On the Existence of Evolutionary Learning Equilibriums

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Masudul Alam Choudhury

2011-12-01

Full Text Available The usual kinds of Fixed-Point Theorems formalized on the existence of competitive equilibrium that explain much of economic theory at the core of economics can operate only on bounded and closed sets with convex mappings. But these conditions are hardly true of the real world of economic and financial complexities and perturbations. The category of learning sets explained by continuous fields of interactive, integrative and evolutionary behaviour caused by dynamic preferences at the individual and institutional and social levels cannot maintain the assumption of closed, bounded and convex sets. Thus learning sets and multi-system inter-temporal relations explained by pervasive complementarities and  participation between variables and entities, and evolution by learning, have evolutionary equilibriums. Such a study requires a new methodological approach. This paper formalizes such a methodology for evolutionary equilibriums in learning spaces. It briefly points out the universality of learning equilibriums in all mathematical structures. For a particular case though, the inter-systemic interdependence between sustainable development and ethics and economics in the specific understanding of learning domain is pointed out.

Biology Needs Evolutionary Software Tools: Let’s Build Them Right

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Team, Galaxy; Goecks, Jeremy; Taylor, James

2018-01-01

Abstract Research in population genetics and evolutionary biology has always provided a computational backbone for life sciences as a whole. Today evolutionary and population biology reasoning are essential for interpretation of large complex datasets that are characteristic of all domains of today’s life sciences ranging from cancer biology to microbial ecology. This situation makes algorithms and software tools developed by our community more important than ever before. This means that we, developers of software tool for molecular evolutionary analyses, now have a shared responsibility to make these tools accessible using modern technological developments as well as provide adequate documentation and training. PMID:29688462

Quantum Mechanics predicts evolutionary biology.

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Torday, J S

2018-07-01

Nowhere are the shortcomings of conventional descriptive biology more evident than in the literature on Quantum Biology. In the on-going effort to apply Quantum Mechanics to evolutionary biology, merging Quantum Mechanics with the fundamentals of evolution as the First Principles of Physiology-namely negentropy, chemiosmosis and homeostasis-offers an authentic opportunity to understand how and why physics constitutes the basic principles of biology. Negentropy and chemiosmosis confer determinism on the unicell, whereas homeostasis constitutes Free Will because it offers a probabilistic range of physiologic set points. Similarly, on this basis several principles of Quantum Mechanics also apply directly to biology. The Pauli Exclusion Principle is both deterministic and probabilistic, whereas non-localization and the Heisenberg Uncertainty Principle are both probabilistic, providing the long-sought after ontologic and causal continuum from physics to biology and evolution as the holistic integration recognized as consciousness for the first time. Copyright © 2018 Elsevier Ltd. All rights reserved.

Integrative Assessment of Evolutionary Theory Acceptance and Knowledge Levels of Biology Undergraduate Students from a Brazilian University

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Tavares, Gustavo Medina; Bobrowski, Vera Lucia

2018-01-01

The integrative role that Evolutionary theory plays within Biology is recognised by most scientific authors, as well as in governmental education policies, including Brazilian policies. However, teaching and learning evolution seems problematic in many countries, and Brazil is among those. Many factors may affect teachers' and students'…

Distribution Learning in Evolutionary Strategies and Restricted Boltzmann Machines

DEFF Research Database (Denmark)

Krause, Oswin

The thesis is concerned with learning distributions in the two settings of Evolutionary Strategies (ESs) and Restricted Boltzmann Machines (RBMs). In both cases, the distributions are learned from samples, albeit with different goals. Evolutionary Strategies are concerned with finding an optimum ...

Transforming Biology Assessment with Machine Learning: Automated Scoring of Written Evolutionary Explanations

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Nehm, Ross H.; Ha, Minsu; Mayfield, Elijah

2012-02-01

This study explored the use of machine learning to automatically evaluate the accuracy of students' written explanations of evolutionary change. Performance of the Summarization Integrated Development Environment (SIDE) program was compared to human expert scoring using a corpus of 2,260 evolutionary explanations written by 565 undergraduate students in response to two different evolution instruments (the EGALT-F and EGALT-P) that contained prompts that differed in various surface features (such as species and traits). We tested human-SIDE scoring correspondence under a series of different training and testing conditions, using Kappa inter-rater agreement values of greater than 0.80 as a performance benchmark. In addition, we examined the effects of response length on scoring success; that is, whether SIDE scoring models functioned with comparable success on short and long responses. We found that SIDE performance was most effective when scoring models were built and tested at the individual item level and that performance degraded when suites of items or entire instruments were used to build and test scoring models. Overall, SIDE was found to be a powerful and cost-effective tool for assessing student knowledge and performance in a complex science domain.

Social traits, social networks and evolutionary biology.

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Fisher, D N; McAdam, A G

2017-12-01

effects) provides the potential to understand how entire networks of social interactions in populations influence phenotypes and predict how these traits may evolve. By theoretical integration of social network analysis and quantitative genetics, we hope to identify areas of compatibility and incompatibility and to direct research efforts towards the most promising areas. Continuing this synthesis could provide important insights into the evolution of traits expressed in a social context and the evolutionary consequences of complex and nuanced social phenotypes. © 2017 European Society For Evolutionary Biology. Journal of Evolutionary Biology © 2017 European Society For Evolutionary Biology.

Evolutionary computation for reinforcement learning

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Whiteson, S.; Wiering, M.; van Otterlo, M.

2012-01-01

Algorithms for evolutionary computation, which simulate the process of natural selection to solve optimization problems, are an effective tool for discovering high-performing reinforcement-learning policies. Because they can automatically find good representations, handle continuous action spaces,

Exploitation of linkage learning in evolutionary algorithms

CERN Document Server

Chen, Ying-ping

2010-01-01

The exploitation of linkage learning is enhancing the performance of evolutionary algorithms. This monograph examines recent progress in linkage learning, with a series of focused technical chapters that cover developments and trends in the field.

Evolutionary Biology: Its Value to Society

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Carson, Hampton L.

1972-01-01

Cites examples of the contribution of basic research in evolutionary biology to the solution of problems facing society (1) by dispelling myths about human origins, the nature of the individual, and the nature of race (2) by providing basic data concerning the effects of overpopulation, the production of improved sources of food, resistance of…

Nanoscopical dissection of ancestral nucleoli in Archaea: a case of study in Evolutionary Cell Biology

KAUST Repository

Islas Morales, Parsifal

2018-04-01

lessons learned from ECB and attempts to find a homologue structure of the eukaryotic nucleolus within the Archaea. We found nanometric structures in S. solfactarius that either are positive to specific nucleolar techniques such as Nucleolar organizer regions NOR silver staining. These is structures are novel and its significance should be revised on the evolutionary cell biology perspective.

Towards a Population Dynamics Theory for Evolutionary Computing: Learning from Biological Population Dynamics in Nature

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Ma, Zhanshan (Sam)

In evolutionary computing (EC), population size is one of the critical parameters that a researcher has to deal with. Hence, it was no surprise that the pioneers of EC, such as De Jong (1975) and Holland (1975), had already studied the population sizing from the very beginning of EC. What is perhaps surprising is that more than three decades later, we still largely depend on the experience or ad-hoc trial-and-error approach to set the population size. For example, in a recent monograph, Eiben and Smith (2003) indicated: "In almost all EC applications, the population size is constant and does not change during the evolutionary search." Despite enormous research on this issue in recent years, we still lack a well accepted theory for population sizing. In this paper, I propose to develop a population dynamics theory forEC with the inspiration from the population dynamics theory of biological populations in nature. Essentially, the EC population is considered as a dynamic system over time (generations) and space (search space or fitness landscape), similar to the spatial and temporal dynamics of biological populations in nature. With this conceptual mapping, I propose to 'transplant' the biological population dynamics theory to EC via three steps: (i) experimentally test the feasibility—whether or not emulating natural population dynamics improves the EC performance; (ii) comparatively study the underlying mechanisms—why there are improvements, primarily via statistical modeling analysis; (iii) conduct theoretical analysis with theoretical models such as percolation theory and extended evolutionary game theory that are generally applicable to both EC and natural populations. This article is a summary of a series of studies we have performed to achieve the general goal [27][30]-[32]. In the following, I start with an extremely brief introduction on the theory and models of natural population dynamics (Sections 1 & 2). In Sections 4 to 6, I briefly discuss three

The Design and Transformation of Biofundamentals: A Nonsurvey Introductory Evolutionary and Molecular Biology Course.

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Klymkowsky, Michael W; Rentsch, Jeremy D; Begovic, Emina; Cooper, Melanie M

2016-01-01

Many introductory biology courses amount to superficial surveys of disconnected topics. Often, foundational observations and the concepts derived from them and students' ability to use these ideas appropriately are overlooked, leading to unrealistic expectations and unrecognized learning obstacles. The result can be a focus on memorization at the expense of the development of a meaningful framework within which to consider biological phenomena. About a decade ago, we began a reconsideration of what an introductory course should present to students and the skills they need to master. The original Web-based course's design presaged many of the recommendations of the Vision and Change report; in particular, a focus on social evolutionary mechanisms, stochastic (evolutionary and molecular) processes, and core ideas (cellular continuity, evolutionary homology, molecular interactions, coupled chemical reactions, and molecular machines). Inspired by insights from the Chemistry, Life, the Universe & Everything general chemistry project, we transformed the original Web version into a (freely available) book with a more unified narrative flow and a set of formative assessments delivered through the beSocratic system. We outline how student responses to course materials are guiding future course modifications, in particular a more concerted effort at helping students to construct logical, empirically based arguments, explanations, and models. © 2016 M. W. Klymkowsky et al. CBE—Life Sciences Education © 2016 The American Society for Cell Biology. This article is distributed by The American Society for Cell Biology under license from the author(s). It is available to the public under an Attribution–Noncommercial–Share Alike 3.0 Unported Creative Commons License (http://creativecommons.org/licenses/by-nc-sa/3.0).

A new evolutionary algorithm with LQV learning for combinatorial problems optimization

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Machado, Marcelo Dornellas; Schirru, Roberto

2000-01-01

Genetic algorithms are biologically motivated adaptive systems which have been used, with good results, for combinatorial problems optimization. In this work, a new learning mode, to be used by the population-based incremental learning algorithm, has the aim to build a new evolutionary algorithm to be used in optimization of numerical problems and combinatorial problems. This new learning mode uses a variable learning rate during the optimization process, constituting a process known as proportional reward. The development of this new algorithm aims its application in the optimization of reload problem of PWR nuclear reactors, in order to increase the useful life of the nuclear fuel. For the test, two classes of problems are used: numerical problems and combinatorial problems. Due to the fact that the reload problem is a combinatorial problem, the major interest relies on the last class. The results achieved with the tests indicate the applicability of the new learning mode, showing its potential as a developing tool in the solution of reload problem. (author)

Entrepreneurs and Evolutionary Biology: The Relationship between Testosterone and New Venture Creation

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White, Roderick E.; Thornhill, Stewart; Hampson, Elizabeth

2006-01-01

Biological evolutionary processes select for heritable behaviors providing a survival and reproductive advantage. Accordingly, how we behave is, at least in part, affected by the evolutionary history of our species. This research uses evolutionary psychology as the theoretical perspective for exploring the relationship between a heritable…

Evolutionary biology and the determinants of morality 2 | Odozor ...

African Journals Online (AJOL)

This second essay continues the reflection on this problem through an exploration of the extra-biological factors and how they effect human moral development—a facet which is only too obvious, but which the evolutionary approach sidelines in its desperate effort to put up purely biological accounts of morality and ethics.

Evolutionary biology: a basic science for medicine in the 21st century.

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Perlman, Robert L

2011-01-01

Evolutionary biology was a poorly developed discipline at the time of the Flexner Report and was not included in Flexner's recommendations for premedical or medical education. Since that time, however, the value of an evolutionary approach to medicine has become increasingly recognized. There are several ways in which an evolutionary perspective can enrich medical education and improve medical practice. Evolutionary considerations rationalize our continued susceptibility or vulnerability to disease; they call attention to the idea that the signs and symptoms of disease may be adaptations that prevent or limit the severity of disease; they help us understand the ways in which our interventions may affect the evolution of microbial pathogens and of cancer cells; and they provide a framework for thinking about population variation and risk factors for disease. Evolutionary biology should become a foundational science for the medical education of the future.

Evolution of microbes and viruses: A paradigm shift in evolutionary biology?

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Eugene V. Koonin

2012-09-01

Full Text Available When Charles Darwin formulated the central principles of evolutionary biology in the Origin of Species in 1859 and the architects of the Modern Synthesis integrated these principles with population genetics almost a century later, the principal if not the sole objects of evolutionary biology were multicellular eukaryotes, primarily animals and plants. Before the advent of efficient gene sequencing, all attempts to extend evolutionary studies to bacteria have been futile. Sequencing of the rRNA genes in thousands of microbes allowed the construction of the three- domain ‘ribosomal Tree of Life’ that was widely thought to have resolved the evolutionary relationships between the cellular life forms. However, subsequent massive sequencing of numerous, complete microbial genomes revealed novel evolutionary phenomena, the most fundamental of these being: i pervasive horizontal gene transfer (HGT, in large part mediated by viruses and plasmids, that shapes the genomes of archaea and bacteria and call for a radical revision (if not abandonment of the Tree of Life concept, ii Lamarckian-type inheritance that appears to be critical for antivirus defense and other forms of adaptation in prokaryotes, and iii evolution of evolvability, i.e. dedicated mechanisms for evolution such as vehicles for HGT and stress-induced mutagenesis systems. In the non-cellular part of the microbial world, phylogenomics and metagenomics of viruses and related selfish genetic elements revealed enormous genetic and molecular diversity and extremely high abundance of viruses that come across as the dominant biological entities on earth. Furthermore, the perennial arms race between viruses and their hosts is one of the defining factors of evolution. Thus, microbial phylogenomics adds new dimensions to the fundamental picture of evolution even as the principle of descent with modification discovered by Darwin and the laws of population genetics remain at the core of evolutionary

Indoor Thermal Comfort, an Evolutionary Biology Perspective

Energy Technology Data Exchange (ETDEWEB)

Stoops, John L.

2006-04-15

As is becoming increasingly clear, the human species evolvedin the East African savannah. Details of the precise evolutionary chainremain unresolved however it appears that the process lasted severalmillion years, culminating with the emergence of modern Homo sapiensroughly 200,000 years ago. Following that final evolutionary developmentmodern Homo sapiens relatively quickly populated the entire world.Clearly modern Homo sapiens is a successful, resourceful and adaptablespecies. In the developed societies, modern humans live an existence farremoved from our evolutionary ancestors. As we have learned over the lastcentury, this "new" lifestyle can often result in unintendedconsequences. Clearly, our modern access to food, shelter, transportationand healthcare has resulted in greatly expanded expected lifespan butthis new lifestyle can also result in the emergence of different kinds ofdiseases and health problems. The environment in modern buildings haslittle resemblance to the environment of the savannah. We strive tocreate environments with little temperature, air movement and lightvariation. Building occupants often express great dissatisfaction withthese modern created environments and a significant fraction even developsomething akin to allergies to specific buildings (sick buildingsyndrome). Are the indoor environments we are creating fundamentallyunhealthy -- when examined from an evolutionary perspective?

Applying evolutionary biology to address global challenges

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Carroll, Scott P.; Jørgensen, Peter Søgaard; Kinnison, Michael T.; Bergstrom, Carl T.; Denison, R. Ford; Gluckman, Peter; Smith, Thomas B.; Strauss, Sharon Y.; Tabashnik, Bruce E.

2014-01-01

Two categories of evolutionary challenges result from escalating human impacts on the planet. The first arises from cancers, pathogens and pests that evolve too quickly, and the second from the inability of many valued species to adapt quickly enough. Applied evolutionary biology provides a suite of strategies to address these global challenges that threaten human health, food security, and biodiversity. This review highlights both progress and gaps in genetic, developmental and environmental manipulations across the life sciences that either target the rate and direction of evolution, or reduce the mismatch between organisms and human-altered environments. Increased development and application of these underused tools will be vital in meeting current and future targets for sustainable development. PMID:25213376

Nanoscopical dissection of ancestral nucleoli in Archaea: a case of study in Evolutionary Cell Biology

KAUST Repository

Islas Morales, Parsifal

2018-01-01

Evolutionary cell biology (ECB) has raised increasing attention in the last decades. Is this a new discipline and an historical opportunity to combine functional and evolutionary biology towards the insight that cell

Evolutionary systems biology: historical and philosophical perspectives on an emerging synthesis.

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O'Malley, Maureen A

2012-01-01

Systems biology (SB) is at least a decade old now and maturing rapidly. A more recent field, evolutionary systems biology (ESB), is in the process of further developing system-level approaches through the expansion of their explanatory and potentially predictive scope. This chapter will outline the varieties of ESB existing today by tracing the diverse roots and fusions that make up this integrative project. My approach is philosophical and historical. As well as examining the recent origins of ESB, I will reflect on its central features and the different clusters of research it comprises. In its broadest interpretation, ESB consists of five overlapping approaches: comparative and correlational ESB; network architecture ESB; network property ESB; population genetics ESB; and finally, standard evolutionary questions answered with SB methods. After outlining each approach with examples, I will examine some strong general claims about ESB, particularly that it can be viewed as the next step toward a fuller modern synthesis of evolutionary biology (EB), and that it is also the way forward for evolutionary and systems medicine. I will conclude with a discussion of whether the emerging field of ESB has the capacity to combine an even broader scope of research aims and efforts than it presently does.

The role of evolutionary biology in research and control of liver flukes in Southeast Asia.

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Echaubard, Pierre; Sripa, Banchob; Mallory, Frank F; Wilcox, Bruce A

2016-09-01

Stimulated largely by the availability of new technology, biomedical research at the molecular-level and chemical-based control approaches arguably dominate the field of infectious diseases. Along with this, the proximate view of disease etiology predominates to the exclusion of the ultimate, evolutionary biology-based, causation perspective. Yet, historically and up to today, research in evolutionary biology has provided much of the foundation for understanding the mechanisms underlying disease transmission dynamics, virulence, and the design of effective integrated control strategies. Here we review the state of knowledge regarding the biology of Asian liver Fluke-host relationship, parasitology, phylodynamics, drug-based interventions and liver Fluke-related cancer etiology from an evolutionary biology perspective. We consider how evolutionary principles, mechanisms and research methods could help refine our understanding of clinical disease associated with infection by Liver Flukes as well as their transmission dynamics. We identify a series of questions for an evolutionary biology research agenda for the liver Fluke that should contribute to an increased understanding of liver Fluke-associated diseases. Finally, we describe an integrative evolutionary medicine approach to liver Fluke prevention and control highlighting the need to better contextualize interventions within a broader human health and sustainable development framework. Copyright © 2016 Elsevier B.V. All rights reserved.

The Microfoundations of Macroeconomics: An Evolutionary Perspective

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Bergh, van den Jeroen C.J.M.; Gowdy, John M.

2000-01-01

We consider the microfoundations controversy from the perspective ofeconomic evolution and show that the debate can benefit from lessons learned in evolutionary biology. Although the analogy between biology and economics has been noted before, it has rarely focused on clarifying the micro-macro

Not just a theory--the utility of mathematical models in evolutionary biology.

Directory of Open Access Journals (Sweden)

Maria R Servedio

2014-12-01

Full Text Available Progress in science often begins with verbal hypotheses meant to explain why certain biological phenomena exist. An important purpose of mathematical models in evolutionary research, as in many other fields, is to act as “proof-of-concept” tests of the logic in verbal explanations, paralleling the way in which empirical data are used to test hypotheses. Because not all subfields of biology use mathematics for this purpose, misunderstandings of the function of proof-of-concept modeling are common. In the hope of facilitating communication, we discuss the role of proof-of-concept modeling in evolutionary biology.

A soul of truth in things erroneous: Popper's "amateurish" evolutionary philosophy in light of contemporary biology.

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Vecchi, Davide; Baravalle, Lorenzo

2015-01-01

This paper will critically assess Popper's evolutionary philosophy. There exists a rich literature on the topic with which we have many reservations. We believe that Popper's evolutionary philosophy should be assessed in light of the intriguing theoretical insights offered, during the last 10 years or so, by the philosophy of biology, evolutionary biology and molecular biology. We will argue that, when analysed in this manner, Popper's ideas concerning the nature of selection, Lamarckism and the theoretical limits of neo-Darwinism can be appreciated in their full biological and philosophical value.

Stochastic noncooperative and cooperative evolutionary game strategies of a population of biological networks under natural selection.

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Chen, Bor-Sen; Yeh, Chin-Hsun

2017-12-01

We review current static and dynamic evolutionary game strategies of biological networks and discuss the lack of random genetic variations and stochastic environmental disturbances in these models. To include these factors, a population of evolving biological networks is modeled as a nonlinear stochastic biological system with Poisson-driven genetic variations and random environmental fluctuations (stimuli). To gain insight into the evolutionary game theory of stochastic biological networks under natural selection, the phenotypic robustness and network evolvability of noncooperative and cooperative evolutionary game strategies are discussed from a stochastic Nash game perspective. The noncooperative strategy can be transformed into an equivalent multi-objective optimization problem and is shown to display significantly improved network robustness to tolerate genetic variations and buffer environmental disturbances, maintaining phenotypic traits for longer than the cooperative strategy. However, the noncooperative case requires greater effort and more compromises between partly conflicting players. Global linearization is used to simplify the problem of solving nonlinear stochastic evolutionary games. Finally, a simple stochastic evolutionary model of a metabolic pathway is simulated to illustrate the procedure of solving for two evolutionary game strategies and to confirm and compare their respective characteristics in the evolutionary process. Copyright © 2017 Elsevier B.V. All rights reserved.

Evolutionary thinking

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Hunt, Tam

2014-01-01

Evolution as an idea has a lengthy history, even though the idea of evolution is generally associated with Darwin today. Rebecca Stott provides an engaging and thoughtful overview of this history of evolutionary thinking in her 2013 book, Darwin's Ghosts: The Secret History of Evolution. Since Darwin, the debate over evolution—both how it takes place and, in a long war of words with religiously-oriented thinkers, whether it takes place—has been sustained and heated. A growing share of this debate is now devoted to examining how evolutionary thinking affects areas outside of biology. How do our lives change when we recognize that all is in flux? What can we learn about life more generally if we study change instead of stasis? Carter Phipps’ book, Evolutionaries: Unlocking the Spiritual and Cultural Potential of Science's Greatest Idea, delves deep into this relatively new development. Phipps generally takes as a given the validity of the Modern Synthesis of evolutionary biology. His story takes us into, as the subtitle suggests, the spiritual and cultural implications of evolutionary thinking. Can religion and evolution be reconciled? Can evolutionary thinking lead to a new type of spirituality? Is our culture already being changed in ways that we don't realize by evolutionary thinking? These are all important questions and Phipps book is a great introduction to this discussion. Phipps is an author, journalist, and contributor to the emerging “integral” or “evolutionary” cultural movement that combines the insights of Integral Philosophy, evolutionary science, developmental psychology, and the social sciences. He has served as the Executive Editor of EnlightenNext magazine (no longer published) and more recently is the co-founder of the Institute for Cultural Evolution, a public policy think tank addressing the cultural roots of America's political challenges. What follows is an email interview with Phipps. PMID:26478766

Evolutionary and adaptive learning in complex markets: a brief summary

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Hommes, Cars H.

2007-06-01

We briefly review some work on expectations and learning in complex markets, using the familiar demand-supply cobweb model. We discuss and combine two different approaches on learning. According to the adaptive learning approach, agents behave as econometricians using time series observations to form expectations, and update the parameters as more observations become available. This approach has become popular in macro. The second approach has an evolutionary flavor and is sometimes referred to as reinforcement learning. Agents employ different forecasting strategies and evaluate these strategies based upon a fitness measure, e.g. past realized profits. In this framework, boundedly rational agents switch between different, but fixed behavioral rules. This approach has become popular in finance. We combine evolutionary and adaptive learning to model complex markets and discuss whether this theory can match empirical facts and forecasting behavior in laboratory experiments with human subjects.

Core principles of evolutionary medicine

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Grunspan, Daniel Z; Nesse, Randolph M; Barnes, M Elizabeth; Brownell, Sara E

2018-01-01

Abstract Background and objectives Evolutionary medicine is a rapidly growing field that uses the principles of evolutionary biology to better understand, prevent and treat disease, and that uses studies of disease to advance basic knowledge in evolutionary biology. Over-arching principles of evolutionary medicine have been described in publications, but our study is the first to systematically elicit core principles from a diverse panel of experts in evolutionary medicine. These principles should be useful to advance recent recommendations made by The Association of American Medical Colleges and the Howard Hughes Medical Institute to make evolutionary thinking a core competency for pre-medical education. Methodology The Delphi method was used to elicit and validate a list of core principles for evolutionary medicine. The study included four surveys administered in sequence to 56 expert panelists. The initial open-ended survey created a list of possible core principles; the three subsequent surveys winnowed the list and assessed the accuracy and importance of each principle. Results Fourteen core principles elicited at least 80% of the panelists to agree or strongly agree that they were important core principles for evolutionary medicine. These principles over-lapped with concepts discussed in other articles discussing key concepts in evolutionary medicine. Conclusions and implications This set of core principles will be helpful for researchers and instructors in evolutionary medicine. We recommend that evolutionary medicine instructors use the list of core principles to construct learning goals. Evolutionary medicine is a young field, so this list of core principles will likely change as the field develops further. PMID:29493660

A Probability-based Evolutionary Algorithm with Mutations to Learn Bayesian Networks

Directory of Open Access Journals (Sweden)

Sho Fukuda

2014-12-01

Full Text Available Bayesian networks are regarded as one of the essential tools to analyze causal relationship between events from data. To learn the structure of highly-reliable Bayesian networks from data as quickly as possible is one of the important problems that several studies have been tried to achieve. In recent years, probability-based evolutionary algorithms have been proposed as a new efficient approach to learn Bayesian networks. In this paper, we target on one of the probability-based evolutionary algorithms called PBIL (Probability-Based Incremental Learning, and propose a new mutation operator. Through performance evaluation, we found that the proposed mutation operator has a good performance in learning Bayesian networks

From experimental systems to evolutionary biology: an impossible journey?

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Morange, Michel

2013-01-01

The historical approach to the sciences has undergone a sea change during recent decades. Maybe the major contribution of Hans-Jörg Rheinberger to this movement was his demonstration of the importance of experimental systems, and of their transformations, in the development of the sciences. To describe these transformations, Hans-Jörg borrows metaphors from evolutionary biology. I want to argue that evolutionary biologists can find in these recent historical studies plenty of models and concepts to address unresolved issues in their discipline. At a time when transdisciplinarity is highly praised, it is useful to provide a precise description of the obstacles that have so far prevented this exchange.

Psychology and evolutionary biology; Causal analysis, evidence, and nomothetic laws

NARCIS (Netherlands)

Van Hezewijk, René

2008-01-01

Published as a chapter in Van Hezewijk, R. (2003). Psychology and evolutionary biology; Causal analysis, evidence, and nomothetic laws. In N. Stephenson, L. Radtke, R. Jorna & H. J. Stam (Eds.), Theoretical psychology; Critical contributions (pp. 405-415). Concord, Ontario: Captus Press.

On the Interplay between the Evolvability and Network Robustness in an Evolutionary Biological Network: A Systems Biology Approach

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Chen, Bor-Sen; Lin, Ying-Po

2011-01-01

In the evolutionary process, the random transmission and mutation of genes provide biological diversities for natural selection. In order to preserve functional phenotypes between generations, gene networks need to evolve robustly under the influence of random perturbations. Therefore, the robustness of the phenotype, in the evolutionary process, exerts a selection force on gene networks to keep network functions. However, gene networks need to adjust, by variations in genetic content, to generate phenotypes for new challenges in the network’s evolution, ie, the evolvability. Hence, there should be some interplay between the evolvability and network robustness in evolutionary gene networks. In this study, the interplay between the evolvability and network robustness of a gene network and a biochemical network is discussed from a nonlinear stochastic system point of view. It was found that if the genetic robustness plus environmental robustness is less than the network robustness, the phenotype of the biological network is robust in evolution. The tradeoff between the genetic robustness and environmental robustness in evolution is discussed from the stochastic stability robustness and sensitivity of the nonlinear stochastic biological network, which may be relevant to the statistical tradeoff between bias and variance, the so-called bias/variance dilemma. Further, the tradeoff could be considered as an antagonistic pleiotropic action of a gene network and discussed from the systems biology perspective. PMID:22084563

Biological causal links on physiological and evolutionary time scales.

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Karmon, Amit; Pilpel, Yitzhak

2016-04-26

Correlation does not imply causation. If two variables, say A and B, are correlated, it could be because A causes B, or that B causes A, or because a third factor affects them both. We suggest that in many cases in biology, the causal link might be bi-directional: A causes B through a fast-acting physiological process, while B causes A through a slowly accumulating evolutionary process. Furthermore, many trained biologists tend to consistently focus at first on the fast-acting direction, and overlook the slower process in the opposite direction. We analyse several examples from modern biology that demonstrate this bias (codon usage optimality and gene expression, gene duplication and genetic dispensability, stem cell division and cancer risk, and the microbiome and host metabolism) and also discuss an example from linguistics. These examples demonstrate mutual effects between the fast physiological processes and the slow evolutionary ones. We believe that building awareness of inference biases among biologists who tend to prefer one causal direction over another could improve scientific reasoning.

Computational Modeling of Teaching and Learning through Application of Evolutionary Algorithms

Directory of Open Access Journals (Sweden)

Richard Lamb

2015-09-01

Full Text Available Within the mind, there are a myriad of ideas that make sense within the bounds of everyday experience, but are not reflective of how the world actually exists; this is particularly true in the domain of science. Classroom learning with teacher explanation are a bridge through which these naive understandings can be brought in line with scientific reality. The purpose of this paper is to examine how the application of a Multiobjective Evolutionary Algorithm (MOEA can work in concert with an existing computational-model to effectively model critical-thinking in the science classroom. An evolutionary algorithm is an algorithm that iteratively optimizes machine learning based computational models. The research question is, does the application of an evolutionary algorithm provide a means to optimize the Student Task and Cognition Model (STAC-M and does the optimized model sufficiently represent and predict teaching and learning outcomes in the science classroom? Within this computational study, the authors outline and simulate the effect of teaching on the ability of a “virtual” student to solve a Piagetian task. Using the Student Task and Cognition Model (STAC-M a computational model of student cognitive processing in science class developed in 2013, the authors complete a computational experiment which examines the role of cognitive retraining on student learning. Comparison of the STAC-M and the STAC-M with inclusion of the Multiobjective Evolutionary Algorithm shows greater success in solving the Piagetian science-tasks post cognitive retraining with the Multiobjective Evolutionary Algorithm. This illustrates the potential uses of cognitive and neuropsychological computational modeling in educational research. The authors also outline the limitations and assumptions of computational modeling.

Evolutionary Science as a Method to Facilitate Higher Level Thinking and Reasoning in Medical Training.

Science.gov (United States)

Graves, Joseph L; Reiber, Chris; Thanukos, Anna; Hurtado, Magdalena; Wolpaw, Terry

2016-10-15

Evolutionary science is indispensable for understanding biological processes. Effective medical treatment must be anchored in sound biology. However, currently the insights available from evolutionary science are not adequately incorporated in either pre-medical or medical school curricula. To illuminate how evolution may be helpful in these areas, examples in which the insights of evolutionary science are already improving medical treatment and ways in which evolutionary reasoning can be practiced in the context of medicine are provided. In order to facilitate the learning of evolutionary principles, concepts derived from evolutionary science that medical students and professionals should understand are outlined. These concepts are designed to be authoritative and at the same time easily accessible for anyone with the general biological knowledge of a first-year medical student. Thus we conclude that medical practice informed by evolutionary principles will be more effective and lead to better patient outcomes.Furthermore, it is argued that evolutionary medicine complements general medical training because it provides an additional means by which medical students can practice the critical thinking skills that will be important in their future practice. We argue that core concepts from evolutionary science have the potential to improve critical thinking and facilitate more effective learning in medical training. © The Author(s) 2016. Published by Oxford University Press on behalf of the Foundation for Evolution, Medicine, and Public Health.

The use of information theory in evolutionary biology.

Science.gov (United States)

Adami, Christoph

2012-05-01

Information is a key concept in evolutionary biology. Information stored in a biological organism's genome is used to generate the organism and to maintain and control it. Information is also that which evolves. When a population adapts to a local environment, information about this environment is fixed in a representative genome. However, when an environment changes, information can be lost. At the same time, information is processed by animal brains to survive in complex environments, and the capacity for information processing also evolves. Here, I review applications of information theory to the evolution of proteins and to the evolution of information processing in simulated agents that adapt to perform a complex task. © 2012 New York Academy of Sciences.

phyloXML: XML for evolutionary biology and comparative genomics.

Science.gov (United States)

Han, Mira V; Zmasek, Christian M

2009-10-27

Evolutionary trees are central to a wide range of biological studies. In many of these studies, tree nodes and branches need to be associated (or annotated) with various attributes. For example, in studies concerned with organismal relationships, tree nodes are associated with taxonomic names, whereas tree branches have lengths and oftentimes support values. Gene trees used in comparative genomics or phylogenomics are usually annotated with taxonomic information, genome-related data, such as gene names and functional annotations, as well as events such as gene duplications, speciations, or exon shufflings, combined with information related to the evolutionary tree itself. The data standards currently used for evolutionary trees have limited capacities to incorporate such annotations of different data types. We developed a XML language, named phyloXML, for describing evolutionary trees, as well as various associated data items. PhyloXML provides elements for commonly used items, such as branch lengths, support values, taxonomic names, and gene names and identifiers. By using "property" elements, phyloXML can be adapted to novel and unforeseen use cases. We also developed various software tools for reading, writing, conversion, and visualization of phyloXML formatted data. PhyloXML is an XML language defined by a complete schema in XSD that allows storing and exchanging the structures of evolutionary trees as well as associated data. More information about phyloXML itself, the XSD schema, as well as tools implementing and supporting phyloXML, is available at http://www.phyloxml.org.

