Earth-like planets orbiting in the habitable zone of K- to G-type stars create an RV effect in amplitude of less than 1 and have orbital periods of hundreds of days. Only long-term RV surveys with sub-meter per second precision instruments can explore the outer regions of Sun-like stars and look for Earth-like planets and super-Earths. Detecting Earth-like or super-Earth planets in the habitable zone of Sun-like stars is crucial to provide targets to the next generation of direct imaging facilities. We present the analysis of the K-type star HD 176986. It has a brightness of V=8.45 mag and a distance from the Sun of d = 27.88 pc. This star hosts a known planetary system of two super-Earths. We utilize historical and recently collected RV measurements to investigate the presence of Earth- and super-Earth-like planets in the habitable zone of HD 176986. We monitored the system with HARPS and HARPS-N. We joined historical datasets with new data collected in an ongoing blind search program. We took advantage of recently developed tools for RV extraction and stellar activity filtering. The analysis of activity indicators permits us to determine the period of the magnetic cycle of the star alongside its rotation period. We performed a joint analysis of RVs and activity indicators through multidimensional GPs to better constrain the activity model in RVs and avoid overfitting. We detected a new planet orbiting the star and retrieved the two known planets. HD 176986 b has an orbital period of 6.49164 +0.00030 _ -0.00029 $ and a minimum mass of 5.36 ± 0.44 M⊕. HD 176986 c has an orbital period of P_c = 16.8124 ± 0.0015 and a minimum mass of 9.75_ -0.64 ^ +0.65 M⊕. HD 176986 d has an orbital period of 61.376^ +0.051 _ -0.049 and a minimum mass of 6.76_ -0.92 ^ +0.91 M⊕. From the analysis of activity indicators, we find evidence of a magnetic cycle with a period of 2432_ -59 ^ +64 , along with a rotation period of 36.05 $_ -0.71 ^ +0.67 . We discover a new planet in the multi-planet system orbiting the K-type star HD 176986. All the planets have minimum masses compatible with super-Earths or mini-Neptunes.

Mesoporous silica and its derivatives might enable applications ranging from biomedicine to petrochemical processing. Transmission electron microscopy (TEM), X-ray diffraction (XRD) and N2 adsorption–desorption measurements are usually used to characterize the ordered porous system. However, none of these methods convey the full surface information. In this work, a low-voltage scanning electron microscope (LVSEM) with beam deceleration technology was employed to image detailed surface structures of ~2 nm pore size silica (MCM-41), SBA-15, KIT-6, and mesoporous silica nanospheres (MSNSs). The prospects for the development of this application of ultra-high-resolution scanning electron microscopy (SEM) are discussed in the characterization of the ordered porous materials. We demonstrate that the complete dimension range of the mesoscopic surface structure (2–50 nm) could be resolved by current low-voltage SEM technology.

OASIS Survey Direct Imaging and Astrometric Discovery of HIP 71618 B: A Substellar Companion Suitable for the Roman Coronagraph Technology Demonstration**Based in part on data collected at the Subaru Telescope, which is operated by the National Astronomical Observatory of Japan.

SCExAO/CHARIS and Gaia Direct Imaging and Astrometric Discovery of a Superjovian Planet 3–4 λ/D from the Accelerating Star HIP 54515**Based in part on data collected at Subaru Telescope, which is operated by the National Astronomical Observatory of Japan.

Effects of drugs covalent binding on DNA: joint use of microRaman spectroscopy and HRTEM imaging.

Synergistic effects of microplastics and bioaerosols: emerging trends in urban air pollution complexification and public health implications.

Direct edge-enhanced imaging through multimode fibers via transmission matrix-improved spiral phase contrast

Association between microplastics exposure and gut microbiota and metabolites in older adults: A cross-sectional study.

