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Streptococcus suis is a significant and emerging zoonotic pathogen. ST1 and ST7 strains are the primary agents responsible for S. suis human infections in China, including the Guangxi Zhuang Autonomous Region (GX). To enhance our understanding of S. suis ST1 population characteristics, we conducted an investigation into the phylogenetic structure, genomic features, and virulence levels of 73 S. suis ST1 human strains from GX between 2005 and 2020. The ST1 GX strains were categorized into three lineages in phylogenetic analysis. Sub-lineage 3-1a exhibited a closer phylogenetic relationship with the ST7 epidemic strain SC84. The strains from lineage 3 predominantly harboured 89K-like pathogenicity islands (PAIs) which were categorized into four clades based on sequence alignment. The acquirement of 89K-like PAIs increased the antibiotic resistance and pathogenicity of corresponding transconjugants. We observed significant diversity in virulence levels among the 37 representative ST1 GX strains, that were classified as follows: epidemic (E)/highly virulent (HV) (32.4%, 12/37), virulent plus (V+) (29.7%, 11/37), virulent (V) (18.9%, 7/37), and lowly virulent (LV) (18.9%, 7/37) strains based on survival curves and mortality rates at different time points in C57BL/6 mice following infection. The E/HV strains were characterized by the overproduction of tumour necrosis factor (TNF)-α in serum and promptly established infection at the early phase of infection. Our research offers novel insights into the population structure, evolution, genomic features, and pathogenicity of ST1 strains. Our data also indicates the importance of establishing a scheme for characterizing and subtyping the virulence levels of S. suis strains.

Carbapenem resistant Klebsiella pneumoniae (CRKP) can cause serious hospital- and community-acquired infections. Treatment for CRKP infection is limited, resulting in prolonged hospitalization and high consultation costs. The KPC genotype has the highest detection rate of CRKP, and its mortality rate is higher than the overall mortality rate of CRKP. However, traditional testing methods have disadvantages such as long time and reliance on complex and sophisticated instruments, which are not conducive to rapid screening for CRKP. Therefore, this study aimed to establish a detection platform for early screening of CRKP so that effective antimicrobial therapy could be administered promptly to prevent the widespread spread of CRKP. We integrated dual RPA with CRISPR/Cas12a to establish a dual platform for the detection of K. pneumoniae (Kp) rcsA-specific gene and KPC resistance gene. Four result reading methods were established, including fluorescence detection (FD), blue light irradiation detection (BLID), ultraviolet irradiation detection (UID), and lateral flow test strips (LFTS). For the rcsA gene, the LOD of FD was 1 × 10 pg/μL, and the other three methods could detect 1 × 101 pg/μL of bacterial DNA. As for the KPC gene, four resultant readout methods were able to detect 1 × 102 pg/μL of bacterial DNA. In 59 clinical strains tested, the dual RPA-CRISPR/Cas12a detection of the rcsA had 100% sensitivity, specificity, and accuracy compared to the culture method. Compared with the drug sensitivity test, the sensitivity of dual RPA-CRISPR/Cas12a detection for the KPC was 85.71%, the specificity was 100%, and the accuracy was 94.92%. In summary, our dual RPA-CRISPR/Cas12a platform proved to be rapid, precise, and convenient for the efficient detection of Kp with KPC in the laboratory or at the point of care.

The bacterium Klebsiella pneumoniae (Kp) was the primary pathogen of hospital-acquired infection, but the current detection method could not rapidly and conveniently identify Kp. Recombinase polymerase amplification (RPA) was a fast and convenient isothermal amplification technology, and the clustered regularly interspaced short palindromic repeats (CRISPR) system could rapidly amplify the signal of RPA and improve its limit of detection (LOD). In this study, we designed three pairs of RPA primers for the rcsA gene of Kp, amplified the RPA signal through single-strand DNA reporter cleavage by CRISPR/Cas12a, and finally analyzed the cleavage signal using fluorescence detection (FD) and lateral flow test strips (LFTS). Our results indicated that the RPA-CRISPR/Cas12a platform could specifically identify Kp from eleven common clinical pathogens. The LOD of FD and LFTS were 1 fg/μL and 10 fg/μL, respectively. In clinical sample testing, the RPA-CRISPR/Cas12a platform was consistent with the culture method and qPCR method, and its sensitivity and specificity were 100% (16/16) and 100% (9/9), respectively. With the advantages of detection speed, simplicity, and accuracy, the RPA-CRISPR/Cas12a platform was expected to be a convenient tool for the early clinical detection of Kp.

