Magic Wavelengths for 1S–nS and 2S–nS Transitions in Hydrogenlike Systems

We study the magic wavelength for two-photon 1S–nS transitions in a hydrogen and deuterium atom, as well as 2S–nS transitions, where the lower level is the metastable 2S state. At the magic wavelength, the dynamic Stark shifts of the ground and the excited state of the transition coincide, so that the transition frequency is independent of the intensity of the trapping laser field. Experimentally feasible magic wavelengths of transitions with small slopes in the atomic polarizabilities are determined; these are the most stable magic wavelengths against variations of the laser frequency. We provide data for the magic wavelengths for the 1S–nS and 2S–nS transitions in hydrogen and deuterium, with n=2,⋯,8. We also analyze the stability of the elimination of the ac Stark shift at the magic wavelength against tiny variations of the trapping laser frequency from the magic value.

Transfreq: a Python package for computing the theta-to-alpha transition frequency from resting state EEG data

A classic approach to estimate the individual theta-to-alpha transition frequency requires two electroencephalographic (EEG) recordings, one acquired in restingstate condition and one showing an alpha de-synchronisation due e.g. to task execution. This translates into longer recording sessions that my be cumbersome in studies involving patients. Moreover, incomplete de-synchronisation of the alpha rhythm may compromise the final estimation of the transition frequency. Here we present transfreq, a Python library that allows the computation of the transition frequency from resting-state data by clustering the spectral profiles at different EEG channels based on their content in the alpha and theta bands. We first provide an overview of the transfreq core algorithm and of the software architecture. Then we demonstrate its feasibility and robustness across different experimental setups on a publicly available EEG data set and on in-house recordings. A detailed documentation of transfreq and the codes for reproducing the analysis of the paper with the open-source data set are available online at https://elisabettavallarino.github.io/transfreq/

Association of Sedentary and Active Bout Frequency With Mortality in Older Men Using Accelerometry

Background Time spent sedentary increases with age and has several negative health consequences. We sought to examine associations between daily sedentary and active bout frequency with all-cause mortality. Methods Data are from 2,918 men in the Osteoporotic Fractures in Men (MrOS) study (mean age at Visit 3±SD: 79.0±5.1 years) with valid activity monitor data (5.1±0.3 days worn>90%) at Year 7 visit (Visit 3, 2007-2009). Sedentary and active bout frequencies are defined as the daily transition frequency from a sedentary bout lasting 5+ minutes to activity of any intensity, and the transition frequency from an active bout lasting 5+ minutes to sedentary. Deaths were centrally adjudicated using death certificates. Cox proportional hazard models were used to examine associations between quartiles of sedentary (Q1 referent, <13.6 bouts/day) or active (Q1 referent, <5 bouts/day) bout frequency and mortality. Models were repeated, stratifying by median daily total time spent sedentary and active. Results After 9.4±3.7 years of follow-up, 1,487 (51.0%) men died. Men averaged 16.9±5.1 and 8.2±4.2 sedentary and active bouts/day, respectively. After full covariate adjustment, each quartile reflecting a higher sedentary (Q4 vs Q1 HR: 0.68, 95%CI: 0.58-0.81, p-trend<0.001) and active bout (Q4 vs Q1 HR: 0.57, 95%CI: 0.48-0.68, p-trend<0.001) frequency was associated with lower mortality risk. There was no evidence that effects differed by total sedentary time (p-interaction for sedentary bout frequency and total sedentary time>0.05). Conclusions More frequent, prolonged sedentary and active bouts are associated with a lower mortality risk in older men and is not moderated by total sedentary time.

Spontaneous emission mediated by graphene/hexagonal boron nitride/graphene sandwich structure

Controlling the spontaneous emission of atoms or molecules is an interesting research topic in the field of quantum optics. Here we provide a perspective on modulating the interaction between two quantum emitters through a sandwich structure composed of graphene and hexagonal boron nitride (hBN). The dependence of interaction between quantum emitters on the thickness of hBN and chemical potential of graphene is investigated in detail. When the transition frequency of quantum emitter is located in the hyperbolic band of type I supported by hBN, the radiative state can be easily modulated by adjusting the chemical potential of graphene. So it is flexible to switch the interaction of quantum emitters from superradiant state to subradiant state by external electric field. When the transition frequency of quantum emitter is located in hyperbolic band of type II, the system can be always in the superradiant state. We predict the further research perspectives of spontaneous emission and superradiance with the help of metasurfaces based on two-dimensional materials.

Sleeping at the switch

Sleep slow waves are studied for their role in brain plasticity, homeostatic regulation, and their changes during aging. Here, we address the possibility that two types of slow waves co-exist in humans. Thirty young and 29 older adults underwent a night of polysomnographic recordings. Using the transition frequency, slow waves with a slow transition (slow switchers) and those with a fast transition (fast switchers) were discovered. Slow switchers had a high electroencephalography (EEG) connectivity along their depolarization transition while fast switchers had a lower connectivity dynamics and dissipated faster during the night. Aging was associated with lower temporal dissipation of sleep pressure in slow and fast switchers and lower EEG connectivity at the microscale of the oscillations, suggesting a decreased flexibility in the connectivity network of older individuals. Our findings show that two different types of slow waves with possible distinct underlying functions coexist in the slow wave spectrum.

Longitudinal Transitions in Initiation, Cessation, and Relapse of Smoking and E-Cigarette Use Among US Youth and Adults

Introduction Longitudinal surveys provide data to estimate transition probabilities between cigarette smoking, e-cigarette use, and dual use of both, facilitating projections of future use and the impact of policies. Methods We fit a continuous time Markov multi-state model for youth (ages 12-17y) and adults (≥18y) in Waves 1-4.5 of the Population Assessment of Tobacco and Health (PATH) longitudinal survey and estimated smoking and e-cigarette transition frequencies, including initiation, cessation, and relapse. We validated transition frequency results in a microsimulation model by projecting smoking and e-cigarette use prevalence over time. Results There was more volatility in smoking and e-cigarette use among youth than among adults. For youth never smokers, annual smoking initiation among never/current/former e-cigarette users occurred in 0.4% (95% CI 0.2-0.6%)/8.8% (7.0-10.7%)/3.1% (2.1-4.2%), and current e-cigarette users were more likely to quit e-cigarettes than to initiate smoking (absolute difference in annual probability 46.5%, 38.7-54.2%). For adult current smokers, annual smoking cessation among never/current/former e-cigarette users occurred in 22.6% (20.9-24.3%)/14.5% (11.5-17.4%)/15.1% (12.1-18.2%). For adult current dual users, 14.5% quit smoking and 49.5% quit e-cigarettes annually. For adult former smokers, annual smoking relapse among never/current/former e-cigarette users occurred in 17.7% (15.8-19.6%)/29.3% (23.8-34.7%)/32.8% (27.1-38.6%). Using these transition probabilities in a microsimulation model accurately projected smoking and e-cigarette use prevalence at 12 and 24 months compared to PATH empirical data (root-mean-square error <0.7%). Discussion PATH Waves 1-4.5 contain sufficient data to generate smoking and e-cigarette use transition frequency estimates for youth and adults in a microsimulation model. E-cigarette use among youth is especially volatile.