Thinking about the bigger picture: Influence of holistic processing on the dishware size effect.

Individuals vary in the extent to which they engage in holistic and analytic information processing styles. Holistic processing involves focusing on the interconnectivity and relatedness of items being evaluated, while analytic processing involves focusing on items being judged as discrete elements and independent of context. We examined the contribution of these basic processing styles to the dishware size effect, which proposes that food consumption patterns may be influenced by the size of the dishware (i.e., larger plates increase the amount of food consumed). We observed that participants self-served and consumed more food when using and eating from a larger plate (LP) compared with a smaller plate (SP) (p≤0.01). Importantly, participants who reported greater levels of holistic information processing related to attitudes towards contradictions and attention allocation exhibited smaller variations in portions of food self-served and consumed based on the dishware size used (SP vs. LP). These findings suggest that the susceptibility of individuals to the dishware size effect may be associated with an individual's dispositional tendency to process information in a holistic (vs. analytic) manner.

Influence of impending healthy food consumption on snacking: Nudging vs. compensatory behaviour.

The use of nudging, prompts or primes in the environment aligned with desired goals, as a strategy to promote healthy behaviour has gained increasing attention. Yet, the adoption of healthy behaviours may ironically be frustrated by licensing of competing goals for indulgence, producing compensatory unhealthy behaviours. However, little is known of these unintended compensatory effects of "health" nudging, and the conditions in which engagement of healthy behaviours may continue to activate health goals versus compensatory goals for indulgence. Over two studies, we examined both behavioural phenomena by investigating the influence of impending healthy food consumption on snack intake. We predicted that anticipated engagement of healthy eating will prime healthier eating behaviour (decreased snack intake), unless consumption of an anticipated healthy meal is expected to lead to an energy deficit (a "low calorie" meal), which may instead activate compensatory indulgence (increased snack intake). Study 1 demonstrated that the increase in the saliency of "healthiness" (unrelated to calorie content) from an anticipated meal resulted in lower snack intake (potato crisp consumption). Study 2 revealed that the anticipated consumption of a healthy meal described as low in caloric content promoted compensatory eating behaviour. This maladaptive behaviour was also observed to be associated with the endorsement of weight-regulation-related compensatory health beliefs. Furthermore, the findings also suggest that more restrained eaters (who experience competing goals for health and indulgence) may be especially likely to engage in healthier eating behaviour when anticipating healthy meals that were not portrayed to be low in calories. Taken together, our findings suggest that nudging by increasing the salience of healthy options without implying resulting negative energy balance is an effective strategy in promoting healthy eating behaviour and may be less prone to maladaptive compensatory behaviour, especially for individuals actively regulating their dietary behaviour.

Big Data in Drug Discovery.

Interpretation of Big Data in the drug discovery community should enhance project timelines and reduce clinical attrition through improved early decision making. The issues we encounter start with the sheer volume of data and how we first ingest it before building an infrastructure to house it to make use of the data in an efficient and productive way. There are many problems associated with the data itself including general reproducibility, but often, it is the context surrounding an experiment that is critical to success. Help, in the form of artificial intelligence (AI), is required to understand and translate the context. On the back of natural language processing pipelines, AI is also used to prospectively generate new hypotheses by linking data together. We explain Big Data from the context of biology, chemistry and clinical trials, showcasing some of the impressive public domain sources and initiatives now available for interrogation.

When exercise does not pay: Counterproductive effects of impending exercise on energy intake among restrained eaters.

Evidence suggests people may overestimate the effectiveness of future positive behaviour, leading to counterproductive behaviours in the present. Applied to weight-management, we hypothesize that inaccurate expectations about impending exercise may impede weight management by promoting overconsumption prior to exercise. This study aimed to determine how expectations about impending exercise and its potential ability to expend energy may influence i) energy intake before exercise and ii) overall energy balance (energy intake minus energy expended via exercise). Using a randomised, counterbalanced design, 21 inactive, overweight males, following a baseline session, completed two experimental trials: i) ad-libitum snack meal (potato-crisps) followed by an exercise session (SE) and ii) ad-libitum snack meal only (SO). There was no main effect of condition (SE vs. SO) on ad-libitum snack intake (p = .917). However, after accounting for dietary restraint (covariate), a difference in snack intake between SE and SO was revealed (p = .050). Specifically, participants who scored higher in dietary restraint consumed more in the SE (vs. SO) session (162 ±â€¯359 kcal more) compared with participants who scored lower in dietary restraint (89 ±â€¯135 kcal less). Among restrained eaters, the relative (net) energy consumed after accounting for energy expended from exercise in SE was not different from the energy consumed in the SO condition, suggesting that energy expended via exercise in SE does not appear to negate extra energy consumed in this condition compared with SO. Of interest, desire to eat and prospective food consumption ratings at the start of the trial were greater (p ≤ .029) in SE compared with SO. Findings suggest that restrained-eaters are at risk of adopting compensatory eating behaviour that may impede negative energy balance typically resulting from exercise (i.e. expending insufficient energy to negate compensatory energy intake).

