Evolutionary genetics of partial migration – the threshold model of migration revis(it)ed

Partial migration is a common and widespread phenomenon in animal populations. Even though the ecological causes for the evolution and maintenance of partial migration have been widely discussed, the consequences of the genetics underlying differences in migration patterns have been little acknowledged. Here, I revise current ideas on the genetics of partial migration and identify open questions, focussing on migration in birds. The threshold model of migration describing the inheritance and phenotypic expression of migratory behaviour is strongly supported by experimental results. As a consequence of migration being a threshold trait, high levels of genetic variation can be preserved, even under strong directional selection. This is partly due to strong environmental canalization. This cryptic genetic variation may explain rapid de novo evolution of migratory behaviour in resident populations and the high prevalence of partial migration in animal populations. To date the threshold model of migration has been tested only under laboratory conditions. For obtaining a more realistic representation of migratory behaviour in the wild, the simple threshold model needs to be extended by considering that the threshold of migration or the liability may be modified by environmental effects. This environmental threshold model is valid for both facultative and obligate migration movements, and identifies genetic accommodation as an important process underlying evolutionary change in migration status. Future research should aim at identifying the major environmental variables modifying migration propensity and at determining reaction norms of the threshold and liability across variation in these variables.     

Granger mediation analysis of multiple time series with an application to functional magnetic resonance imaging

This paper presents Granger mediation analysis, a new framework for causal mediation analysis of multiple time series. This framework is motivated by a functional magnetic resonance imaging (fMRI) experiment where we are interested in estimating the mediation effects between a randomized stimulus time series and brain activity time series from two brain regions. The independent observation assumption is thus unrealistic for this type of time‐series data. To address this challenge, our framework integrates two types of models: causal mediation analysis across the mediation variables, and vector autoregressive (VAR) models across the temporal observations. We use “Granger” to refer to VAR correlations modeled in this paper. We further extend this framework to handle multilevel data, in order to model individual variability and correlated errors between the mediator and the outcome variables. Using Rubin's potential outcome framework, we show that the causal mediation effects are identifiable under our time‐series model. We further develop computationally efficient algorithms to maximize our likelihood‐based estimation criteria. Simulation studies show that our method reduces the estimation bias and improves statistical power, compared with existing approaches. On a real fMRI data set, our approach quantifies the causal effects through a brain pathway, while capturing the dynamic dependence between two brain regions.     

On the modeling of the performances of the human brain in a two-choice task involving decoding and memorization of simple visual patterns

The quantitative, aspects of the decoding and storage processes of simple visual patterns by the human brain are considered, on the basis of the performances obtained in a set of two-choice experiments. Strings of patterns (colored lights or simple geometrical patterns) were used in which the densities of the two patterns throughout the string were either constant or asymmetric. The subjects were required to indicate the pattern which was presented more frequently. A nonlinear model for the storage and decision processes was devised from these data which accurately predicted the performance under experimental conditions different from those originally used. Among the prominent features of the brain processes suggested by the model are the necessity for a nonlinear summation of the decoded information and its decay with time.Finally, it is shown that the experimental design allows a quantitative evaluation of those factors which are relevant to the decoding of patterns of different complexities.     

Mathematical modeling of human glioma growth based on brain topological structures: study of two clinical cases

Gliomas are the most common primary brain tumors and yet almost incurable due mainly to their great invasion capability. This represents a challenge to present clinical oncology. Here, we introduce a mathematical model aiming to improve tumor spreading capability definition. The model consists in a time dependent reaction-diffusion equation in a three-dimensional spatial domain that distinguishes between different brain topological structures. The model uses a series of digitized images from brain slices covering the whole human brain. The Talairach atlas included in the model describes brain structures at different levels. Also, the inclusion of the Brodmann areas allows prediction of the brain functions affected during tumor evolution and the estimation of correlated symptoms. The model is solved numerically using patient-specific parametrization and finite differences. Simulations consider an initial state with cellular proliferation alone (benign tumor), and an advanced state when infiltration starts (malign tumor). Survival time is estimated on the basis of tumor size and location. The model is used to predict tumor evolution in two clinical cases. In the first case, predictions show that real infiltrative areas are underestimated by current diagnostic imaging. In the second case, tumor spreading predictions were shown to be more accurate than those derived from previous models in the literature. Our results suggest that the inclusion of differential migration in glioma growth models constitutes another step towards a better prediction of tumor infiltration at the moment of surgical or radiosurgical target definition. Also, the addition of physiological/psychological considerations to classical anatomical models will provide a better and integral understanding of the patient disease at the moment of deciding therapeutic options, taking into account not only survival but also life quality.     

