Abstract: Artificial intelligence is approaching a problem that cannot be reduced to the accumulation, retrieval, or representation of knowledge: heterogeneous bodies of knowledge must be assembled so that their local semantics can differ while their deeper structural relations remain operationally compatible. This paper proposes Artificial Science as a framework for this problem. Its central premise is that Integration Scienceidentifies portable mathematical structures across scientific domains, whereas Artificial Science converts such structural portability into executable mechanisms of knowledge transformation, composition, control, and stabilization. We develop the first unified model of this program throughgauge-symmetry knowledge assembly and formulate it as a theory of Big Knowledge Dynamics. The framework begins with a global-to-local geometric architecture in which knowledge contexts form a base space, structural symmetries act on internal representations, and connections, covariant derivatives, curvature, and holonomy govern transport across changing local frames. Within this architecture, four mechanism-specific sectors form a staged knowledge dynamics. provides the minimal model of covariant single-charge knowledge transport; provides a non-Abelian model of internally mediated multi-component assembly; supplies a doublet control architecture for externality-driven state transformation; and spontaneous symmetry breaking together with Higgs-like stabilization provides a model of locally stable yet adaptive rational organization. These sectors are not asserted to be ontologically identical to physical gauge theories. Their role is structural: each supplies a mathematically constrained interface that can be mapped into domain-specific knowledge systems and re-grounded in their own semantics. We combine these mechanisms into a staged assembly operator, A_BKD = S_stab ∘ X_SU(2) ∘ B_SU(3) ∘ T_U(1), representing transport, internal binding, externally induced transformation, and stabilization. The model separates representation change from content change, local curvature from global memory, internal mediation from external control, and stability from rigidity. Finally, we formulate a machine-facing layer in which formal objects are translated into computational objects, experimental manipulations, observable metrics, and falsifiable predictions. Gauge symmetry is thereby recast not as an analogy imported from physics but as a candidate structural technology for operational knowledge assembly. Artificial Science is proposed as the science of converting portable deep structures into executable and testable knowledge dynamics.

Abstract: Obesity is a heterogeneous chronic disease in which cardiometabolic and functional risks are influenced not only by total body fat but also by its anatomical distribution, ectopic fat accumulation, and skeletal muscle quality. Traditional anthropometric measurements, such as body mass index and waist circumference, fail to capture these biological differences. Point-of-care ultrasound provides a portable, radiation-free, repeatable, and widely available bedside tool for assessing multiple adipose and muscular compartments, yet a broadly standardized framework integrating these measurements into a multiorgan obesity phenotype remains lacking. We propose OBESUS (OBESity UltraSound), a pragmatic multiorgan point-of-care ultrasound framework for structured obesity phenotyping. To facilitate competency-based and stepwise implementation, OBESUS comprises two complementary tiers: OBESUS-Core evaluates abdominal subcutaneous adiposity, preperitoneal fat thickness, hepatic steatosis, and rectus femoris muscle quality, whereas OBESUS-Plus incorporates epicardial and perirenal adipose tissue as additional cardiorenal ectopic fat depots. Rather than generating a numerical score, the framework integrates these findings into four non-mutually exclusive ultrasound-derived phenotypes: metabolic, myosteatotic, epicardial adiposity, and perirenal adiposity. This narrative review describes the conceptual basis, standardized acquisition methodology, pragmatic reference thresholds, and potential clinical applications of OBESUS. The framework is intended to provide a foundation for prospective evaluation of its reproducibility, clinical validity, prognostic significance and potential utility for longitudinal monitoring.

