Yoshinobu Kawahara’s research while affiliated with Osaka University and other places

Far-field super-resolution fluorescence microscopy has enabled us to visualize live cells in great detail and with an unprecedented resolution. However, the techniques developed thus far have required high-power illumination (102-106 W/cm2), which leads to considerable phototoxicity to live cells and hampers time-lapse observation of the cells. In this study we show a highly biocompatible super-resolution microscopy technique that requires a very low-power illumination. The present technique combines a fast photoswitchable fluorescent protein, Kohinoor, with SPoD-ExPAN (super-resolution by polarization demodulation/excitation polarization angle narrowing). With this technique, we successfully observed Kohinoor-fusion proteins involving vimentin, paxillin, histone and clathrin expressed in HeLa cells at a spatial resolution of 70-80 nm with illumination power densities as low as ~1 W/cm2 for both excitation and photoswitching. Furthermore, although the previous SPoD-ExPAN technique used L1-regularized maximum-likelihood calculations to reconstruct super-resolved images, we devised an extension to the Lp-regularization to obtain super-resolved images that more accurately describe objects at the specimen plane. Thus, the present technique would significantly extend the applicability of super-resolution fluorescence microscopy for live-cell imaging.

Structured regularization is a mathematical technique which incorporates prior structural knowledge among variables into regression analysis to make a sparse estimation reflecting relationships among them. Abundance of structural information in biology, such as pathways formed by genes, transcripts, and proteins, especially suits well its application. Previously, we reported on the first application of latent group Lasso, a group-based regularization method, in toxicogenomics, with genes regulated by the same transcription factor treated as a group. We revealed that it achieved good predictive performances comparable to Lasso and allowed us to discuss mechanisms behind liver weight gain in rats based on selected groups. Latent group Lasso, however, does not lead to a sparse estimation, due to large group sizes in our analytical setting. In this study, we applied graph-based regularization methods, generalized fused Lasso and graph Lasso, for the same data, with regulatory networks formed by transcription factors and their downstream genes as a graph. These methods are expected to make a sparser estimation since they select variables based on edges. Comparisons showed that graph Lasso generated an accurate, biologically relevant and sparse model that could not have been possible with latent group Lasso and generalized fused Lasso.

A vector autoregressive model in discrete time domain (DVAR) is often used to analyze continuous time, multivariate, linear Markov systems through their observed time series data sampled at discrete timesteps. Based on previous studies, the DVAR model is supposed to be a noncanonical representation of the system, that is, it does not correspond to a unique system bijectively. However, in this article, we characterize the relations of the DVAR model with its corresponding Structural Vector AR (SVAR) and Continuous Time Vector AR (CTVAR) models through a finite difference method across continuous and discrete time domain. We further clarify that the DVARmodel of a continuous time,multivariate, linearMarkov system is canonical under a highly generic condition. Our analysis shows that we can uniquely reproduce its SVAR and CTVAR models from the DVAR model. Based on these results, we propose a novel Continuous and Structural Vector Autoregressive (CSVAR) modeling approach to derive the SVAR and the CTVAR models from their DVAR model empirically derived from the observed time series of continuous time linear Markov systems. We demonstrate its superior performance through some numerical experiments on both artificial and real-world data.

Regression analysis such as linear regression and logistic regression has often been employed to construct toxicogenomic predictive models, which forecast toxicological effects of chemical compounds in human or animals based on gene expression data. While in general these techniques can generate an accurate and sparse model when a regularization term is added to a loss function, they ignore structural relationships behind genes which form vast regulatory networks and interact with each other. Recently, several reports proposed structured sparsity-inducing norms to incorporate prior structural information and make a model reflecting relationships between variables. In this study, assuming that genes regulated by the same transcription factor should be selected together, we applied the latent group Lasso technique on toxicogenomic data with transcription factor networks as prior knowledge. We compared generated classifiers for liver weight gain in rats between the latent group Lasso and Lasso. The latent group Lasso was comparable or superior to the Lasso in terms of predictive performances (balanced accuracy: 74% vs. 72%, sensitivity: 62% vs. 62%, specificity: 86% vs. 83%). Besides, groups selected by the latent group Lasso suggested involvement of Wnt/β-catenin signaling pathway. Such mechanism-related analysis could not have been possible with the Lasso and is one of the advantages of the latent group Lasso.

Multi-dimensional data visualization is an important research topic that has been receiving increasing attention. Several techniques that apply scatterplot matrices have been proposed to represent multi-dimensional data as a collection of two-dimensional data visualization spaces. Typically, when using the scatterplot-based approach it is easier to understand relations between particular pairs of dimensions, but it often requires too large display spaces to display all possible scatterplots. This paper presents a technique to display meaningful sets of scatterplots generated from high-dimensional datasets. Our technique first evaluates all possible scatterplots generated from high-dimensional datasets, and selects meaningful sets. It then calculates the similarity between arbitrary pairs of the selected scatterplots, and places relevant scatterplots closer together in the display space while they never overlap each other. This design policy makes users easier to visually compare relevant sets of scatterplots. This paper presents algorithms to place the scatterplots by the combination of ideal position calculation and rectangle packing algorithms, and two examples demonstrating the effectiveness of the presented technique. Graphical Abstract

This paper presents a first application of a novel Continuous and Structural Autoregressive Moving Average (CSARMA) modeling approach to BWR noise analysis. The CSARMA approach derives a unique representation of the system dynamics by more robust and reliable canonical models as basis for signal analysis in general and for reactor diagnostics in particular. In this paper, a stability event that occurred in a Swiss BWR plant during power ascension phase is analyzed as well as the time periods that preceded and followed the event. Focusing only on qualitative trends at this stage, the obtained results clearly indicate a different dynamical state during the unstable event compared to the two other stable periods. Also, they could be interpreted as pointing out a disturbance in the pressure control system as primary cause for the event. To benchmark these findings, the frequency domain based Signal Transmission-Path (STP) method is also applied. And with the STP method, we obtained similar relationships as mentioned above. This consistency between both methods can be considered as being a confirmation that the event was caused by a pressure control system disturbance and not induced by the core. Also, it is worth noting that the STP analysis failed to catch the relations among the processes during the stable periods, that were clearly indicated by the CSARMA method, since the last uses more precise models as basis.

While the recent advent of new technologies in biology such as DNA microarray and next-generation sequencer has given researchers a large volume of data representing genome-wide biological responses, it is not necessarily easy to derive knowledge that is accurate and understandable at the same time. In this study, we applied the Classification Based on Association (CBA) algorithm, one of the Class Association Rule mining techniques, to the TG-GATEs database, where both toxicogenomic and toxicological data of more than 150 compounds in rat and human are stored. We compared the generated classifiers between CBA and linear discriminant analysis (LDA) and showed that CBA is superior to LDA in terms of both predictive performances (accuracy: 83% for CBA vs. 75% for LDA, sensitivity: 82% for CBA vs. 72% for LDA, specificity: 85% for CBA vs. 75% for LDA) and interpretability.

To provide a discriminant model learning device capable of efficiently learning a discriminant model on which domain knowledge indicating user's knowledge or analysis intention for a model is reflected while keeping fitting to data. A query candidate storage means 81 stores candidates of a query as a model to be given with domain knowledge indicating a user's intention. A regularization function generation means 82 generates a regularization function indicating compatibility with domain knowledge based on the domain knowledge to be given to the query candidates. A model learning means 83 learns a discriminant model by optimizing a function defined by a loss function and the regularization function predefined per discriminant model.