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Deep neural network model extraction attack is the process of retraining a surrogate model based on the outputs of a target model with a given set of inputs. Such attacks are hard to defend for the sake of model owners' interest. Recently, some work propose model watermarking scheme for image processing networks, which is able to prove the intellectual property of deep models even after the model extraction attack. This scheme makes sure that, once the target model (an image processing network) is watermarked, we can extract the watermark from the output of the surrogate model. In this paper, we propose a new model extraction attack scheme to fight against the latest method. Instead of directly using the output images of a target model, we propose to use their reconstructed versions for model retraining, where an asymmetrical UNet is proposed for image reconstruction. To thoroughly remove the watermarking traces, we propose and incorporate a referenced subspace attention module in the asymmetrical UNet, which removes the watermark by projecting the outputs of the target model into the subspaces of the reference image. Various experiments demonstrate the effectiveness of our attack. [ABSTRACT FROM AUTHOR]

Motivation BISCAP is a state-of-the-art tool for automatically characterizing biofilm images obtained from Optical Coherence Tomography. Limited availability of other software tools is reported in the field. BISCAP's first version processes 2D images only. Processing 3D images is a problem of greater scientific relevance since it deals with the entire structure of biofilms instead of their 2D slices. Results Building on the image-processing principles and algorithms proposed earlier for 2D images, these were adapted to the 3D case, and a more general implementation of BISCAP was developed. The primary goal concerns the extension of the initial methodology to incorporate the depth axis in 3D images; multiple improvements were also made to boost computational performance. The calculation of structural properties and visual outputs was extended to offer new insights into the 3D structure of biofilms. BISCAP was tested using 3D images of biofilms with different morphologies, consistently delivering accurate characterizations of 3D structures in a few minutes using standard laptop machines. Low user dependency is required for image analysis. Availability and implementation BISCAP is available from https://github.com/diogonarciso/BISCAP. All images used in the tutorials and the validation examples are available from https://web.fe.up.pt/∼fgm/biscap3d. [ABSTRACT FROM AUTHOR]

One of the fundamental questions in plant developmental biology is how cell proliferation and cell expansion coordinately determine organ growth and morphology. An amenable system to address this question is the Arabidopsis root tip, where cell proliferation and elongation occur in spatially separated domains, and cell morphologies can easily be observed using a confocal microscope. While past studies revealed numerous elements of root growth regulation including gene regulatory networks, hormone transport and signaling, cell mechanics and environmental perception, how cells divide and elongate under possible constraints from cell lineages and neighboring cell files has not been analyzed quantitatively. This is mainly due to the technical difficulties in capturing cell division and elongation dynamics at the tip of growing roots, as well as an extremely labor-intensive task of tracing the lineages of frequently dividing cells. Here, we developed a motion-tracking confocal microscope and an Artificial Intelligence (AI)-assisted image-processing pipeline that enables semi-automated quantification of cell division and elongation dynamics at the tip of vertically growing Arabidopsis roots. We also implemented a data sonification tool that facilitates human recognition of cell division synchrony. Using these tools, we revealed previously unnoted lineage-constrained dynamics of cell division and elongation, and their contribution to the root zonation boundaries. [ABSTRACT FROM AUTHOR]

Advancements in imaging technologies have revolutionized our ability to deeply profile pathological tissue architectures, generating large volumes of imaging data with unparalleled spatial resolution. This type of data collection, namely, spatial proteomics, offers invaluable insights into various human diseases. Simultaneously, computational algorithms have evolved to manage the increasing dimensionality of spatial proteomics inherent in this progress. Numerous imaging-based computational frameworks, such as computational pathology, have been proposed for research and clinical applications. However, the development of these fields demands diverse domain expertise, creating barriers to their integration and further application. This review seeks to bridge this divide by presenting a comprehensive guideline. We consolidate prevailing computational methods and outline a roadmap from image processing to data-driven, statistics-informed biomarker discovery. Additionally, we explore future perspectives as the field moves toward interfacing with other quantitative domains, holding significant promise for precision care in immuno-oncology. [ABSTRACT FROM AUTHOR]

STUDY QUESTION Can the density of the inner cell mass (ICM) be a new indicator of the quality of the human blastocyst? SUMMARY ANSWER The densification index (DI) developed in this study can quantify ICM density and provide positive guidance for ploidy, pregnancy, and live birth. WHAT IS KNOWN ALREADY In evaluating the quality of ICM, reproductive care clinics still use size indicators without further evaluation. The main disadvantage of this current method is that the evaluation of blastocyst ICM is relatively rough and cannot meet the needs of clinical embryologists, especially when multiple blastocysts have the same ICM score, which makes them difficult to evaluate further. STUDY DESIGN, SIZE, DURATION This observational study included data from 2272 blastocysts in 1991 frozen–thawed embryo transfer (FET) cycles between January 2018 to November 2021 and 1105 blastocysts in 430 preimplantation genetic testing cycles between January 2019 and February 2023. PARTICIPANTS/MATERIALS, SETTING, METHODS FET, ICSI, blastocyst culture, trophectoderm biopsy, time-lapse (TL) monitoring, and next-generation sequencing were performed. After preliminary sample size selection, the 11 focal plane images captured by the TL system were normalized and the spatial frequency was used to construct the DI of the ICM. MAIN RESULTS AND THE ROLE OF CHANCE This study successfully constructed a quantitative indicator DI that can reflect the degree of ICM density in terms of fusion and texture features. The higher the DI value, the better the density of the blastocyst ICM, and the higher the chances that the blastocyst was euploid (P  < 0.001) and that pregnancy (P  < 0.001) and live birth (P  = 0.005) were reached. In blastocysts with ICM graded B and blastocysts graded 4BB, DI was also positively associated with ploidy, pregnancy, and live birth (P  < 0.05). ROC analysis showed that combining the Gardner scoring system with DI can more effectively predict pregnancy and live births, when compared to using the Gardner scoring system alone. LIMITATIONS, REASONS FOR CAUTION Accurate calculation of the DI value places high demands on image quality, requiring manual selection of the clearest focal plane and exposure control. Images with the ICM not completely within the field of view cannot be used. The association between the density of ICM and chromosomal mosaicism was not evaluated. The associations between the density of ICM and different assisted reproductive technologies and different culture conditions in embryo laboratories were also not evaluated. Prospective studies are needed to further investigate the impact of ICM density on clinical outcomes. WIDER IMPLICATIONS OF THE FINDINGS ICM density assessment is a new direction in blastocyst assessment. This study explores new ways of assessing blastocyst ICM density and develops quantitative indicators and a corresponding qualitative evaluation scheme for ICM density. The DI of the blastocyst ICM developed in this study is easy to calculate and requires only TL equipment and image processing, providing positive guidance for clinical outcomes. The qualitative evaluation scheme of ICM density can assist embryologists without TL equipment to manually evaluate ICM density. ICM density is a simple indicator that can be used in practice and is a good complement to the blastocyst scoring systems currently used in most centers. STUDY FUNDING/COMPETING INTEREST(S) This work was supported by the National Key Research & Development Program of China (2021YFC2700603). The authors report no financial or commercial conflicts of interest. TRIAL REGISTRATION NUMBER N/A. [ABSTRACT FROM AUTHOR]