Thermodynamics, ecology and evolutionary biology: A bridge over troubled water or common ground?

Science.gov (United States)

Skene, Keith R.

2017-11-01

This paper addresses a key issue confronting ecological and evolutionary biology, namely the challenge of a cohesive approach to these fields given significant differences in the concepts and foundations of their study. Yet these two areas of scientific research are paramount in terms addressing the spatial and temporal dynamics and distribution of diversity, an understanding of which is needed if we are to resolve the current crisis facing the biosphere. The importance of understanding how nature responds to change is now of essential rather than of metaphysical interest as our planet struggles with increasing anthropogenic damage. Ecology and evolutionary biology can no longer remain disjointed. While some progress has been made in terms of synthetic thinking across these areas, this has often been in terms of bridge building, where thinking in one aspect is extended over to the other side. We review these bridges and the success or otherwise of such efforts. This paper then suggests that in order to move from a descriptive to a mechanistic understanding of the biosphere, we may need to re-evaluate our approach to the studies of ecology and evolutionary biology, finding a common denominator that will enable us to address the critical issues facing us, particularly in terms of understanding what drives change, what determines tempo and how communities function. Common ground, we argue, is essential if we are to comprehend how resilience operates in the natural world and how diversification can counter increasing extinction rates. This paper suggests that thermodynamics may provide a bridge between ecology and evolutionary biology, and that this will enable us to move forward with otherwise intractable problems.

Comparative Ecophysiology and Evolutionary Biology of Island and Mainland Chaparral Communities

OpenAIRE

Ramirez, Aaron Robert

2015-01-01

The unique nature of island ecosystems have fascinated generations of naturalists, ecologists, and evolutionary biologists. Studying island systems led to the development of keystone biological theories including: Darwin and Wallace's theories of natural selection, Carlquist's insights into the biology of adaptive radiations, MacArthur and Wilson's theory of island biogeography, and many others. Utilizing islands as natural laboratories allows us to discover the underlying fabric of ecology a...

The State of Software for Evolutionary Biology.

Science.gov (United States)

Darriba, Diego; Flouri, Tomáš; Stamatakis, Alexandros

2018-05-01

With Next Generation Sequencing data being routinely used, evolutionary biology is transforming into a computational science. Thus, researchers have to rely on a growing number of increasingly complex software. All widely used core tools in the field have grown considerably, in terms of the number of features as well as lines of code and consequently, also with respect to software complexity. A topic that has received little attention is the software engineering quality of widely used core analysis tools. Software developers appear to rarely assess the quality of their code, and this can have potential negative consequences for end-users. To this end, we assessed the code quality of 16 highly cited and compute-intensive tools mainly written in C/C++ (e.g., MrBayes, MAFFT, SweepFinder, etc.) and JAVA (BEAST) from the broader area of evolutionary biology that are being routinely used in current data analysis pipelines. Because, the software engineering quality of the tools we analyzed is rather unsatisfying, we provide a list of best practices for improving the quality of existing tools and list techniques that can be deployed for developing reliable, high quality scientific software from scratch. Finally, we also discuss journal as well as science policy and, more importantly, funding issues that need to be addressed for improving software engineering quality as well as ensuring support for developing new and maintaining existing software. Our intention is to raise the awareness of the community regarding software engineering quality issues and to emphasize the substantial lack of funding for scientific software development.

Reinforcement Learning for Online Control of Evolutionary Algorithms

NARCIS (Netherlands)

Eiben, A.; Horvath, Mark; Kowalczyk, Wojtek; Schut, Martijn

2007-01-01

The research reported in this paper is concerned with assessing the usefulness of reinforcment learning (RL) for on-line calibration of parameters in evolutionary algorithms (EA). We are running an RL procedure and the EA simultaneously and the RL is changing the EA parameters on-the-fly. We

Evolutionary Stable Strategy

Indian Academy of Sciences (India)

Home; Journals; Resonance – Journal of Science Education; Volume 21; Issue 9. Evolutionary Stable Strategy: Application of Nash Equilibrium in Biology. General Article Volume 21 Issue 9 September 2016 pp 803- ... Keywords. Evolutionary game theory, evolutionary stable state, conflict, cooperation, biological games.

Core principles of evolutionary medicine: A Delphi study.

Science.gov (United States)

Grunspan, Daniel Z; Nesse, Randolph M; Barnes, M Elizabeth; Brownell, Sara E

2018-01-01

Evolutionary medicine is a rapidly growing field that uses the principles of evolutionary biology to better understand, prevent and treat disease, and that uses studies of disease to advance basic knowledge in evolutionary biology. Over-arching principles of evolutionary medicine have been described in publications, but our study is the first to systematically elicit core principles from a diverse panel of experts in evolutionary medicine. These principles should be useful to advance recent recommendations made by The Association of American Medical Colleges and the Howard Hughes Medical Institute to make evolutionary thinking a core competency for pre-medical education. The Delphi method was used to elicit and validate a list of core principles for evolutionary medicine. The study included four surveys administered in sequence to 56 expert panelists. The initial open-ended survey created a list of possible core principles; the three subsequent surveys winnowed the list and assessed the accuracy and importance of each principle. Fourteen core principles elicited at least 80% of the panelists to agree or strongly agree that they were important core principles for evolutionary medicine. These principles over-lapped with concepts discussed in other articles discussing key concepts in evolutionary medicine. This set of core principles will be helpful for researchers and instructors in evolutionary medicine. We recommend that evolutionary medicine instructors use the list of core principles to construct learning goals. Evolutionary medicine is a young field, so this list of core principles will likely change as the field develops further.

Perception-based Co-evolutionary Reinforcement Learning for UAV Sensor Allocation

National Research Council Canada - National Science Library

Berenji, Hamid

2003-01-01

.... A Perception-based reasoning approach based on co-evolutionary reinforcement learning was developed for jointly addressing sensor allocation on each individual UAV and allocation of a team of UAVs...

Evolutionary approaches for the reverse-engineering of gene regulatory networks: A study on a biologically realistic dataset

Directory of Open Access Journals (Sweden)

Gidrol Xavier

2008-02-01

Full Text Available Abstract Background Inferring gene regulatory networks from data requires the development of algorithms devoted to structure extraction. When only static data are available, gene interactions may be modelled by a Bayesian Network (BN that represents the presence of direct interactions from regulators to regulees by conditional probability distributions. We used enhanced evolutionary algorithms to stochastically evolve a set of candidate BN structures and found the model that best fits data without prior knowledge. Results We proposed various evolutionary strategies suitable for the task and tested our choices using simulated data drawn from a given bio-realistic network of 35 nodes, the so-called insulin network, which has been used in the literature for benchmarking. We assessed the inferred models against this reference to obtain statistical performance results. We then compared performances of evolutionary algorithms using two kinds of recombination operators that operate at different scales in the graphs. We introduced a niching strategy that reinforces diversity through the population and avoided trapping of the algorithm in one local minimum in the early steps of learning. We show the limited effect of the mutation operator when niching is applied. Finally, we compared our best evolutionary approach with various well known learning algorithms (MCMC, K2, greedy search, TPDA, MMHC devoted to BN structure learning. Conclusion We studied the behaviour of an evolutionary approach enhanced by niching for the learning of gene regulatory networks with BN. We show that this approach outperforms classical structure learning methods in elucidating the original model. These results were obtained for the learning of a bio-realistic network and, more importantly, on various small datasets. This is a suitable approach for learning transcriptional regulatory networks from real datasets without prior knowledge.

The great opportunity: Evolutionary applications to medicine and public health.

Science.gov (United States)

Nesse, Randolph M; Stearns, Stephen C

2008-02-01

Evolutionary biology is an essential basic science for medicine, but few doctors and medical researchers are familiar with its most relevant principles. Most medical schools have geneticists who understand evolution, but few have even one evolutionary biologist to suggest other possible applications. The canyon between evolutionary biology and medicine is wide. The question is whether they offer each other enough to make bridge building worthwhile. What benefits could be expected if evolution were brought fully to bear on the problems of medicine? How would studying medical problems advance evolutionary research? Do doctors need to learn evolution, or is it valuable mainly for researchers? What practical steps will promote the application of evolutionary biology in the areas of medicine where it offers the most? To address these questions, we review current and potential applications of evolutionary biology to medicine and public health. Some evolutionary technologies, such as population genetics, serial transfer production of live vaccines, and phylogenetic analysis, have been widely applied. Other areas, such as infectious disease and aging research, illustrate the dramatic recent progress made possible by evolutionary insights. In still other areas, such as epidemiology, psychiatry, and understanding the regulation of bodily defenses, applying evolutionary principles remains an open opportunity. In addition to the utility of specific applications, an evolutionary perspective fundamentally challenges the prevalent but fundamentally incorrect metaphor of the body as a machine designed by an engineer. Bodies are vulnerable to disease - and remarkably resilient - precisely because they are not machines built from a plan. They are, instead, bundles of compromises shaped by natural selection in small increments to maximize reproduction, not health. Understanding the body as a product of natural selection, not design, offers new research questions and a framework for

An evolutionary firefly algorithm for the estimation of nonlinear biological model parameters.

Directory of Open Access Journals (Sweden)

Afnizanfaizal Abdullah

Full Text Available The development of accurate computational models of biological processes is fundamental to computational systems biology. These models are usually represented by mathematical expressions that rely heavily on the system parameters. The measurement of these parameters is often difficult. Therefore, they are commonly estimated by fitting the predicted model to the experimental data using optimization methods. The complexity and nonlinearity of the biological processes pose a significant challenge, however, to the development of accurate and fast optimization methods. We introduce a new hybrid optimization method incorporating the Firefly Algorithm and the evolutionary operation of the Differential Evolution method. The proposed method improves solutions by neighbourhood search using evolutionary procedures. Testing our method on models for the arginine catabolism and the negative feedback loop of the p53 signalling pathway, we found that it estimated the parameters with high accuracy and within a reasonable computation time compared to well-known approaches, including Particle Swarm Optimization, Nelder-Mead, and Firefly Algorithm. We have also verified the reliability of the parameters estimated by the method using an a posteriori practical identifiability test.

An evolutionary firefly algorithm for the estimation of nonlinear biological model parameters.

Science.gov (United States)

Abdullah, Afnizanfaizal; Deris, Safaai; Anwar, Sohail; Arjunan, Satya N V

2013-01-01

The development of accurate computational models of biological processes is fundamental to computational systems biology. These models are usually represented by mathematical expressions that rely heavily on the system parameters. The measurement of these parameters is often difficult. Therefore, they are commonly estimated by fitting the predicted model to the experimental data using optimization methods. The complexity and nonlinearity of the biological processes pose a significant challenge, however, to the development of accurate and fast optimization methods. We introduce a new hybrid optimization method incorporating the Firefly Algorithm and the evolutionary operation of the Differential Evolution method. The proposed method improves solutions by neighbourhood search using evolutionary procedures. Testing our method on models for the arginine catabolism and the negative feedback loop of the p53 signalling pathway, we found that it estimated the parameters with high accuracy and within a reasonable computation time compared to well-known approaches, including Particle Swarm Optimization, Nelder-Mead, and Firefly Algorithm. We have also verified the reliability of the parameters estimated by the method using an a posteriori practical identifiability test.

A Graphical Evolutionary Game Approach to Social Learning

Science.gov (United States)

Cao, Xuanyu; Liu, K. J. Ray

2017-06-01

In this work, we study the social learning problem, in which agents of a networked system collaborate to detect the state of the nature based on their private signals. A novel distributed graphical evolutionary game theoretic learning method is proposed. In the proposed game-theoretic method, agents only need to communicate their binary decisions rather than the real-valued beliefs with their neighbors, which endows the method with low communication complexity. Under mean field approximations, we theoretically analyze the steady state equilibria of the game and show that the evolutionarily stable states (ESSs) coincide with the decisions of the benchmark centralized detector. Numerical experiments are implemented to confirm the effectiveness of the proposed game-theoretic learning method.

Evolutionary thinking: "A conversation with Carter Phipps about the role of evolutionary thinking in modern culture".

Science.gov (United States)

Hunt, Tam

2014-12-01

Evolution as an idea has a lengthy history, even though the idea of evolution is generally associated with Darwin today. Rebecca Stott provides an engaging and thoughtful overview of this history of evolutionary thinking in her 2013 book, Darwin's Ghosts: The Secret History of Evolution. Since Darwin, the debate over evolution-both how it takes place and, in a long war of words with religiously-oriented thinkers, whether it takes place-has been sustained and heated. A growing share of this debate is now devoted to examining how evolutionary thinking affects areas outside of biology. How do our lives change when we recognize that all is in flux? What can we learn about life more generally if we study change instead of stasis? Carter Phipps' book, Evolutionaries: Unlocking the Spiritual and Cultural Potential of Science's Greatest Idea, delves deep into this relatively new development. Phipps generally takes as a given the validity of the Modern Synthesis of evolutionary biology. His story takes us into, as the subtitle suggests, the spiritual and cultural implications of evolutionary thinking. Can religion and evolution be reconciled? Can evolutionary thinking lead to a new type of spirituality? Is our culture already being changed in ways that we don't realize by evolutionary thinking? These are all important questions and Phipps book is a great introduction to this discussion. Phipps is an author, journalist, and contributor to the emerging "integral" or "evolutionary" cultural movement that combines the insights of Integral Philosophy, evolutionary science, developmental psychology, and the social sciences. He has served as the Executive Editor of EnlightenNext magazine (no longer published) and more recently is the co-founder of the Institute for Cultural Evolution, a public policy think tank addressing the cultural roots of America's political challenges. What follows is an email interview with Phipps.

THE EVOLUTION OF THE KREBS CYCLE: A PROMISING THEME FOR MEANINGFUL BIOCHEMISTRY LEARNING IN BIOLOGY

Directory of Open Access Journals (Sweden)

C. Costa

2015-08-01

Full Text Available INTRODUCTION: Evolution has been recognized as a key concept for biologists. In order to motivate biology undergraduates for contents of central energetic metabolism, we addressed the Krebs cycle structure and functions to an evolutionary view. To this end, we created a study guide which contextualizes the emergence of the cyclic pathway, in light of the prokaryotic influence since early Earth anaerobic condition to oxygen rise in atmosphere. OBJECTIVES: The main goal is to highlight the educational potential of the material whose subject is scarcely covered in biochemistry textbooks. MATERIALS AND METHODS: The study guide is composed by three interrelated sections, the problem (Section 1, designed to arouse curiosity, inform and motivate students; an introductory text (Section 2 about life evolution, including early micro-organisms and Krebs cycle emergence, and questions (Section 3 for debate. The activity consisted on a peer discussion session, with instructors tutoring. The questions were designed to foster exchange of ideas in an ever-increasing level of complexity, and cover subjects from early atmospheric conditions to organization of the metabolism along the subsequent geological ages. RESULTS AND DISCUSSION: We noticed that students were engaged and motivated by the task, especially during group discussion. Based on students’ feedbacks and class observations, we learned that the material raised curiosity and stimulated discussion among peers. It brought a historical and purposeful way of dealing with difficult biochemical concepts. CONCLUSIONS: The whole experience suggests that the study guide was a stimulus for broadening comprehension of the Krebs cycle, reinforcing the evolutionary stance as an important theme for biology and biochemistry understanding. On the other hand, we do not underestimate the fact that approaching Krebs cycle from an evolutionary standpoint is a quite complex discussion for the majority of students

Evolutionary Demography

DEFF Research Database (Denmark)

Levitis, Daniel

2015-01-01

of biological and cultural evolution. Demographic variation within and among human populations is influenced by our biology, and therefore by natural selection and our evolutionary background. Demographic methods are necessary for studying populations of other species, and for quantifying evolutionary fitness......Demography is the quantitative study of population processes, while evolution is a population process that influences all aspects of biological organisms, including their demography. Demographic traits common to all human populations are the products of biological evolution or the interaction...

Evolutionary molecular medicine.

Science.gov (United States)

Nesse, Randolph M; Ganten, Detlev; Gregory, T Ryan; Omenn, Gilbert S

2012-05-01

Evolution has long provided a foundation for population genetics, but some major advances in evolutionary biology from the twentieth century that provide foundations for evolutionary medicine are only now being applied in molecular medicine. They include the need for both proximate and evolutionary explanations, kin selection, evolutionary models for cooperation, competition between alleles, co-evolution, and new strategies for tracing phylogenies and identifying signals of selection. Recent advances in genomics are transforming evolutionary biology in ways that create even more opportunities for progress at its interfaces with genetics, medicine, and public health. This article reviews 15 evolutionary principles and their applications in molecular medicine in hopes that readers will use them and related principles to speed the development of evolutionary molecular medicine.

An Untold Story in Biology: The Historical Continuity of Evolutionary Ideas of Muslim Scholars from the 8th Century to Darwin's Time

Science.gov (United States)

Malik, Aamina H.; Ziermann, Janine M.; Diogo, Rui

2018-01-01

Textbooks on the history of biology and evolutionary thought do not mention the evolutionary ideas of Muslim scholars before Darwin's time. This is part of a trend in the West to minimise the contributions of non-Western scientists to biology, human anatomy and evolutionary biology. Therefore, this paper focuses on the contributions of…

Pseudorandom numbers: evolutionary models in image processing, biology, and nonlinear dynamic systems

Science.gov (United States)

Yaroslavsky, Leonid P.

1996-11-01

We show that one can treat pseudo-random generators, evolutionary models of texture images, iterative local adaptive filters for image restoration and enhancement and growth models in biology and material sciences in a unified way as special cases of dynamic systems with a nonlinear feedback.

Evolutionary Biology Today

Indian Academy of Sciences (India)

Hindi and English. Port 1. Resonance, Vo1.7 ... they use. Of course, many evolutionary biologists do work with fossils or DNA, or both, but there are also large numbers of ... The first major division that I like to make is between studies focussed ...

Preference learning with evolutionary Multivariate Adaptive Regression Spline model

DEFF Research Database (Denmark)

Abou-Zleikha, Mohamed; Shaker, Noor; Christensen, Mads Græsbøll

2015-01-01

This paper introduces a novel approach for pairwise preference learning through combining an evolutionary method with Multivariate Adaptive Regression Spline (MARS). Collecting users' feedback through pairwise preferences is recommended over other ranking approaches as this method is more appealing...... for function approximation as well as being relatively easy to interpret. MARS models are evolved based on their efficiency in learning pairwise data. The method is tested on two datasets that collectively provide pairwise preference data of five cognitive states expressed by users. The method is analysed...

Co-Evolution of Social Learning and Evolutionary Preparedness in Dangerous Environments.

Science.gov (United States)

Lindström, Björn; Selbing, Ida; Olsson, Andreas

2016-01-01

Danger is a fundamental aspect of the lives of most animals. Adaptive behavior therefore requires avoiding actions, objects, and environments associated with danger. Previous research has shown that humans and non-human animals can avoid such dangers through two types of behavioral adaptions, (i) genetic preparedness to avoid certain stimuli or actions, and (ii) social learning. These adaptive mechanisms reduce the fitness costs associated with danger but still allow flexible behavior. Despite the empirical prevalence and importance of both these mechanisms, it is unclear when they evolve and how they interact. We used evolutionary agent-based simulations, incorporating empirically based learning mechanisms, to clarify if preparedness and social learning typically both evolve in dangerous environments, and if these mechanisms generally interact synergistically or antagonistically. Our simulations showed that preparedness and social learning often co-evolve because they provide complimentary benefits: genetic preparedness reduced foraging efficiency, but resulted in a higher rate of survival in dangerous environments, while social learning generally came to dominate the population, especially when the environment was stochastic. However, even in this case, genetic preparedness reliably evolved. Broadly, our results indicate that the relationship between preparedness and social learning is important as it can result in trade-offs between behavioral flexibility and safety, which can lead to seemingly suboptimal behavior if the evolutionary environment of the organism is not taken into account.

Mathematics and evolutionary biology make bioinformatics education comprehensible

Science.gov (United States)

Weisstein, Anton E.

2013-01-01

The patterns of variation within a molecular sequence data set result from the interplay between population genetic, molecular evolutionary and macroevolutionary processes—the standard purview of evolutionary biologists. Elucidating these patterns, particularly for large data sets, requires an understanding of the structure, assumptions and limitations of the algorithms used by bioinformatics software—the domain of mathematicians and computer scientists. As a result, bioinformatics often suffers a ‘two-culture’ problem because of the lack of broad overlapping expertise between these two groups. Collaboration among specialists in different fields has greatly mitigated this problem among active bioinformaticians. However, science education researchers report that much of bioinformatics education does little to bridge the cultural divide, the curriculum too focused on solving narrow problems (e.g. interpreting pre-built phylogenetic trees) rather than on exploring broader ones (e.g. exploring alternative phylogenetic strategies for different kinds of data sets). Herein, we present an introduction to the mathematics of tree enumeration, tree construction, split decomposition and sequence alignment. We also introduce off-line downloadable software tools developed by the BioQUEST Curriculum Consortium to help students learn how to interpret and critically evaluate the results of standard bioinformatics analyses. PMID:23821621

Mathematics and evolutionary biology make bioinformatics education comprehensible.

Science.gov (United States)

Jungck, John R; Weisstein, Anton E

2013-09-01

The patterns of variation within a molecular sequence data set result from the interplay between population genetic, molecular evolutionary and macroevolutionary processes-the standard purview of evolutionary biologists. Elucidating these patterns, particularly for large data sets, requires an understanding of the structure, assumptions and limitations of the algorithms used by bioinformatics software-the domain of mathematicians and computer scientists. As a result, bioinformatics often suffers a 'two-culture' problem because of the lack of broad overlapping expertise between these two groups. Collaboration among specialists in different fields has greatly mitigated this problem among active bioinformaticians. However, science education researchers report that much of bioinformatics education does little to bridge the cultural divide, the curriculum too focused on solving narrow problems (e.g. interpreting pre-built phylogenetic trees) rather than on exploring broader ones (e.g. exploring alternative phylogenetic strategies for different kinds of data sets). Herein, we present an introduction to the mathematics of tree enumeration, tree construction, split decomposition and sequence alignment. We also introduce off-line downloadable software tools developed by the BioQUEST Curriculum Consortium to help students learn how to interpret and critically evaluate the results of standard bioinformatics analyses.

EvoluCode: Evolutionary Barcodes as a Unifying Framework for Multilevel Evolutionary Data.

Science.gov (United States)

Linard, Benjamin; Nguyen, Ngoc Hoan; Prosdocimi, Francisco; Poch, Olivier; Thompson, Julie D

2012-01-01

Evolutionary systems biology aims to uncover the general trends and principles governing the evolution of biological networks. An essential part of this process is the reconstruction and analysis of the evolutionary histories of these complex, dynamic networks. Unfortunately, the methodologies for representing and exploiting such complex evolutionary histories in large scale studies are currently limited. Here, we propose a new formalism, called EvoluCode (Evolutionary barCode), which allows the integration of different evolutionary parameters (eg, sequence conservation, orthology, synteny …) in a unifying format and facilitates the multilevel analysis and visualization of complex evolutionary histories at the genome scale. The advantages of the approach are demonstrated by constructing barcodes representing the evolution of the complete human proteome. Two large-scale studies are then described: (i) the mapping and visualization of the barcodes on the human chromosomes and (ii) automatic clustering of the barcodes to highlight protein subsets sharing similar evolutionary histories and their functional analysis. The methodologies developed here open the way to the efficient application of other data mining and knowledge extraction techniques in evolutionary systems biology studies. A database containing all EvoluCode data is available at: http://lbgi.igbmc.fr/barcodes.

Evolutionary Cell Biology of Proteins from Protists to Humans and Plants.

Science.gov (United States)

Plattner, Helmut

2018-03-01

During evolution, the cell as a fine-tuned machine had to undergo permanent adjustments to match changes in its environment, while "closed for repair work" was not possible. Evolution from protists (protozoa and unicellular algae) to multicellular organisms may have occurred in basically two lineages, Unikonta and Bikonta, culminating in mammals and angiosperms (flowering plants), respectively. Unicellular models for unikont evolution are myxamoebae (Dictyostelium) and increasingly also choanoflagellates, whereas for bikonts, ciliates are preferred models. Information accumulating from combined molecular database search and experimental verification allows new insights into evolutionary diversification and maintenance of genes/proteins from protozoa on, eventually with orthologs in bacteria. However, proteins have rarely been followed up systematically for maintenance or change of function or intracellular localization, acquirement of new domains, partial deletion (e.g. of subunits), and refunctionalization, etc. These aspects are discussed in this review, envisaging "evolutionary cell biology." Protozoan heritage is found for most important cellular structures and functions up to humans and flowering plants. Examples discussed include refunctionalization of voltage-dependent Ca 2+ channels in cilia and replacement by other types during evolution. Altogether components serving Ca 2+ signaling are very flexible throughout evolution, calmodulin being a most conservative example, in contrast to calcineurin whose catalytic subunit is lost in plants, whereas both subunits are maintained up to mammals for complex functions (immune defense and learning). Domain structure of R-type SNAREs differs in mono- and bikonta, as do Ca 2+ -dependent protein kinases. Unprecedented selective expansion of the subunit a which connects multimeric base piece and head parts (V0, V1) of H + -ATPase/pump may well reflect the intriguing vesicle trafficking system in ciliates, specifically in

Biology Teachers' Conceptions of the Diversity of Life and the Historical Development of Evolutionary Concepts

Science.gov (United States)

da Silva, Paloma Rodrigues; de Andrade, Mariana A. Bologna Soares; de Andrade Caldeira, Ana Maria

2015-01-01

Biology is a science that involves study of the diversity of living organisms. This diversity has always generated questions and has motivated cultures to seek plausible explanations for the differences and similarities between types of organisms. In biology teaching, these issues are addressed by adopting an evolutionary approach. The aim of this…

Democratizing evolutionary biology, lessons from insects

DEFF Research Database (Denmark)

Dunn, Robert Roberdeau; Beasley, DeAnna E.

2016-01-01

The engagement of the public in the scientific process is an old practice. Yet with recent advances in technology, the role of the citizen scientist in studying evolutionary processes has increased. Insects provide ideal models for understanding these evolutionary processes at large scales. This ...

Burney J. Le Boeuf, Professor of Ecology and Evolutionary Biology: Recollections of UCSC, 1966-1994

OpenAIRE

Reti, Irene H.; Burney, Le Boeuf J; Jarrell, Randall

2014-01-01

Burney Le Boeuf was born in southern Louisiana. He attended UC Berkeley, earning his PhD in experimental psychology in 1966. While at Berkeley, he also studied zoology and experimental biology. He arrived at UCSC in 1967 as a member of the psychology board and of Crown College. He already had a strong interest in evolutionary biology and participated in the biology board’s meetings as an outside member. He also began working with biology professor Richard Peterson on seal and sea lion researc...

The Relationships Between Epistemic Beliefs in Biology and Approaches to Learning Biology Among Biology-Major University Students in Taiwan

Science.gov (United States)

Lin, Yi-Chun; Liang, Jyh-Chong; Tsai, Chin-Chung

2012-12-01

The aim of this study was to investigate the relationships between students' epistemic beliefs in biology and their approaches to learning biology. To this end, two instruments, the epistemic beliefs in biology and the approaches to learning biology surveys, were developed and administered to 520 university biology students, respectively. By and large, it was found that the students reflected "mixed" motives in biology learning, while those who had more sophisticated epistemic beliefs tended to employ deep strategies. In addition, the results of paired t tests revealed that the female students were more likely to possess beliefs about biological knowledge residing in external authorities, to believe in a right answer, and to utilize rote learning as a learning strategy. Moreover, compared to juniors and seniors, freshmen and sophomores tended to hold less mature views on all factors of epistemic beliefs regarding biology. Another comparison indicated that theoretical biology students (e.g. students majoring in the Department of Biology) tended to have more mature beliefs in learning biology and more advanced strategies for biology learning than those students studying applied biology (e.g. in the Department of Biotechnology). Stepwise regression analysis, in general, indicated that students who valued the role of experiments and justify epistemic assumptions and knowledge claims based on evidence were more oriented towards having mixed motives and utilizing deep strategies to learn biology. In contrast, students who believed in the certainty of biological knowledge were more likely to adopt rote learning strategies and to aim to qualify in biology.

[Evolutionary medicine].

Science.gov (United States)

Wjst, M

2013-12-01

Evolutionary medicine allows new insights into long standing medical problems. Are we "really stoneagers on the fast lane"? This insight might have enormous consequences and will allow new answers that could never been provided by traditional anthropology. Only now this is made possible using data from molecular medicine and systems biology. Thereby evolutionary medicine takes a leap from a merely theoretical discipline to practical fields - reproductive, nutritional and preventive medicine, as well as microbiology, immunology and psychiatry. Evolutionary medicine is not another "just so story" but a serious candidate for the medical curriculum providing a universal understanding of health and disease based on our biological origin. © Georg Thieme Verlag KG Stuttgart · New York.

Evolutionary biology of bacterial and fungal pathogens

National Research Council Canada - National Science Library

Baquero, F

2008-01-01

... and Evolutionary Dynamics of Pathogens * 21 Keith A. Crandall and Marcos Pérez-Losada II. Evolutionary Genetics of Microbial Pathogens 4. Environmental and Social Influences on Infectious Disea...

A new evolutionary algorithm with LVQ learning for the optimization of combinatory problems as a reload of nuclear reactors

International Nuclear Information System (INIS)

Machado, Marcelo Dornellas

1999-04-01

Genetic algorithms are biologically motivated adaptive systems which have been used, with good results, for function optimization. In this work, a new learning mode, to be used by the Population-Based Incremental Learning (PBIL) algorithm, who combines mechanisms of standard genetic algorithm with simple competitive learning, has the aim to build a new evolutionary algorithm to be used in optimization of numerical problems and combinatorial problems. This new learning mode uses a variable learning rate during the optimization process, constituting a process know as proportional reward. The development of this new algorithm aims its application in the optimization of reload problem of PWR nuclear reactors. This problem can be interpreted as search of a load pattern to be used in the nucleus of the reactor in order to increase the useful life of the nuclear fuel. For the test, two classes of problems are used: numerical problems and combinatorial problem, the major interest relies on the last class. The results achieved with the tests indicate the applicability of the new learning mode, showing its potential as a developing tool in the solution of reload problem. (author)

EVOLUTIONARY FOUNDATIONS FOR MOLECULAR MEDICINE

Science.gov (United States)

Nesse, Randolph M.; Ganten, Detlev; Gregory, T. Ryan; Omenn, Gilbert S.

2015-01-01

Evolution has long provided a foundation for population genetics, but many major advances in evolutionary biology from the 20th century are only now being applied in molecular medicine. They include the distinction between proximate and evolutionary explanations, kin selection, evolutionary models for cooperation, and new strategies for tracing phylogenies and identifying signals of selection. Recent advances in genomics are further transforming evolutionary biology and creating yet more opportunities for progress at the interface of evolution with genetics, medicine, and public health. This article reviews 15 evolutionary principles and their applications in molecular medicine in hopes that readers will use them and others to speed the development of evolutionary molecular medicine. PMID:22544168

Evolutionary Nephrology.

Science.gov (United States)

Chevalier, Robert L

2017-05-01

Progressive kidney disease follows nephron loss, hyperfiltration, and incomplete repair, a process described as "maladaptive." In the past 20 years, a new discipline has emerged that expands research horizons: evolutionary medicine. In contrast to physiologic (homeostatic) adaptation, evolutionary adaptation is the result of reproductive success that reflects natural selection. Evolutionary explanations for physiologically maladaptive responses can emerge from mismatch of the phenotype with environment or evolutionary tradeoffs. Evolutionary adaptation to a terrestrial environment resulted in a vulnerable energy-consuming renal tubule and a hypoxic, hyperosmolar microenvironment. Natural selection favors successful energy investment strategy: energy is allocated to maintenance of nephron integrity through reproductive years, but this declines with increasing senescence after ~40 years of age. Risk factors for chronic kidney disease include restricted fetal growth or preterm birth (life history tradeoff resulting in fewer nephrons), evolutionary selection for APOL1 mutations (that provide resistance to trypanosome infection, a tradeoff), and modern life experience (Western diet mismatch leading to diabetes and hypertension). Current advances in genomics, epigenetics, and developmental biology have revealed proximate causes of kidney disease, but attempts to slow kidney disease remain elusive. Evolutionary medicine provides a complementary approach by addressing ultimate causes of kidney disease. Marked variation in nephron number at birth, nephron heterogeneity, and changing susceptibility to kidney injury throughout life history are the result of evolutionary processes. Combined application of molecular genetics, evolutionary developmental biology (evo-devo), developmental programming and life history theory may yield new strategies for prevention and treatment of chronic kidney disease.

