Ecological determinants of women's status among hunter/gatherers

Women's status in preindustrial communities has been the focus of a number of studies in the past two decades. However, very few of these studies deal exclusively with hunter/gatherers, and none of the hunter/gatherer studies combine empirical tests with explanations. Because of a number of differences with settled agricultural villagers, hunter/gatherers can be viewed as the focus of distinctive substantive ecological theory. A cross-cultural materialist approach to the problem of why women's status is high among some hunter/gatherers and low among others indicates that several factors are strongly related to differences in women's status. Frequent and severe resource stress is the most strongly associated with low female status in the domestic and political domain, while warfare exerts an independent but lesser effect. The importance of hunting constitutes a third and still more weakly associated variable. Thus, women's political and domestic status among hunter/gatherers predominantly appears to have resulted from techno-ecological conditions. In contrast, technoecological conditions appear to have had no strong influence on women's status in ritual roles, including shamanism. Several possible ways that women's status might be linked to techno-economic conditions are proposed. The results of this study demonstrate that Leacock's assumptions of systematic bias in the ethnographic record, and of ubiquitous female equality among band level societies are unjustified.     

Resolving the roles of body size and species identity in driving functional diversity

Efforts to characterize food webs have generated two influential approaches that reduce the complexity of natural communities. The traditional approach groups individuals based on their species identity, while recently developed approaches group individuals based on their body size. While each approach has provided important insights, they have largely been used in parallel in different systems. Consequently, it remains unclear how body size and species identity interact, hampering our ability to develop a more holistic framework that integrates both approaches. We address this conceptual gap by developing a framework which describes how both approaches are related to each other, revealing that both approaches share common but untested assumptions about how variation across size classes or species influences differences in ecological interactions among consumers. Using freshwater mesocosms with dragonfly larvae as predators, we then experimentally demonstrate that while body size strongly determined how predators affected communities, these size effects were species specific and frequently nonlinear, violating a key assumption underlying both size- and species-based approaches. Consequently, neither purely species- nor size-based approaches were adequate to predict functional differences among predators. Instead, functional differences emerged from the synergistic effects of body size and species identity. This clearly demonstrates the need to integrate size- and species-based approaches to predict functional diversity within communities.     

MedScan,a natural language processing engine for MEDLINE abstracts

MOTIVATION: The importance of extracting biomedical information from scientific publications is well recognized. A number of information extraction systems for the biomedical domain have been reported, but none of them have become widely used in practical applications. Most proposals to date make rather simplistic assumptions about the syntactic aspect of natural language. There is an urgent need for a system that has broad coverage and performs well in real-text applications. RESULTS: We present a general biomedical domain-oriented NLP engine called MedScan that efficiently processes sentences from MEDLINE abstracts and produces a set of regularized logical structures representing the meaning of each sentence. The engine utilizes a specially developed context-free grammar and lexicon. Preliminary evaluation of the system's performance, accuracy, and coverage exhibited encouraging results. Further approaches for increasing the coverage and reducing parsing ambiguity of the engine, as well as its application for information extraction are discussed.     

The ecology of differences: assessing community assembly with trait and evolutionary distances

Species enter and persist in local communities because of their ecological fit to local conditions, and recently, ecologists have moved from measuring diversity as species richness and evenness, to using measures that reflect species ecological differences. There are two principal approaches for quantifying species ecological differences: functional (trait‐based) and phylogenetic pairwise distances between species. Both approaches have produced new ecological insights, yet at the same time methodological issues and assumptions limit them. Traits and phylogeny may provide different, and perhaps complementary, information about species' differences. To adequately test assembly hypotheses, a framework integrating the information provided by traits and phylogenies is required. We propose an intuitive measure for combining functional and phylogenetic pairwise distances, which provides a useful way to assess how functional and phylogenetic distances contribute to understanding patterns of community assembly. Here, we show that both traits and phylogeny inform community assembly patterns in alpine plant communities across an elevation gradient, because they represent complementary information. Differences in historical selection pressures have produced variation in the strength of the trait‐phylogeny correlation, and as such, integrating traits and phylogeny can enhance the ability to detect assembly patterns across habitats or environmental gradients.     

