Bacterial community analysis in upflow multilayer anaerobic reactor (UMAR) treating high-solids organic wastes

A novel anaerobic digestion configuration, the upflow multi-layer anaerobic reactor (UMAR), was developed to treat high-solids organic wastes. The UMAR was hypothesized to form multi-layer along depth due to the upflow plug flow; use of a recirculation system and a rotating distributor and baffles aimed to assist treating high-solids influent. The chemical oxygen demand (COD) removal efficiency and methane (CH4) production rate were 89% and 2.10 L CH4/L/day, respectively, at the peak influent COD concentration (110.4 g/L) and organic loading rate (7.5 g COD/L/day). The 454 pyrosequencing results clearly indicated heterogeneous distribution of bacterial communities at different vertical locations (upper, middle, and bottom) of the UMAR. Firmicutes was the dominant (>70%) phylum at the middle and bottom parts, while Deltaproteobacteria and Chloroflexi were only found in the upper part. Potential functions of the bacteria were discussed to speculate on their roles in the anaerobic performance of the UMAR system

Benchmarking calculations of wavelengths and transition rates with spectroscopic accuracy for W XLVIII through W LVI tungsten ions

Atomic properties of n=3 levels for W47+-W55+ ions (Z=74) are systematically calculated using two different and independent methods, namely, the second-order many-body perturbation theory and the multiconfiguration Dirac-Hartree-Fock method combined with the relativistic configuration interaction approach. Wavelengths and transition rates for electric-and magnetic-dipole transitions involving the n=3 levels of W47+-W55+ are calculated. In addition, we discuss in detail the importance of the valence and core-valence electron correlations, the Breit interaction, the higher-order frequency-dependent retardation correction, and the leading quantum electrodynamical corrections for transition wavelengths. Spectroscopic accuracy is achieved for the present calculated wavelengths, and most of them agree with experimental values within 0.05%. Our calculated wavelengths, combined with collisional radiative model simulations, are used to identify the yet unidentified 25 observed lines in the extremely complex spectrum between 27Å and 34Å measured by Lennartsson etal. [Phys. Rev. A 87, 062505 (2013)10.1103/PhysRevA.87.062505]. We provide additional data for 472 strong electric-dipole transitions in the wavelength range of 17-50 Å, and 185 strong magnetic-dipole transitions between 36Å and 4384Å, with a line intensity greater than 1photon/s. These can provide benchmark data for future experiments and theoretical calculations

Association of body mass index with rapid eye movement sleep behavior disorder in Parkinson’s disease

BackgroundThe association between body mass index (BMI) and rapid eye-movement (REM) sleep-related behavioral disorder (RBD) in Parkinson’s disease (PD) remains unknown. Our study was to investigate the association of BMI with RBD in PD patients.MethodsIn this cross-sectional study, a total of 1,115 PD participants were enrolled from Parkinson’s Progression Markers Initiative (PPMI) database. BMI was calculated as weight divided by height squared. RBD was defined as the RBD questionnaire (RBDSQ) score with the cutoff of 5 or more assessed. Univariable and multivariable logistic regression models were performed to examine the associations between BMI and the prevalence of RBD. Non-linear correlations were explored with use of restricted cubic spline (RCS) analysis. And the inflection point was determined by the two-line piecewise linear models.ResultsWe identified 426 (38.2%) RBD. The proportion of underweight, normal, overweight and obese was 2.61, 36.59, 40.36, and 20.44%, respectively. In the multivariate logistic regression model with full adjustment for confounding variables, obese individuals had an odds ratio of 1.77 (95% confidence interval: 1.21 to 2.59) with RBD compared with those of normal weight. In the RCS models with three knots, BMI showed a non-linear association with RBD. The turning points of BMI estimated from piecewise linear models were of 28.16 kg/m2, 28.10 kg/m2, and 28.23 kg/m2 derived from univariable and multivariable adjusted logistic regression models. The effect modification by depression on the association between BMI and RBD in PD was also found in this study. Furthermore, the sensitivity analyses linked with cognition, education, and ethnic groups indicated the robustness of our results.ConclusionThe current study found a significant dose–response association between BMI and RBD with a depression-based difference in the impact of BMI on RBD in PD patients

Emergent Dark Matter, Baryon, and Lepton Numbers

We present a new mechanism for transferring a pre-existing lepton or baryon asymmetry to a dark matter asymmetry that relies on mass mixing which is dynamically induced in the early universe. Such mixing can succeed with only generic scales and operators and can give rise to distinctive relationships between the asymmetries in the two sectors. The mixing eliminates the need for the type of additional higher-dimensional operators that are inherent to many current asymmetric dark matter models. We consider several implementations of this idea. In one model, mass mixing is temporarily induced during a two-stage electroweak phase transition in a two Higgs doublet model. In the other class of models, mass mixing is induced by large field vacuum expectation values at high temperatures - either moduli fields or even more generic kinetic terms. Mass mixing models of this type can readily accommodate asymmetric dark matter masses ranging from 1 GeV to 100 TeV and expand the scope of possible relationships between the dark and visible sectors in such models.Comment: 36 pages, 5 figure

Spintronics: Fundamentals and applications

Spintronics, or spin electronics, involves the study of active control and manipulation of spin degrees of freedom in solid-state systems. This article reviews the current status of this subject, including both recent advances and well-established results. The primary focus is on the basic physical principles underlying the generation of carrier spin polarization, spin dynamics, and spin-polarized transport in semiconductors and metals. Spin transport differs from charge transport in that spin is a nonconserved quantity in solids due to spin-orbit and hyperfine coupling. The authors discuss in detail spin decoherence mechanisms in metals and semiconductors. Various theories of spin injection and spin-polarized transport are applied to hybrid structures relevant to spin-based devices and fundamental studies of materials properties. Experimental work is reviewed with the emphasis on projected applications, in which external electric and magnetic fields and illumination by light will be used to control spin and charge dynamics to create new functionalities not feasible or ineffective with conventional electronics.Comment: invited review, 36 figures, 900+ references; minor stylistic changes from the published versio

Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United States

Short-term probabilistic forecasts of the trajectory of the COVID-19 pandemic in the United States have served as a visible and important communication channel between the scientific modeling community and both the general public and decision-makers. Forecasting models provide specific, quantitative, and evaluable predictions that inform short-term decisions such as healthcare staffing needs, school closures, and allocation of medical supplies. Starting in April 2020, the US COVID-19 Forecast Hub (https://covid19forecasthub.org/) collected, disseminated, and synthesized tens of millions of specific predictions from more than 90 different academic, industry, and independent research groups. A multimodel ensemble forecast that combined predictions from dozens of groups every week provided the most consistently accurate probabilistic forecasts of incident deaths due to COVID-19 at the state and national level from April 2020 through October 2021. The performance of 27 individual models that submitted complete forecasts of COVID-19 deaths consistently throughout this year showed high variability in forecast skill across time, geospatial units, and forecast horizons. Two-thirds of the models evaluated showed better accuracy than a naïve baseline model. Forecast accuracy degraded as models made predictions further into the future, with probabilistic error at a 20-wk horizon three to five times larger than when predicting at a 1-wk horizon. This project underscores the role that collaboration and active coordination between governmental public-health agencies, academic modeling teams, and industry partners can play in developing modern modeling capabilities to support local, state, and federal response to outbreaks