Conceiving relatedness: non-substantial relations among the Kamea of Papua New Guinea

This article considers the implications of imagining kinship as a non-embodied relation. In recent years, it has become commonplace to argue that relatedness is a gradually acquired state that can be built over time and by non-sexual means. In this view, relationships of consanguinity are not given at birth but are created through purposeful acts of feeding and caring. Here, I address a question that has been less commonly asked by anthropologists: need kinship always be imagined as entailing an embodied connection? Is there a way of thinking about cross-generational relationships that does not ground them in bodily connectedness, or, at the very least, one that imagines contexts in which they are not embodied as a substantial link between people? Drawing upon data collected among Kamea of Papua New Guinea, I describe a world in which the parent-child tie is conceptualized as one that is inherently non-embodied.     

Swing it to the left, swing it to the right: enacting flexible spatial language using a neurodynamic framework

Research is continually expanding the empirical and theoretical picture of embodiment and dynamics in language. To date, however, a formalized neural dynamic framework for embodied linguistic processes has yet to emerge. To advance embodied theories of language, the present work develops a formalized neural dynamic framework of spatial language that explicitly integrates linguistic processes and dynamic sensory-motor systems. We then implement and test our spatial language architecture on a robotic platform continuously linked to real-time camera input. In a suite of tasks using everyday objects we demonstrate the framework’s capacity for both contextually-dependent behavioral flexibility and the seamless integration of spatial, non-spatial, and symbolic representations. To our knowledge this is the first unified, neurally-grounded architecture integrating these processes and behaviors.     

Active inference unifies intentional and conflict-resolution imperatives of motor control

The field of motor control has long focused on the achievement of external goals through action (e.g., reaching and grasping objects). However, recent studies in conditions of multisensory conflict, such as when a subject experiences the rubber hand illusion or embodies an avatar in virtual reality, reveal the presence of unconscious movements that are not goal-directed, but rather aim at resolving multisensory conflicts; for example, by aligning the position of a person’s arm with that of an embodied avatar. This second, conflict-resolution imperative of movement control did not emerge in classical studies of motor adaptation and online corrections, which did not allow movements to reduce the conflicts; and has been largely ignored so far in formal theories. Here, we propose a model of movement control grounded in the theory of active inference that integrates intentional and conflict-resolution imperatives. We present three simulations showing that the active inference model is able to characterize movements guided by the intention to achieve an external goal, by the necessity to resolve multisensory conflict, or both. Furthermore, our simulations reveal a fundamental difference between the (active) inference underlying intentional and conflict-resolution imperatives by showing that it is driven by two different (model and sensory) kinds of prediction errors. Finally, our simulations show that when movement is only guided by conflict resolution, the model incorrectly infers that is velocity is zero, as if it was not moving. This result suggests a novel speculative explanation for the fact that people are unaware of their subtle compensatory movements to avoid multisensory conflict. Furthermore, it can potentially help shed light on deficits of motor awareness that arise in psychopathological conditions.     

Behavioral drive versus behavioral inertia in evolution: a null model approach

Some biologists embrace the classical view that changes in behavior inevitably initiate or drive evolutionary changes in other traits, yet others note that behavior sometimes inhibits evolutionary changes. Here we develop a null model that quantifies the impact of regulatory behaviors (specifically, thermoregulatory behaviors) on body temperature and on performance of ectotherms. We apply the model to data on a lizard (Anolis cristatellus) and show that thermoregulatory behaviors likely inhibit selection for evolutionary shifts in thermal physiology with altitude. Because behavioral adjustments are commonly used by ectotherms to regulate physiological performance, regulatory behaviors should generally constrain rather than drive evolution, a phenomenon we call the "Bogert effect." We briefly review a few other examples that contradict the classical view of behavior as the inevitable driving force in evolution. Overall, our analysis and brief review challenge the classical view that behavior is invariably the driving force in evolution, and instead our work supports the alternative view that behavior has diverse--and sometimes conflicting--effects on the directions and rates at which other traits evolve.     