Which drug for the adult epileptic patient: phenytoin or valproate?

A series of 140 previously untreated patients with tonic-clonic or partial seizures were randomised to receive either phenytoin or sodium valproate. There was no difference between the treatment groups in pretreatment variables that might influence outcome. Sodium valproate and phenytoin in the treatment of tonic-clonic or partial seizures showed no difference in efficacy as regards time to two year remission or time to first seizure. When the possible prognostic factors were studied, including history and results of clinical examination and investigations before treatment; the only factor which influenced the proportion of patients achieving two year remission was type of seizure. Patients with a clinical history of partial seizures did significantly less well than those with a history of tonic-clonic seizures only. This study showed no major difference in efficacy between sodium valproate and phenytoin in adults with recent onset of epilepsy, irrespective of the type of seizures that the patient suffered.     

Freshwater diatom ecology: developing an experimental approach as an aid to interpreting field data

The observational and experimental approaches to ecological studies are outlined and contrasted. Most studies involving benthic diatoms are still at the observational stage. Community analyses are interpreted in relation to physico-chemical variables, but without reference to the intrinsic growth characteristics of the species or to biological interactions. By allowing separate investigation of the effects of chosen variables under controlled conditions, culture studies permit field predictions to be tested and our understanding of the constraints on growth of particular species to be improved.Results of culture experiments on four benthic diatom species grown under contrasting light, temperature, and pH regimes are presented and discussed in relation to their field distributions. Synedra ulna showed a positive response to both light and temperature; Meridion circulare was more sensitive to raised water temperature at higher light intensity; Pinnularia viridis and Eunotia exigua grew over wider ranges of temperature and pH than recorded from published data. The experimental results indicate that distribution data provide only partial information on ecological range; growth in the field may be constrained by one or more factors and their interactions.Whereas analysis of field data is a correlative exercise, an experimental approach allows investigation of the physiological responses contributing to the survival of species and the demonstration of causal relationships.     

maSigPro: a method to identify significantly differential expression profiles in time-course microarray experiments

MOTIVATION: Multi-series time-course microarray experiments are useful approaches for exploring biological processes. In this type of experiments, the researcher is frequently interested in studying gene expression changes along time and in evaluating trend differences between the various experimental groups. The large amount of data, multiplicity of experimental conditions and the dynamic nature of the experiments poses great challenges to data analysis. RESULTS: In this work, we propose a statistical procedure to identify genes that show different gene expression profiles across analytical groups in time-course experiments. The method is a two-regression step approach where the experimental groups are identified by dummy variables. The procedure first adjusts a global regression model with all the defined variables to identify differentially expressed genes, and in second a variable selection strategy is applied to study differences between groups and to find statistically significant different profiles. The methodology is illustrated on both a real and a simulated microarray dataset.     

Visual aspects of egg care behaviour in Cichlasoma nigrofasciatum (Günther)

Egg size was found to be an important visual stimulus eliciting egg care behaviour in substrate-spawning Cichlasoma nigrofasciatum. Egg size was manipulated by the use of artificial wax ‘eggs’ while maintaining other visual spects of the spawn unchanged. Egg-care behaviour was compared under two experimental and three control conditions. The experimental conditions consisted of exchanging the eggs for larger or smaller models. The control groups included a baseline record of egg care toward natural eggs, a situation without eggs and one with model ‘eggs’ similar in size to the natural. The results showed that models larger or smaller than real eggs were discriminated; a considerable decrement in several fanning parameters occurred relative to the baseline or natural size model groups. The absence of eggs resulted in the greatest decrement of fanning. These results suggest some hormonal control of egg care behaviour. The length of time spent fanning eggs is a function of the time the eggs are in view; the adaptive significance of this result is discussed.     

