Marginal Probability-Based Integer Handling for CMA-ES Tackling Single-and Multi-Objective Mixed-Integer Black-Box Optimization

This study targets the mixed-integer black-box optimization (MI-BBO) problem where continuous and integer variables should be optimized simultaneously. The CMA-ES, our focus in this study, is a population-based stochastic search method that samples solution candidates from a multivariate Gaussian distribution (MGD), which shows excellent performance in continuous BBO. The parameters of MGD, mean and (co)variance, are updated based on the evaluation value of candidate solutions in the CMA-ES. If the CMA-ES is applied to the MI-BBO with straightforward discretization, however, the variance corresponding to the integer variables becomes much smaller than the granularity of the discretization before reaching the optimal solution, which leads to the stagnation of the optimization. In particular, when binary variables are included in the problem, this stagnation more likely occurs because the granularity of the discretization becomes wider, and the existing integer handling for the CMA-ES does not address this stagnation. To overcome these limitations, we propose a simple integer handling for the CMA-ES based on lower-bounding the marginal probabilities associated with the generation of integer variables in the MGD. The numerical experiments on the MI-BBO benchmark problems demonstrate the efficiency and robustness of the proposed method. Furthermore, in order to demonstrate the generality of the idea of the proposed method, in addition to the single-objective optimization case, we incorporate it into multi-objective CMA-ES and verify its performance on bi-objective mixed-integer benchmark problems.Comment: Camera-ready version for ACM Transactions on Evolutionary Learning and Optimization (TELO). This paper is an extended version of the work presented in arXiv:2205.1348

Machine-Learning Optimization of Multiple Measurement Parameters Nonlinearly Affecting the Signal Quality

Determination of optimal measurement parameters is essential for measurement experiments. They can be manually optimized if the linear correlation between them and the corresponding signal quality is known or easily determinable. However, in practice, this correlation is often nonlinear and not known a priori; hence, complicated trial and error procedures are employed for finding optimal parameters while avoiding local optima. In this work, we propose a novel approach based on machine learning for optimizing multiple measurement parameters, which nonlinearly influence the signal quality. Optically detected magnetic resonance measurements of nitrogen-vacancy centers in fluorescent nanodiamonds were used as a proof-of-concept system. We constructed a suitable dataset of optically detected magnetic resonance spectra for predicting the optimal laser and microwave powers that deliver the highest contrast and signal-to-noise ratio values by means of linear regression, neural networks, and random forests. The model developed by the considered neural network turned out to have a coefficient of determination significantly higher than that of the other methods. The proposed method thus provided a novel approach for the rapid setting of measurement parameters that influence the signal quality in a nonlinear way, opening a gate for fields like nuclear magnetic resonance, electron paramagnetic resonance, and fluorescence microscopy to benefit from it

Magnetic resonance-based visualization of gene expression in mammalian cells using a bacterial polyphosphate kinase reporter gene

Gene expression reporter systems, in which a promoter of interest is cloned upstream of a readily assayed reporter gene, have been developed and used extensively to study gene expression in prokaryotes and eukaryotes. Unfortunately, most of these systems cannot be used to assay gene expression in nonsuperficial tissues in living organisms. This study examines a novel reporter gene system based on the gene encoding Escherichia coli polyphosphate kinase (PPK), which can be used to monitor gene expression in mammalian cells. PPK catalyzes the synthesis of inorganic polyphosphate (polyP) from ATP, and because mammalian cells do not contain detectable levels of polyP, PPK activity can be measured in mammalian cells using 31P-magnetic resonance spectroscopy or 31P-magnetic resonance imaging. The ppk reporter gene system described here is noninvasive, does not require an exogenous substrate, and can potentially be used in internal tissues of living organisms

Structural Insights into Methylated DNA Recognition by the Methyl-CpG Binding Domain of MBD6 from Arabidopsis thaliana

Cytosine methylation is an epigenetic modification essential for formation of mature heterochromatin, gene silencing, and genomic stability. In plants, methylation occurs not only at cytosine bases in CpG but also in CpHpG and CpHpH contexts, where H denotes A, T, or C. Methyl-CpG binding domain (MBD) proteins, which recognize symmetrical methyl-CpG dinucleotides and act as gene repressors in mammalian cells, are also present in plant cells, although their structural and functional properties still remain poorly understood. To fill this gap, in this study, we determined the solution structure of the MBD domain of the MBD6 protein from Arabidopsis thaliana and investigated its binding properties to methylated DNA by binding assays and an in-depth NMR spectroscopic analysis. The AtMBD6 MBD domain folds into a canonical MBD structure in line with its binding specificity toward methyl-CpG and possesses a DNA binding interface similar to mammalian MBD domains. Intriguingly, however, the binding affinity of the AtMBD6 MBD domain toward methyl-CpG-containing DNA was found to be much lower than that of known mammalian MBD domains. The main difference arises from the absence of positively charged residues in AtMBD6 that supposedly interact with the DNA backbone as seen in mammalian MBD/methyl-CpG-containing DNA complexes. Taken together, we have established a structural basis for methyl-CpG recognition by AtMBD6 to develop a deeper understanding how MBD proteins work as mediators of epigenetic signals in plant cells

