Synergizing Roughness Penalization and Basis Selection in Bayesian Spline Regression

Bayesian P-splines and basis determination through Bayesian model selection are both commonly employed strategies for nonparametric regression using spline basis expansions within the Bayesian framework. Despite their widespread use, each method has particular limitations that may introduce potential estimation bias depending on the nature of the target function. To overcome the limitations associated with each method while capitalizing on their respective strengths, we propose a new prior distribution that integrates the essentials of both approaches. The proposed prior distribution assesses the complexity of the spline model based on a penalty term formed by a convex combination of the penalties from both methods. The proposed method exhibits adaptability to the unknown level of smoothness, while achieving the minimax-optimal posterior contraction rate up to a logarithmic factor. We provide an efficient Markov chain Monte Carlo algorithm for implementing the proposed approach. Our extensive simulation study reveals that the proposed method outperforms other competitors in terms of performance metrics or model complexity

The art of BART: On flexibility of Bayesian forests

Considerable effort has been directed to developing asymptotically minimax procedures in problems of recovering functions and densities. These methods often rely on somewhat arbitrary and restrictive assumptions such as isotropy or spatial homogeneity. This work enhances theoretical understanding of Bayesian forests (including BART) under substantially relaxed smoothness assumptions. In particular, we provide a comprehensive study of asymptotic optimality and posterior contraction of Bayesian forests when the regression function has anisotropic smoothness that possibly varies over the function domain. We introduce a new class of sparse piecewise heterogeneous anisotropic H\"{o}lder functions and derive their minimax rate of estimation in high-dimensional scenarios under the $L_2$ loss. Next, we find that the default Bayesian CART prior, coupled with a subset selection prior for sparse estimation in high-dimensional scenarios, adapts to unknown heterogeneous smoothness and sparsity. These results show that Bayesian forests are uniquely suited for more general estimation problems which would render other default machine learning tools, such as Gaussian processes, suboptimal. Beyond nonparametric regression, we also show that Bayesian forests can be successfully applied to many other problems including density estimation and binary classification

Functional clustering methods for binary longitudinal data with temporal heterogeneity

In the analysis of binary longitudinal data, it is of interest to model a dynamic relationship between a response and covariates as a function of time, while also investigating similar patterns of time-dependent interactions. We present a novel generalized varying-coefficient model that accounts for within-subject variability and simultaneously clusters varying-coefficient functions, without restricting the number of clusters nor overfitting the data. In the analysis of a heterogeneous series of binary data, the model extracts population-level fixed effects, cluster-level varying effects, and subject-level random effects. Various simulation studies show the validity and utility of the proposed method to correctly specify cluster-specific varying-coefficients when the number of clusters is unknown. The proposed method is applied to a heterogeneous series of binary data in the German Socioeconomic Panel (GSOEP) study, where we identify three major clusters demonstrating the different varying effects of socioeconomic predictors as a function of age on the working status

Chloroplast and mitochondrial DNA editing in plants

Plant organelles including mitochondria and chloroplasts contain their own genomes, which encode many genes essential for respiration and photosynthesis, respectively. Gene editing in plant organelles, an unmet need for plant genetics and biotechnology, has been hampered by the lack of appropriate tools for targeting DNA in these organelles. In this study, we developed a Golden Gate cloning system(1), composed of 16 expression plasmids (8 for the delivery of the resulting protein to mitochondria and the other 8 for delivery to chloroplasts) and 424 transcription activator-like effector subarray plasmids, to assemble DddA-derived cytosine base editor (DdCBE)(2) plasmids and used the resulting DdCBEs to efficiently promote point mutagenesis in mitochondria and chloroplasts. Our DdCBEs induced base editing in lettuce or rapeseed calli at frequencies of up to 25% (mitochondria) and 38% (chloroplasts). We also showed DNA-free base editing in chloroplasts by delivering DdCBE mRNA to lettuce protoplasts to avoid off-target mutations caused by DdCBE-encoding plasmids. Furthermore, we generated lettuce calli and plantlets with edit frequencies of up to 99%, which were resistant to streptomycin or spectinomycin, by introducing a point mutation in the chloroplast 16S rRNA gene.

Light–Matter Interactions in Cesium Lead Halide Perovskite Nanowire Lasers

Light–matter interactions in inorganic perovskite nanolasers are investigated using single-crystalline cesium lead halide (CsPbX₃, X = Cl, Br, and I) nanowires synthesized by the chemical vapor transport method. The perovskite nanowires exhibit a uniform growth direction, smooth surfaces, straight end facets, and homogeneous composition distributions. Lasing occurs in the perovskite nanowires at low thresholds (3 μJ/cm²) with high quality factors (<i>Q</i> = 1200–1400) under ambient atmospheric environments. The wavelengths of the nanowire lasers are tunable by controlling the stoichiometry of the halide, allowing the lasing of the inorganic perovskite nanowires from blue to red. The unusual spacing of the Fabry–Pérot modes suggests strong light–matter interactions in the reduced mode volume of the nanowires, while the polarization of the lasing indicates that the Fabry–Pérot modes belong to the same fundamental transverse mode. The dispersion curve of the exciton–polariton model suggests that the group refractive index of the polariton is significantly enhanced

Differential modulation of thalamo-parietal interactions by varying depths of isoflurane anesthesia

<div><p>The thalamus is thought to relay peripheral sensory information to the somatosensory cortex in the parietal lobe. Long-range thalamo-parietal interactions play an important role in inducing the effect of anesthetic. However, whether these interaction changes vary with different levels of anesthesia is not known. In the present study, we investigated the influence of different levels of isoflurane-induced anesthesia on the functional connectivity between the thalamus and the parietal region. Microelectrodes were implanted in rats to record local field potentials (LFPs). The rats underwent different levels of isoflurane anesthesia [deep anesthesia: isoflurane (ISO) 2.5 vol%, light anesthesia (ISO 1 vol%), awake, and recovery state] and LFPs were recorded from four different brain areas (left parietal, right parietal, left thalamus, and right thalamus). Partial directed coherence (PDC) was calculated for these areas. With increasing depth of anesthesia, the PDC in the thalamus-to-parietal direction was significantly increased mainly in the high frequency ranges; however, in the parietal-to-thalamus direction, the increase was mainly in the low frequency band. For both directions, the PDC changes were prominent in the alpha frequency band. Functional interactions between the thalamus and parietal area are augmented proportionally to the anesthesia level. This relationship may pave the way for better understanding of the neural processing of sensory inputs from the periphery under different levels of anesthesia.</p></div