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Immunoinformatics-based design of artificial chimeric proteins as universal vaccine candidates against foot-and-mouth disease virus serotypes A, O, and SAT2
Abstract Foot-and-mouth disease virus (FMDV) remains a major constraint to livestock health due to its high mutation rate and serotype diversity. Currently, FMDV vaccines, primarily inactivated whole-virus formulations, have significant limitations, including limited cross-protection, high production costs, and potential biosafety risks. To address the need for broad-spectrum protection, this study aimed to design a universal vaccine candidate by rationally constructing artificial chimeric proteins (ACPs) integrating conserved structural (VP1–VP3) and non-structural (3 A, 3 C) proteins from the predominant Egyptian FMDV serotypes A, O, and SAT 2. Three-dimensional modeling via AlphaFold3 and Swiss-Model confirmed the high structural quality of the constructs, with the ACP2 candidate exhibiting superior stability and reliability metrics (TM-score > 0.95, RMSD < 0.5, and overall quality > 88). Functional annotation revealed three conserved domains critical for virion assembly, receptor interaction, and host immune activation. Immunoinformatics analysis identified a robust antigenic profile for ACP1 and ACP2 proteins, comprising (21 and 36) cytotoxic T-lymphocyte (CTL), (18 and 20) helper T-lymphocyte (THL), and (15 and 19) B-cell epitopes prioritized for conservancy and population coverage. Based on these epitopes, three multiepitope vaccine constructs were assembled and analyzed computationally. Molecular docking demonstrated strong and stable binding affinities between the vaccine constructs and bovine TLR9 and TLR4 receptors (lowest binding energies of − 19.4 and − 16.9 kcal/mol, respectively), supported by stable interactions in 100 ns molecular dynamics simulations. These findings highlight the ACP2 construct as a novel, structurally stable, and highly immunogenic candidate capable of eliciting cross-serotype protection. The study provides a translational blueprint for a universal recombinant FMDV vaccine, warranting immediate in vitro expression and in vivo validation.
Kernel embeddings and the separation of measure phenomenon
We prove that kernel covariance embeddings lead to information-theoretically perfect separation of distinct continuous probability distributions. In statistical terms, we establish that testing for the equality of two nonatomic (Borel) probability measures on a locally compact uncountable Polish space is equivalent to testing for the singularity between two centered Gaussian measures on a reproducing kernel Hilbert space. The corresponding Gaussians are defined via the notion of kernel covariance embedding of a probability measure, and the Hilbert space is that generated by the embedding kernel. Distinguishing singular Gaussians is structurally simpler from an information-theoretic perspective than nonparametric two-sample testing, particularly in complex or high-dimensional domains. This is because singular Gaussians are supported on essentially separate and affine subspaces. Our proof leverages the classical Feldman–Hájek dichotomy, and shows that even a small perturbation of a continuous distribution will be maximally magnified through its Gaussian embedding. This “separation of measure phenomenon” appears to be a blessing of infinite dimensionality, by means of embedding, with the potential to inform the design of efficient inference tools in considerable generality. The elicitation of this phenomenon also appears to crystallize, in a precise and simple mathematical statement, a core mechanism underpinning the empirical effectiveness of kernel methods.
AI and knowledge driven computation of rock mass characteristic parameters across engineering projects
Abstract With the continuous development of underground construction, represented by transportation tunnels, municipal utility tunnels and hydraulic tunnels, accurately perceiving the rock mass quality ahead of the tunnel boring machine (TBM) face has become critical for ensuring construction quality and improving construction efficiency. Over the past 5 years, predicting rock mass quality using machine learning and other artificial intelligence (AI) algorithms has gradually become a research hotspot. However, the raw TBM tunnelling data are massive and noisy, and key challenges remain, including how to reasonably select effective input features and how to cope with the limited data available from newly built projects. Although the academic community has proposed indices such as the TPI and FPI to quantify rock mass boreability and to incorporate them as input features, their application in practical engineering remains challenging. Owing to the complexity of tunnelling operating conditions and the variability in data quality, existing approaches exhibit notable limitations. In particular, physics-based “definition-based” methods typically require the processing of large volumes of high-frequency tunnelling data, and their computed results are highly sensitive to data fluctuations, resulting in poor stability. In contrast, “fitting-based” methods constructed on regression relationships, while showing reasonable effectiveness under single-project conditions, rely heavily on prior assumptions (e.g., penetration-based hypotheses of rock-breaking force) and are strongly influenced by fitting performance. As a result, these methods struggle to adapt to varying geological and engineering conditions, leading to limited generalizability and robustness in multi-project scenarios, and thus remain insufficient to support large-scale engineering applications. To address these issues, this study proposes an AI- and knowledge-driven method for computing rock mass characteristic parameters. Firstly, a rock-breaking data filtering method is developed based on the effective conversion of disc cutter energy. Engineering data from three TBM projects with different diameters and geological conditions are utilized, including the Yinchuo Water Diversion Project (YC), the Yinsong Water Diversion Project (YS), and the Huanbei Water Diversion Project (HB). Based on these datasets, this paper proposes an innovative methodology for identifying high-efficiency rock-breaking stages. Then, knowledge-driven rock mass characteristic parameters ( a , b , and the torque penetration index, TPI) are computed as input features. Lastly, an AI-driven strategy is then adopted to determine the prediction model, whereby CatBoost is selected to develop the rock mass class predictor and is tested across different projects. The results indicate that the proposed characteristic parameters are strongly correlated with rock mass quality. The prediction accuracies in the YC, YS and HB projects reach 85.60%, 86.48% and 88.89%, respectively, outperforming conventional methods overall. The proposed method provides new technical support for cross-project data utilization and real-time prediction of rock mass quality, and has important implications for construction safety and efficiency in newly built tunnel projects.
