Functional Implications of DNA Methylation in Adipose Biology.

The twin epidemics of obesity and type 2 diabetes (T2D) are a serious health, social, and economic issue. The dysregulation of adipose tissue biology is central to the development of these two metabolic disorders, as adipose tissue plays a pivotal role in regulating whole-body metabolism and energy homeostasis (1). Accumulating evidence indicates that multiple aspects of adipose biology are regulated, in part, by epigenetic mechanisms. The precise and comprehensive understanding of the epigenetic control of adipose tissue biology is crucial to identifying novel therapeutic interventions that target epigenetic issues. Here, we review the recent findings on DNA methylation events and machinery in regulating the developmental processes and metabolic function of adipocytes. We highlight the following points: 1) DNA methylation is a key epigenetic regulator of adipose development and gene regulation, 2) emerging evidence suggests that DNA methylation is involved in the transgenerational passage of obesity and other metabolic disorders, 3) DNA methylation is involved in regulating the altered transcriptional landscape of dysfunctional adipose tissue, 4) genome-wide studies reveal specific DNA methylation events that associate with obesity and T2D, and 5) the enzymatic effectors of DNA methylation have physiological functions in adipose development and metabolic function

Multiscale Tensor Decomposition and Rendering Equation Encoding for View Synthesis

Rendering novel views from captured multi-view images has made considerable progress since the emergence of the neural radiance field. This paper aims to further advance the quality of view synthesis by proposing a novel approach dubbed the neural radiance feature field (NRFF). We first propose a multiscale tensor decomposition scheme to organize learnable features so as to represent scenes from coarse to fine scales. We demonstrate many benefits of the proposed multiscale representation, including more accurate scene shape and appearance reconstruction, and faster convergence compared with the single-scale representation. Instead of encoding view directions to model view-dependent effects, we further propose to encode the rendering equation in the feature space by employing the anisotropic spherical Gaussian mixture predicted from the proposed multiscale representation. The proposed NRFF improves state-of-the-art rendering results by over 1 dB in PSNR on both the NeRF and NSVF synthetic datasets. A significant improvement has also been observed on the real-world Tanks & Temples dataset. Code can be found at https://github.com/imkanghan/nrff

Generalized Invariant Matching Property via LASSO

Learning under distribution shifts is a challenging task. One principled approach is to exploit the invariance principle via the structural causal models. However, the invariance principle is violated when the response is intervened, making it a difficult setting. In a recent work, the invariant matching property has been developed to shed light on this scenario and shows promising performance. In this work, by formulating a high-dimensional problem with intrinsic sparsity, we generalize the invariant matching property for an important setting when only the target is intervened. We propose a more robust and computation-efficient algorithm by leveraging a variant of Lasso, improving upon the existing algorithms.Comment: Accepted to the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2023

Learning Invariant Representations under General Interventions on the Response

It has become increasingly common nowadays to collect observations of feature and response pairs from different environments. As a consequence, one has to apply learned predictors to data with a different distribution due to distribution shifts. One principled approach is to adopt the structural causal models to describe training and test models, following the invariance principle which says that the conditional distribution of the response given its predictors remains the same across environments. However, this principle might be violated in practical settings when the response is intervened. A natural question is whether it is still possible to identify other forms of invariance to facilitate prediction in unseen environments. To shed light on this challenging scenario, we focus on linear structural causal models (SCMs) and introduce invariant matching property (IMP), an explicit relation to capture interventions through an additional feature, leading to an alternative form of invariance that enables a unified treatment of general interventions on the response as well as the predictors. We analyze the asymptotic generalization errors of our method under both the discrete and continuous environment settings, where the continuous case is handled by relating it to the semiparametric varying coefficient models. We present algorithms that show competitive performance compared to existing methods over various experimental settings including a COVID dataset.Comment: Accepted to the IEEE Journal on Selected Areas in Information Theory. Special Issue: Causality: Fundamental Limits and Application

A multi-protein receptor-ligand complex underlies combinatorial dendrite guidance choices in C. elegans.

Ligand receptor interactions instruct axon guidance during development. How dendrites are guided to specific targets is less understood. The C. elegans PVD sensory neuron innervates muscle-skin interface with its elaborate dendritic branches. Here, we found that LECT-2, the ortholog of leukocyte cell-derived chemotaxin-2 (LECT2), is secreted from the muscles and required for muscle innervation by PVD. Mosaic analyses showed that LECT-2 acted locally to guide the growth of terminal branches. Ectopic expression of LECT-2 from seam cells is sufficient to redirect the PVD dendrites onto seam cells. LECT-2 functions in a multi-protein receptor-ligand complex that also contains two transmembrane ligands on the skin, SAX-7/L1CAM and MNR-1, and the neuronal transmembrane receptor DMA-1. LECT-2 greatly enhances the binding between SAX-7, MNR-1 and DMA-1. The activation of DMA-1 strictly requires all three ligands, which establishes a combinatorial code to precisely target and pattern dendritic arbors

Deconfinement Phase Transition Heating and Thermal Evolution of Neutron Stars

The deconfinement phase transition will lead to the release of latent heat during spins down of neutron stars if the transition is the first-order one.We have investigated the thermal evolution of neutron stars undergoing such deconfinement phase transition. The results show that neutron stars may be heated to higher temperature.This feature could be particularly interesting for high temperature of low-magnetic field millisecond pulsar at late stage.Comment: 4 pages, to be published by American Institute of Physics, ed. D.Lai, X.D.Li and Y.F.Yuan, as the Proceedings of the conference Astrophysics of Compact Object

Non-classical non-Gaussian state of a mechanical resonator via selectively incoherent damping in three-mode optomechanical systems

We theoretically propose a scheme for the generation of a non-classical single-mode motional state of a mechanical resonator (MR) in the three-mode optomechanical systems, in which two optical modes of the cavities are linearly coupled to each other and one mechanical mode of the MR is optomechanically coupled to the two optical modes with the same coupling strength simultaneously. One cavity is driven by a coherent laser light. By properly tuning the frequency of the weak driving field, we obtain engineered Liouvillian superoperator via engineering the selective interaction Hamiltonian confined to the Fock subspaces. In this case, the motional state of the MR can be prepared into a non-Gaussian state, which possesses the sub-Poisson statistics although its Wigner function is positive.Comment: 6 pages, 5 figure