BackgroundCarbapenem-resistant Acinetobacter baumannii (CRAB) poses a severe nosocomial threat, prompting a need for efficient detection methods. Traditional approaches, such as bacterial culture and PCR, are time-consuming and cumbersome. The CRISPR-based gene editing system offered a potential approach for point-of-care testing of CRAB.MethodsWe integrated recombinase polymerase amplification (RPA) and CRISPR-Cas12a system to swiftly diagnose CRAB-associated genes, OXA-51 and OXA-23. This multiplex RPA-CRISPR-Cas12a system eliminates bulky instruments, ensuring a simplified UV lamp-based outcome interpretation.ResultsOperating at 37°C to 40°C, the entire process achieves CRAB diagnosis within 90 minutes. Detection limits for OXA-51 and OXA-23 genes are 1.3 × 10−6 ng/μL, exhibiting exclusive CRAB detection without cross-reactivity to common pathogens. Notably, the platform shows 100% concordance with PCR when testing 30 clinical Acinetobacter baumannii strains.ConclusionIn conclusion, our multiplex RPA coupled with the CRISPR-Cas12a system provides a fast and sensitive CRAB detection method, overcoming limitations of traditional approaches and holding promise for efficient point-of-care testing.

Introduction:Salmonella was one of the most common bacteria that caused foodborne illness, with S. typhimurium (Salmonella typhimurium) and S. enteritidis (Salmonella enteritidis) infections accounting for more than 75% of human salmonella infections.Methods: In this study, we developed a method of dual recombinase polymerase amplification (RPA) combined with a lateral flow dipstick for the rapid detection of S. typhimurium and S. enteritidis in clinical specimens (stool).Results: The entire reaction process, including amplification and result reading, could be completed within 65 min. The detection limits of S. typhimurium and S. enteritidis in pure culture samples were 5.23 × 101 CFU/mL and 3.59 × 101 CFU/mL, respectively. The detection limits of S. typhimurium and S. enteritidis in artificially contaminated samples were 8.30 × 101 CFU/mL and 2.70 × 102 CFU/mL, respectively. In addition, the method had no cross-reaction with other pathogenic microorganisms. The results in clinical samples were fully consistent with those obtained using Bacterial Analysis Manual, with sensitivity and specificity were 100% (8/8) and 100% (17/17) for S. typhimurium and 100% (4/4) and 100% (21/21) for S. enteritidis, respectively.Discussion: The detection limits of S. typhimurium and S. enteritidis in artificially contaminated samples were higher than those in pure culture samples, which might be attributed to the inherent complex composition of artificially contaminated samples. In addition, the detection limits of S. typhimurium and S. enteritidis in the same sample were also different, which might be attributed to different amplification efficiency of two target genes in the same reaction system.Conclusion: This assay had potential application outdoors, as it could be performed within 1 h at 38°C without a complex instrument, and the results could be observed with the naked eye. In conclusion, the dual RPA-LFD assay established in this study had practical significance for the rapid detection of S. typhimurium and S. enteritidis in the future.

After the outbreak of SARS-CoV-2, nucleic acid testing quickly entered people’s lives. In addition to the polymerase chain reaction (PCR) which was commonly used in nucleic acid testing, isothermal amplification methods were also important nucleic acid testing methods. Among several common isothermal amplification methods like displaced amplification, rolling circle amplification, and so on, recombinase polymerase amplification (RPA) was recently paid more attention to. It had the advantages like a simple operation, fast amplification speed, and reaction at 37-42°C, et al. So it was very suitable for field detection. However, there were still some disadvantages to RPA. Herein, our review mainly summarized the principle, advantages, and disadvantages of RPA. The specific applications of RPA in bacterial detection, fungi detection, virus detection, parasite detection, drug resistance gene detection, genetically modified food detection, and SARS-CoV-2 detection were also described. It was hoped that the latest research progress on RPA could be better delivered to the readers who were interested in RPA.

