Abstract 2504: Melatonin decreases plasma arginine, its precursors and acylcarnitines in breast cancer xenograft model at specific time point during circadian rhythm

AbstractMutualistic interactions play a major role in shaping the Earth’s biodiversity, yet the consistent drivers governing these beneficial interactions are unknown. Using a long-term (8 year, including > 256 h behavioural observations) dataset of the interaction patterns of a service-resource mutualism (the cleaner-client interaction), we identified consistent and dynamic predictors of mutualistic outcomes. We showed that cleaning was consistently more frequent when the presence of third-party species and client partner abundance locally increased (creating choice options), whilst partner identity regulated client behaviours. Eight of our 12 predictors of cleaner and client behaviour played a dynamic role in predicting both the quality (duration) and quantity (frequency) of interactions, and we suggest that the environmental context acting on these predictors at a specific time point will indirectly regulate their role in cleaner-client interaction patterns: context-dependency can hence regulate mutualisms both directly and indirectly. Together our study highlights that consistency in cleaner-client mutualisms relies strongly on the local, rather than wider community—with biodiversity loss threatening all environments this presents a worrying future for the pervasiveness of mutualisms.

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Exploring protocol for breast cancer xenograft model using endothelial colony-forming cells

Translational Cancer Research ◽

10.21037/tcr.2018.09.09 ◽

2018 ◽

Vol 7 (5) ◽

pp. 1228-1234

Author(s):

Hyun Kyung Lim ◽

Hyunsook Lee ◽

Aree Moon ◽

Kyu-Tae Kang ◽

Joohee Jung

Keyword(s):

Breast Cancer ◽

Xenograft Model ◽

Breast Cancer Xenograft ◽

Endothelial Colony Forming Cells

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Estimating the Necessary Amount of Driving Data for Assessing Driving Behavior

Sensors ◽

10.3390/s20092600 ◽

2020 ◽

Vol 20 (9) ◽

pp. 2600 ◽

Cited By ~ 1

Author(s):

Anna-Maria Stavrakaki ◽

Dimitrios I. Tselentis ◽

Emmanouil Barmpounakis ◽

Eleni I. Vlahogianni ◽

George Yannis

Keyword(s):

Discrete Time ◽

Driving Behavior ◽

Methodological Framework ◽

Detailed Data ◽

Specific Time ◽

Speed Limits ◽

Specific Time Point ◽

Smartphone Sensors ◽

Over Time ◽

Time Point

The aim of this paper was to provide a methodological framework for estimating the amount of driving data that should be collected for each driver in order to acquire a clear picture regarding their driving behavior. We examined whether there is a specific discrete time point for each driver, in the form of total driving duration and/or the number of trips, beyond which the characteristics of driving behavior are stabilized over time. Various mathematical and statistical methods were employed to process the data collected and determine the time point at which behavior converges. Detailed data collected from smartphone sensors are used to test the proposed methodology. The driving metrics used in the analysis are the number of harsh acceleration and braking events, the duration of mobile usage while driving and the percentage of time driving over the speed limits. Convergence was tested in terms of both the magnitude and volatility of each metric for different trips and analysis is performed for several trip durations. Results indicated that there is no specific time point or number of trips after which driving behavior stabilizes for all drivers and/or all metrics examined. The driving behavior stabilization is mostly affected by the duration of the trips examined and the aggressiveness of the driver.

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