Rethinking the fast-slow continuum of individual differences

The idea that individual differences in behavior and physiology can be partly understood by linking them to a fast-slow continuum of life history strategies has become popular in the evolutionary behavioral sciences. I refer to this approach as the “fast-slow paradigm” of individual differences. The paradigm has generated a substantial amount of research, but has also come increasingly under scrutiny for theoretical, empirical, and methodological reasons. I start by reviewing the basic empirical facts about the fast-slow continuum across species and the main theoretical accounts of its existence. I then discuss the move from the level of species and populations to that of individuals, and the theoretical and empirical complications that follow. I argue that the fast-slow continuum can be a productive heuristic for individual differences; however, the field needs to update its theoretical assumptions, rethink some methodological practices, and explore new approaches and ideas in light of the specific features of the human ecology.     

Mammalian social systems: Structure and function

Mammalian societies are complex socio-ecological systems controlled by the interactions of numerous internal constraints and external factors. We present a simple model describing these systems functionally in terms of the adaptive behavioural strategies for resource exploitation, predation avoidance and mating and rearing of young to maturity shown by the individuals that comprise them. The relations between species-specific limitations on the range of potential individual social behaviour and the environmental variables to which the system is responsive are analysed and hypotheses from correlational and analytical field studies examined. We advocate the continued development of sophisticated systems-analytical approaches to societal analysis taking into account the contrasting informational provenance of factors of different types. This is preferred to either an over-emphasis on environmental determination or excessively formalised neo-darwinian modelling based on assumptions from genetics and selection theory alone.     

Conflicting biomedical assumptions for mathematical modeling: the case of cancer metastasis

Computational models in biomedicine rely on biological and clinical assumptions. The selection of these assumptions contributes substantially to modeling success or failure. Assumptions used by experts at the cutting edge of research, however, are rarely explicitly described in scientific publications. One can directly collect and assess some of these assumptions through interviews and surveys. Here we investigate diversity in expert views about a complex biological phenomenon, the process of cancer metastasis. We harvested individual viewpoints from 28 experts in clinical and molecular aspects of cancer metastasis and summarized them computationally. While experts predominantly agreed on the definition of individual steps involved in metastasis, no two expert scenarios for metastasis were identical. We computed the probability that any two experts would disagree on k or fewer metastatic stages and found that any two randomly selected experts are likely to disagree about several assumptions. Considering the probability that two or more of these experts review an article or a proposal about metastatic cascades, the probability that they will disagree with elements of a proposed model approaches 1. This diversity of conceptions has clear consequences for advance and deadlock in the field. We suggest that strong, incompatible views are common in biomedicine but largely invisible to biomedical experts themselves. We built a formal Markov model of metastasis to encapsulate expert convergence and divergence regarding the entire sequence of metastatic stages. This model revealed stages of greatest disagreement, including the points at which cancer enters and leaves the bloodstream. The model provides a formal probabilistic hypothesis against which researchers can evaluate data on the process of metastasis. This would enable subsequent improvement of the model through Bayesian probabilistic update. Practically, we propose that model assumptions and hunches be harvested systematically and made available for modelers and scientists.     

Comprehensive approach to analyzing rare genetic variants

Recent findings suggest that rare variants play an important role in both monogenic and common diseases. Due to their rarity, however, it remains unclear how to appropriately analyze the association between such variants and disease. A common approach entails combining rare variants together based on a priori information and analyzing them as a single group. Here one must make some assumptions about what to aggregate. Instead, we propose two approaches to empirically determine the most efficient grouping of rare variants. The first considers multiple possible groupings using existing information. The second is an agnostic "step-up" approach that determines an optimal grouping of rare variants analytically and does not rely on prior information. To evaluate these approaches, we undertook a simulation study using sequence data from genes in the one-carbon folate metabolic pathway. Our results show that using prior information to group rare variants is advantageous only when information is quite accurate, but the step-up approach works well across a broad range of plausible scenarios. This agnostic approach allows one to efficiently analyze the association between rare variants and disease while avoiding assumptions required by other approaches for grouping such variants.     

