Learning Compliant Stiffness by Impedance Control-Aware Task Segmentation and Multi-objective Bayesian Optimization with Priors

Rather than traditional position control, impedance control is preferred to ensure the safe operation of industrial robots programmed from demonstrations. However, variable stiffness learning studies have focused on task performance rather than safety (or compliance). Thus, this paper proposes a novel stiffness learning method to satisfy both task performance and compliance requirements. The proposed method optimizes the task and compliance objectives (T/C objectives) simultaneously via multi-objective Bayesian optimization. We define the stiffness search space by segmenting a demonstration into task phases, each with constant responsible stiffness. The segmentation is performed by identifying impedance control-aware switching linear dynamics (IC-SLD) from the demonstration. We also utilize the stiffness obtained by proposed IC-SLD as priors for efficient optimization. Experiments on simulated tasks and a real robot demonstrate that IC-SLD-based segmentation and the use of priors improve the optimization efficiency compared to existing baseline methods.Comment: Accepted to IROS202

Cell population‐based framework of genetic epidemiology in the single‐cell omics era

Genetic epidemiology is a rapidly advancing field due to the recent availability of large amounts of omics data. In recent years, it has become possible to obtain omics information at the single-cell level, so genetic epidemiological models need to be updated to integrate with single-cell expression data. In this perspective paper, we propose a cell population-based framework for genetic epidemiology in the single-cell era. In this framework, genetic diversity influences phenotypic diversity through the diversity of cell population profiles, which are defined as high-dimensional probability distributions of the state spaces of biomolecules of each omics layer. We discuss how biomolecular experimental measurement data can capture the different properties of this distribution. In particular, single-cell data constitute a sample from this population distribution where only some coordinate values are observable. From a data analysis standpoint, we introduce methodology for feature extraction from cell population profiles. Finally, we discuss how this framework can be applied not only to genetic epidemiology but also to systems biology

Data-driven comparison of multiple high-dimensional single-cell expression profiles

Comparing multiple single-cell expression datasets such as cytometry and scRNA-seq data between case and control donors provides information to elucidate the mechanisms of disease. We propose a completely data-driven computational biological method for this task. This overcomes the challenges of conventional cellular subset-based comparisons and facilitates further analyses such as machine learning and gene set analysis of single-cell expression datasets

Phenotypic change of macrophages in the progression of diabetic nephropathy; sialoadhesin-positive activated macrophages are increased in diabetic kidney

Inflammatory process is involved in pathogenesis of diabetic nephropathy, although the activation and phenotypic change of macrophages in diabetic kidney has remained unclear. Sialoadhesin is a macrophage adhesion molecule containing 17 extracellular immunoglobulin-like domains, and is an I-type lectin which binds to sialic acid ligands expressed on hematopoietic cells. The aim of this study is to clarify the activation and phenotypic change of macrophages in the progression of diabetic nephropathy. We examined the expression of surface markers for pan-macrophages, resident macrophages, sialoadhesin, major histocompatibility complex class II and alpha-smooth muscle actin in the glomeruli of diabetic rats using immunohistochemistry at 0, 1, 4, 12, and 24 weeks after induction of diabetes by streptozotocin. Expression of type IV collagen and the change of mesangial matrix area were also measured. The mechanism for up-regulated expression of sialoadhesin on macrophages was evaluated in vitro. The number of macrophages was increased in diabetic glomeruli at 1 month after induction of diabetes and the increased number was maintained until 6 months. On the other hand, sialoadhesin-positive macrophages were increased during the late stage of diabetes concomitantly with the increase of alpha-smooth muscle actin-positive mesangial cells, mesangial matrix area and type IV collagen. Gene expression of sialoadhesin was induced by stimulation with interleukin (IL)-1 beta and tumor necrosis factor-alpha but not with IL-4, transforming growth factor-beta and high glucose in cultured human macrophages. The present findings suggest that sialoadhesin-positive macrophages may contribute to the progression of diabetic nephropathy

Data-driven identification and classification of nonlinear aging patterns reveals the landscape of associations between DNA methylation and aging

オミックスデータから非線形な加齢変化の全体像を取得する解析手法を開発. 京都大学プレスリリース. 2023-02-13.[Background] Aging affects the incidence of diseases such as cancer and dementia, so the development of biomarkers for aging is an important research topic in medical science. While such biomarkers have been mainly identified based on the assumption of a linear relationship between phenotypic parameters, including molecular markers, and chronological age, numerous nonlinear changes between markers and aging have been identified. However, the overall landscape of the patterns in nonlinear changes that exist in aging is unknown. [Result] We propose a novel computational method, Data-driven Identification and Classification of Nonlinear Aging Patterns (DICNAP), that is based on functional data analysis to identify biomarkers for aging and potential patterns of change during aging in a data-driven manner. We applied the proposed method to large-scale, public DNA methylation data to explore the potential patterns of age-related changes in methylation intensity. The results showed that not only linear, but also nonlinear changes in DNA methylation patterns exist. A monotonous demethylation pattern during aging, with its rate decreasing at around age 60, was identified as the candidate stable nonlinear pattern. We also analyzed the age-related changes in methylation variability. The results showed that the variability of methylation intensity tends to increase with age at age-associated sites. The representative variability pattern is a monotonically increasing pattern that accelerates after middle age. [Conclusion] DICNAP was able to identify the potential patterns of the changes in the landscape of DNA methylation during aging. It contributes to an improvement in our theoretical understanding of the aging process