The Covid-19 pandemic has been identified as a key issue for human society, in recent times. The presence of the infection on any human is identified according to different symptoms like cough, fever, headache, breathless and so on. However, most of the symptoms are shared by various other diseases, which makes it challenging for the medical practitioners to identify the infection. To aid the medical practitioners, there are a number of approaches designed which use different features like blood report, lung and cardiac features to detect the disease. The method captures the lung image using magnetic resonance imaging scan device and records the cardiac features. Using the image, the lung features are extracted and from the cardiac graph, the cardiac features are extracted. Similarly, from the blood samples, the features are extracted. By extracting such features from the person, the method estimates different weight measures to predict the disease. Different methods estimate the similarity of the samples in different ways to classify the input sample. However, the image processing techniques are used for different problems in medical domain; the same has been used in the detection of the disease. Also, the presence of Covid-19 is detected using different set of features by various approaches. [ABSTRACT FROM AUTHOR]

Summary perox-per-cell automates cumbersome, image-based data collection tasks often encountered in peroxisome research. The software processes microscopy images to quantify peroxisome features in yeast cells. It uses off-the-shelf image processing tools to automatically segment cells and peroxisomes and then outputs quantitative metrics including peroxisome counts per cell and spatial areas. In validation tests, we found that perox-per-cell output agrees well with manually quantified peroxisomal counts and cell instances, thereby enabling high-throughput quantification of peroxisomal characteristics. Availability and implementation The software is coded in Python. Compiled executables and source code are available at https://github.com/AitchisonLab/perox-per-cell. [ABSTRACT FROM AUTHOR]

Sewing defect detection is an essential step in garment production quality control. Although sewing defects significantly influence the quality of clothing, they are yet to be studied widely compared to fabric defects. In this study, to address sewing defect detection and develop an appropriate method for small and labor-intensive garment companies, an on-machine broken stitch detection system is proposed. In hardware, a versatile mounting kit, including clamping, display, and adjustable linkage for a camera, is presented for easy installation on a typical industrial sewing machine and for placing the camera close to the sewing position. Additionally, a prototype is implemented using a low-cost single-board computer, Raspberry Pi 4 B, its camera, and Python language. For automated broken stitch detection, a method is proposed that includes removing the texture of the background fabric, image processing in the HSV color space, and edge detection for robust broken detection under various fabric and thread colors and lighting conditions. The proposed system demonstrates reasonable real-time detection accuracy. The maximum accuracy obtained on a sewing stitch dataset with 880 images and on-site tests of various industrial sewing machines is 82.5%, which is 12.1–34.6% higher than that of the two existing methods. [ABSTRACT FROM AUTHOR]

The aim of this work is propose and describe ImagIngDev, a new approach for developing automatic cross-platform mobile applications using image processing techniques. As proof of concept, we presented ImagIng Tool and compared its performance with respect to similar cross-platform application development tools. Our main contribution to software development is ImagingDev, a new, intuitive and agile approach aimed at novice developers for automatically developing cross-platform mobile applications. Our proof of concept, ImagIng Tool, can generate mobile applications on four different software platforms: Android™, Windows Phone™, iOS™ and FirefoxOS™. Also, it relies on image processing techniques to recognize user interface design patterns (UIDPs) inside user interfaces, and generates the source code for cross-platform and multi-device applications. As proof of concept, we developed ImagIng Tool for automatic generation of cross-platform mobile applications by using image processing techniques. Results from the evaluation demonstrated that ImagIng Tool has attractive benefits if compared to similar cross-platform application development tools. Such benefits include higher learnability and usability. ImagIngDev and ImagIng Tool can effectively solve current problems in mobile applications development, such as automatic code generation for cross-platform mobile applications, since they allow users to generate cross-platform and multi-device mobile applications in an easy and intuitive way. [ABSTRACT FROM AUTHOR]

An image identification method was developed with the aid of a deep convolutional neural network (CNN) and applied to the analysis of inorganic particles using electron holography. Despite significant variation in the shapes of α-Fe2O3 particles that were observed by transmission electron microscopy, this CNN-based method could be used to identify isolated, spindle-shaped particles that were distinct from other particles that had undergone pairing and/or agglomeration. The averaging of images of these isolated particles provided a significant improvement in the phase analysis precision of the electron holography observations. This method is expected to be helpful in the analysis of weak electromagnetic fields generated by nanoparticles showing only small phase shifts. [ABSTRACT FROM AUTHOR]

Chitin, poly (β-(1→4)-N-acetyl-d-glucosamine), is an important biopolymer for insects that is utilized as a major component of peritrophic membrane. The chitin content in peritrophic membrane is of expedient interest from a pest control perspective, although it is hard to quantify chitin. In this study, we establish a facile method for the quantification of chitin in peritrophic membrane by image processing. In this method, chitin was indirectly quantified using chitosan–I3− complex, which exhibited a specific red-purple color. A calibration curve using a chitosan solution showed good linearity in a concentration range of 0.05–0.5 μg/μL. We quantified the amount of chitin in peritrophic membrane of Spodoptera litura (Lepidoptera: Noctuidae) larvae using this method. Throughout the study, only common inexpensive regents and easily attainable apparatuses were employed. This method can be easily applied to the sensitive quantification of the amounts of chitin and chitosan in materials by wide range of researchers. Abbreviations: LOD: limit of detection; LOQ: limit of quantification; ROI: region of interest; RSD: relative standard deviation. Chitin in peritrophic membrane of insects was quantified by image processing with an easy, inexpensive and sensitive method. [ABSTRACT FROM AUTHOR]

Photo-plates are an invaluable historical legacy that have been used for over a hundred years to capture images of celestial objects. By digitizing these photo-plates and processing the images with digital image processing methods, scientists can study celestial objects that exhibit temporal variations. Multiple-exposure photo-plates are a unique type of observation data that can capture images of the same sky at different observation times in a single photo-plate. Such photo-plates can be used to discover flares or moving targets with rapid variations, but they are difficult to process automatically due to their complex observation strategies. This paper proposes a pipeline based on classical data-processing algorithms and machine-learning algorithms to detect flares or moving targets in multiple-exposure photo-plate images automatically. The pipeline was used to process several digitized multiple-exposure photo-plate images from the China Astronomical Plates Data, and preliminary results indicate that the pipeline is effective. In the future, we plan to use our method to discover more celestial objects with temporal variations from photo-plate digital archives. [ABSTRACT FROM AUTHOR]

