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COMPLEX TIME: Adaptation, Aging, & Arrow of Time

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Cognitive ageing research examines the cognitive abilities that are preserved and/or those that decline with advanced age. There is great individual variability in cognitive ageing trajectories. Some older adults show little decline in cognitive ability compared with young adults and are thus termed ‘optimally ageing’. By contrast, others exhibit substantial cognitive decline and may develop dementia. Human neuroimaging research has led to a number of important advances in our understanding of the neural mechanisms underlying these two outcomes. However, interpreting the age-related changes and differences in brain structure, activation and functional connectivity that this research reveals is an ongoing challenge. Ambiguous terminology is a major source of difficulty in this venture. Three terms in particular — compensation, maintenance and reserve — have been used in a number of different ways, and researchers continue to disagree about the kinds of evidence or patterns of results that are required to interpret findings related to these concepts. As such inconsistencies can impede progress in both theoretical and empirical research, here, we aim to clarify and propose consensual definitions of these terms.  +
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Consumer–resource interactions are often influenced by other species in the community. At present these ‘trophic interaction modifications’ are rarely included in ecological models despite demonstrations that they can drive system dynamics. Here, we advocate and extend an approach that has the potential to unite and represent this key group of non-trophic interactions by emphasising the change to trophic interactions induced by modifying species. We highlight the opportunities this approach brings in comparison to frameworks that coerce trophic interaction modifications into pairwise relationships. To establish common frames of reference and explore the value of the approach, we set out a range of metrics for the ‘strength’ of an interaction modification which incorporate increasing levels of contextual information about the system. Through demonstrations in three-species model systems, we establish that these metrics capture complimentary aspects of interaction modifications. We show how the approach can be used in a range of empirical contexts; we identify as specific gaps in current understanding experiments with multiple levels of modifier species and the distributions of modifications in networks. The trophic interaction modification approach we propose can motivate and unite empirical and theoretical studies of system dynamics, providing a route to confront ecological complexity.  +
Contemporary niche theory is a powerful conceptual framework for understanding how organisms interact with each other and with their shared environment. Here we show that a large segment of niche theory is equivalent to a Minimum Environmental Perturbation Principle (MEPP): ecosystems self-organize into a state that minimizes the impact of organisms on their environment. Different choices of environmental dynamics naturally give rise to distinct dissimilarity measures for quantifying environmental impact. The MEPP allows for the analysis of ecosystems with large numbers of species and environmental factors and provides a new avenue for analyzing ecological invasions. The MEPP also rigorously connects ecological bistability with the existence of multiple minima in a statistical-physics inspired landscapes. We show that the presence of environmental feedbacks where organisms can produce new resources in addition to depleting them violates the global MEPP. However, even in the presence of such feedbacks, a weaker, local version of the MEPP still applies in a limited region of resource space.  +
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Defined as the invariance of system structure or function following a nontrivial perturbation to one or more important system components, robustness is a characteristic property of all adaptive systems. This chapter reviews the theory of robustness in biology, the design of experiments used to assay robustness (including the functional behavior or outputs of a system), and the adaptive response of those parts or components which are compromised by a perturbation. Emphasis is given to a rigorous logic of measurements that carefully factors apart the many casual contributions to robust function. Insights from the study of robustness in biology are applied to the social and decision-making domains, and modifications of experimental design and theory are proposed to account for challenges unique to human agents.  +
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Delirium occurring in patients with dementia is referred to as delirium superimposed on dementia (DSD). People who are older with dementia and who are institutionalized are at increased risk of developing delirium when hospitalized. In addition, their prior cognitive impairment makes detecting their delirium a challenge. The Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition and the International Statistical Classification of Diseases and Related Health Problems, 10th Revision are considered the standard reference for the diagnosis of delirium and include criteria of impairments in cognitive processes such as attention, additional cognitive disturbances, or altered level of arousal. The Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition and the International Statistical Classification of Diseases and Related Health Problems, 10th Revision does not provide guidance regarding specific tests for assessment of the cognitive process impaired in delirium. Importantly, the assessment or inclusion of preexisting cognitive impairment is also not addressed by these standards. The challenge of DSD gets more complex as types of dementia, particularly dementia with Lewy bodies, which has features of both delirium and dementia, are considered. The objective of this article is to critically review key elements for the diagnosis of DSD, including the challenge of neuropsychological assessment in patients with dementia and the influence of particular tests used to diagnose DSD. To address the challenges of DSD diagnosis, we present a framework for guiding the focus of future research efforts to develop a reliable reference standard to diagnose DSD. A key feature of a reliable reference standard will improve the ability to clinically diagnose DSD in facility-based patients and research studies.  +
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Dietary restriction (DR) increases life-span in organisms from yeast to mammals, presumably by slowing the accumulation of aging-related damage. Here we show that in Drosophila, DR extends life-span entirely by reducing the short-term risk of death. Two days after the application of DR at any age for the first time, previously fully fed flies are no more likely to die than flies of the same age that have been subjected to long-term DR. DR of mammals may also reduce short-term risk of death, and hence DR instigated at any age could generate a full reversal of mortality.  +
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Dynamical brain state transitions are critical for flexible working memory but the network mechanisms are incompletely understood. Here, we show that working memory entails brain-wide switching between activity states. The stability of states relates to dopamine D1 receptor gene expression while state transitions are influenced by D2 receptor expression and pharmacological modulation. Schizophrenia patients show altered network control properties, including a more diverse energy landscape and decreased stability of working memory representations.  +
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Ecology letters (2011) 14: 914–921AbstractThe Arrhenius equation has emerged as the favoured model for describing the temperature dependence of consumption in predator–prey models. To examine the relevance of this equation, we undertook a meta-analysis of published relationships between functional response parameters and temperature. We show that, when plotted in lin-log space, temperature dependence of both attack rate and maximal ingestion rate exhibits a hump-shaped relationship and not a linear one as predicted by the Arrhenius equation. The relationship remains significantly downward concave even when data from temperatures above the peak of the hump are discarded. Temperature dependence is stronger for attack rate than for maximal ingestion rate, but the thermal optima are not different. We conclude that the use of the Arrhenius equation to describe consumption in predator–prey models requires the assumption that temperatures above thermal optima are unimportant for population and community dynamics, an assumption that is untenable given the available data.  +
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Empirical findings in the Cognitive Sciences on the relationship between feeling states and subjective time have led to the assumption that time perception entails emotional and interoceptive states. The perception of time would thereafter be embodied; the bodily self, the continuous input from the body is the functional anchor of phenomenal experience and the mental self. Subjective time emerges through the existence of the self across time as an enduring and embodied entity. This relation is prominently disclosed in studies on altered states of consciousness such as in meditative states, under the influence of hallucinogens as well as in many psychiatric and neurological conditions. An increased awareness of oneself coincides with an increased awareness of time. Conversely, a decreased awareness of the self is associated with diminished awareness of time. The body of empirical work within different conceptual frameworks on the intricate relationship between self and time is presented and discussed.  +
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Environmental factors like temperature, pressure, and pH partly shaped the evolution of life. As life progressed, new stressors (e.g., poisons and antibiotics) arose as part of an arms race among organisms. Here we ask if cells co-opted existing mechanisms to respond to new stressors, or whether new responses evolved de novo. We use a network-clustering approach based purely on phenotypic growth measurements and interactions among the effects of stressors on population growth. We apply this method to two types of stressors—temperature and antibiotics—to discover the extent to which their cellular responses overlap in Escherichia coli. Our clustering reveals that responses to low and high temperatures are clearly separated, and each is grouped with responses to antibiotics that have similar effects to cold or heat, respectively. As further support, we use a library of transcriptional fluorescent reporters to confirm heat-shock and cold-shock genes are induced by antibiotics. We also show strains evolved at high temperatures are more sensitive to antibiotics that mimic the effects of cold. Taken together, our results strongly suggest that temperature stress responses have been co-opted to deal with antibiotic stress.  +
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Environmental temperature has systematic effects on rates of species interactions, primarily through its influence on organismal physiology. We present a mechanistic model for the thermal response of consumer-resource interactions. We focus on how temperature affects species interactions via key traits - body velocity, detection distance, search rate and handling time - that underlie per capita consumption rate. The model is general because it applies to all foraging strategies: active-capture (both consumer and resource body velocity are important), sit-and-wait (resource velocity dominates) and grazing (consumer velocity dominates). The model predicts that temperature influences consumer-resource interactions primarily through its effects on body velocity (either of the consumer, resource or both), which determines how often consumers and resources encounter each other, and that asymmetries in the thermal responses of interacting species can introduce qualitative, not just quantitative, changes in consumer-resource dynamics. We illustrate this by showing how asymmetries in thermal responses determine equilibrium population densities in interacting consumer-resource pairs. We test for the existence of asymmetries in consumer-resource thermal responses by analysing an extensive database on thermal response curves of ecological traits for 309 species spanning 15 orders of magnitude in body size from terrestrial, marine and freshwater habitats. We find that asymmetries in consumer-resource thermal responses are likely to be a common occurrence. Overall, our study reveals the importance of asymmetric thermal responses in consumer-resource dynamics. In particular, we identify three general types of asymmetries: (i) different levels of performance of the response, (ii) different rates of response (e.g. activation energies) and (iii) different peak or optimal temperatures. Such asymmetries should occur more frequently as the climate changes and species' geographical distributions and phenologies are altered, such that previously noninteracting species come into contact. 6. By using characteristics of trophic interactions that are often well known, such as body size, foraging strategy, thermy and environmental temperature, our framework should allow more accurate predictions about the thermal dependence of consumer-resource interactions. Ultimately, integration of our theory into models of food web and ecosystem dynamics should be useful in understanding how natural systems will respond to current and future temperature change.  
