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PROVIDER: S-EPMC6880299 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:IntroductionWhile smoking rates are 3-4 times higher among criminal justice populations than in the general population, no studies have previously examined smoking characteristics in a community corrections population.MethodsThe current study involved descriptive analyses of self-reported survey data from 217 criminal justice supervisees reporting for urine drug screens during a 5-day period at a community corrections facility in the southeastern United States.ResultsMost participants were current smokers (72.3%), males (65.9%), and Black (50.2%) who reported smoking three fourths of a pack of cigarettes per day and had been smoking for about 15 years. More than half of smokers reported that they would be interested in receiving cessation assistance if free help were available and of these, 60% were interested in pharmacotherapy. White smokers used more cigarettes per day, were more likely to have already tried medication to help them quit smoking, and were also more interested in pharmacotherapies and less interested in behavioral therapies compared with Black smokers. Female smokers did not differ from male smokers on key smoking characteristics, but male smokers were more likely to have tried or regularly used other tobacco products, such as cigars. Female smokers were significantly more likely to report interest in using a pharmacotherapy agent for future cessation, while male smokers reported more interest in nonpharmacotherapy approaches to quit smoking.DiscussionResults from this study highlight important differences among smoking groups and may indicate the need to test tailored smoking interventions.
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Project description:Theoretical models capture very precisely the behaviour of magnetic materials at the microscopic level. This makes computer simulations of magnetic materials, such as spin dynamics simulations, accurately mimic experimental results. New approaches to efficient spin dynamics simulations are limited by integration time step barrier to solving the equations-of-motions of many-body problems. Using a short time step leads to an accurate but inefficient simulation regime whereas using a large time step leads to accumulation of numerical errors that render the whole simulation useless. In this paper, we use a Deep Learning method to compute the numerical errors of each large time step and use these computed errors to make corrections to achieve higher accuracy in our spin dynamics. We validate our method on the 3D Ferromagnetic Heisenberg cubic lattice over a range of temperatures. Here we show that the Deep Learning method can accelerate the simulation speed by 10 times while maintaining simulation accuracy and overcome the limitations of requiring small time steps in spin dynamic simulations.
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Project description:Carceral settings in the United States have been the source of many single site COVID-19 outbreaks. Quarantine is a strategy used to mitigate the spread of COVID-19 in correctional settings, and specific quarantine practices differ state to state. To better understand how states are using quarantine in prisons, we reviewed each state's definition of quarantine and compared each state's definition to the Centers for Disease Control's (CDC) definition and recommendations for quarantine in jails and prisons. Most prison systems, 45 of 53, define quarantine, but definitions vary widely. No state published definitions of quarantine that align with all CDC recommendations, and only 9 states provide quarantine data. In these states, the highest recorded quarantine rate occurred in Ohio in May 2020 at 843 per 1,000. It is necessary for prison systems to standardize their definitions of quarantine and to utilize quarantine practices in accordance with CDC recommendations. In addition, data transparency is needed to better understand the use of quarantine and its effectiveness at mitigating COVID-19 outbreaks in carceral settings.
| S-EPMC8491943 | biostudies-literature
Project description:Revelation of emerging exotic states of topological insulators (TIs) for future quantum computing applications relies on breaking time-reversal symmetry and opening a surface energy gap. Here, we report on the transport response of Bi2Te3 TI thin films in the presence of varying Cr dopants. By tracking the magnetoconductance (MC) in a low doping regime we observed a progressive crossover from weak antilocalization (WAL) to weak localization (WL) as the Cr concentration increases. In a high doping regime, however, increasing Cr concentration yields a monotonically enhanced anomalous Hall effect (AHE) accompanied by an increasing carrier density. Our results demonstrate a possibility of manipulating bulk ferromagnetism and quantum transport in magnetic TI, thus providing an alternative way for experimentally realizing exotic quantum states required by spintronic applications.
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Project description:We present two new semiempirical quantum-chemical methods with orthogonalization and dispersion corrections: ODM2 and ODM3 (ODM x). They employ the same electronic structure model as the OM2 and OM3 (OM x) methods, respectively. In addition, they include Grimme's dispersion correction D3 with Becke-Johnson damping and three-body corrections E ABC for Axilrod-Teller-Muto dispersion interactions as integral parts. Heats of formation are determined by adding explicitly computed zero-point vibrational energy and thermal corrections, in contrast to standard MNDO-type and OM x methods. We report ODM x parameters for hydrogen, carbon, nitrogen, oxygen, and fluorine that are optimized with regard to a wide range of carefully chosen state-of-the-art reference data. Extensive benchmarks show that the ODM x methods generally perform better than the available MNDO-type and OM x methods for ground-state and excited-state properties, while they describe noncovalent interactions with similar accuracy as OM x methods with a posteriori dispersion corrections.
