Walks4work: Rationale and study design to investigate walking at lunchtime in the workplace setting

Background: Following recruitment of a private sector company, an 8week lunchtime walking intervention was implemented to examine the effect of the intervention on modifiable cardiovascular disease risk factors, and further to see if walking environment had any further effect on the cardiovascular disease risk factors. Methods. For phase 1 of the study participants were divided into three groups, two lunchtime walking intervention groups to walk around either an urban or natural environment twice a week during their lunch break over an 8week period. The third group was a waiting-list control who would be invited to join the walking groups after phase 1. In phase 2 all participants were encouraged to walk during their lunch break on self-selecting routes. Health checks were completed at baseline, end of phase 1 and end of phase 2 in order to measure the impact of the intervention on cardiovascular disease risk. The primary outcome variables of heart rate and heart rate variability were measured to assess autonomic function associated with cardiovascular disease. Secondary outcome variables (Body mass index, blood pressure, fitness, autonomic response to a stressor) related to cardiovascular disease were also measured. The efficacy of the intervention in increasing physical activity was objectively monitored throughout the 8-weeks using an accelerometer device. Discussion. The results of this study will help in developing interventions with low researcher input with high participant output that may be implemented in the workplace. If effective, this study will highlight the contribution that natural environments can make in the reduction of modifiable cardiovascular disease risk factors within the workplace. © 2012 Brown et al.; licensee BioMed Central Ltd

Heroin versus cocaine: opposite choice as a function of context but not of drug history in the rat

Rationale Previous studies have shown that rats trained to self-administer heroin and cocaine exhibit opposite preferences, as a function of setting, when tested in a choice paradigm. Rats tested at home prefer heroin to cocaine whereas rats tested outside the home prefer cocaine to heroin. Here we investigated whether drug history would influence subsequent drug preference in distinct settings. Based on a theoretical model of drug-setting interaction, we predicted that regardless of drug history rats would prefer heroin at home and cocaine outside the home. Methods Rats with double-lumen catheters were first trained to self-administer either heroin (25 μg/kg) or cocaine (400 μg/kg) for 12 consecutive sessions. Twenty-six rats were housed in the self-administration chambers (thus, they were tested at home) whereas 30 rats lived in distinct home cages and were transferred to self-administration chambers only for the self-administration session (thus, they were tested outside the home). The rats were then allowed to choose repeatedly between heroin and cocaine within the same session for 7 sessions. Results Regardless of the training drug, the rats tested outside the home preferred cocaine to heroin whereas the rats tested at home preferred heroin to cocaine. There was no correlation between drug preference and drug intake during the training phase. Conclusion Drug preferences were powerfully influenced by the setting but, quite surprisingly, not by drug history. This suggests that, under certain conditions, associative learning processes and drug-induced neuroplastic adaptations play a minor role in shaping individual preferences for one drug or the other

Parasympathetic Activity and Blood Catecholamine Responses Following a Single Partial-Body Cryostimulation and a Whole-Body Cryostimulation

The aim of this study was to compare the effects of a single whole-body cryostimulation (WBC) and a partial-body cryostimulation (PBC) (i.e., not exposing the head to cold) on indices of parasympathetic activity and blood catecholamines. Two groups of 15 participants were assigned either to a 3-min WBC or PBC session, while 10 participants constituted a control group (CON) not receiving any cryostimulation. Changes in thermal, physiological and subjective variables were recorded before and during the 20-min after each cryostimulation. According to a qualitative statistical analysis, an almost certain decrease in skin temperature was reported for all body regions immediately after the WBC (mean decrease±90% CL, -13.7±0.7°C) and PBC (-8.3±0.3°C), which persisted up to 20-min after the session. The tympanic temperature almost certainly decreased only after the WBC session (-0.32±0.04°C). Systolic and diastolic blood pressures were very likely increased after the WBC session, whereas these changes were trivial in the other groups. In addition, heart rate almost certainly decreased after PBC (-10.9%) and WBC (-15.2%) sessions, in a likely greater proportion for WBC compared to PBC. Resting vagal-related heart rate variability indices (the root-mean square difference of successive normal R-R intervals, RMSSD, and high frequency band, HF) were very likely increased after PBC (RMSSD: +54.4%, HF: +138%) and WBC (RMSSD: +85.2%, HF: +632%) sessions without any marked difference between groups. Plasma norepinephrine concentrations were likely to very likely increased after PBC (+57.4%) and WBC (+76.2%), respectively. Finally, cold and comfort sensations were almost certainly altered after WBC and PBC, sensation of discomfort being likely more pronounced after WBC than PBC. Both acute cryostimulation techniques effectively stimulated the autonomic nervous system (ANS), with a predominance of parasympathetic tone activation. The results of this study also suggest that a whole-body cold exposure induced a larger stimulation of the ANS compared to partial-body cold exposure

