Feeding and metabolic compensations in response to different foraging costs

How energetic cost of locomotion affects foraging decisions, and its metabolic consequences are poorly understood. In several groups of animals, including hermit crabs, exploratory walking enhances the efficiency of foraging by increasing the probability of finding more and better food items; however, the net gain of energy will only be enhanced if the costs of walking are lower than the benefits of enhanced food acquisition. In hermit crabs, the cost of walking increases with the mass of the shell type occupied. Thus, we expected that hermit crabs should adjust their foraging strategy to the cost of movement in different shells. We assessed the foraging, the quantity and quality of food intake, and the energetic cost of maintenance of hermit crabs paying different costs of foraging in the wild. The exploratory walking negatively correlated with shell mass, showing that hermit crabs use different foraging strategies in response to the expenditure required to move. Hermit crabs deal with high energetic costs of foraging in heavy shells by reduces their exploratory walking and overall metabolic rate, as a strategy to maximize the net energy intake. This study integrates behavioral and metabolic compensations as a response to foraging at different costs in natural conditions.     

The cost of walking downhill: Is the preferred gait energetically optimal?

Humans tend to prefer walking patterns that minimize energetic cost, but must also maintain stability to avoid falling over. The relative importance of these two goals in determining the preferred gait pattern is not currently clear. We investigated the relationship between energetic cost and stability during downhill walking, a context in which gravitational energy will assist propulsion but may also reduce stability. We hypothesized that humans will not minimize energetic cost when walking downhill, but will instead prefer a gait pattern that increases stability. Simulations of a dynamic walking model were used to determine whether stable downhill gaits could be achieved using a simple control strategy. Experimentally, twelve healthy subjects walked downhill at 1.25 m/s (0, 0.05, 0.10, and 0.15 gradients). For each slope, subjects performed normal and relaxed trials, in which they were instructed to reduce muscle activity and allow gravity to maximally assist their gait. We quantified energetic cost, stride timing, and leg muscle activity. In our model simulations, increase in slope reduced the required actuation but also decreased stability. Experimental subjects behaved more like the model when using the relaxed rather than the normal walking strategy; the relaxed strategy decreased energetic cost at the steeper slopes but increased stride period variability, an indicator of instability. These results indicate that subjects do not take optimal advantage of the propulsion provided by gravity to decrease energetic cost, but instead prefer a more stable and more costly gait pattern.     

Bayesian probabilistic approach for predicting backbone structures in terms of protein blocks

By using an unsupervised cluster analyzer, we have identified a local structural alphabet composed of 16 folding patterns of five consecutive C(alpha) ("protein blocks"). The dependence that exists between successive blocks is explicitly taken into account. A Bayesian approach based on the relation protein block-amino acid propensity is used for prediction and leads to a success rate close to 35%. Sharing sequence windows associated with certain blocks into "sequence families" improves the prediction accuracy by 6%. This prediction accuracy exceeds 75% when keeping the first four predicted protein blocks at each site of the protein. In addition, two different strategies are proposed: the first one defines the number of protein blocks in each site needed for respecting a user-fixed prediction accuracy, and alternatively, the second one defines the different protein sites to be predicted with a user-fixed number of blocks and a chosen accuracy. This last strategy applied to the ubiquitin conjugating enzyme (alpha/beta protein) shows that 91% of the sites may be predicted with a prediction accuracy larger than 77% considering only three blocks per site. The prediction strategies proposed improve our knowledge about sequence-structure dependence and should be very useful in ab initio protein modelling.     

The Energetics of Male Mating Strategies in Field Crickets (Orthoptera: Gryllinae: Gryllidae)

Male field crickets (Gryllinae: Gryllidae: Orthoptera) vary in their mating strategies, particularly in whether they call from defended sites to attract phonotactic females or roam silently in search of potential mates. To better understand the economics underlying these alternative strategies, respirometry was employed to examine the energetic costs of each strategy's component behaviors for a representative species, Acheta domesticus. Advertisement calling in this species, composed of low pulse rate chirps, is an order of magnitude less energetically costly than walking per unit time. However, for gryllids that advertise call with higher pulse rate trills, calling and walking appear to be of equivalent energetic cost. Thus, if energetic costs are important in determining grylline mating strategies, trillers and chirpers should have different sensitivities to change in factors affecting the relative payoffs of each strategy. Field studies of changes in mating behavior with increased population density support this argument. This study also determined that courtship calling by A. domesticus was over twice as energetically costly as advertisement calling per unit time, suggesting its importance as a more reliable indicator of signaler, or mate, quality.     

