Photocurrent switchable dual-target bioassay: Signal distinction and interface reconfiguration via pH-responsive triplex DNA programming.

Most multiplexed photoelectrochemical (PEC) sensors require additional instrumentation and cumbersome electrode modification and surface partitioning, which limits their portability and instrument miniaturization. Herein, a pH-responsive programmable triple DNA nanomachine was developed for constructing a reconfigurable multiplex PEC sensing platform. By programming the base sequence, T-A·T-riched triple DNA was designed to construct integrated nano-controlled release machine (INCRM) for simultaneous recognition of multiple targets. The INCRM enables to recognize two targets in one step, and sequentially separate the signal labels by pH adjustment. The detached signal label catalyzes glucose to produce gluconic acid, causing the C-riched DNA fold into a triple structure on the electrode surface. As a result, one target can be detected relying on the enhanced photocurrent due to accelerated electron transfer between the CdS QD labeled at the end of C-riched DNA and the electrode. The triplex DNA dissociation in pH 7.4 buffer reconfigures the electrode interface, which can be continued to detect another target. The feasibility of the multiplexed sensor is verified by the detection of extensively coexisting antibiotics enrofloxacin (ENR) and ciprofloxacin (CIP). Under the optimal conditions, wide linear range (10 fg/mL âˆ¼ 1 µg/mL) and low detection limit (3.27 fg/mL and 9.60 fg/mL) were obtained. The pH-regulated programmable triplex DNA nanomachine-based sensing platform overcomes the technical difficulties of conventional multiplexed PEC assay, which may open the way for miniaturization of multiplexed PEC sensors.

Band Alignment Modulated Polarity-Switchable PEC Ratiometric Sensor through Coupling a pH-Responsive CuTCPP MOF with i-Motif Sensing tool.

In conventional ratiometric photoelectrochemical (PEC) sensors, the detection and reference signals are output sequentially from two independent photosensitive materials. In such a "two-to-two" ratiometric mode, unavoidable difference during dual-interface modification exists, resulting in questionable ratiometric signals and detection results. To address this issue, we propose a novel "one-to-two" ratiometric PEC sensor on a single electrode interface through pH-modulated band alignment engineering. The double ratiometric signals are generated by the synergistic action of a pH-responsive CuTCPP/WS2 photoelectric substrate material and the i-motif sensing tool. Specifically, a ternary heterostructure to generate a photoanodic detection signal is formed under alkaline conditions between CuTCPP/WS2 and signal label CdS QDs binding to the i-motif. While under acidic conditions, a photocurrent polarity conversion and signaling labels detachment, induced by the band realignment of CuTCPP/WS2 and the i-motif conformational switching, produce a reliable internal reference photocathodic signal. The feasibility of this two-wing signal generation strategy is validated by detecting mycotoxin ochratoxin A, which achieves accurate and reliable ratio detection results. Overall, this work provides guidance for the design of a PEC ratiometric determination system and exhibits great potential to be applied in practical analysis research.

Visible-light-induced C(sp3)-H bromination of 4-methylthiophene derivatives with HBr/H2O2.

A convenient method to synthesize ethyl 4-(bromomethyl)thiophene-3-carboxylate derivatives has been developed via a visible-light-induced radical process in good yields and with wide functional group tolerance under air conditions and at ambient temperature. The present protocol has the advantages of a high atom economy, easy purification, and environmental friendliness as it employs HBr as the bromine source and the cheap and low-toxic H2O2 as the oxidant. The synthetic utility of this method is demonstrated by a gram scale reaction and its application in the innovative synthesis of the clinical drug relugolix.

Prediction of Prostate Cancer Risk Stratification Based on A Nonlinear Transformation Stacking Learning Strategy.