Efficient Blind Spectral Unmixing of Fluorescently Labeled Samples Using Multi-Layer Non-Negative Matrix Factorization

The ample variety of labeling dyes and staining methods available in fluorescence microscopy has enabled biologists to advance in the understanding of living organisms at cellular and molecular level. When two or more fluorescent dyes are used in the same preparation, or one dye is used in the presence of autofluorescence, the separation of the fluorescent emissions can become problematic. Various approaches have been recently proposed to solve this problem. Among them, blind non-negative matrix factorization is gaining interest since it requires little assumptions about the spectra and concentration of the fluorochromes. In this paper, we propose a novel algorithm for blind spectral separation that addresses some of the shortcomings of existing solutions: namely, their dependency on the initialization and their slow convergence. We apply this new algorithm to two relevant problems in fluorescence microscopy: autofluorescence elimination and spectral unmixing of multi-labeled samples. Our results show that our new algorithm performs well when compared with the state-of-the-art approaches for a much faster implementation.     

Community-based participatory research and integrated knowledge translation: advancing the co-creation of knowledge

Background

Better use of research evidence (one form of “knowledge”) in health systems requires partnerships between researchers and those who contend with the real-world needs and constraints of health systems. Community-based participatory research (CBPR) and integrated knowledge translation (IKT) are research approaches that emphasize the importance of creating partnerships between researchers and the people for whom the research is ultimately meant to be of use (“knowledge users”). There exist poor understandings of the ways in which these approaches converge and diverge. Better understanding of the similarities and differences between CBPR and IKT will enable researchers to use these approaches appropriately and to leverage best practices and knowledge from each. The co-creation of knowledge conveys promise of significant social impacts, and further understandings of how to engage and involve knowledge users in research are needed.

Main text

We examine the histories and traditions of CBPR and IKT, as well as their points of convergence and divergence. We critically evaluate the ways in which both have the potential to contribute to the development and integration of knowledge in health systems. As distinct research traditions, the underlying drivers and rationale for CBPR and IKT have similarities and differences across the areas of motivation, social location, and ethics; nevertheless, the practices of CBPR and IKT converge upon a common aim: the co-creation of knowledge that is the result of knowledge user and researcher expertise. We argue that while CBPR and IKT both have the potential to contribute evidence to implementation science and practices for collaborative research, clarity for the purpose of the research—social change or application—is a critical feature in the selection of an appropriate collaborative approach to build knowledge.

Conclusion

CBPR and IKT bring distinct strengths to a common aim: to foster democratic processes in the co-creation of knowledge. As research approaches, they create opportunities to challenge assumptions about for whom, how, and what is defined as knowledge, and to develop and integrate research findings into health systems. When used appropriately, CBPR and IKT both have the potential to contribute to and advance implementation science about the conduct of collaborative health systems research.

     

On the use of genetic divergence for identifying species

Degree of genetic divergence is frequently used to infer that two populations belong to separate species, or that several populations belong to a single species. I explore the logical framework of this approach, including the following assumptions: (i) speciation takes place over very long periods of time; (ii) reproductive isolation is based on the slow accumulation many genetic differences throughout the genome; (iii) genetic divergence automatically leads to reproductive isolation between species; and (iv) pre-mating and post-mating reproductive isolation have a similar genetic basis. I argue that so many exceptions to these assumptions have been demonstrated that they cannot be used with any reliability to distinguish different species. In addition, genetic distance as a species criterion is mostly used within the framework of Mayr's Biological Species Concept and is not free of assumptions about the nature of species or of speciation. The use of genetic distance to infer separate species (or the lack of these) is not parsimonious, its theoretical foundations are not well understood, and it cannot be applied over a wide range of plants and animals. I explore alternative approaches towards solving the species problems normally solved using genetic distance. © 2002 The Linnean Society of London, Biological Journal of the Linnean Society , 2002, 75 , 509–516.     