Abstract Science yield studies will drive the development of future direct imaging telescopes, such as the Habitable Worlds Observatory. These studies rely on a metric called completeness, which represents the fraction of planets from an assumed planet population that can be detected for a given observing scenario. Completeness is often calculated by comparing the brightness of planets in the population to the “photometric constraint,” or the dimmest planet detectable in a given observing scenario. The photometric constraint has also been used to calculate the probability of directly imaging a planet detected by the radial velocity method. This work shows how to numerically and analytically invert the analytic exposure time calculator used by the Nancy Grace Roman Space Telescope’s coronagraph instrument to calculate a precise photometric constraint that accounts for planet–star separation, integration time, zodiacal light brightness during observation, and assumed exozodiacal light. This refined photometric constraint is then used to calculate completeness and probability of detection in an efficient manner. Finally, we show that the probability of detection values calculated using the refined photometric constraint closely tracks the planet’s true detectability. We validate our approach by generating realistic planetary systems, simulating an extreme-precision radial velocity survey, performing orbit fits, and computing the probability of directly imaging the fitted planets with a telescope design similar to the future Habitable Worlds Observatory. Our validation tests show that the probability of detection values predicts the number of direct imaging detections to within 4% when scheduling observations at high probability of detection values.

Soft actuators that respond to external stimuli play a fundamental role in microscale robotics, active matter, and bio-inspired systems. Among these actuators, photo-thermal hybrid microgels (HMGs) containing plasmonic nanoparticles enable rapid, spatially controlled actuation via localized heating. Understanding their dynamic behavior at the single-particle level is crucial for optimizing performance. However, traditional bulk characterization methods such as dynamic light scattering (DLS) provide only ensemble-averaged data, thereby limiting analytical insights. Here, a dual-laser optical tweezers approach is introduced for real-time, single-particle analysis of HMGs under controlled light exposure. Combining direct imaging and mean-squared displacement (MSD) analysis, our method quantifies the precise laser power required for actuation and accurately tracks the particle size. The results are benchmarked against dual-laser DLS, demonstrating comparable precision while offering the unique advantage of single-actuator resolution. Thus, this method provides a robust platform for precise optimization of programmable actuators with applications in soft robotics, microswimmers, and biomedical devices.

Designing efficient electrochemical interfaces for sustainable energy conversion requires a detailed microscopic understanding of these complex solid–liquid interfaces. This challenge has driven significant advancements in in situ interface-sensitive techniques, particularly scanning probe microscopy. While electrochemical scanning tunneling methods have provided valuable insights into electrode surface structures and morphology, their ability to resolve the solvent structure at the liquid side remains limited—creating a critical gap in our fundamental understanding of electrochemical interfaces.High-resolution atomic force microscopy (AFM), however, has demonstrated the ability to visualize the vertical arrangement of water molecules at solid-liquid interfaces [1]. Yet, only a few studies have successfully implemented this approach under electrochemical control. To address this gap, we have developed an electrochemical frequency-modulation AFM that can be simultaneously operated with scanning tunneling microscopy (STM) using stiff, self-sensing quartz cantilevers (qPlus sensors) [2,3]. This integrated approach provides high spatial resolution, enabling direct imaging of both the vertical layering of interfacial solvents and the lateral structure of the electrochemical double layer.In this talk, I will discuss how the simultaneous operation of electrochemical STM and AFM with qPlus sensors provides new opportunities to probe the molecular-scale structure of electrified interfaces. I will present recent findings from our studies on well-defined single-crystal electrodes, particularly Au(111) in aqueous electrolytes, where we observe distinct, potential-dependent oscillations in the frequency shift signal along the surface-normal direction. These oscillations, which vary with the applied potential, electrode charge, and ion type, provide valuable insights into water and ion structuring at electrochemical interfaces. By correlating these observations with atomistic molecular dynamics simulations, we enhance our understanding of the interfacial organization in electrochemical systems.[1] T. Fukuma and R. Garcia., ACS Nano. 12, 11785–11797 (2018).[2] F.J. Giessibl, Rev. Sci. Instrum. 90, 011101 (2019).[3] A. Auer, B. Eder and F.J. Giessibl, J. Chem. Phys. 159, 174201 (2023).[4] A. Auer, F.J. Giessibl, J. Kunze-Liebhäuser, ACS Nano 19, 8401 (2025).

