Journals are beginning to consider language models for manuscript handling, but submitted manuscripts are unpublished, and where policy forbids sending them to an external service the model must run on hardware the journal controls. The capability of locally hosted models on editorial work has not been measured. Here we constructed a benchmark of eight editorial tasks from a journal's Instructions for Authors, from manuscripts carrying defects we seeded and verified independently, and from published reviews of a preprint, and evaluated twenty open-weight models spanning a twenty-five-fold range of weight size on a compact desktop workstation of the kind a laboratory or small editorial office can adopt. The strongest model detected 36 of 40 seeded guideline violations and occupied 81 GB; a 17 GB model detected 33. Across the best configurations tested, we observed no consistent monotonic association between weight size and score: rank correlations were negligible on every task (Spearman |rho| <= 0.19), and within one model family the larger member scored below its smaller sibling. A deterministic checker of regular expressions and arithmetic, using no model, detected 31 of the same violations in a fraction of a second, and the union of its detections with those of the strongest model covered all 40. On the single peer-review case, the best model recovered 6 of 12 points from three published reviews. Prompt structure substantially altered scores within individual models. This level of performance is therefore within reach of a workstation of this class, once the deterministic checks are written.
The actin cytoskeleton generates diverse cellular functions, including intracellular positioning, symmetry breaking, and cell migration, through dynamic self-organization. Despite extensive molecular characterization of actin-binding proteins, the physical principles by which local molecular interactions give rise to cell-scale organization remain incompletely understood. Artificial cells provide a powerful platform for addressing this question because they enable precise control over molecular composition, geometry, and physical boundary conditions while minimizing the complexity inherent to living cells. In this review, we describe how artificial cell systems have been used to investigate the emergence of actin-dependent intracellular organization and motility. Using the cytoplasmic extracts of Xenopus eggs encapsulated within cell-sized water-in-oil droplets surrounded by a phospholipid membrane, we reconstructed actomyosin-driven intracellular positioning and spontaneous cell-like migration. By combining quantitative molecular perturbations with theoretical modeling, these studies revealed how competing actomyosin structures, stochastic network formation, and mechanical coupling between the actin cytoskeleton and the membrane collectively determine intracellular organization and directional movement. These findings illustrate how constructive approaches using artificial cells can uncover general physical principles governing the self-organization of cellular architecture and function. This review article is an extended version of the Japanese article, reconstitution of actin cytoskeletal functions in cell-sized confinement, published in SEIBUTSU BUTSURI Vol. 65 p. 192–196 (2025).
Bioluminescence imaging (BLI) detects light emission from samples expressing luciferase in the presence of luciferin. Unlike fluorescence imaging, BLI does not require excitation light, thereby avoiding phototoxicity in living cells and photobleaching of labels. However, the intrinsically slow turnover of the luciferin-luciferase reaction often results in low luminescence intensity, which degrades image quality and limits practical applications, particularly in high-magnification optical microscopy of cells. In this study, we demonstrate that image denoising provides an effective strategy to overcome this fundamental limitation of BLI. We developed a physics-based noise model that incorporates fixed-pattern noise, blooming noise, readout noise, quantization noise, and Poisson shot noise, enabling accurate estimation of noise parameters encountered in EMCCD-based microscopy. In the denoising pipeline, raw noisy images were first corrected for the fixed-pattern and blooming noises, followed by restoration using Uformer neural network trained with paired ground-truth images and synthetically generated noisy images. Quantitative evaluation using the peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM) showed that denoised images derived from raw data acquired with exposure times of ≤100 ms achieved PSNR and SSIM values comparable to those of raw images acquired with exposure times of 1–3 s. These results indicate that the proposed denoising approach substantially extends the practical limit of high-magnification BLI.
