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Achieving "dual carbon" targets by containing carbon emissions while sustaining economic growth is challenging. This study examines the varying carbon dependency levels among China's 30 provincial-level administrative units, considering spatial correlations in emissions. Using a semi-parametric varying coefficient spatial autoregressive panel model on 2004–2019 panel data, this study shows the following: (i) The relationship between economic growth and carbon emissions forms an "S"-shaped curve, with the contribution decreasing as tertiary industry grows, defining three stages of carbon dependency. (ii) There is significant heterogeneity in carbon dependency across provinces, with some advancing to "weak dependency" or an "economic carbon peak" due to advantages and policies. (iii) Dependency levels shift over time, with "weak dependency" being the predominant stage, though transitions occur. (iv) A positive spatial spillover effect in emissions was noted. This study recommends tailored policies for each provincial-level administrative unit based on their carbon dependency and development stage. [ABSTRACT FROM AUTHOR]

In this paper, we introduce a new class of heterogeneous spatial autoregressive models (heterogeneous SAR models) where the variance parameters are modeled in terms of covariates. In order to estimate the model parameters, as well as their corresponding standard error estimates, we proposed a computational efficient MCMC method which combines the Gibbs sampler with Metropolis-Hastings algorithm. The proposed estimate method is illustrated through numerous simulations and is applied to the Boston housing data. [ABSTRACT FROM AUTHOR]

Myostatin is a transforming growth factor-β family member that normally acts to limit skeletal muscle growth. Myostatin gene (MSTN) knockout (KO) mice show possible effects for the prevention or treatment of metabolic disorders such as obesity and type 2 diabetes. We applied chromatography and mass spectrometry based metabonomics to assess system-wide metabolic response of heterozygous MSTN KO (MSTN+/-) swine. Most of the metabolic data for MSTN+/- swine were similar to the data for wild type (WT) control swine. There were, however, metabolic changes related to fatty acid metabolism, glucose utilization, lipid metabolism, as well as BCAA catabolism caused by monoallelic MSTN depletion.The statistical analyses suggested that: (1) most metabolic changes were not significant in MSTN+/- swine compared to WT swine; (2) only a few metabolic properties were significantly different between KO and WT swine, especially for lipid metabolism. Significantly, these minor changes were most evident in female KO swine and suggested differences in gender sensitivity to myostatin.

Logistic mixed-effects models are widely used to study the relationship between the binary response and covariates for longitudinal data analysis, where the random effects are typically assumed to have a fully parametric distribution. As this assumption is likely limited or unreasonable in a multitude of practical researches, a semiparametric Bayesian approach for relaxing it is developed in this paper. In the context of binomial distribution logistic mixed-effects models, a general Bayesian framework is presented in which a semiparametric hierarchical modelling with an approximate truncated Dirichlet process prior distribution is specified for the random effects. The stick-breaking prior and the blocked Gibbs sampler using Pólya-Gamma mixture are employed to efficiently sample in the posterior analysis. Besides, a procedure calculating DIC for Bayesian model comparison is addressed. The methodology is demonstrated through simulation studies and a real example. [ABSTRACT FROM AUTHOR]