Since the discovery of 51 Pegasi b in 1995, exoplanet research has evolved from serendipitous radial-velocity detections to large-scale surveys employing transit photometry, microlensing, astrometry, and high-contrast direct imaging. Each technique probes different physical regimes, enabling the measurement of planetary masses, radii, orbital architectures, and atmospheric compositions. However, as instrument precision and data volume continue to grow, traditional detection algorithms struggle with noise, degeneracy, and the massive data throughput of modern facilities. Recent progress in machine learning,especially deep convolutional and generative models,has begun to transform this field, improving sensitivity and automation across all detection modalities. This review provides a chronological and conceptual overview of exoplanet detection methods, highlighting how data-driven frameworks are reshaping exoplanet discovery and characterization. The paper concludes with a discussion of key challenges, interpretability issues, and prospects for future space and ground-based missions.

This study explores the distinctive physical properties of HoMn6Sn6 single crystal with a kagome lattice structure. The magnetic and electronic transport properties of this material exhibit strong dependence on both the rare-earth element R and the crystallographic orientation. We systematically investigate in-plane magnetism and electronic transport in HoMn6Sn6, revealing a temperature-induced spin reorientation. The observation of a topological Hall effect within the 100–300 K range provides evidence of non-collinear spin textures. These textures are identified as 180° and 360° magnetic domain walls through direct imaging using Lorentz transmission electron microscopy. Our results establish HoMn6Sn6 as a good candidate for investigating the interplay between magnetic domain walls and magnetoelectric transport properties of kagome magnetic materials.

Searches for the putative large-scale X-ray halo around the Geminga pulsar have been extensively performed using various narrow field-of-view X-ray telescopes. In this paper, we present wide-field scanning observation of Geminga with srg art . Our X-ray analysis provides, for the first time, direct imaging of a $3.5^̧irc region in the 4-12 keV energy band, comparable in extent to the expected Geminga emission. The art observation provides a highly uniform sky coverage without strong vignetting effects. The synchrotron X-ray halo flux was predicted using a physical model based on particle injection, diffusion, and cooling over the pulsar’s lifetime, as well as the spectral and spatial properties of the synchrotron X-ray and inverse-Compton gamma-ray emissions. The model is tuned to reproduce existing multiwavelength data from X-ray upper limits and GeV to TeV gamma-ray observations. After accounting for the high particle background and its uncertainties, no significant emission is found in the assumed source region, and X-ray flux upper limits are derived. These limits are less constraining by up to a factor of three with respect to existing results obtained with narrow field-of-view telescopes and longer exposure times. Nonetheless, we place direct and independent constraints on Geminga's ambient magnetic field strength, which are compatible with other studies. Our methodology, including simulation for longer observation times, is applied for the first time to the wide field-of-view search for pulsar halos. Using extensive simulations, we also show that a 68% probability of detecting the Geminga pulsar halo can be achieved with a 20-day srg art exposure for a 3 μ G magnetic field.

Matrix-assisted laser desorption ionization mass spectrometry imaging (MALDI MSI) is widely used for biomolecular mapping but has not been extensively applied for direct element imaging. Established techniques such as LA-ICP-MS and SIMS provide high sensitivity for elemental analysis but lack the ability to simultaneously map elements and biomolecules. Here, we demonstrate the feasibility of MALDI MSI for direct spatial element profiling across multiple biological contexts. We optimized MALDI MSI parameters─including laser power, ionization conditions, and mass resolving power─to enable the detection of endogenous and exogenous elements such as Fe, Ca, Gd, Pt, and Cl. Using a combination of high-resolution FTICR-MS and timsTOF MSI, we assessed ionization efficiency, spectral fidelity, and isotopic accuracy. The method was applied to murine developmental models, genetic metal accumulation disorders, and platinum-based chemotherapy distributions in tumors. Our results demonstrate that MALDI MSI enables element detection while preserving spatial integrity. Computational modeling and spectral similarity analysis confirmed the reliability of isotopologue distributions. These findings establish MALDI MSI as a viable alternative for high-resolution element imaging, offering a complementary approach to LA-ICP-MS and SIMS by integrating atomic and biomolecular spatial distributions. This method expands the analytical capabilities of MALDI MSI and opens new avenues for metallomics, pharmacokinetics, and disease biomarker research.