Multi-messenger observations of a binary neutron star merger

On 2017 August 17 a binary neutron star coalescence candidate (later designated GW170817) with merger time 12:41:04 UTC was observed through gravitational waves by the Advanced LIGO and Advanced Virgo detectors. The Fermi Gamma-ray Burst Monitor independently detected a gamma-ray burst (GRB 170817A) with a time delay of ~1.7 s with respect to the merger time. From the gravitational-wave signal, the source was initially localized to a sky region of 31 deg2 at a luminosity distance of 40+8-8 Mpc and with component masses consistent with neutron stars. The component masses were later measured to be in the range 0.86 to 2.26 Mo. An extensive observing campaign was launched across the electromagnetic spectrum leading to the discovery of a bright optical transient (SSS17a, now with the IAU identification of AT 2017gfo) in NGC 4993 (at ~40 Mpc) less than 11 hours after the merger by the One- Meter, Two Hemisphere (1M2H) team using the 1 m Swope Telescope. The optical transient was independently detected by multiple teams within an hour. Subsequent observations targeted the object and its environment. Early ultraviolet observations revealed a blue transient that faded within 48 hours. Optical and infrared observations showed a redward evolution over ~10 days. Following early non-detections, X-ray and radio emission were discovered at the transient’s position ~9 and ~16 days, respectively, after the merger. Both the X-ray and radio emission likely arise from a physical process that is distinct from the one that generates the UV/optical/near-infrared emission. No ultra-high-energy gamma-rays and no neutrino candidates consistent with the source were found in follow-up searches. These observations support the hypothesis that GW170817 was produced by the merger of two neutron stars in NGC4993 followed by a short gamma-ray burst (GRB 170817A) and a kilonova/macronova powered by the radioactive decay of r-process nuclei synthesized in the ejecta

Effects of antidepressant treatment on heart rate variability in major depression: A quantitative review

<p>Abstract</p> <p>Background</p> <p>The literature measuring effects of antidepressant and electroconvulsive therapy (ECT) for major depression on heart rate variability (HRV) in medically well individuals was reviewed.</p> <p>Methods</p> <p>Fourteen studies evaluating HRV were included. Twenty three pre-post or within group comparisons were available. Treatment impact on measures of HRV was pooled over studies. We examined different classes of antidepressants, and for short and long electrocardiogram (ECG) recordings separately.</p> <p>Results</p> <p>Tricyclic antidepressants (TCAs) were associated with declines in most measures of HRV and significant increase in heart rate (HR) in studies with short recording intervals. No significant changes were found for longer recording times.</p> <p>Treatment effects with selective serotonin reuptake inhibitors (SSRIs) were more variable. Short-recording studies revealed a significant decrease in HR and an increase in one HRV measure. In two 24-hour recording studies no significant changes were observed. No relationship between ECT and HRV has been established in the literature. The effects of other drugs are reported.</p> <p>Limitations</p> <p>Few studies measure the effects of treatment of depression on HRV. Existing studies have generally used very small samples, employing a variety of measurements and methodologies.</p> <p>Conclusion</p> <p>We confirm that TCAs are associated with a large decrease in HRV and increase HR. However, data for SSRIs is not clear. Although the effect of SSRIs on HRV is weaker than for TCAs, evidence shows that SSRIs are associated with a small decrease in HR, and an increase in one measure of HRV. The use of TCAs in depression leads to changes in HRV that are associated with increased risk of mortality.</p

Same data, different analysts: variation in effect sizes due to analytical decisions in ecology and evolutionary biology.

