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Implant Restorations
The fourth edition of Implant Restorations: A Step-by-Step Guide provides a wealth of updated and expanded coverage on detailed procedures for restoring dental implants. Focusing on the most common treatment scenarios, it offers concise literature reviews for each chapter and easy-to-follow descriptions of the techniques, along with high-quality clinical photographs demonstrating each step. Comprehensive throughout, this practical guide begins with introductory information on incorporating implant restorative dentistry in clinical practice. It covers diagnosis and treatment planning and digital dentistry, and addresses advances in cone beam computerized tomography (CBCT), treatment planning software, computer generated surgical guides, rapid prototype printing and impression-less implant restorative treatments, intra-oral scanning, laser sintering, and printing/milling polymer materials. Record-keeping, patient compliance, hygiene regimes, and follow-up are also covered. * Provides an accessible step-by-step guide to commonly encountered treatment scenarios, describing procedures and techniques in an easy-to-follow, highly illustrated format * Offers new chapters on diagnosis and treatment planning and digital dentistry * Covers advances in cone beam computerized tomography (CBCT), computer generated surgical guides, intra-oral scanning, laser sintering, and more An excellent and accessible guide on a burgeoning subject in modern dental practice by one of its most experienced clinicians, Implant Restorations: A Step-by-Step Guide, Fourth Edition will appeal to prosthodontists, general dentists, implant surgeons, dental students, dental assistants, hygienists, and dental laboratory technicians.
eBook
Machine Learning Applied to X-Ray Spectra: Separating Stars in Orion Nebula Cluster from Active Galactic Nuclei in CDFS
Modern X-ray telescopes have detected hundreds of thousands of X-ray sources in the universe. However, current methods to classify these sources using the X-ray data themselves suffer problems—detailed X-ray spectroscopy of individual sources is too time consuming, while hardness ratios often lack accuracy, and can be difficult to use effectively. These methods fail to use the power of X-ray CCD detectors to identify X-ray emission lines and distinguish line-dominated spectra (from chromospherically active stars, supernova remnants, etc.) from continuum-dominated ones (e.g., compact objects or active galactic nuclei, AGN). In this paper, we probe the use of artificial neural networks (ANN) in differentiating Chandra spectra of young stars in the Chandra Orion Ultradeep Project (COUP) survey from AGN in the Chandra Deep Field South (CDFS) survey. We use these surveys to generate 100,000 artificial spectra of stars and AGN, and train our ANN models to separate the two kinds of spectra. We find that our methods reach an accuracy of ∼92% in classifying simulated spectra of moderate-brightness objects in typical exposures, but their performance decreases on the observed COUP and CDFS spectra (∼91%), due in large part to the relatively high background of these long-exposure data sets. We also investigate the performance of our methods with changing properties of the spectra such as the net source counts, the relative contribution of background, the absorption column of the sources, etc. We conclude that these methods have substantial promise for application to large X-ray surveys.
Journal Article
Crystals, X-rays, and proteins : comprehensive protein crystallography
A complete account of the theory of the diffraction of x-rays by crystals with particular reference to the processes of determining the structures of protein molecules, this book is aimed primarily at structural biologists and biochemists but will also be valuable to those entering the field with a background in physical sciences or chemistry.
Book
Fitting AGN/Galaxy X-Ray-to-radio SEDs with CIGALE and Improvement of the Code
Modern and future surveys effectively provide a panchromatic view for large numbers of extragalactic objects. Consistently modeling these multiwavelength survey data is a critical but challenging task for extragalactic studies. The Code Investigating GALaxy Emission (cigale) is an efficient python code for spectral energy distribution (SED) fitting of galaxies and active galactic nuclei (AGNs). Recently, a major extension of cigale (named x-cigale) has been developed to account for AGN/galaxy X-ray emission and improve AGN modeling at UV-to-IR wavelengths. Here, we apply x-cigale to different samples, including Cosmological Evolution Survey (COSMOS) spectroscopic type 2 AGNs, Chandra Deep Field-South X-ray detected normal galaxies, Sloan Digital Sky Survey quasars, and COSMOS radio objects. From these tests, we identify several weaknesses of x-cigale and improve the code accordingly. These improvements are mainly related to AGN intrinsic X-ray anisotropy, X-ray binary emission, AGN accretion-disk SED shape, and AGN radio emission. These updates improve the fit quality and allow for new interpretation of the results, based on which we discuss physical implications. For example, we find that AGN intrinsic X-ray anisotropy is moderate, and can be modeled as LX(θ)∝1+cosθ , where θ is the viewing angle measured from the AGN axis. We merge the new code into the major branch of cigale, and publicly release this new version as cigale v2022.0 on https://cigale.lam.fr.