Discovery of dynamical heterogeneity in a supercooled magnetic monopole fluid
Dynamical heterogeneity, in which transitory local fluctuations occur in the conformation and dynamics of constituent particles, is widely hypothesized to be essential to the evolution of supercooled liquids into the structural glass state. Yet its microscopic spatiotemporal phenomenology is challenging to detect directly in molecular glass forming liquids. Because recent theoretical advances predict that corresponding dynamical heterogeneity could occur in supercooled magnetic monopole fluids (Proc. Nat. Acad. Sci. 112, 8549 (2015)), we searched for such phenomena in Dy 2 Ti 2 O 7 . By measuring its microsecond-resolved spontaneous magnetization fluctuations M ( t , T ) we detected a sharp bifurcation in monopole noise characteristics below T ≈ 1,500 mK , with the appearance of powerful spontaneous monopole current bursts. This intense dynamics emerges upon entering the supercooled monopole fluid regime, reaches maximum strength near T ≈ 750 mK and then collapses along with coincident loss of ergodicity approaching T g ≈ 250 mK . Moreover, when the four-point dynamical susceptibility χ 4 ( τ , T ) is determined directly from temperature dependence of correlations in M ( t , T ) , it evolves as predicted when dynamical heterogeneity is present, revealing its simultaneously and rapidly escalating length and time scales, ξ ( T ) and τ 4 ( T ) . This overall phenomenology greatly expands our empirical knowledge of supercooled monopole fluids and, more generally, demonstrates techniques for detection of the time sequence, magnitude, statistics, and correlations of dynamical heterogeneity, access to which may greatly accelerate fundamental vitrification studies.
Comparing surgical outcomes between the Jones and Wies procedures with lateral tarsal strip for involutional lower eyelid entropion
Advancing AI negotiations: A large-scale autonomous negotiation competition
We conducted an international AI negotiation competition in which participants designed and refined prompts for AI negotiation agents. We then facilitated over 180,000 negotiations between these agents across multiple scenarios with diverse characteristics and objectives. Our findings revealed that principles from human negotiation theory remain crucial even in AI–AI contexts. Surprisingly, warmth—a traditionally human relationship-building trait—was consistently associated with superior outcomes across all key performance metrics. Dominant agents, meanwhile, were especially effective at claiming value. Our analysis also revealed unique dynamics in AI–AI negotiations not fully explained by negotiation theory, including AI-specific technical strategies like chain-of-thought reasoning and prompt injection. When we applied natural language processing methods to the full transcripts of all negotiations, we found positivity, gratitude, and question-asking (associated with warmth) were strongly associated with reaching deals as well as objective and subjective value, whereas conversation lengths (associated with dominance) were strongly associated with impasses. The results suggest the need to establish a new theory of AI negotiation, which integrates classic negotiation theory with AI-specific negotiation theories to better understand autonomous negotiations and optimize agent performance.
Development of risk assessment models for microvascular complications in type 2 diabetes mellitus patients: a cross-sectional study
Structure of domain walls in chiral spin liquids
The chiral spin liquid is a canonical state of quantum spins combining topological and symmetry-breaking order, and possible experimental realizations have attracted growing interest. We examine the physics at interfaces between chiral spin liquid domains of opposite chirality. We show that a self-consistent mean-field description of spinons remains possible in the vicinity of a domain wall and use this to formulate a Ginzburg–Landau theory of the domain wall. The bulk of a chiral spin liquid contains gapped spinon excitations and gauge fluctuations, set by a finite spinon mass and a nonzero spinon Chern number. A third class of excitations consists of amplitude fluctuations of the spinon hoppings, which admit a geometric interpretation in terms of effective vielbein fields. These fluctuations are usually neglected because they are irrelevant for a homogeneous chiral spin liquid and are suppressed in standard large- N treatments. Going beyond the purely topological Chern–Simons limit, we incorporate these fluctuations into an effective field theoretic framework and show that they generate momentum-dependent corrections, including Chern–Simons-like linear terms and higher-order contributions, beyond the universal topological limit. We then analyze nontopological properties, including domain wall tension and edge velocity, and explain how they modify observables relative to the uniform case. These results connect measurable, nonuniversal quantities such as domain wall width, domain wall tension, and edge velocity to microscopic parameters and provide concrete targets for experiments.