| S-EPMC6416713 | biostudies-literature
Project description:In this work, we demonstrate a method to quantify uncertainty in corrections to density functional theory (DFT) energies based on empirical results. Such corrections are commonly used to improve the accuracy of computational enthalpies of formation, phase stability predictions, and other energy-derived properties, for example. We incorporate this method into a new DFT energy correction scheme comprising a mixture of oxidation-state and composition-dependent corrections and show that many chemical systems contain unstable polymorphs that may actually be predicted stable when uncertainty is taken into account. We then illustrate how these uncertainties can be used to estimate the probability that a compound is stable on a compositional phase diagram, thus enabling better-informed assessments of compound stability.
| S-EPMC8322326 | biostudies-literature
Project description:This paper describes the development of the B3LYP localized orbital correction model which improves the accuracy of the B3LYP thermochemical predictions for compounds containing transition metals. The development of this model employs a large data set containing 36 experimental atomic energies and 71 bond dissociation energies. B3LYP calculations were carried out on these systems with different basis sets. Based on an electronic structure analysis and physical arguments, we built a set of 10 parameters to correct atomic data and a set of 21 parameters to correct bond dissociation energies. Using the results from our biggest basis set, the model was shown to reduce the mean absolute deviation from 7.7 to 0.4 kcalmol for the atomic data and from 5.3 to 1.7 kcalmol for the bond dissociation energies. The model was also tested using a second basis set and was shown to give relatively accurate results too. The model was also able to predict an outlier in the experimental data that was further investigated with high level coupled-cluster calculations.
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Project description:Our previous works have demonstrated the ability of our localized orbital correction (LOC) methodology to greatly improve the accuracy of various thermochemical properties at the stationary points of the Density Functional Theory (DFT) reaction coordinate (RC). Herein we extend this methodology from stationary points to the entire RC connecting any stationary points by developing continuous localized orbital corrections (CLOCs). We show that the resultant method, DFT-CLOC, is capable of producing RCs with far greater accuracy than uncorrected DFT and yet requires negligible computational cost beyond the uncorrected DFT calculations. Various post-Hartree-Fock (post-HF) reaction coordinate profiles were used, including a sigmatropic shift, Diels-Alder reaction, electrocyclization, carbon radical and three hydrogen radical reactions to show that this method is robust across multiple reaction types of general interest.
| S-EPMC3030200 | biostudies-literature
Project description:Camera images and video recordings are simple and non-invasive tools to investigate animals in their natural habitat. Quantitative evaluations, however, often require an exact reconstruction of object positions, sizes, and distances in the image. Here, we provide an open source software package to perform such calculations. Our approach allows the user to correct for perspective distortion, transform images to "bird's-eye" view projections, or transform image-coordinates to real-world coordinates and vice versa. The extrinsic camera parameters that are necessary to perform such image corrections and transformations (elevation, tilt/roll angle, and heading of the camera) are obtained from the image using contextual information such as a visible horizon, GPS coordinates of landmarks, known object sizes, or images of the same object obtained from different viewing angles. All mathematical operations are implemented in the Python package CameraTransform. The performance of the implementation is evaluated using computer-generated synthetic images with known camera parameters. Moreover, we test our algorithm on images of emperor penguin colonies, and demonstrate that the camera tilt and roll angles can be estimated with an error of less than one degree, and the camera elevation with an error of less than 5%. The CameraTransform software package simplifies camera matrix-based image transformations and the extraction of quantitative image information. An extensive documentation and usage examples in an ecological context are provided at http://cameratransform.readthedocs.io.
| S-EPMC7453838 | biostudies-literature
Project description:Taking long-range electrostatic effects into account in classical and hybrid quantum mechanics-molecular mechanics (QM/MM) simulations is necessary for an accurate description of the system under study. We have recently developed a method, termed long-range electrostatic corrections (LREC), for monopolar QM/MM calculations. Here, we present an extension of LREC for multipolar/polarizable QM/MM simulations within the LICHEM software package. Reaction barriers and QM-MM interaction energies converge with a LREC cutoff between 20 and 25 Å, in agreement with our previous results. Additionally, the LREC approach for the QM-MM interactions can be smoothly combined with standard shifting or Ewald summation methods in the MM calculations. We recommend the use of QM(LREC)/MM(PME), where the QM region is treated with LREC and the MM region is treated with particle mesh Ewald (PME) summation. This combination is an excellent compromise between simplicity, speed, and accuracy for large QM/MM simulations.
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