Taking species abundance distributions beyond individuals

The species abundance distribution (SAD) is one of the few universal patterns in ecology. Research on this fundamental distribution has primarily focused on the study of numerical counts, irrespective of the traits of individuals. Here we show that considering a set of Generalized Species Abundance Distributions (GSADs) encompassing several abundance measures, such as numerical abundance, biomass and resource use, can provide novel insights into the structure of ecological communities and the forces that organize them. We use a taxonomically diverse combination of macroecological data sets to investigate the similarities and differences between GSADs. We then use probability theory to explore, under parsimonious assumptions, theoretical linkages among them. Our study suggests that examining different GSADs simultaneously in natural systems may help with assessing determinants of community structure. Broadening SADs to encompass multiple abundance measures opens novel perspectives in biodiversity research and warrants future empirical and theoretical developments.     

Linkage analysis in the presence of errors IV: joint pseudomarker analysis of linkage and/or linkage disequilibrium on a mixture of pedigrees and singletons when the mode of inheritance cannot be accurately specified

There is a lot of confusion in the literature about the "differences" between "model-based" and "model-free" methods and about which approach is better suited for detection of the genes predisposing to complex multifactorial phenotypes. By starting from first principles, we demonstrate that the differences between the two approaches have more to do with study design than statistical analysis. When simple data structures are repeatedly ascertained, no assumptions about the genotype-phenotype relationship need to be made for the analysis to be powerful, since simple data structures admit only a small number of df. When more complicated and/or heterogeneous data structures are ascertained, however, the number of df in the underlying probability model is too large to have a powerful, truly "model-free" test. So-called "model-free" methods typically simplify the underlying probability model by implicitly assuming that, in some sense, all meioses connecting two affected individuals are informative for linkage with identical probability and that the affected individuals in a pedigree share as many disease-predisposing alleles as possible. By contrast, "model-based" methods add structure to the underlying parameter space by making assumptions about the genotype-phenotype relationship, making it possible to probabilistically assign disease-locus genotypes to all individuals in the data set on the basis of the observed phenotypes. In this study, we demonstrate the equivalence of these two approaches in a variety of situations and exploit this equivalence to develop more powerful and efficient likelihood-based analogues of "model-free" tests of linkage and/or linkage disequilibrium. Through the use of a "pseudomarker" locus to structure the space of observations, sib-pairs, triads, and singletons can be analyzed jointly, which will lead to tests that are more well-behaved, efficient, and powerful than traditional "model-free" tests such as the affected sib-pair, transmission/disequilibrium, haplotype relative risk, and case-control tests. Also described is an extension of this approach to large pedigrees, which, in practice, is equivalent to affected relative-pair analysis. The proposed methods are equally applicable to two-point and multipoint analysis (using complex-valued recombination fractions).     

Modelling the adaptive dynamics of traits involved in inter- and intraspecific interactions: An assessment of three methods

In recent years, three related methods have been used to model the phenotypic dynamics of traits under the influence of natural selection. The first is based on an approximation to quantitative genetic recursion equations for sexual populations. The second is based on evolution in asexual lineages with mutation-generated variation. The third method finds an evolutionarily stable set of phenotypes for species characterized by a given set of fitness functions, assuming that the mode of reproduction places no constraints on the number of distinct types that can be maintained in the population. The three methods share the property that the rate of change of a trait within a homogeneous population is approximately proportional to the individual fitness gradient. The methods differ in assumptions about the potential magnitude of phenotypic differences in mutant forms, and in their assumptions about the probability that invasion or speciation occurs when a species has a stable, yet invadable phenotype. Determining the range of applicability of the different methods is important for assessing the validity of optimization methods in predicting the evolutionary outcome of ecological interactions. Methods based on quantitative genetic models predict that fitness minimizing traits will often be evolutionarily stable over significant time periods, while other approaches suggest this is likely to be rare. A more detailed study of cases of disruptive selection might reveal whether fitness-minimizing traits occur frequently in natural communities.     

