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Design randomizations and spatial corrections have increased understanding of genotypic, spatial, and residual effects in field experiments, but precisely measuring spatial heterogeneity in the field remains a challenge. To this end, our study evaluated approaches to improve spatial modeling using high-throughput phenotypes (HTP) via unoccupied aerial vehicle (UAV) imagery. The normalized difference vegetation index was measured by a multispectral MicaSense camera and processed using ImageBreed. Contrasting to baseline agronomic trait spatial correction and a baseline multitrait model, a two-stage approach was proposed. Using longitudinal normalized difference vegetation index data, plot level permanent environment effects estimated spatial patterns in the field throughout the growing season. Normalized difference vegetation index permanent environment were separated from additive genetic effects using 2D spline, separable autoregressive models, or random regression models. The Permanent environment were leveraged within agronomic trait genomic best linear unbiased prediction either modeling an empirical covariance for random effects, or by modeling fixed effects as an average of permanent environment across time or split among three growth phases. Modeling approaches were tested using simulation data and Genomes-to-Fields hybrid maize (Zea mays L.) field experiments in 2015, 2017, 2019, and 2020 for grain yield, grain moisture, and ear height. The two-stage approach improved heritability, model fit, and genotypic effect estimation compared to baseline models. Electrical conductance and elevation from a 2019 soil survey significantly improved model fit, while 2D spline permanent environment were most strongly correlated with the soil parameters. Simulation of field effects demonstrated improved specificity for random regression models. In summary, the use of longitudinal normalized difference vegetation index measurements increased experimental accuracy and understanding of field spatio-temporal heterogeneity. [ABSTRACT FROM AUTHOR]

Objective To evaluate the associations between analytical methods, such as microarray and enzyme-linked immunosorbent assay (ELISA); expedient cutoffs; and the lowest possible number of microarrays in analysis for target biomarker estimation in case-control studies. Methods This study included 321 serum specimens, gathered in different case-control studies to test for atherosclerosis and atrial fibrillation. Among them, 48 serum specimens were analyzed using microarray technology. We used ELISA and commercial kits for confirmation of the results. Results Three proteins—cadherin-P, neuronal nitric oxide synthase, and adenovirus fiber—were shown to have distinctly different values in the case group vs the control group. As a result, we used those proteins as the target for confirmation using our alternative analytical method. Also, these protein values represented the limiting range between the highest and lowest differences in case-control groups. The results of microarray assay were confirmed using ELISA and commercial kits in the same specimens, in which microarray profiling was performed, and also in separate large case-control groups. Conclusions A 1.5-fold difference in the protein content, as measured using microarray technology, was shown to be sufficient for further investigation of the candidate proteins. As few as 3 microarrays were considered sufficient for perspective evaluation of the target proteins. Microarray serum profiling, therefore, provides semiquantitative determination of protein in serum. [ABSTRACT FROM AUTHOR]

Disentangling the radio flux contribution from star formation (SF) and active-galactic-nuclei (AGNs) activity is a long-standing problem in extragalactic astronomy, since at frequencies of ≲ 10 GHz, both processes emit synchrotron radiation. We present in this work the general objectives of the PARADIGM (PAnchromatic high-Resolution Analysis of DIstant Galaxy Mergers) project, a multi-instrument concept to explore SF and mass assembly of galaxies. We introduce two novel general approaches for a detailed multiscale study of the radio emission in local (ultra) luminous infrared galaxies (U/LIRGs). In this work, we use archival interferometric data from the Very Large Array (VLA) centred at ∼ 6 GHz (C band) and present new observations from the e -Multi-Element Radio-Linked Interferometer Network (e -MERLIN) for UGC 5101, VV 705, VV 250, and UGC 8696. Using our image decomposition methods, we robustly disentangle the radio emission into distinct components by combining information from the two interferometric arrays. We use e -MERLIN as a probe of the core-compact radio emission (AGN or starburst) at ∼ 20 pc scales, and as a probe of nuclear diffuse emission, at scales ∼100–200 pc. With VLA, we characterize the source morphology and the flux density on scales from ∼200 pc up to and above 1 kpc. As a result, we find deconvolved and convolved sizes for nuclear regions from ∼10 to ∼200 pc. At larger scales, we find sizes of 1.5–2 kpc for diffuse structures (with effective sizes of ∼ 300–400 pc). We demonstrate that the radio emission from nuclear extended structures (∼ 100 pc) can dominate over core-compact components, providing a significant fraction of the total multiscale SF output. We establish a multiscale radio tracer for SF by combining information from different instruments. Consequently, this work sets a starting point to potentially correct for overestimations of AGN fractions and underestimates of SF activity. [ABSTRACT FROM AUTHOR]