Although variation in effect sizes and predicted values among studies of similar phenomena is inevitable, such variation far exceeds what might be produced by sampling error alone. One possible explanation for variation among results is differences among researchers in the decisions they make regarding statistical analyses. A growing array of studies has explored this analytical variability in different fields and has found substantial variability among results despite analysts having the same data and research question. Many of these studies have been in the social sciences, but one small many analyst study found similar variability in ecology. We expanded the scope of this prior work by implementing a large-scale empirical exploration of the variation in effect sizes and model predictions generated by the analytical decisions of different researchers in ecology and evolutionary biology. We used two unpublished datasets, one from evolutionary ecology (blue tit, Cyanistes caeruleus, to compare sibling number and nestling growth) and one from conservation ecology (Eucalyptus, to compare grass cover and tree seedling recruitment). The project leaders recruited 174 analyst teams, comprising 246 analysts, to investigate the answers to prespecified research questions. Analyses conducted by these teams yielded 141 usable effects (compatible with our meta-analyses and with all necessary information provided) for the blue tit dataset, and 85 usable effects for the Eucalyptus dataset. We found substantial heterogeneity among results for both datasets, although the patterns of variation differed between them. For the blue tit analyses, the average effect was convincingly negative, with less growth for nestlings living with more siblings, but there was near continuous variation in effect size from large negative effects to effects near zero, and even effects crossing the traditional threshold of statistical significance in the opposite direction. In contrast, the average relationship between grass cover and Eucalyptus seedling number was only slightly negative and not convincingly different from zero, and most effects ranged from weakly negative to weakly positive, with about a third of effects crossing the traditional threshold of significance in one direction or the other. However, there were also several striking outliers in the Eucalyptus dataset, with effects far from zero. For both datasets, we found substantial variation in the variable selection and random effects structures among analyses, as well as in the ratings of the analytical methods by peer reviewers, but we found no strong relationship between any of these and deviation from the meta-analytic mean. In other words, analyses with results that were far from the mean were no more or less likely to have dissimilar variable sets, use random effects in their models, or receive poor peer reviews than those analyses that found results that were close to the mean. The existence of substantial variability among analysis outcomes raises important questions about how ecologists and evolutionary biologists should interpret published results, and how they should conduct analyses in the future

Same data, different analysts: Variation in effect sizes due to analytical decisions in ecology and evolutionary biology

Although variation in effect sizes and predicted values among studies of similar phenomena is inevitable, such variation far exceeds what might be produced by sampling error alone. One possible explanation for variation among results is differences among researchers in the decisions they make regarding statistical analyses. A growing array of studies has explored this analytical variability in different fields and has found substantial variability among results despite analysts having the same data and research question. Many of these studies have been in the social sciences, but one small “many analyst” study found similar variability in ecology. We expanded the scope of this prior work by implementing a large-scale empirical exploration of the variation in effect sizes and model predictions generated by the analytical decisions of different researchers in ecology and evolutionary biology. We used two unpublished datasets, one from evolutionary ecology (blue tit, Cyanistes caeruleus, to compare sibling number and nestling growth) and one from conservation ecology (Eucalyptus, to compare grass cover and tree seedling recruitment). The project leaders recruited 174 analyst teams, comprising 246 analysts, to investigate the answers to prespecified research questions. Analyses conducted by these teams yielded 141 usable effects (compatible with our meta-analyses and with all necessary information provided) for the blue tit dataset, and 85 usable effects for the Eucalyptus dataset. We found substantial heterogeneity among results for both datasets, although the patterns of variation differed between them. For the blue tit analyses, the average effect was convincingly negative, with less growth for nestlings living with more siblings, but there was near continuous variation in effect size from large negative effects to effects near zero, and even effects crossing the traditional threshold of statistical significance in the opposite direction. In contrast, the average relationship between grass cover and Eucalyptus seedling number was only slightly negative and not convincingly different from zero, and most effects ranged from weakly negative to weakly positive, with about a third of effects crossing the traditional threshold of significance in one direction or the other. However, there were also several striking outliers in the Eucalyptus dataset, with effects far from zero. For both datasets, we found substantial variation in the variable selection and random effects structures among analyses, as well as in the ratings of the analytical methods by peer reviewers, but we found no strong relationship between any of these and deviation from the meta-analytic mean. In other words, analyses with results that were far from the mean were no more or less likely to have dissimilar variable sets, use random effects in their models, or receive poor peer reviews than those analyses that found results that were close to the mean. The existence of substantial variability among analysis outcomes raises important questions about how ecologists and evolutionary biologists should interpret published results, and how they should conduct analyses in the future