Non-invasive in situ simultaneous measurement of multi-parameter mechanical properties of red blood cell membrane

The purpose of this study was to develop a new dynamic image analyzing technique that will give us the ability to measure the viscoelastic parameters of individual living red blood cells non-invasively, in situ and in real time. With this technique, the bending modulus Kc, the shear elasticity μ and their ratio ε were measured under different temperatures, oxygen partial pressures and osmotic pressures. The results not only show the effects of external conditions on mechanical properties of cell membranes including deformability,flexibility, adhesive ability and plasticity, but also demonstrate that the technique can be used to measure cell membrane parameters continuously under several physiological and pathological conditions.     

Four-state models and regulation of contraction of smooth muscle. I. Physical considerations, stability, and solutions.

Cyclic four-state models are frequently used in biology to represent a variety of molecular behaviors. A common experimental strategy to test such models is to follow the behavior of the real system after some of the rate constants are changed in a stepwise manner. We analyze the mathematical behavior of a simple example of such a model applicable to the regulation of contraction of smooth muscle, but our results apply in general to any linear, cyclic four-state model. We discuss detailed balance and requirements for linearity. We find that the only way to have sustained oscillations is for the rate constants of the model to be themselves oscillatory. We state conditions for decaying oscillations and find that in models that do not follow strictly first-order kinetics and do not satisfy detailed balance, these conditions can hold. We show analytically that the response of any state to step changes in the rate constants is the sum of three weighted exponentials plus a constant term, the steady-state value. We provide explicit expressions for the time dependence of all state variables. We discuss a simple way to use these results to obtain numerical solutions in cases where the rate constants change in an arbitrary way.     

BrainSignals Revisited: Simplifying a Computational Model of Cerebral Physiology

Multimodal monitoring of brain state is important both for the investigation of healthy cerebral physiology and to inform clinical decision making in conditions of injury and disease. Near-infrared spectroscopy is an instrument modality that allows non-invasive measurement of several physiological variables of clinical interest, notably haemoglobin oxygenation and the redox state of the metabolic enzyme cytochrome c oxidase. Interpreting such measurements requires the integration of multiple signals from different sources to try to understand the physiological states giving rise to them. We have previously published several computational models to assist with such interpretation. Like many models in the realm of Systems Biology, these are complex and dependent on many parameters that can be difficult or impossible to measure precisely. Taking one such model, BrainSignals, as a starting point, we have developed several variant models in which specific regions of complexity are substituted with much simpler linear approximations. We demonstrate that model behaviour can be maintained whilst achieving a significant reduction in complexity, provided that the linearity assumptions hold. The simplified models have been tested for applicability with simulated data and experimental data from healthy adults undergoing a hypercapnia challenge, but relevance to different physiological and pathophysiological conditions will require specific testing. In conditions where the simplified models are applicable, their greater efficiency has potential to allow their use at the bedside to help interpret clinical data in near real-time.     

Real Time Intraoperative Functional Brain Mapping Based on RGB Imaging

Intraoperative optical imaging is a localization technique for the functional areas of the human brain cortex during neurosurgical procedures. However, it still lacks robustness to be used as a clinical standard. In particular new biomarkers of brain functionality with improved sensitivity and specificity are needed. We present a method for the real time identification of the activated cortical areas based on the analysis of the cortical hemodynamic using a RGB camera and a white light source. We measure the quantitative oxy and deoxy-hemoglobin concentration changes in the human brain cortex with the modified Beer-Lambert law and Monte Carlo simulations. A functional model has been implemented to define in real time a binary biomarker of the cortical activation following neuronal activation by physiological stimuli. The results show a good correlation between the computed activation maps and the brain areas localized by electrical brain stimulation. We demonstrate that a RGB camera combined with a quantitative modeling of brain hemodynamics biomarkers can evaluate in real time the functional areas during neurosurgery and serve as a tool of choice to complement electrical brain stimulation.     

