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Abstract Sulfur-containing units are fundamental components widely found in bioactive compounds, prompting notable efforts toward developing synthetic methodologies for incorporating sulfur functionality into organic precursors. The synthesis of sulfinate esters and sulfinamides has garnered significant interest owing to their immense potential for applications, especially in drug development. However, most existing synthetic protocols suffer from some limitations. To address these challenges, we herein present a practical and efficient approach for the reductive sulfinylation of diverse nucleophiles with sulfonylpyridinium salts (SulPy) through the nucleophilic chain substitution, namely SNC reaction, which involves S(VI) to S(IV) nucleophilic chain isomerization process. These versatile sulfinylation reagents can be readily accessed from diverse commercially available resourses. The late-stage modification of complex molecules and the ability to rapidly synthesize numerous sulfinyl bioisosteres of various drugs highlights the utility of this protocol.

Abstract Background Teat number is one of the most important indicators to evaluate the lactation performance of sows, and increasing the teat number has become an important method to improve the economic efficiency of farms. Therefore, it is particularly important to deeply analyze the genetic mechanism of teat number traits in pigs. In this study, we detected Single Nucleotide Ploymorphism (SNP), Insertion-Deletion (InDel) and Structural variant (SV) by high-coverage whole-genome resequencing data, and selected teat number at birth and functional teat number as two types of teat number traits for genome-wide association study (GWAS) to reveal candidate genes associated with pig teat number traits. Results In this study, we used whole genome resequencing data from 560 Yorkshire sows to detect SNPs, InDels and SVs, and performed GWAS for the traits of born teat number and functional teat number, and detected a total of 85 significant variants and screened 214 candidate genes, including HEG1, XYLT1, SULF1, MUC13, VRTN, RAP1A and NPVF. Among them, HEG1 and XYLT1 were the new candidate genes in this study. The co-screening and population validation of multiple traits suggested that HEG1 may have a critical effect on the born teat number. Conclusion Our study shows that more candidate genes associated with pig teat number traits can be identified by GWAS with different variant types. Through large population validation, we found that HEG1 may be a new key candidate gene affecting pig teat number traits. In conclusion, the results of this study provide new information for exploring the genetic mechanisms affecting pig teat number traits and genetic improvement of pigs.

Spiking Neural Network (SNN) simulation is very important for studying brain function and validating the hypotheses for neuroscience, and it can also be used in artificial intelligence. Recently, GPU-based simulators have been developed to support the real-time simulation of SNN. However, these simulators’ simulating performance and scale are severely limited, due to the random memory access pattern and the global communication between devices. Therefore, we propose an efficient distributed heterogeneous SNN simulator based on the Sunway accelerators (including SW26010 and SW26010pro), named SWsnn, which supports accurate simulation with small time step (1/16 ms), randomly delay sizes for synapses, and larger scale network computing. Compared with existing GPUs, the Local Dynamic Memory (LDM) (similar to cache) in Sunway is much bigger (4 MB or 16 MB in each core group). To improve the simulation performance, we redesign the network data storage structure and the synaptic plasticity flow to make most random accesses occur in LDM. SWsnn hides Message Passing Interface (MPI)-related operations to reduce communication costs by separating SNN general workflow. Besides, SWsnn relies on parallel Compute Processing Elements (CPEs) rather than serial Manage Processing Element (MPE) to control the communicating buffers, using Register-Level Communication (RLC) and Direct Memory Access (DMA). In addition, SWsnn is further optimized using vectorization and DMA hiding techniques. Experimental results show that SWsnn runs 1.4−2.2 times faster than state-of-the-art GPU-based SNN simulator GPU-enhanced Neuronal Networks (GeNN), and supports much larger scale real-time simulation.

Abstract Studies in SIV-infected macaques show that the virus reservoir is particularly refractory to conventional suppressive antiretroviral therapy (ART). We posit that optimized ART regimens designed to have robust penetration in tissue reservoirs and long-lasting antiviral activity may be advantageous for HIV or SIV remission. Here we treat macaques infected with RT-SHIV with oral emtricitabine/tenofovir alafenamide and long-acting cabotegravir/rilpivirine without (n = 4) or with (n = 4) the immune activator vesatolimod after the initial onset of viremia. We document full suppression in all animals during treatment (4-12 months) and no virus rebound after treatment discontinuation (1.5-2 years of follow up) despite CD8 + T cell depletion. We show efficient multidrug penetration in virus reservoirs and persisting rilpivirine in plasma for 2 years after the last dose. Our results document a type of virus remission that is achieved through early treatment initiation and provision of ultra long-lasting antiviral activity that persists after treatment cessation.

Abstract Background The use of mesenchymal stem cells (MSCs) is recognized as a promising strategy for the treatment of androgenetic alopecia (AGA). However, the underlying mechanism remains to be explored. Here, we evaluated the therapeutic effects and potential mechanisms of the use of human umbilical cord mesenchymal stem cells (hUCMSCs) in dihydrotestosterone (DHT)-induced AGA models in vivo and in vitro. Methods Intradermal transplantation of hUCMSCs was performed in AGA model mice and therapeutic effects were evaluated using histological and immunofluorescence staining. Transwell assays were used for co-culture of hUCMSCs and dermal papilla cells (DPCs), and communication was assessed using RT-qPCR, immunofluorescence, and apoptosis analysis. Interactions between DPCs and hair follicle stem cells (HFSCs) were investigated using RT-qPCR, EdU assays, and cell cycle analysis. Results Treatment of AGA mice with hUCMSCs promoted hair growth, HFs density, skin thickness, and anagen phase activation, while inhibiting DPCs apoptosis, and promoting HFSCs proliferation. In vitro, hUCMSCs activated Wnt/β-catenin signaling in DPCs via Wntless (Wls), while stimulating growth factor secretion and HFSCs proliferation. Blocking β-catenin degradation with MSAB increased DPCs apoptosis, reduced growth factor secretion, and retarded HFSCs proliferation. Conclusion hUCMSCs promoted hair regeneration in AGA model mice. This was found to be dependent on reducing DPCs apoptosis, thereby relieving the inhibitory effects of DPCs on the growth of HFSCs. The activation of the Wnt/β-catenin signaling pathway was shown to play a crucial role in the promotion of hair growth by hUCMSCs in AGA mice.