Evolutionary Nephrology

Directory of Open Access Journals (Sweden)

Robert L. Chevalier

2017-05-01

Full Text Available Progressive kidney disease follows nephron loss, hyperfiltration, and incomplete repair, a process described as “maladaptive.” In the past 20 years, a new discipline has emerged that expands research horizons: evolutionary medicine. In contrast to physiologic (homeostatic adaptation, evolutionary adaptation is the result of reproductive success that reflects natural selection. Evolutionary explanations for physiologically maladaptive responses can emerge from mismatch of the phenotype with environment or from evolutionary tradeoffs. Evolutionary adaptation to a terrestrial environment resulted in a vulnerable energy-consuming renal tubule and a hypoxic, hyperosmolar microenvironment. Natural selection favors successful energy investment strategy: energy is allocated to maintenance of nephron integrity through reproductive years, but this declines with increasing senescence after ∼40 years of age. Risk factors for chronic kidney disease include restricted fetal growth or preterm birth (life history tradeoff resulting in fewer nephrons, evolutionary selection for APOL1 mutations (which provide resistance to trypanosome infection, a tradeoff, and modern life experience (Western diet mismatch leading to diabetes and hypertension. Current advances in genomics, epigenetics, and developmental biology have revealed proximate causes of kidney disease, but attempts to slow kidney disease remain elusive. Evolutionary medicine provides a complementary approach by addressing ultimate causes of kidney disease. Marked variation in nephron number at birth, nephron heterogeneity, and changing susceptibility to kidney injury throughout the life history are the result of evolutionary processes. Combined application of molecular genetics, evolutionary developmental biology (evo-devo, developmental programming, and life history theory may yield new strategies for prevention and treatment of chronic kidney disease.

Machine learning and evolutionary techniques in interplanetary trajectory design

OpenAIRE

Izzo, Dario; Sprague, Christopher; Tailor, Dharmesh

2018-01-01

After providing a brief historical overview on the synergies between artificial intelligence research, in the areas of evolutionary computations and machine learning, and the optimal design of interplanetary trajectories, we propose and study the use of deep artificial neural networks to represent, on-board, the optimal guidance profile of an interplanetary mission. The results, limited to the chosen test case of an Earth-Mars orbital transfer, extend the findings made previously for landing ...

Evolutionary games under incompetence.

Science.gov (United States)

Kleshnina, Maria; Filar, Jerzy A; Ejov, Vladimir; McKerral, Jody C

2018-02-26

The adaptation process of a species to a new environment is a significant area of study in biology. As part of natural selection, adaptation is a mutation process which improves survival skills and reproductive functions of species. Here, we investigate this process by combining the idea of incompetence with evolutionary game theory. In the sense of evolution, incompetence and training can be interpreted as a special learning process. With focus on the social side of the problem, we analyze the influence of incompetence on behavior of species. We introduce an incompetence parameter into a learning function in a single-population game and analyze its effect on the outcome of the replicator dynamics. Incompetence can change the outcome of the game and its dynamics, indicating its significance within what are inherently imperfect natural systems.

Introduced species as evolutionary traps

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Schlaepfer, Martin A.; Sherman, P.W.; Blossey, B.; Runge, M.C.

2005-01-01

Invasive species can alter environments in such a way that normal behavioural decision-making rules of native species are no longer adaptive. The evolutionary trap concept provides a useful framework for predicting and managing the impact of harmful invasive species. We discuss how native species can respond to changes in their selective regime via evolution or learning. We also propose novel management strategies to promote the long-term co-existence of native and introduced species in cases where the eradication of the latter is either economically or biologically unrealistic.

Highly Adaptable but Not Invulnerable: Necessary and Facilitating Conditions for Research in Evolutionary Developmental Biology

NARCIS (Netherlands)

Laudel, Grit; Benninghoff, Martin; Lettkemann, Eric; Håkansson, Elias; Whitley, Richard; Gläser, Jochen

2014-01-01

Evolutionary developmental biology is a highly variable scientific innovation because researchers can adapt their involvement in the innovation to the opportunities provided by their environment. On the basis of comparative case studies in four countries, we link epistemic properties of research

Using large-scale public health data to explore the evolutionary biology of human pregnancy and child bearing

DEFF Research Database (Denmark)

Hollegaard, Birgitte

. Consequently, research has increasingly focused on the underlying causes of disease, shaped by human evolution. Evolutionary medicine is a relatively new field, specifically bridging the gap between conventional medicine and evolutionary biology: Instead of asking how we get sick, we can apply evolutionary...... explanations for this phenomenon. This thesis demonstrates how taking an evolutionary perspective can help us to better understand important aspects of health and medicine that remain opaque, using the specific example of pregnancy-related conditions.......Medicine has made a giant leap forward over the last century when it comes to the treatment of human disease, but even the most cutting edge 21st century medicine cannot prevent new diseases arising, nor those thought to be extinct developing resistance to pharmaceuticals and returning...

Origin of the fittest: link between emergent variation and evolutionary change as a critical question in evolutionary biology.

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Badyaev, Alexander V

2011-07-07

In complex organisms, neutral evolution of genomic architecture, associated compensatory interactions in protein networks and emergent developmental processes can delineate the directions of evolutionary change, including the opportunity for natural selection. These effects are reflected in the evolution of developmental programmes that link genomic architecture with a corresponding functioning phenotype. Two recent findings call for closer examination of the rules by which these links are constructed. First is the realization that high dimensionality of genotypes and emergent properties of autonomous developmental processes (such as capacity for self-organization) result in the vast areas of fitness neutrality at both the phenotypic and genetic levels. Second is the ubiquity of context- and taxa-specific regulation of deeply conserved gene networks, such that exceptional phenotypic diversification coexists with remarkably conserved generative processes. Establishing the causal reciprocal links between ongoing neutral expansion of genomic architecture, emergent features of organisms' functionality, and often precisely adaptive phenotypic diversification therefore becomes an important goal of evolutionary biology and is the latest reincarnation of the search for a framework that links development, functioning and evolution of phenotypes. Here I examine, in the light of recent empirical advances, two evolutionary concepts that are central to this framework-natural selection and inheritance-the general rules by which they become associated with emergent developmental and homeostatic processes and the role that they play in descent with modification.

Reconstructing nursing altruism using a biological evolutionary framework.

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Haigh, Carol A

2010-06-01

This paper presents a discussion of the role of altruism in development of the discipline of nursing and an exploration of how nursing altruism compares with current thinking in biological evolutionary theory. There is an assumption that the role of the nurse has its foundations in altruistic behaviours; however, the source of this altruism is never analysed or debated. A search of the biological altruism, altruism and health-related literature encompassing the years 1975-2007 was performed using Google Scholar. The first element of the study is a brief overview of nursing altruism as a way of establishing the conceptual boundaries. Additionally, the major tenets of biological evolution are explored to clarify the theoretical underpinnings of the hypotheses presented. A key premise of this study is that nursing altruism is not solely a manifestation of disinterested sacrifice for the benefit of others, but is more concerned with ensuring the survival of a clearly defined social group. A re-evaluation of altruism as a motivating factor in nursing and as an element of the therapeutic relationship is long overdue. It is time that the nursing profession examined professional driving forces using more than traditional philosophical frameworks. Nursing altruism is programmed to ensure the survival of the meme rather than to act in the best interest of patients. Certainly patients reap the benefits of this selfish altruism, but that can be argued to be a side effect rather than a result.

Unity and disunity in evolutionary sciences: process-based analogies open common research avenues for biology and linguistics.

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List, Johann-Mattis; Pathmanathan, Jananan Sylvestre; Lopez, Philippe; Bapteste, Eric

2016-08-20

For a long time biologists and linguists have been noticing surprising similarities between the evolution of life forms and languages. Most of the proposed analogies have been rejected. Some, however, have persisted, and some even turned out to be fruitful, inspiring the transfer of methods and models between biology and linguistics up to today. Most proposed analogies were based on a comparison of the research objects rather than the processes that shaped their evolution. Focusing on process-based analogies, however, has the advantage of minimizing the risk of overstating similarities, while at the same time reflecting the common strategy to use processes to explain the evolution of complexity in both fields. We compared important evolutionary processes in biology and linguistics and identified processes specific to only one of the two disciplines as well as processes which seem to be analogous, potentially reflecting core evolutionary processes. These new process-based analogies support novel methodological transfer, expanding the application range of biological methods to the field of historical linguistics. We illustrate this by showing (i) how methods dealing with incomplete lineage sorting offer an introgression-free framework to analyze highly mosaic word distributions across languages; (ii) how sequence similarity networks can be used to identify composite and borrowed words across different languages; (iii) how research on partial homology can inspire new methods and models in both fields; and (iv) how constructive neutral evolution provides an original framework for analyzing convergent evolution in languages resulting from common descent (Sapir's drift). Apart from new analogies between evolutionary processes, we also identified processes which are specific to either biology or linguistics. This shows that general evolution cannot be studied from within one discipline alone. In order to get a full picture of evolution, biologists and linguists need to

A Course in Evolutionary Biology: Engaging Students in the "Practice" of Evolution. Research Report.

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Passmore, Cynthia; Stewart, James

Recent education reform documents emphasize the need for students to develop a rich understanding of evolution's power to integrate knowledge of the natural world. This paper describes a nine-week high school course designed to help students understand evolutionary biology by engaging them in developing, elaborating, and using Charles Darwin's…

From the "Modern Synthesis" to cybernetics: Ivan Ivanovich Schmalhausen (1884-1963) and his research program for a synthesis of evolutionary and developmental biology.

Science.gov (United States)

Levit, Georgy S; Hossfeld, Uwe; Olsson, Lennart

2006-03-15

Ivan I. Schmalhausen was one of the central figures in the Russian development of the "Modern Synthesis" in evolutionary biology. He is widely cited internationally even today. Schmalhausen developed the main principles of his theory facing the danger of death in the totalitarian Soviet Union. His great services to evolutionary and theoretical biology are indisputable. However, the received view of Schmalhausen's contributions to evolutionary biology makes an unbiased reading of his texts difficult. Here we show that taking all of his works into consideration (including those only available in Russian) paints a much more dynamic and exciting picture of what he tried to achieve. Schmalhausen pioneered the integration of a developmental perspective into evolutionary thinking. A main tool for achieving this was his approach to living objects as complex multi-level self-regulating systems. Schmalhausen put enormous effort into bringing this idea into fruition during the final stages of his career by combining evolutionary theory with cybernetics. His results and ideas remain thought-provoking, and his texts are of more than just historical interest. Copyright 2006 Wiley-Liss, Inc.

Evolution and the American social sciences: An evolutionary social scientist's view.

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Thayer, Bradley A

2004-03-01

American social scientists rarely ever use evolutionary concepts to explain behavior, despite the potential of such concepts to elucidate major social problems. I argue that this observation can be understood as the product of three influences: an ideologically narrowed political liberalism; a fear of ''Social Darwinism'' as a scientific idea, rather than a scientific apostasy; and a widely believed criticism of evolutionary thinking as deterministic, reductionistic, and Panglossian. I ask what is to be done to encourage social scientists to learn and to apply evolutionary lessons. I answer with four solutions. First, evolutionary social scientists should more effectively educate their non-evolutionary students and colleagues. Second, they should publicize, even popularize, accessible refutations of perennially misleading criticisms. Third, they should more credibly assure skeptics that evolutionary theory not only keeps the ''social'' in social science but better explains social behavior than can any individual-level theory, such as rational-choice theory. Fourth, they should recall that biology took generations to become Darwinian, and they must understand that the social sciences may take as long to become evolutionary.

Deep learning: Using machine learning to study biological vision

OpenAIRE

Majaj, Najib; Pelli, Denis

2017-01-01

Today most vision-science presentations mention machine learning. Many neuroscientists use machine learning to decode neural responses. Many perception scientists try to understand recognition by living organisms. To them, machine learning offers a reference of attainable performance based on learned stimuli. This brief overview of the use of machine learning in biological vision touches on its strengths, weaknesses, milestones, controversies, and current directions.

The Relationships between Epistemic Beliefs in Biology and Approaches to Learning Biology among Biology-Major University Students in Taiwan

Science.gov (United States)

Lin, Yi-Chun; Liang, Jyh-Chong; Tsai, Chin-Chung

2012-01-01

The aim of this study was to investigate the relationships between students' epistemic beliefs in biology and their approaches to learning biology. To this end, two instruments, the epistemic beliefs in biology and the approaches to learning biology surveys, were developed and administered to 520 university biology students, respectively. By and…

Learning bias, cultural evolution of language, and the biological evolution of the language faculty.

Science.gov (United States)

Smith, Kenny

2011-04-01

The biases of individual language learners act to determine the learnability and cultural stability of languages: learners come to the language learning task with biases which make certain linguistic systems easier to acquire than others. These biases are repeatedly applied during the process of language transmission, and consequently should effect the types of languages we see in human populations. Understanding the cultural evolutionary consequences of particular learning biases is therefore central to understanding the link between language learning in individuals and language universals, common structural properties shared by all the world’s languages. This paper reviews a range of models and experimental studies which show that weak biases in individual learners can have strong effects on the structure of socially learned systems such as language, suggesting that strong universal tendencies in language structure do not require us to postulate strong underlying biases or constraints on language learning. Furthermore, understanding the relationship between learner biases and language design has implications for theories of the evolution of those learning biases: models of gene-culture coevolution suggest that, in situations where a cultural dynamic mediates between properties of individual learners and properties of language in this way, biological evolution is unlikely to lead to the emergence of strong constraints on learning.

How can we estimate natural selection on endocrine traits? Lessons from evolutionary biology.

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Bonier, Frances; Martin, Paul R

2016-11-30

An evolutionary perspective can enrich almost any endeavour in biology, providing a deeper understanding of the variation we see in nature. To this end, evolutionary endocrinologists seek to describe the fitness consequences of variation in endocrine traits. Much of the recent work in our field, however, follows a flawed approach to the study of how selection shapes endocrine traits. Briefly, this approach relies on among-individual correlations between endocrine phenotypes (often circulating hormone levels) and fitness metrics to estimate selection on those endocrine traits. Adaptive plasticity in both endocrine and fitness-related traits can drive these correlations, generating patterns that do not accurately reflect natural selection. We illustrate why this approach to studying selection on endocrine traits is problematic, referring to work from evolutionary biologists who, decades ago, described this problem as it relates to a variety of other plastic traits. We extend these arguments to evolutionary endocrinology, where the likelihood that this flaw generates bias in estimates of selection is unusually high due to the exceptional responsiveness of hormones to environmental conditions, and their function to induce adaptive life-history responses to environmental variation. We end with a review of productive approaches for investigating the fitness consequences of variation in endocrine traits that we expect will generate exciting advances in our understanding of endocrine system evolution. © 2016 The Author(s).

Questioning Clerkship: Applying Popper's Evolutionary Analysis of Learning to Medical Student Training

Science.gov (United States)

Chitpin, Jeremy Sebastian; Chitpin, Stephanie

2017-01-01

Purpose: Through a series of critical discussions on Karl Popper's evolutionary analysis of learning and the non-authoritarian values it promotes, the purpose of this paper is to advocate a Popperian approach for building medical student knowledge. Specifically, it challenges positivist assumptions that permeate the design and management of many…

The Schistosoma mansoni phylome: using evolutionary genomics to gain insight into a parasite’s biology

Directory of Open Access Journals (Sweden)

Silva Larissa

2012-11-01

Full Text Available Abstract Background Schistosoma mansoni is one of the causative agents of schistosomiasis, a neglected tropical disease that affects about 237 million people worldwide. Despite recent efforts, we still lack a general understanding of the relevant host-parasite interactions, and the possible treatments are limited by the emergence of resistant strains and the absence of a vaccine. The S. mansoni genome was completely sequenced and still under continuous annotation. Nevertheless, more than 45% of the encoded proteins remain without experimental characterization or even functional prediction. To improve our knowledge regarding the biology of this parasite, we conducted a proteome-wide evolutionary analysis to provide a broad view of the S. mansoni’s proteome evolution and to improve its functional annotation. Results Using a phylogenomic approach, we reconstructed the S. mansoni phylome, which comprises the evolutionary histories of all parasite proteins and their homologs across 12 other organisms. The analysis of a total of 7,964 phylogenies allowed a deeper understanding of genomic complexity and evolutionary adaptations to a parasitic lifestyle. In particular, the identification of lineage-specific gene duplications pointed to the diversification of several protein families that are relevant for host-parasite interaction, including proteases, tetraspanins, fucosyltransferases, venom allergen-like proteins, and tegumental-allergen-like proteins. In addition to the evolutionary knowledge, the phylome data enabled us to automatically re-annotate 3,451 proteins through a phylogenetic-based approach rather than solely sequence similarity searches. To allow further exploitation of this valuable data, all information has been made available at PhylomeDB (http://www.phylomedb.org. Conclusions In this study, we used an evolutionary approach to assess S. mansoni parasite biology, improve genome/proteome functional annotation, and provide insights into

Physiology is rocking the foundations of evolutionary biology.

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Noble, Denis

2013-08-01

The 'Modern Synthesis' (Neo-Darwinism) is a mid-20th century gene-centric view of evolution, based on random mutations accumulating to produce gradual change through natural selection. Any role of physiological function in influencing genetic inheritance was excluded. The organism became a mere carrier of the real objects of selection, its genes. We now know that genetic change is far from random and often not gradual. Molecular genetics and genome sequencing have deconstructed this unnecessarily restrictive view of evolution in a way that reintroduces physiological function and interactions with the environment as factors influencing the speed and nature of inherited change. Acquired characteristics can be inherited, and in a few but growing number of cases that inheritance has now been shown to be robust for many generations. The 21st century can look forward to a new synthesis that will reintegrate physiology with evolutionary biology.

[The Biology of Learning].

Science.gov (United States)

Campo-Cabal, Gerardo

2012-01-01

The effort to relate mental and biological functioning has fluctuated between two doctrines: 1) an attempt to explain mental functioning as a collective property of the brain and 2) as one relatied to other mental processes associated with specific regions of the brain. The article reviews the main theories developed over the last 200 years: phrenology, the psuedo study of the brain, mass action, cellular connectionism and distributed processing among others. In addition, approaches have emerged in recent years that allows for an understanding of the biological determinants and individual differences in complex mental processes through what is called cognitive neuroscience. Knowing the definition of neuroscience, the learning of memory, the ways in which learning occurs, the principles of the neural basis of memory and learning and its effects on brain function, among other things, allows us the basic understanding of the processes of memory and learning and is an important requirement to address the best manner to commit to the of training future specialists in Psychiatry. Copyright © 2012 Asociación Colombiana de Psiquiatría. Publicado por Elsevier España. All rights reserved.

Cognition and Culture in Evolutionary Context.

Science.gov (United States)

Colmenares, Fernando; Hernández-Lloreda, María Victoria

2017-01-09

In humans and other animals, the individuals' ability to adapt efficiently and effectively to the niches they have actively contributed to construct relies heavily on an evolved psychology which has been shaped by biological, social, and cultural processes over evolutionary time. As expected, although many of the behavioral and cognitive components of this evolved psychology are widely shared across species, many others are species-unique. Although many animal species are known to acquire group-specific traditions (or cultures) via social learning, human culture is unique in terms of its contents and characteristics (observable and unobservable products, cumulative effects, norm conformity, and norm enforcement) and of its cognitive underpinnings (imitation, instructed teaching, and language). Here we provide a brief overview of some of the issues that are currently tackled in the field. We also highlight some of the strengths of a biological, comparative, non-anthropocentric and evolutionarily grounded approach to the study of culture. The main contributions of this approach to the science of culture are its emphasis (a) on the integration of information on mechanisms, function, and evolution, and on mechanistic factors located at different levels of the biological hierarchy, and (b) on the search for general principles that account for commonalities and differences between species, both in the cultural products and in the processes of innovation, dissemination, and accumulation involved that operate during developmental and evolutionary timespans.

Relational Analysis of High School Students' Cognitive Self-Regulated Learning Strategies and Conceptions of Learning Biology

Science.gov (United States)

Sadi, Özlem

2017-01-01

The purpose of this study was to analyze the relation between students' cognitive learning strategies and conceptions of learning biology. The two scales, "Cognitive Learning Strategies" and "Conceptions of Learning Biology", were revised and adapted to biology in order to measure the students' learning strategies and…

Evolutionary biology and life histories

Directory of Open Access Journals (Sweden)

Brown, C. R.

2004-06-01

Full Text Available The demographic processes that drive the spread of populations through environments and in turn determine the abundance of organisms are the same demographic processes that drive the spread of genes through populations and in turn determine gene frequencies and fitness. Conceptually, marked similarities exist in the dynamic processes underlying population ecology and those underlying evolutionary biology. Central to an understanding of both disciplines is life history and its component demographic rates, such as survival, fecundity, and age of first breeding, and biologists from both fields have a vested interest in good analytical machinery for the estimation and analysis of these demographic rates. In the EURING conferences, we have been striving since the mid 1980s to promote a quantitative understanding of demographic rates through interdisciplinary collaboration between ecologists and statisticians. From the ecological side, the principal impetus has come from population biology, and in particular from wildlife biology, but the importance of good quantitative insights into demographic processes has long been recognized by a number of evolutionary biologists (e.g., Nichols & Kendall, 1995; Clobert, 1995; Cooch et al., 2002. In organizing this session, we have aimed to create a forum for those committed to gaining the best possible understanding of evolutionary processes through the application of modern quantitative methods for the collection and interpretation of data on marked animal populations. Here we present a short overview of the material presented in the session on evolutionary biology and life histories. In a plenary talk, Brown & Brown (2004 explored how mark–recapture methods have allowed a better understanding of the evolution of group–living and alternative reproductive tactics in colonial cliff swallows (Petrochelidon pyrrhonota. By estimating the number of transient birds passing through colonies of different sizes, they

Revisit of Machine Learning Supported Biological and Biomedical Studies.

Science.gov (United States)

Yu, Xiang-Tian; Wang, Lu; Zeng, Tao

2018-01-01

Generally, machine learning includes many in silico methods to transform the principles underlying natural phenomenon to human understanding information, which aim to save human labor, to assist human judge, and to create human knowledge. It should have wide application potential in biological and biomedical studies, especially in the era of big biological data. To look through the application of machine learning along with biological development, this review provides wide cases to introduce the selection of machine learning methods in different practice scenarios involved in the whole biological and biomedical study cycle and further discusses the machine learning strategies for analyzing omics data in some cutting-edge biological studies. Finally, the notes on new challenges for machine learning due to small-sample high-dimension are summarized from the key points of sample unbalance, white box, and causality.

Generalizing and learning protein-DNA binding sequence representations by an evolutionary algorithm

KAUST Repository

Wong, Ka Chun

2011-02-05

Protein-DNA bindings are essential activities. Understanding them forms the basis for further deciphering of biological and genetic systems. In particular, the protein-DNA bindings between transcription factors (TFs) and transcription factor binding sites (TFBSs) play a central role in gene transcription. Comprehensive TF-TFBS binding sequence pairs have been found in a recent study. However, they are in one-to-one mappings which cannot fully reflect the many-to-many mappings within the bindings. An evolutionary algorithm is proposed to learn generalized representations (many-to-many mappings) from the TF-TFBS binding sequence pairs (one-to-one mappings). The generalized pairs are shown to be more meaningful than the original TF-TFBS binding sequence pairs. Some representative examples have been analyzed in this study. In particular, it shows that the TF-TFBS binding sequence pairs are not presumably in one-to-one mappings. They can also exhibit many-to-many mappings. The proposed method can help us extract such many-to-many information from the one-to-one TF-TFBS binding sequence pairs found in the previous study, providing further knowledge in understanding the bindings between TFs and TFBSs. © 2011 Springer-Verlag.

Generalizing and learning protein-DNA binding sequence representations by an evolutionary algorithm

KAUST Repository

Wong, Ka Chun; Peng, Chengbin; Wong, Manhon; Leung, Kwongsak

2011-01-01

Protein-DNA bindings are essential activities. Understanding them forms the basis for further deciphering of biological and genetic systems. In particular, the protein-DNA bindings between transcription factors (TFs) and transcription factor binding sites (TFBSs) play a central role in gene transcription. Comprehensive TF-TFBS binding sequence pairs have been found in a recent study. However, they are in one-to-one mappings which cannot fully reflect the many-to-many mappings within the bindings. An evolutionary algorithm is proposed to learn generalized representations (many-to-many mappings) from the TF-TFBS binding sequence pairs (one-to-one mappings). The generalized pairs are shown to be more meaningful than the original TF-TFBS binding sequence pairs. Some representative examples have been analyzed in this study. In particular, it shows that the TF-TFBS binding sequence pairs are not presumably in one-to-one mappings. They can also exhibit many-to-many mappings. The proposed method can help us extract such many-to-many information from the one-to-one TF-TFBS binding sequence pairs found in the previous study, providing further knowledge in understanding the bindings between TFs and TFBSs. © 2011 Springer-Verlag.

Building v/s Exploring Models: Comparing Learning of Evolutionary Processes through Agent-based Modeling

Science.gov (United States)

Wagh, Aditi

Two strands of work motivate the three studies in this dissertation. Evolutionary change can be viewed as a computational complex system in which a small set of rules operating at the individual level result in different population level outcomes under different conditions. Extensive research has documented students' difficulties with learning about evolutionary change (Rosengren et al., 2012), particularly in terms of levels slippage (Wilensky & Resnick, 1999). Second, though building and using computational models is becoming increasingly common in K-12 science education, we know little about how these two modalities compare. This dissertation adopts agent-based modeling as a representational system to compare these modalities in the conceptual context of micro-evolutionary processes. Drawing on interviews, Study 1 examines middle-school students' productive ways of reasoning about micro-evolutionary processes to find that the specific framing of traits plays a key role in whether slippage explanations are cued. Study 2, which was conducted in 2 schools with about 150 students, forms the crux of the dissertation. It compares learning processes and outcomes when students build their own models or explore a pre-built model. Analysis of Camtasia videos of student pairs reveals that builders' and explorers' ways of accessing rules, and sense-making of observed trends are of a different character. Builders notice rules through available blocks-based primitives, often bypassing their enactment while explorers attend to rules primarily through the enactment. Moreover, builders' sense-making of observed trends is more rule-driven while explorers' is more enactment-driven. Pre and posttests reveal that builders manifest a greater facility with accessing rules, providing explanations manifesting targeted assembly. Explorers use rules to construct explanations manifesting non-targeted assembly. Interviews reveal varying degrees of shifts away from slippage in both

Hidden Markov models and other machine learning approaches in computational molecular biology

Energy Technology Data Exchange (ETDEWEB)

Baldi, P. [California Inst. of Tech., Pasadena, CA (United States)

1995-12-31

This tutorial was one of eight tutorials selected to be presented at the Third International Conference on Intelligent Systems for Molecular Biology which was held in the United Kingdom from July 16 to 19, 1995. Computational tools are increasingly needed to process the massive amounts of data, to organize and classify sequences, to detect weak similarities, to separate coding from non-coding regions, and reconstruct the underlying evolutionary history. The fundamental problem in machine learning is the same as in scientific reasoning in general, as well as statistical modeling: to come up with a good model for the data. In this tutorial four classes of models are reviewed. They are: Hidden Markov models; artificial Neural Networks; Belief Networks; and Stochastic Grammars. When dealing with DNA and protein primary sequences, Hidden Markov models are one of the most flexible and powerful alignments and data base searches. In this tutorial, attention is focused on the theory of Hidden Markov Models, and how to apply them to problems in molecular biology.

The biology of gall-inducing arthropods.

Science.gov (United States)

Gyuri Csoka; William J. Mattson; Graham N. Stone; Peter W. Price

1998-01-01

This proceedings explores many facets of the ever intriguing and enigmatic relationships between plants and their gall-forming herbivores. The research reported herein ranges from studies on classical biology and systematics of galling to molecular phylogeny, population genetics, and ecological and evolutionary theory. Human kind has much to learn and gain from...

A Conceptual Framework for Organizing Active Learning Experiences in Biology Instruction

Science.gov (United States)

Gardner, Joel; Belland, Brian R.

2012-01-01

Introductory biology courses form a cornerstone of undergraduate instruction. However, the predominantly used lecture approach fails to produce higher-order biology learning. Research shows that active learning strategies can increase student learning, yet few biology instructors use all identified active learning strategies. In this paper, we…

Where Evolutionary Psychology Meets Cognitive Neuroscience: A Précis to Evolutionary Cognitive Neuroscience1

Directory of Open Access Journals (Sweden)

Austen L. Krill

2007-01-01

Full Text Available Cognitive neuroscience, the study of brain-behavior relationships, has long attempted to map the brain. The discipline is flourishing, with an increasing number of functional neuroimaging studies appearing in the scientific literature daily. Unlike biology and even psychology, the cognitive neurosciences have only recently begun to apply evolutionary meta-theory and methodological guidance. Approaching cognitive neuroscience from an evolutionary perspective allows scientists to apply biologically based theoretical guidance to their investigations and can be conducted in both humans and nonhuman animals. In fact, several investigations of this sort are underway in laboratories around the world. This paper and two new volumes (Platek, Keenan, and Shackelford [Eds.], 2007; Platek and Shackelford [Eds.], under contract represent the first formal attempts to document the burgeoning field of evolutionary cognitive neuroscience. Here, we briefly review the current state of the science of evolutionary cognitive neuroscience, the methods available to the evolutionary cognitive neuroscientist, and what we foresee as the future directions of the discipline.

PENERAPAN BLENDED-PROBLEM BASED LEARNING DALAM PEMBELAJARAN BIOLOGI

Directory of Open Access Journals (Sweden)

Samuel Agus Triyanto

2016-07-01

Biologi abad 21 merupakan integrasi dan mengintegrasikan kembali sub disiplin ilmu biologi, serta integrasi biologi dengan disiplin ilmu lain untuk mengatasi permasalahan sosial. Penelitian ini bertujuan untuk mengetahui penerapan Blended-Problem Based Learning, aktivitas belajar, dan respon siswa dalam pembelajaran biologi. Penelitian ini merupakan penelitian survei dengan pendekatan deskriptif kualitatif. Data hasil penelitian menunjukkan bahwa aktivitas positif siswa dalam pembelajaran memuaskan, sedangkan respon siswa baik terhadap pembelajaran. Berdasarkan hasil penelitian, disimpulkan bahwa Blended-Problem Based Learning dapat diterapkan dan diterima sebagai model dalam pembelajaran.

Essays on nonlinear evolutionary game dynamics

NARCIS (Netherlands)

Ochea, M.I.

2010-01-01

Evolutionary game theory has been viewed as an evolutionary repair of rational actor game theory in the hope that a population of boundedly rational players may attain convergence to classic rational solutions, such as the Nash Equilibrium, via some learning or evolutionary process. In this thesis

Evolutionary principles and their practical application.

Science.gov (United States)

Hendry, Andrew P; Kinnison, Michael T; Heino, Mikko; Day, Troy; Smith, Thomas B; Fitt, Gary; Bergstrom, Carl T; Oakeshott, John; Jørgensen, Peter S; Zalucki, Myron P; Gilchrist, George; Southerton, Simon; Sih, Andrew; Strauss, Sharon; Denison, Robert F; Carroll, Scott P

2011-03-01

Evolutionary principles are now routinely incorporated into medicine and agriculture. Examples include the design of treatments that slow the evolution of resistance by weeds, pests, and pathogens, and the design of breeding programs that maximize crop yield or quality. Evolutionary principles are also increasingly incorporated into conservation biology, natural resource management, and environmental science. Examples include the protection of small and isolated populations from inbreeding depression, the identification of key traits involved in adaptation to climate change, the design of harvesting regimes that minimize unwanted life-history evolution, and the setting of conservation priorities based on populations, species, or communities that harbor the greatest evolutionary diversity and potential. The adoption of evolutionary principles has proceeded somewhat independently in these different fields, even though the underlying fundamental concepts are the same. We explore these fundamental concepts under four main themes: variation, selection, connectivity, and eco-evolutionary dynamics. Within each theme, we present several key evolutionary principles and illustrate their use in addressing applied problems. We hope that the resulting primer of evolutionary concepts and their practical utility helps to advance a unified multidisciplinary field of applied evolutionary biology.

Evolutionary Pseudo-Relaxation Learning Algorithm for Bidirectional Associative Memory

Institute of Scientific and Technical Information of China (English)

Sheng-Zhi Du; Zeng-Qiang Chen; Zhu-Zhi Yuan

2005-01-01

This paper analyzes the sensitivity to noise in BAM (Bidirectional Associative Memory), and then proves the noise immunity of BAM relates not only to the minimum absolute value of net inputs (MAV) but also to the variance of weights associated with synapse connections. In fact, it is a positive monotonically increasing function of the quotient of MAV divided by the variance of weights. Besides, the performance of pseudo-relaxation method depends on learning parameters (λ and ζ), but the relation of them is not linear. So it is hard to find a best combination of λ and ζ which leads to the best BAM performance. And it is obvious that pseudo-relaxation is a kind of local optimization method, so it cannot guarantee to get the global optimal solution. In this paper, a novel learning algorithm EPRBAM (evolutionary psendo-relaxation learning algorithm for bidirectional association memory) employing genetic algorithm and pseudo-relaxation method is proposed to get feasible solution of BAM weight matrix. This algorithm uses the quotient as the fitness of each individual and employs pseudo-relaxation method to adjust individual solution when it does not satisfy constraining condition any more after genetic operation. Experimental results show this algorithm improves noise immunity of BAM greatly. At the same time, EPRBAM does not depend on learning parameters and can get global optimal solution.