Abstract: Traditional deterministic fish stock assessment methods, such as the swept-area and Kushnarenko techniques, show extreme volatility and numerical singularities under sparse data. This paper presents a multi-species stochastic Bayesian state-space algorithm that fuses heterogeneous catch data across species, years, and age cohorts into a fixed-shape NetCDF4 tensor built on a consensus reference field constructed from 8 deterministic methods, jointly models recruitment, mortality, and removal dynamics for several species through an age-structured cohort architecture, and embeds a discrete Heaviside threshold into the population-balance equations to represent age-dependent predation vulnerability. The model was fitted with the No-U-Turn Sampler in PyMC on a 10-year (2015–2024) dataset for four species in the Kapchagay Reservoir, Kazakhstan. Kapchagay was selected as a representative single-reservoir case study given the material's scope; the broader four-reservoir programme (Zhaysan, Kapchagay, Balkhash, and Lake Samarkand) involves substantially more material and is addressed in separate work. The sampler achieved robust convergence for all species (R̂ ≤ 1.0047, ESS > 750), and comparison against alternative predation specifications evaluated by leave-one-out cross-validation (LOO-CV) showed that the choice of functional form had little effect on estimated stock (within approximately 1% for two of the three alternatives) but materially affected sampler efficiency, favoring the adopted threshold formulation on computational rather than biological grounds. A temporal hold-out test showed that forecasting accuracy two to three years ahead remains substantially lower than the in-sample fit (mean absolute percentage error 59–91%), a gap not resolved by correcting an identified age-1 initialization artifact and treated here as a central, unresolved limitation. The algorithm reconciles divergent deterministic stock estimates into a single state-estimation output that may inform, but does not by itself validate, total allowable catch analysis; both the biological necessity of the predation mechanism and multi-year forecasting accuracy remain open limitations for future work.

Abstract: Premature ovarian failure, resulting from the premature activation of follicles in animals, has been linked to excessive levels of 5α-dihydrotestosterone (DHT). Additionally, melatonin has been demonstrated to be autonomously synthesized and secreted by sheep follicles. However, it remains unclear whether melatonin can counteract excessive DHT in sheep ovaries to support normal follicular development. Here, we investigated the effects of DHT and melatonin on early folliculogenesis in ovine ovaries. Ovarian cortices (~2×2 mm) and secondary follicles (280–360 μm) were cultured for 4 days with high-dose DHT (10⁻⁷ M) and/or melatonin (10⁻⁹ M). H&E staining, TUNEL assay, Western blotting, and RT-qPCR were used to examine the underlying mechanisms. DHT increased the number and diameter of secondary follicles (p < 0.05), but also induced granulosa cell apoptosis, which melatonin alleviated. Mechanistically, DHT upregulated cleaved caspase-3 and caspase-3 expression in granulosa cells, whereas co-treatment with melatonin reduced the Bcl-2/Bax ratio. Using the MT1 inhibitor Luzindole, melatonin was shown to suppress DHT-stimulated granulosa cell proliferation via MT1-mediated Akt phosphorylation, thereby counteracting the DHT-induced decline in follicular reserve. This study elucidates the pathogenic mechanism of DHT-triggered premature ovarian insufficiency and validates the melatonin-mediated protective regulation of follicular development, which provides a theoretical basis for the improvement and protection of reproductive function in reserve breeding ewes.

Abstract: Background: Hospital units are commonly assessed by the pressure they face: census, patient acuity, admissions and staffing. These measures describe demand, but they do not show whether a unit can still convert that demand into coordinated action. Units with similar pressure profiles can perform very differently. Aim: To present RUISA™ (Roles, Users, Information, Situation, Actions) as a candidate operational coherence model and to state a testable hypothesis about how coordination deteriorates under pressure. Model: The paper does not propose a new theory of workload, patient flow or resilience. It offers a structured method for locating where the chain that converts demand into action becomes disconnected, and for how long. Four constructs are kept distinct: pressure, coherence, separation and persistence. Three propositions follow: pressure is not separation; separation is not failure; and persistent, unresolved separation under pressure is the hypothesised mechanism through which operational coherence deteriorates. Hypothesis: Persistent disruption of RUISA relationships will explain deterioration in operational readiness beyond conventional measures of census, acuity, staffing and workload. A comparison of nested prediction models is proposed as a test, together with the result that would count against the model. Conclusion: If supported, the model would give nursing and operations leaders a way to see not only how hard a unit is being pushed, but where its coordination is breaking and how long the break has lasted.

Abstract: Common buckwheat (Fagopyrum esculentum Moench) is considered phosphorus efficient, but its response to fertilizer regime in hot, arid, irrigated soils is poorly known. We tested whether a P-dominant NPK regime advances flowering and alters grain composition relative to balanced NPK and no fertilizer. In a drip-irrigated field trial in Jazan, Saudi Arabia (randomized complete block design, five blocks), we compared an unfertilized control (C), balanced NPK (F1; 40 kg N, 17.5 kg P, 33.2 kg K ha⁻¹), and P-dominant NPK (F2; 5.8 kg N, 13.1 kg P, 4.8 kg K ha⁻¹), applied by fertigation. The control reached 50% flowering 43.4 days after sowing, F1 after 39.0 days (10.1% earlier), and F2 after 35.2 days (18.9% earlier; p < 0.001). Vegetative traits and seed yield (0.37 to 0.49 t ha⁻¹) did not differ significantly. Grain K was 7.7% lower and crude fiber 12.6% lower under F1 than in the control, and grain Mn was 44.3% higher under F2 than under F1. Fertilizer regime therefore changed flowering time without changing growth or yield. F2 flowered earliest although it supplied less P than F1, which suggests that the N to P ratio matters as well as the P dose.

