Abnormalities in autonomic function in obese boys at-risk for insulin resistance and obstructive sleep apnea.

Study objectivesCurrent evidence in adults suggests that, independent of obesity, obstructive sleep apnea (OSA) can lead to autonomic dysfunction and impaired glucose metabolism, but these relationships are less clear in children. The purpose of this study was to investigate the associations among OSA, glucose metabolism, and daytime autonomic function in obese pediatric subjects.MethodsTwenty-three obese boys participated in: overnight polysomnography; a frequently sampled intravenous glucose tolerance test; and recordings of spontaneous cardiorespiratory data in both the supine (baseline) and standing (sympathetic stimulus) postures.ResultsBaseline systolic blood pressure and reactivity of low-frequency heart rate variability to postural stress correlated with insulin resistance, increased fasting glucose, and reduced beta-cell function, but not OSA severity. Baroreflex sensitivity reactivity was reduced with sleep fragmentation, but only for subjects with low insulin sensitivity and/or low first-phase insulin response to glucose.ConclusionsThese findings suggest that vascular sympathetic activity impairment is more strongly affected by metabolic dysfunction than by OSA severity, while blunted vagal autonomic function associated with sleep fragmentation in OSA is enhanced when metabolic dysfunction is also present

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

Dynamically Weighted Load Evaluation Method Based on Self-adaptive Threshold in Cloud Computing

International audienceCloud resources and their loads possess dynamic characteristics. Current research methods have utilized certain physical indicators and fixed thresholds to evaluate cloud resources, which cannot meet the dynamic needs of cloud resources or accurately reflect their resource states. To address this challenge, this paper proposes a Self-adaptive threshold based Dynamically Weighted load evaluation Method (termed SDWM). It evaluates the load state of the resource through a dynamically weighted evaluation method. First, the work proposes some dynamic evaluation indicators in order to evaluate the resource state more accurately. Second, SDWM divided the resource load into three states, including Overload, Normal and Idle using the self-adaptive threshold. It then migrated those overload resources to a balance load, and releases the idle resources whose idle times exceeded a threshold to save energy, which could effectively improve system utilization. Finally, SDWM leveraged an energy evaluation model to describe energy quantitatively using the migration amount of the resource request. The parameters of the energy model were obtained from a linear regression model according to the actual experimental environment. Experimental results showed that SDWM is superior to other methods in energy conservation, task response time, and resource utilization, and the improvements are 31.5 %, 50 %, 50.8 %, respectively. These results demonstrate the positive effect of the dynamic self-adaptive threshold. More specially, SDWM shows great adaptability when resources dynamically join or exi