Assessment of Student Learning Associated with Tree Thinking in an Undergraduate Introductory Organismal Biology Course

Science.gov (United States)

Smith, James J.; Cheruvelil, Kendra Spence; Auvenshine, Stacie

2013-01-01

Phylogenetic trees provide visual representations of ancestor-descendant relationships, a core concept of evolutionary theory. We introduced "tree thinking" into our introductory organismal biology course (freshman/sophomore majors) to help teach organismal diversity within an evolutionary framework. Our instructional strategy consisted…

Is `Learning' Science Enough? - A Cultural Model of Religious Students of Science in an Australian Government School

Science.gov (United States)

Ferguson, Joseph Paul; Kameniar, Barbara

2014-10-01

This paper investigates the cognitive experiences of four religious students studying evolutionary biology in an inner city government secondary school in Melbourne, Australia. The participants in the study were identified using the Religious Background and Behaviours questionnaire (Connors, Tonigan, & Miller, 1996). Participants were interviewed and asked to respond to questions about their cognitive experiences of studying evolutionary biology. Students' responses were analysed using cultural analysis of discourse to construct a cultural model of religious students of science. This cultural model suggests that these students employ a human schema and a non-human schema, which assert that humans are fundamentally different from non-humans in terms of origins and that humans have a transcendental purpose in life. For these students, these maxims seem to be challenged by their belief that evolutionary biology is dictated by metaphysical naturalism. The model suggests that because the existential foundation of these students is challenged, they employ a believing schema to classify their religious explanations and a learning schema to classify evolutionary biology. These schemas are then hierarchically arranged with the learning schema being made subordinate to the believing schema. Importantly, these students are thus able to maintain their existential foundation while fulfilling the requirements of school science. However, the quality of this "learning" is questionable.

Critical factors in the empirical performance of temporal difference and evolutionary methods for reinforcement learning

NARCIS (Netherlands)

Whiteson, S.; Taylor, M.E.; Stone, P.

2010-01-01

Temporal difference and evolutionary methods are two of the most common approaches to solving reinforcement learning problems. However, there is little consensus on their relative merits and there have been few empirical studies that directly compare their performance. This article aims to address

Developmental biology, the stem cell of biological disciplines.

Science.gov (United States)

Gilbert, Scott F

2017-12-01

Developmental biology (including embryology) is proposed as "the stem cell of biological disciplines." Genetics, cell biology, oncology, immunology, evolutionary mechanisms, neurobiology, and systems biology each has its ancestry in developmental biology. Moreover, developmental biology continues to roll on, budding off more disciplines, while retaining its own identity. While its descendant disciplines differentiate into sciences with a restricted set of paradigms, examples, and techniques, developmental biology remains vigorous, pluripotent, and relatively undifferentiated. In many disciplines, especially in evolutionary biology and oncology, the developmental perspective is being reasserted as an important research program.

Active Learning Not Associated with Student Learning in a Random Sample of College Biology Courses

Science.gov (United States)

Andrews, T. M.; Leonard, M. J.; Colgrove, C. A.; Kalinowski, S. T.

2011-01-01

Previous research has suggested that adding active learning to traditional college science lectures substantially improves student learning. However, this research predominantly studied courses taught by science education researchers, who are likely to have exceptional teaching expertise. The present study investigated introductory biology courses randomly selected from a list of prominent colleges and universities to include instructors representing a broader population. We examined the relationship between active learning and student learning in the subject area of natural selection. We found no association between student learning gains and the use of active-learning instruction. Although active learning has the potential to substantially improve student learning, this research suggests that active learning, as used by typical college biology instructors, is not associated with greater learning gains. We contend that most instructors lack the rich and nuanced understanding of teaching and learning that science education researchers have developed. Therefore, active learning as designed and implemented by typical college biology instructors may superficially resemble active learning used by education researchers, but lacks the constructivist elements necessary for improving learning. PMID:22135373

Language cannot be reduced to biology: perspectives from neuro-developmental disorders affecting language learning.

Science.gov (United States)

Vasanta, D

2005-02-01

The study of language knowledge guided by a purely biological perspective prioritizes the study of syntax. The essential process of syntax is recursion--the ability to generate an infinite array of expressions from a limited set of elements. Researchers working within the biological perspective argue that this ability is possible only because of an innately specified genetic makeup that is specific to human beings. Such a view of language knowledge may be fully justified in discussions on biolinguistics, and in evolutionary biology. However, it is grossly inadequate in understanding language-learning problems, particularly those experienced by children with neurodevelopmental disorders such as developmental dyslexia, Williams syndrome, specific language impairment and autism spectrum disorders. Specifically, syntax-centered definitions of language knowledge completely ignore certain crucial aspects of language learning and use, namely, that language is embedded in a social context; that the role of envrironmental triggering as a learning mechanism is grossly underestimated; that a considerable extent of visuo-spatial information accompanies speech in day-to-day communication; that the developmental process itself lies at the heart of knowledge acquisition; and that there is a tremendous variation in the orthographic systems associated with different languages. All these (socio-cultural) factors can influence the rate and quality of spoken and written language acquisition resulting in much variation in phenotypes associated with disorders known to have a genetic component. Delineation of such phenotypic variability requires inputs from varied disciplines such as neurobiology, neuropsychology, linguistics and communication disorders. In this paper, I discuss published research that questions cognitive modularity and emphasises the role of the environment for understanding linguistic capabilities of children with neuro-developmental disorders. The discussion pertains

A second inheritance system: the extension of biology through culture.

Science.gov (United States)

Whiten, Andrew

2017-10-06

By the mid-twentieth century (thus following the 'Modern Synthesis' in evolutionary biology), the behavioural sciences offered only the sketchy beginnings of a scientific literature documenting evidence for cultural inheritance in animals-the transmission of traditional behaviours via learning from others (social learning). By contrast, recent decades have seen a massive growth in the documentation of such cultural phenomena, driven by long-term field studies and complementary laboratory experiments. Here, I review the burgeoning scope of discoveries in this field, which increasingly suggest that this 'second inheritance system', built on the shoulders of the primary genetic inheritance system, occurs widely among vertebrates and possibly in invertebrates too. Its novel characteristics suggest significant implications for our understanding of evolutionary biology. I assess the extent to which this second system extends the scope of evolution, both by echoing principal properties of the primary, organic evolutionary system, and going beyond it in significant ways. This is well established in human cultural evolution; here, I address animal cultures more generally. The further major, and related, question concerns the extent to which the consequences of widespread animal cultural transmission interact with the primary, genetically based inheritance systems, shaping organic evolution.

The Implementation of Research-based Learning on Biology Seminar Course in Biology Education Study Program of FKIP UMRAH

Science.gov (United States)

Amelia, T.

2018-04-01

Biology Seminar is a course in Biology Education Study Program of Faculty of Teacher Training and Education University of Maritim Raja Ali Haji (FKIP UMRAH) that requires students to have the ability to apply scientific attitudes, perform scientific writing and undertake scientific publications on a small scale. One of the learning strategies that can drive the achievement of learning outcomes in this course is Research-Based Learning. Research-Based Learning principles are considered in accordance with learning outcomes in Biology Seminar courses and generally in accordance with the purpose of higher education. On this basis, this article which is derived from a qualitative research aims at describing Research-based Learning on Biology Seminar course. Based on a case study research, it was known that Research-Based Learning on Biology Seminar courses is applied through: designing learning activities around contemporary research issues; teaching research methods, techniques and skills explicitly within program; drawing on personal research in designing and teaching courses; building small-scale research activities into undergraduate assignment; and infusing teaching with the values of researchers.

Evolutionary learning processes as the foundation for behaviour change.

Science.gov (United States)

Crutzen, Rik; Peters, Gjalt-Jorn Ygram

2018-03-01

We argue that the active ingredients of behaviour change interventions, often called behaviour change methods (BCMs) or techniques (BCTs), can usefully be placed on a dimension of psychological aggregation. We introduce evolutionary learning processes (ELPs) as fundamental building blocks that are on a lower level of psychological aggregation than BCMs/BCTs. A better understanding of ELPs is useful to select the appropriate BCMs/BCTs to target determinants of behaviour, or vice versa, to identify potential determinants targeted by a given BCM/BCT, and to optimally translate them into practical applications. Using these insights during intervention development may increase the likelihood of developing effective interventions - both in terms of behaviour change as well as maintenance of behaviour change.

Evolutionary convergence and biologically embodied cognition

NARCIS (Netherlands)

Keijzer, Franciscus

2017-01-01

The study of evolutionary patterns of cognitive convergence would be greatly helped by a clear demarcation of cognition. Cognition is often used as an equivalent of mind, making it difficult to pin down empirically or to apply it confidently beyond the human condition. Recent developments in

Evolutionary Expectations

DEFF Research Database (Denmark)

Nash, Ulrik William

2014-01-01

, they are correlated among people who share environments because these individuals satisfice within their cognitive bounds by using cues in order of validity, as opposed to using cues arbitrarily. Any difference in expectations thereby arise from differences in cognitive ability, because two individuals with identical...... cognitive bounds will perceive business opportunities identically. In addition, because cues provide information about latent causal structures of the environment, changes in causality must be accompanied by changes in cognitive representations if adaptation is to be maintained. The concept of evolutionary......The concept of evolutionary expectations descends from cue learning psychology, synthesizing ideas on rational expectations with ideas on bounded rationality, to provide support for these ideas simultaneously. Evolutionary expectations are rational, but within cognitive bounds. Moreover...

Learning Partnerships Between Undergraduate Biology Students and Younger Learners

Directory of Open Access Journals (Sweden)

Lee Abrahamsen

2009-12-01

Full Text Available In two upper-level elective biology courses and one beginning-level general biology course, college students participated in Learning Partnerships with middle or high school classes to study some aspect of biology. The goals were to enhance learning by providing resources to middle and high school students and teachers and by encouraging college students to consider teaching as a learning tool and a possible career goal. The college students designed lessons, activities, and laboratories that were done at the schools and at Bates College. Feedback and data suggest that the partnerships have helped teachers enrich their curricula, enhanced student learning, encouraged additional high school students to consider applying to college, and encouraged college students to consider teaching science.

Is evolutionary psychology a metatheory for psychology? A discussion of four major issues in psychology from an evolutionary developmental perspective

NARCIS (Netherlands)

Ploeger, A.; van der Maas, H.L.J.; Raijmakers, M.E.J.

2008-01-01

Evolutionary psychology has been proposed as a metatheoretical framework for psychology. We argue that evolutionary psychology should be expanded if it is to offer new insights regarding the major issues in psychology. Evolutionary developmental biology can provide valuable new insights into issues

The Learning of Biology: A Structural Basis for Future Research

Science.gov (United States)

Murray, Darrel L.

1977-01-01

This article reviews recent research studies and experiences relating the learning theories of Ausubel to biology instruction. Also some suggestions are made for future research on the learning of biology. (MR)

Where is, in 2017, the evo in evo-devo (evolutionary developmental biology)?

Science.gov (United States)

Diogo, Rui

2018-01-01

After the inaugural Pan-American-Evo-Devo meeting (2015, Berkeley), I showed how major concerns about evo-devo (Evolutionary Developmental Biology) research were demonstrated by a simple, non-biased quantitative analysis of the titles/abstracts of that meeting's talks. Here, I apply the same methodology to the titles/abstracts of the recent Pan-American-Evo-Devo meeting (2017, Calgary). The aim is to evaluate if the concerns raised by me in that paper and by other authors have been addressed and/or if there are other types of differences between the two meetings that may reflect trends within the field of evo-devo. This analysis shows that the proportion of presentations referring to "morphology", "organism", "selection", "adaptive", "phylogeny", and their derivatives was higher in the 2017 meeting, which therefore had a more "organismal" feel. However, there was a decrease in the use of "evolution"/its derivatives and of macroevolutionary terms related to the tempo and mode of evolution in the 2017 meeting. Moreover, the disproportionately high use of genetic/genomic terms clearly shows that evo-devo continues to be mainly focused on devo, and particularly on "Geno", that is, on molecular/genetic studies. Furthermore, the vast majority of animal evo-devo studies are focused only on hard tissues, which are just a small fraction of the whole organism-for example, only 15% of the tissue mass of the human body. The lack of an integrative approach is also evidenced by the lack of studies addressing conceptual/long-standing broader questions, including the links between ecology and particularly behavior and developmental/evolutionary variability and between evo-devo and evolutionary medicine. © 2018 Wiley Periodicals, Inc.

Natural Killer T Cells: An Ecological Evolutionary Developmental Biology Perspective.

Science.gov (United States)

Kumar, Amrendra; Suryadevara, Naveenchandra; Hill, Timothy M; Bezbradica, Jelena S; Van Kaer, Luc; Joyce, Sebastian

2017-01-01

Type I natural killer T (NKT) cells are innate-like T lymphocytes that recognize glycolipid antigens presented by the MHC class I-like protein CD1d. Agonistic activation of NKT cells leads to rapid pro-inflammatory and immune modulatory cytokine and chemokine responses. This property of NKT cells, in conjunction with their interactions with antigen-presenting cells, controls downstream innate and adaptive immune responses against cancers and infectious diseases, as well as in several inflammatory disorders. NKT cell properties are acquired during development in the thymus and by interactions with the host microbial consortium in the gut, the nature of which can be influenced by NKT cells. This latter property, together with the role of the host microbiota in cancer therapy, necessitates a new perspective. Hence, this review provides an initial approach to understanding NKT cells from an ecological evolutionary developmental biology (eco-evo-devo) perspective.

Natural Killer T Cells: An Ecological Evolutionary Developmental Biology Perspective

Science.gov (United States)

Kumar, Amrendra; Suryadevara, Naveenchandra; Hill, Timothy M.; Bezbradica, Jelena S.; Van Kaer, Luc; Joyce, Sebastian

2017-01-01

Type I natural killer T (NKT) cells are innate-like T lymphocytes that recognize glycolipid antigens presented by the MHC class I-like protein CD1d. Agonistic activation of NKT cells leads to rapid pro-inflammatory and immune modulatory cytokine and chemokine responses. This property of NKT cells, in conjunction with their interactions with antigen-presenting cells, controls downstream innate and adaptive immune responses against cancers and infectious diseases, as well as in several inflammatory disorders. NKT cell properties are acquired during development in the thymus and by interactions with the host microbial consortium in the gut, the nature of which can be influenced by NKT cells. This latter property, together with the role of the host microbiota in cancer therapy, necessitates a new perspective. Hence, this review provides an initial approach to understanding NKT cells from an ecological evolutionary developmental biology (eco-evo-devo) perspective. PMID:29312339

Natural Killer T Cells: An Ecological Evolutionary Developmental Biology Perspective

Directory of Open Access Journals (Sweden)

Amrendra Kumar

2017-12-01

Full Text Available Type I natural killer T (NKT cells are innate-like T lymphocytes that recognize glycolipid antigens presented by the MHC class I-like protein CD1d. Agonistic activation of NKT cells leads to rapid pro-inflammatory and immune modulatory cytokine and chemokine responses. This property of NKT cells, in conjunction with their interactions with antigen-presenting cells, controls downstream innate and adaptive immune responses against cancers and infectious diseases, as well as in several inflammatory disorders. NKT cell properties are acquired during development in the thymus and by interactions with the host microbial consortium in the gut, the nature of which can be influenced by NKT cells. This latter property, together with the role of the host microbiota in cancer therapy, necessitates a new perspective. Hence, this review provides an initial approach to understanding NKT cells from an ecological evolutionary developmental biology (eco-evo-devo perspective.

Learning Cell Biology as a Team: A Project-Based Approach to Upper-Division Cell Biology

Science.gov (United States)

Wright, Robin; Boggs, James

2002-01-01

To help students develop successful strategies for learning how to learn and communicate complex information in cell biology, we developed a quarter-long cell biology class based on team projects. Each team researches a particular human disease and presents information about the cellular structure or process affected by the disease, the cellular…

Do Sophisticated Epistemic Beliefs Predict Meaningful Learning? Findings from a Structural Equation Model of Undergraduate Biology Learning

Science.gov (United States)

Lee, Silvia Wen-Yu; Liang, Jyh-Chong; Tsai, Chin-Chung

2016-01-01

This study investigated the relationships among college students' epistemic beliefs in biology (EBB), conceptions of learning biology (COLB), and strategies of learning biology (SLB). EBB includes four dimensions, namely "multiple-source," "uncertainty," "development," and "justification." COLB is further…

Peer Learning and Support of Technology in an Undergraduate Biology Course to Enhance Deep Learning

Science.gov (United States)

Tsaushu, Masha; Tal, Tali; Sagy, Ornit; Kali, Yael; Gepstein, Shimon; Zilberstein, Dan

2012-01-01

This study offers an innovative and sustainable instructional model for an introductory undergraduate course. The model was gradually implemented during 3 yr in a research university in a large-lecture biology course that enrolled biology majors and nonmajors. It gives priority to sources not used enough to enhance active learning in higher education: technology and the students themselves. Most of the lectures were replaced with continuous individual learning and 1-mo group learning of one topic, both supported by an interactive online tutorial. Assessment included open-ended complex questions requiring higher-order thinking skills that were added to the traditional multiple-choice (MC) exam. Analysis of students’ outcomes indicates no significant difference among the three intervention versions in the MC questions of the exam, while students who took part in active-learning groups at the advanced version of the model had significantly higher scores in the more demanding open-ended questions compared with their counterparts. We believe that social-constructivist learning of one topic during 1 mo has significantly contributed to student deep learning across topics. It developed a biological discourse, which is more typical to advanced stages of learning biology, and changed the image of instructors from “knowledge transmitters” to “role model scientists.” PMID:23222836

Peer learning and support of technology in an undergraduate biology course to enhance deep learning.

Science.gov (United States)

Tsaushu, Masha; Tal, Tali; Sagy, Ornit; Kali, Yael; Gepstein, Shimon; Zilberstein, Dan

2012-01-01

This study offers an innovative and sustainable instructional model for an introductory undergraduate course. The model was gradually implemented during 3 yr in a research university in a large-lecture biology course that enrolled biology majors and nonmajors. It gives priority to sources not used enough to enhance active learning in higher education: technology and the students themselves. Most of the lectures were replaced with continuous individual learning and 1-mo group learning of one topic, both supported by an interactive online tutorial. Assessment included open-ended complex questions requiring higher-order thinking skills that were added to the traditional multiple-choice (MC) exam. Analysis of students' outcomes indicates no significant difference among the three intervention versions in the MC questions of the exam, while students who took part in active-learning groups at the advanced version of the model had significantly higher scores in the more demanding open-ended questions compared with their counterparts. We believe that social-constructivist learning of one topic during 1 mo has significantly contributed to student deep learning across topics. It developed a biological discourse, which is more typical to advanced stages of learning biology, and changed the image of instructors from "knowledge transmitters" to "role model scientists."

Evolutionary Multiplayer Games

OpenAIRE

Gokhale, Chaitanya S.; Traulsen, Arne

2014-01-01

Evolutionary game theory has become one of the most diverse and far reaching theories in biology. Applications of this theory range from cell dynamics to social evolution. However, many applications make it clear that inherent non-linearities of natural systems need to be taken into account. One way of introducing such non-linearities into evolutionary games is by the inclusion of multiple players. An example is of social dilemmas, where group benefits could e.g.\\ increase less than linear wi...

The causal pie model: an epidemiological method applied to evolutionary biology and ecology.

Science.gov (United States)

Wensink, Maarten; Westendorp, Rudi G J; Baudisch, Annette

2014-05-01

A general concept for thinking about causality facilitates swift comprehension of results, and the vocabulary that belongs to the concept is instrumental in cross-disciplinary communication. The causal pie model has fulfilled this role in epidemiology and could be of similar value in evolutionary biology and ecology. In the causal pie model, outcomes result from sufficient causes. Each sufficient cause is made up of a "causal pie" of "component causes". Several different causal pies may exist for the same outcome. If and only if all component causes of a sufficient cause are present, that is, a causal pie is complete, does the outcome occur. The effect of a component cause hence depends on the presence of the other component causes that constitute some causal pie. Because all component causes are equally and fully causative for the outcome, the sum of causes for some outcome exceeds 100%. The causal pie model provides a way of thinking that maps into a number of recurrent themes in evolutionary biology and ecology: It charts when component causes have an effect and are subject to natural selection, and how component causes affect selection on other component causes; which partitions of outcomes with respect to causes are feasible and useful; and how to view the composition of a(n apparently homogeneous) population. The diversity of specific results that is directly understood from the causal pie model is a test for both the validity and the applicability of the model. The causal pie model provides a common language in which results across disciplines can be communicated and serves as a template along which future causal analyses can be made.

Computational methods using weighed-extreme learning machine to predict protein self-interactions with protein evolutionary information.

Science.gov (United States)

An, Ji-Yong; Zhang, Lei; Zhou, Yong; Zhao, Yu-Jun; Wang, Da-Fu

2017-08-18

Self-interactions Proteins (SIPs) is important for their biological activity owing to the inherent interaction amongst their secondary structures or domains. However, due to the limitations of experimental Self-interactions detection, one major challenge in the study of prediction SIPs is how to exploit computational approaches for SIPs detection based on evolutionary information contained protein sequence. In the work, we presented a novel computational approach named WELM-LAG, which combined the Weighed-Extreme Learning Machine (WELM) classifier with Local Average Group (LAG) to predict SIPs based on protein sequence. The major improvement of our method lies in presenting an effective feature extraction method used to represent candidate Self-interactions proteins by exploring the evolutionary information embedded in PSI-BLAST-constructed position specific scoring matrix (PSSM); and then employing a reliable and robust WELM classifier to carry out classification. In addition, the Principal Component Analysis (PCA) approach is used to reduce the impact of noise. The WELM-LAG method gave very high average accuracies of 92.94 and 96.74% on yeast and human datasets, respectively. Meanwhile, we compared it with the state-of-the-art support vector machine (SVM) classifier and other existing methods on human and yeast datasets, respectively. Comparative results indicated that our approach is very promising and may provide a cost-effective alternative for predicting SIPs. In addition, we developed a freely available web server called WELM-LAG-SIPs to predict SIPs. The web server is available at http://219.219.62.123:8888/WELMLAG/ .

Interpreting Evolutionary Diagrams: When Topology and Process Conflict

Science.gov (United States)

Catley, Kefyn M.; Novick, Laura R.; Shade, Courtney K.

2010-01-01

The authors argue that some diagrams in biology textbooks and the popular press presented as depicting evolutionary relationships suggest an inappropriate (anagenic) conception of evolutionary history. The goal of this research was to provide baseline data that begin to document how college students conceptualize the evolutionary relationships…

Evolutionary game theory: cells as players.

Science.gov (United States)

Hummert, Sabine; Bohl, Katrin; Basanta, David; Deutsch, Andreas; Werner, Sarah; Theissen, Günter; Schroeter, Anja; Schuster, Stefan

2014-12-01

In two papers we review game theory applications in biology below the level of cognitive living beings. It can be seen that evolution and natural selection replace the rationality of the actors appropriately. Even in these micro worlds, competing situations and cooperative relationships can be found and modeled by evolutionary game theory. Also those units of the lowest levels of life show different strategies for different environmental situations or different partners. We give a wide overview of evolutionary game theory applications to microscopic units. In this first review situations on the cellular level are tackled. In particular metabolic problems are discussed, such as ATP-producing pathways, secretion of public goods and cross-feeding. Further topics are cyclic competition among more than two partners, intra- and inter-cellular signalling, the struggle between pathogens and the immune system, and the interactions of cancer cells. Moreover, we introduce the theoretical basics to encourage scientists to investigate problems in cell biology and molecular biology by evolutionary game theory.

Evolutionary engineering of industrial microorganisms-strategies and applications.

Science.gov (United States)

Zhu, Zhengming; Zhang, Juan; Ji, Xiaomei; Fang, Zhen; Wu, Zhimeng; Chen, Jian; Du, Guocheng

2018-06-01

Microbial cells have been widely used in the industry to obtain various biochemical products, and evolutionary engineering is a common method in biological research to improve their traits, such as high environmental tolerance and improvement of product yield. To obtain better integrate functions of microbial cells, evolutionary engineering combined with other biotechnologies have attracted more attention in recent years. Classical laboratory evolution has been proven effective to letting more beneficial mutations occur in different genes but also has some inherent limitations such as a long evolutionary period and uncontrolled mutation frequencies. However, recent studies showed that some new strategies may gradually overcome these limitations. In this review, we summarize the evolutionary strategies commonly used in industrial microorganisms and discuss the combination of evolutionary engineering with other biotechnologies such as systems biology and inverse metabolic engineering. Finally, we prospect the importance and application prospect of evolutionary engineering as a powerful tool especially in optimization of industrial microbial cell factories.

How evolutionary principles improve the understanding of human health and disease.

Science.gov (United States)

Gluckman, Peter D; Low, Felicia M; Buklijas, Tatjana; Hanson, Mark A; Beedle, Alan S

2011-03-01

An appreciation of the fundamental principles of evolutionary biology provides new insights into major diseases and enables an integrated understanding of human biology and medicine. However, there is a lack of awareness of their importance amongst physicians, medical researchers, and educators, all of whom tend to focus on the mechanistic (proximate) basis for disease, excluding consideration of evolutionary (ultimate) reasons. The key principles of evolutionary medicine are that selection acts on fitness, not health or longevity; that our evolutionary history does not cause disease, but rather impacts on our risk of disease in particular environments; and that we are now living in novel environments compared to those in which we evolved. We consider these evolutionary principles in conjunction with population genetics and describe several pathways by which evolutionary processes can affect disease risk. These perspectives provide a more cohesive framework for gaining insights into the determinants of health and disease. Coupled with complementary insights offered by advances in genomic, epigenetic, and developmental biology research, evolutionary perspectives offer an important addition to understanding disease. Further, there are a number of aspects of evolutionary medicine that can add considerably to studies in other domains of contemporary evolutionary studies.

Fixation Time for Evolutionary Graphs

Science.gov (United States)

Nie, Pu-Yan; Zhang, Pei-Ai

Evolutionary graph theory (EGT) is recently proposed by Lieberman et al. in 2005. EGT is successful for explaining biological evolution and some social phenomena. It is extremely important to consider the time of fixation for EGT in many practical problems, including evolutionary theory and the evolution of cooperation. This study characterizes the time to asymptotically reach fixation.

The Biological Basis of Learning and Individuality.

Science.gov (United States)

Kandel, Eric R.; Hawkins, Robert D.

1992-01-01

Describes the biological basis of learning and individuality. Presents an overview of recent discoveries that suggest learning engages a simple set of rules that modify the strength of connection between neurons in the brain. The changes are cited as playing an important role in making each individual unique. (MCO)

Neuro-symbolic representation learning on biological knowledge graphs

KAUST Repository

Alshahrani, Mona

2017-04-21

Biological data and knowledge bases increasingly rely on Semantic Web technologies and the use of knowledge graphs for data integration, retrieval and federated queries. In the past years, feature learning methods that are applicable to graph-structured data are becoming available, but have not yet widely been applied and evaluated on structured biological knowledge.We develop a novel method for feature learning on biological knowledge graphs. Our method combines symbolic methods, in particular knowledge representation using symbolic logic and automated reasoning, with neural networks to generate embeddings of nodes that encode for related information within knowledge graphs. Through the use of symbolic logic, these embeddings contain both explicit and implicit information. We apply these embeddings to the prediction of edges in the knowledge graph representing problems of function prediction, finding candidate genes of diseases, protein-protein interactions, or drug target relations, and demonstrate performance that matches and sometimes outperforms traditional approaches based on manually crafted features. Our method can be applied to any biological knowledge graph, and will thereby open up the increasing amount of SemanticWeb based knowledge bases in biology to use in machine learning and data analytics.https://github.com/bio-ontology-research-group/walking-rdf-and-owl.robert.hoehndorf@kaust.edu.sa.Supplementary data are available at Bioinformatics online.

Experimental studies of animal social learning in the wild: Trying to untangle the mystery of human culture.

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Hill, Kim

2010-08-01

Here I discuss how studies on animal social learning may help us understand human culture. It is an evolutionary truism that complex biological adaptations always evolve from less complex but related adaptations, but occasionally evolutionary transitions lead to major biological changes whose end products are difficult to anticipate. Language-based cumulative adaptive culture in humans may represent an evolutionary transition of this type. Most of the social learning observed in animals (and even plants) may be due to mechanisms that cannot produce cumulative cultural adaptations. Likewise, much of the critical content of socially transmitted human culture seems to show no parallel in nonhuman species. Thus, with regard to the uniquely human extent and quality of culture, we are forced to ask: Are other species only a few small steps away from this transition, or do they lack multiple critical features that make us the only truly cultural species? Only future research into animal social learning can answer these questions.

Evaluating children's conservation biology learning at the zoo.

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Jensen, Eric

2014-08-01

Millions of children visit zoos every year with parents or schools to encounter wildlife firsthand. Public conservation education is a requirement for membership in professional zoo associations. However, in recent years zoos have been criticized for failing to educate the public on conservation issues and related biological concepts, such as animal adaptation to habitats. I used matched pre- and postvisit mixed methods questionnaires to investigate the educational value of zoo visits for children aged 7-15 years. The questionnaires gathered qualitative data from these individuals, including zoo-related thoughts and an annotated drawing of a habitat. A content analysis of these qualitative data produced the quantitative data reported in this article. I evaluated the relative learning outcomes of educator-guided and unguided zoo visits at London Zoo, both in terms of learning about conservation biology (measured by annotated drawings) and changing attitudes toward wildlife conservation (measured using thought-listing data). Forty-one percent of educator-guided visits and 34% of unguided visits resulted in conservation biology-related learning. Negative changes in children's understanding of animals and their habitats were more prevalent in unguided zoo visits. Overall, my results show the potential educational value of visiting zoos for children. However, they also suggest that zoos' standard unguided interpretive materials are insufficient for achieving the best outcomes for visiting children. These results support a theoretical model of conservation biology learning that frames conservation educators as toolmakers who develop conceptual resources to enhance children's understanding of science. © 2014 Society for Conservation Biology.

Biological Dialogues: How to Teach Your Students to Learn Fluency in Biology

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May, S. Randolph; Cook, David L.; May, Marilyn K.

2013-01-01

Biology courses have thousands of words to learn in order to intelligently discuss the subject and take tests over the material. Biological fluency is an important goal for students, and practical methods based on constructivist pedagogies can be employed to promote it. We present a method in which pairs of students write dialogues from…

On the origins of anticipation as an evolutionary framework: functional systems perspective

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Kurismaa, Andres

2015-08-01

This paper discusses the problem of anticipation from an evolutionary and systems-theoretical perspective, developed in the context of Russian/Soviet evolutionary biological and neurophysiological schools in the early and mid-twentieth century. On this background, an outline is given of the epigenetic interpretation of anticipatory capacities formulated and substantiated by the eminent Russian neurophysiologist academician Peter K. Anokhin in the framework of functional systems theory. It is considered that several key positions of this theory are well confirmed by recent evidence on anticipation as an evolutionarily basic adaptive capacity, possibly inherent to the organization of life. In the field of neuroscience, the theory of functional systems may potentially facilitate future studies at the intersection of learning, development and evolution by representing an integrative approach to the problem of anticipation.

Assessing Student Behaviors and Motivation for Actively Learning Biology

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Moore, Michael Edward

Vision and Change states that one of the major changes in the way we design biology courses should be a switch in approach from teacher-centered learning to student-centered learning and identifies active learning as a recommended methods. Studies show performance benefits for students taking courses that use active learning. What is unknown is why active learning is such an effective instructional tool and the limits of this instructional method’s ability to influence performance. This dissertation builds a case in three steps for why active learning is an effective instructional tool. In step one, I assessed the influence of different types of active learning (clickers, group activities, and whole class discussions) on student engagement behavior in one semester of two different introductory biology courses and found that active learning positively influenced student engagement behavior significantly more than lecture. For step two, I examined over four semesters whether student engagement behavior was a predictor of performance and found participation (engagement behavior) in the online (video watching) and in-class course activities (clicker participation) that I measure were significant predictors of performance. In the third, I assessed whether certain active learning satisfied the psychological needs that lead to students’ intrinsic motivation to participate in those activities when compared over two semesters and across two different institutions of higher learning. Findings from this last step show us that student’s perceptions of autonomy, competency, and relatedness in doing various types of active learning are significantly higher than lecture and consistent across two institutions of higher learning. Lastly, I tie everything together, discuss implications of the research, and address future directions for research on biology student motivation and behavior.

The power of associative learning and the ontogeny of optimal behaviour

OpenAIRE

Enquist, Magnus; Lind, Johan; Ghirlanda, Stefano

2016-01-01

Behaving efficiently (optimally or near-optimally) is central to animals' adaptation to their environment. Much evolutionary biology assumes, implicitly or explicitly, that optimal behavioural strategies are genetically inherited, yet the behaviour of many animals depends crucially on learning. The question of how learning contributes to optimal behaviour is largely open. Here we propose an associative learning model that can learn optimal behaviour in a wide variety of ecologically relevant ...

Spore: Spawning Evolutionary Misconceptions?

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Bean, Thomas E.; Sinatra, Gale M.; Schrader, P. G.

2010-10-01

The use of computer simulations as educational tools may afford the means to develop understanding of evolution as a natural, emergent, and decentralized process. However, special consideration of developmental constraints on learning may be necessary when using these technologies. Specifically, the essentialist (biological forms possess an immutable essence), teleological (assignment of purpose to living things and/or parts of living things that may not be purposeful), and intentionality (assumption that events are caused by an intelligent agent) biases may be reinforced through the use of computer simulations, rather than addressed with instruction. We examine the video game Spore for its depiction of evolutionary content and its potential to reinforce these cognitive biases. In particular, we discuss three pedagogical strategies to mitigate weaknesses of Spore and other computer simulations: directly targeting misconceptions through refutational approaches, targeting specific principles of scientific inquiry, and directly addressing issues related to models as cognitive tools.

