Toneyama National Hospital

ABSTRACT We report a time-averaged 2–8 keV X-ray polarization degree (PD) of $8.5 \pm 1.6~{{\ \rm per\ cent}}$ ($\gt 3\sigma$ detection) from the accretion-disc–corona (ADC) neutron-star system 2S 0921–630 (= V395 Car) observed with the Imaging X-ray Polarimetry Explorer (IXPE). As the observation includes an eclipse, we analyse eclipse and out-of-eclipse intervals separately. The eclipse PD is $15\pm 3~{{\ \rm per\ cent}}$, compared to $5.9\pm 1.9~{{\ \rm per\ cent}}$ out of eclipse, with no clear evidence for an associated change in polarization angle (PA). We also search time-averaged, eclipse and non-eclipse spectra and find marginal evidence ($2\sigma$) for a change in PA with energy, and even weaker evidence for an increase in PD with energy. We use a Monte Carlo spectropolarimetric radiation transfer simulation to model the polarization produced from a disc accreting neutron star, combining boundary-layer emission, its disc reflection, and the disc continuum, each with its intrinsic polarization. The model then also includes scattering of this composite spectrum in the column density distribution produced by a thermal–radiative wind launched by X-ray irradiation of the outer disc. At high inclination angles, where the observed flux is seen only via scattering in the wind, this model can reproduce both the observed PD and its (very weakly significant) increase with energy. However, it does not predict the stronger (but still only marginally significant) change in PA with energy. If this is a real effect then it points to a more complex, non-axisymmetric scattering geometry than that assumed in our model.

• Robust and generalized unsupervised continuity-driven machine learning framework for nanoXRD-based SXDM data. • Novel metric for detecting structural discontinuities without labels, usually overlooked by conventional methods. • Visualization of structural change at interfaces/boundaries in bulk GaN. • New insights linking crystal processing to microstructural defects. Nanobeam X-ray diffraction (nanoXRD) enables nanoscale mapping of crystal structures across wafer-scale crystals, offering unique insight into microstructural evolution during crystal growth. However, the resulting large and complex diffraction datasets make it challenging to quantitatively resolve local structural transitions and their connection to growth processes using conventional analysis. Here, we present a continuity-driven, unsupervised, and generalized analysis framework, referred to as the neighborhood-based similarity metric , which integrates spatial coordinates with nanoXRD data to reveal structural variations across growth sectors, interfaces, and defect-related regions without requiring prior knowledge or labels. By introducing Jaccard similarity scores to compare local neighborhoods in spatial and diffraction domains, the method quantitatively detects discontinuities where structural evolution disrupts the local continuity of diffraction patterns. Our unsupervised approach, validated with synthetic data and nanoXRD measurements of bulk GaN crystals, successfully identified both known defects and previously hidden structural discontinuities. The results provide new insights into the relationship between growth conditions, local strain evolution, and defect formation, establishing a robust and interpretable approach for linking processing and structural characteristics in complex crystalline materials.

Abstract A novel dirhodium complex bearing a tetramethylcyclopentadienyl-pyridine (Me4Cp-bpy) as a bridging ligand was synthesized and characterized. The complex is composed of two electronically differentiated Rh centers, and its photophysical and redox properties were investigated by UV–Vis absorption, cyclic voltammetry, and theoretical calculations. Facile generation of a cis-coordination site at the Rh–Rh bond was realized, enabling aerobic oxidation to yield a dirhodium oxo complex. These findings provide a new design for paddlewheel dirhodium complexes potentially capable of cooperative molecular activation and transformations at the Rh–Rh bond.

We demonstrate an integrated artificial intelligence (AI) framework for autonomous atom manipulation of silver atoms on a Si(111)-(7 × 7) surface at room temperature. The framework combines four machine learning models that evaluate tip and surface conditions, detect Ag atoms, locate defect-free half-unit cells (HUCs), and evaluate manipulation conditions. This integration enables autonomous scanning tunneling microscopy operation with key functions including thermal drift correction, probe tip conditioning, and automated atom manipulation. The integrated AI framework demonstrated robust long-term operation, autonomously performing atom manipulation over 25 h. During this period, the system successfully executed both lateral transfer of Ag atoms between adjacent HUCs and vertical pickup operations without human intervention. While the manipulation success rate remains limited by tip stability challenges, the system demonstrates the feasibility of AI-driven autonomous operation at room temperature, providing a foundation for future high-throughput atomic-scale fabrication.