Abstract: This study develops a nonlinear, state-dependent framework for measuring and forecasting shock transmission across global grain markets. Monthly World Bank Pink Sheet prices for maize, U.S. hard red winter wheat, Thai 5% rice, soybeans, and soybean meal are combined with crude-oil returns, a fertilizer-price index, and the Oceanic Niño Index from January 1990 to December 2025. The proposed state-conditioned Bayesian additive regression-tree time-varying vector autoregression (SC-BART–TVP-VAR) treats energy, fertilizer, and climate conditions as lagged effect modifiers of a nonlinear grain-return system. Local posterior Jacobians translate the BART response surface into time-varying propagation matrices; generalized forecast-error variance decompositions then yield total, directional, and net connectedness without recursive ordering. A complementary linear VAR, rolling VAR, regime summaries, and chronologically separated out-of-sample forecasts provide transparent benchmarks. The linear full-sample total connectedness index is 33.44%, whereas the nonlinear local index averages 35.46% and ranges from 31.21% to 57.72%. Rice remains the most segmented market in the linear system, while soybean-related markets form the strongest bilateral transmission channel. High climate states are associated with greater rolling connectedness, but energy and fertilizer regimes do not generate uniformly larger spillovers. Out-of-sample BART forecasts yield lower RMSE than the linear VAR for all five commodities in the common holdout, although RF is slightly better for maize and none of the BART–VAR or BART–RF Diebold–Mariano comparisons are significant at the 5% level. The findings therefore support a state-dependent early-warning interpretation and competitive nonlinear forecasting rather than a claim of universal statistically significant superiority. The framework contributes a reproducible bridge between flexible Bayesian prediction, dynamic connectedness, and food-price surveillance, while retaining explicit safeguards against look-ahead bias and causal overstatement.

Abstract: University rankings publish positions and bands that readers may interpret as precise comparisons. We examine which comparisons the QS World University Rankings: Latin America & The Caribbean 2027 supports under imprecise weights and indicator data, and assess change using both the 2026 and 2027 editions. Nominal weights centre an epsilon-contamination credal set with an imprecise Dirichlet interpretation. Missing indicators enter as observed-range intervals, and one-decimal scores reflect rounding uncertainty. Closed-form necessary orders and sequential typing distinguish conflict, ignorance and resolution among 517 institutions. At ε=0.10, only 4.0% of 5115 near pairs (reference ranks at most 10 positions apart) have a necessary order, against 61.2% of all pairs. Conflict represents 99.3% of undecided neighbouring pairs; ignorance represents 55.6% overall. An eight-coalition factorial analysis and symmetric Shapley attribution allocate 70.1% of additional neighbouring indeterminacy to weights and 57.3% of additional overall indeterminacy to missing indicators. For 494 matched institutions, mixed-integer optimisation closes 1976 marginal rank-endpoint problems under edition-specific admissible weights. Median change-hull width falls from 365.5 positions under outer bounds to 294 under attainable endpoints; exact rational witnesses support both a rise and a fall for 493 institutions. These are model-based possibilities, rather than evidence of stability or institutional quality. The analysis combines necessary comparisons, attainable rank and change bounds, and complementary sequential and symmetric descriptions of indeterminacy. Its findings support reporting comparison precision and underlying indicator coverage alongside ranking positions.