Single Cell Phenotyping Reveals Heterogeneity Among Hematopoietic Stem Cells Following Infection.

The hematopoietic stem cell (HSC) niche provides essential microenvironmental cues for the production and maintenance of HSCs within the bone marrow. During inflammation, hematopoietic dynamics are perturbed, but it is not known whether changes to the HSC-niche interaction occur as a result. We visualize HSCs directly in vivo, enabling detailed analysis of the 3D niche dynamics and migration patterns in murine bone marrow following Trichinella spiralis infection. Spatial statistical analysis of these HSC trajectories reveals two distinct modes of HSC behavior: (a) a pattern of revisiting previously explored space and (b) a pattern of exploring new space. Whereas HSCs from control donors predominantly follow pattern (a), those from infected mice adopt both strategies. Using detailed computational analyses of cell migration tracks and life-history theory, we show that the increased motility of HSCs following infection can, perhaps counterintuitively, enable mice to cope better in deteriorating HSC-niche microenvironments following infection. Stem Cells 2017;35:2292-2304.

Nanog Fluctuations in Embryonic Stem Cells Highlight the Problem of Measurement in Cell Biology.

A number of important pluripotency regulators, including the transcription factor Nanog, are observed to fluctuate stochastically in individual embryonic stem cells. By transiently priming cells for commitment to different lineages, these fluctuations are thought to be important to the maintenance of, and exit from, pluripotency. However, because temporal changes in intracellular protein abundances cannot be measured directly in live cells, fluctuations are typically assessed using genetically engineered reporter cell lines that produce a fluorescent signal as a proxy for protein expression. Here, using a combination of mathematical modeling and experiment, we show that there are unforeseen ways in which widely used reporter strategies can systematically disturb the dynamics they are intended to monitor, sometimes giving profoundly misleading results. In the case of Nanog, we show how genetic reporters can compromise the behavior of important pluripotency-sustaining positive feedback loops, and induce a bifurcation in the underlying dynamics that gives rise to heterogeneous Nanog expression patterns in reporter cell lines that are not representative of the wild-type. These findings help explain the range of published observations of Nanog variability and highlight the problem of measurement in live cells.

Accurate Reconstruction of Cell and Particle Tracks from 3D Live Imaging Data.

Spatial structures often constrain the 3D movement of cells or particles in vivo, yet this information is obscured when microscopy data are analyzed using standard approaches. Here, we present methods, called unwrapping and Riemannian manifold learning, for mapping particle-tracking data along unseen and irregularly curved surfaces onto appropriate 2D representations. This is conceptually similar to the problem of reconstructing accurate geography from conventional Mercator maps, but our methods do not require prior knowledge of the environments' physical structure. Unwrapping and Riemannian manifold learning accurately recover the underlying 2D geometry from 3D imaging data without the need for fiducial marks. They outperform standard x-y projections, and unlike standard dimensionality reduction techniques, they also successfully detect both bias and persistence in cell migration modes. We demonstrate these features on simulated data and zebrafish and Drosophila in vivo immune cell trajectory datasets. Software packages that implement unwrapping and Riemannian manifold learning are provided.

Systems Analysis of the Dynamic Inflammatory Response to Tissue Damage Reveals Spatiotemporal Properties of the Wound Attractant Gradient.

In the acute inflammatory phase following tissue damage, cells of the innate immune system are rapidly recruited to sites of injury by pro-inflammatory mediators released at the wound site. Although advances in live imaging allow us to directly visualize this process in vivo, the precise identity and properties of the primary immune damage attractants remain unclear, as it is currently impossible to directly observe and accurately measure these signals in tissues. Here, we demonstrate that detailed information about the attractant signals can be extracted directly from the in vivo behavior of the responding immune cells. By applying inference-based computational approaches to analyze the in vivo dynamics of the Drosophila inflammatory response, we gain new detailed insight into the spatiotemporal properties of the attractant gradient. In particular, we show that the wound attractant is released by wound margin cells, rather than by the wounded tissue per se, and that it diffuses away from this source at rates far slower than those of previously implicated signals such as H2O2 and ATP, ruling out these fast mediators as the primary chemoattractant. We then predict, and experimentally test, how competing attractant signals might interact in space and time to regulate multi-step cell navigation in the complex environment of a healing wound, revealing a period of receptor desensitization after initial exposure to the damage attractant. Extending our analysis to model much larger wounds, we uncover a dynamic behavioral change in the responding immune cells in vivo that is prognostic of whether a wound will subsequently heal or not. VIDEO ABSTRACT.

