Intraseasonal predictability of natural phytoplankton population dynamics

It is difficult to make skillful predictions about the future dynamics of marine phytoplankton populations. Here, we use a 22‐year time series of monthly average abundances for 198 phytoplankton taxa from Station L4 in the Western English Channel (1992–2014) to test whether and how aggregating phytoplankton into multi‐species assemblages can improve predictability of their temporal dynamics. Using a non‐parametric framework to assess predictability, we demonstrate that the prediction skill is significantly affected by how species data are grouped into assemblages, the presence of noise, and stochastic behavior within species. Overall, we find that predictability one month into the future increases when species are aggregated together into assemblages with more species, compared with the predictability of individual taxa. However, predictability within dinoflagellates and larger phytoplankton (>12 μm cell radius) is low overall and does not increase by aggregating similar species together. High variability in the data, due to observational error (noise) or stochasticity in population growth rates, reduces the predictability of individual species more than the predictability of assemblages. These findings show that there is greater potential for univariate prediction of species assemblages or whole‐community metrics, such as total chlorophyll or biomass, than for the individual dynamics of phytoplankton species.     

Nonlinear sequence-dependent structure of nigral dopamine neuron interspike interval firing patterns.

Firing patterns of 15 dopamine neurons in the rat substantia nigra were studied. These cells alternated between two firing modes, single-spike and bursting, which interwove to produce irregular, aperiodic interspike interval (ISI) patterns. When examined by linear autocorrelation analysis, these patterns appeared to reflect a primarily stochastic or random process. However, dynamical analysis revealed that the sequential behavior of a majority of these cells expressed "higher-dimensional" nonlinear deterministic structure. Dimensionality refers to the number of degrees of freedom or complexity of a time series. Bursting was statistically associated with some aspects of nonlinear ISI sequence dependence. Controlling for the effects of nonstationarity substantially increased overall predictability of ISI sequences. We hypothesize that the nonlinear deterministic structure of ISI firing patterns reflects the neuron's response to coordinated synaptic inputs emerging from neural circuit interactions.     

Predictability of EEG interictal spikes.

To determine whether EEG spikes are predictable, time series of EEG spike intervals were generated from subdural and depth electrode recordings from four patients. The intervals between EEG spikes were hand edited to ensure high accuracy and eliminate false positive and negative spikes. Spike rates (per minute) were generated from longer time series, but for these data hand editing was usually not feasible. Linear and nonlinear models were fit to both types of data. One patient had no linear or nonlinear predictability, two had predictability that could be well accounted for with a linear stochastic model, and one had a degree of nonlinear predictability for both interval and rate data that no linear model could adequately account for.     

Justice blocks and predictability of U.S. Supreme Court votes

Successful attempts to predict judges' votes shed light into how legal decisions are made and, ultimately, into the behavior and evolution of the judiciary. Here, we investigate to what extent it is possible to make predictions of a justice's vote based on the other justices' votes in the same case. For our predictions, we use models and methods that have been developed to uncover hidden associations between actors in complex social networks. We show that these methods are more accurate at predicting justice's votes than forecasts made by legal experts and by algorithms that take into consideration the content of the cases. We argue that, within our framework, high predictability is a quantitative proxy for stable justice (and case) blocks, which probably reflect stable a priori attitudes toward the law. We find that U.S. Supreme Court justice votes are more predictable than one would expect from an ideal court composed of perfectly independent justices. Deviations from ideal behavior are most apparent in divided 5-4 decisions, where justice blocks seem to be most stable. Moreover, we find evidence that justice predictability decreased during the 50-year period spanning from the Warren Court to the Rehnquist Court, and that aggregate court predictability has been significantly lower during Democratic presidencies. More broadly, our results show that it is possible to use methods developed for the analysis of complex social networks to quantitatively investigate historical questions related to political decision-making.     

The Effects of Incidentally Learned Temporal and Spatial Predictability on Response Times and Visual Fixations during Target Detection and Discrimination

Responses are quicker to predictable stimuli than if the time and place of appearance is uncertain. Studies that manipulate target predictability often involve overt cues to speed up response times. However, less is known about whether individuals will exhibit faster response times when target predictability is embedded within the inter-trial relationships. The current research examined the combined effects of spatial and temporal target predictability on reaction time (RT) and allocation of overt attention in a sustained attention task. Participants responded as quickly as possible to stimuli while their RT and eye movements were measured. Target temporal and spatial predictability were manipulated by altering the number of: 1) different time intervals between a response and the next target; and 2) possible spatial locations of the target. The effects of target predictability on target detection (Experiment 1) and target discrimination (Experiment 2) were tested. For both experiments, shorter RTs as target predictability increased across both space and time were found. In addition, the influences of spatial and temporal target predictability on RT and the overt allocation of attention were task dependent; suggesting that effective orienting of attention relies on both spatial and temporal predictability. These results indicate that stimulus predictability can be increased without overt cues and detected purely through inter-trial relationships over the course of repeated stimulus presentations.     