This paper presents the 'NoisyOffice' database. It consists of images of printed text documents with noise mainly caused by uncleanliness from a generic office, such as coffee stains and footprints on documents or folded and wrinkled sheets with degraded printed text. This corpus is intended to train and evaluate supervised learning methods for cleaning, binarization and enhancement of noisy images of grayscale text documents. As an example, several experiments of image enhancement and binarization are presented by using deep learning techniques. Also, double-resolution images are also provided for testing super-resolution methods. The corpus is freely available at UCI Machine Learning Repository. Finally, a challenge organized by Kaggle Inc. to denoise images, using the database, is described in order to show its suitability for benchmarking of image processing systems. [ABSTRACT FROM AUTHOR]

Fault detection in a seismic image is a key step of structural interpretation. Structure-oriented smoothing with edge-preserving removes noise while enhancing seismic structures and sharpening structural edges in a seismic image, which, therefore, facilitates and accelerates the seismic structural interpretation. Estimating seismic normal vectors or reflection slopes is a basic step for many other seismic data processing tasks. All the three seismic image processing tasks are related to each other as they all involve the analysis of seismic structural features. In conventional seismic image processing schemes, however, these three tasks are often independently performed by different algorithms and challenges remain in each of them. We propose to simultaneously perform all the three tasks by using a single convolutional neural network (CNN). To train the network, we automatically create thousands of 3-D noisy synthetic seismic images and corresponding ground truth of fault images, clean seismic images and seismic normal vectors. Although trained with only the synthetic data sets, the network automatically learns to accurately perform all the three image processing tasks in a general seismic image. Multiple field examples show that the network is significantly superior to the conventional methods in all the three tasks of computing a more accurate and sharper fault detection, a smoothed seismic volume with better enhanced structures and structural edges, and more accurate seismic normal vectors or reflection slopes. Using a Titan Xp GPU, the training processing takes about 8 hr and the trained model takes only half a second to process a seismic volume with |$128\, \times \, 128\, \times \, 128$| image samples. [ABSTRACT FROM AUTHOR]

SUMMARY: To address complex subsurface structures, a high-resolution velocity model must be constructed. Conventionally, algorithms such as full waveform inversion (FWI) have been used to derive accurate high-resolution velocity structures, but obstacles such as high computational costs remain. Therefore, we propose a high-resolution U-NET (HR U-NET) machine learning model to derive a high-resolution velocity model from a low-resolution velocity model. The low-resolution velocity model and migration data obtained through the corresponding velocity information were used as input data for training. In addition, we tried to improve the accuracy of the high-resolution velocity model by using prior information containing accurate velocity values. A prior model generated through geophysical logging data and a weight model including the reliability information of the prior model were also utilized. Therefore, the HR U-NET model was trained using the low-resolution velocity model, the migration data, the prior model and the weight model. Numerical experiments conducted using synthetic and field data demonstrated that the proposed model could accurately construct a high-resolution velocity model and verified that the prior model and weight model play important roles in the training process. Additionally, we confirmed that the proposed method derived almost similar results using only 8.2 percent of the computational cost of the conventional inversion method. In other words, there is an advantage that it is possible to predict high-resolution velocity information more efficiently in terms of computational cost. [ABSTRACT FROM AUTHOR]

We present the first release of the Calar Alto CAFOS direct imaging data, a project led by the Spanish Virtual Observatory with the goal of enhancing the use of the Calar Alto archive by the astrophysics community. Data Release 1 contains 23 903 reduced and astrometrically calibrated images taken from 2008 March to 2019 July with a median of the mean uncertainties in the astrometric calibration of 0.04 arcsec. The catalogue associated to 6132 images in the Sloan griz filters provides accurate astrometry and PSF calibrated photometry for 139 337 point-like detections corresponding to 21 985 different sources extracted from a selection of 2338 good-quality images. The mean internal astrometric and photometric accuracies are 0.05 arcsec and 0.04 mag, respectively. In this work we describe the approach followed to process and calibrate the images, and the construction of the associated catalogue, together with the validation quality tests carried out. Finally, we present three cases to prove the science capabilities of the catalogue: discovery and identification of asteroids, identification of potential transients, and identification of cool and ultracool dwarfs. [ABSTRACT FROM AUTHOR]

Adaptive optics (AO) systems correct optical phase aberrations of the incoming light generated by the atmosphere. To do so simultaneous estimators of the atmospheric turbulence parameters are required. For the family of wide-field AO (WFAO) systems, this information must be stratified in altitude. Among these vectorized estimations, wind profiling in altitude is needed for the reduction of temporal errors in AO loops or for the estimation of turbulence coherence time. This paper proposes a turbulence wind profiler called image processing based peak tracking algorithm (ipta). ipta is an image-processing based approach that automatically and reliably estimates wind velocity for several turbulent layers along the line of sight. The estimation of each wind layer is achieved by tracking peaks produced in cross-correlation maps from pairs of wavefront sensors (WFSs) slopes using the technique known as SLODAR (slope detection and ranging). Results for simulated and on-sky WFS data sets demonstrate that ipta outperforms one of the state of the art wind profiler methods [the profiler covariance parametrization of wind velocity (CAW)] in terms of accuracy and speed. Results also show that, in terms of execution time, our method scales better when the number of WFS lenslets is increased. Being an open source and reliable tool, we believe ipta can be a useful wind profiler for the AO community. [ABSTRACT FROM AUTHOR]

Sampling of biological and environmental vouchers in the field is rather challenging, particularly under adverse habitat conditions and when various activities need to be handled simultaneously. The workflow described here includes five procedural steps, which result in professional sampling and the generation of universally identifiable data. In preparation for the field campaign, sample containers need to be labelled with universally unique identifier (UUID)-QR-codes. At the collection site, labelled containers, sampled material and attached supplementary information are imaged using a GNSS- respectively GPS-enabled smartphone or camera. Image processing, tagging and data storage as CSV text file is subsequently achieved in a field station or laboratory. For this purposes, the newly implemented tool DiversityImageInspector (URL: http://diversityworkbench.net/Portal/DiversityImageInspector) is used. It addresses combined image and data processing in such a context including the extraction of the QR-coded UUID from the image content and the extraction of geodata and time information from the Exif image header. The import of the resulting data files into a relational database or other kind of data management systems is optional but recommended. If applied, the import might be guided by a data transformation tool with compliant schema as described here. The new approach is discussed also with regard to implications for virtual research environments and data publication networks. Database URL : http://diversityworkbench.net/Portal/DiversityImageInspector [ABSTRACT FROM AUTHOR]

