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Background Recent research has established the correlation between gut microbiota and periodontitis via oral-gut axis. Intestinal dysbiosis may play a pivotal bridging role in extra-oral inflammatory comorbidities caused by periodontitis. However, it is unclear whether the link is merely correlative or orchestrated by causative mechanistic interactions. This two-sample Mendelian randomization (MR) study was performed to evaluate the potential bidirectional causal relationships between gut microbiota and periodontitis. Materials and Methods A two-sample MR analysis was performed using summary statistics from genome-wide association studies (GWAS) for gut microbiota (n = 18,340) and periodontitis (cases = 12,251; controls = 22,845). The inverse-variance weighted (IVW) method was used for the primary analysis, and we employed sensitivity analyses to assess the robustness of the main results. The PhenoScanner database was then searched for pleiotropy SNPs associated with potential confounders. In order to identify the possibly influential SNPs, we further conducted the leave-one-out analysis. Finally, a reverse MR analysis was performed to evaluate the possibility of links between periodontitis and genetically predicted gut microbiota alternation. Results 2,699 single nucleotide polymorphisms (SNPs) associated with 196 microbiota genera were selected as instrumental variables (IVs). IVW method suggested that order Enterobacteriales (OR: 1.35, 95% CI 1.10–1.66), family Bacteroidales S24.7group (OR: 1.22, 95% CI 1.05–1.41), genus Lachnospiraceae UCG008 (OR: 1.16, 95% CI 1.03–1.31), genus Prevotella 7 (OR: 1.11, 95% CI 1.01–1.23), and order Pasteurellales (OR: 1.12, 95% CI 1.00–1.26) may be associated with a higher risk of periodontitis, while genus Ruminiclostridium 6 may be linked to a lower risk (OR: 0.82, 95% CI 0.70–0.95). The sensitivity and heterogeneity analyses yielded no indication of horizontal pleiotropy or heterogeneity. Only the association between order Enterobacteriales and the likelihood of periodontitis remained consistent across all alternative MR approaches. In the reverse MR analysis, four microbiota genera were genetically predicted to be down-regulated in periodontitis, whereas two were predicted to be up-regulated. Conclusions The present MR analysis demonstrated the potential bidirectional causal relationships between gut microbiota and periodontitis. Our research provided fresh insights for the prevention and management of periodontitis. Future research is required to support the finding of our current study.

In the context of China's resolute advancement of dual carbon goals (carbon peaking and carbon neutrality), urban agglomerations emerge as pivotal areas for carbon emission mitigation due to their dense economic activities and rapid urbanization. Previous studies overlook regional disparities in carbon emission prediction, disregarding the variations and policy directives across different provinces or cities. Therefore, this study addresses the research gap by investigating synergistic strategies to foster regional carbon peaking within the Beijing-Tianjin-Hebei region. Employing a novel approach tailored to regional segmentation policies, we provide more accurate predictions reflecting real-world conditions and distinct policy landscapes. Meanwhile, we integrate carbon emission efficiency into our analysis, emphasizing the dual goals of emission reduction and quality economic growth. Our empirical investigation in the Beijing-Tianjin-Hebei region, utilizing the Super-SBM and extended STIRPAT models, reveals upward trends in carbon emission efficiency, with varying trajectories across cities. Scenario simulations informed by the \"14th Five-Year Plan\" demonstrate that under the green development scenario, carbon peaking accelerates, alongside enhanced efficiency, supporting long-term emission reduction. Moreover, we design seven regional synergy carbon peak strategies for scenario simulations to facilitate the rational layout of dual carbon policies for collaborative development. We find that synergistic strategies have proven more effective in reducing regional carbon emission and increasing efficiency than strategies focusing solely on economic development or energy conservation. This innovative finding emphasizes the necessity of comprehensive green development in the Beijing-Tianjin-Hebei region and provides strong evidence for policymakers. Our research contributes to targeted strategies for improving carbon emission efficiency and reducing emissions, emphasizing the importance of synergistic approaches for regional carbon reduction.

