Maximal Aerobic Power versus Performance in Two Aerobic Endurance Tests among Young and Old Adults

Background: Aerobic fitness is of great value for reducing risk of mortality and cardiovascular diseases. Objective: This study evaluated the performance in and correlations between a new test (five-minute pyramid test, 5MPT), the six-minute walk-test (6MWT) and maximal oxygen uptake (VO(2max)) among old and young adults. Methods: Forty-four habitually active adults (females and males), 23 old (64-79 years) and 21 young (20-32 years) participated. In the 5MPT, the participants moved back and forth along a short walkway (5.5 m) over boxes (height: 'old people' 0.42 m, 'young people' 0.62 m) arranged like an elongated step pyramid for 5 min. Power in the pyramid test (5MPT(power)) was calculated as the product of numbers of laps, body weight, gravity and highest box level divided by time. A 6MWT and a maximal cycle ergometer test for direct measurements of VO(2max) were also performed. In all tests heart rate, with on-line electrocardiography, and perceived exertion were recorded. Results: There was a strong correlation between the 5MPT(power) and VO(2max) for the entire group studied (r = 0.98), and each of the four subgroups old and young females and males separately (r = 0.78-0.98). Contrary to several earlier studies, especially involving people with various diseases, the present data showed that 6MWT cannot be used to predict VO(2max) among old females and young adults. The correlation with VO(2max) was weaker for the 6MWT than for the 5MPT(power). The relative performance values for the old compared to the young (ratio old/young × 100) were considerably lower in 5MPT(power) and VO(2max) (47-55%) than in distance and 'work' in the 6MWT (82-86%). Conclusions: The results, with age and gender variations, can be valuable information in health-fitness contexts, since measuring physical aerobic capacity is very significant in connection with risk evaluations of mortality and various diseases. The 5MPT is a rapid, functional, easy and inexpensive tool for predicting assessed maximal aerobic power

Maxwell-Stefan based modelling of ion exchange systems containing common species (Cd2+, Na+) and distinct sorbents (ETS-4, ETS-10)

Cadmium(II) is a toxic hazardous cation, whose presence in the environment causes great concern because of its bioaccumulation in organisms and bioamplification along food chain. Hence, the removal of cadmium compounds from industrial waters and wastewaters is particularly essential, which requires intensive experimental and modelling studies to deal with the problem. In this work, the ion exchange of Cd2+ ions from aqueous solution using microporous titanosilicates (ETS-4 and ETS-10) has been modelled using adapted Maxwell-Stefan equations for the ions transport inside the sorbent particles. The fundamentals of the Maxwell-Stefan equations along with correlations for the convective mass transfer coefficients have been used with advantage to reduce the number of model parameters. In the whole, the model was able to represent successfully the kinetic behaviour of 11 independent and very distinct curves of both studied systems (Cd2+/Na+/ETS-4 and Cd2+/Na+/ETS-10). The predictive capability of the model has been also shown, since several uptake curves were accurately predicted with parameters fitted previously to different sets of experimental data

Statistical analysis of co-occurrence patterns in microbial presence-absence datasets.

Drawing on a long history in macroecology, correlation analysis of microbiome datasets is becoming a common practice for identifying relationships or shared ecological niches among bacterial taxa. However, many of the statistical issues that plague such analyses in macroscale communities remain unresolved for microbial communities. Here, we discuss problems in the analysis of microbial species correlations based on presence-absence data. We focus on presence-absence data because this information is more readily obtainable from sequencing studies, especially for whole-genome sequencing, where abundance estimation is still in its infancy. First, we show how Pearson's correlation coefficient (r) and Jaccard's index (J)-two of the most common metrics for correlation analysis of presence-absence data-can contradict each other when applied to a typical microbiome dataset. In our dataset, for example, 14% of species-pairs predicted to be significantly correlated by r were not predicted to be significantly correlated using J, while 37.4% of species-pairs predicted to be significantly correlated by J were not predicted to be significantly correlated using r. Mismatch was particularly common among species-pairs with at least one rare species (<10% prevalence), explaining why r and J might differ more strongly in microbiome datasets, where there are large numbers of rare taxa. Indeed 74% of all species-pairs in our study had at least one rare species. Next, we show how Pearson's correlation coefficient can result in artificial inflation of positive taxon relationships and how this is a particular problem for microbiome studies. We then illustrate how Jaccard's index of similarity (J) can yield improvements over Pearson's correlation coefficient. However, the standard null model for Jaccard's index is flawed, and thus introduces its own set of spurious conclusions. We thus identify a better null model based on a hypergeometric distribution, which appropriately corrects for species prevalence. This model is available from recent statistics literature, and can be used for evaluating the significance of any value of an empirically observed Jaccard's index. The resulting simple, yet effective method for handling correlation analysis of microbial presence-absence datasets provides a robust means of testing and finding relationships and/or shared environmental responses among microbial taxa