This work proposed a new method which applied image processing and support vector machine (SVM) for screening of mold strains. Taking Monascus as example, morphological characteristics of Monascus colony were quantified by image processing. And the association between the characteristics and pigment production capability was determined by SVM. On this basis, a highly automated screening strategy was achieved. The accuracy of the proposed strategy is 80.6 %, which is compatible with the existing methods (81.1 % for microplate and 85.4 % for flask). Meanwhile, the screening of 500 colonies only takes 20-30 min, which is the highest rate among all published results. By applying this automated method, 13 strains with high-predicted production were obtained and the best one produced as 2.8-fold (226 U/mL) of pigment and 1.9-fold (51 mg/L) of lovastatin compared with the parent strain. The current study provides us with an effective and promising method for strain improvement. [ABSTRACT FROM AUTHOR]

Optical Mapping is an established single-molecule, whole-genome analysis system, which has been used to gain a comprehensive understanding of genomic structure and to study structural variation of complex genomes. A critical component of Optical Mapping system is the image processing module, which extracts single molecule restriction maps from image datasets of immobilized, restriction digested and fluorescently stained large DNA molecules. In this review, we describe robust and efficient image processing techniques to process these massive datasets and extract accurate restriction maps in the presence of noise, ambiguity and confounding artifacts. We also highlight a few applications of the Optical Mapping system. [ABSTRACT FROM AUTHOR]

Motivation Cell membrane segmentation in electron microscopy (EM) images is a crucial step in EM image processing. However, while popular approaches have achieved performance comparable to that of humans on low-resolution EM datasets, they have shown limited success when applied to high-resolution EM datasets. The human visual system, on the other hand, displays consistently excellent performance on both low and high resolutions. To better understand this limitation, we conducted eye movement and perceptual consistency experiments. Our data showed that human observers are more sensitive to the structure of the membrane while tolerating misalignment, contrary to commonly used evaluation criteria. Additionally, our results indicated that the human visual system processes images in both global–local and coarse-to-fine manners. Results Based on these observations, we propose a computational framework for membrane segmentation that incorporates these characteristics of human perception. This framework includes a novel evaluation metric, the perceptual Hausdorff distance (PHD), and an end-to-end network called the PHD-guided segmentation network (PS-Net) that is trained using adaptively tuned PHD loss functions and a multiscale architecture. Our subjective experiments showed that the PHD metric is more consistent with human perception than other criteria, and our proposed PS-Net outperformed state-of-the-art methods on both low- and high-resolution EM image datasets as well as other natural image datasets. Availability and implementation The code and dataset can be found at https://github.com/EmmaSRH/PS-Net. [ABSTRACT FROM AUTHOR]

Phosphorylation reactions performed by protein kinases are one of the most studied post-translational modifications within cells. Much is understood about conserved residues within protein kinase domains that perform catalysis of the phosphotransfer reaction, yet the identity of the target substrates and downstream biological effects vary widely among cells, tissues, and organisms. Here, we characterize key residues essential for NUAK kinase activity in Drosophila melanogaster myogenesis and homeostasis. Creation of a NUAK kinase-dead mutation using Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR)/Cas9 results in lethality at the embryo to larval transition, while loss of NUAK catalytic function later in development produces aggregation of the chaperone protein αB-crystallin/CryAB in muscle tissue. Yeast 2-hybrid assays demonstrate a physical interaction between NUAK and CryAB. We further show that a phosphomimetic version of NUAK promotes the phosphorylation of CryAB and this post-translational modification occurs at 2 previously unidentified phosphosites that are conserved in the primary sequence of human CryAB. Mutation of these serine residues in D. melanogaster NUAK abolishes CryAB phosphorylation, thus, proving their necessity at the biochemical level. These studies together highlight the importance of kinase activity regulation and provide a platform to further explore muscle tissue proteostasis. [ABSTRACT FROM AUTHOR]

Multispectral imaging--a method for acquiring image data over a series of wavelengths across the light spectrum--is becoming a valuable tool within the cultural and heritage sector for the recovery and enhancement of information contained within primary historical texts. However, most applications of this technique, to date, have been bespoke: analysing particular documents of historic importance. There has been little prior work done on evaluating this technique in a structured fashion, to provide recommendations on how best to capture and process images when working with damaged and abraded textual material. This article introduces a new approach for evaluating the efficacy of image processing algorithms in recovering information from multispectral images of deteriorated primary historical texts. We present a series of experiments that deliberately degrade samples cut from a real historical document to provide a set of images acquired before and after damage. These images then allow us to compare, both objectively and quantitatively, the effectiveness of multispectral imaging and image processing for recovering information from damaged text. We develop a methodological framework for the continuing study of the techniques involved in the analysis and processing of multispectral images of primary historical texts, and a dataset which will be of use to others interested in advanced digitisation techniques within the cultural heritage sector. [ABSTRACT FROM AUTHOR]

Providing high-quality electron images and hyperspectral X-ray maps is a focus of many modern electron microscopy laboratories. Nevertheless, further image processing and annotations are often needed to prepare them for publications and reports. For multi-user facilities, accessibility to processing software can be a limitation either through license costs or availability of processing stations. Open-source software running on multiple platforms allows for post-acquisition data processing in-lab or on user-owned devices. We developed Probelab ReImager to supersede our vendor-supplied acquisition software's exportation by being efficient and highly customizable. This article describes its main features and capabilities. [ABSTRACT FROM AUTHOR]

The main advantage of full-waveform inversion (FWI) is the ability to obtain useful subsurface structure information, such as velocity and density, from complex seismic data. We have developed a novel inversion algorithm to improve the capability of FWI to achieve high-resolution imaging, even under complex conditions caused by random noise contamination, initial model dependence, or the selection of parameters to be estimated. Our algorithm considers an effective image processing and dimension reduction tool, randomized singular value decomposition-weighted truncated nuclear norm regularization, for embedding FWI to achieve high-resolution imaging results. This algorithm obtains a truncated matrix approximating the original matrix by reducing the rank of the velocity increment matrix, thus achieving the truncation of noisy data, with the truncation range controlled by weighted truncated nuclear norm regularization. Subsequently, we employ an inexact augmented Lagrangian method algorithm in the optimization to compress the solution space range, thus relaxing the dependence of FWI and randomized singular value decomposition-weighted truncated nuclear norm regularization on the initial model and accelerating the convergence rate of the objective function. We tested on one set of synthetic data, and the results show that, compared with traditional FWI, our method can more effectively suppress the impact of random noise, thus obtaining higher resolution and more accurate subsurface model information. This work indicates that the combination of randomized singular value decomposition-weighted truncated nuclear norm regularization and FWI is an effective imaging strategy that can help to solve the challenges faced by traditional FWI. [ABSTRACT FROM AUTHOR]