The CRISPR-Cas system revolutionized molecular biology by guiding Cas proteins to target nucleic acid sequences using customizable guide RNAs, offering unparalleled precision and versatility. Inspired by this innovation, we developed RNA-guided green fluorescent protein (RGG), a simple and programmable platform for targeting nucleic acid. Using a streamlined click chemistry approach, known for its high efficiency and specificity, we conjugated dibenzocyclooctyne (DBCO)-modified guide nucleic acids, designed to complement target sequences, with azide-exposed proteins to construct RGG. Systematic optimization identified 30-nt RNA with 3′-DBCO modifications as the most effective configuration for RGG, enabling precise visualization of nuclear-localized RNAs, including NEAT1 and Satellite III RNA, in living cells. This establishes RGG as a customizable and efficient system for RNA imaging and molecular analysis, underscoring the potential of direct conjugation between guide nucleic acids and proteins to enable precise nucleic acid recognition and dynamic molecular modification in living cells.

Cholangiocarcinoma (CC) is a highly lethal malignancy that urgently requires reliable prognostic biomarkers. Although MUC1 expression and promoter methylation have been implicated in CC, the clinical significance of promoter methylation pattern composition, beyond average methylation levels, remains unclear. Here, we investigated the relationship between MUC1 promoter methylation heterogeneity, MUC1 mRNA expression, and prognosis in CC. We analyzed bisulfite amplicon sequencing data and mRNA expression of MUC1, DNA methylation-related enzymes (TET1, TET2, TET3, Dnmt1, and Dnmt3a), and tumor microenvironment stress markers in 131 CC tissues. In the neoplastic region, high MUC1 mRNA expression was associated with poor overall survival (HR = 0.131, 95% CI: 0.02to0.95, p = 0.042) and correlated with the abundance of completely unmethylated promoter patterns (r = 0.386, p < 0.001). Among the enzymes analyzed, only TET3 expression significantly correlated with the abundance of completely unmethylated patterns in the neoplastic region (Cohen's f2 = 0.108, p = 0.009), suggesting a potential region-specific regulatory association. We visualized beta-diversity in methylation pattern composition using t-SNE and classified samples into two groups based on a linear decision boundary in the t-SNE space. This classification stratified prognosis independently of clinical factors (HR = 0.291, 95% CI: 0.06to0.94, p = 0.037; multivariate p = 0.021). These findings propose a novel, composition-based epigenetic stratification framework in CC, revealing that MUC1 promoter methylation pattern structure-rather than average methylation level-has prognostic relevance. Our results highlight the potential of pattern-resolved methylation profiling in the development of clinically applicable epigenetic biomarkers.

Abstract. In the polar middle and upper atmosphere, nitric oxide (NO) is produced in large amounts by both solar EUV and X-ray radiation and energetic particle precipitation, and its chemical loss is driven by photodissociation. As a result, polar atmospheric NO has a clear seasonal variability and a solar cycle dependency which have been measured by satellite-based instruments. On shorter timescales, NO response to magnetospheric electron precipitation has been shown to take place on a day-to-day basis. Despite recent studies using observations and simulations, it remains challenging to understand NO daily distribution in the mesosphere–lower thermosphere during geomagnetic storms and to separate contributions of electron forcing and atmospheric chemistry and dynamics. This is due to the uncertainties existing in the available electron flux observations, differences in representation of NO chemistry in models, and differences between NO observations from satellite instruments. In this paper, we use mesospheric–lower-thermospheric NO column density data measured with a millimeter-wave spectroscopic radiometer at the Syowa station in Antarctica. In the period 2012–2017, we study both the long-term and short-term variability of NO. Comparisons are made with results from the Whole Atmosphere Community Climate Model to understand the shortcomings of current electron forcing in models and how the representation of the NO variability can be improved in simulations. We find that, qualitatively, the simulated year-to-year and day-to-day variability of NO is in agreement with the observations. On the other hand, there is up to a factor of 2 underestimation of the NO column density in wintertime. Also, the model captures only 27 % of the range of observed daily NO values. The observed day-to-day variability has a good correlation with three different geomagnetic indices, indicating the importance of electron forcing in atmospheric NO production. Using electron flux measurements from the Arase satellite, we demonstrate their potential in atmospheric research. Our results call for improved representation of electron forcing in simulations to capture the observed day-to-day variability.