Evolving from bioinformatics in-the-small to bioinformatics in-the-large

We argue the significance of a fundamental shift in bioinformatics, from in-the-small to in-the-large. Adopting a large-scale perspective is a way to manage the problems endemic to the world of the small-constellations of incompatible tools for which the effort required to assemble an integrated system exceeds the perceived benefit of the integration. Where bioinformatics in-the-small is about data and tools, bioinformatics in-the-large is about metadata and dependencies. Dependencies represent the complexities of large-scale integration, including the requirements and assumptions governing the composition of tools. The popular make utility is a very effective system for defining and maintaining simple dependencies, and it offers a number of insights about the essence of bioinformatics in-the-large. Keeping an in-the-large perspective has been very useful to us in large bioinformatics projects. We give two fairly different examples, and extract lessons from them showing how it has helped. These examples both suggest the benefit of explicitly defining and managing knowledge flows and knowledge maps (which represent metadata regarding types, flows, and dependencies), and also suggest approaches for developing bioinformatics database systems. Generally, we argue that large-scale engineering principles can be successfully adapted from disciplines such as software engineering and data management, and that having an in-the-large perspective will be a key advantage in the next phase of bioinformatics development.     

The currency and tempo of extinction

This study examines estimates of extinction rates for the current purported biotic crisis and from the fossil record. Studies that compare current and geological extinctions sometimes use metrics that confound different sources of error and reflect different features of extinction processes. The per taxon extinction rate is a standard measure in paleontology that avoids some of the pitfalls of alternative approaches. Extinction rates reported in the conservation literature are rarely accompanied by measures of uncertainty, despite many elements of the calculations being subject to considerable error. We quantify some of the most important sources of uncertainty and carry them through the arithmetic of extinction rate calculations using fuzzy numbers. The results emphasize that estimates of current and future rates rely heavily on assumptions about the tempo of extinction and on extrapolations among taxa. Available data are unlikely to be useful in measuring magnitudes or trends in current extinction rates.     

Extending and expanding the Darwinian synthesis: the role of complex systems dynamics

Darwinism is defined here as an evolving research tradition based upon the concepts of natural selection acting upon heritable variation articulated via background assumptions about systems dynamics. Darwin's theory of evolution was developed within a context of the background assumptions of Newtonian systems dynamics. The Modern Evolutionary Synthesis, or neo-Darwinism, successfully joined Darwinian selection and Mendelian genetics by developing population genetics informed by background assumptions of Boltzmannian systems dynamics. Currently the Darwinian Research Tradition is changing as it incorporates new information and ideas from molecular biology, paleontology, developmental biology, and systems ecology. This putative expanded and extended synthesis is most perspicuously deployed using background assumptions from complex systems dynamics. Such attempts seek to not only broaden the range of phenomena encompassed by the Darwinian Research Tradition, such as neutral molecular evolution, punctuated equilibrium, as well as developmental biology, and systems ecology more generally, but to also address issues of the emergence of evolutionary novelties as well as of life itself.     

The (mis)concept of species recognition

To many, the concept of 'species recognition' is integral to the origin and maintenance of species. However, the heuristic value of species recognition is hampered by its reliance on the problematic concept of species. In this paper, we first discuss assumptions associated with prevailing use of the term, including the typological implications of the concept, the false dichotomy of compatibility and mate quality, and the commonly held model of species recognition in which animals determine taxonomic status before mate status. Subsequently, we propose research directions aimed to improve our understanding of the role of courtship behavior in speciation. We propose two complementary research approaches, one addressing the processes that drive the evolution of mate recognition systems and the other addressing the phenotypic architecture of behavioral isolation. Our approach emphasizes the fitness consequences and multidimensional nature of mate choice.     

Analytical applications of separation techniques through membranes

An overview about the analytical applications of membrane-based systems is presented. The different types of membranes (microporous, homogenous, ion-exchange and asymmetric) and the main forces related to the transport through them (differences in concentration, pressure or electrical potential) are briefly discussed.     

On statistical energy functions for biomolecular modeling and design

Statistical energy functions are general models about atomic or residue-level interactions in biomolecules, derived from existing experimental data. They provide quantitative foundations for structural modeling as well as for structure-based protein sequence design. Statistical energy functions can be derived computationally either based on statistical distributions or based on variational assumptions. We present overviews on the theoretical assumptions underlying the various types of approaches. Theoretical considerations underlying important pragmatic choices are discussed.