The hot intracluster medium (ICM) provides a unique laboratory to test multiscale physics in numerical simulations and probe plasma physics. Utilizing archival Chandra observations, we measure density fluctuations in the ICM in a sample of 80 nearby (z ≲ 1) galaxy clusters and infer scale-dependent velocities within regions affected by mergers (r < R 2500c), excluding cool-cores. Systematic uncertainties (e.g. substructures, cluster asymmetries) are carefully explored to ensure robust measurements within the bulk ICM. We find typical velocities ∼220 (300) km s−1 in relaxed (unrelaxed) clusters, which translate to non-thermal pressure fractions ∼4 (8) per cent, and clumping factors ∼1.03 (1.06). We show that density fluctuation amplitudes could distinguish relaxed from unrelaxed clusters in these regions. Comparison with density fluctuations in cosmological simulations shows good agreement in merging clusters. Simulations underpredict the amplitude of fluctuations in relaxed clusters on length scales <0.75  R 2500c, suggesting these systems are most sensitive to 'missing' physics in the simulations. In clusters hosting radio haloes, we examine correlations between gas velocities, turbulent dissipation rate, and radio emission strength/efficiency to test turbulent re-acceleration of cosmic ray electrons. We measure a weak correlation, driven by a few outlier clusters, in contrast to some previous studies. Finally, we present upper limits on effective viscosity in the bulk ICM of 16 clusters, showing it is systematically suppressed by at least a factor of 8, and the suppression is a general property of the ICM. Confirmation of our results with direct velocity measurements will be possible soon with XRISM. [ABSTRACT FROM AUTHOR]

The morphological diversity of galaxies is a relevant probe of galaxy evolution and cosmological structure formation. However, in large sky surveys, even the morphological classification of galaxies into two classes, like late-type (LT) and early-type (ET), still represents a significant challenge. In this work, we present a Deep Learning (DL) based morphological catalogue built from images obtained by the Southern Photometric Local Universe Survey (S-PLUS) Data Release 3 (DR3). Our DL method achieves a purity rate of 98.5 per cent in accurately distinguishing between spiral, as part of the larger category of LT galaxies, and elliptical, belonging to ET galaxies. Additionally, we have implemented a secondary classifier that evaluates the quality of each galaxy stamp, which allows to select only high-quality images when studying properties of galaxies on the basis of their DL morphology. From our LT/ET catalogue of galaxies, we recover the expected colour–magnitude diagram in which LT galaxies display bluer colours than ET ones. Furthermore, we also investigate the clustering of galaxies based on their morphology, along with their relationship to the surrounding environment. As a result, we deliver a full morphological catalogue with 164 314 objects complete up to r petro < 18, covering ∼1800 deg2, from which ∼55 000 are classified as high reliability, including a significant area of the Southern hemisphere that was not covered by previous morphology catalogues. [ABSTRACT FROM AUTHOR]

Spectral induced polarization (SIP) and electrical impedance tomography (EIT) are advanced approaches for structural characterisation of soils and rocks based on measurements of the frequency-dependent complex electrical resistivity. Accurate SIP and EIT measurements require the reduction of electrode effects, which have often been attributed to the polarization of metal electrodes in the current pathway. In this study, we demonstrate an alternative electrode effect that influences the accuracy of complex impedance measurements. This alternative electrode effect is caused by a heterogeneous distribution of the contact impedance, which can occur due to ageing or corrosion of the electrode. First, theoretical considerations are given to explain how this electrode effect influences the accuracy of complex impedance measurements. Next, we demonstrate using SIP and EIT measurements that this alternative mechanism better explains the observed electrode effects than previous explanations based only on the presence of metal. The SIP measurements were made on a water-filled container with a metal sphere between two non-polarizing electrodes, which showed that the mere presence of metal in the current pathway only leads to small electrode effects that are typically well below 1 mrad even for small electrode spacings. When the same metal sphere was used as a potential electrode, the observed electrode effect was about 2.5 times larger and varied with rotation. These changes can be explained by a heterogeneous contact impedance resulting in a varying electrode voltage. The EIT measurements were performed with a cylindrical sample holder filled with water. They are used to illustrate how the presented theoretical considerations can be used to investigate the magnitude of the electrode effects for different electrode configurations. We conclude that electrode effects are important to consider for laboratory SIP and EIT measurements and field measurement with closely spaced electrodes. [ABSTRACT FROM AUTHOR]