Inference of random walk models to describe leukocyte migration.

While the majority of cells in an organism are static and remain relatively immobile in their tissue, migrating cells occur commonly during developmental processes and are crucial for a functioning immune response. The mode of migration has been described in terms of various types of random walks. To understand the details of the migratory behaviour we rely on mathematical models and their calibration to experimental data. Here we propose an approximate Bayesian inference scheme to calibrate a class of random walk models characterized by a specific, parametric particle re-orientation mechanism to observed trajectory data. We elaborate the concept of transition matrices (TMs) to detect random walk patterns and determine a statistic to quantify these TM to make them applicable for inference schemes. We apply the developed pipeline to in vivo trajectory data of macrophages and neutrophils, extracted from zebrafish that had undergone tail transection. We find that macrophage and neutrophils exhibit very distinct biased persistent random walk patterns, where the strengths of the persistence and bias are spatio-temporally regulated. Furthermore, the movement of macrophages is far less persistent than that of neutrophils in response to wounding.

Great cities look small.

Great cities connect people; failed cities isolate people. Despite the fundamental importance of physical, face-to-face social ties in the functioning of cities, these connectivity networks are not explicitly observed in their entirety. Attempts at estimating them often rely on unrealistic over-simplifications such as the assumption of spatial homogeneity. Here we propose a mathematical model of human interactions in terms of a local strategy of maximizing the number of beneficial connections attainable under the constraint of limited individual travelling-time budgets. By incorporating census and openly available online multi-modal transport data, we are able to characterize the connectivity of geometrically and topologically complex cities. Beyond providing a candidate measure of greatness, this model allows one to quantify and assess the impact of transport developments, population growth, and other infrastructure and demographic changes on a city. Supported by validations of gross domestic product and human immunodeficiency virus infection rates across US metropolitan areas, we illustrate the effect of changes in local and city-wide connectivities by considering the economic impact of two contemporary inter- and intra-city transport developments in the UK: High Speed 2 and London Crossrail. This derivation of the model suggests that the scaling of different urban indicators with population size has an explicitly mechanistic origin.

Goldstein-Kac telegraph processes with random speeds: Path probabilities, likelihoods, and reported Lévy flights.

The Goldstein-Kac telegraph process describes the one-dimensional motion of particles with constant speed undergoing random changes in direction. Despite its resemblance to numerous real-world phenomena, the singular nature of the resultant spatial distribution of each particle precludes the possibility of any a posteriori empirical validation of this random-walk model from data. Here we show that by simply allowing for random speeds, the ballistic terms are regularized and that the diffusion component can be well-approximated via the unscented transform. The result is a computationally efficient yet robust evaluation of the full particle path probabilities and, hence, the parameter likelihoods of this generalized telegraph process. We demonstrate how a population diffusing under such a model can lead to non-Gaussian asymptotic spatial distributions, thereby mimicking the behavior of an ensemble of Lévy walkers.

Effects of High-Intensity Intermittent Exercise Training on Appetite Regulation.

OBJECTIVE: An acute bout of high-intensity intermittent exercise suppresses ad libitum energy intake at the postexercise meal. The present study examined the effects of 12 wk of high-intensity intermittent exercise training (HIIT) compared with moderate-intensity continuous exercise training (MICT) on appetite regulation. METHODS: Thirty overweight inactive men (body mass index, 27.2 ± 1.3 kg·m(-2); V˙O2peak, 35.3 ± 5.3 mL·kg(-1)·min(-1) were randomized to either HIIT or MICT (involving 12 wk of training, three sessions per week) or a control group (CON) (n = 10 per group). Ad libitum energy intake from a laboratory test meal was assessed after both a low-energy (847 kJ) and a high-energy preload (2438 kJ) before and after the intervention. Perceived appetite and appetite-related blood variables were also measured. RESULTS: There was no significant effect of the intervention period on energy intake at the test meal after the two different preloads (P ≥ 0.05). However, the 95% confidence interval indicated a clinically meaningful decrease in energy intake after the high-energy preload compared with the low-energy preload in response to HIIT (516 ± 395 kJ decrease), but not for MICT or CON, suggesting improved appetite regulation. This was not associated with alterations in the perception of appetite or the circulating concentration of a number of appetite-related peptides or metabolites, although insulin sensitivity was enhanced with HIIT only (P = 0.003). CONCLUSIONS: HIIT seems to benefit appetite regulation in overweight men. The mechanisms for this remain to be elucidated.