Optimization, conflict, and nonoverlapping foraging ranges in ants

An organism's foraging range depends on the behavior of neighbors, the dynamics of resources, and the availability of information. We use a well-studied population of the red harvester ant Pogonomyrmex barbatus to develop and independently parameterize models that include these three factors. The models solve for an allocation of foraging ants in the area around the nest in response to other colonies. We compare formulations that optimize at the colony or individual level and those that do or do not include costs of conflict. Model predictions were compared with data collected on ant time budgets and ant density. The strategy that optimizes at the colony level but neglects costs of conflict predicts unrealistic levels of overlap. In contrast, the strategy that optimizes at the individual level predicts realistic foraging ranges with or without inclusion of conflict costs. Both the individual model and the colony model that includes conflict costs show good quantitative agreement with data. Thus, an optimal foraging response to a combination of exploitation and interference competition can largely explain how individual foraging behavior creates the foraging range of a colony. Deviations between model predictions and data indicate that colonies might allocate a larger than optimal number of foragers to areas near boundaries between foraging ranges.     

Task-Specific Response Strategy Selection on the Basis of Recent Training Experience

The goal of training is to produce learning for a range of activities that are typically more general than the training task itself. Despite a century of research, predicting the scope of learning from the content of training has proven extremely difficult, with the same task producing narrowly focused learning strategies in some cases and broadly scoped learning strategies in others. Here we test the hypothesis that human subjects will prefer a decision strategy that maximizes performance and reduces uncertainty given the demands of the training task and that the strategy chosen will then predict the extent to which learning is transferable. To test this hypothesis, we trained subjects on a moving dot extrapolation task that makes distinct predictions for two types of learning strategy: a narrow model-free strategy that learns an input-output mapping for training stimuli, and a general model-based strategy that utilizes humans'' default predictive model for a class of trajectories. When the number of distinct training trajectories is low, we predict better performance for the mapping strategy, but as the number increases, a predictive model is increasingly favored. Consonant with predictions, subject extrapolations for test trajectories were consistent with using a mapping strategy when trained on a small number of training trajectories and a predictive model when trained on a larger number. The general framework developed here can thus be useful both in interpreting previous patterns of task-specific versus task-general learning, as well as in building future training paradigms with certain desired outcomes.     

Do Humans Optimally Exploit Redundancy to Control Step Variability in Walking?

It is widely accepted that humans and animals minimize energetic cost while walking. While such principles predict average behavior, they do not explain the variability observed in walking. For robust performance, walking movements must adapt at each step, not just on average. Here, we propose an analytical framework that reconciles issues of optimality, redundancy, and stochasticity. For human treadmill walking, we defined a goal function to formulate a precise mathematical definition of one possible control strategy: maintain constant speed at each stride. We recorded stride times and stride lengths from healthy subjects walking at five speeds. The specified goal function yielded a decomposition of stride-to-stride variations into new gait variables explicitly related to achieving the hypothesized strategy. Subjects exhibited greatly decreased variability for goal-relevant gait fluctuations directly related to achieving this strategy, but far greater variability for goal-irrelevant fluctuations. More importantly, humans immediately corrected goal-relevant deviations at each successive stride, while allowing goal-irrelevant deviations to persist across multiple strides. To demonstrate that this was not the only strategy people could have used to successfully accomplish the task, we created three surrogate data sets. Each tested a specific alternative hypothesis that subjects used a different strategy that made no reference to the hypothesized goal function. Humans did not adopt any of these viable alternative strategies. Finally, we developed a sequence of stochastic control models of stride-to-stride variability for walking, based on the Minimum Intervention Principle. We demonstrate that healthy humans are not precisely “optimal,” but instead consistently slightly over-correct small deviations in walking speed at each stride. Our results reveal a new governing principle for regulating stride-to-stride fluctuations in human walking that acts independently of, but in parallel with, minimizing energetic cost. Thus, humans exploit task redundancies to achieve robust control while minimizing effort and allowing potentially beneficial motor variability.     

