Measuring of mandible bone density in dogs using/digital radiography/radiovisigraphy

Radiological diagnostics serves as one of the basic monitoring techniques in veterinary dental practice. The recent up-to-date literature data based on the findings of digital radiology/radiovisiography (RVG) in general dentistry inspired the authors to present its possible use in clinical veterinary dentistry. The digital radiography used in this study was RVG Trophy Radiologie SA 2001 device equipped with software for linear measurements (readings), densitometry, setting of contrast of radiography image, 3D image manipulation, zooming of detail and orientation handling. The aim of the study was to evaluate the bone mineral density of the alveolar part of the lower jaw in seven Scottish terriers. Bone mineral density measurement was performed around the central lower incisors by converting gray scale values into equivalent aluminum thickness (mm Al). The mean bone mineral density was in the range of 4.31-6.20 mm Al with no significant statistical difference between left and right incisors (p>0.01). Our results showed that the combination of RVG and aluminum step wedge etalon is a reliable tool to measure bone mineral density around the lower central incisors in dogs. This method can be considered as comfortable for manipulation in everyday use in clinical veterinary practice

The LSST AGN Data Challenge: Selection methods

Development of the Rubin Observatory Legacy Survey of Space and Time (LSST) includes a series of Data Challenges (DC) arranged by various LSST Scientific Collaborations (SC) that are taking place during the projects preoperational phase. The AGN Science Collaboration Data Challenge (AGNSCDC) is a partial prototype of the expected LSST AGN data, aimed at validating machine learning approaches for AGN selection and characterization in large surveys like LSST. The AGNSC-DC took part in 2021 focusing on accuracy, robustness, and scalability. The training and the blinded datasets were constructed to mimic the future LSST release catalogs using the data from the Sloan Digital Sky Survey Stripe 82 region and the XMM-Newton Large Scale Structure Survey region. Data features were divided into astrometry, photometry, color, morphology, redshift and class label with the addition of variability features and images. We present the results of four DC submitted solutions using both classical and machine learning methods. We systematically test the performance of supervised (support vector machine, random forest, extreme gradient boosting, artificial neural network, convolutional neural network) and unsupervised (deep embedding clustering) models when applied to the problem of classifying/clustering sources as stars, galaxies or AGNs. We obtained classification accuracy 97.5% for supervised and clustering accuracy 96.0% for unsupervised models and 95.0% with a classic approach for a blinded dataset. We find that variability features significantly improve the accuracy of the trained models and correlation analysis among different bands enables a fast and inexpensive first order selection of quasar candidatesComment: Accepted by ApJ. 21 pages, 14 figures, 5 table

From Data to Software to Science with the Rubin Observatory LSST

The Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST) dataset will dramatically alter our understanding of the Universe, from the origins of the Solar System to the nature of dark matter and dark energy. Much of this research will depend on the existence of robust, tested, and scalable algorithms, software, and services. Identifying and developing such tools ahead of time has the potential to significantly accelerate the delivery of early science from LSST. Developing these collaboratively, and making them broadly available, can enable more inclusive and equitable collaboration on LSST science. To facilitate such opportunities, a community workshop entitled "From Data to Software to Science with the Rubin Observatory LSST" was organized by the LSST Interdisciplinary Network for Collaboration and Computing (LINCC) and partners, and held at the Flatiron Institute in New York, March 28-30th 2022. The workshop included over 50 in-person attendees invited from over 300 applications. It identified seven key software areas of need: (i) scalable cross-matching and distributed joining of catalogs, (ii) robust photometric redshift determination, (iii) software for determination of selection functions, (iv) frameworks for scalable time-series analyses, (v) services for image access and reprocessing at scale, (vi) object image access (cutouts) and analysis at scale, and (vii) scalable job execution systems. This white paper summarizes the discussions of this workshop. It considers the motivating science use cases, identified cross-cutting algorithms, software, and services, their high-level technical specifications, and the principles of inclusive collaborations needed to develop them. We provide it as a useful roadmap of needs, as well as to spur action and collaboration between groups and individuals looking to develop reusable software for early LSST science.Comment: White paper from "From Data to Software to Science with the Rubin Observatory LSST" worksho