The microtubule motor dynein is critical for the assembly and positioning of mitotic spindles. In Caenorhabditis elegans , these dynein functions have been extensively studied in the early embryo but remain poorly explored in other developmental contexts. Here, we use a hypomorphic dynein mutant to investigate the motor's contribution to asymmetric stem cell–like divisions in the larval epidermis. Live imaging of seam cell divisions that precede formation of the seam syncytium shows that mutant cells properly assemble but frequently misorient their spindle. Misoriented divisions misplace daughter cells from the seam cell row, generate anucleate compartments due to aberrant cytokinesis, and disrupt asymmetric cell fate inheritance. Consequently, the seam becomes disorganized and populated with extra cells that have lost seam identity, leading to fatal epidermal rupture. We show that dynein orients the spindle through the cortical GOA-1Gα–LIN-5NuMA pathway by directing the migration of prophase centrosomes along the anterior–posterior axis. Spindle misorientation in the dynein mutant can be partially rescued by elongating cells, implying that dynein-dependent force generation and cell shape jointly promote correct asymmetric division of epithelial stem cells. [ABSTRACT FROM AUTHOR]

In this paper, we introduce a novel data augmentation methodology based on Conditional Progressive Generative Adversarial Networks (CPGAN) to generate diverse black hole (BH) images, accounting for variations in spin and electron temperature prescriptions. These generated images are valuable resources for training deep learning algorithms to accurately estimate black hole parameters from observational data. Our model can generate BH images for any spin value within the range of [−1, 1], given an electron temperature distribution. To validate the effectiveness of our approach, we employ a convolutional neural network to predict the BH spin using both the GRMHD images and the images generated by our proposed model. Our results demonstrate a significant performance improvement when training is conducted with the augmented data set while testing is performed using GRMHD simulated data, as indicated by the high R 2 score. Consequently, we propose that GANs can be employed as cost-effective models for black hole image generation and reliably augment training data sets for other parametrization algorithms. [ABSTRACT FROM AUTHOR]

Seismic interrogation of the upper mantle from the base of the crust to the top of the mantle transition zone has revealed discontinuities that are variable in space, depth, lateral extent, amplitude and lack a unified explanation for their origin. Improved constraints on the detectability and properties of mantle discontinuities can be obtained with P-to-S receiver function (Ps-RF) where energy scatters from P to S as seismic waves propagate across discontinuities of interest. However, due to the interference of crustal multiples, uppermost mantle discontinuities are more commonly imaged with lower resolution S-to-P receiver function (Sp-RF). In this study, a new method called CRISP-RF (Clean Receiver-function Imaging using SParse Radon Filters) is proposed, which incorporates ideas from compressive sensing and model-based image reconstruction. The central idea involves applying a sparse Radon transform to effectively decompose the Ps-RF into its underlying wavefield contributions, that is direct conversions, multiples, and noise, based on the phase moveout and coherence. A masking filter is then designed and applied to create a multiple-free and denoised Ps-RF. We demonstrate, using synthetic experiment, that our implementation of the Radon transform using a sparsity-promoting regularization outperforms the conventional least-squares methods and can effectively isolate direct Ps conversions. We further apply the CRISP-RF workflow on real data, including single station data on cratons, common-conversion-point stack at continental margins and seismic data from ocean islands. The application of CRISP-RF to global data sets will advance our understanding of the enigmatic origins of the upper mantle discontinuities like the ubiquitous mid-lithospheric discontinuity and the elusive X-discontinuity. [ABSTRACT FROM AUTHOR]

