Acute whole body UVA irradiation combined with nitrate ingestion enhances time trial performance in trained cyclists
- Muggeridge, David, Sculthorpe, Nicholas, Grace, Fergal, Willis, Gareth, Thornhill, Laurence, Weller, Richard, James, Philip, Easton, Chris
- Authors: Muggeridge, David , Sculthorpe, Nicholas , Grace, Fergal , Willis, Gareth , Thornhill, Laurence , Weller, Richard , James, Philip , Easton, Chris
- Date: 2015
- Type: Text , Journal article
- Relation: Nitric Oxide : Biology and Chemistry Vol. 48, no. (2015), p. 3-9
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- Description: Dietary nitrate supplementation has been shown to increase nitric oxide (NO) metabolites, reduce blood pressure (BP) and enhance exercise performance. Acute exposure to ultraviolet (UV)-A light also increases NO bioavailability and reduces BP. We conducted a randomized, counterbalanced placebo-controlled trial to determine the effects of UV-A light alone and in combination with nitrate on the responses to sub-maximal steady-state exercise and time trial (TT) performance. Nine cyclists (VO2max 53.1 +/- 4.4 ml/kg/min) completed five performance trials comprising 10 min submaximal steady-state cycling followed by a 16.1 km TT. Following a familiarization the final four trials were preceded, in random order, by either (1) Nitrate gels (NIT) + UV-A, (2) Placebo (PLA) + UV-A, (3) NIT + Sham light (SHAM) and (4) PLA + SHAM (control). The NIT gels (2 x 60 ml gels, ~8.1 mmol nitrate) or a low-nitrate PLA were ingested 2.5 h prior to the trial. The light exposure consisted of 20 J/cm(2) whole body irradiation with either UV-A or SHAM light. Plasma nitrite was measured pre- and post-irradiation and VO2 was measured continuously during steady-state exercise. Plasma nitrite was higher for NIT + SHAM (geometric mean (95% CI), 332 (292-377) nM; P = 0.029) and NIT + UV-A (456 (312-666) nM; P = 0.014) compared to PLA + SHAM (215 (167-277) nM). Differences between PLA + SHAM and PLA + UV-A (282 (248-356) nM) were small and non-significant. During steady-state exercise VO2 was reduced following NIT + UVA (P = 0.034) and tended to be lower in NIT + SHAM (P = 0.086) but not PLA + UV-A (P = 0.381) compared to PLA + SHAM. Performance in the TT was significantly faster following NIT + UV-A (mean +/- SD 1447 +/- 41 s P = 0.005; d = 0.47), but not PLA + UV-A (1450 +/- 40 s; d = 0.41) or NIT + SHAM (1455 +/- 47 s; d = 0.28) compared to PLA + SHAM (1469 +/- 52 s). These findings demonstrate that exposure to UV-A light alone does not alter the physiological responses to exercise or improve performance in a laboratory setting. A combination of UV-A and NIT, however, does improve cycling TT performance in this environment, which may be due to a larger increase in NO availability.