Journal Article
Simultaneous Femtosecond X-ray Spectroscopy and Diffraction of Photosystem II at Room Temperature
Intense femtosecond x-ray pulses produced at the Linac Coherent Light Source (LCLS) were used for simultaneous x-ray diffraction (XRD) and x-ray emission spectroscopy (XES) of microcrystals of photosystem II (PS II) at room temperature. This method probes the overall protein structure and the electronic structure of the Mn₄ CaO₅ cluster in the oxygen-evolving complex of PS II. XRD data are presented from both the dark state (S₁) and the first illuminated state (S₂) of PS II. Our simultaneous XRD-XES study shows that the PS II crystals are intact during our measurements at the LCLS, not only with respect to the structure of PS II, but also with regard to the electronic structure of the highly radiation-sensitive Mn₄CaO₅ cluster, opening new directions for future dynamics studies.
Journal Article
First X-Ray Polarization Measurement Confirms the Low Black Hole Spin in LMC X-3
X-ray polarization is a powerful tool to investigate the geometry of accreting material around black holes, allowing independent measurements of the black hole spin and orientation of the innermost parts of the accretion disk. We perform X-ray spectropolarimetric analysis of an X-ray binary system in the Large Magellanic Cloud, LMC X-3, that hosts a stellar-mass black hole, known to be persistently accreting since its discovery. We report the first detection of the X-ray polarization in LMC X-3 with the Imaging X-ray Polarimetry Explorer, and find the average polarization degree (PD) of 3.2% ± 0.6% and a constant polarization angle of −42° ± 6° over the 2–8 keV range. Using accompanying spectroscopic observations by NICER, NuSTAR, and the Neil Gehrels Swift observatories, we confirm previous measurements of the black hole spin via the X-ray continuum method, a ≈ 0.2. From polarization analysis only, we found consistent results with low black hole spin, with an upper limit of a < 0.7 at a 90% confidence level. A slight increase in the PD with energy, similar to other black hole X-ray binaries in the soft state, is suggested from the data but with a low statistical significance.
Journal Article
Explainable Machine Learning Classification of Chandra X-Ray Sources: SHAP Analysis of Multiwavelength Features
Extensive astronomical surveys, like those conducted with the Chandra X-ray Observatory, detect hundreds of thousands of unidentified cosmic sources. Machine learning (ML) methods offer an efficient, probabilistic approach to classifying them, which can be useful for making discoveries and conducting deeper studies. In earlier work, we applied the LightGBM (ML model) to classify 277,069 Chandra point sources into eight categories: active galactic nuclei (AGNs), X-ray emitting stars, young stellar objects (YSO), high-mass X-ray binaries, low-mass X-ray binaries, ultraluminous X-ray sources, cataclysmic variables, and pulsars. In this work, we present the classification table of 54,770 robustly classified sources (over 3σ confidence), including 14,066 sources at >4σ significance. To ensure classification reliability and gain a deeper insight, we investigate the multiwavelength feature relationships learned by the LightGBM model, focusing on AGNs, stars, and YSOs. We employ explainable artificial intelligence (XAI) techniques, specifically, Shapley Additive Explanations, to quantify the contribution of individual features and their interactions to the predicted classification probabilities. Among other things, we find infrared-optical and X-ray decision boundaries for separating AGN/stars, and infrared-X-ray boundaries for YSOs. These results are crucial for estimating object classes even with limited multiwavelength data. This study represents one of the earliest applications of XAI to large-scale astronomical data sets, demonstrating ML models’ potential for uncovering physically meaningful patterns in data in addition to classification. Finally, our publicly available, extensive, and interactive catalog will be helpful to explore the contributions of features and their combinations in greater detail in the future.
Journal Article