Estimating supermassive black hole masses in active galactic nuclei using polarization of broad Mg ii, H α, and H β lines

International audienceFor type-1 active galactic nuclei (AGNs) for which the equatorial scattering is the dominant broad-line polarization mechanism, it is possible to measure the supermassive black hole (SMBH) mass by tracing the Keplerian motion across the polarization plane position angle ϕ. So far, this method has been used for 30 objects but only for H α emission line. We explore the possibilities of this method for determining SMBH masses using polarization in broad emission lines by applying it for the first time to Mg II λ2798 Å spectral line. We use three-dimensional (3-D) Monte Carlo radiative transfer code STOKES for simultaneous modelling of equatorial scattering of H α, H β, and Mg II lines. We included vertical inflows and outflows in the Mg II broad-line region (BLR). We find that polarization states of H α and H β lines are almost identical and SMBH-mass estimates differ by 7 per cent. For Mg II line, we find that ϕ exhibits an additional 'plateau' with a constant ϕ, which deviates than the profiles expected for pure Keplerian motion. SMBH-mass estimates using Mg II line are higher by up to 35 per cent than those obtained from H α and H β lines. Our model shows that for vertical inflows and outflows in the BLR that are higher or comparable to the Keplerian velocity, this method can be applied as a first approximation for obtaining SMBH mass

High amount of lecithin facilitates oral delivery of a poorly soluble pyrazoloquinolinone ligand formulated in lipid nanoparticles: Physicochemical, structural and pharmacokinetic performances

Preclinical development of deuterated pyrazoloquinolinone ligands, promising drug candidates for various neuropsychiatric disorders, was hindered by unusually low solubility in water and oils. DK-I-60-3 (7-methoxy-d3- 2-(4-methoxy-d3-phenyl)-2,5-dihydro-3Hpyrazolo[4,3-c]quinolin-3-one) is one of such pyrazoloquinolinones, and we recently reported about increased oral bioavailability of its nanocrystal formulation (NC). Lipid nano- particles (LNP) with a high concentration of lecithin, which enhances loading capacity of the lipid matrix, may give rise to further improvement. After preformulation studies by differential scanning calorimetry and polarized light microscopy, LNP were prepared by the hot high pressure homogenization, and characterized in terms of particle size, morphology, and encapsulation efficacy. The layered structure visible on atomic force micrographs was confirmed by nuclear magnetic resonance. Obtained formulations were desirably stable, with small particle size (99 %). Lecithin was partially fluid and most probably located in the outer shell of the particle, together with DK-I-60-3. While the hydrophobic part of polysorbate 80 was completely immobilized, its hydrophilic part was free in the aqueous phase. In oral neuropharmacokinetic study in rats, an around 1.5-fold increase of area under the curve with LNP compared to NC was noticed both in brain and plasma. In bioavailability study, F value of LNP (34.7 ± 12.4 %) was 1.4-fold higher than of NC (24.5 ± 7.8 %); however, this difference did not reach statistical significance. Therefore, employment of LNP platform in preclinical formulation of DK-I-60-3 imparted an incremental improvement of its physicochemical as well as pharmacokinetic behavior

Vascular effects of midazolam, flumazenil, and a novel imidazobenzodiazepine MP-III-058 on isolated rat aorta