Gastrulation marks a pivotal stage in mammalian embryonic development, establishing the three germ layers and body axis through lineage diversification and morphogenetic movements. However, studying human gastrulating embryos is challenging due to limited access to early tissues. Here we show the use of spatial transcriptomics to analyse a fully intact Carnegie stage 7 human embryo at single-cell resolution, along with immunofluorescence validations in a second embryo. Employing 82 serial cryosections and Stereo-seq technology, we reconstructed a three-dimensional model of the embryo. Our findings reveal early specification of distinct mesoderm subtypes and the presence of the anterior visceral endoderm. Notably, primordial germ cells were located in the connecting stalk, and haematopoietic stem cell-independent haematopoiesis was observed in the yolk sac. This study advances our understanding of human gastrulation and provides a valuable dataset for future research in early human development. Guo and colleagues characterize an intact Carnegie stage 7 human embryo at single-cell resolution in a spatially resolved manner.

Objectives. To study the influence of electroacupuncture (EA) and moxibustion on the hippocampus astrocyte and microglia activation in the ulcerative colitis model and to evaluate the mitochondria activity. Methods. 2.5% dextran sodium sulfate-induced colitis mice were treated by EA or moxibustion. Intestinal pathological structure was observed by hematoxylin and eosin (H&E) staining; the expression of GFAP or S100b (markers for astrocyte), Iba-1 (a marker for microglia), and Mitofilin (a marker for mitochondria) in hippocampus was detected by immunofluorescence staining or western blot. Results. The results demonstrated that both EA and moxibustion could improve the morphology of distal colonic mucosal epithelia in DSS-induced colitis mice. Expression of GFAP in the hippocampus was significantly increased after EA or moxibustion treatment. The effects were further supported by WB results. Meanwhile, expression of mitofilin in the hippocampus CA1 and CA3 regions showed the same trend as that of GFAP. Expression of Iba-1 in the hippocampus showed no significant difference after EA or moxibustion treatment, while the state of microglia changed from resting in control mice to activated state in colitis mice. Conclusion. EA and moxibustion were able to modulate the activation of astrocyte, microglial, and mitochondria in the hippocampus area in the colitis model.

Many causal quantities are only partially identifiable due to the inherent missingness of potential outcomes, and the associated partial identification (PI) sets can be obtained by solving an optimal transport (OT) problem. Covariates often provide additional information about the potential outcomes and thus yield tighter PI sets, which can be obtained via conditional optimal transport (COT). However, COT-based PI set estimators are susceptible to the curse of dimensionality in the covariates and outcomes, which precludes the asymptotic normality and hinders statistical inference. In this paper, we exploit smoothness in the marginal densities of covariates and potential outcomes and develop a wavelet-based primal method for COT with multivariate outcomes and covariates. Moreover, for quadratic cost functions, we establish a stability result for COT and prove asymptotic normality of the proposed estimator. This characterization of the asymptotic distribution enables valid statistical inference for the partial identification set. Empirically, we validate the estimation and inference performance of our approach through numerical experiments in comparison with existing benchmarks.

We study the estimation of causal estimand involving the joint distribution of treatment and control outcomes for a single unit. In typical causal inference settings, it is impossible to observe both outcomes simultaneously, which places our estimation within the domain of partial identification (PI). Pre-treatment covariates can substantially reduce estimation uncertainty by shrinking the partially identified set. Recent work has shown that covariate-assisted PI sets can be characterized through conditional optimal transport (COT) problems. However, finite-sample estimation of COT poses significant challenges, primarily because the COT functional is discontinuous under the weak topology, rendering the direct plug-in estimator inconsistent. To address this issue, existing literature relies on relaxations or indirect methods involving the estimation of non-parametric nuisance statistics. In this work, we demonstrate the continuity of the COT functional under a stronger topology induced by the adapted Wasserstein distance. Leveraging this result, we propose a direct, consistent, non-parametric estimator for COT value that avoids nuisance parameter estimation. We derive the convergence rate for our estimator and validate its effectiveness through comprehensive simulations, demonstrating its improved performance compared to existing approaches.