Recently discovered 2D van der Waals magnetic materials, and specifically iron-germanium-telluride (Fe5GeTe2), have attracted significant attention both from a fundamental perspective and for potential applications. Key open questions concern their domain structure and magnetic phase transition temperature as a function of sample thickness and external field, as well as implications for integration into devices such as magnetic memories and logic. Here we address key questions using a nitrogen-vacancy center based quantum magnetic microscope, enabling direct imaging of the magnetization of Fe5GeTe2 at submicrometer spatial resolution as a function of temperature, magnetic field, and thickness. This quantum imaging technique provides noninvasive, high-sensitivity measurements with high spatial resolution under ambient conditions, making it particularly well suited for probing 2D magnets. We employ spatially resolved measures, including magnetization variance and cross-correlation, and find a significant spread in transition temperature yet with no clear dependence on thickness down to 15 nm. We also identify previously unknown stripe features in the optical as well as magnetic images, which we attribute to modulations of the constituting elements during crystal synthesis and subsequent oxidation. Our results suggest that the magnetic anisotropy in this material does not play a crucial role in their magnetic properties, leading to a magnetic phase transition of Fe5GeTe2 which is largely thickness-independent down to 15 nm. Our findings could be significant in designing future spintronic devices, magnetic memories, and logic with 2D van der Waals magnetic materials.

High-contrast imaging relies on advanced coronagraphs and adaptive optics (AO) to attenuate the starlight. However, residual aberrations, especially non-common path aberrations between the AO channel and the coronagraph channel, limit the instrument performance. While post-processing techniques such as spectral or angular differential imaging (ADI) can partially address those issues, they suffer from self-subtraction and inefficiencies at small angular separations or when observations are conducted far from transit. We previously demonstrated the on-sky performance of coherent differential imaging (CDI), which offers a promising alternative. It allows for isolating coherent starlight residuals through speckle modulation, which can then be subtracted from the raw images during post-processing. This work aims to validate a CDI method on real science targets using VLT/SPHERE, demonstrating its effectiveness in imaging almost face-on circumstellar disks, which are typically challenging to retrieve with ADI. We temporally modulated the speckle field in VLT/SPHERE images, applying small phase offsets on the AO deformable mirror while observing stars surrounded by circumstellar material: HR 4796A, CPD-36 6759, HD 169142, and HD 163296. We hence separated the astrophysical scene from the stellar speckle field, whose lights are mutually incoherent. Combining a dozen of data frames and reference coronagraph point spread functions through a Karhunen–Loève image projection framework, we recover the circumstellar disks without the artifacts that are usually introduced by common post-processing algorithms (e.g., self-subtraction). The CDI method therefore represents a promising strategy for calibrating the effect of static and quasi-static aberrations in future direct imaging surveys. Indeed, it is efficient, does not require frequent telescope slewing, and does not introduce image artifacts to first order.

Strongly scattering conditions are detrimental to imaging. Conventional methods rely solely on post-processing techniques to enhance the ballistic components of an obscured target to recover the image; however, they struggle in complex conditions where the ballistic components are heavily overwhelmed by scattering. In this work, we present an optical meta-image-processor (MIP) that tailors the scattered point spread function of the imaging system to enable high-quality, deep imaging through strongly scattering media. The MIP performs both Laplacian and Gaussian operations in a single device, effectively suppressing background interference and Gaussian noise in the obscured image. Experimental results demonstrate that clear information can be recognized with the MIP, even when the optical thickness of the scattering medium reaches a challenging value of 17.05. Without the MIP, such imaging depth cannot be achieved through direct imaging, even when combined with any other post-processing techniques. Additionally, the MIP shows potential for enhancing the diagnostic performance of fundus cameras in the presence of cataracts. Both simulated and experimental evidence confirms the MIP’s capability to reveal hidden information under strongly scattering conditions, underscoring its promising potential to enhance imaging depth in biomedical imaging, machine vision, and artificial intelligence, especially in complex environments.