Bayesian Estimation of Conditional Independence Graphs Improves Functional Connectivity Estimates

Functional connectivity concerns the correlated activity between neuronal populations in spatially segregated regions of the brain, which may be studied using functional magnetic resonance imaging (fMRI). This coupled activity is conveniently expressed using covariance, but this measure fails to distinguish between direct and indirect effects. A popular alternative that addresses this issue is partial correlation, which regresses out the signal of potentially confounding variables, resulting in a measure that reveals only direct connections. Importantly, provided the data are normally distributed, if two variables are conditionally independent given all other variables, their respective partial correlation is zero. In this paper, we propose a probabilistic generative model that allows us to estimate functional connectivity in terms of both partial correlations and a graph representing conditional independencies. Simulation results show that this methodology is able to outperform the graphical LASSO, which is the de facto standard for estimating partial correlations. Furthermore, we apply the model to estimate functional connectivity for twenty subjects using resting-state fMRI data. Results show that our model provides a richer representation of functional connectivity as compared to considering partial correlations alone. Finally, we demonstrate how our approach can be extended in several ways, for instance to achieve data fusion by informing the conditional independence graph with data from probabilistic tractography. As our Bayesian formulation of functional connectivity provides access to the posterior distribution instead of only to point estimates, we are able to quantify the uncertainty associated with our results. This reveals that while we are able to infer a clear backbone of connectivity in our empirical results, the data are not accurately described by simply looking at the mode of the distribution over connectivity. The implication of this is that deterministic alternatives may misjudge connectivity results by drawing conclusions from noisy and limited data.     

A hidden Markov model-based approach for identifying timing differences in gene expression under different experimental factors

MOTIVATION: Time series experiments of cDNA microarrays have been commonly used in various biological studies and conducted under a lot of experimental factors. A popular approach of time series microarray analysis is to compare one gene with another in their expression profiles, and clustering expression sequences is a typical example. On the other hand, a practically important issue in gene expression is to identify the general timing difference that is caused by experimental factors. This type of difference can be extracted by comparing a set of time series expression profiles under a factor with those under another factor, and so it would be difficult to tackle this issue by using only a current approach for time series microarray analysis. RESULTS: We have developed a systematic method to capture the timing difference in gene expression under different experimental factors, based on hidden Markov models. Our model outputs a real-valued vector at each state and has a unique state transition diagram. The parameters of our model are trained from a given set of pairwise (generally multiplewise) expression sequences. We evaluated our model using synthetic as well as real microarray datasets. The results of our experiment indicate that our method worked favourably to identify the timing ordering under different experimental factors, such as that gene expression under heat shock tended to start earlier than that under oxidative stress. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.     

Kinetics of milk coagulation: II. Kinetics of the secondary phase: micelle flocculation

A kinetic model for the agglomeration of milk micelles following kappa-casein hydrolysis is described. The key features of the model are: (1) the surface potential of casein micelles is sufficient to explain the colloidal stability of the milk system; (2) the reduction in surface potential following kappa-casein hydrolysis explains the loss of stability; (3) partial hydrolysis leads to limited agglomeration; and (4) the kinetics of agglomeration are compatible with the theory that completely hydrolyzed micelles have only a limited number of interaction sites. The model accurately predicts solution turbidity increase assuming that micelles have only circa 1.2 interaction sites on the average under the experimental conditions of this study.     