Motivation Cryo-electron microscopy (cryo-EM) is a powerful technique for determining the structures of large protein complexes. Picking single protein particles from cryo-EM micrographs (images) is a crucial step in reconstructing protein structures from them. However, the widely used template-based particle picking process requires some manual particle picking and is labor-intensive and time-consuming. Though machine learning and artificial intelligence (AI) can potentially automate particle picking, the current AI methods pick particles with low precision or low recall. The erroneously picked particles can severely reduce the quality of reconstructed protein structures, especially for the micrographs with low signal-to-noise ratio. Results To address these shortcomings, we devised CryoTransformer based on transformers, residual networks, and image processing techniques to accurately pick protein particles from cryo-EM micrographs. CryoTransformer was trained and tested on the largest labeled cryo-EM protein particle dataset—CryoPPP. It outperforms the current state-of-the-art machine learning methods of particle picking in terms of the resolution of 3D density maps reconstructed from the picked particles as well as F1-score, and is poised to facilitate the automation of the cryo-EM protein particle picking. Availability and implementation The source code and data for CryoTransformer are openly available at: https://github.com/jianlin-cheng/CryoTransformer. [ABSTRACT FROM AUTHOR]

We present the joint astrometric and direct imaging discovery, mass measurement, and orbital analysis of HD 63754 B (HIP 38216 B), a companion near the stellar-substellar boundary orbiting |$\sim$| 20 au from its Sun-like host. HD 63754 was observed in our ongoing high-contrast imaging survey targeting stars with significant proper-motion accelerations between Hipparcos and Gaia consistent with wide-separation substellar companions. We utilized archival High Resolution Echelle Spectrograph and High Accuracy Radial Velocity Planet Searcher radial velocity (RV) data, together with the host star's astrometric acceleration extracted from the Hipparcos – Gaia Catalog of Accelerations, to predict the location of the candidate companion around HD 63754 A. We subsequently imaged HD 63754 B at its predicted location using the Near Infrared Camera 2 (NIRC2) in the |$L^{\prime }$| band at the W. M. Keck Observatory. We then jointly modelled the orbit of HD 63754 B with RVs, Hipparcos – Gaia accelerations, and our new relative astrometry, measuring a dynamical mass of |${81.9}_{-5.8}^{+6.4} {M_{\rm Jup}}$|⁠ , an eccentricity of |${0.260}_{-0.059}^{+0.065}$|⁠ , and a nearly face-on inclination of |$174.\!\!^\circ 81_{-0.50}^{+0.48}$|⁠. For HD 63754 B, we obtain an |$L^{\prime }$| -band absolute magnitude of |$L^{\prime } = 11.39\pm 0.06$| mag, from which we infer a bolometric luminosity of |$\rm{log(L_{\rm bol}/{\rm{L}_{\odot}})= -4.55 \pm 0.08}$| dex using a comparison sample of L and T dwarfs with measured luminosities. Although uncertainties linger in age and dynamical mass estimates, our analysis points towards HD 63754 B's identity as a brown dwarf on the L/T transition rather than a low-mass star, indicated by its inferred bolometric luminosity and model-estimated effective temperature. Future RV, spectroscopic, and astrometric data such as those from JWST and Gaia Data Release 4 will clarify HD 63754 B's mass, and enable spectral typing and atmospheric characterization. [ABSTRACT FROM AUTHOR]

Objectives Epigenetically modified fibroblasts contribute to chronicity in inflammatory diseases. Reasons for the relapsing character of large vessel vasculitis (LVV) remain obscure, including the role of fibroblasts, in part due to limited access to biopsies of involved tissue.68Ga FAPI-46 (FAPI)-PET/CT detects activated fibroblasts in vivo. In this exploratory pilot study, we tested the detection of fibroblast activation in vessel walls using FAPI-PET/CT in LVV with aortitis. Methods Eight LVV patients with aortitis and eight age- and gender-matched controls were included. The distribution of FAPI uptake was evaluated in the aorta and large vessels. FAPI-uptake was compared with MRI inflammatory activity scores. Imaging results were compared with clinical parameters such as serum inflammatory markers, time of remission and medication. Results Three aortitis patients were clinically active and five in remission. Irrespective of activity, FAPI uptake was significantly enhanced in aortitis compared with controls. Patients in remission had a mean duration of remission of 2.8 years (range 1–4 years), yet significant FAPI uptake in the vessel wall was found. In remitted aortitis, MRI inflammatory scores were close to be negative, while in 4/5 patients visually identifiable FAPI uptake was observed. Conclusions This pilot feasibility study shows significant tracer uptake in the aortic walls in LVV. FAPI positivity indicates ongoing fibroblast pathology in clinically remitted LVV. [ABSTRACT FROM AUTHOR]

In a strong gravitational lensing system, the distorted light from a source is analysed to infer the properties of the lens. However, light emitted by the lens itself can contaminate the image of the source, introducing systematic errors in the analysis. We present a simple and efficient lens light model based on the well-tested multi-Gaussian expansion (MGE) method for representing galaxy surface brightness profiles, which we combine with a semi-linear inversion scheme for pixelized source modelling. Testing it against realistic mock lensing images, we show that our scheme can fit the lensed images to the noise level, with relative differences between the true input and best-fitting lens light model remaining below 5 per cent. We apply the MGE lens light model to 38 lenses from the HST SLACS sample. We find that the new scheme provides a good fit for the majority of the sample with only 3 exceptions – these show clear asymmetric residuals in the lens light. We examine the radial dependence of the ellipticity and position angles and confirm that it is common for a typical lens galaxy to exhibit twisting non-elliptical isophotes and boxy / disky isophotes. Our MGE lens light model will be a valuable tool for understanding the hidden complexity of the lens mass distribution. [ABSTRACT FROM AUTHOR]

Observation datasets acquired by the Hyper Suprime-Cam (HSC) on the Subaru Telescope for NASA's New Horizons mission target search were analyzed through a method devised by JAXA. The method makes use of Field Programmable Gate arrays and was originally used to detect fast-moving objects such as space debris or near-Earth asteroids. Here we present an application of the method to detect slow-moving Kuiper Belt Objects (KBOs) in the New Horizons target search observations. A cadence that takes continuous images of one HSC field of view for half a night fits the method well. The observations for the New Horizons Kuiper Belt Extended Mission (NH/KEM) using HSC began in 2020 May, and are ongoing. Here we show our result of the analysis of the dataset acquired from 2020 May through 2021 June that have already passed the proprietary period and are open to the public. We detected 84 KBO candidates in the 2020 June and 2021 June datasets, when the observation field was close to opposition. [ABSTRACT FROM AUTHOR]