Using Active Learning to Teach Concepts and Methods in Quantitative Biology.

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Waldrop, Lindsay D; Adolph, Stephen C; Diniz Behn, Cecilia G; Braley, Emily; Drew, Joshua A; Full, Robert J; Gross, Louis J; Jungck, John A; Kohler, Brynja; Prairie, Jennifer C; Shtylla, Blerta; Miller, Laura A

2015-11-01

This article provides a summary of the ideas discussed at the 2015 Annual Meeting of the Society for Integrative and Comparative Biology society-wide symposium on Leading Students and Faculty to Quantitative Biology through Active Learning. It also includes a brief review of the recent advancements in incorporating active learning approaches into quantitative biology classrooms. We begin with an overview of recent literature that shows that active learning can improve students' outcomes in Science, Technology, Engineering and Math Education disciplines. We then discuss how this approach can be particularly useful when teaching topics in quantitative biology. Next, we describe some of the recent initiatives to develop hands-on activities in quantitative biology at both the graduate and the undergraduate levels. Throughout the article we provide resources for educators who wish to integrate active learning and technology into their classrooms. © The Author 2015. Published by Oxford University Press on behalf of the Society for Integrative and Comparative Biology. All rights reserved. For permissions please email: journals.permissions@oup.com.

Neuro-symbolic representation learning on biological knowledge graphs.

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Alshahrani, Mona; Khan, Mohammad Asif; Maddouri, Omar; Kinjo, Akira R; Queralt-Rosinach, Núria; Hoehndorf, Robert

2017-09-01

Biological data and knowledge bases increasingly rely on Semantic Web technologies and the use of knowledge graphs for data integration, retrieval and federated queries. In the past years, feature learning methods that are applicable to graph-structured data are becoming available, but have not yet widely been applied and evaluated on structured biological knowledge. Results: We develop a novel method for feature learning on biological knowledge graphs. Our method combines symbolic methods, in particular knowledge representation using symbolic logic and automated reasoning, with neural networks to generate embeddings of nodes that encode for related information within knowledge graphs. Through the use of symbolic logic, these embeddings contain both explicit and implicit information. We apply these embeddings to the prediction of edges in the knowledge graph representing problems of function prediction, finding candidate genes of diseases, protein-protein interactions, or drug target relations, and demonstrate performance that matches and sometimes outperforms traditional approaches based on manually crafted features. Our method can be applied to any biological knowledge graph, and will thereby open up the increasing amount of Semantic Web based knowledge bases in biology to use in machine learning and data analytics. https://github.com/bio-ontology-research-group/walking-rdf-and-owl. robert.hoehndorf@kaust.edu.sa. Supplementary data are available at Bioinformatics online. © The Author(s) 2017. Published by Oxford University Press.

Fuzzy control in robot-soccer, evolutionary learning in the first layer of control

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Peter J Thomas

2003-02-01

Full Text Available In this paper an evolutionary algorithm is developed to learn a fuzzy knowledge base for the control of a soccer playing micro-robot from any configuration belonging to a grid of initial configurations to hit the ball along the ball to goal line of sight. The knowledge base uses relative co-ordinate system including left and right wheel velocities of the robot. Final path positions allow forward and reverse facing robot to ball and include its physical dimensions.

Discovering Learning Strategy to Increase Metacognitive Knowledge in Biology Learning in Secondary School

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Y. Herlanti

2017-04-01

Full Text Available The study is aimed at finding an effective learning strategy that can increase metacognitive knowledge. Metacognitive knowledge is a standard that based on 2016-revised edition of 2013 curriculum needs to be achieved by every graduate in all level of education in Indonesia. The study is conducted in three different schools and engages 207 students, which then divided into six groups. The groups are students who study under mind mapping strategy, concept mapping, reciprocal teaching using summary notes, reciprocal teaching using mind mapping, problem-based learning, and investigation group. The results showed that those studying under problem-based learning strategy spent a significantly higher numbers in metacognitive knowledge in biology learning and followed by students who study under reciprocal teaching using mind mapping. According to the finding, it is expected that teachers of Biology will practice problem-based learning strategy in their classroom in order to increase the Metacognitive knowledge.

Assessing Student Behaviors and Motivation for Actively Learning Biology

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Moore, Michael Edward

2017-01-01

Vision and Change states that one of the major changes in the way we design biology courses should be a switch in approach from teacher-centered learning to student-centered learning and identifies active learning as a recommended methods. Studies show performance benefits for students taking courses that use active learning. What is unknown is…

Improving creative thinking skills and scientific attitude through inquiry-based learning in basic biology lecture toward student of biology education

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Bayu Sandika

2018-03-01

Full Text Available Inquiry-based learning is one of the learning methods which can provide an active and authentic scientific learning process in order students are able to improve the creative thinking skills and scientific attitude. This study aims at improving creative thinking skills and scientific attitude through inquiry-based learning in basic biology lecture toward students of biology education at the Institut Agama Islam Negeri (IAIN Jember, Indonesia. This study is included in a descriptive quantitative research. The research focused on the topic of cell transport which was taught toward 25 students of Biology 2 class from 2017 academic year of Biology Education Department at the IAIN Jember. The learning process was conducted in two meetings in November 2017. The enhancement of students' creative thinking skills was determined by one group pre-test and post-test research design using test instrument meanwhile the scientific attitude focused on curiosity and objectivity were observed using the non-test instrument. Research result showed that students' creative thinking skills enhanced highly and students' scientific attitude improved excellently through inquiry-based learning in basic biology lecture.

Cultural evolutionary theory: How culture evolves and why it matters.

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Creanza, Nicole; Kolodny, Oren; Feldman, Marcus W

2017-07-24

Human cultural traits-behaviors, ideas, and technologies that can be learned from other individuals-can exhibit complex patterns of transmission and evolution, and researchers have developed theoretical models, both verbal and mathematical, to facilitate our understanding of these patterns. Many of the first quantitative models of cultural evolution were modified from existing concepts in theoretical population genetics because cultural evolution has many parallels with, as well as clear differences from, genetic evolution. Furthermore, cultural and genetic evolution can interact with one another and influence both transmission and selection. This interaction requires theoretical treatments of gene-culture coevolution and dual inheritance, in addition to purely cultural evolution. In addition, cultural evolutionary theory is a natural component of studies in demography, human ecology, and many other disciplines. Here, we review the core concepts in cultural evolutionary theory as they pertain to the extension of biology through culture, focusing on cultural evolutionary applications in population genetics, ecology, and demography. For each of these disciplines, we review the theoretical literature and highlight relevant empirical studies. We also discuss the societal implications of the study of cultural evolution and of the interactions of humans with one another and with their environment.

Integrating genomics into evolutionary medicine.

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Rodríguez, Juan Antonio; Marigorta, Urko M; Navarro, Arcadi

2014-12-01

The application of the principles of evolutionary biology into medicine was suggested long ago and is already providing insight into the ultimate causes of disease. However, a full systematic integration of medical genomics and evolutionary medicine is still missing. Here, we briefly review some cases where the combination of the two fields has proven profitable and highlight two of the main issues hindering the development of evolutionary genomic medicine as a mature field, namely the dissociation between fitness and health and the still considerable difficulties in predicting phenotypes from genotypes. We use publicly available data to illustrate both problems and conclude that new approaches are needed for evolutionary genomic medicine to overcome these obstacles. Copyright © 2014 Elsevier Ltd. All rights reserved.

Evolutionary Robotics: What, Why, and Where to

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Stephane eDoncieux

2015-03-01

Full Text Available Evolutionary robotics applies the selection, variation, and heredity principles of natural evolution to the design of robots with embodied intelligence. It can be considered as a subfield of robotics that aims to create more robust and adaptive robots. A pivotal feature of the evolutionary approach is that it considers the whole robot at once, and enables the exploitation of robot features in a holistic manner. Evolutionary robotics can also be seen as an innovative approach to the study of evolution based on a new kind of experimentalism. The use of robots as a substrate can help address questions that are difficult, if not impossible, to investigate through computer simulations or biological studies. In this paper we consider the main achievements of evolutionary robotics, focusing particularly on its contributions to both engineering and biology. We briefly elaborate on methodological issues, review some of the most interesting findings, and discuss important open issues and promising avenues for future work.

Can a multimedia tool help students' learning performance in complex biology subjects?

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Pinar Koseoglu

2015-11-01

Full Text Available The aim of the present study was to determine the effects of multimedia-based biology teaching (Mbio and teacher-centered biology (TCbio instruction approaches on learners' biology achievements, as well as their views towards learning approaches. During the research process, an experimental design with two groups, TCbio (n = 22 and Mbio (n = 26, were used. The results of the study proved that the Mbio approach was more effective than the TCbio approach with regard to supporting meaningful learning, academic achievement, enjoyment and motivation. Moreover, the TCbio approach is ineffective in terms of time management, engaging attention, and the need for repetition of subjects. Additionally, the results were discussed in terms of teaching, learning, multimedia design as well as biology teaching/learning.

Learning and coding in biological neural networks

Science.gov (United States)

Fiete, Ila Rani

How can large groups of neurons that locally modify their activities learn to collectively perform a desired task? Do studies of learning in small networks tell us anything about learning in the fantastically large collection of neurons that make up a vertebrate brain? What factors do neurons optimize by encoding sensory inputs or motor commands in the way they do? In this thesis I present a collection of four theoretical works: each of the projects was motivated by specific constraints and complexities of biological neural networks, as revealed by experimental studies; together, they aim to partially address some of the central questions of neuroscience posed above. We first study the role of sparse neural activity, as seen in the coding of sequential commands in a premotor area responsible for birdsong. We show that the sparse coding of temporal sequences in the songbird brain can, in a network where the feedforward plastic weights must translate the sparse sequential code into a time-varying muscle code, facilitate learning by minimizing synaptic interference. Next, we propose a biologically plausible synaptic plasticity rule that can perform goal-directed learning in recurrent networks of voltage-based spiking neurons that interact through conductances. Learning is based on the correlation of noisy local activity with a global reward signal; we prove that this rule performs stochastic gradient ascent on the reward. Thus, if the reward signal quantifies network performance on some desired task, the plasticity rule provably drives goal-directed learning in the network. To assess the convergence properties of the learning rule, we compare it with a known example of learning in the brain. Song-learning in finches is a clear example of a learned behavior, with detailed available neurophysiological data. With our learning rule, we train an anatomically accurate model birdsong network that drives a sound source to mimic an actual zebrafinch song. Simulation and

Do sophisticated epistemic beliefs predict meaningful learning? Findings from a structural equation model of undergraduate biology learning

Science.gov (United States)

Lee, Silvia Wen-Yu; Liang, Jyh-Chong; Tsai, Chin-Chung

2016-10-01

This study investigated the relationships among college students' epistemic beliefs in biology (EBB), conceptions of learning biology (COLB), and strategies of learning biology (SLB). EBB includes four dimensions, namely 'multiple-source,' 'uncertainty,' 'development,' and 'justification.' COLB is further divided into 'constructivist' and 'reproductive' conceptions, while SLB represents deep strategies and surface learning strategies. Questionnaire responses were gathered from 303 college students. The results of the confirmatory factor analysis and structural equation modelling showed acceptable model fits. Mediation testing further revealed two paths with complete mediation. In sum, students' epistemic beliefs of 'uncertainty' and 'justification' in biology were statistically significant in explaining the constructivist and reproductive COLB, respectively; and 'uncertainty' was statistically significant in explaining the deep SLB as well. The results of mediation testing further revealed that 'uncertainty' predicted surface strategies through the mediation of 'reproductive' conceptions; and the relationship between 'justification' and deep strategies was mediated by 'constructivist' COLB. This study provides evidence for the essential roles some epistemic beliefs play in predicting students' learning.

Part E: Evolutionary Computation

DEFF Research Database (Denmark)

2015-01-01

of Computational Intelligence. First, comprehensive surveys of genetic algorithms, genetic programming, evolution strategies, parallel evolutionary algorithms are presented, which are readable and constructive so that a large audience might find them useful and – to some extent – ready to use. Some more general...... kinds of evolutionary algorithms, have been prudently analyzed. This analysis was followed by a thorough analysis of various issues involved in stochastic local search algorithms. An interesting survey of various technological and industrial applications in mechanical engineering and design has been...... topics like the estimation of distribution algorithms, indicator-based selection, etc., are also discussed. An important problem, from a theoretical and practical point of view, of learning classifier systems is presented in depth. Multiobjective evolutionary algorithms, which constitute one of the most...

Evolutionary principles and their practical application

DEFF Research Database (Denmark)

Hendry, A. P.; Kinnison, M. T.; Heino, M.

2011-01-01

Evolutionary principles are now routinely incorporated into medicine and agriculture. Examples include the design of treatments that slow the evolution of resistance by weeds, pests, and pathogens, and the design of breeding programs that maximize crop yield or quality. Evolutionary principles...... are also increasingly incorporated into conservation biology, natural resource management, and environmental science. Examples include the protection of small and isolated populations from inbreeding depression, the identification of key traits involved in adaptation to climate change, the design...... of harvesting regimes that minimize unwanted life-history evolution, and the setting of conservation priorities based on populations, species, or communities that harbor the greatest evolutionary diversity and potential. The adoption of evolutionary principles has proceeded somewhat independently...

Evolutionary online behaviour learning and adaptation in real robots.

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Silva, Fernando; Correia, Luís; Christensen, Anders Lyhne

2017-07-01

Online evolution of behavioural control on real robots is an open-ended approach to autonomous learning and adaptation: robots have the potential to automatically learn new tasks and to adapt to changes in environmental conditions, or to failures in sensors and/or actuators. However, studies have so far almost exclusively been carried out in simulation because evolution in real hardware has required several days or weeks to produce capable robots. In this article, we successfully evolve neural network-based controllers in real robotic hardware to solve two single-robot tasks and one collective robotics task. Controllers are evolved either from random solutions or from solutions pre-evolved in simulation. In all cases, capable solutions are found in a timely manner (1 h or less). Results show that more accurate simulations may lead to higher-performing controllers, and that completing the optimization process in real robots is meaningful, even if solutions found in simulation differ from solutions in reality. We furthermore demonstrate for the first time the adaptive capabilities of online evolution in real robotic hardware, including robots able to overcome faults injected in the motors of multiple units simultaneously, and to modify their behaviour in response to changes in the task requirements. We conclude by assessing the contribution of each algorithmic component on the performance of the underlying evolutionary algorithm.

Evolutionary strategy to develop learning-based decision systems. Application to breast cancer and liver fibrosis stadialization.

Science.gov (United States)

Gorunescu, Florin; Belciug, Smaranda

2014-06-01

The purpose of this paper is twofold: first, to propose an evolutionary-based method for building a decision model and, second, to assess and validate the model's performance using five different real-world medical datasets (breast cancer and liver fibrosis) by comparing it with state-of-the-art machine learning techniques. The evolutionary-inspired approach has been used to develop the learning-based decision model in the following manner: the hybridization of algorithms has been considered as "crossover", while the development of new variants which can be thought of as "mutation". An appropriate hierarchy of the component algorithms was established based on a statistically built fitness measure. A synergetic decision-making process, based on a weighted voting system, involved the collaboration between the selected algorithms in making the final decision. Well-established statistical performance measures and comparison tests have been extensively used to design and implement the model. Finally, the proposed method has been tested on five medical datasets, out of which four publicly available, and contrasted with state-of-the-art techniques, showing its efficiency in supporting the medical decision-making process. Copyright © 2014 Elsevier Inc. All rights reserved.

ADAPTIVE SELECTION OF AUXILIARY OBJECTIVES IN MULTIOBJECTIVE EVOLUTIONARY ALGORITHMS

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I. A. Petrova

2016-05-01

Full Text Available Subject of Research.We propose to modify the EA+RL method, which increases efficiency of evolutionary algorithms by means of auxiliary objectives. The proposed modification is compared to the existing objective selection methods on the example of travelling salesman problem. Method. In the EA+RL method a reinforcement learning algorithm is used to select an objective – the target objective or one of the auxiliary objectives – at each iteration of the single-objective evolutionary algorithm.The proposed modification of the EA+RL method adopts this approach for the usage with a multiobjective evolutionary algorithm. As opposed to theEA+RL method, in this modification one of the auxiliary objectives is selected by reinforcement learning and optimized together with the target objective at each step of the multiobjective evolutionary algorithm. Main Results.The proposed modification of the EA+RL method was compared to the existing objective selection methods on the example of travelling salesman problem. In the EA+RL method and its proposed modification reinforcement learning algorithms for stationary and non-stationary environment were used. The proposed modification of the EA+RL method applied with reinforcement learning for non-stationary environment outperformed the considered objective selection algorithms on the most problem instances. Practical Significance. The proposed approach increases efficiency of evolutionary algorithms, which may be used for solving discrete NP-hard optimization problems. They are, in particular, combinatorial path search problems and scheduling problems.

Passing thoughts on the evolutionary stability of implicit motor behaviour: performance retention under physiological fatigue.

Science.gov (United States)

Poolton, J M; Masters, R S W; Maxwell, J P

2007-06-01

Heuristics of evolutionary biology (e.g., survival of the fittest) dictate that phylogenetically older processes are inherently more stable and resilient to disruption than younger processes. On the grounds that non-declarative behaviour emerged long before declarative behaviour, Reber (1992) argues that implicit (non-declarative) learning is supported by neural processes that are evolutionarily older than those supporting explicit learning. Reber suggested that implicit learning thus leads to performance that is more robust than explicit learning. Applying this evolutionary framework to motor performance, we examined whether implicit motor learning, relative to explicit motor learning, conferred motor output that was resilient to physiological fatigue and durable over time. In Part One of the study a fatigued state was induced by a double Wingate Anaerobic test protocol. Fatigue had no affect on performance of participants in the implicit condition; whereas, performance of participants in the explicit condition deteriorated significantly. In Part Two of the study a convenience sample of participants was recalled following a one-year hiatus. In both the implicit and the explicit condition retention of performance was seen and, contrary to the findings in Part One, so was resilience to fatigue. The resilient performance in the explicit condition after one year may have resulted from forgetting (the decay of declarative knowledge) or from consolidation of declarative knowledge as implicit memories. In either case, implicit processes were left to more effectively support motor performance.

Evolutionary analyses of non-genealogical bonds produced by introgressive descent.

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Bapteste, Eric; Lopez, Philippe; Bouchard, Frédéric; Baquero, Fernando; McInerney, James O; Burian, Richard M

2012-11-06

All evolutionary biologists are familiar with evolutionary units that evolve by vertical descent in a tree-like fashion in single lineages. However, many other kinds of processes contribute to evolutionary diversity. In vertical descent, the genetic material of a particular evolutionary unit is propagated by replication inside its own lineage. In what we call introgressive descent, the genetic material of a particular evolutionary unit propagates into different host structures and is replicated within these host structures. Thus, introgressive descent generates a variety of evolutionary units and leaves recognizable patterns in resemblance networks. We characterize six kinds of evolutionary units, of which five involve mosaic lineages generated by introgressive descent. To facilitate detection of these units in resemblance networks, we introduce terminology based on two notions, P3s (subgraphs of three nodes: A, B, and C) and mosaic P3s, and suggest an apparatus for systematic detection of introgressive descent. Mosaic P3s correspond to a distinct type of evolutionary bond that is orthogonal to the bonds of kinship and genealogy usually examined by evolutionary biologists. We argue that recognition of these evolutionary bonds stimulates radical rethinking of key questions in evolutionary biology (e.g., the relations among evolutionary players in very early phases of evolutionary history, the origin and emergence of novelties, and the production of new lineages). This line of research will expand the study of biological complexity beyond the usual genealogical bonds, revealing additional sources of biodiversity. It provides an important step to a more realistic pluralist treatment of evolutionary complexity.

Biologically-inspired Learning in Pulsed Neural Networks

DEFF Research Database (Denmark)

Lehmann, Torsten; Woodburn, Robin

1999-01-01

Self-learning chips to implement many popular ANN (artificial neural network) algorithms are very difficult to design. We explain why this is so and say what lessons previous work teaches us in the design of self-learning systems. We offer a contribution to the `biologically-inspired' approach......, explaining what we mean by this term and providing an example of a robust, self-learning design that can solve simple classical-conditioning tasks. We give details of the design of individual circuits to perform component functions, which can then be combined into a network to solve the task. We argue...

Motivating Students to Learn Biology Vocabulary with Wikipedia

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Boriana Marintcheva

2012-02-01

Full Text Available Timely learning of specialized science vocabulary is critical for building a solid knowledge base in any scientific discipline. To motivate students to dedicate time and effort mastering biology vocabulary, I have designed a vocabulary exercise utilizing the popular web encyclopedia Wikipedia. The exercise creates an opportunity for students to connect the challenge of vocabulary learning to a prior positive experience of self-guided learning using a content source they are familiar and comfortable with.

Investigating Climate Change and Reproduction: Experimental Tools from Evolutionary Biology

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Oliver Y. Martin

2012-09-01

Full Text Available It is now generally acknowledged that climate change has wide-ranging biological consequences, potentially leading to impacts on biodiversity. Environmental factors can have diverse and often strong effects on reproduction, with obvious ramifications for population fitness. Nevertheless, reproductive traits are often neglected in conservation considerations. Focusing on animals, recent progress in sexual selection and sexual conflict research suggests that reproductive costs may pose an underestimated hurdle during rapid climate change, potentially lowering adaptive potential and increasing extinction risk of certain populations. Nevertheless, regime shifts may have both negative and positive effects on reproduction, so it is important to acquire detailed experimental data. We hence present an overview of the literature reporting short-term reproductive consequences of exposure to different environmental factors. From the enormous diversity of findings, we conclude that climate change research could benefit greatly from more coordinated efforts incorporating evolutionary approaches in order to obtain cross-comparable data on how individual and population reproductive fitness respond in the long term. Therefore, we propose ideas and methods concerning future efforts dealing with reproductive consequences of climate change, in particular by highlighting the advantages of multi-generational experimental evolution experiments.

Incorporating evolutionary principles into environmental management and policy

DEFF Research Database (Denmark)

Lankau, Richard; Jørgensen, Peter Søgaard; Harris, David J.

2011-01-01

As policymakers and managers work to mitigate the effects of rapid anthropogenic environmental changes, they need to consider organisms’ responses. In light of recent evidence that evolution can be quite rapid, this now includes evolutionary responses. Evolutionary principles have a long history...... in conservation biology, and the necessary next step for the field is to consider ways in which conservation policy makers and managers can proactively manipulate evolutionary processes to achieve their goals. In this review, we aim to illustrate the potential conservation benefits of an increased understanding...... of evolutionary history and prescriptive manipulation of three basic evolutionary factors: selection, variation, and gene flow. For each, we review and propose ways that policy makers and managers can use evolutionary thinking to preserve threatened species, combat pest species, or reduce undesirable evolutionary...

EFFECTS OF 5E LEARNING CYCLE ON STUDENTS ACHIEVEMENT IN BIOLOGY AND CHEMISTRY

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Patrick Osawaru Ajaja,

2012-01-01

Full Text Available The major purpose of this study was to determine the effects of learning cycle as an instructional strategy on biology andchemistry students achievement. To guide this study, six research hypotheses were stated and tested at 0.05 level ofsignificance. The design of this study was 2x2x3x6 Pre-test Post-test non-equivalent control group quasi experimental design.These included two instructional groups (experimental and control groups, sex (male and female, repeated testing (Pre,Post and follow-up tests, and six weeks of experience. The samples of the study included six senior secondary schools, 112science students, and 12 biology and chemistry teachers. The instruments used for this study were: teacher’s questionnaireon knowledge and use of learning cycle (KULC; and Biology and Chemistry Achievement Test (BCAT. The data collected wereanalyzed with simple percentage, Analysis of Covariance (ANCOVA and student t-test statistics. The major findings of thestudy included that only 30.43% and 26.31% of biology and chemistry teachers have the knowledge that learning cycle is aninstructional method; all the biology and chemistry teachers sampled have never used learning cycle as an instructionalmethod; learning cycle had a significant effect on students achievement in biology and chemistry; students taught withlearning cycle significantly achieved better in biology/chemistry Post-test than those taught with lecture method; the posttestscores of students in the learning cycle group increased over the period of experience; non-significant difference in Posttestscores between males and females taught with learning cycle; non-significant interaction effect between method andsex on achievement; and a significant higher retention of biology and chemistry knowledge by students taught with learningcycle than those taught with lecture method. It was concluded that the method seems an appropriate instructional modelthat could be used to solve the problems of

Eco-Evo-Devo: developmental symbiosis and developmental plasticity as evolutionary agents.

Science.gov (United States)

Gilbert, Scott F; Bosch, Thomas C G; Ledón-Rettig, Cristina

2015-10-01

The integration of research from developmental biology and ecology into evolutionary theory has given rise to a relatively new field, ecological evolutionary developmental biology (Eco-Evo-Devo). This field integrates and organizes concepts such as developmental symbiosis, developmental plasticity, genetic accommodation, extragenic inheritance and niche construction. This Review highlights the roles that developmental symbiosis and developmental plasticity have in evolution. Developmental symbiosis can generate particular organs, can produce selectable genetic variation for the entire animal, can provide mechanisms for reproductive isolation, and may have facilitated evolutionary transitions. Developmental plasticity is crucial for generating novel phenotypes, facilitating evolutionary transitions and altered ecosystem dynamics, and promoting adaptive variation through genetic accommodation and niche construction. In emphasizing such non-genomic mechanisms of selectable and heritable variation, Eco-Evo-Devo presents a new layer of evolutionary synthesis.

New insights into the evolutionary history of biological nitrogen fixation

Directory of Open Access Journals (Sweden)

Eric eBoyd

2013-08-01

Full Text Available Nitrogenase, which catalyzes the ATP-dependent reduction of dinitrogen (N2 to ammonia (NH3, accounts for roughly half of the bioavailable nitrogen supporting extant life. The fundamental requirement for fixed forms of nitrogen for life on Earth, both at present and in the past, has led to broad and significant interest in the origin and evolution of this fundamental biological process. One key question is whether the limited availability of fixed nitrogen was a factor in life’s origin or whether there were ample sources of fixed nitrogen produced by abiotic processes or delivered through the weathering of bolide impact materials to support this early life. If the latter, the key questions become what were the characteristics of the environment that precipitated the evolution of this oxygen sensitive process, when did this occur, and how was its subsequent evolutionary history impacted by the advent of oxygenic photosynthesis and the rise of oxygen in the Earth’s biosphere. Since the availability of fixed sources of nitrogen capable of supporting early life is difficult to glean from the geologic record, there are limited means to get direct insights into these questions. Indirect insights, however, can be gained by deep phylogenetic studies of nitrogenase structural gene products and additional gene products involved in the biosynthesis of the complex metal-containing prosthetic groups associated with this enzyme complex. Insights gained from such studies, as reviewed herein, challenge traditional models for the evolution of biological nitrogen fixation and provide the basis for the development of new conceptual models that explain the stepwise evolution of this highly complex and life sustaining process.

Applying Computerized-Scoring Models of Written Biological Explanations across Courses and Colleges: Prospects and Limitations

Science.gov (United States)

Ha, Minsu; Nehm, Ross H.; Urban-Lurain, Mark; Merrill, John E.

2011-01-01

Our study explored the prospects and limitations of using machine-learning software to score introductory biology students' written explanations of evolutionary change. We investigated three research questions: 1) Do scoring models built using student responses at one university function effectively at another university? 2) How many human-scored…

Design and validation of general biology learning program based on scientific inquiry skills

Science.gov (United States)

Cahyani, R.; Mardiana, D.; Noviantoro, N.

2018-03-01

Scientific inquiry is highly recommended to teach science. The reality in the schools and colleges is that many educators still have not implemented inquiry learning because of their lack of understanding. The study aims to1) analyze students’ difficulties in learning General Biology, 2) design General Biology learning program based on multimedia-assisted scientific inquiry learning, and 3) validate the proposed design. The method used was Research and Development. The subjects of the study were 27 pre-service students of general elementary school/Islamic elementary schools. The workflow of program design includes identifying learning difficulties of General Biology, designing course programs, and designing instruments and assessment rubrics. The program design is made for four lecture sessions. Validation of all learning tools were performed by expert judge. The results showed that: 1) there are some problems identified in General Biology lectures; 2) the designed products include learning programs, multimedia characteristics, worksheet characteristics, and, scientific attitudes; and 3) expert validation shows that all program designs are valid and can be used with minor revisions. The first section in your paper.

The ABCs of an evolutionary education science: The academic, behavioral, and cultural implications of an evolutionary approach to education theory and practice

Science.gov (United States)

Kauffman, Rick, Jr.

Calls for improving research-informed policy in education are everywhere. Yet, while there is an increasing trend towards science-based practice, there remains little agreement over which of the sciences to consult and how to organize a collective effort between them. What Education lacks is a general theoretical framework through which policies can be constructed, implemented, and assessed. This dissertation submits that evolutionary theory can provide a suitable framework for coordinating educational policies and practice, and can provide the entire field of education with a clearer sense of how to better manage the learning environment. This dissertation explores two broad paths that outline the conceptual foundations for an Evolutionary Education Science: "Teaching Evolution" and "Using Evolution to Teach." Chapter 1 introduces both of these themes. After describing why evolutionary science is best suited for organizing education research and practice, Chapter 1 proceeds to "teach" an overview of the "evolutionary toolkit"---the mechanisms and principles that underlie the modern evolutionary perspective. The chapter then employs the "toolkit" in examining education from an evolutionary perspective, outlining the evolutionary precepts that can guide theorizing and research in education, describing how educators can "use evolution to teach.". Chapters 2-4 expand on this second theme. Chapters 2 and 3 describe an education program for at-risk 9th and 10th grade students, the Regents Academy, designed entirely with evolutionary principles in mind. The program was rigorously assessed in a randomized control design and has demonstrated success at improving students' academic performance (Chapter 2) and social & behavioral development (Chapter 3). Chapter 4 examines current teaching strategies that underlie effective curriculum-instruction-assessment practices and proposes a framework for organizing successful, evidence-based strategies for neural

An open future for ecological and evolutionary data?

Science.gov (United States)

Kenall, Amye; Harold, Simon; Foote, Christopher

2014-04-02

As part of BioMed Central's open science mission, we are pleased to announce that two of our journals have integrated with the open data repository Dryad. Authors submitting their research to either BMC Ecology or BMC Evolutionary Biology will now have the opportunity to deposit their data directly into the Dryad archive and will receive a permanent, citable link to their dataset. Although this does not affect any of our current data deposition policies at these journals, we hope to encourage a more widespread adoption of open data sharing in the fields of ecology and evolutionary biology by facilitating this process for our authors. We also take this opportunity to discuss some of the wider issues that may concern researchers when making their data openly available. Although we offer a number of positive examples from different fields of biology, we also recognise that reticence to data sharing still exists, and that change must be driven from within research communities in order to create future science that is fit for purpose in the digital age. This editorial was published jointly in both BMC Ecology and BMC Evolutionary Biology.

Unweaving misconceptions: Guided learning, simulations, and misconceptions in learning principles of natural selection

Science.gov (United States)

Weeks, Brian E.

College students often come to the study of evolutionary biology with many misconceptions of how the processes of natural selection and speciation occur. How to relinquish these misconceptions with learners is a question that many educators face in introductory biology courses. Constructivism as a theoretical framework has become an accepted and promoted model within the epistemology of science instruction. However, constructivism is not without its skeptics who see some problems of its application in lacking necessary guidance for novice learners. This study within a quantitative, quasi-experimental format tested whether guided online instruction in a video format of common misconceptions in evolutionary biology produced higher performance on a survey of knowledge of natural selection versus more constructivist style learning in the form of student exploration of computer simulations of the evolutionary process. Performances on surveys were also explored for a combination of constructivist and guided techniques to determine if a consolidation of approaches produced higher test scores. Out of the 94 participants 95% displayed at least one misconception of natural selection in the pre-test while the study treatments produced no statistically significant improvements in post-test scores except within the video (guided learning treatment). These overall results demonstrated the stubbornness of misconceptions involving natural selection for adult learners and the difficulty of helping them overcome them. It also bolsters the idea that some misconceptions of natural selection and evolution may be hardwired in a neurological sense and that new, more long-term teaching techniques may be warranted. Such long-term strategies may not be best implemented with constructivist techniques alone, and it is likely that some level of guidance may be necessary for novice adult learners. A more substantial, nuanced approach for undergraduates is needed that consolidates successful

Evolution and development: some insights from evolutionary theory

Directory of Open Access Journals (Sweden)

DAVID JEAN R.

2001-01-01

Full Text Available Developmental biology and evolutionary biology are both mature integrative disciplines which started in the 19th century and then followed parallel and independent scientific pathways. Recently, a genetical component has stepped into both disciplines (developmental genetics and evolutionary genetics pointing out the need for future convergent maturation. Indeed, the Evo-Devo approach is becoming popular among developmental biologists, based on the facts that distant groups share a common ancestry, that precise phylogenies can be worked out and that homologous genes often play similar roles during the development of very different organisms. In this essay, I try to show that the real future of Evo-Devo thinking is still broader. The evolutionary theory is a set of diverse concepts which can and should be used in any biological field. Evolutionary thinking trains to ask « why » questions and to provide logical and plausible answers. It can shed some light on a diversity of general problems such as how to distinguish homologies from analogies, the costs and benefits of multicellularity, the origin of novel structures (e.g. the head, or the evolution of sexual reproduction. In the next decade, we may expect a progressive convergence between developmental genetics and quantitative genetics.

Cooperative learning in industrial-sized biology classes.