Abstract: Background: Robotic-assisted spine surgery has evolved from mechanically guided pedicle screw placement into increasingly integrated platforms combining preoperative planning, intraoperative three-dimensional imaging, navigation, and robotic trajectory guidance. Although pedicle screw insertion remains the most extensively studied application, robotic technology is progressively being incorporated into minimally invasive fusion procedures, deformity surgery, trauma, revision surgery, and oncological reconstruction. The principal proposed benefits include improved instrumentation accuracy, reduced dependence on intraoperative fluoroscopy, reproducible execution of preplanned trajectories, and facilitation of minimally invasive workflows. However, uncertainty remains regarding its effect on patient-centered clinical outcomes, cost-effectiveness, radiation exposure, and superiority over contemporary navigation systems. Methods: A structured literature review was conducted to evaluate contemporary evidence concerning robotic-assisted thoracic and lumbar spinal instrumentation, as well as to assess potential future developments in this field. Comparative studies and systematic reviews/meta-analysis evaluating robotic-assisted techniques against free hand, fluoroscopy-guided, or comput-er-assisted navigation techniques were considered. Outcomes of interest included pedicle screw accuracy, intraoperative and postoperative screw revision, radiation exposure, operative time, blood loss, hospital length of stay, complications, patient-reported outcomes, learning curve, and economic considerations. Robotic platforms were additionally considered separately because differences between imaging, registration, mechanical guidance, and workflow may limit generalization of pulled results across systems. Results: The most consistent advantage of robotic-assisted spinal instrumentation concerns pedicle screw accuracy. Contemporary systematic reviews and meta-analysis generally demonstrate higher rates of optimal od clinically acceptable screw placement compared with conventional freehand or fluoroscopy-guided techniques, although the magnitude of benefit varies among robotic platforms, indications, and comparator techniques. Recent evidence also suggests that robotic assistance may provide an accuracy advantage over image-based navigation, however, differences in revision rates, operative time, blood loss, and complications are substantially less consistent. Radiation outcomes depend strongly on how exposure is defined. Robotic workflows can reduce occupational exposure to the surgical team by shifting imaging toward preoperative or intraoperative three-dimensional acquisition, but total patient radiation dose vary according to imaging modality, registration strategy, and institutional protocol. Evidence regarding operative duration is similarly heterogeneous and influenced by the learning curve and workflow maturity. Some studies report reductions in blood loss and length of stay, particularly when robotic assistance facilitates minimally invasive surgery, although these advantages cannot always be attributed to robotics independently of the operative approach. Evidence demonstrating superior long-term patient-reported outcomes, lower neurological complication rates, or reduced reoperation remains limited. High acquisition and maintenance cost, platform-specific workflows, registration errors, technical failure, and the need for dedicated team training remain important barriers to widespread implementation. Conclusions: Robotic assistance has become an established precision technology for spinal instrumentation, with the strongest evidence supporting improved pedicle screw placement accuracy. Its clinical value extends beyond accuracy through integration with navigation, advanced imaging, and minimally invasive surgical workflows, however, improved technical precision should not be equated automatically with superior patient outcomes. Future research should increasingly compare robotic systems with contemporary three-dimensional navigation rather than fluoroscopy alone and should prioritize standardized patient-centered outcomes, radiation dosimetry, workflow efficiency, long-term revision rates, and cost-effectiveness. Advances in artificial intelligence, automated surgical planning, augmented reality, and increasingly autonomous robotic functions my further expand the role of robotics from trajectory guidance toward comprehensive digital surgical platforms.

Abstract: Background: ACMG/AMP 2015 variant classification requires combining criteria of varying strength into a single five-tier class. The combining rules are specified, but in many tools the combining step is treated as a black box, and the resulting classifications cannot be inspected or reconstructed. This is a problem for clinical laboratories, which must defend classifications to accreditation bodies and ethics committees, and it is a more universal gap than the network-access constraints that motivate offline alternatives. Results: GenomicsOps is built around a strength-aware combining engine that implements the ACMG/AMP 2015 rules together with the ClinGen VCEP strength modifiers and the Tavtigian points-based extension. The combining engine achieves 95.8% concordance with curator classifications on 12,644 ClinGen ERepo variants when curator-supplied criteria are provided. This figure describes the combining step in isolation, not end-to-end accuracy. The criterion-extraction path that precedes combining is a partial automation: on average it recovers 68.9% of the criteria a human curator would report, with 92.5% agreement on a well-annotated subset of the evaluation corpus. Curators should expect to supplement its output. The pipeline runs fully offline and writes every classification to a SHA-256 hash-chained audit log. End-to-end accuracy on a user-supplied VCF is bounded by the criterion-extraction recall reported above and will be lower than the combining-only figure for variants carrying less structured annotation than the evaluation corpus. Conclusions: The central contribution is a reproducible implementation of the ACMG/AMP combining logic, evaluated on a large expert-curated corpus. Transparency of the classification record is an architectural property of the design, not an empirically validated benefit; a user study measuring the effect of criterion-level reporting on review behaviour is left as future work. Stratified analysis of the two combining paths shows that aggregate agreement (95.7% points-based vs 96.2% generic) conceals substantial subgroup differences: the choice of path affects per-class and per-criterion-count agreement by up to 10 percentage points in opposite directions, and the generic aggregate is dominated by a single panel.