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Evolution drives, and is driven by, demography. A genotype moulds its phenotype’s age patterns of mortality and fertility in an environment; these two patterns in turn determine the genotype’s fitness in that environment. Hence, to understand the evolution of ageing, age patterns of mortality and reproduction need to be compared for species across the tree of life. However, few studies have done so and only for a limited range of taxa. Here we contrast standardized patterns over age for 11 mammals, 12 other vertebrates, 10 invertebrates, 12 vascular plants and a green alga. Although it has been predicted that evolution should inevitably lead to increasing mortality and declining fertility with age after maturity, there is great variation among these species, including increasing, constant, decreasing, humped and bowed trajectories for both long- and short-lived species. This diversity challenges theoreticians to develop broader perspectives on the evolution of ageing and empiricists to study the demography of more species.  +
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Explaining the strong variation in lifespan among organisms remains a major challenge in evolutionary biology. Whereas previous work has concentrated mainly on differences in selection regimes and selection pressures, we hypothesize that differences in genetic drift may explain some of this variation. We develop a model to formalize this idea and show that the strong positive relationship between lifespan and genetic diversity predicted by this model indeed exists among populations of Daphnia magna, and that ageing is accelerated in small populations. Additional results suggest that this is due to increased drift in small populations rather than adaptation to environments favoring faster life histories. First, the correlation between genetic diversity and lifespan remains significant after statistical correction for potential environmental covariates. Second, no trade-offs are observed; rather, all investigated traits show clear signs of increased genetic load in the small populations. Third, hybrid vigor with respect to lifespan is observed in crosses between small but not between large populations. Together, these results suggest that the evolution of lifespan and ageing can be strongly affected by genetic drift, especially in small populations, and that variation in lifespan and ageing may often be nonadaptive, due to a strong contribution from mutation accumulation.  +
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Fisher's geometrical model (FGM) has been widely used to depict the fitness effects of mutations. It is a general model with few underlying assumptions that gives a large and comprehensive view of adaptive processes. It is thus attractive in several situations, for example adaptation to antibiotics, but comes with limitations, so that more mechanistic approaches are often preferred to interpret experimental data. It might be possible however to extend FGM assumptions to better account for mutational data. This is theoretically challenging in the context of antibiotic resistance because resistance mutations are assumed to be rare. In this article, we show with Escherichia coli how the fitness effects of resistance mutations screened at different doses of nalidixic acid vary across a dose-gradient. We found experimental patterns qualitatively consistent with the basic FGM (rate of resistance across doses, gamma distributed costs) but also unexpected patterns such as a decreasing mean cost of resistance with increasing screen dose. We show how different extensions involving mutational modules and variations in trait covariance across environments, can be discriminated based on these data. Overall, simple extensions of the FGM accounted well for complex mutational effects of resistance mutations across antibiotic doses.  +
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Food webs have markedly non-random network structure. Ecologists maintain that this non-random structure is key for stability, since large random ecological networks would invariably be unstable and thus should not be observed empirically. Here we show that a simple yet overlooked feature of natural food webs, the correlation between the effects of consumers on resources and those of resources on consumers, substantially accounts for their stability. Remarkably, random food webs built by preserving just the distribution and correlation of interaction strengths have stability properties similar to those of the corresponding empirical systems. Surprisingly, we find that the effect of topological network structure on stability, which has been the focus of countless studies, is small compared to that of correlation. Hence, any study of the effects of network structure on stability must first take into account the distribution and correlation of interaction strengths.  +
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Graphical Abstract Highlights d Partial reprogramming erases cellular markers of aging in mouse and human cells d Induction of OSKM in progeria mice ameliorates signs of aging and extends lifespan d In vivo reprogramming improves regeneration in 12-month-old wild-type mice  +
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Healthy aging has been associated with decreased specialization in brain function. This characterization has focused largely on describing age-accompanied differences in specialization at the level of neurons and brain areas. We expand this work to describe systems-level differences in specialization in a healthy adult lifespan sample (n = 210; 20-89 y). A graph-theoretic framework is used to guide analysis of functional MRI resting-state data and describe systems-level differences in connectivity of individual brain networks. Young adults' brain systems exhibit a balance of within- and between-system correlations that is characteristic of segregated and specialized organization. Increasing age is accompanied by decreasing segregation of brain systems. Compared with systems involved in the processing of sensory input and motor output, systems mediating "associative" operations exhibit a distinct pattern of reductions in segregation across the adult lifespan. Of particular importance, the magnitude of association system segregation is predictive of long-term memory function, independent of an individual's age.  +
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High (H) and low (L) antibody responder lines of mice separated by selective breeding present a maximal interline difference in antibody (Ab) response to Ag of different specificities (general genetic regulation). The analysis of SRBC agglutinin response in H line, L line, F1 hybrids, F2, and backcross segregants demonstrates that Ab responsiveness is a polygenic trait regulated by the additive interaction of 5 to 7 independent loci, with an incomplete dominance (44% +/- 7%) of the high response character, and a 30% +/- 10% impact of the environmental factors. The life span of H, L, F1, F2, and backcross populations is correlated positively with 2-ME-resistant agglutinin response (r = 0.97, p less than 0.001) and negatively with 2-ME-sensitive agglutinin response (r = 0.95, p = 0.01) (interpopulation correlation). Similar correlations are also observed in individuals of the various populations, especially in F1 x L backcross, in which the largest phenotypic variance is found. The positive correlation between Ab responsiveness and life span was confirmed by ELISA titration for distinct IgG isotypes (intrapopulation correlation). Malignant lymphomas and chronic nephritis were the two most common diseases observed. The age-adjusted incidence of such diseases, which is largely affected by environmental factors, accounts for the longer life span of H, as compared with L, mouse populations. The longevity of the 30% or less survivors, chiefly determined by the rate of physiologic aging, is a polygenic character regulated by the cumulative interaction of 3 to 7 independent loci, with a complete dominance of the long life trait and an impact of the environmental factors of about 60%. Thus we have grounds for regarding general Ab responsiveness and life span as polygenic traits regulated by a small number of identical or closely linked gene loci, and immune responsiveness as a defense mechanism against neoplastic and inflammatory diseases.  +
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How do children and adults differ in their search for rewards? We consider three different hypotheses that attribute developmental differences to either children’s increased random sampling, more directed exploration towards uncertain options, or narrower generalization. Using a search task in which noisy rewards are spatially correlated on a grid, we compare 55 younger children (age 7-8), 55 older children (age 9-11), and 50 adults (age 19-55) in their ability to successfully generalize about unobserved outcomes and balance the exploration-exploitation dilemma. Our results show that children explore more eagerly than adults, but obtain lower rewards. Building a predictive model of search to disentangle the unique contributions of the three hypotheses of developmental differences, we find robust and recoverable parameter estimates indicating that children generalize less and rely on directed exploration more than adults. We do not, however, find reliable differences in terms of random sampling.  +
How producers of public goods persist inmicrobial communities is amajor question in 8 evolutionary biology. Cooperation is evolutionarily unstable, since cheating strains can reproduce 9 quicker and take over. Spatial structure has been shown to be a robustmechanism for the 0 1 evolution of cooperation. Here we study how spatial assortmentmight emerge from native 1 1 dynamics and show that fluid flow shear promotes cooperative behavior. Social structures arise 2 1 naturally from our advection-diffusion-reactionmodel as self-reproducing Turing patterns. We 13 computationally study the effects of fluid advection on these patterns as amechanism to enable or 4 1 enhance social behavior. Our central finding is that flow shear enables and promotes social 5 1 behavior inmicrobes by increasing the group fragmentation rate and thereby limiting the spread of 6 1 cheating strains. Regions of the flow domain with higher shear admit high cooperativity and large 7 1 population density, whereas low shear regions are devoid of life due to opportunisticmutations.  +
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Hubs are network components that hold positions of high importance for network function. Previous research has identified hubs in human brain networks derived from neuroimaging data; however, there is little consensus on the localization of such hubs. Moreover, direct evidence regarding the role of various proposed hubs in network function (e.g., cognition) is scarce. Regions of the default mode network (DMN) have been frequently identified as "cortical hubs" of brain networks. On theoretical grounds, we have argued against some of the methods used to identify these hubs and have advocated alternative approaches that identify different regions of cortex as hubs. Our framework predicts that our proposed hub locations may play influential roles in multiple aspects of cognition, and, in contrast, that hubs identified via other methods (including salient regions in the DMN) might not exert such broad influence. Here we used a neuropsychological approach to directly test these predictions by studying long-term cognitive and behavioral outcomes in 30 patients, 19 with focal lesions to six "target" hubs identified by our approaches (high system density and participation coefficient) and 11 with focal lesions to two "control" hubs (high degree centrality). In support of our predictions, we found that damage to target locations produced severe and widespread cognitive deficits, whereas damage to control locations produced more circumscribed deficits. These findings support our interpretation of how neuroimaging-derived network measures relate to cognition and augment classic neuroanatomically based predictions about cognitive and behavioral outcomes after focal brain injury.  +