Toward a Typology of Dynamic and Hazardous Work Environments

The most hazardous work environments share one feature in common: constant change. Many different, but constantly changing hazards are found in agriculture, construction, mining, and transport. This dynamic feature of workplace hazards varies by: (1) degree of control, (2) predictability, (3) visibility, (4) movement, and (5) degree of speed and force. In some cases the actions of the dynamic hazards are required for production to take place, and in many cases, several different hazards may overlap and interact. Finally, whether intentional or unintentional, some dynamic hazards are human generated. These are some of the features that distinguish dynamic and hazardous work environments across a variety of industries. The authors propose a preliminary typology of dynamic and hazardous work environments, along with a schema to systematically observe the dynamic characteristics of these hazards. The implications of this typology are considered with respect to worker training, hazard awareness, and safe work practices. For example, the implementation of the Hierarchy of Control is shown to require active worker involvement at every level in the hierarchy, except where an environmental hazard has been completely eliminated.     

Towards physical dynamic tolerance: an approach to resolve the conflict between free will and physical determinism

This paper attempts to resolve the conflict between free will and determinism. The problem is approached by demonstrating that: (a) some well-established experimental observations indicate that irreversibility persists at the molecular level, (b) microscopic reversibility is not fully compatible with macroscopic irreversibility, (c) an overall consistency can be maintained if microscopic reversibility is regarded only as an excellent approximation, whereas microscopic irreversibility together with chaos can account for macroscopic reversibility, and (d) endogenous noise serves a vital function of nerve excitation. Thus, the mean of position and momentum specified by a non-deterministic law of motion gives the law its superficially deterministic behavior and predictability, whereas its dispersion grants dynamic tolerance and irreversibility. Therefore, causality is preserved while a limited degree of freedom allows for the exercise of free will. However, it is argued that free will can never be proven or disproven by a conventional behavioral experiment.     

Dynamic response landscape of immune cells identified immune dysfunction which predicts disease progression in COVID-19 infected patients

During the development of COVID-19 caused by SARS-CoV-2 infection from mild disease to severe disease, it can trigger a series of complications and stimulate a strong cellular and humoral immune response. However, the precise identification of blood immune cell response dynamics and the relevance to disease progression in COVID-19 patients remains unclear. We propose for the first time to use changes in cell numbers to establish new subgroups, which were divided into four groups: first from high to low cell number (H_L_Group), first from low to high (L_H_Group), continuously high (H_Group), and continuously low (L_Group). It was found that in the course of disease development. In the T cell subgroup, the immune response is mainly concentrated in the H_L_Group cell type, and the complications are mainly in the L_H_Group cell type. In the NK cell subgroup, the moderate patients are mainly related to cellular immunity, and the severe patients are mainly caused by the disease, while severe patients are mainly related to complications caused by diseases. Our study provides a dynamic response of immune cells in human blood during SARS-CoV-2 infection and the first subgroup analysis using dynamic changes in cell numbers, providing a new reference for clinical treatment of COVID-19.     

Deterministic chaоs and the problem of predictability in population dynamics

The studies of the processes that can significantly influence the predictability in population dynamics are reviewed and the results of mathematical simulations of population dynamics are compared to the time series obtained in field observations. Considerable attention is given to the chaotic changes in population abundance. Some methods of numerical analysis of chaoticity and predictability of the time series are considered. The importance of comparing the results of mathematical simulation and observation data is tightly linked to problems in detecting chaos in the dynamics of natural populations and estimating the prevalence of chaotic regimes in nature. Insight into these problems can allow identification of the functional role of chaotic regimes in population dynamics.     

