ANOSIM, PERMANOVA, and the Mantel test are all resemblance‐based permutation methods widely used in ecology. Here, we report the results of the first simulation study, to our knowledge, specifically designed to examine the effects of heterogeneity of multivariate dispersions on the rejection rates of these tests and on a classical MANOVA test (Pillai's trace). Increasing differences in dispersion among groups were simulated under scenarios of changing sample sizes, correlation structures, error distributions, numbers of variables, and numbers of groups for balanced and unbalanced one‐way designs. The power of these tests to detect environmental impacts or natural large‐scale biogeographic gradients was also compared empirically under simulations based on parameters derived from real ecological data sets. Overall, ANOSIM and the Mantel test were very sensitive to heterogeneity in dispersions, with ANOSIM generally being more sensitive than the Mantel test. In contrast, PERMANOVA and Pillai's trace were largely unaffected by heterogeneity for balanced designs. PERMANOVA was also unaffected by differences in correlation structure, unlike Pillai's trace. For unbalanced designs, however, all of the tests were (1) too liberal when the smaller group had greater dispersion and (2) overly conservative when the larger group had greater dispersion, especially ANOSIM and the Mantel test. For simulations based on real ecological data sets, PERMANOVA was generally, but not always, more powerful than the others to detect changes in community structure, and the Mantel test was usually more powerful than ANOSIM. Both the error distributions and the resemblance measure affected results concerning power. Differences in the underlying construction of these test statistics result in important differences in the nature of the null hypothesis they are testing, their sensitivity to heterogeneity, and their power to detect important changes in ecological communities. For balanced designs, PERMANOVA and PERMDISP can be used to rigorously identify location vs. dispersion effects, respectively, in the space of the chosen resemblance measure. ANOSIM and the Mantel test can be used as more “omnibus” tests, being sensitive to differences in location, dispersion or correlation structure among groups. Unfortunately, none of the tests (PERMANOVA, Mantel, or ANOSIM) behaved reliably for unbalanced designs in the face of heterogeneity.

A range of psychophysical taste measurements are used to characterize an individual’s sweet taste perception and to assess links between taste perception and dietary intake. The aims of this study were to investigate the relationship between four different psychophysical measurements of sweet taste perception, and to explore which measures of sweet taste perception relate to sweet food intake. Forty-four women aged 20–40 years were recruited for the study. Four measures of sweet taste perception (detection and recognition thresholds, and sweet taste intensity and hedonic liking of suprathreshold concentrations) were assessed using glucose as the tastant. Dietary measurements included a four-day weighed food record, a sweet food-food frequency questionnaire and a sweet beverage liking questionnaire. Glucose detection and recognition thresholds showed no correlation with suprathreshold taste measurements or any dietary intake measurement. Importantly, sweet taste intensity correlated negatively with total energy and carbohydrate (starch, total sugar, fructose, glucose) intakes, frequency of sweet food intake and sweet beverage liking. Furthermore, sweet hedonic liking correlated positively with total energy and carbohydrate (total sugar, fructose, glucose) intakes. The present study shows a clear link between sweet taste intensity and hedonic liking with sweet food liking, and total energy, carbohydrate and sugar intake.

Spreading depolarizations occur spontaneously and frequently in injured human brain. They propagate slowly through injured tissue often cycling around a local area of damage. Tissue recovery after an spreading depolarization requires greatly augmented energy utilisation to normalise ionic gradients from a virtually complete loss of membrane potential. In the injured brain, this is difficult because local blood flow is often low and unreactive. In this study, we use a new variant of microdialysis, continuous on-line microdialysis, to observe the effects of spreading depolarizations on brain metabolism. The neurochemical changes are dynamic and take place on the timescale of the passage of an spreading depolarization past the microdialysis probe. Dialysate potassium levels provide an ionic correlate of cellular depolarization and show a clear transient increase. Dialysate glucose levels reflect a balance between local tissue glucose supply and utilisation. These show a clear transient decrease of variable magnitude and duration. Dialysate lactate levels indicate non-oxidative metabolism of glucose and show a transient increase. Preliminary data suggest that the transient changes recover more slowly after the passage of a sequence of multiple spreading depolarizations giving rise to a decrease in basal dialysate glucose and an increase in basal dialysate potassium and lactate levels.

Air displacement plethysmography (ADP) and dual‐energy X‐ray absorptiometry (DXA) are well‐regarded methods for predicting body fat percentage (BF%). Bioelectrical impedance analysis (BIA) also predicts BF% and has distinct advantages in research settings. Aim To assess the validity of BIA against ADP and DXA to measure BF%, and to test the reliability of each method. Methods Adults (n = 166) with a wide range of body mass index (19–38 kg/m2) were tested twice during a 5‐day period. ADP was conducted in a BodPod (Life Measurement Inc, Concord, CA, USA); DXA measurements on a QDR Discovery A (Hologic) and BIA measurements used the InBody 230 (Biospace Ltd., Seoul, Korea). Agreement between measurements was analysed using t‐tests, effect size, linear regression models and method of triads (estimating true value). Results BIA showed excellent relative agreement to the estimated true value (ρ = 0.97 (0.96, 0.98)) and to ADP (R2 = 0.88) and DXA (R2 = 0.92), but wide limits of agreement (−4.25 to 8.37%). BIA underestimated BF% by 2%, across all values. DXA showed excellent relative agreement to the estimated true value (ρ = 0.97 (0.96, 0.98)) and with ADP (R2 = 0.92), good absolute agreement but wide limits of agreement (−6.13 to 6.91%) and under‐ and overestimation at high and low BF% levels, respectively. All methods showed excellent reliability with repeat measurements differing by less than 0.2% with very small 95% CIs. Conclusions BIA may be a valid method in research and population samples. All three methods showed excellent reliability.