Causal estimands can vary significantly depending on the relationship between outcomes in treatment and control groups, potentially leading to wide partial identification (PI) intervals that impede decision making. Incorporating covariates can substantially tighten these bounds, but requires determining the range of PI over probability models consistent with the joint distributions of observed covariates and outcomes in treatment and control groups. This problem is known to be equivalent to a conditional optimal transport (COT) optimization task, which is more challenging than standard optimal transport (OT) due to the additional conditioning constraints. In this work, we study a tight relaxation of COT that effectively reduces it to standard OT, leveraging its well-established computational and theoretical foundations. Our relaxation incorporates covariate information and ensures narrower PI intervals for any value of the penalty parameter, while becoming asymptotically exact as a penalty increases to infinity. This approach preserves the benefits of covariate adjustment in PI and results in a data-driven estimator for the PI set that is easy to implement using existing OT packages. We analyze the convergence rate of our estimator and demonstrate the effectiveness of our approach through extensive simulations, highlighting its practical use and superior performance compared to existing methods.

We study the estimation of causal estimand involving the joint distribution of treatment and control outcomes for a single unit. In typical causal inference settings, it is impossible to observe both outcomes simultaneously, which places our estimation within the domain of partial identification (PI). Pre-treatment covariates can substantially reduce estimation uncertainty by shrinking the partially identified set. Recently, it was shown that covariate-assisted PI sets can be characterized through conditional optimal transport (COT) problems. However, finite-sample estimation of COT poses significant challenges, primarily because, as we explain, the COT functional is discontinuous under the weak topology, rendering the direct plug-in estimator inconsistent. To address this issue, existing literature relies on relaxations or indirect methods involving the estimation of non-parametric nuisance statistics. In this work, we demonstrate the continuity of the COT functional under a stronger topology induced by the adapted Wasserstein distance. Leveraging this result, we propose a direct, consistent, non-parametric estimator for COT value that avoids nuisance parameter estimation. We derive the convergence rate for our estimator and validate its effectiveness through comprehensive simulations, demonstrating its improved performance compared to existing approaches.

The empirical Wasserstein projection (WP) distance quantifies the Wasserstein distance from the empirical distribution to a set of probability measures satisfying given expectation constraints. The WP is a powerful tool because it mitigates the curse of dimensionality inherent in the Wasserstein distance, making it valuable for various tasks, including constructing statistics for hypothesis testing, optimally selecting the ambiguity size in Wasserstein distributionally robust optimization, and studying algorithmic fairness. While the weak convergence analysis of the WP as the sample size \\(n\\) grows is well understood, higher-order (i.e., sharp) asymptotics of WP remain unknown. In this paper, we study the second-order asymptotic expansion and the Edgeworth expansion of WP, both expressed as power series of \\(n^-1/2\\). These expansions are essential to develop improved confidence level accuracy and a power expansion analysis for the WP-based tests for moment equations null against local alternative hypotheses. As a by-product, we obtain insightful criteria for comparing the power of the Empirical Likelihood and Hotelling's \\(T^2\\) tests against the WP-based test. This insight provides the first comprehensive guideline for selecting the most powerful local test among WP-based, empirical-likelihood-based, and Hotelling's \\(T^2\\) tests for a null. Furthermore, we introduce Bartlett-type corrections to improve the approximation to WP distance quantiles and, thus, improve the coverage in WP applications.

We develop a novel generative model to simulate vehicle health and forecast faults, conditioned on practical operational considerations. The model, trained on data from the US Army's Predictive Logistics program, aims to support predictive maintenance. It forecasts faults far enough in advance to execute a maintenance intervention before a breakdown occurs. The model incorporates real-world factors that affect vehicle health. It also allows us to understand the vehicle's condition by analyzing operating data, and characterizing each vehicle into discrete states. Importantly, the model predicts the time to first fault with high accuracy. We compare its performance to other models and demonstrate its successful training.