22 research outputs found
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 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 (CsPbX<sub>3</sub>, 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/cm<sup>2</sup>) 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