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In 1943, a Greek war veteran named Stamatis Moraitis came to the United States for treatment of a combat-mangled arm. He’d survived a gunshot wound, escaped to Turkey and eventually talked his way onto the Queen Elizabeth, then serving as a troopship, to cross the Atlantic. Moraitis settled in Port Jefferson, N.Y., an enclave of countrymen from his native island, Ikaria. He quickly landed a job doing manual labor. Later, he moved to Boynton Beach, Fla. Along the way, Moraitis married a Greek-American woman, had three children and bought a three-bedroom house and a 1951 Chevrolet.\r\n\r\nOne day in 1976, Moraitis felt short of breath. Climbing stairs was a chore; he had to quit working midday. After X-rays, his doctor concluded that Moraitis had lung cancer. As he recalls, nine other doctors confirmed the diagnosis. They gave him nine months to live. He was in his mid-60s.\r\n\r\nMoraitis considered staying in America and seeking aggressive cancer treatment at a local hospital. That way, he could also be close to his adult children. But he decided instead to return to Ikaria, where he could be buried with his ancestors in a cemetery shaded by oak trees that overlooked the Aegean Sea. He figured a funeral in the United States would cost thousands, a traditional Ikarian one only $200, leaving more of his retirement savings for his wife, Elpiniki. Moraitis and Elpiniki moved in with his elderly parents, into a tiny, whitewashed house on two acres of stepped vineyards near Evdilos, on the north side of Ikaria. At first, he spent his days in bed, as his mother and wife tended to him. He reconnected with his faith. On Sunday mornings, he hobbled up the hill to a tiny Greek Orthodox chapel where his grandfather once served as a priest. When his childhood friends discovered that he had moved back, they started showing up every afternoon. They’d talk for hours, an activity that invariably involved a bottle or two of locally produced wine. I might as well die happy, he thought.\r\n\r\nIn the ensuing months, something strange happened. He says he started to feel stronger. One day, feeling ambitious, he planted some vegetables in the garden. He didn’t expect to live to harvest them, but he enjoyed being in the sunshine, breathing the ocean air. Elpiniki could enjoy the fresh vegetables after he was gone.\r\n\r\nSix months came and went. Moraitis didn’t die. Instead, he reaped his garden and, feeling emboldened, cleaned up the family vineyard as well. Easing himself into the island routine, he woke up when he felt like it, worked in the vineyards until midafternoon, made himself lunch and then took a long nap. In the evenings, he often walked to the local tavern, where he played dominoes past midnight. The years passed. His health continued to improve. He added a couple of rooms to his parents’ home so his children could visit. He built up the vineyard until it produced 400 gallons of wine a year. Today, three and a half decades later, he’s 97 years old — according to an official document he disputes; he says he’s 102 — and cancer-free. He never went through chemotherapy, took drugs or sought therapy of any sort. All he did was move home to Ikaria.  
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In order to maintain brain function, neural activity needs to be tightly coordinated within the brain network. How this coordination is achieved and related to behavior is largely unknown. It has been previously argued that the study of the link between brain and behavior is impossible without a guiding vision. Here we propose behavioral-level concepts and mechanisms embodied as structured flows on manifold (SFM) that provide a formal description of behavior as a low-dimensional process emerging from a network's dynamics dependent on the symmetry and invariance properties of the network connectivity. Specifically, we demonstrate that the symmetry breaking of network connectivity constitutes a timescale hierarchy resulting in the emergence of an attractive functional subspace. We show that behavior emerges when appropriate conditions imposed upon the couplings are satisfied, justifying the conductance-based nature of synaptic couplings. Our concepts propose design principles for networks predicting how behavior and task rules are represented in real neural circuits and open new avenues for the analyses of neural data.  +
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In the ocean, organic particles harbour diverse bacterial communities, which collectively digest and recycle essential nutrients. Traits like motility and exo-enzyme production allow individual taxa to colonize and exploit particle resources, but it remains unclear how community dynamics emerge from these individual traits. Here we track the taxon and trait dynamics of bacteria attached to model marine particles and demonstrate that particle-attached communities undergo rapid, reproducible successions driven by ecological interactions. Motile, particle-degrading taxa are selected for during early successional stages. However, this selective pressure is later relaxed when secondary consumers invade, which are unable to use the particle resource but, instead, rely on carbon from primary degraders. This creates a trophic chain that shifts community metabolism away from the particle substrate. These results suggest that primary successions may shape particle-attached bacterial communities in the ocean and that rapid community-wide metabolic shifts could limit rates of marine particle degradation.  +
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In the past decades, reductionism has dominated both research directions and funding policies in clinical psychology and psychiatry. The intense search for the biological basis of mental disorders, however, has not resulted in conclusive reductionist explanations of psychopathology. Recently, network models have been proposed as an alternative framework for the analysis of mental disorders, in which mental disorders arise from the causal interplay between symptoms. In this target article, we show that this conceptualization can help explain why reductionist approaches in psychiatry and clinical psychology are on the wrong track. First, symptom networks preclude the identification of a common cause of symptomatology with a neurobiological condition; in symptom networks, there is no such common cause. Second, symptom network relations depend on the content of mental states and, as such, feature intentionality. Third, the strength of network relations is highly likely to depend partially on cultural and historical contexts as well as external mechanisms in the environment. Taken together, these properties suggest that, if mental disorders are indeed networks of causally related symptoms, reductionist accounts cannot achieve the level of success associated with reductionist disease models in modern medicine. As an alternative strategy, we propose to interpret network structures in terms of D. C. Dennett's (1987) notion of real patterns , and suggest that, instead of being reducible to a biological basis, mental disorders feature biological and psychological factors that are deeply intertwined in feedback loops. This suggests that neither psychological nor biological levels can claim causal or explanatory priority, and that a holistic research strategy is necessary for progress in the study of mental disorders.  +
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It is clear today that the immune system is constituted by a coordinated network of perfectly integrated and interacting cells and molecules subject to strict cooperation to ensure the highest possible efficiency in antiinfectious immunity. A simplified scheme of the immune system in higher vertebrates is represented in this chapter. The enzyme equipment of macrophage phagosomes endows these cells with bactericidal or bacteriostatic activity on ingested microorganisms, therefore, constituting the first important mechanism in antiinfectious defense. The metabolic activity of macrophages on engulfed antigens also regulates the specific response of T and B lymphocytes through a complex process of antigen handling and antigen presentation, establishing a sort of symbiotic relationship between lymphocytes and macrophages. There are two essential components in the immune response: one is specific and the other nonspecific. The specific response involves the stereospecific selective recognition. The nonspecific aspect of the immune response includes the handling of the phagocytized antigen and the rate at which the process of multiplication and differentiation of small lymphocytes takes place. The protective effect of specific vaccination is essentially based on immunological memory. The antibody molecules, according to their isotypes, play specialized defensive roles against various types of invading microorganisms, particularly in collaboration with the complement system, inducing bactericidal or opsonizing effects. © 1984, Academic Press, Inc. All rights reserved.  +
Laboratory experiments show us that the deleterious character of accumulated novel age-specific mutations is reduced and made less variable with increased age. While theories of aging predict that the frequency of deleterious mutations at mutation-selection equilibrium will increase with the mutation's age of effect, they do not account for these age-related changes in the distribution of de novo mutational effects. Furthermore, no model predicts why this dependence of mutational effects upon age exists. Because the nature of mutational distributions plays a critical role in shaping patterns of senescence, we need to develop aging theory that explains and incorporates these effects. Here we propose a model that explains the age dependency of mutational effects by extending Fisher's geometrical model of adaptation to include a temporal dimension. Using a combination of simple analytical arguments and simulations, we show that our model predicts age-specific mutational distributions that are consistent with observations from mutation-accumulation experiments. Simulations show us that these age-specific mutational effects may generate patterns of senescence at mutation-selection equilibrium that are consistent with observed demographic patterns that are otherwise difficult to explain.  +
Machine Learning (ML) is one of the most exciting and dynamic areas of modern research and application. The purpose of this review is to provide an introduction to the core concepts and tools of machine learning in a manner easily understood and intuitive to physicists. The review begins by covering fundamental concepts in ML and modern statistics such as the bias-variance tradeoff, overfitting, regularization, and generalization before moving on to more advanced topics in both supervised and unsupervised learning. Topics covered in the review include ensemble models, deep learning and neural networks, clustering and data visualization, energy-based models (including MaxEnt models and Restricted Boltzmann Machines), and variational methods. Throughout, we emphasize the many natural connections between ML and statistical physics. A notable aspect of the review is the use of Jupyter notebooks to introduce modern ML/statistical packages to readers using physics-inspired datasets (the Ising Model and Monte-Carlo simulations of supersymmetric decays of proton-proton collisions). We conclude with an extended outlook discussing possible uses of machine learning for furthering our understanding of the physical world as well as open problems in ML where physicists maybe able to contribute.  +