High-throughput plant phenotyping is an effective approach to bridge the genotype-to-phenotype gap in crops. Phenomics experiments typically result in large-scale image datasets, which are not amenable for processing on desktop computers, thus creating a bottleneck in the image-analysis pipeline. Here, we present an open-source, flexible image-analysis framework, called Image Harvest (IH), for processing images originating from high-throughput plant phenotyping platforms. Image Harvest is developed to perform parallel processing on computing grids and provides an integrated feature for metadata extraction from large-scale file organization. Moreover, the integration of IH with the Open Science Grid provides academic researchers with the computational resources required for processing large image datasets at no cost. Image Harvest also offers functionalities to extract digital traits from images to interpret plant architecture-related characteristics. To demonstrate the applications of these digital traits, a rice (Oryza sativa) diversity panel was phenotyped and genome-wide association mapping was performed using digital traits that are used to describe different plant ideotypes. Three major quantitative trait loci were identified on rice chromosomes 4 and 6, which co-localize with quantitative trait loci known to regulate agronomically important traits in rice. Image Harvest is an open-source software for high-throughput image processing that requires a minimal learning curve for plant biologists to analyzephenomics datasets. [ABSTRACT FROM AUTHOR]

The purpose of this study was to evaluate rater agreement and the accuracy of a semi-automated software and its fully automated tool for osteoporosis risk assessment in intraoral radiographs. A total of 567 intraoral radiographs was selected retrospectively from women aged 75–80 years participating in a large population-based study (SUPERB) based in Gothenburg, Sweden. Five raters assessed participants' risk of osteoporosis in the intraoral radiographs using a semi-automated software. Assessments were repeated after 4 weeks on 121 radiographs (20%) randomly selected from the original 567. Radiographs were also assessed by the softwares' fully automated tool for analysis. Overall interrater agreement for the five raters was 0.37 (95% CI 0.32–0.41), and for the five raters with the fully automated tool included as 'sixth rater' the overall Kappa was 0.34 (0.30–0.38). Intrarater agreement varied from moderate to substantial according to the Landis and Koch interpretation scale. Diagnostic accuracy was calculated in relation to reference standard for osteoporosis diagnosis which is T-score values for spine, total hip and femoral neck and presented in form of sensitivities, specificities, predictive values, likelihood ratios and odds ratios. All raters' mean sensitivity, including the fully automated tool, was 40,4% (range 14,3%–57,6%). Corresponding values for specificity was 69,5% (range 59,7%–90,4%). The diagnostic odds ratios ranged between 1 and 2.7. The low diagnostic odds ratio and agreement between raters in osteoporosis risk assessment using the software for analysis of the trabecular pattern in intraoral radiographs shows that more work needs to be done to optimise the automation of trabecular pattern analysis in intraoral radiographs. [ABSTRACT FROM AUTHOR]

The upcoming Nancy Grace Roman Space Telescope will carry out a wide-area survey in the near-infrared. A key science objective is the measurement of cosmic structure via weak gravitational lensing. Roman data will be undersampled, which introduces new challenges in the measurement of source galaxy shapes; a potential solution is to use linear algebra-based coaddition techniques such as imcom that combine multiple undersampled images to produce a single oversampled output mosaic with a desired 'target' point spread function (PSF). We present here an initial application of imcom to 0.64 square degrees of simulated Roman data, based on the Roman branch of the Legacy Survey of Space and Time (LSST) Dark Energy Science Collaboration (DESC) Data Challenge 2 (DC2) simulation. We show that imcom runs successfully on simulated data that includes features such as plate scale distortions, chip gaps, detector defects, and cosmic ray masks. We simultaneously propagate grids of injected sources and simulated noise fields as well as the full simulation. We quantify the residual deviations of the PSF from the target (the 'leakage'), as well as noise properties of the output images; we discuss how the overall tiling pattern as well as Moiré patterns appear in the final leakage and noise maps. We include appendices on interpolation algorithms and the interaction of undersampling with image processing operations that may be of broader applicability. The companion paper ('Paper II') explores the implications for weak lensing analyses. [ABSTRACT FROM AUTHOR]

Disruptions to dopamine and noradrenergic neurotransmission are noted in several neurodegenerative and psychiatric disorders. Neuromelanin-sensitive (NM)-MRI offers a non-invasive approach to visualize and quantify the structural and functional integrity of the substantia nigra and locus coeruleus. This method may aid in the diagnosis and quantification of longitudinal changes of disease and could provide a stratification tool for predicting treatment success of pharmacological interventions targeting the dopaminergic and noradrenergic systems. Given the growing clinical interest in NM-MRI, understanding the contrast mechanisms that generate this signal is crucial for appropriate interpretation of NM-MRI outcomes and for the continued development of quantitative MRI biomarkers that assess disease severity and progression. To date, most studies associate NM-MRI measurements to the content of the neuromelanin pigment and/or density of neuromelanin-containing neurons, while recent studies suggest that the main source of the NM-MRI contrast is not the presence of neuromelanin but the high-water content in the dopaminergic and noradrenergic neurons. In this review, we consider the biological and physical basis for the NM-MRI contrast and discuss a wide range of interpretations of NM-MRI. We describe different acquisition and image processing approaches and discuss how these methods could be improved and standardized to facilitate large-scale multisite studies and translation into clinical use. We review the potential clinical applications in neurological and psychiatric disorders and the promise of NM-MRI as a biomarker of disease, and finally, we discuss the current limitations of NM-MRI that need to be addressed before this technique can be utilized as a biomarker and translated into clinical practice and offer suggestions for future research. [ABSTRACT FROM AUTHOR]

We present an overview of image-processing methods for Affymetrix GeneChips. All GeneChips are affected to some extent by spatially coherent defects and image processing has a number of potential impacts on the downstream analysis of GeneChip data. Fortunately, there are now a number of robust and accurate algorithms, which identify the most disabling defects. One group of algorithms concentrate on the transformation from the original hybridisation DAT image to the representative CEL file. Another set uses dedicated pattern recognition routines to detect different types of hybridisation defect in replicates. A third type exploits the information provided by public repositories of GeneChips (such as GEO). The use of these algorithms improves the sensitivity of GeneChips, and should be a prerequisite for studies in which there are only few probes per relevant biological signal, such as exon arrays and SNP chips. [ABSTRACT FROM AUTHOR]

This article discusses the need for improved representation of skin of colour (SOC) in medical education, particularly in dermatology curricula. The lack of diverse skin tones in educational materials has been shown to negatively impact patient satisfaction and dermatological health outcomes for SOC populations. The study aims to objectively assess the distribution of skin tones depicted in a first-year medical student course using quantitative digital image analysis. The results show that there is a need for greater inclusivity and diversity in medical education regarding SOC. [Extracted from the article]