The study of jet-inflated X-ray cavities provides a powerful insight into the energetics of hot galactic atmospheres and radio-mechanical AGN feedback. By estimating the volumes of X-ray cavities, the total energy and thus also the corresponding mechanical jet power required for their inflation can be derived. Properly estimating their total extent is, however, non-trivial, prone to biases, nearly impossible for poor-quality data, and so far has been done manually by scientists. We present a novel machine-learning pipeline called Cavity Detection Tool (CADET), developed as an assistive tool that detects and estimates the sizes of X-ray cavities from raw Chandra images. The pipeline consists of a convolutional neural network trained for producing pixel-wise cavity predictions and a DBSCAN clustering algorithm, which decomposes the predictions into individual cavities. The convolutional network was trained using mock observations of early-type galaxies simulated to resemble real noisy Chandra -like images. The network's performance has been tested on simulated data obtaining an average cavity volume error of 14 per cent at an 89 per cent true-positive rate. For simulated images without any X-ray cavities inserted, we obtain a 5 per cent false-positive rate. When applied to real Chandra images, the pipeline recovered 93 out of 97 previously known X-ray cavities in nearby early-type galaxies and all 14 cavities in chosen galaxy clusters. Besides that, the CADET pipeline discovered seven new cavity pairs in atmospheres of early-type galaxies (IC 4765, NGC 533, NGC 2300, NGC 3091, NGC 4073, NGC 4125, and NGC 5129) and a number of potential cavity candidates. [ABSTRACT FROM AUTHOR]

We release photometric redshifts, reaching ∼0.7, for ∼14M galaxies at r ≤ 20 in the 11 500 deg2 of the SDSS north and south Galactic caps. These estimates were inferred from a convolution neural network (CNN) trained on ugriz stamp images of galaxies labelled with a spectroscopic redshift from the SDSS, GAMA, and BOSS surveys. Representative training sets of ∼370k galaxies were constructed from the much larger combined spectroscopic data to limit biases, particularly those arising from the over-representation of luminous red galaxies. The CNN outputs a redshift classification that offers all the benefits of a well-behaved PDF, with a width efficiently signalling unreliable estimates due to poor photometry or stellar sources. The dispersion, mean bias, and rate of catastrophic failures of the median point estimate are of order σMAD = 0.014, <Δ z norm>=0.0015, |$\eta (|\Delta z_{\rm norm}|\gt 0.05)=4{{\, \rm per\ cent}}$| on a representative test sample at r < 19.8, outperforming currently published estimates. The distributions in narrow intervals of magnitudes of the redshifts inferred for the photometric sample are in good agreement with the results of tomographic analyses. The inferred redshifts also match the photometric redshifts of the redMaPPer galaxy clusters for the probable cluster members. [ABSTRACT FROM AUTHOR]

We present wide-field, deep K -band photometry of 98 luminous early-type galaxies (ETGs) from the MASSIVE survey based on observations taken with the WIRCam instrument on the Canada–France–Hawaii Telescope. Using these images, we extract accurate total K -band luminosities (LK) and half-light radii (Re) for this sample of galaxies. We use these new values to explore the size–luminosity and Faber–Jackson relations for massive ETGs. Within this volume-limited sample, we find clear evidence for curvature in both relations, indicating that the most luminous galaxies tend to have larger sizes and smaller velocity dispersions than expected from a simple power-law fit to less luminous galaxies. Our measured relations are qualitatively consistent with the most massive elliptical galaxies forming largely through dissipationless mergers. When the sample is separated into fast and slow rotators, we find the slow rotators to exhibit similar changes in slope with increasing LK , suggesting that low-mass and high-mass slow rotators have different formation histories. The curvatures in the Re – LK and σ– LK relations cancel, leading to a relation between dynamical mass and luminosity that is well described by a single power law: Re σ2 ∝  LK b with b ≈ 1.2. This is consistent with the tilt of the fundamental plane observed in lower mass elliptical galaxies. [ABSTRACT FROM AUTHOR]

Direct imaging is an active research topic in astronomy for the detection and the characterization of young substellar objects. The very high contrast between the host star and its companions makes the observations particularly challenging. In this context, post-processing methods combining several images recorded with the pupil tracking mode of telescope are needed. In previous works, we have presented a data-driven algorithm, PACO , capturing locally the spatial correlations of the data with a multivariate Gaussian model. PACO delivers better detection sensitivity and confidence than the standard post-processing methods of the field. However, there is room for improvement due to the approximate fidelity of the PACO statistical model to the time evolving observations. In this paper, we propose to combine the statistical model of PACO with supervised deep learning. The data are first pre-processed with the PACO framework to improve the stationarity and the contrast. A convolutional neural network (CNN) is then trained in a supervised fashion to detect the residual signature of synthetic sources. Finally, the trained network delivers a detection map. The photometry of detected sources is estimated by a second CNN. We apply the proposed approach to several data sets from the VLT/SPHERE instrument. Our results show that its detection stage performs significantly better than baseline methods (cADI and PCA), and leads to a contrast improvement up to half a magnitude compared to PACO. The characterization stage of the proposed method performs on average on par with or better than the comparative algorithms (PCA and PACO) for angular separation above 0.5 arcsec. [ABSTRACT FROM AUTHOR]