Science.gov (United States)

Armstrong, Norris; Chang, Shu-Mei; Brickman, Marguerite

2007-01-01

This study examined the impact of cooperative learning activities on student achievement and attitudes in large-enrollment (>250) introductory biology classes. We found that students taught using a cooperative learning approach showed greater improvement in their knowledge of course material compared with students taught using a traditional lecture format. In addition, students viewed cooperative learning activities highly favorably. These findings suggest that encouraging students to work in small groups and improving feedback between the instructor and the students can help to improve student outcomes even in very large classes. These results should be viewed cautiously, however, until this experiment can be replicated with additional faculty. Strategies for potentially improving the impact of cooperative learning on student achievement in large courses are discussed.

Challenges and Opportunities for Learning Biology in Distance-Based Settings

Science.gov (United States)

Hallyburton, Chad L.; Lunsford, Eddie

2013-01-01

The history of learning biology through distance education is documented. A review of terminology and unique problems associated with biology instruction is presented. Using published research and their own teaching experience, the authors present recommendations and best practices for managing biology in distance-based formats. They offer ideas…

Opportunities and obstacles for deep learning in biology and medicine

Science.gov (United States)

2018-01-01

Deep learning describes a class of machine learning algorithms that are capable of combining raw inputs into layers of intermediate features. These algorithms have recently shown impressive results across a variety of domains. Biology and medicine are data-rich disciplines, but the data are complex and often ill-understood. Hence, deep learning techniques may be particularly well suited to solve problems of these fields. We examine applications of deep learning to a variety of biomedical problems—patient classification, fundamental biological processes and treatment of patients—and discuss whether deep learning will be able to transform these tasks or if the biomedical sphere poses unique challenges. Following from an extensive literature review, we find that deep learning has yet to revolutionize biomedicine or definitively resolve any of the most pressing challenges in the field, but promising advances have been made on the prior state of the art. Even though improvements over previous baselines have been modest in general, the recent progress indicates that deep learning methods will provide valuable means for speeding up or aiding human investigation. Though progress has been made linking a specific neural network's prediction to input features, understanding how users should interpret these models to make testable hypotheses about the system under study remains an open challenge. Furthermore, the limited amount of labelled data for training presents problems in some domains, as do legal and privacy constraints on work with sensitive health records. Nonetheless, we foresee deep learning enabling changes at both bench and bedside with the potential to transform several areas of biology and medicine. PMID:29618526

Opportunities and obstacles for deep learning in biology and medicine.

Science.gov (United States)

Ching, Travers; Himmelstein, Daniel S; Beaulieu-Jones, Brett K; Kalinin, Alexandr A; Do, Brian T; Way, Gregory P; Ferrero, Enrico; Agapow, Paul-Michael; Zietz, Michael; Hoffman, Michael M; Xie, Wei; Rosen, Gail L; Lengerich, Benjamin J; Israeli, Johnny; Lanchantin, Jack; Woloszynek, Stephen; Carpenter, Anne E; Shrikumar, Avanti; Xu, Jinbo; Cofer, Evan M; Lavender, Christopher A; Turaga, Srinivas C; Alexandari, Amr M; Lu, Zhiyong; Harris, David J; DeCaprio, Dave; Qi, Yanjun; Kundaje, Anshul; Peng, Yifan; Wiley, Laura K; Segler, Marwin H S; Boca, Simina M; Swamidass, S Joshua; Huang, Austin; Gitter, Anthony; Greene, Casey S

2018-04-01

Deep learning describes a class of machine learning algorithms that are capable of combining raw inputs into layers of intermediate features. These algorithms have recently shown impressive results across a variety of domains. Biology and medicine are data-rich disciplines, but the data are complex and often ill-understood. Hence, deep learning techniques may be particularly well suited to solve problems of these fields. We examine applications of deep learning to a variety of biomedical problems-patient classification, fundamental biological processes and treatment of patients-and discuss whether deep learning will be able to transform these tasks or if the biomedical sphere poses unique challenges. Following from an extensive literature review, we find that deep learning has yet to revolutionize biomedicine or definitively resolve any of the most pressing challenges in the field, but promising advances have been made on the prior state of the art. Even though improvements over previous baselines have been modest in general, the recent progress indicates that deep learning methods will provide valuable means for speeding up or aiding human investigation. Though progress has been made linking a specific neural network's prediction to input features, understanding how users should interpret these models to make testable hypotheses about the system under study remains an open challenge. Furthermore, the limited amount of labelled data for training presents problems in some domains, as do legal and privacy constraints on work with sensitive health records. Nonetheless, we foresee deep learning enabling changes at both bench and bedside with the potential to transform several areas of biology and medicine. © 2018 The Authors.

Editorial overview: Evolutionary psychology

NARCIS (Netherlands)

Gangestad, S.W.; Tybur, J.M.

2016-01-01

Functional approaches in psychology - which ask what behavior is good for - are almost as old as scientific psychology itself. Yet sophisticated, generative functional theories were not possible until developments in evolutionary biology in the mid-20th century. Arising in the last three decades,

Development and Assessment of Service Learning Projects in General Biology

Science.gov (United States)

Felzien, Lisa; Salem, Laura

2008-01-01

Service learning involves providing service to the community while requiring students to meet learning goals in a specific course. A service learning project was implemented in a general biology course at Rockhurst University to involve students in promoting scientific education in conjunction with community partner educators. Students were…

Improving the reviewing process in Ecology and Evolutionary Biology

Directory of Open Access Journals (Sweden)

Grossman, G. D.

2014-06-01

Full Text Available I discuss current issues in reviewing and editorial practices in ecology and evolutionary biology and suggest possible solutions for current problems. The reviewing crisis is unlikely to change unless steps are taken by journals to provide greater inclusiveness and incentives to reviewers. In addition, both journals and institutions should reduce their emphasis on publication numbers (least publishable units and impact factors and focus instead on article synthesis and quality which will require longer publications. Academic and research institutions should consider reviewing manuscripts and editorial positions an important part of a researcher’s professional activities and reward them accordingly. Rewarding reviewers either monetarily or via other incentives such as free journal subscriptions may encourage participation in the reviewing process for both profit and non–profit journals. Reviewer performance will likely be improved by measures that increase inclusiveness, such as sending reviews and decision letters to reviewers. Journals may be able to evaluate the efficacy of their reviewing process by comparing citations of rejected but subsequently published papers with those published within the journal at similar times. Finally, constructive reviews: 1 identify important shortcomings and suggest solutions when possible, 2 distinguish trivial from non–trivial problems, and 3 include editor’s evaluations of the reviews including identification of trivial versus substantive comments (i.e., those that must be addressed.

High School Students' Epistemological Beliefs, Conceptions of Learning, and Self-Efficacy for Learning Biology: A Study of Their Structural Models

Science.gov (United States)

Sadi, Özlem; Dagyar, Miray

2015-01-01

The current work reveals the data of the study which examines the relationships among epistemological beliefs, conceptions of learning, and self-efficacy for biology learning with the help of the Structural Equation Modeling. Three questionnaires, the Epistemological Beliefs, the Conceptions of Learning Biology and the Self-efficacy for Learning…

The extended evolutionary synthesis: its structure, assumptions and predictions

Science.gov (United States)

Laland, Kevin N.; Uller, Tobias; Feldman, Marcus W.; Sterelny, Kim; Müller, Gerd B.; Moczek, Armin; Jablonka, Eva; Odling-Smee, John

2015-01-01

Scientific activities take place within the structured sets of ideas and assumptions that define a field and its practices. The conceptual framework of evolutionary biology emerged with the Modern Synthesis in the early twentieth century and has since expanded into a highly successful research program to explore the processes of diversification and adaptation. Nonetheless, the ability of that framework satisfactorily to accommodate the rapid advances in developmental biology, genomics and ecology has been questioned. We review some of these arguments, focusing on literatures (evo-devo, developmental plasticity, inclusive inheritance and niche construction) whose implications for evolution can be interpreted in two ways—one that preserves the internal structure of contemporary evolutionary theory and one that points towards an alternative conceptual framework. The latter, which we label the ‘extended evolutionary synthesis' (EES), retains the fundaments of evolutionary theory, but differs in its emphasis on the role of constructive processes in development and evolution, and reciprocal portrayals of causation. In the EES, developmental processes, operating through developmental bias, inclusive inheritance and niche construction, share responsibility for the direction and rate of evolution, the origin of character variation and organism–environment complementarity. We spell out the structure, core assumptions and novel predictions of the EES, and show how it can be deployed to stimulate and advance research in those fields that study or use evolutionary biology. PMID:26246559

Problem-based learning through field investigation: Boosting questioning skill, biological literacy, and academic achievement

Science.gov (United States)

Suwono, Hadi; Wibowo, Agung

2018-01-01

Biology learning emphasizes problem-based learning as a learning strategy to develop students ability in identifying and solving problems in the surrounding environment. Problem identification skills are closely correlated with questioning skills. By holding this skill, students tend to deliver a procedural question instead of the descriptive one. Problem-based learning through field investigation is an instruction model which directly exposes the students to problems or phenomena that occur in the environment, and then the students design the field investigation activities to solve these problems. The purpose of this research was to describe the improvement of undergraduate biology students on questioning skills, biological literacy, and academic achievement through problem-based learning through field investigation (PBFI) compared with the lecture-based instruction (LBI). This research was a time series quasi-experimental design. The research was conducted on August - December 2015 and involved 26 undergraduate biology students at the State University of Malang on the Freshwater Ecology course. The data were collected during the learning with LBI and PBFI, in which questioning skills, biological literacy, and academic achievement were collected 3 times in each learning model. The data showed that the procedural correlative and causal types of questions are produced by the students to guide them in conducting investigations and problem-solving in PBFI. The biological literacy and academic achievement of the students at PBFI are significantly higher than those at LBI. The results show that PBFI increases the questioning skill, biological literacy, and the academic achievement of undergraduate biology students.

What Is Sexual Orientation All About? Explaining an Evolutionary Paradox

OpenAIRE

Brad Bowins

2015-01-01

Numerous psychological, biological, and evolutionary theories have been proposed to explain sexual orientation. For a theory to be valid it must account for the evolutionary or Darwinian paradox of how homosexual behavior seemingly blocking evolutionary fitness could have evolved. Typically it is only evolutionary based theories that attempt to address this issue. All theories proposed to date have limitations, a major one being that they tend to be specific for male or female sexual orientat...

Effects of demographic stochasticity on biological community assembly on evolutionary time scales

KAUST Repository

Murase, Yohsuke; Shimada, Takashi; Ito, Nobuyasu; Rikvold, Per Arne

2010-01-01

We study the effects of demographic stochasticity on the long-term dynamics of biological coevolution models of community assembly. The noise is induced in order to check the validity of deterministic population dynamics. While mutualistic communities show little dependence on the stochastic population fluctuations, predator-prey models show strong dependence on the stochasticity, indicating the relevance of the finiteness of the populations. For a predator-prey model, the noise causes drastic decreases in diversity and total population size. The communities that emerge under influence of the noise consist of species strongly coupled with each other and have stronger linear stability around the fixed-point populations than the corresponding noiseless model. The dynamics on evolutionary time scales for the predator-prey model are also altered by the noise. Approximate 1/f fluctuations are observed with noise, while 1/ f2 fluctuations are found for the model without demographic noise. © 2010 The American Physical Society.

Effects of demographic stochasticity on biological community assembly on evolutionary time scales

KAUST Repository

Murase, Yohsuke

2010-04-13

We study the effects of demographic stochasticity on the long-term dynamics of biological coevolution models of community assembly. The noise is induced in order to check the validity of deterministic population dynamics. While mutualistic communities show little dependence on the stochastic population fluctuations, predator-prey models show strong dependence on the stochasticity, indicating the relevance of the finiteness of the populations. For a predator-prey model, the noise causes drastic decreases in diversity and total population size. The communities that emerge under influence of the noise consist of species strongly coupled with each other and have stronger linear stability around the fixed-point populations than the corresponding noiseless model. The dynamics on evolutionary time scales for the predator-prey model are also altered by the noise. Approximate 1/f fluctuations are observed with noise, while 1/ f2 fluctuations are found for the model without demographic noise. © 2010 The American Physical Society.

Sex in an Evolutionary Perspective: Just Another Reaction Norm

OpenAIRE

Ah-King, Malin; Nylin, S?ren

2010-01-01

It is common to refer to all sorts of clear-cut differences between the sexes as something that is biologically almost inevitable. Although this does not reflect the status of evolutionary theory on sex determination and sexual dimorphism, it is probably a common view among evolutionary biologists as well, because of the impact of sexual selection theory. To get away from thinking about biological sex and traits associated with a particular sex as something static, it should be recognized tha...

Polemics and Synthesis: Ernst Mayr and Evolutionary Biology

Indian Academy of Sciences (India)

A hundred years is but an instant in evolutionary time; however during his life that spanned a century, Ernst Mayr (1904-2005) made outstanding contributions to our understanding of the pat- tern and process of evolution. An ornithologist and systematist by training, Mayr embraced Darwinism and championed the cause of.

Computing the Quartet Distance Between Evolutionary Trees in Time O(n log n)

DEFF Research Database (Denmark)

Brodal, Gerth Sølfting; Fagerberg, Rolf; Pedersen, Christian Nørgaard Storm

2003-01-01

Evolutionary trees describing the relationship for a set of species are central in evolutionary biology, and quantifying differences between evolutionary trees is therefore an important task. The quartet distance is a distance measure between trees previously proposed by Estabrook, McMorris, and ...... unrooted evolutionary trees of n species, where all internal nodes have degree three, in time O(n log n. The previous best algorithm for the problem uses time O(n 2).......Evolutionary trees describing the relationship for a set of species are central in evolutionary biology, and quantifying differences between evolutionary trees is therefore an important task. The quartet distance is a distance measure between trees previously proposed by Estabrook, Mc......Morris, and Meacham. The quartet distance between two unrooted evolutionary trees is the number of quartet topology differences between the two trees, where a quartet topology is the topological subtree induced by four species. In this paper we present an algorithm for computing the quartet distance between two...

Developmental biology, the stem cell of biological disciplines

OpenAIRE

Gilbert, Scott F.

2017-01-01

Developmental biology (including embryology) is proposed as "the stem cell of biological disciplines.” Genetics, cell biology, oncology, immunology, evolutionary mechanisms, neurobiology, and systems biology each has its ancestry in developmental biology. Moreover, developmental biology continues to roll on, budding off more disciplines, while retaining its own identity. While its descendant disciplines differentiate into sciences with a restricted set of paradigms, examples, and techniques, ...

The use of an active learning approach in a SCALE-UP learning space improves academic performance in undergraduate General Biology.

Science.gov (United States)

Hacisalihoglu, Gokhan; Stephens, Desmond; Johnson, Lewis; Edington, Maurice

2018-01-01

Active learning is a pedagogical approach that involves students engaging in collaborative learning, which enables them to take more responsibility for their learning and improve their critical thinking skills. While prior research examined student performance at majority universities, this study focuses on specifically Historically Black Colleges and Universities (HBCUs) for the first time. Here we present work that focuses on the impact of active learning interventions at Florida A&M University, where we measured the impact of active learning strategies coupled with a SCALE-UP (Student Centered Active Learning Environment with Upside-down Pedagogies) learning environment on student success in General Biology. In biology sections where active learning techniques were employed, students watched online videos and completed specific activities before class covering information previously presented in a traditional lecture format. In-class activities were then carefully planned to reinforce critical concepts and enhance critical thinking skills through active learning techniques such as the one-minute paper, think-pair-share, and the utilization of clickers. Students in the active learning and control groups covered the same topics, took the same summative examinations and completed identical homework sets. In addition, the same instructor taught all of the sections included in this study. Testing demonstrated that these interventions increased learning gains by as much as 16%, and students reported an increase in their positive perceptions of active learning and biology. Overall, our results suggest that active learning approaches coupled with the SCALE-UP environment may provide an added opportunity for student success when compared with the standard modes of instruction in General Biology.

Darwin in Mind: New Opportunities for Evolutionary Psychology

Science.gov (United States)

Bolhuis, Johan J.; Brown, Gillian R.; Richardson, Robert C.; Laland, Kevin N.

2011-01-01

Evolutionary Psychology (EP) views the human mind as organized into many modules, each underpinned by psychological adaptations designed to solve problems faced by our Pleistocene ancestors. We argue that the key tenets of the established EP paradigm require modification in the light of recent findings from a number of disciplines, including human genetics, evolutionary biology, cognitive neuroscience, developmental psychology, and paleoecology. For instance, many human genes have been subject to recent selective sweeps; humans play an active, constructive role in co-directing their own development and evolution; and experimental evidence often favours a general process, rather than a modular account, of cognition. A redefined EP could use the theoretical insights of modern evolutionary biology as a rich source of hypotheses concerning the human mind, and could exploit novel methods from a variety of adjacent research fields. PMID:21811401

An Evolutionary Perspective on Toxic Leadership

Directory of Open Access Journals (Sweden)

Lucia Ovidia VREJA

2016-12-01

Full Text Available Charles Darwin’s prediction from 1859, that future psychology was going to be built on principles derived from evolutionary theory came at last to be fulfilled. Nowadays, there are at least four disciplines that attempt to explain human behaviours as evolutionary adaptations (or maladaptations to the natural and/or social environment: human sociobiology, human behavioural ecology, evolutionary psychology, memetics and gene–culture coevolution theory (in our view, the most adequate of all. According to gene–culture coevolution theory, articulated language was the singular phenomenon that permitted humans to become a cultural species, and from that moment on culture become itself a selection factor. Culture means transmission of information from one generation to the next and learning from other individuals’ experiences, trough language. So, it is of critical importance to have good criteria for the selection of those individuals from whom we should learn. Yet when humans also choose their leaders from among those role-models, according to the same criteria, this mechanism can become a maladaptation and the result can be toxic leadership.

The Relationship between Grade 11 Palestinian Attitudes toward Biology and Their Perceptions of the Biology Learning Environment

Science.gov (United States)

Zeidan, Afif

2010-01-01

The aims of the study were to investigate (a) the relationship between the attitudes toward biology and perceptions of the biology learning environment among grade 11 students in Tulkarm District, Palestine and (b) the effect of gender and residence of these students on their attitudes toward biology and on their perceptions of the biology…

Organisms as natural purposes: the contemporary evolutionary perspective.

Science.gov (United States)

Walsh, D M

2006-12-01

Kant's conception of organisms as natural purposes raises a challenge to the adequacy of mechanistic explanation in biology. Certain features of organisms appear to be inexplicable by appeal to mechanical law alone. Some biological phenomena, it seems, can only be accounted for teleologically. Contemporary evolutionary biology has by and large ignored this challenge. It is widely held that Darwin's theory of natural selection gives us an adequate, wholly mechanical account of the nature of organisms. In contemporary biology, the category of the organism plays virtually no explanatory role. Contemporary evolutionary biology is a science of sub-organismal entities-replicators. I argue that recent advances in developmental biology demonstrate the inadequacy of sub-organismal mechanism. The category of the organism, construed as a 'natural purpose' should play an ineliminable role in explaining ontogenetic development and adaptive evolution. According to Kant the natural purposiveness of organisms cannot be demonstrated to be an objective principle in nature, nor can purposiveness figure in genuine explain. I attempt to argue, by appeal to recent work on self-organization, that the purposiveness of organisms is a natural phenomenon, and, by appeal to the apparatus of invariance explanation, that biological purposiveness provides genuine, ineliminable biological explanations.

Historical change and evolutionary theory.

Science.gov (United States)

Masters, Roger D

2007-09-01

Despite advances in fields like genetics, evolutionary psychology, and human behavior and evolution--which generally focus on individual or small group behavior from a biological perspective--evolutionary biology has made little impact on studies of political change and social history. Theories of natural selection often seem inapplicable to human history because our social behavior is embedded in language (which makes possible the concepts of time and social identity on which what we call "history" depends). Peter Corning's Holistic Darwinism reconceptualizes evolutionary biology, making it possible to go beyond the barriers separating the social and natural sciences. Corning focuses on two primary processes: "synergy" (complex multivariate interactions at multiple levels between a species and its environment) and "cybernetics" (the information systems permitting communication between individuals and groups over time). Combining this frame of reference with inclusive fitness theory, it is possible to answer the most important (and puzzling) question in human history: How did a species that lived for millennia in hunter-gatherer bands form centralized states governing large populations of non-kin (including multi-ethnic empires as well as modern nation-states)? The fragility and contemporary ethnic violence in Kenya and the Congo should suffice as evidence that these issues need to be taken seriously. To explain the rise and fall of states as well as changes in human laws and customs--the core of historical research--it is essential to show how the provision of collective goods can overcome the challenge of self-interest and free-riding in some instances, yet fail to do so in others. To this end, it is now possible to consider how a state providing public goods can--under circumstances that often include effective leadership--contribute to enhanced inclusive fitness of virtually all its members. Because social behavior needs to adapt to ecology, but ecological

Casual Games and Casual Learning About Human Biological Systems

Science.gov (United States)

Price, C. Aaron; Gean, Katherine; Christensen, Claire G.; Beheshti, Elham; Pernot, Bryn; Segovia, Gloria; Person, Halcyon; Beasley, Steven; Ward, Patricia

2016-02-01

Casual games are everywhere. People play them throughout life to pass the time, to engage in social interactions, and to learn. However, their simplicity and use in distraction-heavy environments can attenuate their potential for learning. This experimental study explored the effects playing an online, casual game has on awareness of human biological systems. Two hundred and forty-two children were given pretests at a Museum and posttests at home after playing either a treatment or control game. Also, 41 children were interviewed to explore deeper meanings behind the test results. Results show modest improvement in scientific attitudes, ability to identify human biological systems and in the children's ability to describe how those systems work together in real-world scenarios. Interviews reveal that children drew upon their prior school learning as they played the game. Also, on the surface they perceived the game as mainly entertainment but were easily able to discern learning outcomes when prompted. Implications for the design of casual games and how they can be used to enhance transfer of knowledge from the classroom to everyday life are discussed.

Applying ecological and evolutionary theory to cancer: a long and winding road.

Science.gov (United States)

Thomas, Frédéric; Fisher, Daniel; Fort, Philippe; Marie, Jean-Pierre; Daoust, Simon; Roche, Benjamin; Grunau, Christoph; Cosseau, Céline; Mitta, Guillaume; Baghdiguian, Stephen; Rousset, François; Lassus, Patrice; Assenat, Eric; Grégoire, Damien; Missé, Dorothée; Lorz, Alexander; Billy, Frédérique; Vainchenker, William; Delhommeau, François; Koscielny, Serge; Itzykson, Raphael; Tang, Ruoping; Fava, Fanny; Ballesta, Annabelle; Lepoutre, Thomas; Krasinska, Liliana; Dulic, Vjekoslav; Raynaud, Peggy; Blache, Philippe; Quittau-Prevostel, Corinne; Vignal, Emmanuel; Trauchessec, Hélène; Perthame, Benoit; Clairambault, Jean; Volpert, Vitali; Solary, Eric; Hibner, Urszula; Hochberg, Michael E

2013-01-01

Since the mid 1970s, cancer has been described as a process of Darwinian evolution, with somatic cellular selection and evolution being the fundamental processes leading to malignancy and its many manifestations (neoangiogenesis, evasion of the immune system, metastasis, and resistance to therapies). Historically, little attention has been placed on applications of evolutionary biology to understanding and controlling neoplastic progression and to prevent therapeutic failures. This is now beginning to change, and there is a growing international interest in the interface between cancer and evolutionary biology. The objective of this introduction is first to describe the basic ideas and concepts linking evolutionary biology to cancer. We then present four major fronts where the evolutionary perspective is most developed, namely laboratory and clinical models, mathematical models, databases, and techniques and assays. Finally, we discuss several of the most promising challenges and future prospects in this interdisciplinary research direction in the war against cancer.

Neuro-symbolic representation learning on biological knowledge graphs

KAUST Repository

AlShahrani, Mona; Khan, Mohammed Asif; Maddouri, Omar; Kinjo, Akira R; Queralt-Rosinach, Nú ria; Hoehndorf, Robert

2017-01-01

Biological data and knowledge bases increasingly rely on Semantic Web technologies and the use of knowledge graphs for data integration, retrieval and federated queries. In the past years, feature learning methods that are applicable to graph

Dual Causality and the Autonomy of Biology.

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Bock, Walter J

2017-03-01

Ernst Mayr's concept of dual causality in biology with the two forms of causes (proximate and ultimate) continues to provide an essential foundation for the philosophy of biology. They are equivalent to functional (=proximate) and evolutionary (=ultimate) causes with both required for full biological explanations. The natural sciences can be classified into nomological, historical nomological and historical dual causality, the last including only biology. Because evolutionary causality is unique to biology and must be included for all complete biological explanations, biology is autonomous from the physical sciences.

Biology learning evaluation model in Senior High Schools

Directory of Open Access Journals (Sweden)

Sri Utari

2017-06-01

Full Text Available The study was to develop a Biology learning evaluation model in senior high schools that referred to the research and development model by Borg & Gall and the logic model. The evaluation model included the components of input, activities, output and outcomes. The developing procedures involved a preliminary study in the form of observation and theoretical review regarding the Biology learning evaluation in senior high schools. The product development was carried out by designing an evaluation model, designing an instrument, performing instrument experiment and performing implementation. The instrument experiment involved teachers and Students from Grade XII in senior high schools located in the City of Yogyakarta. For the data gathering technique and instrument, the researchers implemented observation sheet, questionnaire and test. The questionnaire was applied in order to attain information regarding teacher performance, learning performance, classroom atmosphere and scientific attitude; on the other hand, test was applied in order to attain information regarding Biology concept mastery. Then, for the analysis of instrument construct, the researchers performed confirmatory factor analysis by means of Lisrel 0.80 software and the results of this analysis showed that the evaluation instrument valid and reliable. The construct validity was between 0.43-0.79 while the reliability of measurement model was between 0.88-0.94. Last but not the least, the model feasibility test showed that the theoretical model had been supported by the empirical data.

Evolutionary games on graphs

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Szabó, György; Fáth, Gábor

2007-07-01

Game theory is one of the key paradigms behind many scientific disciplines from biology to behavioral sciences to economics. In its evolutionary form and especially when the interacting agents are linked in a specific social network the underlying solution concepts and methods are very similar to those applied in non-equilibrium statistical physics. This review gives a tutorial-type overview of the field for physicists. The first four sections introduce the necessary background in classical and evolutionary game theory from the basic definitions to the most important results. The fifth section surveys the topological complications implied by non-mean-field-type social network structures in general. The next three sections discuss in detail the dynamic behavior of three prominent classes of models: the Prisoner's Dilemma, the Rock-Scissors-Paper game, and Competing Associations. The major theme of the review is in what sense and how the graph structure of interactions can modify and enrich the picture of long term behavioral patterns emerging in evolutionary games.

Selectionist and evolutionary approaches to brain function: a critical appraisal

Directory of Open Access Journals (Sweden)

Chrisantha Thomas Fernando

2012-04-01

Full Text Available We consider approaches to brain dynamics and function that have been claimed to be Darwinian. These include Edelman’s theory of neuronal group selection, Changeux’s theory of synaptic selection and selective stabilization of pre-representations, Seung’s Darwinian synapse, Loewenstein’s synaptic melioration, Adam’s selfish synapse and Calvin’s replicating activity patterns. Except for the last two, the proposed mechanisms are selectionist but not truly Darwinian, because no replicators with information transfer to copies and hereditary variation can be identified in them. All of them fit, however, a generalized selectionist framework conforming to the picture of Price’s covariance formulation, which deliberately was not specific even to selection in biology, and therefore does not imply an algorithmic picture of biological evolution. Bayesian models and reinforcement learning are formally in agreement with selection dynamics. A classification of search algorithms is shown to include Darwinian replicators (evolutionary units with multiplication, heredity and variability as the most powerful mechanism in a sparsely occupied search space. Examples of why parallel competitive search with information transfer among the units is efficient are given. Finally, we review our recent attempts to construct and analyze simple models of true Darwinian evolutionary units in the brain in terms of connectivity and activity copying of neuronal groups. Although none of the proposed neuronal replicators include miraculous mechanisms, their identification remains a challenge but also a great promise.

Using Active Learning in a Studio Classroom to Teach Molecular Biology

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Nogaj, Luiza A.

2013-01-01

This article describes the conversion of a lecture-based molecular biology course into an active learning environment in a studio classroom. Specific assignments and activities are provided as examples. The goal of these activities is to involve students in collaborative learning, teach them how to participate in the learning process, and give…

Dynamically analyzing cell interactions in biological environments using multiagent social learning framework.

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Zhang, Chengwei; Li, Xiaohong; Li, Shuxin; Feng, Zhiyong

2017-09-20

Biological environment is uncertain and its dynamic is similar to the multiagent environment, thus the research results of the multiagent system area can provide valuable insights to the understanding of biology and are of great significance for the study of biology. Learning in a multiagent environment is highly dynamic since the environment is not stationary anymore and each agent's behavior changes adaptively in response to other coexisting learners, and vice versa. The dynamics becomes more unpredictable when we move from fixed-agent interaction environments to multiagent social learning framework. Analytical understanding of the underlying dynamics is important and challenging. In this work, we present a social learning framework with homogeneous learners (e.g., Policy Hill Climbing (PHC) learners), and model the behavior of players in the social learning framework as a hybrid dynamical system. By analyzing the dynamical system, we obtain some conditions about convergence or non-convergence. We experimentally verify the predictive power of our model using a number of representative games. Experimental results confirm the theoretical analysis. Under multiagent social learning framework, we modeled the behavior of agent in biologic environment, and theoretically analyzed the dynamics of the model. We present some sufficient conditions about convergence or non-convergence and prove them theoretically. It can be used to predict the convergence of the system.

Research traditions and evolutionary explanations in medicine.

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Méthot, Pierre-Olivier

2011-02-01

In this article, I argue that distinguishing 'evolutionary' from 'Darwinian' medicine will help us assess the variety of roles that evolutionary explanations can play in a number of medical contexts. Because the boundaries of evolutionary and Darwinian medicine overlap to some extent, however, they are best described as distinct 'research traditions' rather than as competing paradigms. But while evolutionary medicine does not stand out as a new scientific field of its own, Darwinian medicine is united by a number of distinctive theoretical and methodological claims. For example, evolutionary medicine and Darwinian medicine can be distinguished with respect to the styles of evolutionary explanations they employ. While the former primarily involves 'forward looking' explanations, the latter depends mostly on 'backward looking' explanations. A forward looking explanation tries to predict the effects of ongoing evolutionary processes on human health and disease in contemporary environments (e.g., hospitals). In contrast, a backward looking explanation typically applies evolutionary principles from the vantage point of humans' distant biological past in order to assess present states of health and disease. Both approaches, however, are concerned with the prevention and control of human diseases. In conclusion, I raise some concerns about the claim that 'nothing in medicine makes sense except in the light of evolution'.

Peer Learning and Support of Technology in an Undergraduate Biology Course to Enhance Deep Learning

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Tsaushu, Masha; Tal, Tali; Sagy, Ornit; Kali, Yael; Gepstein, Shimon; Zilberstein, Dan

2012-01-01

This study offers an innovative and sustainable instructional model for an introductory undergraduate course. The model was gradually implemented during 3 yr in a research university in a large-lecture biology course that enrolled biology majors and nonmajors. It gives priority to sources not used enough to enhance active learning in higher…

Modalities of gene action predicted by the classical evolutionary biological theory of aging.

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Martin, George M

2007-04-01

What might now be referred to as the "classical" evolutionary biological theory of why we age has had a number of serious challenges in recent years. While the theory might therefore have to be modified under certain circumstances, in the author's opinion, it still provides the soundest theoretical basis for thinking about how we age. Nine modalities of gene action that have the potential to modulate processes of aging are reviewed, including the two most widely reviewed and accepted concepts ("antagonistic pleiotropy" and "mutation accumulation"). While several of these nine mechanisms can be regarded as derivatives of the antagonistic pleiotropic concept, they frame more specific questions for future research. Such research should pursue what appears to be the dominant factor in the determination of intraspecific variations in longevity-stochastic mechanisms, most likely based upon epigenetics. This contrasts with the dominant factor in the determination of interspecific variations in longevity-the constitutional genome, most likely based upon variations in regulatory loci.

Charles Darwin and the origins of plant evolutionary developmental biology.

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Friedman, William E; Diggle, Pamela K

2011-04-01

Much has been written of the early history of comparative embryology and its influence on the emergence of an evolutionary developmental perspective. However, this literature, which dates back nearly a century, has been focused on metazoans, without acknowledgment of the contributions of comparative plant morphologists to the creation of a developmental view of biodiversity. We trace the origin of comparative plant developmental morphology from its inception in the eighteenth century works of Wolff and Goethe, through the mid nineteenth century discoveries of the general principles of leaf and floral organ morphogenesis. Much like the stimulus that von Baer provided as a nonevolutionary comparative embryologist to the creation of an evolutionary developmental view of animals, the comparative developmental studies of plant morphologists were the basis for the first articulation of the concept that plant (namely floral) evolution results from successive modifications of ontogeny. Perhaps most surprisingly, we show that the first person to carefully read and internalize the remarkable advances in the understanding of plant morphogenesis in the 1840s and 1850s is none other than Charles Darwin, whose notebooks, correspondence, and (then) unpublished manuscripts clearly demonstrate that he had discovered the developmental basis for the evolutionary transformation of plant form.