PURPOSE: Prostate cancer (PCa) is an epithelial malignancy that originates in the prostate gland and is generally categorized into low, intermediate, and high-risk groups. The primary diagnostic indicator for PCa is the measurement of serum prostate-specific antigen (PSA) values. However, reliance on PSA levels can result in false positives, leading to unnecessary biopsies and an increased risk of invasive injuries. Therefore, it is imperative to develop an efficient and accurate method for PCa risk stratification. Many recent studies on PCa risk stratification based on clinical data have employed a binary classification, distinguishing between low to intermediate and high risk. In this paper, we propose a novel machine learning (ML) approach utilizing a stacking learning strategy for predicting the tripartite risk stratification of PCa. METHODS: Clinical records, featuring attributes selected using the lasso method, were utilized with 5 ML classifiers. The outputs of these classifiers underwent transformation by various nonlinear transformers and were then concatenated with the lasso-selected features, resulting in a set of new features. A stacking learning strategy, integrating different ML classifiers, was developed based on these new features. RESULTS: Our proposed approach demonstrated superior performance, achieving an accuracy of 0.83 and an area under the receiver operating characteristic curve value of 0.88 in a dataset comprising 197 PCa patients with 42 clinical characteristics. CONCLUSION: This study aimed to improve clinicians' ability to rapidly assess PCa risk stratification while reducing the burden on patients. This was achieved by using artificial intelligence-related technologies as an auxiliary method for diagnosing PCa.

Development of a multiplex reverse transcription-quantitative PCR (qPCR) method for detecting common causative agents of swine viral diarrhea in China.

BACKGROUND: Diarrheal diseases caused by viral agents have led to a great morbidity, mortality, and economic loss in global pig industry. Porcine epidemic diarrhea virus (PEDV), transmissible gastroenteritis virus (TGEV), porcine deltacoronavirus (PDCoV), and group A porcine rotavirus (RVA) are main causative agents of swine viral diarrhea with similar clinical signs on Chinese farms and their co-infection is also common. However, it is still lack of a convenient method to detect these four agents. METHODS: A TaqMan multiplex qPCR method was developed to detect PEDV, TGEV, PDCoV, and RVA, simultaneously. This method was then applied to investigate 7,342 swine fecal samples or rectal swabs, as well as 1,246 swine intestinal samples collected from 2075 farms in China in 2022. RESULTS: Minimum detection limits of this method were 3 copies/µL for PEDV, 4 copies/µL for TGEV, 8 copies/µL for RVA, and 8 copies/µL for PDCoV, suggesting a good sensitivity. No signals were observed by using this method detecting other viral agents commonly prevalent in pigs, which is suggestive of a good specificity. Application of this method on investigating clinical samples demonstrated a relatively high positive rate for PEDV (22.21%, 1907/8588) and RVA (44.00%, 3779/8588). In addition, co-infection between PEDV and RVA was observed on 360 investigated farms, accounting for 17.35% (360/2075) of the farms where co-infection events were screened. CONCLUSIONS: A TaqMan multiplex qPCR method targeting PEDV, TGEV, PDCoV, and RVA was developed in this study. This method demonstrated a good specificity and sensitivity on investigating these four common viruses responsible for viral diarrhea on Chinese pig farms, which represents a convenient method for the monitoring and differential diagnosis of swine viral diarrhea.

Coinfection and nonrandom recombination drive the evolution of swine enteric coronaviruses.

Coinfection with multiple viruses is a common phenomenon in clinical settings and is a crucial driver of viral evolution. Although numerous studies have demonstrated viral recombination arising from coinfections of different strains of a specific species, the role of coinfections of different species or genera during viral evolution is rarely investigated. Here, we analyzed coinfections of and recombination events between four different swine enteric coronaviruses that infect the jejunum and ileum in pigs, including porcine epidemic diarrhea virus (PEDV), transmissible gastroenteritis virus (TGEV), and swine acute diarrhea syndrome coronavirus (SADS-CoV), and a deltacoronavirus, porcine deltacoronavirus (PDCoV). Various coinfection patterns were observed in 4,468 fecal and intestinal tissue samples collected from pigs in a 4-year survey. PEDV/PDCoV was the most frequent coinfection. However, recombination analyses have only detected events involving PEDV/TGEV and SADS-CoV/TGEV, indicating that inter-species recombination among coronaviruses is most likely to occur within the same genus. We also analyzed recombination events within the newly identified genus Deltacoronavirus and found that sparrows have played a unique host role in the recombination history of the deltacoronaviruses. The emerging virus PDCoV, which can infect humans, has a different recombination history. In summary, our study demonstrates that swine enteric coronaviruses are a valuable model for investigating the relationship between viral coinfection and recombination, which provide new insights into both inter- and intraspecies recombination events among swine enteric coronaviruses, and extend our understanding of the relationship between coronavirus coinfection and recombination.