Abstract Background Gliomas are the most common malignant brain tumors characterized by angiogenesis and invasive growth. A detailed understanding of its molecular characteristics could provide potential therapeutic targets. In the present study, we sought to explore the key gene CXCL8 in methionine metabolism in gliomas and its potential role in angiogenesis. Methods U251 glioma cells were divided into control and methionine-restriction tolerant (constructed with 1/4 of the standard level of methionine in the culture medium) groups for transcriptome and metabolome analysis. To confirm the functions and mechanism of CXCL8 in glioma, heat map, volcano map, Go enrichment, gene set enrichment analysis (GSEA), protein–protein interaction network analysis, RT-PCR, western blotting assays, chicken embryo chorioallantoic membrane (CAM) test, chicken embryo yolk sac membrane (YSM) test and transplantation tumor nude mice model were performed. The TCGA database, CGGA database and clinical tissue samples were used to analyze CXCL8’s significance on prognosis for patients with glioma. Results CXCL8 expression was significantly up-regulated in methionine-restricted tolerance cells, it also activated vascular system development and triggered angiogenesis. CXCL8 expression is negatively correlated with survival prognosis in gliomas. Conclusions Glioma cells promote angiogenesis in methionine-restricted environments through the activation of CXCL8, compensating for nutrient deprivation, and possibly contributing to the failure of antiangiogenic therapy.

Introduction Material and methods Results Conclusions

Oil shale, the most important unconventional oil and gas reservoir resource, is characterized by large geological reserves, difficult development technology, and great development potential. Although it cannot be exploited in a large area due to cost issues, with the development and utilization of conventional oil and gas reservoir resources, it is the main direction of future oil exploitation. Based on the classification of in situ conversion technologies of oil shale electric heating into in situ conversion process technology, ElectrofracTM technology, geothermal fuel cell heating technology, high-voltage power frequency electric heating technology, and other electric heating technology, this paper summarizes the research progress on existing electric heating technologies to provide a reference for the engineering research and development of oil shale electric heating in situ mining technology.

In the engineering of oil shale, in situ extraction technology using electric heating involves heating the oil shale reservoir with an electric heater at high temperatures to convert the solid kerogen in oil shale into liquid hydrocarbons. These liquid hydrocarbons are then extracted from underground using traditional oil and gas drilling and production techniques. This paper discusses the pyrolysis mechanism, pore evolution, mineral transformation, and classification of electric heating technology for oil shale. It also summarizes research progress aimed at improving in situ extraction technology for oil shale, providing valuable insights for further research and development in this field.

Accurately diagnosing Alzheimer’s disease is essential for improving elderly health. Meanwhile, accurate prediction of the mini-mental state examination score also can measure cognition impairment and track the progression of Alzheimer’s disease. However, most of the existing methods perform Alzheimer’s disease diagnosis and mini-mental state examination score prediction separately and ignore the relation between these two tasks. To address this challenging problem, we propose a novel multi-task learning method, which uses feature interaction to explore the relationship between Alzheimer’s disease diagnosis and mini-mental state examination score prediction. In our proposed method, features from each task branch are firstly decoupled into candidate and non-candidate parts for interaction. Then, we propose feature sharing module to obtain shared features from candidate features and return shared features to task branches, which can promote the learning of each task. We validate the effectiveness of our proposed method on multiple datasets. In Alzheimer’s disease neuroimaging initiative 1 dataset, the accuracy in diagnosis task and the root mean squared error in prediction task of our proposed method is 87.86% and 2.5, respectively. Experimental results show that our proposed method outperforms most state-of-the-art methods. Our proposed method enables accurate Alzheimer’s disease diagnosis and mini-mental state examination score prediction. Therefore, it can be used as a reference for the clinical diagnosis of Alzheimer’s disease, and can also help doctors and patients track disease progression in a timely manner.

Autism Spectrum Disorder (ASD) is a highly disabling mental disease that brings significant impairments of social interaction ability to the patients, making early screening and intervention of ASD critical. With the development of the machine learning and neuroimaging technology, extensive research has been conducted on machine classification of ASD based on structural Magnetic Resonance Imaging (s-MRI). However, most studies involve with datasets where participants’ age are above 5 and lack interpretability. In this paper, we propose a machine learning method for ASD classification in children with age range from 0.92 to 4.83 years, based on s-MRI features extracted using Contrastive Variational AutoEncoder (CVAE). 78 s-MRIs, collected from Shenzhen Children’s Hospital, are used for training CVAE, which consists of both ASD-specific feature channel and common-shared feature channel. The ASD participants represented by ASD-specific features can be easily discriminated from Typical Control (TC) participants represented by the common-shared features. In case of degraded predictive accuracy when data size is extremely small, a transfer learning strategy is proposed here as a potential solution. Finally, we conduct neuroanatomical interpretation based on the correlation between s-MRI features extracted from CVAE and surface area of different cortical regions, which discloses potential biomarkers that could help target treatments of ASD in the future.

Abstract Single-cell RNA sequencing (scRNA-seq) is a key technology for investigating cell development and analysing cell diversity across various diseases. However, the high dimensionality and extreme sparsity of scRNA-seq data pose great challenges for accurate cell type annotation. To address this, we developed a new cell-type annotation model called scGAA (general gated axial-attention model for accurate cell-type annotation of scRNA-seq). Based on the transformer framework, the model decomposes the traditional self-attention mechanism into horizontal and vertical attention, considerably improving computational efficiency. This axial attention mechanism can process high-dimensional data more efficiently while maintaining reasonable model complexity. Additionally, the gated unit was integrated into the model to enhance the capture of relationships between genes, which is crucial for achieving an accurate cell type annotation. The results revealed that our improved transformer model is a promising tool for practical applications. This theoretical innovation increased the model performance and provided new insights into analytical tools for scRNA-seq data.

BackgroundDiagnosing bipolar disorder poses a challenge in clinical practice and demands a substantial time investment. With the growing utilization of artificial intelligence in mental health, researchers are endeavoring to create AI-based diagnostic models. In this context, some researchers have sought to develop machine learning models for bipolar disorder diagnosis. Nevertheless, the accuracy of these diagnoses remains a subject of controversy. Consequently, we conducted this systematic review to comprehensively assess the diagnostic value of machine learning in the context of bipolar disorder.MethodsWe searched PubMed, Embase, Cochrane, and Web of Science, with the search ending on April 1, 2023. QUADAS-2 was applied to assess the quality of the literature included. In addition, we employed a bivariate mixed-effects model for the meta-analysis.Results18 studies were included, covering 3152 participants, including 1858 cases of bipolar disorder. 28 machine learning models were encompassed. Sensitivity and specificity in discriminating between bipolar disorder and normal individuals were 0.88 (9.5% CI: 0.74~0.95) and 0.89 (95% CI: 0.73~0.96) respectively, and the SROC curve was 0.94(95% CI: 0.92~0.96). The sensitivity and specificity for distinguishing between bipolar disorder and depression were 0.84 (95%CI: 0.80~0.87) and 0.82 (95%CI: 0.75~0.88) respectively. The SROC curve was 0.89 (95%CI: 0.86~0.91).ConclusionsMachine learning methods can be employed for discriminating and diagnosing bipolar disorder. However, in current research, they are predominantly utilized for binary classification tasks, limiting their progress in clinical practice. Therefore, in future studies, we anticipate the development of more multi-class classification tasks to enhance the clinical applicability of these methods.Systematic review registrationhttps://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42023427290, identifier CRD42023427290.