To date, three Streptococcus parasuis strains, BS26, BS27, and NN1, have been isolated from the blood cultures of patients with peritonitis, pneumonia, and arthritis, indicating that S. parasuis is an emerging threat to susceptible people. There is thus an urgent need to further evaluate the pathogenesis of S. parasuis clinical strains in order to design efficient anti-inflammatory strategies. Our previous study demonstrated the capacity of S. parasuis clinical strains to enter the central nervous system (CNS) of infected mice. However, the characteristics and inflammatory mechanism of CNS infections caused by S. parasuis are still non-available. In the present study, we investigated the proportion and time of two clinical S. parasuis strains NN1 and BS26 infected mice that developed neurological symptoms. The characteristics of histopathological changes and the cerebral immune response in mice with neurological symptoms were analyzed. Furthermore, we evaluated the roles of microglia and astrocytes in the S. parasuis clinical strain-induced cerebral inflammation. Our data indicated that S. parasuis clinical strains possess a high potential to induce cerebral inflammation in susceptible people at the early phase of infection. Our study contributes to increasing the understanding of the pathogenicity of S. parasuis and the inflammatory mechanisms of the brain against infection caused by S. parasuis.

Background: L. monocytogenes and L. ivanovii, the only two pathogens of Listeria, can survive in various environments, having different pathogenic characteristics. However, the genetic basis of their excellent adaptability and differences in pathogenicity has still not been completely elucidated. Methods: We performed a comparative genomic analysis based on 275 L. monocytogenes, 10 L. ivanovii, and 22 non-pathogenic Listeria strains. Results: Core/pan-genome analysis revealed that 975 gene families were conserved in all the studied strains. Additionally, 204, 242, and 756 gene families existed uniquely in L. monocytogenes, L. ivanovii, and both, respectively. Functional annotation partially verified that these unique gene families were closely related to their adaptability and pathogenicity. Moreover, the protein–protein interaction (PPI) network analysis of these unique gene sets showed that plenty of carbohydrate transport systems and energy metabolism enzymes were clustered in the networks. Interestingly, ethanolamine-metabolic-process-related proteins were significantly enriched in the PPI network of the unique genes of the Listeria pathogens, which can be understood as a determining factor of their pathogenicity. Conclusions: The utilization capacity of multiple carbon sources of Listeria pathogens, especially ethanolamine, is the key genetic basis for their ability to adapt to various environments and pathogenic lifestyles.

Recently, Streptococcus suis reference strains of serotype 20, 22, and 26 were reclassified as Streptococcus parasuis. The public health significance of S. parasuis is underestimated due to the lack of clinical isolates. In the present study, we first reported two sporadic S. parasuis infections in humans, after using full-length 16S rRNA and housekeeping genes’ phylogeny and ANI values of genome sequence comparisons to determine the species of their isolates BS26 and BS27. Compared to highly pathogenic S. suis strain P1/7, S. parasuis strains BS26 and BS27 possessed a delayed capacity to initiate lethal infection, which may attribute to the later production of higher level of pro-inflammatory cytokines. Differed to S. suis strain P1/7, S. parasuis strains did not induce significant inflammatory response in the brain of mice. Histopathological changes in liver and lungs were widely present in mice infected with S. parasuis strains. Our data indicated that the pathogenic mechanism of S. parasuis may be different from that of S. suis. Three lineages in the core-genome phylogenetic tree and ten types of cps gene cluster were found in 13 S. parasuis genomes, indicating high heterogeneity of this species. The similarity of CPS structure and antibiotic-resistant genes relative to S. suis indicated the evolutionary affinity between the two species. Our data suggested S. parasuis is a potential zoonotic pathogen and poses severe threat to health of susceptible people. Further study on the epidemiology and public health significance of S. parasuis is urgently necessary.

Introduction: Ischemic stroke (IS) is a global health concern, often tied to dyslipidemia and vascular endothelial dysfunction. MicroRNA-34a (miR-34a) was reported to be up-regulated in the blood samples of patients with IS, but the specific role of miR-34a and methylenetetrahydrofolate reductase (MTHFR) in IS remains to be elucidated. Methods: We studied 143 subjects: 71 IS patients, and 72 healthy controls. Human umbilical vein endothelial cells (HUVECs) were cultured and transfected with a miR-34a mimic, inhibitor, or negative control. The miR-34a expression in serum and HUVECs was quantified via quantitative reverse transcription polymerase chain reaction (qRT-PCR). Viability and apoptosis of HUVECs were assessed using CCK-8 assay and flow cytometry. The expression levels of bcl-2, bax, cyt-c, cleaved caspase 3, MTHFR, and homocysteine were measured by Western blot or enzyme-linked immunosorbent assay (ELISA). The relationship between miR-34a and MTHFR was verified by luciferase reporter assay. The levels of MTHFR and homocysteine in serum were examined by ELISA. Results: MiR-34a expression was increased in IS patients and inhibited viability of HUVECs while promoting their apoptosis. Overexpression of miR-34a up-regulated pro-apoptotic proteins (bax, cyt-c and cleaved caspase 3) and down-regulated anti-apoptotic protein bcl-2 in HUVECs. MTHFR was identified as the downstream target of miR-34a and its expression was reduced by miR-34a overexpression, while homocysteine levels increased. Consistently, MTHFR levels were lower and homocysteine levels were higher in IS patients compared with controls. Discussion: Our results suggest that up-regulated miR-34a plays a role in the pathogenesis of IS, potentially through inhibiting MTHFR expression and increasing homocysteine in endothelial cells. Therefore, miR-34a might be a therapeutic target for IS [ABSTRACT FROM AUTHOR]