Abstract By employing backward ray-tracing techniques, we investigate the shadow image of rotating black holes in Kalb-Ramond gravity. We consider two primary emission models: a spherical source and a thin accretion disk, with the latter assumed to be optically and geometrically thin. The results reveal that enhanced black hole rotation parameter $a$ amplifies the shadow's departure from circular symmetry, whereas spontaneous Lorentz symmetry-breaking parameters $\mathcal{G}$ and $\lambda$ suppress the shadow radius. For accretion disk models, observer inclination angle $\theta_o$ predominantly governs the inner shadow morphology and photon ring brightness asymmetry, while $a$, $\mathcal{G}$, and $\lambda$ primarily modulate the inner shadow scale. An increase in $\theta_o$ induces a morphological transition of the inner shadow from a circular to a D-shaped geometry, accompanied by enhanced brightness in a crescent-shaped region on the left side. Meanwhile, increasing the values of $a$, $\mathcal{G}$, or $\lambda$ consistently reduces the shadow dimensions. Furthermore, higher inclination angles $\theta_o$ further enhance spectral differentiation, that is, low inclination angles exhibit exclusively redshifted emission, whereas those at high inclination angles produce blueshifted components in both direct and lensed images. These characteristic signatures provide observational discriminators between rotating Kalb-Ramond black holes and alternative spacetime.

Introduction: Abnormalities in the function and structure of the left atrium, called atrial cardiopathy, are a precursor of atrial fibrillation (AF) and an important risk factor for several other cardiovascular outcomes, yet detecting abnormalities is challenging due to the cost and limited accessibility of high-quality cardiac imaging. Objective: To develop and validate a deep learning model (ECG-AI) that estimates left atrial (LA) structure and function from the resting 12-lead electrocardiogram and reliably predicts cardiovascular outcomes. Methods: We trained ECG-AI models on cardiac magnetic resonance imaging data from the UK Biobank (n=21,749) to estimate LA minimum volumes, maximum volumes and ejection fraction. Volumes were indexed to body surface area. Cox regression models adjusting for clinical risk factors evaluated associations of LA with incident AF, ischemic stroke and cardioembolic stroke in an external cohort of adults aged ≥ 65, the Cardiovascular Health Study. We compared prediction models for incident AF and cardioembolic stroke that included [1] age and sex only (base), [2] base + ECG-AI LA measures (ECG-AI), [3] CHARGE-AF risk score alone, and [4] CHARGE-AF + ECG-AI (combined). Results: ECG-AI estimates were moderately correlated with direct imaging measures (r=0.40-0.50) but demonstrated strong independent associations with outcomes in the external cohort and outperformed a conventional ECG measure of atrial cardiopathy, P terminal force in V1. In the Cardiovascular Health Study, each standard deviation increase in ECG-AI LA minimum volume yielded HRs of 1.44 (95% CI 1.35-1.47) for AF, 1.45 (95% CI 1.37-1.53) for ischemic stroke, and 1.66 (95% CI 1.47-1.86) for cardioembolic stroke, the hallmark complication of AF and atrial cardiopathy (Figure 1). In contrast, none of the ECG-AI measures was associated with large-artery atherosclerotic or small vessel stroke. In 5-year prediction models (Figure 2), the ECG-AI measures outperformed the CHARGE-AF risk prediction tool for both AF (delta AUC 0.03, 95% CI 0.01-0.05) and cardioembolic stroke (delta AUC 0.01, 95% CI -0.05-0.08). Conclusion: Our fully-trained ECG-AI tool estimates LA function and identifies individuals at elevated-risk for cardiovascular disease using only standard data from the inexpensive and widely-available 12-lead ECG.