Introduction: EGFR tyrosine kinase inhibitor (TKI) is the standard adjuvant treatment for patients with stages IB to IIIA EGFR-mutated NSCLC. Nevertheless, adapting this approach to include a molecular residual disease (MRD)-guided de-escalation strategy warrants further investigation. Methods: From January 2019 to December 2022, 71 patients with stages I to III NSCLC and EGFR (exon 19 deletion or L858R) mutations were enrolled in this observational study. A total of 375 blood samples were analyzed using the MRD_Navigator assay. Among them, 27 patients suspended EGFR TKI treatment based on undetectable MRD and were thus included in the adaptive, de-escalation group. Results: Overall, the sensitivity of longitudinal MRD was 86.2%. Only four patients (11.8%) recurred with longitudinal undetectable MRD, indicating a negative predictive value of 88.2%. Of the patients who had detectable MRD after surgery, nine subsequently received EGFR TKI treatment, with only one (11.1%) achieving persistent circulating tumor DNA clearance post–EGFR TKI. Furthermore, 22 patients with stages IB to III disease who had previously suspended their TKI treatment based on undetectable MRD were included in the adaptive group, with an average duration of TKI 3.9 (range: 0–35.0) months. The 2-year disease-free survival rate of these 22 patients was 80.2%, and the median was not reached. Five patients (n = 5 of 22, 22.7%) had disease recurrence during the period of drug cessation but were stable under EGFR TKI treatment until the latest follow-up. Two patients remained in complete remission. Conclusions: Our initial findings underscore the potential of an adaptive, de-escalation approach to adjuvant EGFR TKIs based on circulating tumor DNA-MRD monitoring.

Medical images and radiology reports are essential for physicians to diagnose medical conditions. However, the vast diversity and cross-source heterogeneity inherent in these data have posed significant challenges to the generalizability of current data-mining methods for clinical decision-making. Recently, multimodal large language models (MLLMs), especially Gemini-Vision-series (Gemini) and GPT-4-series (GPT-4) models, have revolutionized numerous domains, significantly impacting the medical field. In this study, we conducted a detailed evaluation of the performance of the Gemini series models (including Gemini-1.0-Pro-Vision, Gemini-1.5-Pro, and Gemini-1.5-Flash) and GPT series models (including GPT-4o, GPT-4-Turbo, and GPT-3.5-Turbo) across 14 medical datasets, covering 5 medical imaging categories (dermatology, radiology, dentistry, ophthalmology, and endoscopy) and 3 radiology report datasets. The investigated tasks encompass disease classification, lesion segmentation, anatomical localization, disease diagnosis, report generation, and lesion detection. Moreover, we also validated the performance of the Claude-3-Opus, Yi-Large, Yi-Large-Turbo, and LLaMA 3 models to gain a comprehensive understanding of the MLLM models in the medical field. Our experimental results demonstrated that Gemini-series models excelled in report generation and lesion detection but faces challenges in disease classification and anatomical localization. Conversely, GPT-series models exhibited proficiency in lesion segmentation and anatomical localization but encountered difficulties in disease diagnosis and lesion detection. Additionally, both the Gemini series and GPT series contain models that have demonstrated commendable generation efficiency. While both models hold promise in reducing physician workload, alleviating pressure on limited healthcare resources, and fostering collaboration between clinical practitioners and artificial intelligence technologies, substantial enhancements and comprehensive validations remain imperative before clinical deployment.

With the rapidly development of biotechnology, it is now possible to obtain single-cell multi-omics data in the same cell. However, how to integrate and analyze these single-cell multi-omics data remains a great challenge. Herein, we introduce an interpretable multitask framework (scMoMtF) for comprehensively analyzing single-cell multi-omics data. The scMoMtF can simultaneously solve multiple key tasks of single-cell multi-omics data including dimension reduction, cell classification and data simulation. The experimental results shows that scMoMtF outperforms current state-of-the-art algorithms on these tasks. In addition, scMoMtF has interpretability which allowing researchers to gain a reliable understanding of potential biological features and mechanisms in single-cell multi-omics data.

Porcine hemagglutinating encephalomyelitis virus (PHEV), porcine pseudorabies virus (PRV), and classical swine fever virus (CSFV) are currently prevalent worldwide and cause similar neurological symptoms in infected pigs. It is very important to establish a detection method that can rapidly and accurately detect and differentiate these three viruses. Targeting the PHEV N gene, PRV gB gene, and CSFV 5′ untranslated region (5′UTR), three pairs of specific primers and probes were designed, and a triplex crystal digital reverse transcription-PCR (cdRT-PCR) was developed to detect PHEV, PRV, and CSFV. The results indicated that this assay had high sensitivity, and the limitation of detection (LODs) for PHEV, PRV, and CSFV were 4.812, 4.047, and 5.243 copies/reaction, respectively, which was about 50 times higher than that of multiplex real-time quantitative RT-PCR (RT-qPCR). This assay showed good specificity, without cross-reaction with other important swine pathogens, i.e., FMDV, PRRSV, PEDV, SIV, TGEV, PoRV, and PCV2. This assay had high repeatability, with intra-assay coefficients of variation (CVs) of 0.73–1.87%, and inter-assay CVs of 0.57–2.95%. The developed assay was used to test 1,367 clinical tissue samples from Guangxi province in China, and the positive rates of PHEV, PRV, and CSFV were 3.44% (47/1,367), 1.24% (17/1,367), and 1.90% (26/1,367), respectively, with a coincidence rate of 98.98% and a Kappa value of 0.94 to the reference multiplex RT-qPCR. The established triplex cdRT-PCR was a highly rapid, sensitive, and accurate assay to detect and differentiate PHEV, PRV, and CSFV.

Abstract Background Glucagon-like peptide-1 receptor (GLP1R) agonists have been shown to reduce major cardiovascular events in diabetic patients, but their role in heart failure (HF) remains controversial. Recent evidence implies their potential benefits on cardiometabolism such as lipid metabolism, which may contribute to lowering the risk of HF. Consequently, we designed a Mendelian randomization (MR) study to investigate the causal relationships of circulating lipids mediating GLP1R agonists in HF. Methods The available cis-eQTLs for GLP1R target gene were selected as instrumental variables (IVs) of GLP1R agonism. Positive control analyses of type 2 diabetes mellitus (T2DM) and body mass index (BMI) were conducted to validate the enrolled IVs. Two-sample MR was performed to evaluate the associations between GLP1R agonism and HF as well as left ventricular ejection fraction (LVEF). Summary data for HF and LVEF were obtained from two genome-wide association studies (GWASs), which included 977,323 and 40,000 individuals of European ancestry, respectively. The primary method employed was the random-effects inverse variance weighted, with several other methods used for sensitivity analyses, including MR-Egger, MR PRESSO, and weighted median. Additionally, multivariable MR and mediation MR were applied to identify potentially causal lipid as mediator. Results A total of 18 independent IVs were included. The positive control analyses showed that GLP1R agonism significantly reduced the risk of T2DM (OR = 0.79, 95% CI = 0.75–0.85, p