The effects of gait strategy on metabolic rate and indicators of stability during downhill walking

When walking at a given speed, humans often appear to prefer gait patterns that minimize metabolic rate, thereby maximizing metabolic economy. However, recent experiments have demonstrated that humans do not maximize economy when walking downhill. The purpose of this study was to investigate whether this non-metabolically optimal behavior is the result of a trade-off between metabolic economy and gait stability. We hypothesized that humans have the ability to modulate their gait strategy to increase either metabolic economy or stability, but that increase in one measure will be accompanied by decrease in the other. Subjects walked downhill using gait strategies ranging from risky to conservative, which were either prescribed by verbal instructions or induced by the threat of perturbations. We quantified spatiotemporal gait characteristics, metabolic rate and several indicators of stability previously associated with fall risk: stride period variability; step width variability; Lyapunov exponents; Floquet multipliers; and stride period fractal index. When subjects walked using conservative gait strategies, stride periods and lengths decreased, metabolic rate increased, and anteroposterior maximum Lyapunov exponents increased, which has previously been interpreted as an indicator of decreased stability. These results do not provide clear support for the proposed trade-off between economy and stability, particularly when stability is approximated using complex metrics. However, several gait pattern changes previously linked to increased fall risk were observed when our healthy subjects walked with a conservative strategy, suggesting that these changes may be a response to, rather than a cause of, increased fall risk.     

The effects of sloped surfaces on locomotion: a kinematic and kinetic analysis

Previous findings from studies of demanding tasks in humans and slope walking in quadrupeds suggest that human slope walking may require specialized neural control strategies. The goal of this investigation was to gain insight into these strategies by quantifying lower limb kinematics and kinetics during up- and downslope walking. Nine healthy volunteers walked at a self-selected speed on an instrumented ramp at each of five grades (-39%, -15%, 0%, +15%, +39%; or -21 degrees, -8.5 degrees, 0 degrees, +8.5 degrees, +21 degrees, respectively). For each subject, the selected speed was maintained at all grades to minimize the effect of speed on gait dynamics. Points of interest were identified in the kinematic and kinetic outcome measures and compared across grades; a significant grade effect was found for all points except the magnitude of the peak hip extensor moment during late stance. Kinematic postural changes were consistent with the need to raise the limb for toe clearance and heel strike and to lift the body during upslope walking, and to control the descent of the body during downslope walking. The support moment increased significantly during both upslope and downslope walking compared to level: the increases were predominantly due to the increasing hip extensor moment during upslope walking, and to the increasing knee extensor moment during downslope walking. In addition, the hip and knee joint moment patterns showed significant differences from the patterns observed during level walking. This non-uniform distribution of joint moment increases during up- and downslope walking compared to level walking suggests that these three tasks are not governed by the same control strategy.     

Resistance management: the stable zone strategy

The different strategies of insecticide resistance management that have been formulated so far consist of delaying the appearance and spread of resistance genes. In this paper, we propose a strategy that can be used even if resistance genes are already present. This strategy consists of applying insecticides in an area smaller than a certain critical size, so that gene flow from the untreated area, combined with the fitness cost of the resistance genes, prevents its frequency reaching high equilibrium value. A two-locus model was analysed numerically to determine population densities at equilibrium as a function of selection coefficients (insecticide selection, fitness costs of resistance genes and dominances), gene flow and size of the treated area. This model indicates that there is an optimal size for the treated area where a minimal and stable density reach equilibrium, and where resistance genes cannot invade. This resistance management strategy seems applicable to a large variety of field situations, but eventually it may encounter obstacles due to a modifier which reduces the fitness costs of resistance genes.     

Estimation of temporal gait parameters using Bayesian models on acceleration signals

The purpose of this study is to develop a system capable of performing calculation of temporal gait parameters using two low-cost wireless accelerometers and artificial intelligence-based techniques as part of a larger research project for conducting human gait analysis. Ten healthy subjects of different ages participated in this study and performed controlled walking tests. Two wireless accelerometers were placed on their ankles. Raw acceleration signals were processed in order to obtain gait patterns from characteristic peaks related to steps. A Bayesian model was implemented to classify the characteristic peaks into steps or nonsteps. The acceleration signals were segmented based on gait events, such as heel strike and toe-off, of actual steps. Temporal gait parameters, such as cadence, ambulation time, step time, gait cycle time, stance and swing phase time, simple and double support time, were estimated from segmented acceleration signals. Gait data-sets were divided into two groups of ages to test Bayesian models in order to classify the characteristic peaks. The mean error obtained from calculating the temporal gait parameters was 4.6%. Bayesian models are useful techniques that can be applied to classification of gait data of subjects at different ages with promising results     

