The Value of Using Predictive Information Optimally

2020 ◽  
Author(s):  
Michael Ashby
2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Kaitlin M. Love ◽  
Linda A. Jahn ◽  
Lee M. Hartline ◽  
James T. Patrie ◽  
Eugene J. Barrett ◽  
...  

AbstractInsulin increases muscle microvascular perfusion and enhances tissue insulin and nutrient delivery. Our aim was to determine phenotypic traits that foretell human muscle microvascular insulin responses. Hyperinsulinemic euglycemic clamps were performed in 97 adult humans who were lean and healthy, had class 1 obesity without comorbidities, or controlled type 1 diabetes without complications. Insulin-mediated whole-body glucose disposal rates (M-value) and insulin-induced changes in muscle microvascular blood volume (ΔMBV) were determined. Univariate and multivariate analyses were conducted to examine bivariate and multivariate relationships between outcomes, ΔMBV and M-value, and predictor variables, body mass index (BMI), total body weight (WT), percent body fat (BF), lean body mass, blood pressure, maximum consumption of oxygen (VO2max), plasma LDL (LDL-C) and HDL cholesterol, triglycerides (TG), and fasting insulin (INS) levels. Among all factors, only M-value (r = 0.23, p = 0.02) and VO2max (r = 0.20, p = 0.047) correlated with ΔMBV. Conversely, INS (r = − 0.48, p ≤ 0.0001), BF (r = − 0.54, p ≤ 0.001), VO2max (r = 0.5, p ≤ 0.001), BMI (r = − 0.40, p < 0.001), WT (r = − 0.33, p = 0.001), LDL-C (r = − 0.26, p = 0.009), TG (r = − 0.25, p = 0.012) correlated with M-value. While both ΔMBV (p = 0.045) and TG (p = 0.03) provided significant predictive information about M-value in the multivariate regression model, only M-value was uniquely predictive of ΔMBV (p = 0.045). Thus, both M-value and VO2max correlated with ΔMBV but only M-value provided unique predictive information about ΔMBV. This suggests that metabolic and microvascular insulin responses are important predictors of one another, but most metabolic insulin resistance predictors do not predict microvascular insulin responses.


Neuron ◽  
2018 ◽  
Vol 100 (5) ◽  
pp. 1022-1024 ◽  
Author(s):  
Anne-Lise Giraud ◽  
Luc H. Arnal

2001 ◽  
Vol 54 (4) ◽  
pp. 1105-1124 ◽  
Author(s):  
Yuhong Jiang ◽  
Marvin M. Chun

The effect of selective attention on implicit learning was tested in four experiments using the “contextual cueing” paradigm (Chun & Jiang, 1998, 1999). Observers performed visual search through items presented in an attended colour (e.g., red) and an ignored colour (e.g., green). When the spatial configuration of items in the attended colour was invariant and was consistently paired with a target location, visual search was facilitated, showing contextual cueing (Experiments 1, 3, and 4). In contrast, repeating and pairing the configuration of the ignored items with the target location resulted in no contextual cueing (Experiments 2 and 4). We conclude that implicit learning is robust only when relevant, predictive information is selectively attended.


Sign in / Sign up

Export Citation Format

Share Document