We have developed a quantitative particle size analytical method at the single atomic level employing electron microscopy and image processing for the investigation of supported metal catalysts. In the present study, a supported gold (Au) catalyst containing sub-nano clusters and individual atoms was globally observed by high-resolution high-angle annular dark-field scanning transmission electron microscopy (HAADF-STEM) using spherical aberration (Cs)-corrected TEM. To fully extract structural information of the Au clusters and individual atoms from the HAADF-STEM images, a morphological image-processing operation was applied. The resulting mean particle size was in good agreement with particle sizes estimated from average information provided by X-ray absorption fine structure analysis. It is demonstrated that the present HAADF-STEM image analysis gives a quantitative particle size distribution measurement of supported Au clusters and individual atoms. [ABSTRACT FROM PUBLISHER]

In this article, we propose a smart image-analysis method suitable for extracting target features with hierarchical dimension from original data. The method was applied to threedimensional volume data of an all-solid lithium-ion battery obtained by the automated sequential sample milling and imaging process using a focused ion beam/scanning electron microscope to investigate the spatial configuration of voids inside the battery. To automatically fully extract the shape and location of the voids, three types of filters were consecutively applied: a median blur filter to extract relatively larger voids, a morphological opening operation filter for small dot-shaped voids and a morphological closing operation filter for small voids with concave contrasts. Three data cubes separately processed by the above-mentioned filters were integrated by a union operation to the final unified volume data, which confirmed the correct extraction of the voids over the entire dimension contained in the original data. [ABSTRACT FROM AUTHOR]

Rapid sample processing and interpretation of estimated exposures will be critical for triaging exposed individuals after a major radiation incident. The dicentric chromosome (DC) assay assesses absorbed radiation using metaphase cells from blood. The Automated Dicentric Chromosome Identifier and Dose Estimator System (ADCI) identifies DCs and determines radiation doses. This study aimed to broaden accessibility and speed of this system, while protecting data and software integrity. ADCI Online is a secure web-streaming platform accessible worldwide from local servers. Cloud-based systems containing data and software are separated until they are linked for radiation exposure estimation. Dose estimates are identical to ADCI on dedicated computer hardware. Image processing and selection, calibration curve generation, and dose estimation of 9 test samples completed in < 2 days. ADCI Online has the capacity to alleviate analytic bottlenecks in intermediate-to-large radiation incidents. Multiple cloned software instances configured on different cloud environments accelerated dose estimation to within clinically relevant time frames. [ABSTRACT FROM AUTHOR]

We present a search for transient radio sources on time-scales of seconds to hours at 144 MHz using the LOFAR Two-metre Sky Survey (LoTSS). This search is conducted by examining short time-scale images derived from the LoTSS data. To allow imaging of LoTSS on short time-scales, a modern imaging procedure and fast filtering strategy are introduced. This includes sky model source subtraction, no cleaning or primary beam correction, a simple source finder, fast filtering schemes, and source catalogue matching. This new strategy is first tested by injecting simulated transients, with a range of flux densities and durations, into the data. We find the limiting sensitivity to be 113 and 6 mJy for 8 s and 1 h transients, respectively. The new imaging and filtering strategies are applied to 58 fields of the LoTSS survey, corresponding to LoTSS-DR1 (2 per cent of the survey). One transient source is identified in the 8 s and 2 min snapshot images. The source shows 1 min duration flare in the 8 h observation. Our method puts the most sensitive constraints on/estimates of the transient surface density at low frequencies at time-scales of seconds to hours; <4.0 × 10−4 deg−2 at 1 h at a sensitivity of 6.3 mJy; 5.7 × 10−7 deg−2 at 2 min at a sensitivity of 30 mJy; and 3.6 × 10−8 deg−2 at 8 s at a sensitivity of 113 mJy. In the future, we plan to apply the strategies presented in this paper to all LoTSS data. [ABSTRACT FROM AUTHOR]

Background We quantified the pathological spatial intratumor heterogeneity of programmed death-ligand 1 (PD-L1) expression and investigated its relevance to patient outcomes in surgically resected non–small cell lung carcinoma (NSCLC). Methods This study enrolled 239 consecutive surgically resected NSCLC specimens of pathological stage IIA-IIIB. To characterize the spatial intratumor heterogeneity of PD-L1 expression in NSCLC tissues, we developed a mathematical model based on texture image analysis and determined the spatial heterogeneity index of PD-L1 for each tumor. The correlation between the spatial heterogeneity index of PD-L1 values and clinicopathological characteristics, including prognosis, was analyzed. Furthermore, an independent cohort of 70 cases was analyzed for model validation. Results Clinicopathological analysis showed correlations between high spatial heterogeneity index of PD-L1 values and histological subtype (squamous cell carcinoma; P  < .001) and vascular invasion (P  = .004). Survival analysis revealed that patients with high spatial heterogeneity index of PD-L1 values presented a significantly worse recurrence-free rate than those with low spatial heterogeneity index of PD-L1 values (5-year recurrence-free survival [RFS] = 26.3% vs 47.1%, P  < .005). The impact of spatial heterogeneity index of PD-L1 on cancer survival rates was verified through validation in an independent cohort. Additionally, high spatial heterogeneity index of PD-L1 values were associated with tumor recurrence in squamous cell carcinoma (5-year RFS = 29.2% vs 52.8%, P  < .05) and adenocarcinoma (5-year RFS = 19.6% vs 43.0%, P  < .01). Moreover, we demonstrated that a high spatial heterogeneity index of PD-L1 value was an independent risk factor for tumor recurrence. Conclusions We presented an image analysis model to quantify the spatial intratumor heterogeneity of protein expression in tumor tissues. This model demonstrated that the spatial intratumor heterogeneity of PD-L1 expression in surgically resected NSCLC predicts poor patient outcomes. [ABSTRACT FROM AUTHOR]