Abstract As the power industry becomes increasingly complex, Digital Twin (DT) technology has emerged as a crucial tool for enhancing grid resilience and operational efficiency by creating dynamic digital replicas of physical systems. These replicas enable accurate simulations and proactive management, but the vast amount of data generated by DT systems poses significant challenges for processing and analysis. Cloud computing offers a flexible solution by offloading these computational tasks to distributed resources, allowing for real-time analysis and scalable operations. However, this approach introduces complexities in task distribution and maintaining quality of service. Recent efforts have applied Deep Reinforcement Learning (DRL) to address these challenges, primarily using single-agent methods. However, these methods struggle with scalability and performance in increasingly complex cloud environments. To overcome these limitations, we propose an efficient task scheduling framework based on Multi-Agent Deep Q-Network (MADQN) principles, specifically designed to optimize both response times and operational costs. We provide a comprehensive design overview of our approach and conduct a thorough evaluation of its performance. The experimental results clearly indicate that our approach can considerably reduce response times amd lower operational costs compared to current methods, including state-of-the-art single-agent approaches.

The simulation and prediction of landscape patterns can provide a reference for the formulation of ecological space security policies and management and control measures. Therefore, it is necessary to use objective and accurate scientific methods to make accurate simulation predictions. In this study, the quantitative prediction of the Markov model is combined with the advantages of the space-time simulation of the CA model, and the artificial neural network (ANN) module is used for calculation and coupling correction. The Kappa coefficient and the FoM (figure of merit) index are used to test the simulation accuracy. Based on this process, the changes of the landscape pattern in Mudanjiang City at three time points in 2000, 2010 and 2020 were analyzed, and the simulation prediction was made based on this. The experimental results show that the CA-Markov model using the ANN module for coupling correction can be tested for accuracy in the study area, and its Kappa coefficient is 0.834 and the FoM index is 0.001. The model meets the requirements of the landscape pattern for the accuracy of simulation and prediction, and the prediction and analysis of the study area is carried out, and the accurate prediction results are obtained.The results of the study were: Looking at the change in landscape pattern over the 20 years from 2000 to 2020, significant changes occurred in the areas of arable land and artificial surface. The total area of arable land decreased by nearly 130 km2, while the area of artificial surface (i.e., built-up land) increased by about 167 km2. The forest area initially decreased and then increased, with an overall increase of approximately 56 km2, while the water and grassland areas first increased and then decreased. In terms of spatial distribution, the changes in agricultural and forest land are mainly concentrated in the central area where urban development is intensive and in the northern forest area. The results of this study are quite important for policy formulation, according to which it was found that human activities, especially urbanization, have led to a decrease in the area of forested land, and therefore the implementation of the natural forest protection policy can lead to a sustained increase in the area of forested land.

In order to better describe the uncertainty of renewable energy output, this paper proposed a novel robust optimization method for new distribution systems based on adaptive data-driven polyhedral sets. First, an ellipsoidal uncertainty set was established using historical data on renewable energy output, and a data-driven convex hull polyhedral set was established by connecting high-dimensional ellipsoidal vertices; on this basis, an adaptive data-driven polyhedral set model was established to address the problem of high conservatism in the scaling process of convex hull polyhedral sets. Furthermore, a novel adaptive data-driven robust scheduling model for new distribution systems was established, and a column-and-constraint generation (C&CG) algorithm was used to solve the robust scheduling model. Finally, the improved IEEE-33 bus system simulation verification shows that the robust scheduling model for new distribution systems based on adaptive data-driven polyhedral sets can reduce conservatism and improve the robustness of optimization results.

