenge in using NETs for therapeutics is to synthesize an artificial system that can function as NETs in vivo. Here, we developed an in vivo supramolecular assembly system to imitate the innate immune process of NETs to inhibit methicillin-resistant staphylococcus epidermidis (MRSE) infection. Our synthesized small molecules undergo oxidation to form supramolecular nanofibers at inflammatory loci. The in situ formed nanofibers network efficiently traps MRSE cells and prevent them from aggressive dissemination. The extended interactions between nanofibers and bacteria directly result in the death of MRSE via the transcriptomes alterations. In clinically relevant models (intraperitoneal infection and catheter implantation), our supramolecular nets show significant antibacterial activity, yielding a three times efficacy comparing to vancomycin. The spontaneous consumption of ROS and the formation of antibacterial networks create a steady negative feedback system to combat bacterial infections.

Enzyme-Instructed Supramolecular Self-Assembly with Anticancer Activity.

Cancer remains one of the leading causes of death, which has continuously stimulated the development of numerous functional biomaterials with anticancer activities. Herein is reviewed one recent trend of biomaterials focusing on the advances in enzyme-instructed supramolecular self-assembly (EISA) with anticancer activity. EISA relies on enzymatic transformations to convert designed small-molecular precursors into corresponding amphiphilic residues that can form assemblies in living systems. EISA has shown some advantages in controlling cell fate from three aspects. 1) Based on the abnormal activity of specific enzymes, EISA can differentiate cancer cells from normal cells. In contrast to the classical ligand-receptor recognition, the targeting capability of EISA relies on dynamic control of the self-assembly process. 2) The interactions between EISA and cellular components directly disrupt cellular processes or pathways, resulting in cell death phenotypes. 3) EISA spatiotemporally controls the distribution of therapeutic agents, which boosts drug delivery efficiency. Therefore, with regard to the development of EISA, the aim is to provide a perspective on the future directions of research into EISA as anticancer theranostics.

Enzyme-Instructed Self-assembly in Biological Milieu for Theranostics Purpose.

Precision medicine is in an urgent need for public healthcare. Among the past several decades, the flourishing development in nanotechnology significantly advances the realization of precision nanomedicine. Comparing to well-documented nanoparticlebased strategy, in this review, we focus on the strategy using enzyme instructed selfassembly (EISA) in biological milieu for theranostics purpose. In principle, the design of small molecules for EISA requires two aspects: (1) the substrate of enzyme of interest; and (2) self-assembly potency after enzymatic conversion. This strategy has shown its irreplaceable advantages in nanomedicne, specifically for cancer treatments and Vaccine Adjuvants. Interestingly, all the reported examples rely on only one kind of enzymehydrolase. Therefore, we envision that the application of EISA strategy just begins and will lead to a new paradigm in nanomedicine.

Hydrogen sulfide induced supramolecular self-assembly in living cells.

Here we developed a critical gasotransmitter (H2S) mediated reduction to induce supramolecular self-assembly in multiple living cell lines. Specifically, the reduction converted an azido group to an amine which allowed the formation of intramolecular hydrogen bonds. The hydrogen bonds planarized the assembling molecules and promoted the supramolecular self-assembly.

Redox supramolecular self-assemblies nonlinearly enhance fluorescence to identify cancer cells.

Based on the nonlinear fluorescence enhancement, our H2O2 induced supramolecular self-assembly reveals a H2O2 threshold among multiple cancer and normal cells. Oxidative elimination restores an intramolecular hydrogen bond which can planarize the molecule to generate a fluorophore. The planarization enhances the intermolecular π-π stacking to promote self-assembly.

A dual-mode turn-on fluorescent BODIPY-based probe for visualization of mercury ions in living cells.

A novel turn-on fluorescent 8-amino BODIPY-based probe carrying a thiourea unit as the mercury ion recognition unit has been developed. Due to the cascade reaction processes, consecutive color changes reflecting the electronic absorption and emission responses were observed upon addition of increased concentrations of mercury(ii) ions. The likely sensing mechanism was proposed as mercury ion-promoted cyclization and subsequent hydrolysis. The probe displayed a selective response to mercury ions over other metal ions. Additionally, experiments with living Human Hepatoma SMMC-7721 cells to visualize intracellular mercury ions in biological systems were carried out with the probe.

Dual emission channels for sensitive discrimination of Cys/Hcy and GSH in plasma and cells.

A new selective fluorescent and colorimetric chemosensor for the detection of GSH was developed. The discrimination of GSH from Cys and Hcy is achieved through two emission channel detection. The detection limit of probe 1 for GSH reached 10 nM (3 ppb). The excellent sensitivity and selectivity of probe 1 allow the selective detection of GSH over Cys and Hcy, which can be visualized colorimetrically and/or fluorescently. The sensitive detection of GSH allowed for convenient measurement of the GSH content in human plasma. The presence of GSH in cells was demonstrated through cell imaging.

Highly selective and sensitive 1-amino BODIPY-based red fluorescent probe for thiophenols with high off-to-on contrast ratio.

A highly selective and sensitive turn-on red fluorescent 1-amino BODIPY-based probe (where BODIPY denotes indole-based boron-dipyrromethene) with high off-to-on contrast ratio has been developed. The probe displayed selective response to thiophenols over aliphatic thiols. Probe 1 is promising for the quantitative detection of thiophenol with a linear response from 6 × 10(-6) M to 1 × 10(-4) M, and the detection limit for thiophenol (PhSH) reaches 4 × 10(-6) M measured in acetonitrile/PBS buffer. The detection limit could be improved to 37 nM (detection limit to 4 ppb) in water when 1% Tween 20 was used to assist the dissolvation of probe 1 in water. Probe 1 is also a useful fluorescent probe for detecting thiophenols in living cells in red emission, which may greatly improve the detectable sensitivity.