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Many bacterial species are composed of multiple lineages distinguished by extensive variation in gene content. These often co-circulate in the same habitat, but the evolutionary and ecological processes that shape these complex populations are poorly understood. Addressing these questions is particularly important for Streptococcus pneumoniae, a nasopharyngeal commensal and respiratory pathogen, as the changes in population structure associated with the recent introduction of partial-coverage vaccines have significantly reduced pneumococcal disease. Here we show pneumococcal lineages from multiple populations each have a distinct combination of intermediate frequency genes. Functional analysis suggested these loci were likely subject to negative frequency-dependent selection (NFDS) through interactions with other bacteria, hosts, or mobile elements. Correspondingly, these genes had similar frequencies in four populations with dissimilar lineage compositions. These frequencies were maintained following substantial alterations in lineage prevalences once vaccination programmes began. Fitting a multilocus NFDS model of post-vaccine population dynamics to three genomic datasets using Approximate Users may view, print, copy, and download text and data-mine the content in such documents, for the purposes of academic research, subject always to the full Conditions of use:  +
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Microbes assemble into complex, dynamic, and species-rich communities that play critical roles in human health and in the environment. The complexity of natural environments and the large number of niches present in most habitats are often invoked to explain the maintenance of microbial diversity in the presence of competitive exclusion. Here we show that soil and plant-associated microbiota, cultivated ex situ in minimal synthetic environments with a single supplied source of carbon, universally re-assemble into large and dynamically stable communities with strikingly predictable coarse-grained taxonomic and functional compositions. We find that generic, non-specific metabolic cross-feeding leads to the assembly of dense facilitation networks that enable the coexistence of multiple competitors for the supplied carbon source. The inclusion of universal and non-specific cross-feeding in ecological consumer-resource models is sufficient to explain our observations, and predicts a simple determinism in community structure, a property reflected in our experiments.  +
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Models relating phenotype space to fitness (phenotype-fitness landscapes) have seen important developments recently. They can roughly be divided into mechanistic models (e.g., metabolic networks) and more heuristic models like Fisher's geometrical model. Each has its own drawbacks, but both yield testable predictions on how the context (genomic background or environment) affects the distribution of mutation effects on fitness and thus adaptation. Both have received some empirical validation. This article aims at bridging the gap between these approaches. A derivation of the Fisher model "from first principles" is proposed, where the basic assumptions emerge from a more general model, inspired by mechanistic networks. I start from a general phenotypic network relating unspecified phenotypic traits and fitness. A limited set of qualitative assumptions is then imposed, mostly corresponding to known features of phenotypic networks: a large set of traits is pleiotropically affected by mutations and determines a much smaller set of traits under optimizing selection. Otherwise, the model remains fairly general regarding the phenotypic processes involved or the distribution of mutation effects affecting the network. A statistical treatment and a local approximation close to a fitness optimum yield a landscape that is effectively the isotropic Fisher model or its extension with a single dominant phenotypic direction. The fit of the resulting alternative distributions is illustrated in an empirical data set. These results bear implications on the validity of Fisher's model's assumptions and on which features of mutation fitness effects may vary (or not) across genomic or environmental contexts.  +
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Much research debates whether properties of ecological networks such as nestedness and con- nectance stabilise biological communities while ignoring key behavioural aspects of organisms within these networks. Here, we computationally assess how adaptive foraging (AF) behaviour interacts with network architecture to determine the stability of plant–pollinator networks. We find that AF reverses negative effects of nestedness and positive effects of connectance on the sta- bility of the networks by partitioning the niches among species within guilds. This behaviour enables generalist pollinators to preferentially forage on the most specialised of their plant part- ners which increases the pollination services to specialist plants and cedes the resources of general- ist plants to specialist pollinators. We corroborate these behavioural preferences with intensive field observations of bee foraging. Our results show that incorporating key organismal behaviours with well-known biological mechanisms such as consumer-resource interactions into the analysis of ecological networks may greatly improve our understanding of complex ecosystems.  +
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Phenotypic variation is common in most pathogens, yet the mechanisms that maintain this diver- sity are still poorly understood. We asked whether continuous host variation in susceptibility helps maintain phenotypic variation, using experiments conducted with a baculovirus that infects gypsy moth (Lymantria dispar) larvae. We found that an empirically observed tradeoff between mean transmission rate and variation in transmission, which results from host heterogeneity, promotes long-term coexistence of two pathogen types in simulations of a population model. This tradeoff introduces an alternative strategy for the pathogen: a low-transmission, low-variability type can coexist with the high-transmission type favoured by classical non-heterogeneity models. In addi- tion, this tradeoff can help explain the extensive phenotypic variation we observed in field-col- lected pathogen isolates, in traits affecting virus fitness including transmission and environmental persistence. Similar heterogeneity tradeoffs might be a general mechanism promoting phenotypic variation in any pathogen for which hosts vary continuously in susceptibility.  +
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Phytoplankton are key components of aquatic ecosystems, fixing CO2 from the atmosphere through photosynthesis and supporting secondary production, yet relatively little is known about how future global warming might alter their biodiversity and associated ecosystem functioning. Here, we explore how the structure, function, and biodiversity of a planktonic metacommunity was altered after five years of experimental warming. Our outdoor mesocosm experiment was open to natural dispersal from the regional species pool, allowing us to explore the effects of experimental warming in the context of metacommunity dynamics. Warming of 4°C led to a 67% increase in the species richness of the phytoplankton, more evenly-distributed abundance, and higher rates of gross primary productivity. Warming elevated productivity indirectly, by increasing the biodiversity and biomass of the local phytoplankton communities. Warming also systematically shifted the taxonomic and functional trait composition of the phytoplankton, favoring large, colonial, inedible phytoplankton taxa, suggesting stronger top-down control, mediated by zooplankton grazing played an important role. Overall, our findings suggest that temperature can modulate species coexistence, and through such mechanisms, global warming could, in some cases, increase the species richness and productivity of phytoplankton communities.  +
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Pollination systems are recognized as critical for the maintenance of biodiversity in terrestrial ecosystems. Therefore, the understanding of mechanisms that promote the integrity of those mutualistic assemblages is an important issue for the conservation of biodiversity and ecosystem function. In this study we present a new population dynamics model for plant–pollinator interactions that is based on the consumer–resource approach and incorporates a few essential features of pollination ecology. The model was used to project the temporal dynamics of three empirical pollination network, in order to analyze how adaptive foraging of pollinators (AF) shapes the outcome of community dynamics in terms of biodiversity and network robustness to species loss. We found that the incorporation of AF into the dynamics of the pollination networks increased the persistence and diversity of its constituent species, and reduced secondary extinctions of both plants and animals. These findings were best explained by the following underlying processes: 1) AF increased the amount of floral resources extracted by specialist pollinators, and 2) AF raised the visitation rates received by specialist plants. We propose that the main mechanism by which AF enhanced those processes is (trophic) niche partitioning among animals, which in turn generates (pollen vector) niche partitioning among plants. Our results suggest that pollination networks can maintain their stability and diversity by the adaptive foraging of generalist pollinators.  +
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Predictions of how a population will respond to a selective pressure are valuable, especially in the case of infectious diseases, which often adapt to the interventions we use to control them. Yet attempts to predict how pathogen populations will change, for example in response to vaccines, are challenging. Such has been the case with Streptococcus pneumoniae , an important human colonizer and pathogen, and the pneumococcal conjugate vaccines (PCVs), which target only a fraction of the strains in the population. Here, we use recent advances in knowledge of negative-frequency dependent selection (NFDS) acting on frequencies of accessory genes (i.e., flexible genome) to predict the changes in the pneumococcal population after intervention. Implementing a deterministic NFDS model using the replicator equation, we can accurately predict which pneumococcal lineages will increase after intervention. Analyzing a population genomic sample of pneumococci collected before and after vaccination, we find that the predicted fitness of a lineage post-vaccine is significantly and positively correlated with the observed change in its prevalence. Then, using quadratic programming to numerically solve the frequencies of non-vaccine type lineages that best restored the pre-vaccine accessory gene frequencies, we accurately predict the post-vaccine population composition. Additionally, we also test the predictive ability of frequencies of core genome loci, a subset of metabolic loci, and naive estimates of prevalence change based on pre-vaccine lineages frequencies. Finally, we show how this approach can assess the migration and invasion capacity of emerging lineages, on the basis of their accessory genome. In general, we provide a method for predicting the impact of an intervention on pneumococcal populations and other bacterial pathogens for which NFDS is a main driving force.  +
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Quantitative computational models play an increasingly important role in modern biology. Such models typically involve many free parameters, and assigning their values is often a substantial obstacle to model development. Directly measuring in vivo biochemical parameters is difficult, and collectively fitting them to other experimental data often yields large parameter uncertainties. Nevertheless, in earlier work we showed in a growth-factor-signaling model that collective fitting could yield well-constrained predictions, even when it left individual parameters very poorly constrained. We also showed that the model had a "sloppy" spectrum of parameter sensitivities, with eigenvalues roughly evenly distributed over many decades. Here we use a collection of models from the literature to test whether such sloppy spectra are common in systems biology. Strikingly, we find that every model we examine has a sloppy spectrum of sensitivities. We also test several consequences of this sloppiness for building predictive models. In particular, sloppiness suggests that collective fits to even large amounts of ideal time-series data will often leave many parameters poorly constrained. Tests over our model collection are consistent with this suggestion. This difficulty with collective fits may seem to argue for direct parameter measurements, but sloppiness also implies that such measurements must be formidably precise and complete to usefully constrain many model predictions. We confirm this implication in our growth-factor-signaling model. Our results suggest that sloppy sensitivity spectra are universal in systems biology models. The prevalence of sloppiness highlights the power of collective fits and suggests that modelers should focus on predictions rather than on parameters.  +