Bionic ankle-foot prosthesis normalizes walking gait for persons with leg amputation

Over time, leg prostheses have improved in design, but have been incapable of actively adapting to different walking velocities in a manner comparable to a biological limb. People with a leg amputation using such commercially available passive-elastic prostheses require significantly more metabolic energy to walk at the same velocities, prefer to walk slower and have abnormal biomechanics compared with non-amputees. A bionic prosthesis has been developed that emulates the function of a biological ankle during level-ground walking, specifically providing the net positive work required for a range of walking velocities. We compared metabolic energy costs, preferred velocities and biomechanical patterns of seven people with a unilateral transtibial amputation using the bionic prosthesis and using their own passive-elastic prosthesis to those of seven non-amputees during level-ground walking. Compared with using a passive-elastic prosthesis, using the bionic prosthesis decreased metabolic cost by 8 per cent, increased trailing prosthetic leg mechanical work by 57 per cent and decreased the leading biological leg mechanical work by 10 per cent, on average, across walking velocities of 0.75-1.75 m s(-1) and increased preferred walking velocity by 23 per cent. Using the bionic prosthesis resulted in metabolic energy costs, preferred walking velocities and biomechanical patterns that were not significantly different from people without an amputation.     

Identifying Stride-To-Stride Control Strategies in Human Treadmill Walking

Variability is ubiquitous in human movement, arising from internal and external noise, inherent biological redundancy, and from the neurophysiological control actions that help regulate movement fluctuations. Increased walking variability can lead to increased energetic cost and/or increased fall risk. Conversely, biological noise may be beneficial, even necessary, to enhance motor performance. Indeed, encouraging more variability actually facilitates greater improvements in some forms of locomotor rehabilitation. Thus, it is critical to identify the fundamental principles humans use to regulate stride-to-stride fluctuations in walking. This study sought to determine how humans regulate stride-to-stride fluctuations in stepping movements during treadmill walking. We developed computational models based on pre-defined goal functions to compare if subjects, from each stride to the next, tried to maintain the same speed as the treadmill, or instead stay in the same position on the treadmill. Both strategies predicted average behaviors empirically indistinguishable from each other and from that of humans. These strategies, however, predicted very different stride-to-stride fluctuation dynamics. Comparisons to experimental data showed that human stepping movements were generally well-predicted by the speed-control model, but not by the position-control model. Human subjects also exhibited no indications they corrected deviations in absolute position only intermittently: i.e., closer to the boundaries of the treadmill. Thus, humans clearly do not adopt a control strategy whose primary goal is to maintain some constant absolute position on the treadmill. Instead, humans appear to regulate their stepping movements in a way most consistent with a strategy whose primary goal is to try to maintain the same speed as the treadmill at each consecutive stride. These findings have important implications both for understanding how biological systems regulate walking in general and for being able to harness these mechanisms to develop more effective rehabilitation interventions to improve locomotor performance.     

Seasonal and daily walking activity patterns of free-ranging adult red deer (<Emphasis Type="Italic">Cervus elaphus</Emphasis>) at the individual level

We studied the walking activity over the year of free-ranging adult red deer (Cervus elaphus) in a mountainous area with the aim of describing the dynamics of movement patterns at the individual level. We monitored the distance walked by two males and two females fitted with global positioning system collars to test the hypothesis that deer adopt behaviours to reduce costs of locomotion. We predicted that both sexes would travel less in winter when disadvantageous environmental conditions occurred. We also predicted that the males would (1) reduce their movement soon after the rut due to very high energy expenditure during the breeding season and (2) travel less than the females due to their larger body mass. As we expected, minimum walking activity occurred after the rut from November to February for the males and in late February for the females. The walking activity of males peaked during the rut whereas that of females decreased. But compared to males, females moved more both during winter and daylight hours. Although our study stems from just four individuals, these results and the methodology used can be inspirational for red deer research as well as for ungulate research in general.     