Abstract Seed size/weight plays an important role in determining crop yield, yet only few genes controlling seed size have been characterized in soybean. Here, we perform a genome-wide association study and identify a major quantitative trait locus (QTL), named GmSW17 (Seed Width 17), on chromosome 17 that determine soybean seed width/weight in natural population. GmSW17 encodes a ubiquitin-specific protease, an ortholog to UBP22, belonging to the ubiquitin-specific protease (USPs/UBPs) family. Further functional investigations reveal that GmSW17 interacts with GmSGF11 and GmENY2 to form a deubiquitinase (DUB) module, which influences H2Bub levels and negatively regulates the expression of GmDP-E2F-1, thereby inhibiting the G1-to-S transition. Population analysis demonstrates that GmSW17 undergo artificial selection during soybean domestication but has not been fixed in modern breeding. In summary, our study identifies a predominant gene related to soybean seed weight, providing potential advantages for high-yield breeding in soybean.

Abstract Alkylamine structures represent one of the most functional and widely used in organic synthesis and drug design. However, the general methods for the functionalization of the shielded and deshielded alkyl radicals remain elusive. Here, we report a general deoxygenative amination protocol using alcohol-derived carbazates and nitrobenzene under electrochemical conditions. A range of primary, secondary, and tertiary alkylamines are obtained. This practical procedure can be scaled up through electrochemical continuous flow technique.

This study explores the potential of Artificial Intelligence (AI) in early screening and prognosis of Dry Eye Disease (DED), aiming to enhance the accuracy of therapeutic approaches for eye-care practitioners. Despite the promising opportunities, challenges such as diverse diagnostic evidence, complex etiology, and interdisciplinary knowledge integration impede the interpretability, reliability, and applicability of AI-based DED detection methods. The research conducts a comprehensive review of datasets, diagnostic evidence, and standards, as well as advanced algorithms in AI-based DED detection over the past five years. The DED diagnostic methods are categorized into three groups based on their relationship with AI techniques: (1) those with ground truth and/or comparable standards, (2) potential AI-based methods with significant advantages, and (3) supplementary methods for AI-based DED detection. The study proposes suggested DED detection standards, the combination of multiple diagnostic evidence, and future research directions to guide further investigations. Ultimately, the research contributes to the advancement of ophthalmic disease detection by providing insights into knowledge foundations, advanced methods, challenges, and potential future perspectives, emphasizing the significant role of AI in both academic and practical aspects of ophthalmology.

Abstract Recently, environmental temperature has been shown to regulate bone homeostasis. However, the mechanisms by which cold exposure affects bone mass remain unclear. In our present study, we observed that exposure to cold temperature (CT) decreased bone mass and quality in mice. Furthermore, a transplant of exosomes derived from the plasma of mice exposed to cold temperature (CT-EXO) can also impair the osteogenic differentiation of BMSCs and decrease bone mass by inhibiting autophagic activity. Rapamycin, a potent inducer of autophagy, can reverse cold exposure or CT-EXO-induced bone loss. Microarray sequencing revealed that cold exposure increases the miR-25-3p level in CT-EXO. Mechanistic studies showed that miR-25-3p can inhibit the osteogenic differentiation and autophagic activity of BMSCs. It is shown that inhibition of exosomes release or downregulation of miR-25-3p level can suppress CT-induced bone loss. This study identifies that CT-EXO mediates CT-induced osteoporotic effects through miR-25-3p by inhibiting autophagy via targeting SATB2, presenting a novel mechanism underlying the effect of cold temperature on bone mass.

Adipose tissue inflammation is now considered to be a key process underlying metabolic diseases in obese individuals. However, it remains unclear how adipose inflammation is initiated and maintained or the mechanism by which inflammation develops. We found that microRNA-802 (Mir802) expression in adipose tissue is progressively increased with the development of dietary obesity in obese mice and humans. The increasing trend of Mir802 preceded the accumulation of macrophages. Adipose tissue-specific knockout of Mir802 lowered macrophage infiltration and ameliorated systemic insulin resistance. Conversely, the specific overexpression of Mir802 in adipose tissue aggravated adipose inflammation in mice fed a high-fat diet. Mechanistically, Mir802 activates noncanonical and canonical NF-κB pathways by targeting its negative regulator, TRAF3. Next, NF-κB orchestrated the expression of chemokines and SREBP1, leading to strong recruitment and M1-like polarization of macrophages. Our findings indicate that Mir802 endows adipose tissue with the ability to recruit and polarize macrophages, which underscores Mir802 as an innovative and attractive candidate for miRNA-based immune therapy for adipose inflammation.

Porcine hemagglutinating encephalomyelitis virus (PHEV) is the only known porcine neurotropic coronavirus, which is prevalent worldwide at present. It is of great significance to understand the genetic and evolutionary characteristics of PHEV in order to perform effective measures for prevention and control of this disease. In this study, a total of 6,986 tissue samples and nasopharyngeal swabs were collected from different regions of Guangxi province in southern China during 2021-2024, and were tested for PHEV using a quadruplex RT-qPCR. The positivity rate of PHEV was 2.81% (196/6,986), of which tissue samples and nasopharyngeal swabs had 2.05% (87/4,246) and 3.98% (109/2,740) positivity rates, respectively. Fifty PHEV positive samples were selected for PCR amplification and gene sequencing. Sequence analysis revealed that the nucleotide homology and amino acid similarities of S, M, and N genes were 94.3%-99.3% and 92.3%-99.2%, 95.0%-99.7% and 94.7%-100.0%, 94.0%-99.5% and 93.5%-99.3%, respectively, indicating M and N genes were more conservative than S gene. Phylogenetic trees based on these three genes revealed that PHEV strains from different countries could be divided into two groups G1 and G2, and the PHEV strains from Guangxi province obtained in this study distributed in subgroups G1c and G2b. Bayesian analysis revealed that the population size of PHEV has been in a relatively stable state since its discovery until it expanded sharply around 2015, and still on the slow rise thereafter. S gene sequences analysis indicated that PHEV strains existed variation of mutation, and recombination. The results indicated that the prevalent PHEV strains in Guangxi province had complex evolutionary trajectories and high genetic diversity. To the best of our knowledge, this is the first report on the genetic and evolutionary characteristics of PHEV in southern China.