The influence of complex and threatening environments in early life on brain size and behaviour

The ways in which challenging environments during development shape the brain and behaviour are increasingly being addressed. To date, studies typically consider only single variables, but the real world is more complex. Many factors simultaneously affect the brain and behaviour, and whether these work independently or interact remains untested. To address this, zebrafish (Danio rerio) were reared in a two-by-two design in housing that varied in structural complexity and/or exposure to a stressor. Fish experiencing both complexity (enrichment objects changed over time) and mild stress (daily net chasing) exhibited enhanced learning and were less anxious when tested as juveniles (between 77 and 90 days). Adults tested (aged 1 year) were also less anxious even though fish were kept in standard housing after three months of age (i.e. no chasing or enrichment). Volumetric measures of the brain using magnetic resonance imaging (MRI) showed that complexity alone generated fish with a larger brain, but this increase in size was not seen in fish that experienced both complexity and chasing, or chasing alone. The results highlight the importance of looking at multiple variables simultaneously, and reveal differential effects of complexity and stressful experiences during development of the brain and behaviour.     

Effect of Time of Day and Partial Sleep Deprivation on Short‐Term,High‐Power Output

The purpose of this study was to determine whether delaying bedtime or advancing rising time by 4 h affects anaerobic performance of individuals the following day in the morning and afternoon. Eleven subjects participated in the study, during which we measured the maximal, peak, and mean powers (i.e., P_max [force‐velocity test], P_peak, and P_mean [Wingate test], respectively). Measurements were performed twice daily, at 07∶00 and 18∶00 h, following a reference normal sleep night (RN), a partial sleep deprivation timed at the beginning of the night (SDB), and a partial sleep deprivation timed at the end of the night (SDE), and oral temperature was measured every 4 h. Each of the three experimental conditions was separated by a one‐week period. Our results showed a circadian rhythm in oral temperature, and analysis of variance revealed a significant sleep×test‐time effect on peak power (P_peak), mean power (P_mean), and maximal power (P_max). These variables improved significantly from the morning to the afternoon for all three experimental conditions. Whereas the morning‐afternoon improvement in the measures was similar after the RN and SDB conditions, it was smaller following the SDE condition. There was no significant difference in the effect of the two sleep‐deprivation conditions on anaerobic performances at 07∶00 and at 18∶00 h under the SDB condition in comparison with the post‐reference night. However, the performance variables were significantly lower at 18∶00 h after the SDE condition. In conclusion, a 4 h partial sleep deprivation at the end of the night appears to be more disturbing than partial sleep deprivation at the beginning of the night.     

Bridging the gap between chemistry, physiology, and evolution: quantifying the functionality of sperm whale myoglobin mutants

This work merges a large set of previously reported thermochemical data for myoglobin (Mb) mutants with a physiological model of O₂-transport and -storage. The model allows a quantification of the functional proficiency of myoglobin (Mb) mutants under various physiological conditions, i.e. O₂-consumption rate resembling workload, O₂ partial pressure resembling hypoxic stress, muscle cell size, and Mb concentration, resembling different organism-specific and compensatory variables. We find that O₂-storage and -transport are distinct functions that rank mutants and wild type differently depending on O₂ partial pressure. Specifically, the wild type is near-optimal for storage at all conditions, but for transport only at severely hypoxic conditions. At normoxic conditions, low-affinity mutants are in fact better O₂-transporters because they still have empty sites for O₂, giving rise to a larger [MbO₂] gradient (more varying saturation curve). The distributions of functionality reveal that many mutants are near-neutral with respect to function, whereas only a few are strongly affected, and the variation in functionality increases dramatically at lower O₂ pressure. These results together show that conserved residues in wild type (WT) Mb were fixated under a selection pressure of low P_O2.     

A random-walk model of human mortality and aging

Consideration is made of the roles of certain types of state space and time scales for a random-walk model of individual physiological status change and death. Because the actual measurement of physiological variables omits many variables relevant to survival, we are forced to view this model as operating in a stochastic state space for a population of individuals where only the frequency distributions are deterministic. In this stochastic state space, under the assumption that the “history” of prior movement contains no additional information, the forward partial differential equation is obtained for the distribution of a population whose movement in the selected space is determined by the randomwalk equations. If the initial distribution of the population in the state space is normal, then certain assumptions about movement and mortality will operate to preserve normality thereafter. Under the assumption of normality, simultaneous ordinary differential equations can be derived from the forward partial differential equation defining the distribution function. Examination of the ordinary simultaneous differential equations shows how parameters for certain models of aging and mortality can be obtained.