Single-cell transcriptome landscape of circulating CD4+ T cell populations in autoimmune diseases.

CD4+ T cells are key mediators of various autoimmune diseases; however, their role in disease progression remains unclear due to cellular heterogeneity. Here, we evaluated CD4+ T cell subpopulations using decomposition-based transcriptome characterization and canonical clustering strategies. This approach identified 12 independent gene programs governing whole CD4+ T cell heterogeneity, which can explain the ambiguity of canonical clustering. In addition, we performed a meta-analysis using public single-cell datasets of over 1.8 million peripheral CD4+ T cells from 953 individuals by projecting cells onto the reference and cataloging cell frequency and qualitative alterations of the populations in 20 diseases. The analyses revealed that the 12 transcriptional programs were useful in characterizing each autoimmune disease and predicting its clinical status. Moreover, genetic variants associated with autoimmune diseases showed disease-specific enrichment within the 12 gene programs. The results collectively provide a landscape of single-cell transcriptomes of CD4+ T cell subpopulations involved in autoimmune disease.

Deep sound-field denoiser: optically-measured sound-field denoising using deep neural network.

This paper proposes a deep sound-field denoiser, a deep neural network (DNN) based denoising of optically measured sound-field images. Sound-field imaging using optical methods has gained considerable attention due to its ability to achieve high-spatial-resolution imaging of acoustic phenomena that conventional acoustic sensors cannot accomplish. However, the optically measured sound-field images are often heavily contaminated by noise because of the low sensitivity of optical interferometric measurements to airborne sound. Here, we propose a DNN-based sound-field denoising method. Time-varying sound-field image sequences are decomposed into harmonic complex-amplitude images by using a time-directional Fourier transform. The complex images are converted into two-channel images consisting of real and imaginary parts and denoised by a nonlinear-activation-free network. The network is trained on a sound-field dataset obtained from numerical acoustic simulations with randomized parameters. We compared the method with conventional ones, such as image filters, a spatiotemporal filter, and other DNN architectures, on numerical and experimental data. The experimental data were measured by parallel phase-shifting interferometry and holographic speckle interferometry. The proposed deep sound-field denoiser significantly outperformed the conventional methods on both the numerical and experimental data. Code is available on GitHub (https://github.com/nttcslab/deep-sound-field-denoiser).

Source directivity approximation for finite-difference time-domain simulation by estimating initial value.

In order to incorporate a directive sound source into acoustic simulation using the finite-difference time-domain method (FDTD), this paper proposes an optimization-based method to estimate the initial value which approximates a desired directional pattern after propagation. The proposed method explicitly considers a discretized FDTD scheme and optimizes the initial value directly in the time domain so that every effect of the discretization error of FDTD, including numerical dispersion, is taken into account. It is also able to consider a frequency-wise directivity by integrating the Fourier transform into the optimization procedure, even though the estimated result is defined in the time domain. After the optimization, the obtained result can be utilized in any acoustic simulation based on the same FDTD scheme without modification because the result is represented as the initial value to be propagated and no additional procedure is required.

Total Syntheses and Configuration Assignments of JBIR-06 and Related Depsipeptides.

The first total syntheses of JBIR-06 and two analogous depsipeptides, 12-membered antimycin-class antibiotics, have been accomplished via Shiina macrolactonization. Comparison of the spectroscopic data of the synthesized compounds with those reported for natural products verified that the absolute configutation of the natural products was (2 S,4 S,6 S,7 R,14 S).