Objective: This study utilized network meta-analysis (NMA) to compare the efficacy of five commonly used traditional Chinese medicine monomers in reducing intimal hyperproliferation in arterial balloon injury models. Methods: Relevant literature up to January 2024 was systematically retrieved from seven major databases. The intima-to-media (I/M) ratio was chosen as the primary outcome measure. The risk of bias in animal studies was assessed using the SYstematic Review Centre for Laboratory Animal Experimentation (SYRCLE) tool. Statistical analysis was conducted using Stata 17 software. Results: A total of 43 studies were included in this meta-analysis. NMA results showed that in the rat model, compared to the control group, GS (SMD: 0.99, 95%CI: 1.25 to −0.73), ASIV (SMD: 1.16, 95%CI: 1.65 to −0.67), TMP (SMD: 0.68, 95%CI: 1.31 to −0.05), and TPNS (SMD: 1.36, 95%CI: 1.91 to −0.80) exhibited inhibitory effects on postoperative intimal hyperproliferation, reducing the I/M ratio. In the rabbit model, compared to the control group, TPNS (SMD: 1.23, 95%CI: 1.97 to −0.49) inhibited postoperative intimal hyperproliferation and reduced the I/M ratio. Superiority ranking analysis suggested that total Panax notoginseng saponin (TPNS) might be the most effective traditional Chinese medicine monomer in reducing intimal hyperproliferation in arterial balloon injury models, lowering the I/M ratio. Conclusion: NMA indicates that traditional Chinese medicine monomers can effectively reduce postoperative intimal hyperproliferation in arterial balloon injury models, lowering the I/M ratio, with TPNS showing optimal efficacy. However, the research on TIIA is insufficient, and the limited sample size may affect the robustness of the results. Furthermore, the majority of research on traditional Chinese medicine monomers is currently limited to the experimental stage, lacking further clinical validation. Conducting standardized animal experiments and reporting their findings can enhance the quality of evidence from animal studies, laying the foundation for future clinical trials.

Abstract It remains a challenge to produce soft robots that can mimic the responsive adaptability of living organisms. Rather than fabricating soft robots from bulk hydrogels,hydrogels are integrated into the interfacial assembly of aqueous two‐phase systems to generate ultra‐soft and elastic all‐aqueous aquabots that exhibit responsive adaptability, that can shrink on demand and have electrically conductive functions. The adaptive functions of the aquabots provide a new platform to develop minimally invasive surgical devices, targeted drug delivery systems, and flexible electronic sensors and actuators.

Abstract Background To establish a method to induce Campylobacter jejuni colonization in the intestines of C57BL/6 mice through antibiotic-induced microbiome depletion. Results Fifty-four female C57BL/6 mice were divided into the normal, control, and experimental groups. The experimental group was administered intragastric cefoperazone sodium and sulbactam sodium (50 mg/mL) for 2 days; then, the experimental and control mice were intragastrically administered 200 µL C. jejuni, which was repeated once more after 2 days. Animal feces were collected, and the HipO gene of C. jejuni was detected using TaqMan qPCR from day 1 to day 14 after modeling completion. Immunofluorescence was used to detect intestinal C. jejuni colonization on day 14, and pathological changes were observed using hematoxylin and eosin staining. Additionally, 16S rDNA analyses of the intestinal contents were conducted on day 14. In the experimental group, C. jejuni was detected in the feces from days 1 to 14 on TaqMan qPCR, and immunofluorescence-labeled C. jejuni were visibly discernable in the intestinal lumen. The intestinal mucosa was generally intact and showed no significant inflammatory-cell infiltration. Diversity analysis of the colonic microbiota showed significant inter-group differences. In the experimental group, the composition of the colonic microbiota differed from that in the other 2 groups at the phylum level, and was characterized by a higher proportion of Bacteroidetes and a lower proportion of Firmicutes. Conclusions Microbiome depletion induced by cefoperazone sodium and sulbactam sodium could promote long-term colonization of C. jejuni in the intestines of mice.

Glucagon-like peptide-1 (GLP-1) is a 30-amino acid peptide hormone that is mainly expressed in the intestine and hypothalamus. In recent years, basic and clinical studies have shown that GLP-1 is closely related to lipid metabolism, and it can participate in lipid metabolism by inhibiting fat synthesis, promoting fat differentiation, enhancing cholesterol metabolism, and promoting adipose browning. GLP-1 plays a key role in the occurrence and development of metabolic diseases such as obesity, nonalcoholic fatty liver disease, and atherosclerosis by regulating lipid metabolism. It is expected to become a new target for the treatment of metabolic disorders. The effects of GLP-1 and dual agonists on lipid metabolism also provide a more complete treatment plan for metabolic diseases. This article reviews the recent research progress of GLP-1 in lipid metabolism.

Abstract Necroptosis is a lytic form of regulated cell death reported to contribute to inflammatory diseases of the gut, skin and lung, as well as ischemic-reperfusion injuries of the kidney, heart and brain. However, precise identification of the cells and tissues that undergo necroptotic cell death in vivo has proven challenging in the absence of robust protocols for immunohistochemical detection. Here, we provide automated immunohistochemistry protocols to detect core necroptosis regulators – Caspase-8, RIPK1, RIPK3 and MLKL – in formalin-fixed mouse and human tissues. We observed surprising heterogeneity in protein expression within tissues, whereby short-lived immune barrier cells were replete with necroptotic effectors, whereas long-lived cells lacked RIPK3 or MLKL expression. Local changes in the expression of necroptotic effectors occurred in response to insults such as inflammation, dysbiosis or immune challenge, consistent with necroptosis being dysregulated in disease contexts. These methods will facilitate the precise localisation and evaluation of necroptotic signaling in vivo.

Abstract Macroautophagy is a process that cells engulf cytosolic materials by autophagosomes and deliver them to lysosomes for degradation. The biogenesis of autophagosomes requires ATG2 as a lipid transfer protein to transport lipids from existing membranes to phagophores. It is generally believed that endoplasmic reticulum is the main source for lipid supply of the forming autophagosomes; whether ATG2 can transfer lipids from other organelles to phagophores remains elusive. In this study, we identified a new ATG2A-binding protein, ANKFY1. Depletion of this endosome-localized protein led to the impaired autophagosome growth and the reduced autophagy flux, which largely phenocopied ATG2A/B depletion. A pool of ANKFY1 co-localized with ATG2A between endosomes and phagophores and depletion of UVRAG, ANKFY1 or ATG2A/B led to reduction of PI3P distribution on phagophores. Purified recombinant ANKFY1 bound to PI3P on membrane through its FYVE domain and enhanced ATG2A-mediated lipid transfer between PI3P-containing liposomes. Therefore, we propose that ANKFY1 recruits ATG2A to PI3P-enriched endosomes and promotes ATG2A-mediated lipid transfer from endosomes to phagophores. This finding implicates a new lipid source for ATG2A-mediated phagophore expansion, where endosomes donate PI3P and other lipids to phagophores via lipid transfer.