Evolutionary aspects of migratory behavior in European warblers

During the past 20 years, European Sylvia warblers have been used for a model study of the control mechanisms of bird migration and of evolutionary aspects of migratory behavior. Endogenous annual rhythms (‘circannual’ rhythms) and photo-period have proved to be the essential internal and external controlling factors. It is unknown whether this basic system, that also controls migration in other bird species, is currently evolutionarily stable or is instead adapting birds to the present slightly changing environmental conditions. Using the Blackcap, the control of partial migration in a bird species was analyzed. Two-way selective breeding experiments demonstrated a large selection response and high heritability values. These experiments have also indicated that a partially migratory population can become either almost completely migratory, or sedentary, in two to five generations. Hence, genetic influences are very important and presumably dominant over environmental factors in the expression of migratory or sedentary behavior. The large selection response implies a strikingly high evolutionary potential with respect to strong selection pressures. Further, in the Blackcap, migratory orientation behavior (in addition to migratory activity) was immediately transmitted into a F₁-generation when a cross-breeding experiment was performed using birds from a migratory and a resident population. The hybrids displayed their migratory activity along an axis that is used by their migratory parents. Finally, a rapidly developing novel migratory habit (new migratory direction to new wintering areas) in the Blackcap is discussed with respect to a positive feedback-mechanism, possibly including a series of advantages leading to above-average fitness.     

Service control with the preemptive parallel job scheduler Scojo-PECT

User satisfaction and scheduling on grids makes predictability of response times and quality-of-service highly desirable. However, existing approaches for response-time prediction still show significant prediction errors, mostly due to problems in dynamic arrival of jobs with potentially higher priority and hard-to-anticipate packing and backfilling effects. The same problems imply that quality-of-service cannot be solved with standard approaches from communication systems. Thus, this paper presents a scheduling approach which provides a more suitable framework for service guarantees and predictability. The approach is based on coarse-grain preemption, combined with an innovative separation of job classes. Resource shares can be determined as necessary to meet target service levels. A further extension permits limited dynamic resource allocation to adapt to variations in machine load and job mixes. The feasibility of service control is demonstrated with various workloads.     

Analyzing time series gene expression data

MOTIVATION: Time series expression experiments are an increasingly popular method for studying a wide range of biological systems. However, when analyzing these experiments researchers face many new computational challenges. Algorithms that are specifically designed for time series experiments are required so that we can take advantage of their unique features (such as the ability to infer causality from the temporal response pattern) and address the unique problems they raise (e.g. handling the different non-uniform sampling rates). RESULTS: We present a comprehensive review of the current research in time series expression data analysis. We divide the computational challenges into four analysis levels: experimental design, data analysis, pattern recognition and networks. For each of these levels, we discuss computational and biological problems at that level and point out some of the methods that have been proposed to deal with these issues. Many open problems in all these levels are discussed. This review is intended to serve as both, a point of reference for experimental biologists looking for practical solutions for analyzing their data, and a starting point for computer scientists interested in working on the computational problems related to time series expression analysis.     

Disruption of Eaat2b, a glutamate transporter, results in abnormal motor behaviors in developing zebrafish

Analysis of zebrafish mutants that have defects in motor behavior can allow entrée into the hindbrain and spinal cord networks that control locomotion. Here, we report that zebrafish techno trousers (tnt) locomotor mutants harbor a mutation in slc1a2b, which encodes Eaat2b, a plasma membrane glutamate transporter. We used tnt mutants to explore the effects of impaired glutamate transporter activity on locomotor network function. Wild-type larvae perform robust swimming behavior in response to touch stimuli at two and four days after fertilization. In contrast, tnt mutant larvae demonstrate aberrant, exaggerated body bends beginning two days after fertilization and they are almost paralyzed four days after fertilization. We show that slc1a2b is expressed in glial cells in a dynamic fashion across development, which may explain the abnormal sequence of motor behaviors demonstrated by tnt mutants. We also show that tnt larvae demonstrate enhanced excitation of neurons, consistent with the predicted effects of excessive glutamate. These findings illustrate the dynamic regulation and importance of glutamate transporters during development. Since glutamate toxicity caused by EAAT2 dysfunction is thought to promote several different neurological disorders in humans, including epilepsy and neurodegenerative diseases, tnt mutants hold promise as a new tool to better understand these pathologies.     

