CBS International Business School

Crowley S.J., Poole E., Adams J., Eastman C.I.

Abstract Study Objectives Our sleep extension intervention in adolescents showed that gradually shifting weekday bedtime earlier plus one weekend of morning bright light advanced circadian phase and increased weeknight sleep duration. Here, we examine at-home maintenance of these changes. Methods Fourteen adolescents (15.3-17.9 years; 7 female) completed a 7-week study. After usual sleep at home (2-week baseline), intervention participants (n=8) gradually advanced weekday bedtime (1h earlier than baseline during week 3; 2h earlier in week 4) and received bright light (~6000lux; 2.5h) on both mornings of the intervening weekend. During 3 maintenance weeks, intervention participants were instructed to maintain their school-day wake-up time on all days, keep their early week 4 bedtimes, except on weekends when they could go to bed up to 1h later, and get a 2.5-h light box exposure within 5 min of waking on one morning (Saturday/Sunday) of both weekends at home. Control participants (n=6) slept as usual at home and did not receive weekend bright light. DLMO was measured after the 2-week baseline, 2-week intervention, and 3-week maintenance in all participants. Actigraphic sleep/wake was collected throughout. Results After the 2-week intervention, DLMOs advanced more compared to control (37.0±40.0 mins vs. -14.7±16.6 mins), weekday sleep duration increased by 69.7±27.8 min and sleep onset was 103.7±14.2 mins earlier compared to baseline. After 3 maintenance weeks, intervention participants showed negligible DLMO delays (-4.9±22.9 mins); weekday fall asleep times and sleep durations also remained stable. Conclusions Early circadian phase and extended sleep can be maintained with at-home weekend bright light.

Abstract Introduction Previously, we reported that evening room light and sleep restriction due to late bedtimes can reduce the expected circadian phase advances in response to morning bright light in adolescents or can shift rhythms later (delay). Here, we examine whether reducing evening light with sunglasses can rescue this effect and help rhythms shift earlier (advance). Methods So far, 57 adolescents (29 females; 14.1-18.0 years) completed a 14-day protocol. On days 1-7, they kept individualized 10-h sleep/dark schedules at home. On days 8-14, they lived in the laboratory. On day 8, we measured their Dim Light Melatonin Onset (DLMO). On days 9-10, they went to bed 1.5h, 3.0h, or 4.5h later. Wake time was unchanged; thus, sleep opportunity was 8.5h, 7h, and 5.5h, respectively. In each sleep restriction group, participants remained awake in room light (104±11 photopic lux; 8.5h n=9; 7h n=12; 5.5h n=8) or wore amber-lensed sunglasses (transmitted 14% of light and 10% of short wavelength light; 8.5h n=5; 7h n=4; 5.5h n=9). A control group (n=10) kept their baseline bedtime, so was not exposed to additional evening light or sleep restriction. On days 11-13, sleep/dark was gradually shifted earlier, and adolescents received three 30-min exposures of bright light (8599±885 photopic lux) upon waking. Final DLMO was measured on day 14. Multiple linear regression examined sleep restriction dose, sunglasses, and the dose-by-sunglasses interaction effects on phase shift. Results The control group advanced +2.1±1.0h. Greater sleep restriction reduced phase advances (dose: β=-1.31, p<.001), but evening sunglasses attenuated this effect (dose-by-sunglasses: β=1.01, p<.05). When sleep/dark was 8.5h, sunglasses produced larger advances (+1.3±0.6h) compared to no sunglasses (+0.5±0.6h) [t(12)=-2.4, p=.04]. Compared to no sunglasses, evening sunglasses prevented delays when sleep/dark was 7.0h (-0.8±1.1h vs +0.5±0.9; t(14)=-2.2, p=.04) and 5.5h (-2.6±1.2h vs +0.0±1.1h; t(15)=4.8, p<.001). With evening sunglasses and 7.0h or 5.5h sleep/dark, advances were still smaller than control (p’s≤.01). Conclusion Reducing evening light with amber-lensed glasses during a phase-advancing protocol with morning bright light facilitates larger advances or prevents delays in adolescents. However, ≤7h sleep/dark reduces phase advances in response to morning bright light even when evening light is reduced. Support (if any) R01HL146772 (Crowley)