Generating novel molecules to satisfy specific properties is a challenging task in modern drug discovery, which requires the optimization of a specific objective based on satisfying chemical rules. Herein, we aim to optimize the properties of a specific molecule to satisfy the specific properties of the generated molecule. The Matched Molecular Pairs (MMPs), which contain the source and target molecules, are used herein, and logD and solubility are selected as the optimization properties. The main innovative work lies in the calculation related to a specific transformer from the perspective of a matrix dimension. Threshold intervals and state changes are then used to encode logD and solubility for subsequent tests. During the experiments, we screen the data based on the proportion of heavy atoms to all atoms in the groups and select 12365, 1503, and 1570 MMPs as the training, validation, and test sets, respectively. Transformer models are compared with the baseline models with respect to their abilities to generate molecules with specific properties. Results show that the transformer model can accurately optimize the source molecules to satisfy specific properties.

With the rapid development of the electric vehicle (EV) industry, various issues, such as the suboptimal deployment of charging facilities and inadequate distribution, hinder improvements in user satisfaction with EVs. Moreover, the interests of multiple stakeholders, including power grid companies and transportation departments, are not sufficiently addressed. To tackle these challenges, this paper proposes a comprehensive evaluation method for EV charging network service quality that integrates user satisfaction. First, considering the coupled dynamics of the EV charging network, we construct a service quality evaluation index system. Then, using a combination of the entropy weight method (EWM) and the analytic hierarchy process (AHP) with game theory, we determine the composite weights for the index system. Finally, we use the improved R-method evaluation model to theoretically verify the charging network in a particular area. The results show that the evaluation method proposed in this paper realizes differentiated evaluation for different planning schemes, highlights the weak links in the operation process of the charging network, and provides a theoretical reference for decision making about charging station planning.

Summary: Leptomeningeal metastases (LMs) remain a devastating complication of non-small cell lung cancer (NSCLC), particularly following osimertinib resistance. We conducted single-cell RNA sequencing on cerebrospinal fluid (CSF) from EGFR-mutant NSCLC with central nervous system metastases. We found that macrophages of LMs displayed functional and phenotypic heterogeneity and enhanced immunosuppressive properties. A population of lipid-associated macrophages, namely RNASE1_M, were linked to osimertinib resistance and LM development, which was regulated by Midkine (MDK) from malignant epithelial cells. MDK exhibited significant elevation in both CSF and plasma among patients with LMs, with higher MDK levels correlating to poorer outcomes in an independent cohort. Moreover, MDK could promote macrophage M2 polarization with lipid metabolism and phagocytic function. Furthermore, malignant epithelial cells in CSF, particularly after resistance to osimertinib, potentially achieved immune evasion through CD47-SIRPA interactions with RNASE1_M. In conclusion, we revealed a specific subtype of macrophages linked to osimertinib resistance and LM development, providing a potential target to overcome LMs.

In this paper, an experimental and theoretical study on the uniaxial tensile behavior of a fiber-reinforced polymer (FRP) grid-engineered cementitious composite (ECC) composite layer is described. Based on the test parameters, including the ECC material properties, the ECC cross-sectional area and the FRP grid cross-sectional area, three groups of ECC specimens and six groups of composite layer specimens were tested to reveal their joint action mechanism and tensile behavior. The experimental results showed two failure modes: fibers were pulled off or pulled out in the ECC specimens, and in the composite layer specimens, part of the fiber bars in the FRP grid was pulled off. The stress–strain curves show three stages: the elastic stage, softening stage and hardening stage. Due to the reinforcement of the FRP grid, the ultimate stress of the composite layer increased significantly compared with that of the ECC specimens. The composite layer softening strain was primarily related to the ECC softening strain, and the ultimate strain was primarily related to the FRP grid fracture strain. Moreover, a three-stage model was proposed to predict the stressstrain relationship, exhibiting a higher prediction accuracy and smaller dispersion compared with the three existing models.

The 2H‐MoTe2 is a well‐known layered 2D semiconductor that is considered as a promising material for next‐generation microelectronic and optoelectronic devices. Te‐deficiency‐induced defective structures, like Te vacancy and mirror twin boundary (MTB), would be generated at elevated temperatures. However, the temperature‐dependent evolution of such defects and their influence on the macroscopic electrical transport property of 2H‐MoTe2 is unclear. Herein, the semiconductor–metal transition phenomenon in 2H‐MoTe2−x mediated by the evolving disordered MTB network with increasing Te deficiency is reported on. The samples are grown by molecular beam epitaxy, while the Te deficiency is tuned by post‐growth flash annealing in ultra‐high vacuum. Low‐temperature scanning tunneling microscope investigation discloses the medium‐range disorder evolution of the MTB network incorporated in the 2H‐MoTe2, which eventually transforms to an ordered metallic Mo5Te8 metastable phase. The scanning tunneling spectroscopy shows rich in‐gap states localized at the MTBs, which provide a conducting channel in the semiconductor. The ultra‐high vacuum in situ transport measurement shows a gradual decrease of resistance of the sample upon flash annealing from 50 to 480 °C, confirming the influence of Te deficiency on the transport property, which would play an essential role in the device performance and durability.

Abstract Ir(III) carbene complexes have been explored as one of the best blue phosphors for their high performance. Herein, the authors designed and synthesized a series of blue‐emitting Ir(III) phosphors (f‐ct9a–c), featuring fac‐coordinated cyano‐imidazo[4,5‐b]pyridin‐2‐ylidene cyclometalates. These Ir(III) complexes exhibit true‐blue emission with a peak maximum spanning 448–467 nm, with high photoluminescence quantum yields of 81–88% recorded in degassed toluene. Moreover, OLED devices bearing phosphors f‐ct9a and f‐ct9b deliver maximum external quantum efficiencies (EQEmax) of 25.9% and 30.3%, together with Commission Internationale de L'Eclairage (CIEx,y) coordinates of (0.157, 0.225) and (0.142, 0.169), respectively. Remarkably, the f‐ct9b‐based device displays an incredible EQE of 29.0% at 5000 cd·m−2. The hyper‐OLED device based on f‐ct9b and ν‐DABNA exhibits an EQEmax of 34.7% and CIEx,y coordinates of (0.122, 0.131), affirming high potentials in achieving efficient blue electroluminescence.

The increasing demand for immune cell applications, both in clinical settings and in research laboratories, has highlighted the critical need for cryopreservation (banking) methods for T cells. While conventional techniques such as freezing with liquid nitrogen remain prevalent, they pose significant challenges including high equipment costs, safety considerations, and logistical hurdles in transportation. Our cryopreservation medium, C80EZ®, represents a novel approach, leveraging biocompatible polysaccharides as cryoprotectants to enable safe storage at −80°C. This paper presents a comprehensive series of tests assessing the effectiveness of C80EZ® in shielding T cells from the detrimental effects of cryopreservation. Importantly, our findings demonstrate that C80EZ® not only ensures the survival of T cells, with a particular emphasis on preserving the CD8+ subsets, but also maintains their critical function in targeting and eliminating cancer cells.