Here the authors use in situ mooring data in the Maluku Channel and the Lifamatola Passage of the Indonesian seas to show that a western boundary current transports Antarctic water from the South Pacific to the Indian Ocean.

Introduction: The pathogenesis of keloids remains unclear.Methods: In this study, we analyzed RNA-Seq data (GSE113619) of the local skin tissue of 8 keloid-prone individuals (KPI) and 6 healthy controls (HC) before and 42 days after trauma from the gene expression omnibus (GEO) database. The differential alternative splicing (AS) events associated with trauma healing between KPIs and HCs were identifified, and their functional differences were analyzed by gene ontology (GO) and kyoto encyclopedia of genes and genomes (KEGG) pathways. The co-expression relationship of differentially alternative splicing genes and differentially expressed RNA binding proteins (RBPs) was established subsequently.Results: A total of 674 differential AS events between the KD42 and the KD0 and 378 differential AS events between the HD42 and the HD0 were discovered. Notably, most of the differential genes related to keloids are enriched in actin, microtubule cells, and cortical actin cytoskeletal tissue pathway. We observed a signifificant association between AS genes (EPB41, TPM1, NF2, PARD3) and trauma healing in KPIs and HCs. We also found that the differential expression of healthy controls-specifific trauma healing-related RBPs (TKT, FDPS, SAMHD1) may affect the response of HCs to trauma healing by regulating the AS of downstream trauma healing-related genes such as DCN and DST. In contrast, KPIs also has specifific differential expression of trauma healing related RBPs (S100A9, HspB1, LIMA1, FBL), which may affect the healing response of KPIs to trauma by regulating the AS of downstream trauma healing-related genes such as FN1 and TPM1.Discussion: Our results were innovative in revealing early wound healing-related genes (EPB41, TPM1, NF2, PARD3) in KPI from the perspective of AS regulated by RBPs.

Smart power grid is a two-way interconnected network in which information plays a fundamental role in the process of energy transmission and distribution. The smart grid is based on combining ICT with the processing capabilities of computers and electrical systems to increase communication between users. However, smart grids are exposed to a variety of malicious cyber-attacks that can lead to the destruction of basic infrastructure and disrupt communication between the grid and users. Meanwhile, by authenticating the authenticated users, cyber-security can be created in the smart grid. One of the main components of a smart power grid is smart meters. However, to maintain security in a communication, smart meter authentication alone is not enough and it must be ensured that the electricity company’s server is not forgery. Therefore, there is a need for two-way authentication between the smart meter on the home side and the electricity company server. The present work provides a key management layout (based on the elliptic curve cryptography) with two-way authentication for communication outside the home area network The proposed solution is two-way authentication among the smart meter and the security associate in the power company. In other words, the proposed methodology creates a solution to establish a connection between valid users and the smart grid. The proposed protocol evaluates the behavior of cyber-attacks on the smart grid. Attacks such as man-in the middle attacks and retransmission attacks are considered. It was found that, the proposed methodology improves the reliance between smart grid and authenticated users with the increase of secure communications. Moreover, the proposed protocol can prevent an extensive experiment attack. The results also showed that, as the false factor increases, the detection time by the proposed algorithm increases. As the false factor increases from 0.1 to 0.3, the detection time increases by 1.4 s.

In this study, a tobramycin concentration-dependent whole-cell micro-biosensor (tob-HHAz) was constructed by fusing a tobramycin aptamer with a hammerhead ribozyme (HHR) from Schistosoma mansoni. The biosensor was obtained by integrating all the modules into one complete RNA sequence, which was easily introduced into E. coli without suffering from harsh external environments. Three independent tobramycin-sensitive RNA structures were identified via high-throughput screening in vivo and were further verified in vitro to undergo the desired self-cleavage reaction. The computation prediction of the RNA structure was performed to help analyze the mechanisms of various conformations by performing a qualitative and rapid detection of tobramycin in practical samples; two sensors exhibited high responsiveness to spiked milk, with a detection limit of around 40 nM, which is below the EU’s antibiotic maximum residual level. One of the structures provides a linear range from 30 to 650 nM with a minimum detection limit of 30 nM and showed relatively good selectivity in spiked urine. This study is the first in which in vivo screening was combined with computation analysis to optimize the pivotal structure of sensors. This strategy enables researchers to use artificial ribozyme-based biosensors not only for antibiotic detection but also as a generally applicable method for the further detection of substances in living cells.