Discovery of the tigecycline resistance gene cluster tmexCD3-toprJ1 in Pasteurella multocida strains isolated from pigs in China.

Pasteurella multocida is a leading cause of respiratory disorders in pigs. However, the genotypes and antimicrobial resistance characteristics of P. multocida from pigs in China have not been reported frequently. In this study, we investigated 381 porcine strains of P. multocida collected in China between 2013 and 2022. These strains were assigned to capsular genotypes A (69.55%, n = 265), D (27.82%, n =106), and F (2.62%, n = 10); or lipopolysaccharide genotypes L1 (1.31%, n = 5), L3 (24.41%, n = 93), and L6 (74.28%, n = 283). Overall, P. multocida genotype A:L6 (46.46%) was the most-commonly identified type, followed by D:L6 (27.82%), A:L3 (21.78%), F:L3 (2.62%), and A:L1 (1.31%). Antimicrobial susceptibility testing showed that a relatively high proportion of strains were resistant to tetracycline (66.67%, n = 254), and florfenicol (35.17%, n = 134), while a small proportion of strains showed resistance phenotypes to enrofloxacin (10.76%, n = 41), ampicillin (8.40%, n = 32), tilmicosin (7.09%, n = 27), and ceftiofur (2.89%, n = 11). Notably, Illumina short-read and Nanopore long-read sequencing identified a chromosome-borne tigecycline-resistance gene cluster tmexCD3-toprJ1 in P. multocida. The structure of this cluster was highly similar to the respective structures found in several members of Proteus or Pseudomonas. It is assumed that the current study identified the tmexCD3-toprJ1 cluster for the first time in P. multocida.

Fecal PCR survey and genome analysis of Lawsonia intracellularis in China.

Proliferative enteropathy caused by Lawsonia intracellularis is an important economic associated disease to pig industry, but the knowledge about the prevalence of L. intracellularis in pig farms in China is limited. In addition, there is no complete genome sequence available for L. intracellularis isolates from China. In this study, we developed a TaqMan qPCR for the screening of L. intracellularis by targeting the bacterial 16S rDNA gene. Laboratory evaluations revealed a good sensitivity and specificity on detecting L. intracellularis nucleic acid. Using this method, we investigated 891 fecal samples from apparently healthy pigs in 47 farms. The results demonstrated a screening positive rate of 37.3% (95% CI, 34.1-40.5%) for the samples, and a farm screening positive rate of 93.6% (95% CI, 65.3-94.4%). The screening positive rate at herd level ranged from 6.67% (95% CI, 0.2-31.9%) to 40% (95% CI, 38-79.6%), while at animal level, the highest screening positive rate was found in 12-week-old pigs [85.7% (95% CI, 67.3-96.0%)]. Investigation of 705 diarrheal or bloody feces from symptomatic pigs revealed that the highest positive rate was found in replacement gilts which was 37.18% (95% CI, 45.1-89.5%). Secondly, we conducted the complete genome sequence of a L. intracellularis PPE-GX01-2022 from China through PacBio sequencing. The genome of PPE-GX01-2022 consisted of a chromosome of 1,439,110 bp in length and three plasmids of 193,063, 39,799, and 27,067 bp, respectively. This genome encoded 1,428 predicted proteins, 44 tRNAs, and 6 rRNAs. Sequence comparisons demonstrated that the genome sequence of PPE-GX01-2022 was highly homologous to those of two isolates from US, and these three isolates shared 1,378 core genes. The screening results suggest a high prevalence rate of L. intracellularis in Chinese pig farms. In addition, the genome sequence of the Chinese isolate was highly homologous to those of the field isolates from the US.