High schools contain various sources that can release semivolatile organic compounds (SVOCs) including phthalates and other plasticizers, polybrominated diphenyl ethers (PBDEs) and organophosphate flame retardants (OPFRs) and pesticides. In this study, concentrations of 42 SVOCs were measured in settled dust (N = 70), heating, ventilation, and air conditioning (HVAC) filter dust (N = 13), and indoor air (N = 24) across four sampling events in each of seven high schools in central Texas, United States. Both legacy and emerging SVOCs were widely detected. Statistically significant positive correlations were found amongst the concentrations of several SVOCs in pairwise settled dust, HVAC filter dust, and indoor air, suggesting that HVAC filter dust is a promising quantitative forensic technique for investigating SVOC concentrations indoors. OPFRs had higher concentrations in air for classrooms with carpet, while PBDEs had higher air concentrations in computer classrooms. The concentrations of SVOCs in portable classrooms were generally higher than those in permanent classrooms. Significantly higher concentrations of several SVOCs were found in settled dust during the colder season compared to those of the warmer season. These results suggest the substantial influences of flooring types, building types, and seasonal variations on indoor SVOC concentrations in high schools.

When we encounter toxic comments online, how might individual efforts to reply to those comments improve others’ experiences conversing in that forum? Is it more helpful for others to publicly, but benevolently (with a polite tone, demonstrated understanding of the original comment, and empathy for the commenter; Young Reusser et al., 2021), correct the post? Is going along with or joking along with the commenter in a benevolent way helpful? Or is retaliating – returning toxicity for toxicity – the best strategy? Using real Reddit conversation pairs – a toxic comment followed by a reply – as stimuli, we conducted a pilot study (n = 126 participants) and pre-registered experiment (n = 1357 participants) investigating the impact of three kinds of replies to online toxicity (benevolent correction, benevolent going-along, or retaliation) on observers’ self-reported freedom to contribute to the conversation, their belief that the toxicity will be reduced, and their overall impression that justice has been restored. We found evidence that benevolently correcting the toxicity helped participants feel freer to contribute than retaliating against it. Benevolently correcting was also seen as the best option for dissuading the toxicity and restoring justice. These findings suggest that treating toxic commenters with empathy, understanding, and politeness while correcting their toxicity can be a useful strategy for online bystanders who want to intervene to improve the health of online discourse. Preregistered Stage 1 protocol: https://osf.io/hfjnb (date of in-principle acceptance: 01/23/2023).

Monterastelli A.J., Adams J., Eastman C.I., Crowley S.J.

IntroductionThe propensity for sleep shifts later as puberty progresses. The present analysis examines whether the circadian-dependent wake maintenance zone, or forbidden zone for sleep observed in the evening just before habitual bedtime is more pronounced in late to post-pubertal adolescents compared to adults and may partly explain late sleep onset in maturing adolescents.MethodsForty four healthy late/post-pubertal adolescents (aged 14.3–17.8 years, 23 female) and 44 healthy adults (aged 30.8–45.8 years, 21 female) participated in an ultradian light/dark protocol for 3 days cycling between 2-h wake periods (~20 lux) and 2-h nap periods (~0 lux) without external time cues. The dim light melatonin onset (DLMO), a measure of circadian phase, was measured immediately before the ultradian protocol by sampling saliva every 30 min in dim light. Wrist actigraphs were used to assess sleep onset latency and total sleep time during the naps that occurred during the ultradian sleep/wake schedule. Sleep episodes were grouped into 2-h bins relative to individual DLMOs (28–56 naps/bin). Sleep onset and total sleep time were compared between adolescents and adults as well as between males and females within each age group.ResultsAdolescents took significantly longer to fall asleep compared to adults during naps that occurred in the 4 h window surrounding the DLMO [2h before DLMO t(50) = 2.13, p = 0.04; 2 h after DLMO t(33) = 3.25, p = 0.003]. Adolescents also slept significantly less than adults during naps that occurred in the 4-h window surrounding DLMO [2 h before DLMO t(51) = −2.91, p = 0.01; 2 h after DLMO t(33) = −1.99, p = 0.05]. Adolescent males slept less than adolescent females in naps that occurred in the 2 h window after the DLMO [t(14) = −2.24, p = 0.04].DiscussionCompared to adults, late/post-pubertal adolescents showed greater difficulty falling asleep and maintaining sleep around the time of their DLMO, which usually occurs a few hours before habitual sleep onset. A greater amplitude in the circadian-driven forbidden zone for sleep could be an additional physiological mechanism explaining why maturing adolescents find it difficult to fall asleep early, increasing the risk for restricted sleep in the context of early school start times.