ADAPTATION OF THE STUDENTS' MOTIVATION TOWARDS SCIENCE LEARNING QUESTIONNAIRE TO MEASURE GREEK STUDENTS’ MOTIVATION TOWARDS BIOLOGY LEARNING

OpenAIRE

Andressa, Helen; Mavrikaki, Evangelia; Dermitzaki, Irini

2015-01-01

The purpose of this study was to investigate students’ motivation towards biology learning and to determine the factors that are related to it: students’ gender and their parents’ occupation (relevant with biology or not) were investigated. The sample of the study consisted of 360 Greek high school students of the 10th grade (178 boys and 182 girls). The data were collected through Students’ Motivation Toward Science Learning (SMTSL) questionnaire. It was found that it was a valid and reliabl...

Learning Styles of the Students of Biology Department and Prospective Biology Teachers in Turkey and Their Relationship with Some Demographic Variables

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Günes, M. Handan

2018-01-01

This study has been carried out with the aim of researching dominant learning styles of the students studying at the biology departments of the faculty of science or the faculty of arts and sciences as well as the dominant learning styles of the prospective biology teachers studying at the faculty of education of universities in Turkey, by taking…

Trans-algorithmic nature of learning in biological systems.

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Shimansky, Yury P

2018-05-02

Learning ability is a vitally important, distinctive property of biological systems, which provides dynamic stability in non-stationary environments. Although several different types of learning have been successfully modeled using a universal computer, in general, learning cannot be described by an algorithm. In other words, algorithmic approach to describing the functioning of biological systems is not sufficient for adequate grasping of what is life. Since biosystems are parts of the physical world, one might hope that adding some physical mechanisms and principles to the concept of algorithm could provide extra possibilities for describing learning in its full generality. However, a straightforward approach to that through the so-called physical hypercomputation so far has not been successful. Here an alternative approach is proposed. Biosystems are described as achieving enumeration of possible physical compositions though random incremental modifications inflicted on them by active operating resources (AORs) in the environment. Biosystems learn through algorithmic regulation of the intensity of the above modifications according to a specific optimality criterion. From the perspective of external observers, biosystems move in the space of different algorithms driven by random modifications imposed by the environmental AORs. A particular algorithm is only a snapshot of that motion, while the motion itself is essentially trans-algorithmic. In this conceptual framework, death of unfit members of a population, for example, is viewed as a trans-algorithmic modification made in the population as a biosystem by environmental AORs. Numerous examples of AOR utilization in biosystems of different complexity, from viruses to multicellular organisms, are provided.

Biological Principles and Threshold Concepts for Understanding Natural Selection: Implications for Developing Visualizations as a Pedagogic Tool

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Tibell, Lena A. E.; Harms, Ute

2017-01-01

Modern evolutionary theory is both a central theory and an integrative framework of the life sciences. This is reflected in the common references to evolution in modern science education curricula and contexts. In fact, evolution is a core idea that is supposed to support biology learning by facilitating the organization of relevant knowledge. In…

Visualizing Biological Data in Museums: Visitor Learning with an Interactive Tree of Life Exhibit

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Horn, Michael S.; Phillips, Brenda C.; Evans, Evelyn Margaret; Block, Florian; Diamond, Judy; Shen, Chia

2016-01-01

In this study, we investigate museum visitor learning and engagement at an interactive visualization of an evolutionary tree of life consisting of over 70,000 species. The study was conducted at two natural history museums where visitors collaboratively explored the tree of life using direct touch gestures on a multi-touch tabletop display. In the…

The Current Status of the Philosophy of Biology

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Takacs, Peter; Ruse, Michael

2013-01-01

The philosophy of biology today is one of the most exciting areas of philosophy. It looks critically across the life sciences, teasing out conceptual issues and difficulties bringing to bear the tools of philosophical analysis to achieve clarification and understanding. This essay surveys work in all of the major directions of research: evolutionary theory and the units/levels of selection; evolutionary developmental biology; reductionism; ecology; the species problem; teleology; evolutionary epistemology; evolutionary ethics; and progress. There is a comprehensive bibliography.

Students’ learning activities while studying biological process diagrams

NARCIS (Netherlands)

Kragten, M.; Admiraal, W.; Rijlaarsdam, G.

2015-01-01

Process diagrams describe how a system functions (e.g. photosynthesis) and are an important type of representation in Biology education. In the present study, we examined students’ learning activities while studying process diagrams, related to their resulting comprehension of these diagrams. Each

How Much Can Evolutionary Psychology Inform the Educational Sciences?

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Halpern, Diane F.

2008-01-01

In response to a stimulating article by David C. Geary on the value of understanding the evolutionary basis of learning as a guide to instruction, I raise several objections. When evolutionary theory is used to explain everything from sex differences in math and reading to why children are bored in school, it loses its explanatory power. There is…

Quantum information and the problem of mechanisms of biological evolution.

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Melkikh, Alexey V

2014-01-01

One of the most important conditions for replication in early evolution is the de facto elimination of the conformational degrees of freedom of the replicators, the mechanisms of which remain unclear. In addition, realistic evolutionary timescales can be established based only on partially directed evolution, further complicating this issue. A division of the various evolutionary theories into two classes has been proposed based on the presence or absence of a priori information about the evolving system. A priori information plays a key role in solving problems in evolution. Here, a model of partially directed evolution, based on the learning automata theory, which includes a priori information about the fitness space, is proposed. A potential repository of such prior information is the states of biologically important molecules. Thus, the need for extended evolutionary synthesis is discussed. Experiments to test the hypothesis of partially directed evolution are proposed. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

[Charles Darwin and the problem of evolutionary progress].

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Iordanskiĭ, N N

2010-01-01

According to Ch. Darwin's evolutionary theory, evolutionary progress (interpreted as morpho-physiological progress or arogenesis in recent terminology) is one of logical results of natural selection. At the same time, natural selection does not hold any factors especially promoting evolutionary progress. Darwin emphasized that the pattern of evolutionary changes depends on organism nature more than on the pattern of environment changes. Arogenesis specificity is determined by organization of rigorous biological systems - integral organisms. Onward progressive development is determined by fundamental features of living organisms: metabolism and homeostasis. The concept of social Darwinism differs fundamentally from Darwin's ideas about the most important role of social instincts in progress of mankind. Competition and selection play secondary role in socio-cultural progress of human society.

Evolutionary medicine: its scope, interest and potential.

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Stearns, Stephen C

2012-11-07

This review is aimed at readers seeking an introductory overview, teaching courses and interested in visionary ideas. It first describes the range of topics covered by evolutionary medicine, which include human genetic variation, mismatches to modernity, reproductive medicine, degenerative disease, host-pathogen interactions and insights from comparisons with other species. It then discusses priorities for translational research, basic research and health management. Its conclusions are that evolutionary thinking should not displace other approaches to medical science, such as molecular medicine and cell and developmental biology, but that evolutionary insights can combine with and complement established approaches to reduce suffering and save lives. Because we are on the cusp of so much new research and innovative insights, it is hard to estimate how much impact evolutionary thinking will have on medicine, but it is already clear that its potential is enormous.

Conscious knowledge of learning: accessing learning strategies in a final year high school biology class

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Conner, Lindsey; Gunstone, Richard

2004-12-01

This paper reports on a qualitative case study investigation of the knowledge and use of learning strategies by 16 students in a final year high school biology class to expand their conscious knowledge of learning. Students were provided with opportunities to engage in purposeful inquiry into the biological, social and ethical aspects of cancer. A constructivist approach was implemented to access prior content and procedural knowledge in various ways. Students were encouraged to develop evaluation of their learning skills independently through activities that promoted metacognition. Those students who planned and monitored their work produced essays of higher quality. The value and difficulties of promoting metacognitive approaches in this context are discussed, as well as the idea that metacognitive processes are difficult to research, because they have to be conscious in order to be identified by the learner, thereby making them accessible to the researcher.

Darwin's Difficulties and Students' Struggles with Trait Loss: Cognitive-Historical Parallelisms in Evolutionary Explanation

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Ha, Minsu; Nehm, Ross H.

2014-05-01

Although historical changes in scientific ideas sometimes display striking similarities with students' conceptual progressions, some scholars have cautioned that such similarities lack meaningful commonalities. In the history of evolution, while Darwin and his contemporaries often used natural selection to explain evolutionary trait gain or increase, they struggled to use it to convincingly account for cases of trait loss or decrease. This study examines Darwin's evolutionary writings about trait gain and loss in the Origin of Species (On the origin of species by means of natural selection, or the preservation of favoured races in the struggle for life. D. Appleton, New York, 1859) and compares them to written evolutionary explanations for trait gain and loss in a large (n > 500), cross-cultural and cross-sectional sample (novices and experts from the USA and Korea). Findings indicate that significantly more students and experts applied natural selection to cases of trait gain, but like Darwin and his contemporaries, they more often applied `use and disuse' and `inheritance of acquired characteristics' to episodes of trait loss. Although the parallelism between Darwin's difficulties and students' struggles with trait loss are striking, significant differences also characterize explanatory model structure. Overall, however, students and scientists struggles to explain trait loss—which is a very common phenomenon in the history of life—appear to transcend time, place, and level of biological expertise. The significance of these findings for evolution education are discussed; in particular, the situated nature of biological reasoning, and the important role that the history of science can play in understanding cognitive constraints on science learning.

Positive feelings in learning and interest development in biology education

DEFF Research Database (Denmark)

Petersen, Morten Rask; Dohn, Niels Bonderup

2015-01-01

for learning (e.g. Krapp, 2002). Here we turn the interplay and see learning as a facilitator for interest development. This interplay was studied in upper secondary biology education. Student’s conducted an exercise on modelling natural selection with LEGO® bricks (Christensen-Dalsgaard & Kanneworf, 2009...... support our initial hypothesis that learning can be a facilitator for interest development. This is an argument for focusing more on didactical approaches and learning environments if the goal is to have interested students. As stated by Dewey: “If we can discover a child’s urgent needs and powers...

Testing evolutionary hypotheses for phenotypic divergence using landscape genetics.

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Funk, W Chris; Murphy, Melanie A

2010-02-01

Understanding the evolutionary causes of phenotypic variation among populations has long been a central theme in evolutionary biology. Several factors can influence phenotypic divergence, including geographic isolation, genetic drift, divergent natural or sexual selection, and phenotypic plasticity. But the relative importance of these factors in generating phenotypic divergence in nature is still a tantalizing and unresolved problem in evolutionary biology. The origin and maintenance of phenotypic divergence is also at the root of many ongoing debates in evolutionary biology, such as the extent to which gene flow constrains adaptive divergence (Garant et al. 2007) and the relative importance of genetic drift, natural selection, and sexual selection in initiating reproductive isolation and speciation (Coyne & Orr 2004). In this issue, Wang & Summers (2010) test the causes of one of the most fantastic examples of phenotypic divergence in nature: colour pattern divergence among populations of the strawberry poison frog (Dendrobates pumilio) in Panama and Costa Rica (Fig. 1). This study provides a beautiful example of the use of the emerging field of landscape genetics to differentiate among hypotheses for phenotypic divergence. Using landscape genetic analyses, Wang & Summers were able to reject the hypotheses that colour pattern divergence is due to isolation-by-distance (IBD) or landscape resistance. Instead, the hypothesis left standing is that colour divergence is due to divergent selection, in turn driving reproductive isolation among populations with different colour morphs. More generally, this study provides a wonderful example of how the emerging field of landscape genetics, which has primarily been applied to questions in conservation and ecology, now plays an essential role in evolutionary research.

An Evolutionary Model of the Environmental Conditions that Shape the Development of Prosociality

Directory of Open Access Journals (Sweden)

Daniel Tumminelli O'Brien

2014-04-01

Full Text Available The current review presents a model for how prosocial development is driven by sociocognitive mechanisms that have been shaped by natural selection to translate critical environmental factors into locally adaptive levels of prosociality. This is done through a synthesis of two existing literatures. Evolutionary developmental psychologists have demonstrated a biological basis for the emergence of prosocial behavior early in youth, and work based on social learning theory has explored how social experiences can influence prosociality across development. The model forwarded organizes this latter literature in a way that is specific to how the biological mechanisms underpinning prosociality have evolved. This consists of two main psychological mechanisms. 1 A domain-specific program that is responsive to environmental factors that determine the relative success of different levels of prosociality. It uses the local prevalence of prosocial others (i.e., support and expectations for prosocial behavior (i.e., structure to guide prosocial development. 2 The domain-general process of cultural learning, by which youth adopt local social norms based on the examples of others. Implications and hypotheses are articulated for both the sociocognitive structure of the individual and the role of social contexts.

Testing the cranial evolutionary allometric 'rule' in Galliformes.

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Linde-Medina, M

2016-09-01

Recent comparative studies have indicated the existence of a common cranial evolutionary allometric (CREA) pattern in mammals and birds, in which smaller species have relatively smaller faces and bigger braincases than larger species. In these studies, cranial allometry was tested using a multivariate regression between shape (described using landmarks coordinates) and size (i.e. centroid size), after accounting for phylogenetic relatedness. Alternatively, cranial allometry can be determined by comparing the sizes of two anatomical parts using a bivariate regression analysis. In this analysis, a slope higher or lower than one indicates the existence of positive or negative allometry, respectively. Thus, in those species that support the CREA 'rule', positive allometry is expected for the association between face size and braincase size, which would indicate that larger species have disproportionally larger faces. In this study, I applied these two approaches to explore cranial allometry in 83 Galliformes (Aves, Galloanserae), ranging in mean body weight from 30 g to 2.5 kg. The multivariate regression between shape and centroid size revealed the existence of a significant allometric pattern resembling CREA, whereas the second analysis revealed a negative allometry for beak size and braincase size (i.e. contrary to the CREA 'rule', larger galliform species have disproportionally shorter beaks than smaller galliform species). This study suggests that the presence of CREA may be overestimated when using cranium size as the standard measurement. © 2016 European Society For Evolutionary Biology. Journal of Evolutionary Biology © 2016 European Society For Evolutionary Biology.

Evolutionary institutionalism.

Science.gov (United States)

Fürstenberg, Dr Kai

Institutions are hard to define and hard to study. Long prominent in political science have been two theories: Rational Choice Institutionalism (RCI) and Historical Institutionalism (HI). Arising from the life sciences is now a third: Evolutionary Institutionalism (EI). Comparative strengths and weaknesses of these three theories warrant review, and the value-to-be-added by expanding the third beyond Darwinian evolutionary theory deserves consideration. Should evolutionary institutionalism expand to accommodate new understanding in ecology, such as might apply to the emergence of stability, and in genetics, such as might apply to political behavior? Core arguments are reviewed for each theory with more detailed exposition of the third, EI. Particular attention is paid to EI's gene-institution analogy; to variation, selection, and retention of institutional traits; to endogeneity and exogeneity; to agency and structure; and to ecosystem effects, institutional stability, and empirical limitations in behavioral genetics. RCI, HI, and EI are distinct but complementary. Institutional change, while amenable to rational-choice analysis and, retrospectively, to criticaljuncture and path-dependency analysis, is also, and importantly, ecological. Stability, like change, is an emergent property of institutions, which tend to stabilize after change in a manner analogous to allopatric speciation. EI is more than metaphorically biological in that institutional behaviors are driven by human behaviors whose evolution long preceded the appearance of institutions themselves.

The uncertain foundation of neo-Darwinism: metaphysical and epistemological pluralism in the evolutionary synthesis.

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Delisle, Richard G

2009-06-01

The Evolutionary Synthesis is often seen as a unification process in evolutionary biology, one which provided this research area with a solid common theoretical foundation. As such, neo-Darwinism is believed to constitute from this time onward a single, coherent, and unified movement offering research guidelines for investigations. While this may be true if evolutionary biology is solely understood as centred around evolutionary mechanisms, an entirely different picture emerges once other aspects of the founding neo-Darwinists' views are taken into consideration, aspects potentially relevant to the elaboration of an evolutionary worldview: the tree of life, the ontological distinctions of the main cosmic entities (inert matter, biological organisms, mind), the inherent properties of self-organizing matter, evolutionary ethics, and so on. Profound tensions and inconsistencies are immediately revealed in the neo-Darwinian movement once this broader perspective is adopted. This pluralism is such that it is possible to identify at least three distinct and quasi-incommensurable epistemological/metaphysical frameworks as providing a proper foundation for neo-Darwinism. The analysis of the views of Theodosius Dobzhansky, Bernhard Rensch, and Ernst Mayr will illustrate this untenable pluralism, one which requires us to conceive of the neo-Darwinian research agenda as being conducted in more than one research programme or research tradition at the same time.

The Effect of Using Cooperative Learning Method on Tenth Grade Students' Learning Achievement and Attitude towards Biology

Science.gov (United States)

Rabgay, Tshewang

2018-01-01

The study investigated the effect of using cooperative learning method on tenth grade students' learning achievement in biology and their attitude towards the subject in a Higher Secondary School in Bhutan. The study used a mixed method approach. The quantitative component included an experimental design where cooperative learning was the…

Proteomics in evolutionary ecology.

Science.gov (United States)

Baer, B; Millar, A H

2016-03-01

Evolutionary ecologists are traditionally gene-focused, as genes propagate phenotypic traits across generations and mutations and recombination in the DNA generate genetic diversity required for evolutionary processes. As a consequence, the inheritance of changed DNA provides a molecular explanation for the functional changes associated with natural selection. A direct focus on proteins on the other hand, the actual molecular agents responsible for the expression of a phenotypic trait, receives far less interest from ecologists and evolutionary biologists. This is partially due to the central dogma of molecular biology that appears to define proteins as the 'dead-end of molecular information flow' as well as technical limitations in identifying and studying proteins and their diversity in the field and in many of the more exotic genera often favored in ecological studies. Here we provide an overview of a newly forming field of research that we refer to as 'Evolutionary Proteomics'. We point out that the origins of cellular function are related to the properties of polypeptide and RNA and their interactions with the environment, rather than DNA descent, and that the critical role of horizontal gene transfer in evolution is more about coopting new proteins to impact cellular processes than it is about modifying gene function. Furthermore, post-transcriptional and post-translational processes generate a remarkable diversity of mature proteins from a single gene, and the properties of these mature proteins can also influence inheritance through genetic and perhaps epigenetic mechanisms. The influence of post-transcriptional diversification on evolutionary processes could provide a novel mechanistic underpinning for elements of rapid, directed evolutionary changes and adaptations as observed for a variety of evolutionary processes. Modern state-of the art technologies based on mass spectrometry are now available to identify and quantify peptides, proteins, protein

Evolutionary accounts of human behavioural diversity

Science.gov (United States)

Brown, Gillian R.; Dickins, Thomas E.; Sear, Rebecca; Laland, Kevin N.

2011-01-01

Human beings persist in an extraordinary range of ecological settings, in the process exhibiting enormous behavioural diversity, both within and between populations. People vary in their social, mating and parental behaviour and have diverse and elaborate beliefs, traditions, norms and institutions. The aim of this theme issue is to ask whether, and how, evolutionary theory can help us to understand this diversity. In this introductory article, we provide a background to the debate surrounding how best to understand behavioural diversity using evolutionary models of human behaviour. In particular, we examine how diversity has been viewed by the main subdisciplines within the human evolutionary behavioural sciences, focusing in particular on the human behavioural ecology, evolutionary psychology and cultural evolution approaches. In addition to differences in focus and methodology, these subdisciplines have traditionally varied in the emphasis placed on human universals, ecological factors and socially learned behaviour, and on how they have addressed the issue of genetic variation. We reaffirm that evolutionary theory provides an essential framework for understanding behavioural diversity within and between human populations, but argue that greater integration between the subfields is critical to developing a satisfactory understanding of diversity. PMID:21199836

A New Strategy to Control and Eradicate "Undruggable" Oncogenic K-RAS-Driven Pancreatic Cancer: Molecular Insights and Core Principles Learned from Developmental and Evolutionary Biology.

Science.gov (United States)

Van Sciver, Robert E; Lee, Michael P; Lee, Caroline Dasom; Lafever, Alex C; Svyatova, Elizaveta; Kanda, Kevin; Colliver, Amber L; Siewertsz van Reesema, Lauren L; Tang-Tan, Angela M; Zheleva, Vasilena; Bwayi, Monicah N; Bian, Minglei; Schmidt, Rebecca L; Matrisian, Lynn M; Petersen, Gloria M; Tang, Amy H

2018-05-14

Oncogenic K-RAS mutations are found in virtually all pancreatic cancers, making K-RAS one of the most targeted oncoproteins for drug development in cancer therapies. Despite intense research efforts over the past three decades, oncogenic K-RAS has remained largely "undruggable". Rather than targeting an upstream component of the RAS signaling pathway (i.e., EGFR/HER2) and/or the midstream effector kinases (i.e., RAF/MEK/ERK/PI3K/mTOR), we propose an alternative strategy to control oncogenic K-RAS signal by targeting its most downstream signaling module, Seven-In-Absentia Homolog (SIAH). SIAH E3 ligase controls the signal output of oncogenic K-RAS hyperactivation that drives unchecked cell proliferation, uncontrolled tumor growth, and rapid cancer cell dissemination in human pancreatic cancer. Therefore, SIAH is an ideal therapeutic target as it is an extraordinarily conserved downstream signaling gatekeeper indispensable for proper RAS signaling. Guided by molecular insights and core principles obtained from developmental and evolutionary biology, we propose an anti-SIAH-centered anti-K-RAS strategy as a logical and alternative anticancer strategy to dampen uncontrolled K-RAS hyperactivation and halt tumor growth and metastasis in pancreatic cancer. The clinical utility of developing SIAH as both a tumor-specific and therapy-responsive biomarker, as well as a viable anti-K-RAS drug target, is logically simple and conceptually innovative. SIAH clearly constitutes a major tumor vulnerability and K-RAS signaling bottleneck in pancreatic ductal adenocarcinoma (PDAC). Given the high degree of evolutionary conservation in the K-RAS/SIAH signaling pathway, an anti-SIAH-based anti-PDAC therapy will synergize with covalent K-RAS inhibitors and direct K-RAS targeted initiatives to control and eradicate pancreatic cancer in the future.

Biologically-inspired On-chip Learning in Pulsed Neural Networks

DEFF Research Database (Denmark)

Lehmann, Torsten; Woodburn, Robin

1999-01-01

Self-learning chips to implement many popular ANN (artificial neural network) algorithms are very difficult to design. We explain why this is so and say what lessons previous work teaches us in the design of self-learning systems. We offer a contribution to the "biologically-inspired" approach......, explaining what we mean by this term and providing an example of a robust, self-learning design that can solve simple classical-conditioning tasks, We give details of the design of individual circuits to perform component functions, which can then be combined into a network to solve the task. We argue...

Learning style and concept acquisition of community college students in introductory biology

Science.gov (United States)

Bobick, Sandra Burin

This study investigated the influence of learning style on concept acquisition within a sample of community college students in a general biology course. There are two subproblems within the larger problem: (1) the influence of demographic variables (age, gender, number of college credits, prior exposure to scientific information) on learning style, and (2) the correlations between prior scientific knowledge, learning style and student understanding of the concept of the gene. The sample included all students enrolled in an introductory general biology course during two consecutive semesters at an urban community college. Initial data was gathered during the first week of the semester, at which time students filled in a short questionnaire (age, gender, number of college credits, prior exposure to science information either through reading/visual sources or a prior biology course). Subjects were then given the Inventory of Learning Processes-Revised (ILP-R) which measures general preferences in five learning styles; Deep Learning; Elaborative Learning, Agentic Learning, Methodical Learning and Literal Memorization. Subjects were then given the Gene Conceptual Knowledge pretest: a 15 question objective section and an essay section. Subjects were exposed to specific concepts during lecture and laboratory exercises. At the last lab, students were given the Genetics Conceptual Knowledge Posttest. Pretest/posttest gains were correlated with demographic variables and learning styles were analyzed for significant correlations. Learning styles, as the independent variable in a simultaneous multiple regression, were significant predictors of results on the gene assessment tests, including pretest, posttest and gain. Of the learning styles, Deep Learning accounted for the greatest positive predictive value of pretest essay and pretest objective results. Literal Memorization was a significant negative predictor for posttest essay, essay gain and objective gain. Simultaneous

Applications of Deep Learning and Reinforcement Learning to Biological Data.

Science.gov (United States)

Mahmud, Mufti; Kaiser, Mohammed Shamim; Hussain, Amir; Vassanelli, Stefano

2018-06-01

Rapid advances in hardware-based technologies during the past decades have opened up new possibilities for life scientists to gather multimodal data in various application domains, such as omics, bioimaging, medical imaging, and (brain/body)-machine interfaces. These have generated novel opportunities for development of dedicated data-intensive machine learning techniques. In particular, recent research in deep learning (DL), reinforcement learning (RL), and their combination (deep RL) promise to revolutionize the future of artificial intelligence. The growth in computational power accompanied by faster and increased data storage, and declining computing costs have already allowed scientists in various fields to apply these techniques on data sets that were previously intractable owing to their size and complexity. This paper provides a comprehensive survey on the application of DL, RL, and deep RL techniques in mining biological data. In addition, we compare the performances of DL techniques when applied to different data sets across various application domains. Finally, we outline open issues in this challenging research area and discuss future development perspectives.

Literary study and evolutionary theory : A review essay.

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Carroll, J

1998-09-01

Several recent books have claimed to integrate literary study with evolutionary biology. All of the books here considered, except Robert Storey's, adopt conceptions of evolutionary theory that are in some way marginal to the Darwinian adaptationist program. All the works attempt to connect evolutionary study with various other disciplines or methodologies: for example, with cultural anthropology, cognitive psychology, the psychology of emotion, neurobiology, chaos theory, or structuralist linguistics. No empirical paradigm has yet been established for this field, but important steps have been taken, especially by Storey, in formulating basic principles, identifying appropriate disciplinary connections, and marking out lines of inquiry. Reciprocal efforts are needed from biologists and social scientists.

Can a Multimedia Tool Help Students' Learning Performance in Complex Biology Subjects?

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Koseoglu, Pinar; Efendioglu, Akin

2015-01-01

The aim of the present study was to determine the effects of multimedia-based biology teaching (Mbio) and teacher-centered biology (TCbio) instruction approaches on learners' biology achievements, as well as their views towards learning approaches. During the research process, an experimental design with two groups, TCbio (n = 22) and Mbio (n =…

Structure versus time in the evolutionary diversification of avian carotenoid metabolic networks.

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Morrison, Erin S; Badyaev, Alexander V

2018-05-01

Historical associations of genes and proteins are thought to delineate pathways available to subsequent evolution; however, the effects of past functional involvements on contemporary evolution are rarely quantified. Here, we examined the extent to which the structure of a carotenoid enzymatic network persists in avian evolution. Specifically, we tested whether the evolution of carotenoid networks was most concordant with phylogenetically structured expansion from core reactions of common ancestors or with subsampling of biochemical pathway modules from an ancestral network. We compared structural and historical associations in 467 carotenoid networks of extant and ancestral species and uncovered the overwhelming effect of pre-existing metabolic network structure on carotenoid diversification over the last 50 million years of avian evolution. Over evolutionary time, birds repeatedly subsampled and recombined conserved biochemical modules, which likely maintained the overall structure of the carotenoid metabolic network during avian evolution. These findings explain the recurrent convergence of evolutionary distant species in carotenoid metabolism and weak phylogenetic signal in avian carotenoid evolution. Remarkable retention of an ancient metabolic structure throughout extensive and prolonged ecological diversification in avian carotenoid metabolism illustrates a fundamental requirement of organismal evolution - historical continuity of a deterministic network that links past and present functional associations of its components. © 2018 European Society For Evolutionary Biology. Journal of Evolutionary Biology © 2018 European Society For Evolutionary Biology.

Evolutionary theory of ageing and the problem of correlated Gompertz parameters.

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Burger, Oskar; Missov, Trifon I

2016-11-07

The Gompertz mortality model is often used to evaluate evolutionary theories of ageing, such as the Medawar-Williams' hypothesis that high extrinsic mortality leads to faster ageing. However, fits of the Gompertz mortality model to data often find the opposite result that mortality is negatively correlated with the rate of ageing. This negative correlation has been independently discovered in several taxa and is known in actuarial studies of ageing as the Strehler-Mildvan correlation. We examine the role of mortality selection in determining late-life variation in susceptibility to death, which has been suggested to be the cause of this negative correlation. We demonstrate that fixed-frailty models that account for heterogeneity in frailty do not remove the correlation and that the correlation is an inherent statistical property of the Gompertz distribution. Linking actuarial and biological rates of ageing will continue to be a pressing challenge, but the Strehler-Mildvan correlation itself should not be used to diagnose any biological, physiological, or evolutionary process. These findings resolve some key tensions between theory and data that affect evolutionary and biological studies of ageing and mortality. Tests of evolutionary theories of ageing should include direct measures of physiological performance or condition. Copyright © 2016 Elsevier Ltd. All rights reserved.

Evolutionary Models for Simple Biosystems

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Bagnoli, Franco

The concept of evolutionary development of structures constituted a real revolution in biology: it was possible to understand how the very complex structures of life can arise in an out-of-equilibrium system. The investigation of such systems has shown that indeed, systems under a flux of energy or matter can self-organize into complex patterns, think for instance to Rayleigh-Bernard convection, Liesegang rings, patterns formed by granular systems under shear. Following this line, one could characterize life as a state of matter, characterized by the slow, continuous process that we call evolution. In this paper we try to identify the organizational level of life, that spans several orders of magnitude from the elementary constituents to whole ecosystems. Although similar structures can be found in other contexts like ideas (memes) in neural systems and self-replicating elements (computer viruses, worms, etc.) in computer systems, we shall concentrate on biological evolutionary structure, and try to put into evidence the role and the emergence of network structure in such systems.

MEGA5: Molecular Evolutionary Genetics Analysis Using Maximum Likelihood, Evolutionary Distance, and Maximum Parsimony Methods

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Tamura, Koichiro; Peterson, Daniel; Peterson, Nicholas; Stecher, Glen; Nei, Masatoshi; Kumar, Sudhir

2011-01-01

Comparative analysis of molecular sequence data is essential for reconstructing the evolutionary histories of species and inferring the nature and extent of selective forces shaping the evolution of genes and species. Here, we announce the release of Molecular Evolutionary Genetics Analysis version 5 (MEGA5), which is a user-friendly software for mining online databases, building sequence alignments and phylogenetic trees, and using methods of evolutionary bioinformatics in basic biology, biomedicine, and evolution. The newest addition in MEGA5 is a collection of maximum likelihood (ML) analyses for inferring evolutionary trees, selecting best-fit substitution models (nucleotide or amino acid), inferring ancestral states and sequences (along with probabilities), and estimating evolutionary rates site-by-site. In computer simulation analyses, ML tree inference algorithms in MEGA5 compared favorably with other software packages in terms of computational efficiency and the accuracy of the estimates of phylogenetic trees, substitution parameters, and rate variation among sites. The MEGA user interface has now been enhanced to be activity driven to make it easier for the use of both beginners and experienced scientists. This version of MEGA is intended for the Windows platform, and it has been configured for effective use on Mac OS X and Linux desktops. It is available free of charge from http://www.megasoftware.net. PMID:21546353

The evolutionary implications of epigenetic inheritance.

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Jablonka, Eva

2017-10-06

The Modern Evolutionary Synthesis (MS) forged in the mid-twentieth century was built on a notion of heredity that excluded soft inheritance, the inheritance of the effects of developmental modifications. However, the discovery of molecular mechanisms that generate random and developmentally induced epigenetic variations is leading to a broadening of the notion of biological heredity that has consequences for ideas about evolution. After presenting some old challenges to the MS that were raised, among others, by Karl Popper, I discuss recent research on epigenetic inheritance, which provides experimental and theoretical support for these challenges. There is now good evidence that epigenetic inheritance is ubiquitous and is involved in adaptive evolution and macroevolution. I argue that the many evolutionary consequences of epigenetic inheritance open up new research areas and require the extension of the evolutionary synthesis beyond the current neo-Darwinian model.

Visual Literacy Skills of Students in College-Level Biology: Learning Outcomes Following Digital or Hand-Drawing Activities

Science.gov (United States)

Bell, Justine C.

2014-01-01

To test the claim that digital learning tools enhance the acquisition of visual literacy in this generation of biology students, a learning intervention was carried out with 33 students enrolled in an introductory college biology course. This study compared learning outcomes following two types of learning tools: a traditional drawing activity, or…

Evolutionary Aesthetics and Print Advertising

Directory of Open Access Journals (Sweden)

Kamil Luczaj

2015-06-01

Full Text Available The article analyzes the extent to which predictions based on the theory of evolutionary aesthetics are utilized by the advertising industry. The purpose of a comprehensive content analysis of print advertising is to determine whether the items indicated by evolutionists such as animals, flowers, certain types of landscapes, beautiful humans, and some colors are part of real advertising strategies. This article has shown that many evolutionary hypotheses (although not all of them are supported by empirical data. Along with these hypotheses, some inferences from Bourdieu’s cultural capital theory were tested. It turned out that advertising uses both biological schemata and cultural patterns to make an image more likable.

The Colorado Learning Attitudes about Science Survey (CLASS) for Use in Biology

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Semsar, Katharine; Knight, Jennifer K.; Birol, Gülnur; Smith, Michelle K.