Structure and Electron Engineering for Nitrate Electrocatalysis to Ammonia: Identification and Modification of Active Sites in Spinel Oxides.

Cobalt spinel oxides, which consist of tetrahedral site (AO4) and octahedral site (BO6), are a potential group of transition metal oxides (TMO) for electrocatalytic nitrate reduction reactions to ammonia (NRA). Identifying the true active site in spinel oxides is crucial to designing advanced catalysts. This work reveals that the CoO6 site is the dominant site for NRA through the site substitution strategy. The suitable electronic configuration of Co at the octahedral site leads to a stronger interaction between the Co d-orbital and the O p-orbital in O-containing intermediates, resulting in a high-efficiency nitrate-to-ammonia reduction. Furthermore, the substitution of metallic elements at the AO4 site can affect the charge density at the BO6 site via the structure of A-O-B. Thereafter, Ni and Cu are introduced to replace the tetrahedral site in spinel oxides and optimize the electronic structure of CoO6. As a result, NiCo2O4 exhibits the best activity for NRA with an outstanding yield of NH3 (15.49 mg cm-2 h-1) and FE (99.89%). This study introduces a novel paradigm for identifying the active site and proposes an approach for constructing high-efficiency electrocatalysts for NRA.

Burden, determinants, consequences and care of multimorbidity in rural and urbanising Telangana, India: protocol for a mixed-methods study within the APCAPS cohort.

INTRODUCTION: The epidemiological and demographic transitions are leading to a rising burden of multimorbidity (co-occurrence of two or more chronic conditions) worldwide. Evidence on the burden, determinants, consequences and care of multimorbidity in rural and urbanising India is limited, partly due to a lack of longitudinal and objectively measured data on chronic health conditions. We will conduct a mixed-methods study nested in the prospective Andhra Pradesh Children and Parents' Study (APCAPS) cohort to develop a data resource for understanding the epidemiology of multimorbidity in rural and urbanising India and developing interventions to improve the prevention and care of multimorbidity. METHODS AND ANALYSIS: We aim to recruit 2100 APCAPS cohort members aged 45+ who have clinical and lifestyle data collected during a previous cohort follow-up (2010-2012). We will screen for locally prevalent non-communicable, infectious and mental health conditions, alongside cognitive impairments, disabilities and frailty, using a combination of self-reported clinical diagnosis, symptom-based questionnaires, physical examinations and biochemical assays. We will conduct in-depth interviews with people with varying multimorbidity clusters, their informal carers and local healthcare providers. Deidentified data will be made available to external researchers. ETHICS AND DISSEMINATION: The study has received approval from the ethics committees of the National Institute of Nutrition and Indian Institute of Public Health Hyderabad, India and the London School of Hygiene and Tropical Medicine, UK. Meta-data and data collection instruments will be published on the APCAPS website alongside details of existing APCAPS data and the data access process (www.lshtm.ac.uk/research/centres-projects-groups/apcaps).

Assessment of different factors on the influence of glass wool concentration for detection of main swine viruses in water samples.

Viruses existed in wastewaters might pose a biosecurity risk to human and animal health. However, it is generally difficult to detect viruses in wastewater directly as they usually occur in low numbers in water. Therefore, processing large volumes of water to concentrate viruses in a much smaller final volume for detection is necessary. Glass wool has been recognized as an effective material to concentrate multiple in water, and in this study, we assessed the use of glass wools on concentrating pseudorabies virus (PRV), African swine fever virus (ASFV), and porcine epidemic diarrhea virus (PEDV) in water samples. The influence of pH values, water matrix, water volume, filtration rate, temperature on the effect of the method concentrating these viruses for detection was evaluated in laboratory. Our results revealed that glass wool was suitable for the concentration of above-mentioned viruses from different water samples, and demonstrated a good application effect for water with pH between 6.0-9.0. Furthermore, glass wool also showed a good recovery effect on concentrating viral nucleic acids and viral particles, as well as living viruses. In addition, combining use of glass wool with skim milk, polyethylene glycol (PEG)-NaCl, or ultracentrifuge had good effects on concentrating ASFV, PRV, and PEDV. Detection of wastewater samples (n = 70) collected from 70 pig farms in 13 regions across Hubei Province in Central China after glass-wool-concentration determined one sample positive for ASFV, eighteen samples positive for PRV, but no sample positive for PEDV. However, these positive samples were detected to be negative before glass wool enrichment was implemented. Our results suggest that glass wool-based water concentration method developed in this study represents an effective tool for detecting viruses in wastewater.