Abstract: Background: Pulmonary fibrosis (PF) is a progressive interstitial lung disease characterized by high mortality rates and limited therapeutic options. Although Asiaticoside (AS) exhibits potent anti-fibrotic activity, its clinical potential is hindered by low bioavailability and insufficient pulmonary targeting. Methods: In this study, pH-responsive and charge-tunable AS-loaded liposomes were prepared using the thin-film hydration method and subsequently optimized. The liposomes were characterized in terms of their physicochemical properties, pH-triggered release, stability, and mucus-penetrating capability. Furthermore, cellular uptake by MH-S cells, in vivo biodistribution, and the anti-fibrotic efficacy and safety were evaluated in bleomycin-induced PF mice model following pulmonary administration. Results: The optimized neutral DPPC-based liposomes exhibited high encapsulation efficiency, excellent stability, pH-sensitive release, and enhanced mucus penetration with reduced macrophage uptake. These liposomes significantly inhibited fibroblast activation in vitro and attenuated lung injury, collagen deposition, and inflammatory cytokine levels in vivo, demonstrating superior therapeutic efficacy compared to Free AS, coupled with a favorable biosafety profile. Conclusions: This optimized pH-responsive, pulmonary-targeted liposomal system remarkably enhances the anti-fibrotic efficacy of AS and represents a promising nanodelivery strategy for the treatment of PF.

Abstract: Although actigraphy is widespread across disciplines to quantify human locomotor activity, its methodology remains unstandardized: the measurement parameters used to record acceleration and the methods used to compress it into epoch-level activity signals vary substantially across manufacturers. While we previously characterized the impact of the latter by emulating device-specific activity determination methods, the effects of acceleration sampling frequency, bit resolution, and full-scale range remain unclear. To systematically investigate these effects, we had to characterize the correlation-based relationships among activity determination methods during sleep and wakefulness to distinguish differences arising from their distinct motion characteristics from those introduced by reducing acceleration data quality. Then, by modifying measurement parameters to emulate different hardware settings, we found that parameter alterations had no effect over multiday and wakeful timeframes. Even when focusing on sleep periods, modifying the measurement parameters affected only a few activity determination methods, with the extent of these effects also influenced by the post-calibration of acceleration data. Overall, how activity is derived from acceleration has a substantially greater influence on the resulting activity signals than the measurement parameters used to record the underlying acceleration data, although high-fidelity acceleration collection remains particularly important for calibration-dependent activity determination methods in sleep-focused analyses.

Abstract: Traumatic brain injury (TBI) is a dynamic and diverse clinical phenomenon characterized by neurologic damage, post-traumatic seizures, impairment of functioning, and mortality. Conventional diagnostics may fail to provide a complete picture of the complicated interrelationship between factors relating to the clinical, imaging, neurophysiological, and treatment aspects. This study introduces a multimodal artificial intelligence clinical decision-support system (AI-CDSS) for TBI severity assessment, post-traumatic seizure occurrence forecasting, risk stratification, and individualized management aid. The model utilizes information on demographic and trauma-related factors, as well as medical history, computed tomography, electroencephalography data, treatment, and outcomes. The logistic regression, random forests, support vector machines, and extreme gradient boosting models were tested, and with the help of stratified cross-validation, the best model XGBoost demonstrated 92% accuracy, sensitivity, and specificity, as well as 92% precision, recall, and F1-score with the area under the ROC curve equal to 95% and Brier score equal to 82%. SHapley Additive exPlanations determined admission Glasgow coma scale, cortical contusion, intracranial bleeding, EEG abnormalities, type of Marshall CT class, pre-hospital seizures, age, and skull fractures as key predictors. The predicted probabilities of seizures were grouped into low (<20%), moderate (20–49%), and high (≥50%) risk categories, and the resulting probabilities were linked to evidence-based recommendations for follow-up, EEG evaluation, specialist referral, and re-evaluation. In spite of the fact that the proposed framework shows promise for personalized TBI management, the data that is currently reported still needs to be validated against patient data, needs to be verified externally, needs to be calibrated, evaluated prospectively, and assessed clinically before they can be employed in practice.