Describe the consequences of introducing common allergy foods to infants early in life and developing allergies in toddlers/preschool age children.

Upon completion, participants will be able to list two factors that provide support for regular breakfast consumption that may also strengthen students GPA.

Describe how stress affects student eating patterns and what category of foods, nutrient-dense versus calorically dense, are eaten when stressed.

An Italian dominating function of a graph G is a function f:V(G)→{0,1,2} such that for each vertex v∈V(G) either f(v)≠0, or ∑u∈N(v)f(u)≥2. If a family F={f1,f2,…,ft} of Italian dominating functions satisfy ∑i=1tfi(v)≤2 for each vertex v, then this is called an Italian dominating family. The Italian domatic number of a graph G, denoted dI(G), is the maximum cardinality of any such family of distinct Italian dominating functions. This paper considers graphs with small Italian domatic number. In particular, it is shown that a graph must have leaves if it has an Italian domatic number of two, but that determining whether or not a graph has Italian domatic number of three is NP-complete. In addition, trees with an Italian domatic number of three are classified, completely determining the Italian domatic number of trees.

Jasper M., Shahbazi A., Schimmel K., Li F., Wang L.

AbstractChemical Looping Combustion (CLC) is a technology that efficiently combines power generation and CO2 capture. In CLC, the fuel is oxidized by a metal oxide called an oxygen carrier (OC). CLC uses two reactors: a fuel reactor and an air reactor. The fuel reactor oxidizes the fuel and reduces the OC. The air reactor oxidizes the OC using air and then the OC is cycled back to the fuel reactor. It is typical for both the fuel and the air reactors to be fluidized beds (FBs). In this research, an Aspen Plus model was developed to simulate a CLC system. Aspen Plus has recently included a built-in FB unit operation module. To our knowledge, no literature has been reported using this FB module for simulating fluidized bed combustion or gasification. This FB unit process was investigated in Aspen Plus and a kinetic based model was used and compared the simulation results to experimental data and the commonly used Gibbs equilibrium model. The FB unit and the kinetic model well fit the experimental data for syngas and methane combustion within 2% of the molar composition of syngas combustion and within 4% for the methane combustion. An advantage of this model over other kinetic models in literature is that the core shrinking model kinetic rate equations have been converted into a power law form. This allows Aspen Plus to use a calculator instead of an external Fortran compiler. This greatly simplifies the modeling process. The reaction rate equations are given for all reactions. A sensitivity analysis of the reaction kinetics was conducted. All data, code, and simulation files are given.

Abstract Background Considerable heterogeneity exists in treatment response to first-line posttraumatic stress disorder (PTSD) treatments, such as Cognitive Processing Therapy (CPT). Relatively little is known about the timing of when during a course of care the treatment response becomes apparent. Novel machine learning methods, especially continuously updating prediction models, have the potential to address these gaps in our understanding of response and optimize PTSD treatment. Methods Using data from a 3-week (n = 362) CPT-based intensive PTSD treatment program (ITP), we explored three methods for generating continuously updating prediction models to predict endpoint PTSD severity. These included Mixed Effects Bayesian Additive Regression Trees (MixedBART), Mixed Effects Random Forest (MERF) machine learning models, and Linear Mixed Effects models (LMM). Models used baseline and self-reported PTSD symptom severity data collected every other day during treatment. We then validated our findings by examining model performances in a separate, equally established, 2-week CPT-based ITP (n = 108). Results Results across approaches were very similar and indicated modest prediction accuracy at baseline (R2 ~ 0.18), with increasing accuracy of predictions of final PTSD severity across program timepoints (e.g. mid-program R2 ~ 0.62). Similar findings were obtained when the models were applied to the 2-week ITP. Neither the MERF nor the MixedBART machine learning approach outperformed LMM prediction, though benefits of each may differ based on the application. Conclusions Utilizing continuously updating models in PTSD treatments may be beneficial for clinicians in determining whether an individual is responding, and when this determination can be made.