Ginsenosides on stem cells fate specification-a novel perspective.

Recent studies have demonstrated that stem cells have attracted much attention due to their special abilities of proliferation, differentiation and self-renewal, and are of great significance in regenerative medicine and anti-aging research. Hence, finding natural medicines that intervene the fate specification of stem cells has become a priority. Ginsenosides, the key components of natural botanical ginseng, have been extensively studied for versatile effects, such as regulating stem cells function and resisting aging. This review aims to summarize recent progression regarding the impact of ginsenosides on the behavior of adult stem cells, particularly from the perspective of proliferation, differentiation and self-renewal.

Multimodality deep learning radiomics nomogram for preoperative prediction of malignancy of breast cancer: a multicenter study.

Background. Breast cancer is the most prevalent cancer diagnosed in women worldwide. Accurately and efficiently stratifying the risk is an essential step in achieving precision medicine prior to treatment. This study aimed to construct and validate a nomogram based on radiomics and deep learning for preoperative prediction of the malignancy of breast cancer (MBC).Methods. The clinical and ultrasound imaging data, including brightness mode (B-mode) and color Doppler flow imaging, of 611 breast cancer patients from multiple hospitals in China were retrospectively analyzed. Patients were divided into one primary cohort (PC), one validation cohort (VC) and two test cohorts (TC1 and TC2). A multimodality deep learning radiomics nomogram (DLRN) was constructed for predicting the MBC. The performance of the proposed DLRN was comprehensively assessed and compared with three unimodal models via the calibration curve, the area under the curve (AUC) of receiver operating characteristics and the decision curve analysis.Results. The DLRN discriminated well between the MBC in all cohorts [overall AUC (95% confidence interval): 0.983 (0.973-0.993), 0.972 (0.952-0.993), 0.897 (0.823-0.971), and 0.993 (0.977-1.000) on the PC, VC, test cohorts1 (TC1) and test cohorts2 TC2 respectively]. In addition, the DLRN performed significantly better than three unimodal models and had good clinical utility.Conclusion. The DLRN demonstrates good discriminatory ability in the preoperative prediction of MBC, can better reveal the potential associations between clinical characteristics, ultrasound imaging features and disease pathology, and can facilitate the development of computer-aided diagnosis systems for breast cancer patients. Our code is available publicly in the repository athttps://github.com/wupeiyan/MDLRN.

HCR/DNAzyme-triggered cascaded feedback cycle amplification for self-powered dual-photoelectrode detection of femtomolar HPV16.

For high-performance dual-photoelectrode assay, developing a pair of photoactive materials with well-matched band structure and the design of a powerful sensing strategy are highly desirable. Herein, the Zn-TBAPy pyrene-based MOF and BiVO4/Ti3C2 Schottky junction were employed as photocathode and photoanode to form an efficient dual-photoelectrode system. The integration of the cascaded hybridization chain reaction (HCR)/DNAzyme-assisted feedback amplification with DNA walker-mediated cycle amplification strategy realizes femtomolar HPV16 dual-photoelectrode bioassay. Through the activation of the HCR cascaded with the DNAzyme system in the presence of HPV16, plentiful HPV16 analogs are generated that leads to exponential positive feedback signal amplification. Meanwhile on the Zn-TBAPy photocathode, the NDNA hybridizes with the bipedal DNA walker followed by circular cleavage by Nb.BbvCI NEase, producing a dramatically enhanced PEC readout. The achieved ultralow detection limit of 0.57 fM and a wide linear range of 10-6 nM-103 nM showcase the excellent performance of the developed dual-photoelectrode system.