The principle and application of oil shale in-situ mining technology are reviewed. Oil shale is rich in resources and is an important supplement to petroleum and natural gas as a source of energy. Currently, ground retorting technology is the primary way to produce shale oil because of its elaborated processing method and equipment. However, this technology has several disadvantages such as low thermal efficiency, high environmental pollution and inability to exploit deeply buried oil shale. A novel method called oil shale in-situ mining technology is efficient and environmentally friendly. This technology can exploit deep oil shale while reducing pollution and greenhouse effect. Based on the data collected, this paper summarizes the development of global underground in-situ oil shale mining technology, introduces four heating transfer principles and mathematical models of conduction heating, convection heating, radiation heating and combustion heating, clarifies the process flow, as well as presents advantages and disadvantages of different technologies. It provides a reference for the research of oil shale in-situ mining technology, and also looks into the technologyâs prospects for industrialization, integrating with greenization, information and intelligence.

Abstract Background Cell-free DNA (cfDNA) contains a large amount of molecular information that can be used for multi-cancer early detection (MCED), including changes in epigenetic status of cfDNA, such as cfDNA fragmentation profile. The fragmentation of cfDNA is non-random and may be related to cfDNA methylation. This study provides clinical evidence for the feasibility of inferring cfDNA methylation levels based on cfDNA fragmentation patterns. We performed whole-genome bisulfite sequencing and whole-genome sequencing (WGS) on both healthy individuals and cancer patients. Using the information of whole-genome methylation levels, we investigated cytosine–phosphate–guanine (CpG) cleavage profile and validated the method of predicting the methylation level of individual CpG sites using WGS data. Results We conducted CpG cleavage profile biomarker analysis on data from both healthy individuals and cancer patients. We obtained unique or shared potential biomarkers for each group and built models accordingly. The modeling results proved the feasibility to predict the methylation status of single CpG sites in cfDNA using cleavage profile model from WGS data. Conclusion By combining cfDNA cleavage profile of CpG sites with machine learning algorithms, we have identified specific CpG cleavage profile as biomarkers to predict the methylation status of individual CpG sites. Therefore, methylation profile, a widely used epigenetic biomarker, can be obtained from a single WGS assay for MCED.

Abstract In vascular networks, the transport across different vessel walls regulates chemical compositions in blood over space and time. Replicating such trans-wall transport with spatial heterogeneity can empower synthetic fluidic systems to program fluid compositions spatiotemporally. However, it remains challenging as existing synthetic channel walls are typically impermeable or composed of homogeneous materials without functional heterogeneity. This work presents a vascular network-inspired fluidic system (VasFluidics), which is functionalizable for spatially different trans-wall transport. Facilitated by embedded three-dimensional (3D) printing, elastic, ultrathin, and semipermeable walls self-assemble electrostatically. Physicochemical reactions between fluids and walls are localized to vary the trans-wall molecules among separate regions, for instance, by confining solutions or locally immobilizing enzymes on the outside of channels. Therefore, fluid compositions can be regulated spatiotemporally, for example, to mimic blood changes during glucose absorption and metabolism. Our VasFluidics expands opportunities to replicate biofluid processing in nature, providing an alternative to traditional fluidics.

Abstract Background Despite major advancements, effective treatment for patients with SMARCB1-deficient cancers has remained elusive. Here, we report the first case of a SMARCB1-deficient undifferentiated carcinoma in the rectum expressing high PD-L1 and responding to a PD-1 inhibitor, as well as with low tumor mutation burden (TMB), proficient mismatch repair (MMR) and BRAF V600E mutation. Case presentation A 35-year-old man visited our hospital complaining of increased defecation frequency, bloody stools and weight loss of 3 kg for one month. Colonoscopy revealed an ulcerated and irregular mass approximately 8–12 cm from the anus. Surgical resection was performed. Histopathological findings revealed that the tumor cells had poor connectivity with each other; each cell had eosinophilic cytoplasm and a polymorphic nucleus. Brisk mitotic activity and necrosis were frequently observed in the tumor cells. Immunohistochemical examination showed that the tumor cells were negative for SMARCB1. The tumor proportion score (TPS) of PD-L1 (22C3) expression was 95%, and the combined positive score (CPS) was 100; the tumor was mismatch repair (MMR) proficient. Next-generation sequencing showed a low tumor mutation burden (TMB), as well as the BRAF V600E mutation. The final diagnosis was SMARCB1-deficient undifferentiated carcinoma. Chemotherapy was useless in this case. His tumor recurred during chemotherapy, and he then received targeted therapy with tirelizumab, an inhibitor of PD-1. At present, his general condition is good. A recent computed tomography (CT) scan showed that the tumor had disappeared, indicating that the immunotherapy was effective. Astonishingly, his most recent follow-up was in August, and his condition continued to improve with the tumor has disappeared. Conclusion SMARCB1‑deficient undifferentiated carcinoma in the rectum is extremely rare, and it has aggressive histological malignancy and poor progression. The observed response to PD-1 inhibitors suggests a role for prospective use of SMARCB1 alterations as a predictive marker for immune checkpoint blockade.

Canine coronavirus (CCoV), canine respiratory coronavirus (CRCoV), canine adenovirus type 2 (CAV-2), and canine norovirus (CNV) are important pathogens for canine viral gastrointestinal and respiratory diseases. Especially, co-infections with these viruses exacerbate the damages of diseases. In this study, four pairs of primers and probes were designed to specifically amplify the conserved regions of the CCoV M gene, CRCoV N gene, CAV-2 hexon gene, and CNV RdRp gene. After optimizing different reaction conditions, a quadruplex RT-qPCR was established for the detection of CCoV, CRCoV, CAV-2, and CNV. The specificity, sensitivity, and repeatability of the established assay were evaluated. Then, the assay was used to test 1688 clinical samples from pet hospitals in Guangxi province of China during 2022–2024 to validate its clinical applicability. In addition, these samples were also assessed using the reported reference RT-qPCR assays, and the agreements between the developed and reference assays were determined. The results indicated that the quadruplex RT-qPCR could specifically test only CCoV, CRCoV, CAV-2, and CNV, without cross-reaction with other canine viruses. The assay had high sensitivity with limits of detection (LODs) of 1.0 × 102 copies/reaction for CCoV, CRCoV, CAV-2, and CNV. The repeatability was excellent, with intra-assay variability of 0.19–1.31% and inter-assay variability of 0.10–0.88%. The positivity rates of CCoV, CRCoV, CAV-2, and CNV using the developed assay were 8.59% (145/1688), 8.65% (146/1688), 2.84% (48/1688), and 1.30% (22/1688), respectively, while the positivity rates using the reference assays were 8.47% (143/1688), 8.53% (144/1688), 2.78% (47/1688), and 1.24% (21/1688), respectively, with agreements of more than 99.53% between two methods. In conclusion, a quadruplex RT-qPCR with high sensitivity, specificity, and repeatability was developed for rapid, and accurate detection of CCoV, CRCoV, CAV-2, and CNV.