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Recent results have shown that deep neural networks (DNN) may have significant potential to serve as quantitatively precise models of sensory cortex neural populations. However, the implications these results have for our conceptual understanding of neural mechanisms are subtle. This is because many modern DNN brain models are best understood as the products of task-constrained optimization processes, unlike the intuitively simpler hand-crafted models from earlier approaches. In this chapter, we illustrate these issues by first discussing the nature of information processing in the primate ventral visual pathway, and review results comparing the response properties of units in goal-optimized DNN models to neural responses found throughout the ventral pathway. We then show how DNN visual system models are just one instance of a more general optimization framework whose logic may be applicable to understanding the underlying constraints that shape neural mechanisms throughout the brain. An important part of a scientist's job is to answer "why" questions. For cognitive neuroscientists, a core objective is to uncover the underlying reasons why the structures of the human brain are as they are. Since brains are biological systems, answering such questions is ultimately a matter of identifying the evolutionary and developmental constraints that shape brain structure and function. Such constraints are in part architectural: what large-scale brain structures are put in place genetically to help a brain help its host organism better meet evolutionary challenges? In light of the centrality of behavior in understanding the brain, an ethological investigation is also indicated: what behavioral goals most strongly constrain a given neural system? And since many complex behaviors in higher organisms are not entirely genetically determined and must instead be partly derived through experience of the world, a core question of learning is also involved: how do learning rules that absorb experiential data constrain what brains look like? The interactions between architectural structure, behavioral goals, and learning rules suggest a quantitative optimization framework as one route toward answering these "why" questions. Put simply, this means: postulating one or several goal behavior(s) as driving the evolution and/or development of a neural system of interest; finding architecturally plausible computational models that (attempt to) optimize for the behavior; and then quantitatively comparing the internal structures arrived at in the optimized models to measurements from large-scale neuroscience experiments. To the extent that there is a match between optimized models and the real data that is very substantially better than that found for various controls (e.g. models designed by hand or optimized for other tasks), this is evidence that something important has been understood about the underlying constraints that shape the brain system under investigation. Though it might sound challenging to put this approach into practice, recent successes suggest we might add to our list of maxims the observation that nothing in computational cognitive neuroscience makes sense except in light of optimization.  
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Research on the brain basis of speech and language faces theoretical and empirical challenges. Most current research, dominated by imaging, deficit-lesion, and electrophysiological techniques, seeks to identify regions that underpin aspects of language processing such as phonology, syntax, or semantics. The emphasis lies on localization and spatial characterization of function. The first part of the paper deals with a practical challenge that arises in the context of such a research programme. This maps problem concerns the extent to which spatial information and localization can satisfy the explanatory needs for perception and cognition. Several areas of investigation exemplify how the neural basis of speech and language is discussed in those terms (regions, streams, hemispheres, networks). The second part of the paper turns to a more troublesome challenge, namely how to formulate the formal links between neurobiology and cognition. This principled problem thus addresses the relation between the primitives of cognition (here speech, language) and neurobiology. Dealing with this mapping problem invites the development of linking hypotheses between the domains. The cognitive sciences provide granular, theoretically motivated claims about the structure of various domains (the "cognome"); neurobiology, similarly, provides a list of the available neural structures. However, explanatory connections will require crafting of computationally explicit linking hypotheses at the right level of abstraction. For both the practical maps problem and the principled mapping problem, developmental approaches and evidence can play a central role in the resolution. © 2012 Copyright Psychology Press.  +
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Sleep is crucial for daytime functioning, cognitive performance and general well-being. These aspects of daily life are known to be impaired after extended wake, yet, the underlying neuronal correlates have been difficult to identify. Accumulating evidence suggests that normal functioning of the brain is characterized by long-range temporal correlations (LRTCs) in cortex, which are supportive for decision-making and working memory tasks. Here we assess LRTCs in resting state human EEG data during a 40-hour sleep deprivation experiment by evaluating the decay in autocorrelation and the scaling exponent of the detrended fluctuation analysis from EEG amplitude fluctuations. We find with both measures that LRTCs decline as sleep deprivation progresses. This decline becomes evident when taking changes in signal power into appropriate consideration. Our results demonstrate the importance of sleep to maintain LRTCs in the human brain. In complex networks, LRTCs naturally emerge in the vicinity of a critical state. The observation of declining LRTCs during wake thus provides additional support for our hypothesis that sleep reorganizes cortical networks towards critical dynamics for optimal functioning.  +
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Starting with Darwin, biologists have asked how populations evolve from a low fitness state that is evolutionarily stable to a high fitness state that is not. Specifically of interest is the emergence of cooperation and multicellularity where the fitness of individuals often appears in conflict with that of the population. Theories of social evolution and evolutionary game theory have produced a number of fruitful results employing two-state two-body frameworks. In this study, we depart from this tradition and instead consider a multi-player, multi-state evolutionary game, in which the fitness of an agent is determined by its relationship to an arbitrary number of other agents. We show that populations organize themselves in one of four distinct phases of interdependence depending on one parameter, selection strength. Some of these phases involve the formation of specialized large-scale structures. We then describe how the evolution of independence can be manipulated through various external perturbations.  +
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Studies of the human microbiome have revealed that even healthy individuals differ remarkably in the microbes that occupy habitats such as the gut, skin, and vagina. Much of this diversity remains unexplained, although diet, environment, host genetics, and early microbial exposure have all been implicated. Accordingly, to characterize the ecology of human-associated microbial communities, the Human Microbiome Project has analyzed the largest cohort and set of distinct, clinically relevant body habitats to date. We found the diversity and abundance of each habitat’s signature microbes to vary widely even among healthy subjects, with strong niche specialization both within and among individuals. The project encountered an estimated 81–99% of the genera, enzyme families, and community configurations occupied by the healthy Western microbiome. Metagenomic carriage of metabolic pathways was stable among individuals despite variation in community structure, and ethnic/racial background proved to be one of the strongest associationa of both pathways and microbes with clinical metadata. These results thus delineate the range of structural and functional configurations normal in the microbial communities of a healthy population, enabling future characterization of the epidemiology, ecology, and translational applications of the human microbiome.  +
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The accumulation of data on the genomic bases of adaptation has triggered renewed interest in theoretical models of adaptation. Among these models, Fisher's geometric model (FGM) has received a lot of attention over the past two decades. FGM is based on a continuous multidimensional phenotypic landscape, but it is mostly used for the emerging properties of individual mutation effects. Despite its apparent simplicity and limited number of pa-rameters, FGM integrates a full model of mutation and epistatic interactions that allows the study of both beneficial and deleterious mutations and, subse-quently, the fate of evolving populations. In this review, I present the different properties of FGM and the qualitative and quantitative support they have received from experimental evolution data. I then discuss how to estimate the different parameters of the model and outline some future directions to connect FGM and the molecular determinants of adaptation.  +
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The brain comprises complex structural and functional networks, but much remains to be determined regarding how these networks support the communication processes that underlie neuronal computation. In this Review, Avena-Koenigsberger, Misic and Sporns discuss the network basis of communication dynamics in the brain.  +
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The diversification of life involved enormous increases in size and complexity. The evolutionary transitions from prokaryotes to unicellular eukaryotes to metazoans were accompanied by major innovations in metabolic design. Here we show that the scalings of metabolic rate, population growth rate, and production efficiency with body size have changed across the evolutionary transitions. Metabolic rate scales with body mass superlinearly in prokaryotes, linearly in protists, and sublinearly in metazoans, so Kleiber's 3/4 power scaling law does not apply universally across organisms. The scaling of maximum population growth rate shifts from positive in prokaryotes to negative in protists and metazoans, and the efficiency of production declines across these groups. Major changes in metabolic processes during the early evolution of life overcame existing constraints, exploited new opportunities, and imposed new constraints.  +