ULS4US: universal lesion segmentation framework for 2D ultrasound images.

Objective. Deep learning (DL) methods have been widely utilized in ultrasound (US) image segmentation tasks. However, current DL segmentation methods for US images are typically developed only for lesion segmentation of specific organs; e.g. breast or thyroid US. So far, there is currently no general-purpose lesion segmentation framework for US images that can be implemented across various organs in computer aided diagnosis scenarios. Considering that most lesion locations in US images have abnormal ultrasonic echo intensities or patterns that may be visually distinct from surrounding normal tissues or organs, it is thus possible to develop a universal lesion segmentation framework for US images (named as ULS4US), focusing on effectively identifying and segmenting lesions of various sizes in different organs.Approach. The proposed ULS4US framework comprises three components: (1) a multiple-in multi-out (MIMO) UNet that incorporates multiscale features extracted from the US image and lesion, (2) a novel two-stage lesion-aware learning algorithm that recursively locates and segments the lesions in a reinforced manner, and (3) a lesion-adaptive loss function for the MIMO-UNet that integrates two weighted components and one self-supervised component designed for intra- and inter-branches of network outputs, respectively.Main Results. Compared to six state-of-the-art segmentation models, ULS4US has achieved superior performance (accuracy of 0.956, DSC of 0.836, HD of 7.849, and mIoU of 0.731) in a unified dataset consisting of two public and three private US image datasets, which include over 2200 images of three specific types of organs. Comparative experiments on both individual and unified datasets suggest that ULS4US is likely scalable with additional data.Significance. The study demonstrates the potential of DL-based universal lesion segmentation approaches in clinical US, which would substantially reduce clinician workload and enhance diagnostic accuracy.

Self-Cleaning Recyclable Multiplexed Photoelectrochemical Sensing Strategy Based on Exonuclease III-Assisted Signal Discrimination.

For discriminating the signals of multi-targets, multiplexed photoelectrochemical (PEC) detection is generally accomplished by modulating the light source or voltage, which prospect is usually limited by expensive instrumentation, tedious operational steps, and time-consuming material screening. To realize multiplexed determination on single photoelectric interface using the routine technique, a non-instrument-assisted strategy for signal discrimination needs to be explored. Herein, we propose an exonuclease III-mediated multiple PEC signals resolution strategy and construct a self-cleaning recyclable multiplexed PEC sensor using a porphyrin-bipyridine-based covalent organic framework (Por-Bpy COF) photocathode. Specifically, following the dual-target recognition event, exonuclease III cleaves the DNA strand attached to the magnetic bead so that the two signal labels can be separated. Once the signal label binds to the DNA on the electrode surface (E-DNA), exonuclease III turns to excise the DNA strand of the signal label and consequently the E-DNA can repeatedly bind different signal labels. As a result, distinguishable photocurrent signals of different targets can be generated on a single photoelectric interface. The feasibility of this multiplexed sensor is verified by detecting two coexisting mycotoxins aflatoxin B1 and zearalenone. On account of eliminating the instrumentation constraints and simplifying the experimental procedures, the proposed sensing strategy may provide a brand-new idea for the exploration of portable multiplexed PEC sensing devices.

Isolation, structural and bioactivities of polysaccharides from Anoectochilus roxburghii (Wall.) Lindl.: A review.

Anoectochilus roxburghii (Wall.) Lindl. (A. roxburghii), a valuable herbal medicine in China, has great medicinal and edible value. Polysaccharides, as one of the main active components of A. roxburghii, comprise glucose, arabinose, xylose, galactose, rhamnose, and mannose in different molar ratios and glycosidic bond types. By varying the sources and extraction methods of A. roxburghii polysaccharides (ARPS), different structural characteristics and pharmacological activities can be elucidated. ARPS has been reported to exhibit antidiabetic, hepatoprotective, anti-inflammatory, antioxidant, antitumor, and immune regulation activities. This review summarizes the available literature on the extraction and purification methods, structural features, biological activities, and applications of ARPS. The shortcomings of the current research and potential focus in future studies are also highlighted. This review provides systematic and current information on ARPS to promote their further exploitation and application.