Detailed qualitative dynamic knowledge representation using a BioNetGen model of TLR-4 signaling and preconditioning

INTRODUCTION: Intracellular signaling/synthetic pathways are being increasingly extensively characterized. However, while these pathways can be displayed in static diagrams, in reality they exist with a degree of dynamic complexity that is responsible for heterogeneous cellular behavior. Multiple parallel pathways exist and interact concurrently, limiting the ability to integrate the various identified mechanisms into a cohesive whole. Computational methods have been suggested as a means of concatenating this knowledge to aid in the understanding of overall system dynamics. Since the eventual goal of biomedical research is the identification and development of therapeutic modalities, computational representation must have sufficient detail to facilitate this 'engineering' process. Adding to the challenge, this type of representation must occur in a perpetual state of incomplete knowledge. We present a modeling approach to address this challenge that is both detailed and qualitative. This approach is termed 'dynamic knowledge representation,' and is intended to be an integrated component of the iterative cycle of scientific discovery. METHODS: BioNetGen (BNG), a software platform for modeling intracellular signaling pathways, was used to model the toll-like receptor 4 (TLR-4) signal transduction cascade. The informational basis of the model was a series of reference papers on modulation of (TLR-4) signaling, and some specific primary research papers to aid in the characterization of specific mechanistic steps in the pathway. This model was detailed with respect to the components of the pathway represented, but qualitative with respect to the specific reaction coefficients utilized to execute the reactions. Responsiveness to simulated lipopolysaccharide (LPS) administration was measured by tumor necrosis factor (TNF) production. Simulation runs included evaluation of initial dose-dependent response to LPS administration at 10, 100, 1000 and 10,000, and a subsequent examination of preconditioning behavior with increasing LPS at 10, 100, 1000 and 10,000 and a secondary dose of LPS at 10,000 administered at approximately 27h of simulated time. Simulations of 'knockout' versions of the model allowed further examination of the interactions within the signaling cascade. RESULTS: The model demonstrated a dose-dependent TNF response curve to increasing stimulus by LPS. Preconditioning simulations demonstrated a similar dose-dependency of preconditioning doses leading to attenuation of response to subsequent LPS challenge - a 'tolerance' dynamic. These responses match dynamics reported in the literature. Furthermore, the simulated 'knockout' results suggested the existence and need for dual negative feedback control mechanisms, represented by the zinc ring-finger protein A20 and inhibitor kappa B proteins (IkappaB), in order for both effective attenuation of the initial stimulus signal and subsequent preconditioned 'tolerant' behavior. CONCLUSIONS: We present an example of detailed, qualitative dynamic knowledge representation using the TLR-4 signaling pathway, its control mechanisms and overall behavior with respect to preconditioning. The intent of this approach is to demonstrate a method of translating the extensive mechanistic knowledge being generated at the basic science level into an executable framework that can provide a means of 'conceptual model verification.' This allows for both the 'checking' of the dynamic consequences of a mechanistic hypothesis and the creation of a modular component of an overall model directed at the engineering goal of biomedical research. It is hoped that this paper will increase the use of knowledge representation and communication in this fashion, and facilitate the concatenation and integration of community-wide knowledge.     

A new method for wide frequency range dynamic olfactory stimulation and characterization

Sensory receptors often receive strongly dynamic, or time varying, inputs in their natural environments. Characterizing their dynamic properties requires control and measurement of the stimulus over a frequency range that equals or exceeds the receptor response. Techniques for dynamic stimulation of olfactory receptors have lagged behind other major sensory modalities because of difficulties in controlling and measuring the concentration of odorants at the receptor. We present a new method for delivering olfactory stimulation that gives linear, low-noise, wide frequency range control of odorant concentration. A servo-controlled moving bead of silicone elastomer occludes the tip of a Pasteur pipette that releases odorant plus tracer gas into a flow tube. Tracer gas serves as a surrogate indicator of odorant concentration and is measured by a photoionization detector. The system has well-defined time-dependent behavior (frequency response and impulse response functions) and gives predictable control of odorant over a significant volume surrounding the animal. The frequency range of the system is about 0-100 Hz. System characterization was based on random (white noise) stimulation, which allows more rapid and accurate estimation of dynamic behavior than deterministic signals such as sinusoids or step functions. Frequency response functions of Drosophila electroantennograms stimulated by fruit odors were used to demonstrate a typical application of the system.     