- Authors: Muggeridge, David , Sculthorpe, Nicholas , Grace, Fergal , Willis, Gareth , Thornhill, Laurence , Weller, Richard , James, Philip , Easton, Chris
- Date: 2015
- Type: Text , Journal article
- Relation: Nitric Oxide : Biology and Chemistry Vol. 48, no. (2015), p. 3-9
- Full Text:
- Reviewed:
- Description: Dietary nitrate supplementation has been shown to increase nitric oxide (NO) metabolites, reduce blood pressure (BP) and enhance exercise performance. Acute exposure to ultraviolet (UV)-A light also increases NO bioavailability and reduces BP. We conducted a randomized, counterbalanced placebo-controlled trial to determine the effects of UV-A light alone and in combination with nitrate on the responses to sub-maximal steady-state exercise and time trial (TT) performance. Nine cyclists (VO2max 53.1 +/- 4.4 ml/kg/min) completed five performance trials comprising 10 min submaximal steady-state cycling followed by a 16.1 km TT. Following a familiarization the final four trials were preceded, in random order, by either (1) Nitrate gels (NIT) + UV-A, (2) Placebo (PLA) + UV-A, (3) NIT + Sham light (SHAM) and (4) PLA + SHAM (control). The NIT gels (2 x 60 ml gels, ~8.1 mmol nitrate) or a low-nitrate PLA were ingested 2.5 h prior to the trial. The light exposure consisted of 20 J/cm(2) whole body irradiation with either UV-A or SHAM light. Plasma nitrite was measured pre- and post-irradiation and VO2 was measured continuously during steady-state exercise. Plasma nitrite was higher for NIT + SHAM (geometric mean (95% CI), 332 (292-377) nM; P = 0.029) and NIT + UV-A (456 (312-666) nM; P = 0.014) compared to PLA + SHAM (215 (167-277) nM). Differences between PLA + SHAM and PLA + UV-A (282 (248-356) nM) were small and non-significant. During steady-state exercise VO2 was reduced following NIT + UVA (P = 0.034) and tended to be lower in NIT + SHAM (P = 0.086) but not PLA + UV-A (P = 0.381) compared to PLA + SHAM. Performance in the TT was significantly faster following NIT + UV-A (mean +/- SD 1447 +/- 41 s P = 0.005; d = 0.47), but not PLA + UV-A (1450 +/- 40 s; d = 0.41) or NIT + SHAM (1455 +/- 47 s; d = 0.28) compared to PLA + SHAM (1469 +/- 52 s). These findings demonstrate that exposure to UV-A light alone does not alter the physiological responses to exercise or improve performance in a laboratory setting. A combination of UV-A and NIT, however, does improve cycling TT performance in this environment, which may be due to a larger increase in NO availability.
- Jha, Devki Nandan, Alwasel, Khaled, Alshoshan, Areeb, Huang, Xianghua, Naha, Ranesh, Battula, Sudheer, Garg, Saurabh, Puthal, Deepak, James, Philip, Zomaya, Albert, Dustdar, Schahram, Ranjan, Rajiv
- Authors: Jha, Devki Nandan , Alwasel, Khaled , Alshoshan, Areeb , Huang, Xianghua , Naha, Ranesh , Battula, Sudheer , Garg, Saurabh , Puthal, Deepak , James, Philip , Zomaya, Albert , Dustdar, Schahram , Ranjan, Rajiv
- Date: 2020
- Type: Text , Journal article
- Relation: Software, practice & experience Vol. 50, no. 6 (2020), p. 844-867
- Full Text: false
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- Description: Summary With the proliferation of Internet of Things (IoT) and edge computing paradigms, billions of IoT devices are being networked to support data‐driven and real‐time decision making across numerous application domains, including smart homes, smart transport, and smart buildings. These ubiquitously distributed IoT devices send the raw data to their respective edge device (eg, IoT gateways) or the cloud directly. The wide spectrum of possible application use cases make the design and networking of IoT and edge computing layers a very tedious process due to the: (i) complexity and heterogeneity of end‐point networks (eg, Wi‐Fi, 4G, and Bluetooth) (ii) heterogeneity of edge and IoT hardware resources and software stack (iv) mobility of IoT devices and (iii) the complex interplay between the IoT and edge layers. Unlike cloud computing, where researchers and developers seeking to test capacity planning, resource selection, network configuration, computation placement, and security management strategies had access to public cloud infrastructure (eg, Amazon and Azure), establishing an IoT and edge computing testbed that offers a high degree of verisimilitude is not only complex, costly, and resource‐intensive but also time‐intensive. Moreover, testing in real IoT and edge computing environments is not feasible due to the high cost and diverse domain knowledge required in order to reason about their diversity, scalability, and usability. To support performance testing and validation of IoT and edge computing configurations and algorithms at scale, simulation frameworks should be developed. Hence, this article proposes a novel simulator IoTSim‐Edge, which captures the behavior of heterogeneous IoT and edge computing infrastructure and allows users to test their infrastructure and framework in an easy and configurable manner. IoTSim‐Edge extends the capability of CloudSim to incorporate the different features of edge and IoT devices. The effectiveness of IoTSim‐Edge is described using three test cases. Results show the varying capability of IoTSim‐Edge in terms of application composition, battery‐oriented modeling, heterogeneous protocols modeling, and mobility modeling along with the resources provisioning for IoT applications.
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