From Nov. 2019 to May 2020, the red supergiant star Betelgeuse experienced an unprecedented drop of brightness in the visible domain called the Great Dimming event (GDE). Large atmospheric dust clouds and large photospheric convective features are suspected to be responsible for it. To better understand the dimming event, we used mid-infrared long-baseline spectro-interferometric measurements of Betelgeuse taken with the Very Large Telescope Interferometer/Multi AperTure mid-Infrared SpectroScopic Experiment (VLTI/MATISSE) instrument before (Dec. 2018), during (Feb. 2020), and after (Dec. 2020) the GDE. We present data in the 3.98–4.15 µm range to cover SiO spectral features molecules as well as adjacent continuum. We have employed geometrical models, image reconstruction, as well as radiative transfer models to monitor the spatial distribution of SiO over the stellar surface. We find a strongly inhomogeneous spatial distribution of SiO that appears to be looking very different between our observing epochs, indicative of a vigorous activity in the stellar atmosphere. The contrast of our images is small in the pseudo-continuum for all epochs, implying that our MATISSE observations support both cold spot and dust cloud model. [ABSTRACT FROM AUTHOR]

The treatment of recurrent ovarian cancer has been based on systemic therapy. The role of secondary cytoreductive surgery has been addressed recently in several trials. Imaging plays a key role in helping the surgical team to decide which patients will have resectable disease and benefit from surgery. The role of staging laparoscopy and several imaging and clinical scores has been extensively debated in the field. In other surgical fields there have been reports of using 3D imaging software and 3D printed models to help surgeons better plan the surgical approach. To the best of our knowledge, we report the first case of a patient with recurrent ovarian cancer undergoing 3D modeling before secondary cytoreductive surgery. The 3D modeling was of most value to evaluate the extension of the disease in our patient who underwent a successful secondary cytoreductive surgery and is currently free of the disease. [ABSTRACT FROM AUTHOR]

Background The application of deep learning (DL) to diagnostic dermatology has been the subject of numerous studies, with some reporting skin lesion classification performance on curated datasets comparable to that of experienced dermatologists. Most skin disease images encountered in clinical settings are macroscopic, without dermoscopic information, and exhibit considerable variability. Further research is necessary to determine the generalizability of DL algorithms across populations and acquisition settings. Objectives To assess the extent to which DL can generalize to nondermoscopic datasets acquired at the primary–secondary care interface in the National Health Service (NHS); to explore how to obtain a clinically satisfactory performance on nonstandardized, real-world local data without the availability of large diagnostically labelled local datasets; and to measure the impact of pretraining DL algorithms on external, public datasets. Methods Diagnostic macroscopic image datasets were created from previous referrals from primary to secondary care. These included 2213 images referred from primary care practitioners in NHS Tayside and 1510 images from NHS Forth Valley acquired by medical photographers. Two further datasets with identical diagnostic labels were obtained from sources in the public domain, namely the International Skin Imaging Collaboration (ISIC) dermoscopic dataset and the SD-260 nondermoscopic dataset. DL algorithms, specifically EfficientNets and Self-attention with Window-wise Inner-product based Network (SWIN) transformers, were trained using data from each of these datasets. Algorithms were also fine-tuned on images from the NHS datasets after pretraining on different data combinations, including the larger public-domain datasets. Receiver operating characteristic curves and the area under such curves (AUC) were used to assess performance. Results SWIN transformers tested on Forth Valley data had AUCs of 0.85 and 0.89 when trained on SD-260 and Forth Valley data, respectively. Training on SD-260 followed by fine tuning of Forth Valley data gave an AUC of 0.91. Similar effects of pretraining and tuning on local data were observed using Tayside data and EfficientNets. Pretraining on the larger dermoscopic image dataset (ISIC 2019) provided no additional benefit. Conclusions Pretraining on public macroscopic images followed by tuning to local data gave promising results. Further improvements are needed to afford deployment in real clinical pathways. Larger datasets local to the target domain might be expected to yield further improved performance. [ABSTRACT FROM AUTHOR]