Correction: Enhanced characterization of hydraulic conductivity via standard penetration test for sandy soils and weathered rocks
Nondisclosure agreements and externalities from silence
How do contractual restrictions on worker voice affect information flows about employers? We develop a framework in which the legal risk from violating a nondisclosure agreement (NDA) reduces the willingness of workers to share negative information, making it more difficult for high-road employers to differentiate themselves to workers. Empirical support for these ideas comes from studying the relationship between NDA use and the content of Glassdoor reviews after three states prohibited employers from using NDAs to conceal unlawful conduct. By curtailing the flow of negative information, NDAs impose negative externalities on workers who value such information and on competing employers who are less able to stand out.
Marine macroalgae-mediated ultrasonic-assisted green synthesis of CuO nanoparticles embedded in starch/PVA electrospun nanoscaffolds for in vitro α-amylase and α-glucosidase inhibitory activity
Virus-induced transgene- and tissue culture-free heritable genome editing in tomato
Genome editing has emerged as a powerful tool for genome manipulation and trait improvement in crops. However, most commonly used approaches rely on tissue culture and transgenic materials, which are time-consuming, labor-intensive, and often strongly genotype-dependent. Here, we developed a Tobacco rattle virus (TRV)-based system to deliver the compact ISYmu1 TnpB endonuclease, coupled with in planta shoot regeneration, to achieve somatic and heritable genome editing across different tomato cultivars without tissue culture. By targeting SlPDS , we successfully generated virus-free homozygous mutant progeny in a single generation. Furthermore, we extended this system to the functional analysis of the previously uncharacterized SlDA1 locus, revealing its involvement in organ size regulation, and recovered transgene-free SlDA1 mutants displaying enlarged fruits. Given the wide host range of TRV, our system should be broadly applicable for rapid, nontransgenic and less genotype-dependent heritable genome editing, thereby advancing both functional genomics and crop improvement.
Promoting shared decision-making in colorectal cancer screening in primary care: A cluster randomized controlled trial
Introduction In Switzerland, primary care physicians (PCP) prescribe colonoscopy for colorectal cancer (CRC) screening rather than offering a choice between colonoscopy and faecal occult blood test (FOBT). This study evaluated a training program promoting shared decision-making for CRC screening. Methods PCP from a research network were randomized 1:1 into intervention or control. The intervention group received study materials, patient decision aids, evidence summary, FOBT sample kit, and personalized feedback on CRC screening practices. PCP documented CRC screening decisions of 40 consecutive patients (ages 50–75) four months post-intervention. The control group received no materials before data collection. Results Of 110 PCP randomized, 83 (76%) collected data on 3,171 patients (mean age 62, 50% women). PCP in the intervention group were more likely than controls to have at least one patient tested or planning FOBT (84% vs. 56%; unadjusted RR: 1.52; 95% CI: 1.13 to 2.04). In a sensitivity analysis restricted to 62 PCP who participated in a previous data collection, 72% (21/29) already met the primary outcome in the intervention group at baseline and 49% (16/33) in the control group (RR: 1.49; 95% CI: 0.98 to 2.28). When contrasting the change within PCP from the 2017 and 2018 data collection, there was no significant increase in proportion of PCP who met primary outcome between intervention and control group, while it might have increased the proportion of PCP already prescribing FOBT to prescribe it to more of their patients. Conclusion A mailed intervention increased FOBT prescriptions, but selection bias may have influenced results.
The impact of mental fatigue on drop landing injury risk and lower limb asymmetry in elite collegiate american football players
How nature discovers rare Turing islands: Exploration by common limit cycles
Turing patterns are a cornerstone of biological self-organization, yet their emergence typically requires finely tuned parameters occupying narrow regions of high-dimensional space. This poses a fundamental challenge: how can evolving biological systems reliably find and exploit such rare conditions? In this work, we propose that common biochemical limit cycles, such as those arising from genetic feedback loops, can act as natural explorers of Turing space. By coupling a reaction–diffusion system to an orbit that modulates some of its parameters, we show that the system can dynamically sweep through Turing-permissive regimes and generate transient spatial patterns. We use an entropy-based measure in Fourier space to quantify pattern formation and demonstrate how cycles enhance the detectability and robustness of Turing islands. We further explore how coupling to positional gradients increases reproducibility, suggesting a route from oscillatory dynamics to stable developmental programs. Our results highlight a powerful mechanism by which nature might bootstrap complex spatial structure from simple temporal motifs.