The buckling of spherical liposomes

In the classical "first approximation" theory of thin-shell structures, the constitutive relations for a generic shell element--i.e. the elastic relations between the bending moments and membrane stresses and the corresponding changes in curvature and strain, respectively-are written as if an element of the shell is flat, although in reality it is curved. In this theory it is believed that discrepancies on account of the use of "flat" constitutive relations will be negligible provided the ratio shell-radius/thickness is of sufficiently large order. In the study of drawing of narrow, cylindrical "tethers" from liposomes it has been known for many years that it is necessary to use instead a constitutive law which explicitly describes a curved element in order to make sense of the mechanics; and indeed such tethers are generally of "thick-walled" proportions. In this paper we show that the proper constitutive relations for a curved element must also be used in the study, by means of shell equations, of the buckling of initially spherical thin-walled giant liposomes under exterior pressure: these involve the inclusion of what we call the "Mkappa" terms, which are not present in the standard "first-approximation" theory. We obtain analytical expressions for both the bifurcation buckling pressure and the slope of the post-buckling path, in terms of the dimensions and elastic constants of the lipid bi-layer, and also the initial state of bending moment in the vesicle. We explain physically how the initial bending moment can affect the bifurcation pressure, whereas it cannot in "first-approximation" theory. We use these results to map the conditions under which the vesicle buckles into an oblate, as distinct from a prolate ("rugby-ball") shape. Some of our results were obtained long ago by the use of energy methods; but our aim here has been to identify precisely what is lacking in "first-approximation" theory in relation to liposomes, and so to put the "shell equations" approach onto a firm footing in mechanics.     

Multilevel selection 1: Quantitative genetics of inheritance and response to selection

Interaction among individuals is universal, both in animals and in plants, and substantially affects evolution of natural populations and responses to artificial selection in agriculture. Although quantitative genetics has successfully been applied to many traits, it does not provide a general theory accounting for interaction among individuals and selection acting on multiple levels. Consequently, current quantitative genetic theory fails to explain why some traits do not respond to selection among individuals, but respond greatly to selection among groups. Understanding the full impacts of heritable interactions on the outcomes of selection requires a quantitative genetic framework including all levels of selection and relatedness. Here we present such a framework and provide expressions for the response to selection. Results show that interaction among individuals may create substantial heritable variation, which is hidden to classical analyses. Selection acting on higher levels of organization captures this hidden variation and therefore always yields positive response, whereas individual selection may yield response in the opposite direction. Our work provides testable predictions of response to multilevel selection and reduces to classical theory in the absence of interaction. Statistical methodology provided elsewhere enables empirical application of our work to both natural and domestic populations.     

Quantum Iterative Deepening with an Application to the Halting Problem

Classical models of computation traditionally resort to halting schemes in order to enquire about the state of a computation. In such schemes, a computational process is responsible for signaling an end of a calculation by setting a halt bit, which needs to be systematically checked by an observer. The capacity of quantum computational models to operate on a superposition of states requires an alternative approach. From a quantum perspective, any measurement of an equivalent halt qubit would have the potential to inherently interfere with the computation by provoking a random collapse amongst the states. This issue is exacerbated by undecidable problems such as the Entscheidungsproblem which require universal computational models, e.g. the classical Turing machine, to be able to proceed indefinitely. In this work we present an alternative view of quantum computation based on production system theory in conjunction with Grover''s amplitude amplification scheme that allows for (1) a detection of halt states without interfering with the final result of a computation; (2) the possibility of non-terminating computation and (3) an inherent speedup to occur during computations susceptible of parallelization. We discuss how such a strategy can be employed in order to simulate classical Turing machines.     

Efficiently computing the Robinson-Foulds metric.

The Robinson-Foulds (RF) metric is the measure most widely used in comparing phylogenetic trees; it can be computed in linear time using Day's algorithm. When faced with the need to compare large numbers of large trees, however, even linear time becomes prohibitive. We present a randomized approximation scheme that provides, in sublinear time and with high probability, a (1 + epsilon) approximation of the true RF metric. Our approach is to use a sublinear-space embedding of the trees, combined with an application of the Johnson-Lindenstrauss lemma to approximate vector norms very rapidly. We complement our algorithm by presenting an efficient embedding procedure, thereby resolving an open issue from the preliminary version of this paper. We have also improved the performance of Day's (exact) algorithm in practice by using techniques discovered while implementing our approximation scheme. Indeed, we give a unified framework for edge-based tree algorithms in which implementation tradeoffs are clear. Finally, we present detailed experimental results illustrating the precision and running-time tradeoffs as well as demonstrating the speed of our approach. Our new implementation, FastRF, is available as an open-source tool for phylogenetic analysis.     

