Consumers discard a lot more food than widely believed: Estimates of global food waste using an energy gap approach and affluence elasticity of food waste.

This work provides an internationally comparable consumer food waste dataset based on food availability, energy gap and consumer affluence. Such data can be used for constructing meaningful and internationally comparable metrics on food waste, such as those for Sustainable Development Goal 12. The data suggests that consumer food waste follows a linear-log relationship with consumer affluence and starts to emerge when consumers reach a threshold of approximately $6.70/day/capita level of expenditure. These findings also imply that most empirical models overestimate consumption by not accounting for the possibility of food waste in their analysis. The results also show that the most widely cited global estimate of food waste is underestimated by a factor greater than 2 (214 Kcal/day/capita versus 527 Kcal/day/capita). Comparison with estimates of US consumer food waste based on national survey data shows this approach can reasonably reproduce the results without needing extensive data from national surveys.

Individual Differences in Motor Noise and Adaptation Rate Are Optimally Related.

Individual variations in motor adaptation rate were recently shown to correlate with movement variability or "motor noise" in a forcefield adaptation task. However, this finding could not be replicated in a meta-analysis of adaptation experiments. Possibly, this inconsistency stems from noise being composed of distinct components that relate to adaptation rate in different ways. Indeed, previous modeling and electrophysiological studies have suggested that motor noise can be factored into planning noise, originating from the brain, and execution noise, stemming from the periphery. Were the motor system optimally tuned to these noise sources, planning noise would correlate positively with adaptation rate, and execution noise would correlate negatively with adaptation rate, a phenomenon familiar in Kalman filters. To test this prediction, we performed a visuomotor adaptation experiment in 69 subjects. Using a novel Bayesian fitting procedure, we succeeded in applying the well-established state-space model of adaptation to individual data. We found that adaptation rate correlates positively with planning noise (ß = 0.44; 95% HDI = [0.27 0.59]) and negatively with execution noise (ß = -0.39; 95% HDI = [-0.50 -0.30]). In addition, the steady-state Kalman gain calculated from planning and execution noise correlated positively with adaptation rate (r = 0.54; 95% HDI = [0.38 0.66]). These results suggest that motor adaptation is tuned to approximate optimal learning, consistent with the "optimal control" framework that has been used to explain motor control. Since motor adaptation is thought to be a largely cerebellar process, the results further suggest the sensitivity of the cerebellum to both planning noise and execution noise.

Cerebellar transcranial direct current stimulation interacts with BDNF Val66Met in motor learning.

BACKGROUND: Cerebellar transcranial direct current stimulation has been reported to enhance motor associative learning and motor adaptation, holding promise for clinical application in patients with movement disorders. However, behavioral benefits from cerebellar tDCS have been inconsistent. OBJECTIVE: Identifying determinants of treatment success is necessary. BDNF Val66Met is a candidate determinant, because the polymorphism is associated with motor skill learning and BDNF is thought to mediate tDCS effects. METHODS: We undertook two cerebellar tDCS studies in subjects genotyped for BDNF Val66Met. Subjects performed an eyeblink conditioning task and received sham, anodal or cathodal tDCS (N = 117, between-subjects design) or a vestibulo-ocular reflex adaptation task and received sham and anodal tDCS (N = 51 subjects, within-subjects design). Performance was quantified as a learning parameter from 0 to 100%. We investigated (1) the distribution of the learning parameter with mixture modeling presented as the mean (M), standard deviation (S) and proportion (P) of the groups, and (2) the role of BDNF Val66Met and cerebellar tDCS using linear regression presented as the regression coefficients (B) and odds ratios (OR) with equally-tailed intervals (ETIs). RESULTS: For the eyeblink conditioning task, we found distinct groups of learners (MLearner = 67.2%; SLearner = 14.7%; PLearner = 61.6%) and non-learners (MNon-learner = 14.2%; SNon-learner = 8.0%; PNon-learner = 38.4%). Carriers of the BDNF Val66Met polymorphism were more likely to be learners (OR = 2.7 [1.2 6.2]). Within the group of learners, anodal tDCS supported eyeblink conditioning in BDNF Val66Met non-carriers (B = 11.9% 95%ETI = [0.8 23.0]%), but not in carriers (B = 1.0% 95%ETI = [-10.2 12.1]%). For the vestibulo-ocular reflex adaptation task, we found no effect of BDNF Val66Met (B = -2.0% 95%ETI = [-8.7 4.7]%) or anodal tDCS in either carriers (B = 3.4% 95%ETI = [-3.2 9.5]%) or non-carriers (B = 0.6% 95%ETI = [-3.4 4.8]%). Finally, we performed additional saccade and visuomotor adaptation experiments (N = 72) to investigate the general role of BDNF Val66Met in cerebellum-dependent learning and found no difference between carriers and non-carriers for both saccade (B = 1.0% 95%ETI = [-8.6 10.6]%) and visuomotor adaptation (B = 2.7% 95%ETI = [-2.5 7.9]%). CONCLUSIONS: The specific role for BDNF Val66Met in eyeblink conditioning, but not vestibulo-ocular reflex adaptation, saccade adaptation or visuomotor adaptation could be related to dominance of the role of simple spike suppression of cerebellar Purkinje cells with a high baseline firing frequency in eyeblink conditioning. Susceptibility of non-carriers to anodal tDCS in eyeblink conditioning might be explained by a relatively larger effect of tDCS-induced subthreshold depolarization in this group, which might increase the spontaneous firing frequency up to the level of that of the carriers.