Hypotensive influences of benzodiazepines and other GABAA receptor ligands, recognized in clinical practice, seem to stem from the existence of “vascular” GABAA receptors in peripheral blood vessels, besides any mechanisms in the central and peripheral nervous systems. We aimed to further elucidate the vasodilatatory effects of ligands acting through GABAA receptors. Using immunohistochemistry, the rat aortic smooth muscle layer was found to express GABAA γ 2 and α1-5 subunit proteins. To confirm the role of “vascular” GABAA receptors, we investigated the vascular effects of standard benzodiazepines, mida-zolam, and flumazenil, as well as the novel compound MP-III-058. Using two-electrode voltage clamp electrophysiology and radioligand binding assays, MP-III-058 was found to have modest binding but substantial functional selectivity for α5β3γ 2 over other αxβ3γ 2 GABAA receptors. Tissue bath assays revealed comparable vasodilatory effects of MP-III-058 and midazo-lam, both of which at 100 μmol/L concentrations had efficacy similar to prazosin. Flumazenil exhibited weak vasoactivity per se, but significantly prevented the relaxant effects of midazolam and MP-III-058. These studies indicate the existence of functional GABAA receptors in the rat aorta, where ligands exert vasodilatory effects by positive modulation of the benzodiazepine binding site, suggesting the potential for further quest for leads with optimized pharmacokinetic properties as prospective adjuvant vasodilators

The LSST AGN Data Challenge: Selection Methods

Abstract Development of the Rubin Observatory Legacy Survey of Space and Time (LSST) includes a series of Data Challenges (DCs) arranged by various LSST Scientific Collaborations that are taking place during the project's preoperational phase. The AGN Science Collaboration Data Challenge (AGNSC-DC) is a partial prototype of the expected LSST data on active galactic nuclei (AGNs), aimed at validating machine learning approaches for AGN selection and characterization in large surveys like LSST. The AGNSC-DC took place in 2021, focusing on accuracy, robustness, and scalability. The training and the blinded data sets were constructed to mimic the future LSST release catalogs using the data from the Sloan Digital Sky Survey Stripe 82 region and the XMM-Newton Large Scale Structure Survey region. Data features were divided into astrometry, photometry, color, morphology, redshift, and class label with the addition of variability features and images. We present the results of four submitted solutions to DCs using both classical and machine learning methods. We systematically test the performance of supervised models (support vector machine, random forest, extreme gradient boosting, artificial neural network, convolutional neural network) and unsupervised ones (deep embedding clustering) when applied to the problem of classifying/clustering sources as stars, galaxies, or AGNs. We obtained classification accuracy of 97.5% for supervised models and clustering accuracy of 96.0% for unsupervised ones and 95.0% with a classic approach for a blinded data set. We find that variability features significantly improve the accuracy of the trained models, and correlation analysis among different bands enables a fast and inexpensive first-order selection of quasar candidates.</jats:p

The LSST AGN Data Challenge: Selection Methods

Development of the Rubin Observatory Legacy Survey of Space and Time (LSST) includes a series of Data Challenges (DCs) arranged by various LSST Scientific Collaborations that are taking place during the project's preoperational phase. The AGN Science Collaboration Data Challenge (AGNSC-DC) is a partial prototype of the expected LSST data on active galactic nuclei (AGNs), aimed at validating machine learning approaches for AGN selection and characterization in large surveys like LSST. The AGNSC-DC took place in 2021, focusing on accuracy, robustness, and scalability. The training and the blinded data sets were constructed to mimic the future LSST release catalogs using the data from the Sloan Digital Sky Survey Stripe 82 region and the XMM-Newton Large Scale Structure Survey region. Data features were divided into astrometry, photometry, color, morphology, redshift, and class label with the addition of variability features and images. We present the results of four submitted solutions to DCs using both classical and machine learning methods. We systematically test the performance of supervised models (support vector machine, random forest, extreme gradient boosting, artificial neural network, convolutional neural network) and unsupervised ones (deep embedding clustering) when applied to the problem of classifying/clustering sources as stars, galaxies, or AGNs. We obtained classification accuracy of 97.5% for supervised models and clustering accuracy of 96.0% for unsupervised ones and 95.0% with a classic approach for a blinded data set. We find that variability features significantly improve the accuracy of the trained models, and correlation analysis among different bands enables a fast and inexpensive first-order selection of quasar candidates