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Recent studies have suggested that changing direction is associated with significant additional energy expenditure. A failure to account for this additional energy expenditure of turning has significant implications in the design and interpretation of health interventions. The purpose of this study was therefore to investigate the influence of walking speed and angle, and their interaction, on energy expenditure in 20 healthy adults (7 female; 28±7 yrs). On two separate days, participants completed a turning protocol at one of 16 speed- (2.5, 3.5, 4.5, 5.5 km∙h-1) and angle (0, 45, 90, 180°) combinations, involving three minute bouts of walking, interspersed by three minutes seated rest. Each condition involved 5 m of straight walking before turning through the pre-determined angle with the speed dictated by a digital, auditory metronome. Tri-axial accelerometry and magnetometry were measured at 60 Hz, in addition to gas exchange on a breath-by-breath basis. Mixed models revealed a significant main effect for speed (F = 121.609, P < 0.001) and angle (F = 19.186, P < 0.001) on oxygen uptake ([Formula: see text]) and a significant interaction between these parameters (F = 4.433, P < 0.001). Specifically, as speed increased, [Formula: see text] increased but significant increases in [Formula: see text] relative to straight line walking were only observed for 90° and 180° turns at the two highest speeds (4.5 and 5.5 km∙hr-1). These findings therefore highlight the importance of accounting for the quantity and magnitude of turns completed when estimating energy expenditure and have significant implications within both sport and health contexts.

Advances in the “omics” field bring about the need for a high number of good quality samples. Many omics studies take advantage of biobanked samples to meet this need. Most of the laboratory errors occur in the pre-analytical phase. Therefore evidence-based standard operating procedures for the pre-analytical phase as well as markers to distinguish between ‘good’ and ‘bad’ quality samples taking into account the desired downstream analysis are urgently needed. We studied concentration changes of metabolites in serum samples due to pre-storage handling conditions as well as due to repeated freeze-thaw cycles. We collected fasting serum samples and subjected aliquots to up to four freeze-thaw cycles and to pre-storage handling delays of 12, 24 and 36 hours at room temperature (RT) and on wet and dry ice. For each treated aliquot, we quantified 127 metabolites through a targeted metabolomics approach. We found a clear signature of degradation in samples kept at RT. Storage on wet ice led to less pronounced concentration changes. 24 metabolites showed significant concentration changes at RT. In 22 of these, changes were already visible after only 12 hours of storage delay. Especially pronounced were increases in lysophosphatidylcholines and decreases in phosphatidylcholines. We showed that the ratio between the concentrations of these molecule classes could serve as a measure to distinguish between ‘good’ and ‘bad’ quality samples in our study. In contrast, we found quite stable metabolite concentrations during up to four freeze-thaw cycles. We concluded that pre-analytical RT handling of serum samples should be strictly avoided and serum samples should always be handled on wet ice or in cooling devices after centrifugation. Moreover, serum samples should be frozen at or below -80°C as soon as possible after centrifugation. © 2015 Anton et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

In Table 3, the substrate/incline conditions are not aligned correctly with the corresponding metrics. Please see the corrected Table 3 here

Penguins are major consumers in the southern oceans although quantification of this has been problematic. One suggestion proposes the use of points of inflection in diving profiles (´wiggles´) for this, a method that has been validated for the estimation of prey consumption by Magellanic penguins (Spheniscus magellanicus) by Simeone and Wilson (2003). Following them, we used wiggles from 31 depth logger-equipped Magellanic penguins foraging from four Patagonian colonies; Punta Norte (PN), Bahía Bustamente (BB), Puerto Deseado (PD) and Puerto San Julián (PSJ), all located in Argentina between 42-49° S, to estimate the prey captured and calculate the catch per unit time (CPUT) for birds foraging during the early chick-rearing period. Numbers of prey caught and CPUT were significantly different between colonies. Birds from PD caught the highest number of prey per foraging trip, with CPUT values of 68 ± 19 prey per hour underwater (almost two times greater than for the three remaining colonies). We modeled consumption from these data and calculate that the world Magellanic penguin population consumes about 2 million tons of prey per year. Possible errors in this calculation are discussed. Despite this, the analysis of wiggles seems a powerful and simple tool to begin to quantify prey consumption by Magellanic penguins, allowing comparison between different breeding sites. The total number of wiggles and/or CPUT do not reflect, by themselves, the availability of food for each colony, as the number of prey consumed by foraging trip is strongly associated with the energy content and wet mass of each colony-specific ´prey type´. Individuals consuming more profitable prey could be optimizing the time spent underwater, thereby optimizing the energy expenditure associated with the dives. Fil: Sala, Juan Emilio. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Nacional Patagónico; Argentina Fil: Wilson, Rory P.. Universidad de Swansea, Gales, Reino Unido; Reino Unido; Reino Unido Fil: Quintana, Flavio Roberto. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Nacional Patagónico; Argentina