Fractal fluctuations in spatiotemporal variables when walking on a self-paced treadmill

This study investigated the fractal dynamic properties of stride time (ST), stride length (SL) and stride speed (SS) during walking on a self-paced treadmill (STM) in which the belt speed is automatically controlled by the walking speed. Twelve healthy young subjects participated in the study. The subjects walked at their preferred walking speed under four conditions: STM, STM with a metronome (STM+met), fixed-speed (conventional) treadmill (FTM), and FTM with a metronome (FTM+met). To compare the fractal dynamics between conditions, the mean, variability, and fractal dynamics of ST, SL, and SS were compared. Moreover, the relationship among the variables was examined under each walking condition using three types of surrogates. The mean values of all variables did not differ between the two treadmills, and the variability of all variables was generally larger for STM than for FTM. The use of a metronome resulted in a decrease in variability in ST and SS for all conditions. The fractal dynamic characteristics of SS were maintained with STM, in contrast to FTM, and only the fractal dynamic characteristics of ST disappeared when using a metronome. In addition, the fractal dynamic patterns of the cross-correlated surrogate results were identical to those of all variables for the two treadmills. In terms of the fractal dynamic properties, STM walking was generally closer to overground walking than FTM walking. Although further research is needed, the present results will be useful in research on gait fractal dynamics and rehabilitation.     

Stochastic Optimal Foraging: Tuning Intensive and Extensive Dynamics in Random Searches

Recent theoretical developments had laid down the proper mathematical means to understand how the structural complexity of search patterns may improve foraging efficiency. Under information-deprived scenarios and specific landscape configurations, Lévy walks and flights are known to lead to high search efficiencies. Based on a one-dimensional comparative analysis we show a mechanism by which, at random, a searcher can optimize the encounter with close and distant targets. The mechanism consists of combining an optimal diffusivity (optimally enhanced diffusion) with a minimal diffusion constant. In such a way the search dynamics adequately balances the tension between finding close and distant targets, while, at the same time, shifts the optimal balance towards relatively larger close-to-distant target encounter ratios. We find that introducing a multiscale set of reorientations ensures both a thorough local space exploration without oversampling and a fast spreading dynamics at the large scale. Lévy reorientation patterns account for these properties but other reorientation strategies providing similar statistical signatures can mimic or achieve comparable efficiencies. Hence, the present work unveils general mechanisms underlying efficient random search, beyond the Lévy model. Our results suggest that animals could tune key statistical movement properties (e.g. enhanced diffusivity, minimal diffusion constant) to cope with the very general problem of balancing out intensive and extensive random searching. We believe that theoretical developments to mechanistically understand stochastic search strategies, such as the one here proposed, are crucial to develop an empirically verifiable and comprehensive animal foraging theory.     

Movement behavior of high-heeled walking: how does the nervous system control the ankle joint during an unstable walking condition?

The human locomotor system is flexible and enables humans to move without falling even under less than optimal conditions. Walking with high-heeled shoes constitutes an unstable condition and here we ask how the nervous system controls the ankle joint in this situation? We investigated the movement behavior of high-heeled and barefooted walking in eleven female subjects. The movement variability was quantified by calculation of approximate entropy (ApEn) in the ankle joint angle and the standard deviation (SD) of the stride time intervals. Electromyography (EMG) of the soleus (SO) and tibialis anterior (TA) muscles and the soleus Hoffmann (H-) reflex were measured at 4.0 km/h on a motor driven treadmill to reveal the underlying motor strategies in each walking condition. The ApEn of the ankle joint angle was significantly higher (p<0.01) during high-heeled (0.38±0.08) than during barefooted walking (0.28±0.07). During high-heeled walking, coactivation between the SO and TA muscles increased towards heel strike and the H-reflex was significantly increased in terminal swing by 40% (p<0.01). These observations show that high-heeled walking is characterized by a more complex and less predictable pattern than barefooted walking. Increased coactivation about the ankle joint together with increased excitability of the SO H-reflex in terminal swing phase indicates that the motor strategy was changed during high-heeled walking. Although, the participants were young, healthy and accustomed to high-heeled walking the results demonstrate that that walking on high-heels needs to be controlled differently from barefooted walking. We suggest that the higher variability reflects an adjusted neural strategy of the nervous system to control the ankle joint during high-heeled walking.     