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The geometric mean of fitness is considered to be the main indicator of evolutionary change in stochastic models. However, this measure was initially derived for models with infinite population sizes, where the long-term evolutionary behavior can be described with certainty. In this paper we begin an exploration of the limitations and utility of this approach to evolution in finite populations and discuss alternate methods for predicting evolutionary dynamics. We reanalyze a model of lottery competition under environmental stochasticity by including population finiteness, and show that the geometric mean predictions do not always agree with those based on the fixa- tion probability of rare alleles. Further, the fixation probability can be inserted into adaptive dynamics equations to derive the mean state of the population. We explore the effects of increasing population size on these conclusions through simulations. These simulations show that for small population sizes the fixation probability accurately predicts the course of evolution, but as population size becomes large the geometric mean predictions are upheld. The two approaches are reconciled because the time scale on which the fixation probability approach applies becomes very large as population size grows.  +
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The interspecies exchange of metabolites plays a key role in the spatiotemporal dynamics of microbial communities. This raises the question of whether ecosystem-level behavior of structured communities can be predicted using genome-scale metabolic models for multiple organisms. We developed a modeling framework that integrates dynamic flux balance analysis with diffusion on a lattice and applied it to engineered communities. First, we predicted and experimentally confirmed the species ratio to which a two-species mutualistic consortium converges and the equilibrium composition of a newly engineered three-member community. We next identified a specific spatial arrangement of colonies, which gives rise to what we term the "eclipse dilemma": does a competitor placed between a colony and its cross-feeding partner benefit or hurt growth of the original colony? Our experimentally validated finding that the net outcome is beneficial highlights the complex nature of metabolic interactions in microbial communities while at the same time demonstrating their predictability. © 2014 The Authors.  +
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The meaning of language is represented in regions of the cerebral cortex collectively known as the ‘semantic system’. However, little of the semantic system has been mapped comprehensively, and the semantic selectivity of most regions is unknown. Here we systematically map semantic selectivity across the cortex using voxel-wise modelling of functional MRI (fMRI) data collected while subjects listened to hours of narrative stories. We show that the semantic system is organized into intricate patterns that seem to be consistent across individuals. We then use a novel generative model to create a detailed semantic atlas. Our results suggest that most areas within the semantic system represent information about specific semantic domains, or groups of related concepts, and our atlas shows which domains are represented in each area. This study demonstrates that data-driven methods—commonplace in studies of human neuroanatomy and functional connectivity—provide a powerful and efficient means for mapping functional representations in the brain.  +
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The organization of human brain networks can be measured by capturing correlated brain activity with fMRI. There is considerable interest in understanding how brain networks vary across individuals or neuropsychiatric populations or are altered during the performance of specific behaviors. However, the plausibility and validity of such measurements is dependent on the extent to which functional networks are stable over time or are state dependent. We analyzed data from nine high-quality, highly sampled individuals to parse the magnitude and anatomical distribution of network variability across subjects, sessions, and tasks. Critically, we find that functional networks are dominated by common organizational principles and stable individual features, with substantially more modest contributions from task-state and day-to-day variability. Sources of variation were differentially distributed across the brain and differentially linked to intrinsic and task-evoked sources. We conclude that functional networks are suited to measuring stable individual characteristics, suggesting utility in personalized medicine. Gratton et al. comprehensively measure individual, day-to-day, and task variance in functional brain networks, revealing that networks are dominated by stable individual factors, not cognitive content. These findings suggest utility of functional network measurements in personalized medicine.  +
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The past twenty years have seen a resurgence of interest in nonequilibrium thermodynamics, thanks to advances in the theory of stochastic processes and in their thermodynamic interpretation. Fluctuation theorems provide fundamental constraints on the dynamics of systems arbitrarily far from thermal equilibrium. Thermodynamic uncertainty relations bound the dissipative cost of precision in a wide variety of processes. Concepts of excess work and excess heat provide the basis for a complete thermodynamics of nonequilibrium steady states, including generalized Clausius relations and thermodynamic potentials. But these general results carry their own limitations: fluctuation theorems involve exponential averages that can depend sensitively on unobservably rare trajectories; steady-state thermodynamics makes use of a dual dynamics that lacks any direct physical interpretation. This review aims to present these central results of contemporary nonequilibrium thermodynamics in such a way that the power of each claim for making physical predictions can be clearly assessed, using examples from current topics in soft matter and biophysics.  +
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The primary somatosensory (S1) and motor (M1) cortices are somatotopically organized, with distinct brain areas receiving information from—and controlling the movements of specific body parts. But once this specialized organization has formed, can it be altered to support the changing needs of the organism? In this chapter, we reexamine the extent to which reorganization of the cortical hand representation occurs in the adult primate brain, with respect to both physiology and behavior. We review seminal findings reporting changes in the cortical maps of humans and nonhuman primates, which have been interpreted as evidence of brain reorganization: a qualitative change in the response of neurons. For example, a hand sensory area now responds to tactile stimulation of the face. We focus on three potential triggers for brain reorganization: changed input (due to training), input loss (due to amputation), and substrate loss (due to stroke). We review recent research demonstrating that the canonical functional organization in S1 and M1 is more resilient than originally assumed. Instead, experience or injury-dependent cortical map changes more likely result either from the unmasking of preexisting cortical connections or from subcortical reorganization, rather than true cortical reorganization. Finally, we examine whether cortical map changes, whatever their physiological origin, have any causal adaptive or maladaptive consequence on perception and action and conclude that they do not. Overall, our review suggests that although the adult brain can change based on experience or injury, cortical reorganization does not need to be invoked as the driving mechanism.  +
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The purpose of this paper is to describe a framework for the understanding of rules that govern how neural system dynamics are coordinated to produce behavior. The framework, structured flows on manifolds (SFM), posits that neural processes are flows depicting system interactions that occur on relatively low-dimension manifolds, which constrain possible functional configurations. Although this is a general framework, we focus on the application to brain disorders. We first explain the Epileptor, a phenomenological computational model showing fast and slow dynamics, but also a hidden repertoire whose expression is similar to refractory status epilepticus. We suggest that epilepsy represents an innate brain state whose potential may be realized only under certain circumstances. Conversely, deficits from damage or disease processes, such as stroke or dementia, may reflect both the disease process per se and the adaptation of the brain. SFM uniquely captures both scenarios.  +
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The role of climate forcing in the population dynamics of infectious diseases has typically been revealed via retrospective analyses of incidence records aggregated across space and, in particular, over whole cities. Here, we focus on the transmission dynamics of rotavirus, the main diarrheal disease in infants and young children, within the megacity of Dhaka, Bangladesh. We identify two zones, the densely urbanized core and the more rural periphery, that respond differentially to flooding. Moreover, disease seasonality differs substantially between these regions, spanning variation comparable to the variation from tropical to temperate regions. By combining process-based models with an extensive disease surveillance record, we show that the response to climate forcing is mainly seasonal in the core, where a more endemic transmission resulting from an asymptomatic reservoir facilitates the response to the monsoons. The force of infection in this monsoon peak can be an order of magnitude larger than the force of infection in the more epidemic periphery, which exhibits little or no postmonsoon outbreak in a pattern typical of nearby rural areas. A typically smaller peak during the monsoon season nevertheless shows sensitivity to interannual variability in flooding. High human density in the core is one explanation for enhanced transmission during troughs and an associated seasonal monsoon response in this diarrheal disease, which unlike cholera, has not been widely viewed as climate-sensitive. Spatial demographic, socioeconomic, and environmental heterogeneity can create reservoirs of infection and enhance the sensitivity of disease systems to climate forcing, especially in the populated cities of the developing world.  +
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The underlying cause of aging remains one of the central mysteries of biology. Recent studies in several different systems suggest that not only may the rate of aging be modified by environmental and genetic factors, but also that the aging clock can be reversed, restoring characteristics of youthfulness to aged cells and tissues. This Review focuses on the emerging biology of rejuvenation through the lens of epigenetic reprogramming. By defining youthfulness and senescence as epigenetic states, a framework for asking new questions about the aging process emerges.  +
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There is growing concern over tipping points arising in ecosystems due to the crossing of environmental thresholds. Tipping points lead to strong and possibly irreversible shifts between alternative ecosystem states incurring high societal costs. Traits are central to the feedbacks that maintain alternative ecosystem states, as they govern the responses of populations to environmental change that could stabilize or destabilize ecosystem states. However, we know little about how evolutionary changes in trait distributions over time affect the occurrence of tipping points, and even less about how big scale ecological shifts reciprocally interact with trait dynamics. We argue that interactions between ecological and evolutionary processes should be taken into account for understanding the balance of feedbacks governing tipping points in nature.  +
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Thermodynamics is the paradigm example in physics of a time-asymmetric theory, but the origin of the asymmetry lies deeper than the second law. A primordial arrow can be defined by the way of the equilibration principle (“minus first law”). By appealing to this arrow, the nature of the well-known ambiguity in Carathéodory's 1909 version of the second law becomes clear. Following Carathéodory's seminal work, formulations of thermodynamics have gained ground that highlight the role of the binary relation of adiabatic accessibility between equilibrium states, the most prominent recent example being the important 1999 axiomatization due to Lieb and Yngvason. This formulation can be shown to contain an ambiguity strictly analogous to that in Carathéodory's treatment.  +