Spatial distribution and risk assessment of dengue incidence at district level across major climatic zones in India
Over the past decade, dengue incidence has been steadily increasing across the different climatic zones of India. The role of climatic variability in the spatial and temporal distribution of dengue at the district level across India is to be determined. District-level dengue incidence data from 2010 to 2022 were obtained from the National Centre for Vector-Borne Disease Control. Indian districts were categorized into eleven climatic zones based on the Köppen-Geiger climate classification scheme and subsequently grouped into three significant climatic zones (tropical, temperate and arid). Temporal trends were assessed using the Prais-Winsten regression model that accounted for serial autocorrelation, while climate zonal differences in annual incidence were evaluated using Kruskal-Wallis tests and pairwise comparisons. The global Moran’s I test was used to assess overall spatial autocorrelation, followed by Anselin’s local Moran’s I test to identify clustering and hotspots of dengue incidences at the district level. There is significant heterogeneity in dengue distribution across the districts in India. Prais-Winsten regression analysis shows the strongest upward trend in dengue incidence in polar tundra (ET) zone [AIR = 126.9%; p = 0.01], temperate, no dry season, hot summer (Cfa) zone [AIR: 94.8%; p = 0.01], and cold, no dry season, warm summer (Dfb) zone [AIR = 85.1%; p < 0.001], indicating a substantial intensification of dengue transmission even in cooler climatic regions. Kruskal-Wallis tests confirmed persistent and significant disparities between tropical, temperate, and arid regions. Spatial analysis revealed clustering (Global Moran’s I = 0.06, p < 0.001), with 31 high-incidence clusters concentrated primarily in the semi-arid regions of Punjab and Haryana, and humid regions of Tamil Nadu and Kerala. Overall, the identified clustering of high-incidence districts in semi-arid and humid regions, along with the upward trends in multiple climatic zones, highlights an urgent need to embed climate-sensitive planning, early warning systems, and geographically targeted vector-control measures into India’s dengue prevention framework.
Polymer-stabilized amorphous CuO–ZnO hybrid nanocomplex as a promising candidate for antimicrobial therapy and controlled drug delivery with molecular docking insights
In vivo binding by <i>Arabidopsis</i> SPLICING FACTOR 1 shifts 3′ splice-site choice, regulating circadian rhythms and immunity in plants
Alternative splicing expands proteome diversity and enables phenotypic plasticity across eukaryotes. In plants, mutations in spliceosomal components impair development and stress responses, but the molecular mechanisms remain unclear. Here, we define the molecular function of SPLICING FACTOR 1 (AtSF1) in Arabidopsis thaliana using individual-nucleotide resolution UV crosslinking and immunoprecipitation combined with RNA sequencing of sf1 mutants. We identify the in vivo branch point sequences bound by AtSF1 and delineate its RNA-binding landscape, revealing pervasive splicing defects dominated by aberrant 3′ splice-site selection. Structural comparison with human SF1 indicates that AtSF1 retains branch point recognition capacity but features a distinct domain organization, including a restructured C-terminal region absent in metazoans, suggesting a divergent RNA-binding mode that evolved to meet plant-specific splicing demands. AtSF1 targets are enriched for core circadian clock and defense genes, consistent with the long-period phenotype and immune-compromised phenotypes of sf1 mutants. Together, these findings establish that AtSF1 orchestrates alternative 3′ splice-site choice through intron binding and branch point recognition, coupling RNA processing with circadian and immune regulation in plants.
Indirect state-level estimation of sexual minority adolescent populations by sex, age, and race/ethnicity using random forests
Purpose Estimating population sizes of adolescents who identify as lesbian, gay, or bisexual (LGB) is important for addressing health needs and disparities. Most states have Youth Risk Behavior Surveys (YRBS) among high school students, but not all include an item about sexual identity. This study’s aim was to estimate the percentages of students identifying as LGB stratified by sex, age, and race and ethnicity where state data is incomplete. States where 2021 YRBS data are not available are outside the scope of this study. Methods We developed two random forests trained separately for each sex and evaluated the models’ performance in predicting percentages of respondents identifying as LGB by state and demographic strata. We then estimated percentages for states that did not include or have responses to the LGB identity question available in 2021. Results The random forests outperformed benchmark comparison models based on a simple logistic regression approach. Estimates of students who identify as LGB across demographic strata and states ranged 5%–30%. The estimated percentages for states that did not ask students about sexual identity fell within the same range. Conclusion Our approach to deriving state-level estimates of LGB students by sex, race and ethnicity, and age performs well and can be used to inform efforts to improve health and well-being of LGB youth.