Abstract: Background: Tumor mutational burden (TMB) is widely used as a prognostic biomarker, but its prognostic direction in microsatellite-stable (MSS) cancers remains contradictory. We hypothesized that the prognostic direction of TMB is determined by the balance of the trinucleotide mutation spectrum (TMS) model—the degree to which protective and risk mutational pathways offset each other. Methods: We trained TMS models in four independent MSS cohorts (SYSUCC-COADREAD as discovery, N=665, DFS; TCGA-COAD, N=437, OS; TCGA-STAD, N=337, OS; TCGA-UCEC, N=400, OS). For each cohort, we stratified patients by TMS tertiles and, within the extreme strata, by stratum-specific median TMB. We tested the TMS×TMB interaction using Cox models and meta-analyzed the interaction coefficients. We quantified TMS model balance using |cor(TMB, TMS)|. Results: In balanced TMS models (SYSUCC: |cor|=0.05; TCGA-STAD: |cor|=0.43; TCGA-COAD: |cor|=0.16), TMB was protective in TMS-Low patients and harmful in TMS-High patients (SYSUCC: HR=0.61 vs 1.47, interaction LRT P=0.0014; TCGA-STAD: HR=0.84 vs 1.28, interaction LRT P=0.047; TCGA-COAD: HR=0.89 vs 1.15, P=0.71, limited by power). In the unbalanced model (TCGA-UCEC: |cor|=0.93), the interaction was not statistically significant (P=0.30), even after removing TMB-TMS collinearity (P=0.56). Meta-analysis showed a consistent interaction direction (pooled HR=1.81, 95% CI 1.29–2.55, P=0.0006; I²=0%). In 100 repeated 70:30 splits in SYSUCC, the primary HR was >1 in 99.0% of held-out test sets (median HR=3.67; C-index=0.643). Conclusions: The prognostic direction of TMB in MSS cancers is determined by the balance of the TMS model. In balanced models, TMB is protective in TMS-Low patients and harmful in TMS-High patients; the two effects cancel out in unstratified analysis. In unbalanced models, TMB systematically shifts the TMS score, masking the interaction. This framework provides a unified explanation for the contradictory TMB literature and offers a practical four-quadrant risk stratification approach using routine sequencing data.

Abstract: Intracellular microbes that deploy actin-based motility (ABM) as a virulence mechanism are phylogenetically diverse pathogens that cause a wide range of infections in animals and humans. These include bacteria that share pathogenic features such as the invasion of host cells, the rupture of intracellular compartments, and exploitation of host cytoskeletal proteins for cytosolic movement. The virulence factors that mediate ABM and the underlying molecular mechanisms differ substantially across genera and even within species when multiple proteins are encoded within a genome. Emerging evidence from these pathogens now reveals significant heterogeneity in bacterial actin-tail architecture, motility behaviors across distinct host-cell contexts, and susceptibility to immune restriction. This review provides an update on the recent advances in bacterial ABM, highlighting the emerging complexities of the interplay between host immunity and this important microbial virulence mechanism.

Abstract: We show that the Buzano–Dragomir inequality holds for octonionic Hilbert spaces. Using this generalization, we derive the Dragomir inequality for the octonionic numerical radius and an octonionic version of Deutsch entropic uncertainty principle.

Abstract: Voronov, Neopryatnaya and Dergachev constructed 5-chromatic unit-distance graphs on the sphere \( S^2(r_1), r_1=\cos\frac{\pi}{10} \), with \( 372 \) vertices, and on \( S^2(r_2) \), \( r_2=\cos\frac{3\pi}{10} \), with \( 972 \) vertices, and noted that the first graph stays 5-chromatic after deleting any single vertex. By SAT-based vertex minimization we find a 5-chromatic subgraph of the first graph with \( 231 \) vertices and \( 938 \) edges, and one of the second with \( 961 \) vertices and \( 4028 \) edges. Both are vertex-critical and contain no Moser spindle; the smaller one is the best found by our search, not a proved minimum. All coordinates are given exactly in nested radicals and verified in exact arithmetic; non-4-colourability is certified by DRAT proofs checked with DRAT-TRIM.