The DNA methyltransferase Dnmt1 directly interacts with the SET and RING finger-associated (SRA) domain of the multifunctional protein Uhrf1 to facilitate accession of the catalytic center to hemi-methylated DNA

This research was originally published in Journal of Biological Chemistry. Ahmet Can Berkyurek, Isao Suetake, Kyohei Arita, Kohei Takeshita, Atsushi Nakagawa, Masahiro Shirakawa and Shoji Tajima. The DNA methyltransferase Dnmt1 directly interacts with the SET and RING finger-associated (SRA) domain of the multifunctional protein Uhrf1 to facilitate accession of the catalytic center to hemi-methylated DNA. Journal of Biological Chemistry. 2014; 289, 379-386. © the American Society for Biochemistry and Molecular Biology

High-Sensitivity Rheo-NMR Spectroscopy for Protein Studies

Shear stress can induce structural deformation of proteins, which might result in aggregate formation. Rheo-NMR spectroscopy has the potential to monitor structural changes in proteins under shear stress at the atomic level; however, existing Rheo-NMR methodologies have insufficient sensitivity to probe protein structure and dynamics. Here we present a simple and versatile approach to Rheo-NMR, which maximizes sensitivity by using a spectrometer equipped with a cryogenic probe. As a result, the sensitivity of the instrument ranks highest among the Rheo-NMR spectrometers reported so far. We demonstrate that the newly developed Rheo-NMR instrument can acquire high-quality relaxation data for a protein under shear stress and can trace structural changes in a protein during fibril formation in real time. The described approach will facilitate rheological studies on protein structural deformation, thereby aiding a physical understanding of shear-induced amyloid fibril formation

A novel magnetic resonance-based method to measure gene expression in living cells

In unicellular and multicellular eukaryotes, elaborate gene regulatory mechanisms facilitate a broad range of biological processes from cell division to morphological differentiation. In order to fully understand the gene regulatory networks involved in these biological processes, the spatial and temporal patterns of expression of many thousands of genes will need to be determined in real time in living organisms. Currently available techniques are not sufficient to achieve this goal; however, novel methods based on magnetic resonance (MR) imaging may be particularly useful for sensitive detection of gene expression in opaque tissues. This report describes a novel reporter gene system that monitors gene expression dynamically and quantitatively, in yeast cells, by measuring the accumulation of inorganic polyphosphate (polyP) using MR spectroscopy (MRS) or MR spectroscopic imaging (MRI). Because this system is completely non-invasive and does not require exogenous substrates, it is a powerful tool for studying gene expression in multicellular organisms, as well

Small multimodal thermometry with detonation-created multi-color centers in detonation nanodiamond

微小ナノダイヤモンド量子センサで安定的に温度計測実現--細胞内などの微小領域での量子センシングに期待--.京都大学プレスリリース. 2024-05-16.Detonation nanodiamond (DND) is the smallest class of diamond nanocrystal capable of hosting various color centers with a size akin to molecular pores. Their negatively charged nitrogen-vacancy center (NV⁻) is a versatile tool for sensing a wide range of physical and even chemical parameters at the nanoscale. The NV⁻ is, therefore, attracting interest as the smallest quantum sensor in biological research. Nonetheless, recent NV⁻ enhancement in DND has yet to yield sufficient fluorescence per particle, leading to efforts to incorporate other group-IV color centers into DND. An example is adding a silicon dopant to the explosive mixture to create negatively charged silicon-vacancy centers (SiV⁻). In this paper, we report on efficient observation (∼50% of randomly selected spots) of the characteristic optically detected magnetic resonance (ODMR) NV⁻ signal in silicon-doped DND (Si-DND) subjected to boiling acid surface cleaning. The NV⁻ concentration is estimated by continuous-wave electron spin resonance spectroscopy to be 0.35 ppm without the NV⁻ enrichment process. A temperature sensitivity of 0.36 K/√HZ in an NV⁻ ensemble inside an aggregate of Si-DND is achieved via the ODMR-based technique. Transmission electron microscopy survey reveals that the Si-DNDs core sizes are ∼11.2 nm, the smallest among the nanodiamond’s temperature sensitivity studies. Furthermore, temperature sensing using both SiV⁻ (all-optical technique) and NV⁻ (ODMR-based technique) in the same confocal volume is demonstrated, showing Si-DND’s multimodal temperature sensing capability. The results of the study thereby pave a path for multi-color and multimodal biosensors and for decoupling the detected electrical field and temperature effects on the NV⁻ center