Aims Hypertensive patients of African ancestry (Afr-a) have higher incidences of heart failure and worse clinical outcomes than hypertensive patients of European ancestry (Eu-a), yet the underlying mechanisms remain misunderstood. This study investigated right (RV) and left (LV) ventricular remodelling alongside myocardial tissue derangements between Afr-a and Eu-a hypertensives. Methods and results 63 Afr-a and 47 Eu-a hypertensives underwent multi-parametric cardiovascular magnetic resonance. Biventricular volumes, mass, function, mass/end-diastolic volume (M / V) ratios, T2 and pre-/post-contrast T1 relaxation times, synthetic extracellular volume, and myocardial fibrosis (MF) were measured. 3D shape modelling was implemented to delineate ventricular geometry. LV and RV mass (indexed to body-surface-area) and M/V ratio were significantly greater in Afr-a than Eu-a hypertensives (67.1 ± 21.7 vs. 58.3 ± 16.7 g/m2, 12.6 ± 3.48 vs. 10.7 ± 2.71 g/m2, 0.79 ± 0.21 vs. 0.70 ± 0.14 g/mL, and 0.16 ± 0.04 vs. 0.13 ± 0.03 g/mL, respectively; P < 0.03). Afr-a patients showed greater basal interventricular septum thickness than Eu-a patients, influencing LV hypertrophy and RV cavity changes. This biventricular remodelling was associated with prolonged T2 relaxation time (47.0 ± 2.2 vs. 45.7 ± 2.2 ms, P = 0.005) and higher prevalence (23% vs. 4%, P = 0.001) and extent of MF [2.3 (0.6–14.3) vs. 1.6 (0.9–2.5) % LV mass, P = 0.008] in Afr-a patients. Multivariable linear regression showed that modifiable cardiovascular risk factors and greater end-diastolic volume, but not ethnicity, were independently associated with greater LV mass. Conclusion Afr-a hypertensives had distinctive biventricular remodelling, including increased RV mass, septal thickening and myocardial tissue abnormalities compared with Eu-a hypertensives. From this study, modifiable cardiovascular risk factors and ventricular geometry, but not ethnicity, were independently associated with greater LV myocardial mass. [ABSTRACT FROM AUTHOR]

Aims To compare the novel 2D multi-velocity encoding (venc) and 4D flow acquisitions with the standard 2D flow acquisition for the assessment of paravalvular regurgitation (PVR) after transcatheter aortic valve replacement (TAVR) using cardiac magnetic resonance (CMR)-derived regurgitant fraction (RF). Methods and results In this prospective study, patients underwent CMR 1 month after TAVR for the assessment of PVR, for which 2D multi-venc and 4D flow were used, in addition to standard 2D flow. Scatterplots and Bland–Altman plots were used to assess correlation and visualize agreement between techniques. Reproducibility of measurements was assessed with intraclass correlation coefficients. The study included 21 patients (mean age ± SD 80 ± 5 years, 9 men). The mean RF was 11.7 ± 10.0% when standard 2D flow was used, 10.6 ± 7.0% when 2D multi-venc flow was used, and 9.6 ± 7.3% when 4D flow was used. There was a very strong correlation between the RFs assessed with 2D multi-venc and standard 2D flow (r = 0.88, P < 0.001), and a strong correlation between the RFs assessed with 4D flow and standard 2D flow (r = 0.74, P < 0.001). Bland–Altman plots revealed no substantial bias between the RFs (2D multi-venc: 1.3%; 4D flow: 0.3%). Intra-observer and inter-observer reproducibility for 2D multi-venc flow were 0.98 and 0.97, respectively, and 0.92 and 0.90 for 4D flow, respectively. Conclusion Two-dimensional multi-venc and 4D flow produce an accurate quantification of PVR after TAVR. The fast acquisition of the 2D multi-venc sequence and the free-breathing acquisition with retrospective plane selection of the 4D flow sequence provide useful advantages in clinical practice, especially in the frail TAVR population. [ABSTRACT FROM AUTHOR]

We present extended point spread function (PSF) models for the Hyper Suprime-Cam Subaru Strategic Program Public Data Release 3 (HSC-SSP PDR3) in all g, r, i, Z , and Y -bands. Due to its 8.2 m primary mirror and long exposure periods, HSC combines deep images with wide-field coverage. Both properties make HSC one of the most suitable observing facilities for low-surface brightness (LSB) studies, which are particularly sensitive to the PSF. By applying a median stacking technique of point-like sources with different brightness, we show how to construct the HSC-SSP PDR3 PSF models to an extent of R ∼ 5.6 arcmin. These models are appropriate for the HSC-PDR3 intermediate-state data which do not have applied the final aggressive background subtraction. The intermediate-state data is especially stored for users interested in large extended objects, where our new PSFs provide them with a crucial tool to characterise LSB properties at large angles. We demonstrate that our HSC PSFs behave reasonably in two scenarios. In the first one, we generate 2D models of a bright star, showing no evidence of residual structures across the five bands. In the second scenario, we recreate the PSF-scattered light on mock images with special consideration of the effect of this additional flux on LSB measurements. Despite the well-behaved nature of the HSC-PDR3 PSFs, there is a non-negligible impact on the faint light present in the mock images. This impact could lead to incorrect LSB measurements if a proper star subtraction is not applied. The PSF models are publicly available to the astronomical community at github.com/luciagarate/HSC_PSFs (DOI: 10.5281/zenodo.11273118). [ABSTRACT FROM AUTHOR]

We introduce a novel technique to mitigate the adverse effects of atmospheric turbulence on astronomical imaging. Utilizing a video-to-image neural network trained on simulated data, our method processes a sliding sequence of short-exposure (∼0.2 s) stellar field images to reconstruct an image devoid of both turbulence and noise. We demonstrate the method with simulated and observed stellar fields, and show that the brief exposure sequence allows the network to accurately associate speckles to their originating stars and effectively disentangle light from adjacent sources across a range of seeing conditions, all while preserving flux to a lower signal-to-noise ratio than an average stack. This approach results in a marked improvement in angular resolution without compromising the astrometric stability of the final image. [ABSTRACT FROM AUTHOR]

Since the manufacture, import, and use of asbestos products have been completely abolished in Japan, the main cause of asbestos emissions into the atmosphere is the demolition and removal of buildings built with asbestos-containing materials. To detect and correct asbestos emissions from inappropriate demolition and removal operations at an early stage, a rapid method to measure atmospheric asbestos fibers is required. The current rapid measurement method is a combination of short-term atmospheric sampling and phase-contrast microscopy counting. However, visual counting takes a considerable amount of time and is not sufficiently fast. Using artificial intelligence (AI) to analyze microscope images to detect fibers may greatly reduce the time required for counting. Therefore, in this study, we investigated the use of AI image analysis for detecting fibers in phase-contrast microscope images. A series of simulated atmospheric samples prepared from standard samples of amosite and chrysotile were observed using a phase-contrast microscope. Images were captured, and training datasets were created from the counting results of expert analysts. We adopted 2 types of AI models—an instance segmentation model, namely the mask region-based convolutional neural network (Mask R-CNN), and a semantic segmentation model, namely the multi-level aggregation network (MA-Net)—that were trained to detect asbestos fibers. The accuracy of fiber detection achieved with the Mask R-CNN model was 57% for recall and 46% for precision, whereas the accuracy achieved with the MA-Net model was 95% for recall and 91% for precision. Therefore, satisfactory results were obtained with the MA-Net model. The time required for fiber detection was less than 1 s per image in both AI models, which was faster than the time required for counting by an expert analyst. [ABSTRACT FROM AUTHOR]