Embedded-Cluster Calculations in a Numeric Atomic Orbital Density-Functional Theory Framework

We integrate the all-electron electronic structure code FHI-aims into the general ChemShell package for solid-state embedding (QM/MM) calculations. A major undertaking in this integration is the implementation of pseudopotential functionality into FHI-aims to describe cations at the QM/MM boundary through effective core potentials and therewith prevent spurious overpolarization of the electronic density. Based on numeric atomic orbital basis sets, FHI-aims offers particularly efficient access to exact exchange and second order perturbation theory, rendering the established QM/MM setup an ideal tool for hybrid and double-hybrid level DFT calculations of solid systems. We illustrate this capability by calculating the reduction potential of Fe in the Fe-substituted ZSM-5 zeolitic framework and the reaction energy profile for (photo-)catalytic water oxidation at TiO2(110).Comment: 12 pages, 4 figure

Supervoid Origin of the Cold Spot in the Cosmic Microwave Background

We use a WISE-2MASS-Pan-STARRS1 galaxy catalog to search for a supervoid in the direction of the Cosmic Microwave Background Cold Spot. We obtain photometric redshifts using our multicolor data set to create a tomographic map of the galaxy distribution. The radial density profile centred on the Cold Spot shows a large low density region, extending over 10's of degrees. Motivated by previous Cosmic Microwave Background results, we test for underdensities within two angular radii, $5^\circ$, and $15^\circ$. Our data, combined with an earlier measurement by Granett et al 2010, are consistent with a large $R_{\rm void}=(192 \pm 15)h^{-1} Mpc$ $(2\sigma)$ supervoid with $\delta \simeq -0.13 \pm 0.03$ centered at $z=0.22\pm0.01$. Such a supervoid, constituting a $\sim3.5 \sigma$ fluctuation in the $\Lambda CDM$ model, is a plausible cause for the Cold Spot.Comment: 4 pages, 2 figures, Proceedings of IAU 306 Symposium: Statistical Challenges in 21st Century Cosmolog

Dual-Pump Coherent Anti-Stokes Raman Scattering Temperature and CO2 Concentration Measurements

Measurements of temperature and CO2 concentration using dual-pump coherent anti-Stokes Raman scattering, (CARS) are described. The measurements were performed in laboratory flames,in a room-temperature gas cell, and on an engine test stand at the U.S. Air Force Research Laboratory, Wright-Patterson Air Force Base. A modeless dye laser, a single-mode Nd:YAG laser, and an unintensified back-illuminated charge-coupled device digital camera were used for these measurements. The CARS measurements were performed on a single-laser-shot basis. The standard deviations of the temperatures and CO2 mole fractions determined from single-shot dual-pump CARS spectra in steady laminar propane/air flames were approximately 2 and 10% of the mean values of approximately 2000 K and 0.10, respectively. The precision and accuracy of single-shot temperature measurements obtained from the nitrogen part of the dual-pump CARS system were investigated in detail in near-adiabatic hydrogen/air/CO2 flames. The precision of the CARS temperature measurements was found to be comparable to the best results reported in the literature for conventional two-laser, single-pump CARS. The application of dual-pump CARS for single-shot measurements in a swirl-stabilized combustor fueled with JP-8 was also demonstrated

Cytoplasmic expression systems triggered by mRNA yield increased gene expression in post-mitotic neurons

Non-viral vectors are promising vehicles for gene therapy but delivery of plasmid DNA to post-mitotic cells is challenging as nuclear entry is particularly inefficient. We have developed and evaluated a hybrid mRNA/DNA system designed to bypass the nuclear barrier to transfection and facilitate cytoplasmic gene expression. This system, based on co-delivery of mRNA(A64) encoding for T7 RNA polymerase (T7 RNAP) with a T7-driven plasmid, produced between 10- and 2200-fold higher gene expression in primary dorsal root ganglion neuronal (DRGN) cultures isolated from Sprague–Dawley rats compared to a cytomegalovirus (CMV)-driven plasmid, and 30-fold greater expression than the enhanced T7-based autogene plasmid pR011. Cell-free assays and in vitro transfections highlighted the versatility of this system with small quantities of T7 RNAP mRNA required to mediate expression at levels that were significantly greater than with the T7-driven plasmid alone or supplemented with T7 RNAP protein. We have also characterized a number of parameters, such as mRNA structure, intracellular stability and persistence of each nucleic acid component that represent important factors in determining the transfection efficiency of this hybrid expression system. The results from this study demonstrate that co-delivery of mRNA is a promising strategy to yield increased expression with plasmid DNA, and represents an important step towards improving the capability of non-viral vectors to mediate efficient gene transfer in cell types, such as in DRGN, where the nuclear membrane is a significant barrier to transfection