Generally, essential genes identified using shRNA and CRISPR are not always the same, raising questions about the choice between these two screening platforms. To address this, we systematically compared the performance of CRISPR and shRNA to identify essential genes across different gene expression levels in 254 cell lines. As both platforms have a notable false positive rate, to correct this confounding factor, we first developed a graph-based unsupervised machine learning model to predict common essential genes. Furthermore, to maintain the unique characteristics of individual cell lines, we intersect essential genes derived from the biological experiment with the predicted common essential genes. Finally, we employed statistical methods to compare the ability of these two screening platforms to identify essential genes that exhibit differential expression across various cell lines. Our analysis yielded several noteworthy findings: (1) shRNA outperforms CRISPR in the identification of lowly expressed essential genes; (2) both screening methodologies demonstrate strong performance in identifying highly expressed essential genes but with limited overlap, so we suggest using a combination of these two platforms for highly expressed essential genes; (3) notably, we did not observe a single gene that becomes universally essential across all cancer cell lines.

Adversarial attacks on Graph Neural Networks (GNNs) have emerged as a significant threat to the security of graph learning. Compared with Graph Modification Attacks (GMAs), Graph Injection Attacks (GIAs) are considered more realistic attacks, in which attackers perturb GNN models by injecting a small number of fake nodes. However, most existing black-box GIA methods either require comprehensive knowledge of the dataset and the ground-truth labels or a large number of queries to execute the attack, which is often unfeasible in many scenarios. In this paper, we propose an unsupervised method for leveraging the rich knowledge contained in the graph data themselves to enhance the success rate of graph injection attacks on the initial query. Specifically, we introduce GraphContrastive Learning-based Graph Injection Attack (GCIA), which consists of a node encoder, a reward predictor, and a fake node generator. The Graph Contrastive Learning (GCL)-based node encoder transforms nodes for low-dimensional continuous embedding, the reward predictor acts as a simplified surrogate for the target model, and the fake node generator produces fake nodes and edges based on several carefully designed loss functions, utilizing the node encoder and reward predictor. Extensive results demonstrate that the proposed GCIA method achieves a first query success rate of 91.2% on the Reddit dataset and improves the success rate to over 99.7% after 10 queries.

Atom manipulation by scanning tunneling microscopy was used to construct quantum dots on the InAs(111)A surface. Each dot comprised six ionized indium adatoms. The positively charged adatoms create a confining potential acting on surface-state electrons, leading to the emergence of a bound state associated with the dot. By lining up the dots into N-dot chains with alternating tunnel coupling between them, quantum-dot molecules were constructed that revealed electronic boundary states as predicted by the Su-Schrieffer-Heeger (SSH) model of one-dimensional topological phases. Dot chains with odd N were constructed such that they host a single end or domain-wall state, allowing one to probe the localization of the boundary state on a given sublattice by scanning tunneling spectroscopy. We found probability density also on the forbidden sublattice together with an asymmetric energy spectrum of the chain-confined states. This deviation from the SSH model arises because the dots are charged and create a variation in on-site potential along the chain—which does not remove the boundary states but shifts their energy away from the midgap position. Our results demonstrate that topological boundary states can be created in quantum-dot arrays engineered with atomic-scale precision.

As the penetration rate of electric vehicles (EVs) increases, how to reasonably distribute the ensuing large charging load to various charging stations is an issue that cannot be ignored. This problem can be solved by developing a suitable charging guidance strategy, the development of which needs to be based on the establishment of a realistic EV charging behaviour model and charging station queuing system. Thus, in this paper, a guidance and pricing strategy for fast charging that considers different types of EV users’ willingness to charge is proposed. Firstly, the EVs are divided into two categories: private cars and online ride-hailing cars. These categories are then used to construct charging behaviour models. Based on this, a charging decision model for EV users is constructed. At the same time, a first-come-first-served (FCFS) charging station queuing system is constructed to model the real-time charging situation in the charging station in a more practical way. Finally, a dynamic tariff updating model is used to obtain the optimal time-of-use tariff for each charging station, and then the tariffs are used to guide the fast-charging demand. By comparing the spatial and temporal distribution of charging demand loads at charging stations under different scenarios and considering whether the tariffs at each charging station play a guiding role, it is verified that the proposed strategy effectively optimises the balanced distribution of EV charging loads and alleviates the congestion at charging stations.

Canine coronavirus (CCoV) is an important gastrointestinal pathogen that causes serious harm to pet dogs worldwide. Here, 1791 clinical samples were collected from pet dogs in different pet hospitals in Guangxi Province, southern China, for the 2021–2024 period and detected for CCoV by a multiplex RT-qPCR. The results showed that 8.43% (151/1791) of samples were positive for CCoV. Sixty-five positive samples were selected to amplify, sequence, and analyze S, M, and N genes. A sequence comparison revealed that the nucleotide and amino acid similarities of the S, M, and N genes were 94.86% and 94.62%, 96.85% and 97.80%, and 96.85% and 97.80%, respectively. Phylogenetic analysis indicated that 65 CCoV strains obtained in this study belonged to the CCoV-II genotype, of which 56 CCoV strains belonged to the CCoV-IIa subtype and 9 CCoV strains belonged to the CCoV-IIb subtype. A potential recombination event analysis of S gene sequences indicated that two CCoV strains, i.e., GXBSHM0328-34 and GXYLAC0318-35, have recombination signals. A Bayesian analysis indicated that the evolutionary rates of the S, M, and N genes were 1.791 × 10−3, 6.529 × 10−4, and 4.775 × 10−4 substitutions/site/year, respectively. The population size grew slowly before 1980 and then began to shrink slowly; it then shrank rapidly in 2005 and expanded sharply in 2020, leveling off thereafter. These results indicated the CCoV strains prevalent in Guangxi Province, southern China, showed a high level of genetic diversity and maintained continuous variation among clinical epidemic strains.

Feline coronavirus (FCoV), as one of the important pathogens of feline viral gastroenteritis, has been attracting great attention. A total of 1869 rectal and nasal swabs, feces, and ascites samples were collected from eight regions in Guangxi province during 2021–2024. The multiplex RT-qPCR established in our laboratory was used to test these samples for FCoV, and 17.66% (330/1869) of the samples were positive for FCoV. The S, M, and N genes of 63 FCoV-positive samples were amplified and sequenced, and the genetic and evolutionary characteristics were analyzed. Similarity analysis showed that the nucleotide and amino acid homologies of S, M, and N genes were 81.2–99.6% and 70.2–99.5%, 89.9–100% and 91.6–100%, and 90.1–100% and 91.5–100%, respectively. Phylogenetic analysis revealed that all 63 FCoV strains, based on S gene sequences, belonged to type I FCoV (FCoV-I), and were clustered with Chinese strains and the Netherlands UU strains. Recombinant signals were detected in the S gene of strains GXLZ03-2022, GXLZ08-2022, and CCoV GD/2020/X9. The results suggest that FCoV is still prevalent in the Guangxi province of southern China, and the prevalent FCoV strains show high genetic diversity and novel epidemic characteristics.