Devices attached to flying birds can hugely enhance our understanding of their behavioural ecology for periods when they cannot be observed directly. For this, scientists routinely attach units to either birds' backs or their tails. However, inappropriate payload distribution is critical in aircraft and, since birds and planes are subject to the same laws of physics during flight, we considered aircraft aerodynamic constraints to explain flight patterns displayed by northern gannets Sula bassana equipped with (small ca. 14 g) tail- and back-mounted accelerometers and (larger ca. 30 g) tail-mounted GPS units. Tail-mounted GPS-fitted birds showed significantly higher cumulative numbers of flap-glide cycles and a higher pitch angle of the tail than accelerometer-equipped birds, indicating problems with balancing inappropriately placed weights with knock-on consequences relating to energy expenditure. These problems can be addressed by carefully choosing where to place tags on birds according to the mass of the tags and the lifestyle of the subject species.

Researchers hoping to elucidate the behaviour of species that aren’t readily observed are able to do so using biotelemetry methods. Accelerometers in particular are proving particularly effective and have been used on terrestrial, aquatic and volant species with success. In the past, behavioural modes were detected in accelerometer data through manual inspection, but with developments in technology, modern accelerometers now record at frequencies that make this impractical. In light of this, some researchers have suggested the use of various machine learning approaches as a means to classify accelerometer data automatically. We feel uptake of this approach by the scientific community is inhibited for two reasons; 1) Most machine learning algorithms require selection of summary statistics which obscure the decision mechanisms by which classifications are arrived, and 2) they are difficult to implement without appreciable computational skill. We present a method which allows researchers to classify accelerometer data into behavioural classes automatically using a primitive machine learning algorithm, k-nearest neighbour (KNN). Raw acceleration data may be used in KNN without selection of summary statistics, and it is easily implemented using the freeware program R. The method is evaluated by detecting 5 behavioural modes in 8 species, with examples of quadrupedal, bipedal and volant species. Accuracy and Precision were found to be comparable with other, more complex methods. In order to assist in the application of this method, the script required to run KNN analysis in R is provided. We envisage that the KNN method may be coupled with methods for investigating animal position, such as GPS telemetry or dead-reckoning, in order to implement an integrated approach to movement ecology research. Fil: Bidder, Owen R.. Swansea University; Reino Unido Fil: Campbell, Hamish A.. The University Of Queensland; Australia Fil: Gómez Laich, Agustina Marta. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Nacional Patagónico; Argentina Fil: Urgé, Patricia. Swansea University; Reino Unido Fil: Walker, James. Swansea University; Reino Unido Fil: Cai, Yuzhi. Swansea University; Reino Unido Fil: Gao, Lianli. The University Of Queensland; Australia Fil: Quintana, Flavio Roberto. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Centro Nacional Patagónico; Argentina Fil: Wilson, Rory P.. Swansea University; Reino Unido

Introduction Animal travel speed is an ecologically significant parameter, with implications for the study of energetics and animal behaviour. It is also necessary for the calculation of animal paths by dead-reckoning. Dead-reckoning uses heading and speed to calculate an animal’s path through its environment on a fine scale. It is often used in aquatic environments, where transmission telemetry is difficult. However, its adoption for tracking terrestrial animals is limited by our ability to measure speed accurately on a fine scale. Recently, tri-axial accelerometers have shown promise for estimating speed, but their accuracy appears affected by changes in substrate and surface gradients. The purpose of the present study was to evaluate four metrics of acceleration; Overall dynamic body acceleration (ODBA), vectorial dynamic body acceleration (VDBA), acceleration peak frequency and acceleration peak amplitude, as proxies for speed over hard, soft and inclined surfaces, using humans as a model species. Results A general linear model (GLM) showed a significant difference in the relationships between the metrics and speed depending on substrate or surface gradient. When the data from all surface types were considered together, VeDBA had the highest coefficient of determination. Conclusions All of the metrics showed some variation in their relationship with speed according to the surface type. This indicates that changes in the substrate or surface gradient during locomotion by animals would produce errors in speed estimates, and also in dead-reckoned tracks if they were calculated from speeds based entirely on a priori calibrations. However, we describe a method by which the relationship between acceleration metrics and speed can be corrected ad hoc, until tracks accord with periodic ground truthed positions, obtained via a secondary means (e.g. VHF or GPS telemetry). In this way, dead-reckoning provides a means to obtain fine scale movement data for terrestrial animals, without the need for additional data on substrate or gradient.