Machine Learning Outperforms Regression Analysis to Predict Next-Season Major League Baseball Player Injuries: Epidemiology and Validation of 13,982 Player-Years From Performance and Injury Profile Trends, 2000-2017

Background: Machine learning (ML) allows for the development of a predictive algorithm capable of imbibing historical data on a Major League Baseball (MLB) player to accurately project the player\u27s future availability. Purpose: To determine the validity of an ML model in predicting the next-season injury risk and anatomic injury location for both position players and pitchers in the MLB. Study Design: Descriptive epidemiology study. Methods: Using 4 online baseball databases, we compiled MLB player data, including age, performance metrics, and injury history. A total of 84 ML algorithms were developed. The output of each algorithm reported whether the player would sustain an injury the following season as well as the injury\u27s anatomic site. The area under the receiver operating characteristic curve (AUC) primarily determined validation. Results: Player data were generated from 1931 position players and 1245 pitchers, with a mean follow-up of 4.40 years (13,982 player-years) between the years of 2000 and 2017. Injured players spent a total of 108,656 days on the disabled list, with a mean of 34.21 total days per player. The mean AUC for predicting next-season injuries was 0.76 among position players and 0.65 among pitchers using the top 3 ensemble classification. Back injuries had the highest AUC among both position players and pitchers, at 0.73. Advanced ML models outperformed logistic regression in 13 of 14 cases. Conclusion: Advanced ML models generally outperformed logistic regression and demonstrated fair capability in predicting publicly reportable next-season injuries, including the anatomic region for position players, although not for pitchers

Resourcing Scholar-Activism: Collaboration, Transformation, and the Production of Knowledge

In this article we offer a set of resources for scholar-activists to reflect on and guide their practice. We begin by suggesting that research questions should be triangulated to consider not only their scholarly merit but the intellectual and political projects the findings will advance and the research questions of interest to community and social movement collaborators

The Cold Spot in the Cosmic Microwave Background: the Shadow of a Supervoid

Standard inflationary hot big bang cosmology predicts small fluctuations in the Cosmic Microwave Background (CMB) with isotropic Gaussian statistics. All measurements support the standard theory, except for a few anomalies discovered in the Wilkinson Microwave Anisotropy Probe maps and confirmed recently by the Planck satellite. The Cold Spot is one of the most significant of such anomalies, and the leading explanation of it posits a large void that imprints this extremely cold area via the linear Integrated Sachs-Wolfe (ISW) effect due to the decay of gravitational potentials over cosmic time, or via the Rees- Sciama (RS) effect due to late-time non-linear evolution. Despite several observational campaigns targeting the Cold Spot region, to date no suitably large void was found at higher redshifts z>0.3. Here we report the detection of an R=(192±15)h −1Mpc size supervoid of depth δ=−0.13±0.03, and centred at redshift z=0.22. This supervoid, possibly the largest ever found, is large enough to significantly affect the CMB via the non-linear RS effect, as shown in our Lemaitre-Tolman-Bondi framework. This discovery presents the first plausible explanation for any of the physical CMB anomalies, and raises the possibility that local large-scale structure could be responsible for other anomalies as well

Results from the centers for disease control and prevention's predict the 2013-2014 Influenza Season Challenge

Background: Early insights into the timing of the start, peak, and intensity of the influenza season could be useful in planning influenza prevention and control activities. To encourage development and innovation in influenza forecasting, the Centers for Disease Control and Prevention (CDC) organized a challenge to predict the 2013-14 Unites States influenza season. Methods: Challenge contestants were asked to forecast the start, peak, and intensity of the 2013-2014 influenza season at the national level and at any or all Health and Human Services (HHS) region level(s). The challenge ran from December 1, 2013-March 27, 2014; contestants were required to submit 9 biweekly forecasts at the national level to be eligible. The selection of the winner was based on expert evaluation of the methodology used to make the prediction and the accuracy of the prediction as judged against the U.S. Outpatient Influenza-like Illness Surveillance Network (ILINet). Results: Nine teams submitted 13 forecasts for all required milestones. The first forecast was due on December 2, 2013; 3/13 forecasts received correctly predicted the start of the influenza season within one week, 1/13 predicted the peak within 1 week, 3/13 predicted the peak ILINet percentage within 1 %, and 4/13 predicted the season duration within 1 week. For the prediction due on December 19, 2013, the number of forecasts that correctly forecasted the peak week increased to 2/13, the peak percentage to 6/13, and the duration of the season to 6/13. As the season progressed, the forecasts became more stable and were closer to the season milestones. Conclusion: Forecasting has become technically feasible, but further efforts are needed to improve forecast accuracy so that policy makers can reliably use these predictions. CDC and challenge contestants plan to build upon the methods developed during this contest to improve the accuracy of influenza forecasts. © 2016 The Author(s)