Information theory and signal transduction systems: from molecular information processing to network inference.

Sensing and responding to the environment are two essential functions that all biological organisms need to master for survival and successful reproduction. Developmental processes are marshalled by a diverse set of signalling and control systems, ranging from systems with simple chemical inputs and outputs to complex molecular and cellular networks with non-linear dynamics. Information theory provides a powerful and convenient framework in which such systems can be studied; but it also provides the means to reconstruct the structure and dynamics of molecular interaction networks underlying physiological and developmental processes. Here we supply a brief description of its basic concepts and introduce some useful tools for systems and developmental biologists. Along with a brief but thorough theoretical primer, we demonstrate the wide applicability and biological application-specific nuances by way of different illustrative vignettes. In particular, we focus on the characterisation of biological information processing efficiency, examining cell-fate decision making processes, gene regulatory network reconstruction, and efficient signal transduction experimental design.

Quantification of HTLV-1 clonality and TCR diversity.

Estimation of immunological and microbiological diversity is vital to our understanding of infection and the immune response. For instance, what is the diversity of the T cell repertoire? These questions are partially addressed by high-throughput sequencing techniques that enable identification of immunological and microbiological "species" in a sample. Estimators of the number of unseen species are needed to estimate population diversity from sample diversity. Here we test five widely used non-parametric estimators, and develop and validate a novel method, DivE, to estimate species richness and distribution. We used three independent datasets: (i) viral populations from subjects infected with human T-lymphotropic virus type 1; (ii) T cell antigen receptor clonotype repertoires; and (iii) microbial data from infant faecal samples. When applied to datasets with rarefaction curves that did not plateau, existing estimators systematically increased with sample size. In contrast, DivE consistently and accurately estimated diversity for all datasets. We identify conditions that limit the application of DivE. We also show that DivE can be used to accurately estimate the underlying population frequency distribution. We have developed a novel method that is significantly more accurate than commonly used biodiversity estimators in microbiological and immunological populations.

Random forests on distance matrices for imaging genetics studies.

We propose a non-parametric regression methodology, Random Forests on Distance Matrices (RFDM), for detecting genetic variants associated to quantitative phenotypes, obtained using neuroimaging techniques, representing the human brain's structure or function. RFDM, which is an extension of decision forests, requires a distance matrix as the response that encodes all pair-wise phenotypic distances in the random sample. We discuss ways to learn such distances directly from the data using manifold learning techniques, and how to define such distances when the phenotypes are non-vectorial objects such as brain connectivity networks. We also describe an extension of RFDM to detect espistatic effects while keeping the computational complexity low. Extensive simulation results and an application to an imaging genetics study of Alzheimer's Disease are presented and discussed.

Effects of static stretching in warm-up on repeated sprint performance.

The aim of this study was to examine the effects of static stretching during warm-up on repeated sprint performance and also to assess any influence of the order in which dynamic activities (i.e., run-throughs and drills) and static stretching are conducted. Thirteen male team sport players completed a repeated sprint ability test consisting of three sets of maximal 6 x 20-m sprints (going every 25 seconds) after performing one of three different warm-up protocols in a within-subjects counterbalanced design. Each warm-up protocol involved an initial 1000-m jog, followed by either dynamic activities only (D), static stretching followed by dynamic activities (S-D), or dynamic activities followed by static stretching (D-S). First (FST), best (BST) and total (TST) 20-m sprint times were determined for each individual set of the repeated sprint ability test and overall (3 sets combined). Although consistent significant differences were not observed between trials for TST, BST, and FST, the mean values for TST in all individual sets and overall were generally slowest in the D-S condition (D = 60.264 +/- 1.127 seconds; S-D = 60.347 +/- 1.774 seconds; D-S = 60.830 +/- 1.786 seconds). This trend was supported by moderate to large effect sizes and qualitative indications of "possible" or "likely" benefits for TST, BST, and FST for the D and S-D warm-ups compared to D-S. No significant differences or large effect sizes were noted between D and S-D, indicating similar repeated sprint ability performance. Overall, these results suggest that 20-m repeated sprint ability may be compromised when static stretching is conducted after dynamic activities and immediately prior to performance (D-S).