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This paper explains some implications of Markov-process theory for models of mortality. We show that an important qualitative feature common to empirical mortality data, and which has been found in certain models - the convergence to a "mortality plateau" - is, in fact, a generic consequence of the models' convergence to a "quasistationary distribution". This phenomenon has been explored extensively in the mathematical literature. Not only does this generalization free important results from specifics of the models, it also suggests a new explanation for the convergence to constant mortality. At the same time that we show that the late behavior of these models - convergence to a finite asymptote - is almost logically immutable, we also point out that the early behavior of the mortality rates can be more flexible than has been generally acknowledged. We show, in particular, that an appropriate choice of initial conditions enables one popular model to approximate any reasonable hazard-rate data. This illustrates how precarious it can be to read a model's vindication from its consilience with a favored hazard-rate function, such as the Gompertz exponential. © 2004 Elsevier Inc. All rights reserved.  +
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This study sought to examine the effect of meditation experience on brain networks underlying cognitive actions employed during contemplative practice. In a previous study, we proposed a basic model of naturalistic cognitive fluctuations that occur during the practice of focused attention meditation. This model specifies four intervals in a cognitive cycle: mind wandering (MW), awareness of MW, shifting of attention, and sustained attention. Using subjective input from experienced practitioners during meditation, we identified activity in salience network regions during awareness of MW and executive network regions during shifting and sustained attention. Brain regions associated with the default mode were active during MW. In the present study, we reasoned that repeated activation of attentional brain networks over years of practice may induce lasting functional connectivity changes within relevant circuits. To investigate this possibility, we created seeds representing the networks that were active during the four phases of the earlier study, and examined functional connectivity during the resting state in the same participants. Connectivity maps were then contrasted between participants with high vs. low meditation experience. Participants with more meditation experience exhibited increased connectivity within attentional networks, as well as between attentional regions and medial frontal regions. These neural relationships may be involved in the development of cognitive skills, such as maintaining attention and disengaging from distraction, that are often reported with meditation practice. Furthermore, because altered connectivity of brain regions in experienced meditators was observed in a non-meditative (resting) state, this may represent a transference of cognitive abilities off the cushion into daily life.  +
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To explain diversity in forests, niche theory must show how multiple plant species coexist while competing for the same resources. Although successional processes are widespread in forests, theoretical work has suggested that differentiation in successional strategy allows only a few species stably to coexist, including only a single shade tolerant. However, this conclusion is based on current niche models, which encode a very simplified view of plant communities, suggesting that the potential for niche differentiation has remained unexplored. Here, we show how extending successional niche models to include features common to all vegetation-height-structured competition for light under a prevailing disturbance regime and two trait-mediated tradeoffs in plant function-enhances the diversity of species that can be maintained, including a diversity of shade tolerants. We identify two distinct axes of potential niche differentiation, corresponding to the traits leaf mass per unit leaf area and height at maturation. The first axis allows for coexistence of different shade tolerances and the second axis for coexistence among species with the same shade tolerance. Addition of this second axis leads to communities with a high diversity of shade tolerants. Niche differentiation along the second axis also generates regions of trait space wherein fitness is almost equalized, an outcome we term "evolutionarily emergent near-neutrality." For different environmental conditions, our model predicts diverse vegetation types and trait mixtures, akin to observations. These results indicate that the outcomes of successional niche differentiation are richer than previously thought and potentially account for mixtures of traits and species observed in forests worldwide.  +
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To understand the effects of temperature on biological systems, we compile, organize, and analyze a database of 1,072 thermal responses for microbes, plants, and animals. The unprecedented diversity of traits (n = 112), species (n = 309), body sizes (15 orders of magnitude), and habitats (all major biomes) in our database allows us to quantify novel features of the temperature response of biological traits. In particular, analysis of the rising component of within-species (intraspecific) responses reveals that 87% are fit well by the Boltzmann-Arrhenius model. The mean activation energy for these rises is 0.66 +/- 0.05 eV, similar to the reported across-species (interspecific) value of 0.65 eV. However, systematic variation in the distribution of rise activation energies is evident, including previously unrecognized right skewness around a median of 0.55 eV. This skewness exists across levels of organization, taxa, trophic groups, and habitats, and it is partially explained by prey having increased trait performance at lower temperatures relative to predators, suggesting a thermal version of the life-dinner principle-stronger selection on running for your life than running for your dinner. For unimodal responses, habitat (marine, freshwater, and terrestrial) largely explains the mean temperature at which trait values are optimal but not variation around the mean. The distribution of activation energies for trait falls has a mean of 1.15 +/- 0.39 eV (significantly higher than rises) and is also right-skewed. Our results highlight generalities and deviations in the thermal response of biological traits and help to provide a basis to predict better how biological systems, from cells to communities, respond to temperature change.  +
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Traditional neuropsychological approaches emphasize the specificity of behavioral deficits and the modular organization of the brain. At the population level, however, there is emerging evidence that deficits are correlated resulting in a low dimensional structure of post-stroke neurological impairments. Here we consider the implications of low dimensionality for the three-way mapping between structural damage, altered physiology, and behavioral deficits. Understanding this mapping will be aided by large-sample studies that apply multivariate models and focus on explained percentage of variance, as opposed to univariate lesion-symptom techniques that report statistical significance. The low dimensionality of behavioral deficits following stroke is paralleled by widespread, yet relatively consistent, changes in functional connectivity (FC), including a reduction in modularity. Both are related to the structural damage to white matter and subcortical grey commonly produced by stroke. We suggest that large-scale physiological abnormalities following a stroke reduce the variety of neural states visited during task processing and at rest, resulting in a limited repertoire of behavioral states.  +
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Translation in cognitive neuroscience remains beyond the horizon, brought no closer by supposed major advances in our understanding of the brain. Unless our explanatory models descend to the individual level—a cardinal requirement for any intervention—their real-world applications will always be limited. Drawing on an analysis of the informational properties of the brain, here we argue that adequate individualisation needs models of far greater dimensionality than has been usual in the field. This necessity arises from the widely distributed causality of neural systems, a consequence of the fundamentally adaptive nature of their developmental and physiological mechanisms. We discuss how recent advances in high-performance computing, combined with collections of large-scale data, enable the high-dimensional modelling we argue is critical to successful translation, and urge its adoption if the ultimate goal of impact on the lives of patients is to be achieved.  +
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Understanding how changes in temperature affect interspecific competition is critical for predicting changes in ecological communities as the climate warms. Here we develop a simple theoretical model that links interspecific differences in metabolic traits, which capture the temperature-dependence of resource acquisition, to the outcome of pairwise competition in phytoplankton. We parameterised our model with metabolic traits derived from six species of freshwater phytoplankton and tested its ability to predict the outcome of competition in all pairwise combinations of the species in a factorial experiment, manipulating temperature and nutrient availability. The model correctly predicted the outcome of competition in 71% of the pairwise experiments. These results demonstrate that metabolic traits play a key role in determining how changes in temperature influence interspecific competition and lay the foundation for developing theory to predict the effects of warming in complex, multi-species communities.  +
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Understanding the functional impacts of pollinator species losses on plant populations is critical given ongoing pollinator declines. Simulation models of pollination networks suggest that plant communities will be resilient to losing many or even most of the pollinator species in an ecosystem. These predictions, however, have not been tested empirically and implicitly assume that pollination efficacy is unaffected by interactions with interspecific competitors. By contrast, ecological theory and data from a wide range of ecosystems show that interspecific competition can drive variation in ecological specialization over short timescales via behavioral or morphological plasticity, although the potential implications of such changes in specialization for ecosystem functioning remain unexplored. We conducted manipulative field experiments in which we temporarily removed single pollinator species from study plots in subalpine meadows, to test the hypothesis that interactions between pollinator species can shape individual species’ functional roles via changes in foraging specialization. We show that loss of a single pollinator species reduces floral fidelity (short-term specialization) in the remaining pollinators, with significant implications for ecosystem functioning in terms of reduced plant reproduction, even when potentially effective pollinators remained in the system. Our results suggest that ongoing pollinator declines may have more serious negative implications for plant communities than is currently assumed. More broadly, we show that the individual functional contributions of species can be dynamic and shaped by the community of interspecific competitors, thereby documenting a distinct mechanism for how biodiversity can drive ecosystem functioning, with potential relevance to a wide range of taxa and systems.  +