Development of Line-Detected UV-Vis Absorption Microscope and Its Application to Quantitative Evaluation of Lithium Surface Reactivity.

Electrochemical reactions in practical batteries occur in confined environments where anode and cathode electrodes are separated only by a thin separator. Therefore, their electrochemical behaviors may differ from those obtained in the conventional experimental cells, where the two electrodes (working and counter electrodes) are largely separated compared to the batteries. The spatial and temporal distributions of the chemical species in the vicinity of each electrode are highly expected to be determined for quantitatively understanding the phenomena in confined environments. In the present study, we developed a line-detected UV-vis absorption microscope that simultaneously measures space-resolved UV-vis absorption spectra. This novel technique has been successfully applied to evaluate the reactivities of the highly reactive lithium (Li) surfaces in organic electrolyte solutions under in situ conditions. The quantitative evaluations of the dissolution rate of Li and the diffusion constant of the product were successfully realized by analyzing the space- and time-resolved absorption spectra based on Fick's law of diffusion. The microscopic technique is expected to open the door to understanding the fundamental electrochemistry in batteries.

Fully-Conjugated Covalent Organic Frameworks with Two Metal Sites for Oxygen Electrocatalysis and Zn-Air Battery.

Covalent organic frameworks (COFs) are a promising alternative toward catalysis, due to the unique framework structure and the excellent chemical stability. However, the scarcity of unsaturated metal sites and the low conductivity have constrained the advancement of these materials for catalysis of electrochemical reactions. Exploring next-generation conductive metal-covalent organic frameworks (M-COFs) with extra metal active sites is crucial for improving their catalytic activity. Herein, a novel fully-conjugated M-COFs (Co-PorBpy-Co) with two types of metal sites is proposed and achieved by solvothermal method in the presence of carbon nanotube (CNT). The electrocatalyst constructed by the Co-PorBpy-Co exhibits excellent oxygen reduction reaction (ORR) activity (E1/2 = 0.84 V vs RHE, n = 3.86), superior to most COFs-based catalysts. Theoretical result shows the CoN2 sites are extremely active for ORR, and Co-PorBpy-Co exhibits excellent conductivity for electron transfer. The Zn-air battery constructed by Co-PorBpy-Co/CNT manifests excellent power density (159.4 mW cm-2 ) and great cycling stability, surpassing that of 20 wt% Pt/C catalyst. This work not only proposes a novel design concept for electrocatalysts, but establishes a mechanism platform for single-metal atom electrocatalysis and synergistic effect.

Complex Network Evolution Model Based on Turing Pattern Dynamics.

Complex network models are helpful to explain the evolution rules of network structures, and also are the foundations of understanding and controlling complex networks. The existing studies (e.g., scale-free model, small-world model) are insufficient to uncover the internal mechanisms of the emergence and evolution of communities in networks. To overcome the above limitation, in consideration of the fact that a network can be regarded as a pattern composed of communities, we introduce Turing pattern dynamic as theory support to construct the network evolution model. Specifically, we develop a Reaction-Diffusion model according to Q-Learning technology (RDQL), in which each node regarded as an intelligent agent makes a behavior choice to update its relationships, based on the utility and behavioral strategy at every time step. Extensive experiments indicate that our model not only reveals how communities form and evolve, but also can generate networks with the properties of scale-free, small-world and assortativity. The effectiveness of the RDQL model has also been verified by its application in real networks. Furthermore, the depth analysis of the RDQL model provides a conclusion that the proportion of exploration and exploitation behaviors of nodes is the only factor affecting the formation of communities. The proposed RDQL model has potential to be the basic theoretical tool for studying network stability and dynamics.