Two biomechanical strategies for locomotor adaptation to split-belt treadmill walking in subjects with and without transtibial amputation

Locomotor adaptation is commonly studied using split-belt treadmill walking, in which each foot is placed on a belt moving at a different speed. As subjects adapt to split-belt walking, they reduce metabolic power, but the biomechanical mechanism behind this improved efficiency is unknown. Analyzing mechanical work performed by the legs and joints during split-belt adaptation could reveal this mechanism. Because ankle work in the step-to-step transition is more efficient than hip work, we hypothesized that control subjects would reduce hip work on the fast belt and increase ankle work during the step-to-step transition as they adapted. We further hypothesized that subjects with unilateral, trans-tibial amputation would instead increase propulsive work from their intact leg on the slow belt. Control subjects reduced hip work and shifted more ankle work to the step-to-step transition, supporting our hypothesis. Contrary to our second hypothesis, intact leg work, ankle work and hip work in amputees were unchanged during adaptation. Furthermore, all subjects increased collisional energy loss on the fast belt, but did not increase propulsive work. This was possible because subjects moved further backward during fast leg single support in late adaptation than in early adaptation, compensating by reducing backward movement in slow leg single support. In summary, subjects used two strategies to improve mechanical efficiency in split-belt walking adaptation: a CoM displacement strategy that allows for less forward propulsion on the fast belt; and, an ankle timing strategy that allows efficient ankle work in the step-to-step transition to increase while reducing inefficient hip work.     

Ribosomal protein-sequence block structure suggests complex prokaryotic evolution with implications for the origin of eukaryotes

Amino acid sequence alignments of orthologous ribosomal proteins found in Bacteria, Archaea, and Eukaryota display, relative to one another, an unusual segment or block structure, with major evolutionary implications. Within each of the prokaryotic phylodomains the sequences exhibit substantial similarity, but cross-domain alignments break up into (a) universal blocks (conserved in both phylodomains), (b) bacterial blocks (unalignable with any archaeal counterparts), and (c) archaeal blocks (unalignable with any bacterial counterparts). Sequences of those eukaryotic cytoplasmic riboproteins that have orthologs in both Bacteria and Archaea, exclusively match the archaeal block structure. The distinct blocks do not correlate consistently with any identifiable functional or structural feature including RNA and protein contacts. This phylodomain-specific block pattern also exists in a number of other proteins associated with protein synthesis, but not among enzymes of intermediary metabolism. While the universal blocks imply that modern Bacteria and Archaea (as defined by their translational machinery) clearly have had a common ancestor, the phylodomain-specific blocks imply that these two groups derive from single, phylodomain-specific types that came into existence at some point long after that common ancestor. The simplest explanation for this pattern would be a major evolutionary bottleneck, or other scenario that drastically limited the progenitors of modern prokaryotic diversity at a time considerably after the evolution of a fully functional translation apparatus. The vast range of habitats and metabolisms that prokaryotes occupy today would thus reflect divergent evolution after such a restricting event. Interestingly, phylogenetic analysis places the origin of eukaryotes at about the same time and shows a closer relationship of the eukaryotic ribosome-associated proteins to crenarchaeal rather than euryarchaeal counterparts.     

A manipulability analysis of human walking

From the literature of biomechanics, it is now clear that humans use elevating, lowering and delayed-lowering strategies in order to maintain stability during perturbed walking. The main purpose of this study is to provide insights into the role of manipulability in selection of these strategies. A 37 degrees of freedom (DoFs) model of the human body is developed to evaluate the manipulability indices during walking. The model is considered as a tree-like structure and its forward kinematics equations and the Jacobian are derived based on the Denavit-Hartenberg (DH) convention. A hybrid genetic algorithm (HGA) is then employed to map the experimental kinematics of a human to the model. The kinematic and dynamic manipulability indices of the swing phase of walking are evaluated concentrating on early, mid and late swing phases. The results indicate that the manipulability indices can characterize well the selection of elevating, lowering and delayed-lowering strategies at different stages of the swing phase. The results kinematically describe the reason of selecting delayed-lowering strategy at mid-swing phase that was not obvious in previous studies. Moreover, the results show that at mid-swing phase of walking the kinematic maneuverability is lower than that of the early and late swing phases.