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We present a population model to examine the forces that determined the quality and quantity of human life in early agricultural societies where cultivable area is limited. The model is driven by the non-linear and interdependent relationships between the age distribution of a population, its behavior and technology, and the nature of its environment. The common currency in the model is the production of food, on which age-specific rates of birth and death depend. There is a single non-trivial equilibrium population at which productivity balances caloric needs. One of the most powerful controls on equilibrium hunger level is fertility control. Gains against hunger are accompanied by decreases in population size. Increasing worker productivity does increase equilibrium population size but does not improve welfare at equilibrium. As a case study we apply the model to the population of a Polynesian valley before European contact.  +
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We typically observe large-scale outcomes that arise from the interactions of many hidden, small-scale processes. Examples include age of disease onset, rates of amino acid substitutions, and composition of ecological communities. The macroscopic patterns in each problem often vary around a characteristic shape that can be generated by neutral processes. A neutral generative model assumes that each microscopic process follows unbiased stochastic fluctuations: random connections of network nodes; amino acid substitutions with no effect on fitness; species that arise or disappear from communities randomly. These neutral generative models often match common patterns of nature. In this paper, I present the theoretical background by which we can understand why these neutral generative models are so successful. I show how the classic patterns such as Poisson and Gaussian arise. Each classic pattern was often discovered by a simple neutral generative model. The neutral patterns share a special characteristic: they describe the patterns of nature that follow from simple constraints on information. For example, any aggregation of processes that preserves information only about the mean and variance attracts to the Gaussian pattern; any aggregation that preserves information only about the mean attracts to the exponential pattern; any aggregation that preserves information only about the geometric mean attracts to the power law pattern. I present an informational framework of the common patterns of nature based on the method of maximum entropy. This framework shows that each neutral generative model is a special case that helps to discover a particular set of informational constraints; those informational constraints define a much wider domain of non-neutral generative processes that attract to the same neutral pattern.  +
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Wrack supply is a common feature of beaches around the world. The species composition and spatial distribution of wrack deposits have been widely recognized as two significant factors in the ecological role of macroalgae within beach ecosystems. These accumulation processes may be intensified by bloom events of opportunistic algae, which frequently occur in estuarine environments. For this study, wrack supply was assessed over 13 months in two estuarine beaches of the Galician coastline, NW Spain. Algal biomass and wrack species distribution were characterized, paying special attention to possible relationships between beach slope, tidal height and algal morphology. The results show that the average values in total monthly biomass of stranded macroalgae and seagrasses were similar for both beaches, but certain differences in temporal and spatial variability throughout the year's cycle were detected. Despite this, the distribution of algal species along the beach face was similar in both beaches: large amounts of sheet-like and thin branched species (e.g. Ulva spp., Gracilaria gracilis) dominated the wrack deposits on the low-tide terraces, whereas thick leathery species with positive flotation properties (e.g. Fucus spp., Ascophyllum nodosum) showed larger amounts in the upper foreshore zones. The relative contribution of the major morphological groups of algal wrack shifted throughout the year, suggesting an inverse relation between thick leathery group and sheet-like species, which could be associated with factors such as differences in macroalgae's life cycles and nutrient requirements of fast- and slow-growing species. This study demonstrates that beach topography, together with the morphology of macroalgae species influence the distribution of wrack deposits in estuarine beaches. Further research on the implications of wrack dynamics for sedimentary characteristics and faunal distribution is needed. © 2012 Elsevier B.V.  +
abstract: Eco-evolutionary theory argues that population cycles in consumer-resource interactions are partly driven by natural selection, such that changes in densities and changes in trait values are mutually reinforcing. Evidence that the theory explains cycles in nature, how-ever, is almost nonexistent. Experimental tests of model assumptions are logistically impractical for most organisms, while for others, evi-dence that population cycles occur in nature is lacking. For insect bac-uloviruses in contrast, tests of model assumptions are straightforward, and there is strong evidence that baculoviruses help drive population cycles in many insects, including the gypsy moth that we study here. We therefore used field experiments with the gypsy moth baculovi-rus to test two key assumptions of eco-evolutionary models of host-pathogen population cycles: that reduced host infection risk is heritable and that it is costly. Our experiments confirm both assumptions, and inserting parameters estimated from our data into eco-evolutionary insect-outbreak models gives cycles closely resembling gypsy moth out-break cycles in North America, whereas standard models predict unre-alistic stable equilibria. Our work shows that eco-evolutionary models are useful for explaining outbreaks of forest insect defoliators, while widespread observations of intense selection on defoliators in nature and of heritable and costly resistance in defoliators in the lab together suggest that eco-evolutionary dynamics may play a general role in de-foliator outbreaks.  +
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umans communicate using systems of interconnected stimuli or concepts -- from language and music to literature and science -- yet it remains unclear how, if at all, the structure of these networks supports the communication of information. Although information theory provides tools to quantify the information produced by a system, traditional metrics do not account for the inefficient and biased ways that humans process this information. Here we develop an analytical framework to study the information generated by a system as perceived by a human observer. We demonstrate experimentally that this perceived information depends critically on a system's network topology. Applying our framework to several real networks, we find that they communicate a large amount of information (having high entropy) and do so efficiently (maintaining low divergence from human expectations). Moreover, we show that such efficient communication arises in networks that are simultaneously heterogeneous, with high-degree hubs, and clustered, with tightly-connected modules -- the two defining features of hierarchical organization. Together, these results suggest that many real networks are constrained by the pressures of information transmission, and that these pressures select for specific structural features.  +
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© 2017 The Author(s). Disturbed activity patterns in cortical networks contribute to the pathophysiology of schizophrenia (SZ). Several lines of evidence implicate NMDA receptor hypofunction in SZ, and blocking NMDA receptor signaling during early neurodevelopment produces cognitive deficits in rodent models that resemble those seen in schizophrenic patients. However, the altered network dynamics underlying these cognitive impairments largely remain to be characterized, especially at the cellular level. Here, we use in vivo two-photon calcium imaging to describe pathological dynamics, occurring in parallel with cognitive dysfunction, in a developmental NMDA receptor hypofunction model. We observed increased synchrony and specific alterations in spatiotemporal activity propagation, which could be causally linked to a previously unidentified persistent bursting phenotype. This phenotype was rescued by acute treatment with the NMDA receptor co-agonist D-serine or the GABAB receptor agonist baclofen, which similarly rescued working memory performance. It was not reproduced by optogenetic inhibition of fast-spiking interneurons. These results provide novel insight into network-level abnormalities mediating the cognitive impairment induced by NMDA receptor hypofunction.  +
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© 2018 The Author(s). Species invasions constitute a major and poorly understood threat to plant-pollinator systems. General theory predicting which factors drive species invasion success and subsequent effects on native ecosystems is particularly lacking. We address this problem using a consumer-resource model of adaptive behavior and population dynamics to evaluate the invasion success of alien pollinators into plant-pollinator networks and their impact on native species. We introduce pollinator species with different foraging traits into network models with different levels of species richness, connectance, and nestedness. Among 31 factors tested, including network and alien properties, we find that aliens with high foraging efficiency are the most successful invaders. Networks exhibiting high alien-native diet overlap, fraction of alien-visited plant species, most-generalist plant connectivity, and number of specialist pollinator species are the most impacted by invaders. Our results mimic several disparate observations conducted in the field and potentially elucidate the mechanisms responsible for their variability.  +
© 2018, The Institute of Navigation, Inc. All rights reserved. An individual’s environmental surroundings interact with the development and maturation of their brain. An important aspect of an individual’s environment is his or her socioeconomic status (SES), which estimates access to material resources and social prestige. Previous characterizations of the relation between SES and the brain have primarily focused on earlier or later epochs of the lifespan (i.e., childhood, older age). We broaden this work to examine the relationship between SES and the brain across a wide range of human adulthood (20–89 years), including individuals from the less studied middle-age range. SES, defined by education attainment and occupational socioeconomic characteristics, moderates previously reported age-related differences in the brain’s functional network organization and whole-brain cortical structure. Across middle age (35–64 years), lower SES is associated with reduced resting-state system segregation (a measure of effective functional network organization). A similar but less robust relationship exists between SES and age with respect to brain anatomy: Lower SES is associated with reduced cortical gray matter thickness in middle age. Conversely, younger and older adulthood do not exhibit consistent SES-related difference in the brain measures. The SES–brain relationships persist after controlling for measures of physical and mental health, cognitive ability, and participant demographics. Critically, an individual’s childhood SES cannot account for the relationship between their current SES and functional network organization. These findings provide evidence that SES relates to the brain’s functional network organization and anatomy across adult middle age, and that higher SES may be a protective factor against age-related brain decline.  +