Space mediates coexistence of females and hermaphrodites

In gynodioecious populations of flowering plants females and hermaphrodites coexist. Gynodioecy is widespread and occurs in both asexual and sexual species but does not admit a satisfactory explanation from classical sex ratio theory. In sexual populations male fertility restoring genes have evolved to counter non-nuclear male sterility mutations. In pseudogamous asexual populations pollen retention and increased self-fertilization can make male sterility costly. Both of these mechanisms can promote coexistence. However, it remains unclear how either of these mechanisms could evolve if coexistence was not initially possible. In the absence of these adaptations non-spatial models predict that females either fail to invade hermaphrodite populations or else displace them until pollen shortage drives the population to extinction. We develop a pair approximation to a probabilistic cellular automata model in which females and hermaphrodites interact on a regular lattice. The model features independent pollination and colonization processes which take place on different timescales. The timescale separation is exploited to obtain, with perturbation methods, a more manageable aggregated pair approximation. We present both the mean field model which recreates the classical non-spatial predictions and the pair approximation, which strikingly predicts different invasion criteria and coexistence under a wide range of parameters. The pair approximation is shown to correspond well qualitatively with simulation behaviour.     

Probability density function learning by unsupervised neurons.

In a recent work, we introduced the concept of pseudo-polynomial adaptive activation function neuron (FAN) and presented an unsupervised information-theoretic learning theory for such structure. The learning model is based on entropy optimization and provides a way of learning probability distributions from incomplete data. The aim of the present paper is to illustrate some theoretical features of the FAN neuron, to extend its learning theory to asymmetrical density function approximation, and to provide an analytical and numerical comparison with other known density function estimation methods, with special emphasis to the universal approximation ability. The paper also provides a survey of PDF learning from incomplete data, as well as results of several experiments performed on real-world problems and signals.     

Ellen Dissanayake’s Evolutionary Aesthetic

Dissanayake argues that art behaviors – which she characterizes first as patterns or syndromes of creation and response and later as rhythms and modes of mutuality – are universal, innate, old, and a source of intrinsic pleasure, these being hallmarks of biological adaptation. Art behaviors proved to enhance survival by reinforcing cooperation, interdependence, and community, and, hence, became selected for at the genetic level. Indeed, she claims that art is essential to the fullest realization of our human nature. I make three criticisms: Dissanayake’s theory cannot account adequately for differences in the aesthetic value of artworks; the connections drawn between art and reproductive success are too stretched to account for art's production, nature, and reception; indeed, art enters the picture only because it is so thinly characterized that it remains in doubt that her topic is art as we understand it.     

Model reduction by extended quasi-steady-state approximation

We extend the quasi-steady-state approximation (QSSA) with respect to the class of differential systems as well as with respect to the order of approximation. We illustrate the first extension by an example which cannot be treated in the frame of the classical approach. As an application of the second extension we prove that the trimolecular autocatalator can be approximated by a fast bimolecular reaction system. Finally we describe a class of singularly perturbed systems for which a higher order QSSA can easily be obtained. Received: 30 March 1999 / Published online: 5 May 2000     

Multilevel selection 2: Estimating the genetic parameters determining inheritance and response to selection

Interactions among individuals are universal, both in animals and in plants and in natural as well as domestic populations. Understanding the consequences of these interactions for the evolution of populations by either natural or artificial selection requires knowledge of the heritable components underlying them. Here we present statistical methodology to estimate the genetic parameters determining response to multilevel selection of traits affected by interactions among individuals in general populations. We apply these methods to obtain estimates of genetic parameters for survival days in a population of layer chickens with high mortality due to pecking behavior. We find that heritable variation is threefold greater than that obtained from classical analyses, meaning that two-thirds of the full heritable variation is hidden to classical analysis due to social interactions. As a consequence, predicted responses to multilevel selection applied to this population are threefold greater than classical predictions. This work, combined with the quantitative genetic theory for response to multilevel selection presented in an accompanying article in this issue, enables the design of selection programs to effectively reduce competitive interactions in livestock and plants and the prediction of the effects of social interactions on evolution in natural populations undergoing multilevel selection.     