Abstract: Background/Objectives: Acute urticaria (AU) is generally self-limiting, but prospective data on its natural course and progression to chronic urticaria (CU) remain limited. To characterize the natural course of AU, determine the proportion of patients progressing to CU, and explore baseline clinical characteristics associated with subsequent progression. Methods: We conducted a prospective ob-servational study of 247 consecutive patients with physician-diagnosed AU presenting to a tertiary university emergency department. Patients were assessed at the index visit and followed at Day 15 ±1 and Day 45 ±1. Total disease duration was calculated from original symptom onset, and CU was defined as recurrent wheals and/or angioedema persisting for ≥6 weeks. Baseline associations with CU progression were explored with correction for multiple comparisons. Results: Overall, 229/247 patients (92.7%) achieved remission before 6 weeks and 18/247 (7.3%) progressed to CU. Among patients achieving remission, median episode duration was 7 days (IQR, 2–13); 78.6% and 96.1% achieved complete resolution within 14 and 28 days, respectively. Patients progressing to CU had longer symptom duration before emergency department assessment than those achieving remission (median, 144 vs 8 hours; FDR-adjusted p=0.001), although this reflected prior disease persistence and was not considered an independent prognostic factor. No other baseline characteristic remained significantly associated with CU progression after multiple-comparison correction. Conclusions: AU was predominantly self-limiting, with most patients achieving remission within the first weeks after symptom onset and 7.3% progressing to CU. These findings provide prospective data on the natural course of AU and the transition from acute to chronic disease. Further identification and validation of clinical and biological markers associated with disease persistence may support per-sonalized risk assessment and individualized follow-up.

Abstract: This exploratory pilot used targeted bisulfite sequencing to profile 16 imprinting control regions (ICRs) across 8 imprinted genes in 12 individuals from 4 families, testing whether ICR methylation carries molecular signals of two behavioral dimensions of innate predisposition. Personality traits are moderately heritable, yet their molecular carriers remain largely unknown. Within a dual-layer framework of innate predisposition and acquired personality, the author identified through long-term observation a set of highly reproducible non-Mendelian intergenerational transmission patterns, suggesting that innate predisposition may be linked to methylation at imprinting control regions. Using molecular features selected under the independently observed ABCD framework, the author found that the signals resided not in the average level of ICR methylation but in its internal spatial structure. The rationality dimension corresponded to methylation at specific KvDMR1 subregions (KCNQ1 difference r = +0.760), and the dominance dimension to the asymmetric methylation distribution within the PEG3 ICR (3′-half skewness r = +0.785). The two axes were nearly completely orthogonal at the epigenetic level (r = −0.038), and nearest-centroid classification matched behavioral labeling in 91.7% (11/12, full-sample resubstitution; exploratory finding).

Abstract: Modelled small-area population estimates are increasingly needed where census data are outdated, spatially coarse, incomplete or unavailable. These estimates are often produced at 100-m grids and aggregated to operational units totals, by combining population observations from surveys, health campaigns and partial censuses with satellite-derived settlement data and geospatial covariates. However, the effects of mapped number of buildings, spatial dependence and changes in spatial support on predictive performance and uncertainty remain insufficiently understood. We developed a Bayesian geospatial modelling framework comparing a Gamma people-per-building (PPB) model with a negative binomial (NB) population-count formulation. Both formulations incorporated hierarchical effects, geospatial covariates and a Matérn spatial field implemented in INLA-SPDE. A factorial simulation study, which evaluated the effects of spatial range, spatial variance and missingness, showed that prediction error increased with spatial variance and short-range spatial dependence, with the Gamma formulation producing higher precision and accuracy than the NB model. However, when applied to household listing datasets from five data sources across 2,285 Enumeration Areas (EAs) in Cameroon, both models produced similar 100-km spatial cross-validation RMSEs (1,293.6 and 1,294.4, respectively). Errors increased when predicting regions or data sources excluded from the training sets. After 100-m grid predictions were reaggregated to EA support, predicted-to-observed population ratios were 1.248 for Gamma and 1.388 for NB, with national total posterior population estimates of 29.20 million people (95% CrI: 25.71 – 33.07) and 33.21 million people (28.96 – 38.60), respectively. Similar EA-level predictive performance can mask important differences in geographic transferability, fine-scale downscaling and aggregation; thus, explicit evaluation of spatial support and model form uncertainty is therefore important for high-resolution population estimation. The framework provides a scalable computationally feasible approach to integrating heterogeneous sparse observations with geospatial information while propagating spatial and hierarchical uncertainty.