Uncertainties in biotic indicators and a corrigendum to : Implications for biomonitoring

Many bioindicators based on species composition have been proposed during the past 30 years in both terrestrial and freshwater ecosystems. Kelly (2011) raised the need to better communicate the usefulness and meaning of the indices and their uncertainties. A survey of European bioindicators of freshwater ecosystems has recently highlighted that most indices have not even considered uncertainties. While recording errors have sometimes been taken into account into the calculation of index uncertainties, the reliability of biotic indices in terms of causality and predictability between a given environmental pressure and bioindicator response have seldom been considered. This has led to serious misunderstanding of what bioindicators may be able to achieve. Here a correction to Ponader et al. (2007) is presented showing that the tolerance (ecological niche) of individual species of diatoms along nutrient enrichment gradients is much wider than formerly presented. The correction, together with the comments by Kelly (2011) and recent whole ecosystem experiments seriously question the reliability of diatoms as nutrient indicators, similarly to recent findings with aquatic macrophytes. A more mechanistic basis for biomonitoring is needed based on current ecological science, rather than early 20th century community ecology.     

Dynamic versus instantaneous models of diet choice

We investigate the dynamics of a series of two-prey-one-predator models in which the predator exhibits adaptive diet choice based on the different energy contents and/or handling times of the two prey species. The predator is efficient at exploiting its prey and has a saturating functional response; these two features combine to produce sustained population cycles over a wide range of parameter values. Two types of models of behavioral change are compared. In one class of models ("instantaneous choice"), the probability of acceptance of the poorer prey by the predator instantaneously approximates the optimal choice, given current prey densities. In the second class of models ("dynamic choice"), the probability of acceptance of the poorer prey is a dynamic variable, which begins to change in an adaptive direction when prey densities change but which requires a finite amount of time to approach the new optimal behavior. The two types of models frequently predict qualitatively different population dynamics of the three-species system, with chaotic dynamics and complex cycles being a common outcome only in the dynamic choice models. In dynamic choice models, factors that reduce the rate of behavioral change when the probability of accepting the poorer prey approaches extreme values often produce complex population dynamics. Instantaneous and dynamic models often predict different average population densities and different indirect interactions between prey species. Alternative dynamic models of behavior are analyzed and suggest, first, that instantaneous choice models may be good approximations in some circumstances and, second, that different types of dynamic choice models often lead to significantly different population dynamics. The results suggest possible behavioral mechanisms leading to complex population dynamics and highlight the need for more empirical study of the dynamics of behavioral change.     

A meta-analysis of community response predictability to anthropogenic disturbances

Abstract Disturbances often lead to changes in average values of community properties; however, disturbances can also affect the predictability of a community's response. We performed a meta-analysis to determine how response predictability, defined as among-replicate variance in diversity and community abundance, is affected by species removals, species invasions, nutrient addition, temperature increase, and habitat loss/fragmentation, and we further determined whether response predictability differed according to habitat and trophic role. Species removals and nutrient addition decreased response predictability, while species invasions increased response predictability. In aquatic habitats, disturbances generally led to a decrease in response predictability, whereas terrestrial habitats showed no overall change in response predictability, suggesting that differences in food web and ecosystem structure affect how communities respond to disturbance. Producers were also more likely to show decreases in response predictability, particularly following species removals, highlighting widespread destabilizing effects of species loss at the producer level. Overall, our results show that whether disturbances cause changes in response predictability is highly contingent on disturbance type, habitat, and trophic role. The nature of changes in response predictability-for example, strong decreases following species invasions and increases following species removals-will likely play a major role in how communities recover from disturbance.     

Both ligand- and cell-specific parameters control ligand agonism in a kinetic model of g protein-coupled receptor signaling

G protein-coupled receptors (GPCRs) exist in multiple dynamic states (e.g., ligand-bound, inactive, G protein-coupled) that influence G protein activation and ultimately response generation. In quantitative models of GPCR signaling that incorporate these varied states, parameter values are often uncharacterized or varied over large ranges, making identification of important parameters and signaling outcomes difficult to intuit. Here we identify the ligand- and cell-specific parameters that are important determinants of cell-response behavior in a dynamic model of GPCR signaling using parameter variation and sensitivity analysis. The character of response (i.e., positive/neutral/inverse agonism) is, not surprisingly, significantly influenced by a ligand's ability to bias the receptor into an active conformation. We also find that several cell-specific parameters, including the ratio of active to inactive receptor species, the rate constant for G protein activation, and expression levels of receptors and G proteins also dramatically influence agonism. Expressing either receptor or G protein in numbers several fold above or below endogenous levels may result in system behavior inconsistent with that measured in endogenous systems. Finally, small variations in cell-specific parameters identified by sensitivity analysis as significant determinants of response behavior are found to change ligand-induced responses from positive to negative, a phenomenon termed protean agonism. Our findings offer an explanation for protean agonism reported in beta2--adrenergic and alpha2A-adrenergic receptor systems.