Perturbation avalanches and criticality in gene regulatory networks

Boolean networks are simplified models of gene regulatory networks. We derive an approximation of the size distribution of perturbation avalanches in Boolean networks based on known results in the theory of branching processes. We show numerically that the approximation works well for different kinds of Boolean networks. It has been suggested that gene regulatory networks may be dynamically critical. To study this, as an application of the presented theory we present a novel method for estimating an order parameter from microarray data. According to the available data and our method, we find that gene regulatory networks appear to be stable and reside near the phase transition between order and chaos.     

Chelex without boiling, a rapid and easy technique to obtain stable amplifiable DNA from small amounts of ethanol-stored spiders

DNA barcoding projects require high-throughput generation of sequence data to assemble the comprehensive reference databases that are required to perform large-scale biodiversity inventories and molecular ecology studies. With the advent of new sequencing technologies, the extraction step, which often requires a considerable amount of time and money, represents a significant bottleneck in many studies. Here, we present a one-step Chelex double-stranded DNA extraction protocol that is quick, cheap, easy and works with a small quantity of ethanol-stored tissue. We developed this protocol by removing the denaturation step appearing in classic methods. This modification reduces the number of handling steps to one, thus simplifying the extraction procedure and reducing the risk of sample contamination, and yields double-stranded DNA instead of the single-stranded form that classical Chelex extraction protocols usually release. DNA obtained through our method is then suitable for long-term conservation (over 1.5 years). We tested our protocol on a highly diverse genus of spiders comprised of mainly very small species. We also apply the method to two other genera of spiders, one with average size species, the other one with giant species, to test the efficacy of the method with varying amounts of input tissue. We also discuss the advantages and limitations of this DNA extraction technique when working with arthropods.     

Predictors of enhancing human physical attractiveness: Data from 93 countries

People across the world and throughout history have gone to great lengths to enhance their physical appearance. Evolutionary psychologists and ethologists have largely attempted to explain this phenomenon via mating preferences and strategies. Here, we test one of the most popular evolutionary hypotheses for beauty-enhancing behaviors, drawn from mating market and parasite stress perspectives, in a large cross-cultural sample. We also test hypotheses drawn from other influential and non-mutually exclusive theoretical frameworks, from biosocial role theory to a cultural media perspective. Survey data from 93,158 human participants across 93 countries provide evidence that behaviors such as applying makeup or using other cosmetics, hair grooming, clothing style, caring for body hygiene, and exercising or following a specific diet for the specific purpose of improving ones physical attractiveness, are universal. Indeed, 99% of participants reported spending >10 min a day performing beauty-enhancing behaviors. The results largely support evolutionary hypotheses: more time was spent enhancing beauty by women (almost 4 h a day, on average) than by men (3.6 h a day), by the youngest participants (and contrary to predictions, also the oldest), by those with a relatively more severe history of infectious diseases, and by participants currently dating compared to those in established relationships. The strongest predictor of attractiveness-enhancing behaviors was social media usage. Other predictors, in order of effect size, included adhering to traditional gender roles, residing in countries with less gender equality, considering oneself as highly attractive or, conversely, highly unattractive, TV watching time, higher socioeconomic status, right-wing political beliefs, a lower level of education, and personal individualistic attitudes. This study provides novel insight into universal beauty-enhancing behaviors by unifying evolutionary theory with several other complementary perspectives.     

Blocking primers to enhance PCR amplification of rare sequences in mixed samples – a case study on prey DNA in Antarctic krill stomachs

Background

Identification of DNA sequence diversity is a powerful means for assessing the species present in environmental samples. The most common molecular strategies for estimating taxonomic composition depend upon PCR with universal primers that amplify an orthologous DNA region from a range of species. The diversity of sequences within a sample that can be detected by universal primers is often compromised by high concentrations of some DNA templates. If the DNA within the sample contains a small number of sequences in relatively high concentrations, then less concentrated sequences are often not amplified because the PCR favours the dominant DNA types. This is a particular problem in molecular diet studies, where predator DNA is often present in great excess of food-derived DNA.

Results

We have developed a strategy where a universal PCR simultaneously amplifies DNA from food items present in DNA purified from stomach samples, while the predator's own DNA is blocked from amplification by the addition of a modified predator-specific blocking primer. Three different types of modified primers were tested out; one annealing inhibiting primer overlapping with the 3' end of one of the universal primers, another annealing inhibiting primer also having an internal modification of five dI molecules making it a dual priming oligo, and a third elongation arrest primer located between the two universal primers. All blocking primers were modified with a C3 spacer. In artificial PCR mixtures, annealing inhibiting primers proved to be the most efficient ones and this method reduced predator amplicons to undetectable levels even when predator template was present in 1000 fold excess of the prey template. The prey template then showed strong PCR amplification where none was detectable without the addition of blocking primer. Our method was applied to identifying the winter food of one of the most abundant animals in the world, the Antarctic krill, Euphausia superba. Dietary item DNA was PCR amplified from a range of species in krill stomachs for which we had no prior sequence knowledge.

Conclusion

We present a simple, robust and cheap method that is easily adaptable to many situations where a rare DNA template is to be PCR amplified in the presence of a higher concentration template with identical PCR primer binding sites.     

Understanding the thermal properties of amorphous solids using machine-learning-based interatomic potentials

Abstract

Understanding the thermal properties of disordered systems is of fundamental importance for condensed matter physics - and for practical applications as well. While quantities such as the thermal conductivity are usually well characterised experimentally, their microscopic origin is often largely unknown - hence the pressing need for molecular simulations. However, the time and length scales involved with thermal transport phenomena are typically well beyond the reach of ab initio calculations. On the other hand, many amorphous materials are characterised by a complex structure, which prevents the construction of classical interatomic potentials. One way to get past this deadlock is to harness machine-learning (ML) algorithms to build interatomic potentials: these can be nearly as computationally efficient as classical force fields while retaining much of the accuracy of first-principles calculations. Here, we discuss neural network potentials (NNPs) and Gaussian approximation potentials (GAPs), two popular ML frameworks. We review the work that has been devoted to investigate, via NNPs, the thermal properties of phase-change materials, systems widely used in non-volatile memories. In addition, we present recent results on the vibrational properties of amorphous carbon, studied via GAPs. In light of these results, we argue that ML-based potentials are among the best options available to further our understanding of the vibrational and thermal properties of complex amorphous solids.     

Robust Concentration and Frequency Control in Oscillatory Homeostats

Homeostatic and adaptive control mechanisms are essential for keeping organisms structurally and functionally stable. Integral feedback is a control theoretic concept which has long been known to keep a controlled variable robustly (i.e. perturbation-independent) at a given set-point by feeding the integrated error back into the process that generates . The classical concept of homeostasis as robust regulation within narrow limits is often considered as unsatisfactory and even incompatible with many biological systems which show sustained oscillations, such as circadian rhythms and oscillatory calcium signaling. Nevertheless, there are many similarities between the biological processes which participate in oscillatory mechanisms and classical homeostatic (non-oscillatory) mechanisms. We have investigated whether biological oscillators can show robust homeostatic and adaptive behaviors, and this paper is an attempt to extend the homeostatic concept to include oscillatory conditions. Based on our previously published kinetic conditions on how to generate biochemical models with robust homeostasis we found two properties, which appear to be of general interest concerning oscillatory and homeostatic controlled biological systems. The first one is the ability of these oscillators (“oscillatory homeostats”) to keep the average level of a controlled variable at a defined set-point by involving compensatory changes in frequency and/or amplitude. The second property is the ability to keep the period/frequency of the oscillator tuned within a certain well-defined range. In this paper we highlight mechanisms that lead to these two properties. The biological applications of these findings are discussed using three examples, the homeostatic aspects during oscillatory calcium and p53 signaling, and the involvement of circadian rhythms in homeostatic regulation.