2011-01-01

This paper describes a newly adapted instrument for measuring novice-to-expert-like perceptions about biology: the Colorado Learning Attitudes about Science Survey for Biology (CLASS-Bio). Consisting of 31 Likert-scale statements, CLASS-Bio probes a range of perceptions that vary between experts and novices, including enjoyment of the discipline, propensity to make connections to the real world, recognition of conceptual connections underlying knowledge, and problem-solving strategies. CLASS-Bio has been tested for response validity with both undergraduate students and experts (biology PhDs), allowing student responses to be directly compared with a consensus expert response. Use of CLASS-Bio to date suggests that introductory biology courses have the same challenges as introductory physics and chemistry courses: namely, students shift toward more novice-like perceptions following instruction. However, students in upper-division biology courses do not show the same novice-like shifts. CLASS-Bio can also be paired with other assessments to: 1) examine how student perceptions impact learning and conceptual understanding of biology, and 2) assess and evaluate how pedagogical techniques help students develop both expertise in problem solving and an expert-like appreciation of the nature of biology. PMID:21885823

The Colorado Learning Attitudes about Science Survey (CLASS) for use in Biology.

Science.gov (United States)

Semsar, Katharine; Knight, Jennifer K; Birol, Gülnur; Smith, Michelle K

2011-01-01

This paper describes a newly adapted instrument for measuring novice-to-expert-like perceptions about biology: the Colorado Learning Attitudes about Science Survey for Biology (CLASS-Bio). Consisting of 31 Likert-scale statements, CLASS-Bio probes a range of perceptions that vary between experts and novices, including enjoyment of the discipline, propensity to make connections to the real world, recognition of conceptual connections underlying knowledge, and problem-solving strategies. CLASS-Bio has been tested for response validity with both undergraduate students and experts (biology PhDs), allowing student responses to be directly compared with a consensus expert response. Use of CLASS-Bio to date suggests that introductory biology courses have the same challenges as introductory physics and chemistry courses: namely, students shift toward more novice-like perceptions following instruction. However, students in upper-division biology courses do not show the same novice-like shifts. CLASS-Bio can also be paired with other assessments to: 1) examine how student perceptions impact learning and conceptual understanding of biology, and 2) assess and evaluate how pedagogical techniques help students develop both expertise in problem solving and an expert-like appreciation of the nature of biology.

Active Learning Outside the Classroom: Implementation and Outcomes of Peer-Led Team-Learning Workshops in Introductory Biology

OpenAIRE

Kudish, Philip; Shores, Robin; McClung, Alex; Smulyan, Lisa; Vallen, Elizabeth A.; Siwicki, Kathleen K.

2016-01-01

Study group meetings (SGMs) are voluntary-attendance peer-led team-learning workshops that supplement introductory biology lectures at a selective liberal arts college. While supporting all students? engagement with lecture material, specific aims are to improve the success of underrepresented minority (URM) students and those with weaker backgrounds in biology. Peer leaders with experience in biology courses and training in science pedagogy facilitate work on faculty-generated challenge prob...

The Predictive Power of Evolutionary Biology and the Discovery of Eusociality in the Naked Mole-Rat.

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Braude, Stanton

1997-01-01

Discusses how biologists use evolutionary theory and provides examples of how evolutionary biologists test hypotheses on specific modes of selection and evolution. Presents an example of the successful predictive power of one evolutionary hypothesis. Contains 38 references. (DDR)

A teleofunctional account of evolutionary mismatch.

Science.gov (United States)

Cofnas, Nathan

When the environment in which an organism lives deviates in some essential way from that to which it is adapted, this is described as "evolutionary mismatch," or "evolutionary novelty." The notion of mismatch plays an important role, explicitly or implicitly, in evolution-informed cognitive psychology, clinical psychology, and medicine. The evolutionary novelty of our contemporary environment is thought to have significant implications for our health and well-being. However, scientists have generally been working without a clear definition of mismatch. This paper defines mismatch as deviations in the environment that render biological traits unable, or impaired in their ability, to produce their selected effects (i.e., to perform their proper functions in Neander's sense). The machinery developed by Millikan in connection with her account of proper function, and with her related teleosemantic account of representation, is used to identify four major types, and several subtypes, of evolutionary mismatch. While the taxonomy offered here does not in itself resolve any scientific debates, the hope is that it can be used to better formulate empirical hypotheses concerning the effects of mismatch. To illustrate, it is used to show that the controversial hypothesis that general intelligence evolved as an adaptation to handle evolutionary novelty can, contra some critics, be formulated in a conceptually coherent way.

Research and Teaching: Instructor Use of Group Active Learning in an Introductory Biology Sequence

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Auerbach, Anna Jo; Schussler, Elisabeth E.

2016-01-01

Active learning (or learner-centered) pedagogies have been shown to enhance student learning in introductory biology courses. Student collaboration has also been shown to enhance student learning and may be a critical part of effective active learning practices. This study focused on documenting the use of individual active learning and group…

An Evolutionary Approach to Driving Tendency Recognition for Advanced Driver Assistance Systems

Directory of Open Access Journals (Sweden)

Lee Jong-Hyun

2016-01-01

Full Text Available Driving tendency recognition is important for constructing Advanced Driver Assistance Systems (ADAS. However, it had not been a lot of research using vehicle sensing data, due to the high difficulty to define it. In this paper, we attempt to improve the learning capability of a machine learning method using evolutionary computation. We propose a driving tendency recognition method, with consideration of data characteristics. Comparison of our classification system with conventional methods demonstrated the effectiveness and accuracy over 92% in our system. Our proposed evolutionary approach is confirmed that improve the classification accuracy of the learning method through evolution in the experiment.

Comparative systems biology across an evolutionary gradient within the Shewanella genus.

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Konstantinidis, Konstantinos T; Serres, Margrethe H; Romine, Margaret F; Rodrigues, Jorge L M; Auchtung, Jennifer; McCue, Lee-Ann; Lipton, Mary S; Obraztsova, Anna; Giometti, Carol S; Nealson, Kenneth H; Fredrickson, James K; Tiedje, James M

2009-09-15

To what extent genotypic differences translate to phenotypic variation remains a poorly understood issue of paramount importance for several cornerstone concepts of microbiology including the species definition. Here, we take advantage of the completed genomic sequences, expressed proteomic profiles, and physiological studies of 10 closely related Shewanella strains and species to provide quantitative insights into this issue. Our analyses revealed that, despite extensive horizontal gene transfer within these genomes, the genotypic and phenotypic similarities among the organisms were generally predictable from their evolutionary relatedness. The power of the predictions depended on the degree of ecological specialization of the organisms evaluated. Using the gradient of evolutionary relatedness formed by these genomes, we were able to partly isolate the effect of ecology from that of evolutionary divergence and to rank the different cellular functions in terms of their rates of evolution. Our ranking also revealed that whole-cell protein expression differences among these organisms, when the organisms were grown under identical conditions, were relatively larger than differences at the genome level, suggesting that similarity in gene regulation and expression should constitute another important parameter for (new) species description. Collectively, our results provide important new information toward beginning a systems-level understanding of bacterial species and genera.

A review on machine learning principles for multi-view biological data integration.

Science.gov (United States)

Li, Yifeng; Wu, Fang-Xiang; Ngom, Alioune

2018-03-01

Driven by high-throughput sequencing techniques, modern genomic and clinical studies are in a strong need of integrative machine learning models for better use of vast volumes of heterogeneous information in the deep understanding of biological systems and the development of predictive models. How data from multiple sources (called multi-view data) are incorporated in a learning system is a key step for successful analysis. In this article, we provide a comprehensive review on omics and clinical data integration techniques, from a machine learning perspective, for various analyses such as prediction, clustering, dimension reduction and association. We shall show that Bayesian models are able to use prior information and model measurements with various distributions; tree-based methods can either build a tree with all features or collectively make a final decision based on trees learned from each view; kernel methods fuse the similarity matrices learned from individual views together for a final similarity matrix or learning model; network-based fusion methods are capable of inferring direct and indirect associations in a heterogeneous network; matrix factorization models have potential to learn interactions among features from different views; and a range of deep neural networks can be integrated in multi-modal learning for capturing the complex mechanism of biological systems.

Skill learning and the evolution of social learning mechanisms.

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van der Post, Daniel J; Franz, Mathias; Laland, Kevin N

2016-08-24

Social learning is potentially advantageous, but evolutionary theory predicts that (i) its benefits may be self-limiting because social learning can lead to information parasitism, and (ii) these limitations can be mitigated via forms of selective copying. However, these findings arise from a functional approach in which learning mechanisms are not specified, and which assumes that social learning avoids the costs of asocial learning but does not produce information about the environment. Whether these findings generalize to all kinds of social learning remains to be established. Using a detailed multi-scale evolutionary model, we investigate the payoffs and information production processes of specific social learning mechanisms (including local enhancement, stimulus enhancement and observational learning) and their evolutionary consequences in the context of skill learning in foraging groups. We find that local enhancement does not benefit foraging success, but could evolve as a side-effect of grouping. In contrast, stimulus enhancement and observational learning can be beneficial across a wide range of environmental conditions because they generate opportunities for new learning outcomes. In contrast to much existing theory, we find that the functional outcomes of social learning are mechanism specific. Social learning nearly always produces information about the environment, and does not always avoid the costs of asocial learning or support information parasitism. Our study supports work emphasizing the value of incorporating mechanistic detail in functional analyses.

Women, behavior, and evolution: understanding the debate between feminist evolutionists and evolutionary psychologists.

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Liesen, Laurette T

2007-03-01

Often since the early 1990s, feminist evolutionists have criticized evolutionary psychologists, finding fault in their analyses of human male and female reproductive behavior. Feminist evolutionists have criticized various evolutionary psychologists for perpetuating gender stereotypes, using questionable methodology, and exhibiting a chill toward feminism. Though these criticisms have been raised many times, the conflict itself has not been fully analyzed. Therefore, I reconsider this conflict, both in its origins and its implications. I find that the approaches and perspectives of feminist evolutionists and evolutionary psychologists are distinctly different, leading many of the former to work in behavioral ecology, primatology, and evolutionary biology. Invitingly to feminist evolutionists, these three fields emphasize social behavior and the influences of environmental variables; in contrast, evolutionary psychology has come to rely on assumptions deemphasizing the pliability of psychological mechanisms and the flexibility of human behavior. In behavioral ecology, primatology, and evolutionary biology, feminist evolutionists have found old biases easy to correct and new hypotheses practical to test, offering new insights into male and female behavior, explaining the emergence and persistence of patriarchy, and potentially bringing closer a prime feminist goal, sexual equality.

An Examination of Science High School Students' Motivation towards Learning Biology and Their Attitude towards Biology Lessons

Science.gov (United States)

Kisoglu, Mustafa

2018-01-01

The purpose of this study is to examine motivation of science high school students towards learning biology and their attitude towards biology lessons. The sample of the study consists of 564 high school students (308 females, 256 males) studying at two science high schools in Aksaray, Turkey. In the study, the relational scanning method, which is…

Strengths and Weaknesses of McNamara's Evolutionary Psychological Model of Dreaming

Directory of Open Access Journals (Sweden)

Sandra Olliges

2010-10-01

Full Text Available This article includes a brief overview of McNamara's (2004 evolutionary model of dreaming. The strengths and weaknesses of this model are then evaluated in terms of its consonance with measurable neurological and biological properties of dreaming, its fit within the tenets of evolutionary theories of dreams, and its alignment with evolutionary concepts of cooperation and spirituality. McNamara's model focuses primarily on dreaming that occurs during rapid eye movement (REM sleep; therefore this article also focuses on REM dreaming.

Impact of e-AV Biology Website for Learning about Renewable Energy

Science.gov (United States)

Nugraini, Siti Hadiati; Choo, Koo Ah; Hin, Hew Soon; Hoon, Teoh Sian

2013-01-01

This paper considers the design and development of a Website for Biology in senior high schools in Indonesia. The teaching media, namely e-AV Biology, was developed with the main features of video lessons and other features in supporting the students' learning process. Some video lessons describe the production process of Biofuel or Renewable…

Experimental evolution reveals differences between phenotypic and evolutionary responses to population density.

Science.gov (United States)

McNamara, K B; Simmons, L W

2017-09-01

Group living can select for increased immunity, given the heightened risk of parasite transmission. Yet, it also may select for increased male reproductive investment, given the elevated risk of female multiple mating. Trade-offs between immunity and reproduction are well documented. Phenotypically, population density mediates both reproductive investment and immune function in the Indian meal moth, Plodia interpunctella. However, the evolutionary response of populations to these traits is unknown. We created two replicated populations of P. interpunctella, reared and mated for 14 generations under high or low population densities. These population densities cause plastic responses in immunity and reproduction: at higher numbers, both sexes invest more in one index of immunity [phenoloxidase (PO) activity] and males invest more in sperm. Interestingly, our data revealed divergence in PO and reproduction in a different direction to previously reported phenotypic responses. Males evolving at low population densities transferred more sperm, and both males and females displayed higher PO than individuals at high population densities. These positively correlated responses to selection suggest no apparent evolutionary trade-off between immunity and reproduction. We speculate that the reduced PO activity and sperm investment when evolving under high population density may be due to the reduced population fitness predicted under increased sexual conflict and/or to trade-offs between pre- and post-copulatory traits. © 2017 European Society For Evolutionary Biology. Journal of Evolutionary Biology © 2017 European Society For Evolutionary Biology.

Evolutionary game theory for physical and biological scientists. I. Training and validating population dynamics equations.

Science.gov (United States)

Liao, David; Tlsty, Thea D

2014-08-06

Failure to understand evolutionary dynamics has been hypothesized as limiting our ability to control biological systems. An increasing awareness of similarities between macroscopic ecosystems and cellular tissues has inspired optimism that game theory will provide insights into the progression and control of cancer. To realize this potential, the ability to compare game theoretic models and experimental measurements of population dynamics should be broadly disseminated. In this tutorial, we present an analysis method that can be used to train parameters in game theoretic dynamics equations, used to validate the resulting equations, and used to make predictions to challenge these equations and to design treatment strategies. The data analysis techniques in this tutorial are adapted from the analysis of reaction kinetics using the method of initial rates taught in undergraduate general chemistry courses. Reliance on computer programming is avoided to encourage the adoption of these methods as routine bench activities.

Cultural and climatic changes shape the evolutionary history of the Uralic languages.

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Honkola, T; Vesakoski, O; Korhonen, K; Lehtinen, J; Syrjänen, K; Wahlberg, N

2013-06-01

Quantitative phylogenetic methods have been used to study the evolutionary relationships and divergence times of biological species, and recently, these have also been applied to linguistic data to elucidate the evolutionary history of language families. In biology, the factors driving macroevolutionary processes are assumed to be either mainly biotic (the Red Queen model) or mainly abiotic (the Court Jester model) or a combination of both. The applicability of these models is assumed to depend on the temporal and spatial scale observed as biotic factors act on species divergence faster and in smaller spatial scale than the abiotic factors. Here, we used the Uralic language family to investigate whether both 'biotic' interactions (i.e. cultural interactions) and abiotic changes (i.e. climatic fluctuations) are also connected to language diversification. We estimated the times of divergence using Bayesian phylogenetics with a relaxed-clock method and related our results to climatic, historical and archaeological information. Our timing results paralleled the previous linguistic studies but suggested a later divergence of Finno-Ugric, Finnic and Saami languages. Some of the divergences co-occurred with climatic fluctuation and some with cultural interaction and migrations of populations. Thus, we suggest that both 'biotic' and abiotic factors contribute either directly or indirectly to the diversification of languages and that both models can be applied when studying language evolution. © 2013 The Authors. Journal of Evolutionary Biology © 2013 European Society For Evolutionary Biology.

Evolutionary game theory and social learning can determine how vaccine scares unfold.

Science.gov (United States)

Bauch, Chris T; Bhattacharyya, Samit

2012-01-01

Immunization programs have often been impeded by vaccine scares, as evidenced by the measles-mumps-rubella (MMR) autism vaccine scare in Britain. A "free rider" effect may be partly responsible: vaccine-generated herd immunity can reduce disease incidence to such low levels that real or imagined vaccine risks appear large in comparison, causing individuals to cease vaccinating. This implies a feedback loop between disease prevalence and strategic individual vaccinating behavior. Here, we analyze a model based on evolutionary game theory that captures this feedback in the context of vaccine scares, and that also includes social learning. Vaccine risk perception evolves over time according to an exogenously imposed curve. We test the model against vaccine coverage data and disease incidence data from two vaccine scares in England & Wales: the whole cell pertussis vaccine scare and the MMR vaccine scare. The model fits vaccine coverage data from both vaccine scares relatively well. Moreover, the model can explain the vaccine coverage data more parsimoniously than most competing models without social learning and/or feedback (hence, adding social learning and feedback to a vaccine scare model improves model fit with little or no parsimony penalty). Under some circumstances, the model can predict future vaccine coverage and disease incidence--up to 10 years in advance in the case of pertussis--including specific qualitative features of the dynamics, such as future incidence peaks and undulations in vaccine coverage due to the population's response to changing disease incidence. Vaccine scares could become more common as eradication goals are approached for more vaccine-preventable diseases. Such models could help us predict how vaccine scares might unfold and assist mitigation efforts.

Draft genome of the medaka fish: a comprehensive resource for medaka developmental genetics and vertebrate evolutionary biology.

Science.gov (United States)

Takeda, Hiroyuki

2008-06-01

The medaka Oryzias latipes is a small egg-laying freshwater teleost, and has become an excellent model system for developmental genetics and evolutionary biology. The medaka genome is relatively small in size, approximately 800 Mb, and the genome sequencing project was recently completed by Japanese research groups, providing a high-quality draft genome sequence of the inbred Hd-rR strain of medaka. In this review, I present an overview of the medaka genome project including genome resources, followed by specific findings obtained with the medaka draft genome. In particular, I focus on the analysis that was done by taking advantage of the medaka system, such as the sex chromosome differentiation and the regional history of medaka species using single nucleotide polymorphisms as genomic markers.

Oxytocin mediated behavior in invertebrates: An evolutionary perspective.

Science.gov (United States)

Lockard, Meghan A; Ebert, Margaret S; Bargmann, Cornelia I

2017-02-01

The molecular and functional conservation of oxytocin-related neuropeptides in behavior is striking. In animals separated by at least 600 million years of evolution, from roundworms to humans, oxytocin homologs play critical roles in the modulation of reproductive behavior and other biological functions. Here, we review the roles of oxytocin in invertebrate behavior from an evolutionary perspective. We begin by tracing the evolution of oxytocin through the invertebrate animal lineages, and then describe common themes in invertebrate behaviors that are mediated by oxytocin-related peptides, including reproductive behavior, learning and memory, food arousal, and predator/prey relationships. Finally, we discuss interesting future directions that have recently become experimentally tractable. Studying oxytocin in invertebrates offers precise insights into the activity of neuropeptides on well-defined neural circuits; the principles that emerge may also be represented in the more complex vertebrate brain. © 2016 Wiley Periodicals, Inc. Develop Neurobiol 77: 128-142, 2017. © 2016 Wiley Periodicals, Inc.

Measuring the evolutionary rewiring of biological networks.

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Chong Shou

Full Text Available We have accumulated a large amount of biological network data and expect even more to come. Soon, we anticipate being able to compare many different biological networks as we commonly do for molecular sequences. It has long been believed that many of these networks change, or "rewire", at different rates. It is therefore important to develop a framework to quantify the differences between networks in a unified fashion. We developed such a formalism based on analogy to simple models of sequence evolution, and used it to conduct a systematic study of network rewiring on all the currently available biological networks. We found that, similar to sequences, biological networks show a decreased rate of change at large time divergences, because of saturation in potential substitutions. However, different types of biological networks consistently rewire at different rates. Using comparative genomics and proteomics data, we found a consistent ordering of the rewiring rates: transcription regulatory, phosphorylation regulatory, genetic interaction, miRNA regulatory, protein interaction, and metabolic pathway network, from fast to slow. This ordering was found in all comparisons we did of matched networks between organisms. To gain further intuition on network rewiring, we compared our observed rewirings with those obtained from simulation. We also investigated how readily our formalism could be mapped to other network contexts; in particular, we showed how it could be applied to analyze changes in a range of "commonplace" networks such as family trees, co-authorships and linux-kernel function dependencies.

Unified Deep Learning Architecture for Modeling Biology Sequence.

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Wu, Hongjie; Cao, Chengyuan; Xia, Xiaoyan; Lu, Qiang

2017-10-09

Prediction of the spatial structure or function of biological macromolecules based on their sequence remains an important challenge in bioinformatics. When modeling biological sequences using traditional sequencing models, characteristics, such as long-range interactions between basic units, the complicated and variable output of labeled structures, and the variable length of biological sequences, usually lead to different solutions on a case-by-case basis. This study proposed the use of bidirectional recurrent neural networks based on long short-term memory or a gated recurrent unit to capture long-range interactions by designing the optional reshape operator to adapt to the diversity of the output labels and implementing a training algorithm to support the training of sequence models capable of processing variable-length sequences. Additionally, the merge and pooling operators enhanced the ability to capture short-range interactions between basic units of biological sequences. The proposed deep-learning model and its training algorithm might be capable of solving currently known biological sequence-modeling problems through the use of a unified framework. We validated our model on one of the most difficult biological sequence-modeling problems currently known, with our results indicating the ability of the model to obtain predictions of protein residue interactions that exceeded the accuracy of current popular approaches by 10% based on multiple benchmarks.

Evidence Combination From an Evolutionary Game Theory Perspective.

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Deng, Xinyang; Han, Deqiang; Dezert, Jean; Deng, Yong; Shyr, Yu

2016-09-01

Dempster-Shafer evidence theory is a primary methodology for multisource information fusion because it is good at dealing with uncertain information. This theory provides a Dempster's rule of combination to synthesize multiple evidences from various information sources. However, in some cases, counter-intuitive results may be obtained based on that combination rule. Numerous new or improved methods have been proposed to suppress these counter-intuitive results based on perspectives, such as minimizing the information loss or deviation. Inspired by evolutionary game theory, this paper considers a biological and evolutionary perspective to study the combination of evidences. An evolutionary combination rule (ECR) is proposed to help find the most biologically supported proposition in a multievidence system. Within the proposed ECR, we develop a Jaccard matrix game to formalize the interaction between propositions in evidences, and utilize the replicator dynamics to mimick the evolution of propositions. Experimental results show that the proposed ECR can effectively suppress the counter-intuitive behaviors appeared in typical paradoxes of evidence theory, compared with many existing methods. Properties of the ECR, such as solution's stability and convergence, have been mathematically proved as well.

Evolution in Mind: Evolutionary Dynamics, Cognitive Processes, and Bayesian Inference.

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Suchow, Jordan W; Bourgin, David D; Griffiths, Thomas L

2017-07-01

Evolutionary theory describes the dynamics of population change in settings affected by reproduction, selection, mutation, and drift. In the context of human cognition, evolutionary theory is most often invoked to explain the origins of capacities such as language, metacognition, and spatial reasoning, framing them as functional adaptations to an ancestral environment. However, evolutionary theory is useful for understanding the mind in a second way: as a mathematical framework for describing evolving populations of thoughts, ideas, and memories within a single mind. In fact, deep correspondences exist between the mathematics of evolution and of learning, with perhaps the deepest being an equivalence between certain evolutionary dynamics and Bayesian inference. This equivalence permits reinterpretation of evolutionary processes as algorithms for Bayesian inference and has relevance for understanding diverse cognitive capacities, including memory and creativity. Copyright © 2017 Elsevier Ltd. All rights reserved.

Bridging developmental systems theory and evolutionary psychology using dynamic optimization.

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Frankenhuis, Willem E; Panchanathan, Karthik; Clark Barrett, H

2013-07-01

Interactions between evolutionary psychologists and developmental systems theorists have been largely antagonistic. This is unfortunate because potential synergies between the two approaches remain unexplored. This article presents a method that may help to bridge the divide, and that has proven fruitful in biology: dynamic optimization. Dynamic optimization integrates developmental systems theorists' focus on dynamics and contingency with the 'design stance' of evolutionary psychology. It provides a theoretical framework as well as a set of tools for exploring the properties of developmental systems that natural selection might favor, given particular evolutionary ecologies. We also discuss limitations of the approach. © 2013 Blackwell Publishing Ltd.

Selfish genetic elements, genetic conflict, and evolutionary innovation.

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Werren, John H

2011-06-28

Genomes are vulnerable to selfish genetic elements (SGEs), which enhance their own transmission relative to the rest of an individual's genome but are neutral or harmful to the individual as a whole. As a result, genetic conflict occurs between SGEs and other genetic elements in the genome. There is growing evidence that SGEs, and the resulting genetic conflict, are an important motor for evolutionary change and innovation. In this review, the kinds of SGEs and their evolutionary consequences are described, including how these elements shape basic biological features, such as genome structure and gene regulation, evolution of new genes, origin of new species, and mechanisms of sex determination and development. The dynamics of SGEs are also considered, including possible "evolutionary functions" of SGEs.

Support Vector Machines Trained with Evolutionary Algorithms Employing Kernel Adatron for Large Scale Classification of Protein Structures.

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Arana-Daniel, Nancy; Gallegos, Alberto A; López-Franco, Carlos; Alanís, Alma Y; Morales, Jacob; López-Franco, Adriana

2016-01-01

With the increasing power of computers, the amount of data that can be processed in small periods of time has grown exponentially, as has the importance of classifying large-scale data efficiently. Support vector machines have shown good results classifying large amounts of high-dimensional data, such as data generated by protein structure prediction, spam recognition, medical diagnosis, optical character recognition and text classification, etc. Most state of the art approaches for large-scale learning use traditional optimization methods, such as quadratic programming or gradient descent, which makes the use of evolutionary algorithms for training support vector machines an area to be explored. The present paper proposes an approach that is simple to implement based on evolutionary algorithms and Kernel-Adatron for solving large-scale classification problems, focusing on protein structure prediction. The functional properties of proteins depend upon their three-dimensional structures. Knowing the structures of proteins is crucial for biology and can lead to improvements in areas such as medicine, agriculture and biofuels.

Biologically Predisposed Learning and Selective Associations in Amygdalar Neurons

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Chung, Ain; Barot, Sabiha K.; Kim, Jeansok J.; Bernstein, Ilene L.

2011-01-01

Modern views on learning and memory accept the notion of biological constraints--that the formation of association is not uniform across all stimuli. Yet cellular evidence of the encoding of selective associations is lacking. Here, conditioned stimuli (CSs) and unconditioned stimuli (USs) commonly employed in two basic associative learning…

The social production of health: critical contributions from evolutionary, biological, and cultural anthropology.

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Levin, Betty Wolder; Browner, C H

2005-08-01

In 1946, the newly formed World Health Organization boldly sought to conceptualize "health" as wellbeing in the positive sense, "not merely the absence of disease or infirmity." Yet nearly six decades later, researchers are still principally concerned with pathology and its characteristics and consequences. This special issue is the result of an effort to broaden the focus. Anthropologists working from evolutionary, biological and sociocultural perspectives and in diverse geographic regions were asked to examine meanings associated with health and/or to identify social conditions and practices that have contributed to positive physiological and psychological states in particular cultures, times, or across time. Most notable, perhaps, was discovering how difficult it is for Western social scientists to move beyond pathology-based thinking; most authors represented here regard health primarily as the absence of disease. Still, these papers articulate and address questions key to understanding health in and of itself, including: How is health conceptualized? What kinds of social conditions lead to health? And, how do social inequalities affect health? This introduction critically discusses previous work on the subject to contextualize the original research papers offered here.

Learning-style preferences of Latino/Hispanic community college students enrolled in an introductory biology course

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Sarantopoulos, Helen D.

Purpose. The purpose of this study was to identify, according to the Productivity Environment Preference Survey (PEPS) instrument, which learning-style domains (environmental, emotional, sociological, and physiological) were favored among Latino/Hispanic community college students enrolled in introductory biology classes in a large, urban community college. An additional purpose of this study was to determine whether statistically significant differences existed between the learning-style preferences and the demographic variables of age, gender, number of prior science courses, second language learner status, and earlier exposure to scientific information. Methodology. The study design was descriptive and ex post facto. The sample consisted of a total of 332 Latino/Hispanic students enrolled in General Biology 3. Major findings. The study revealed that Latino/Hispanic students enrolled in introductory biology at a large urban community college scored higher for the learning preference element of structure. Students twenty-five years and older scored higher for the learning preference elements of light, design, persistence, responsibility, and morning time (p learning-style preferences were found between second English language learners and those who learned English as their primary language (p tactile (p learning-style model and instruments and on recent learning-style research articles on ethnically diverse groups of adult learners; and (2) Instructors should plan their instruction to incorporate the learning-style preferences of their students.

Human compulsivity: A perspective from evolutionary medicine.

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Stein, Dan J; Hermesh, Haggai; Eilam, David; Segalas, Cosi; Zohar, Joseph; Menchon, Jose; Nesse, Randolph M

2016-05-01

Biological explanations address not only proximal mechanisms (for example, the underlying neurobiology of obsessive-compulsive disorder), but also distal mechanisms (that is, a consideration of how particular neurobiological mechanisms evolved). Evolutionary medicine has emphasized a series of explanations for vulnerability to disease, including constraints, mismatch, and tradeoffs. The current paper will consider compulsive symptoms in obsessive-compulsive and related disorders and behavioral addictions from this evolutionary perspective. It will argue that while obsessive-compulsive disorder (OCD) is typically best conceptualized as a dysfunction, it is theoretically and clinically valuable to understand some symptoms of obsessive-compulsive and related disorders in terms of useful defenses. The symptoms of behavioral addictions can also be conceptualized in evolutionary terms (for example, mismatch), which in turn provides a sound foundation for approaching assessment and intervention. Copyright © 2016. Published by Elsevier B.V.

2004 Structural, Function and Evolutionary Genomics

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Douglas L. Brutlag Nancy Ryan Gray

2005-03-23

This Gordon conference will cover the areas of structural, functional and evolutionary genomics. It will take a systematic approach to genomics, examining the evolution of proteins, protein functional sites, protein-protein interactions, regulatory networks, and metabolic networks. Emphasis will be placed on what we can learn from comparative genomics and entire genomes and proteomes.

Altruism, egoism, or neither: A cognitive-efficiency-based evolutionary biological perspective on helping behavior.

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Schulz, Armin W

2016-04-01

I argue for differences in the cognitive efficiency of different psychologies underlying helping behavior, and present an account of the adaptive pressures that result from these differences. Specifically, I argue that organisms often face pressure to move away from only being egoistically motivated to help: non-egoistic organisms are often able to determine how to help other organisms more quickly and with less recourse to costly cognitive resources like concentration and attention. Furthermore, I also argue that, while these pressures away from pure egoism can lead to the evolution of altruists, they can also lead to the evolution of reciprocation-focused behaviorist helpers or even of reflex-driven helpers (who are neither altruists nor egoists). In this way, I seek to broaden the set of considerations typically taken into account when assessing the evolution of the psychology of helping behavior-which tend to be restricted to matters of reliability-and also try to make clearer the role of evolutionary biological considerations in the discussion of this apparently straightforwardly psychological phenomenon. Copyright © 2015 Elsevier Ltd. All rights reserved.

Evolutionary Computation and Its Applications in Neural and Fuzzy Systems

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Biaobiao Zhang

2011-01-01

Full Text Available Neural networks and fuzzy systems are two soft-computing paradigms for system modelling. Adapting a neural or fuzzy system requires to solve two optimization problems: structural optimization and parametric optimization. Structural optimization is a discrete optimization problem which is very hard to solve using conventional optimization techniques. Parametric optimization can be solved using conventional optimization techniques, but the solution may be easily trapped at a bad local optimum. Evolutionary computation is a general-purpose stochastic global optimization approach under the universally accepted neo-Darwinian paradigm, which is a combination of the classical Darwinian evolutionary theory, the selectionism of Weismann, and the genetics of Mendel. Evolutionary algorithms are a major approach to adaptation and optimization. In this paper, we first introduce evolutionary algorithms with emphasis on genetic algorithms and evolutionary strategies. Other evolutionary algorithms such as genetic programming, evolutionary programming, particle swarm optimization, immune algorithm, and ant colony optimization are also described. Some topics pertaining to evolutionary algorithms are also discussed, and a comparison between evolutionary algorithms and simulated annealing is made. Finally, the application of EAs to the learning of neural networks as well as to the structural and parametric adaptations of fuzzy systems is also detailed.

Evolutionary ethics from Darwin to Moore.

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Allhoff, Fritz

2003-01-01

Evolutionary ethics has a long history, dating all the way back to Charles Darwin. Almost immediately after the publication of the Origin, an immense interest arose in the moral implications of Darwinism and whether the truth of Darwinism would undermine traditional ethics. Though the biological thesis was certainly exciting, nobody suspected that the impact of the Origin would be confined to the scientific arena. As one historian wrote, 'whether or not ancient populations of armadillos were transformed into the species that currently inhabit the new world was certainly a topic about which zoologists could disagree. But it was in discussing the broader implications of the theory...that tempers flared and statements were made which could transform what otherwise would have been a quiet scholarly meeting into a social scandal' (Farber 1994, 22). Some resistance to the biological thesis of Darwinism sprung from the thought that it was incompatible with traditional morality and, since one of them had to go, many thought that Darwinism should be rejected. However, some people did realize that a secular ethics was possible so, even if Darwinism did undermine traditional religious beliefs, it need not have any effects on moral thought. Before I begin my discussion of evolutionary ethics from Darwin to Moore, I would like to make some more general remarks about its development. There are three key events during this history of evolutionary ethics. First, Charles Darwin published On the Origin of the Species (Darwin 1859). Since one did not have a fully developed theory of evolution until 1859, there exists little work on evolutionary ethics until then. Shortly thereafter, Herbert Spencer (1898) penned the first systematic theory of evolutionary ethics, which was promptly attacked by T.H. Huxley (Huxley 1894). Second, at about the turn of the century, moral philosophers entered the fray and attempted to demonstrate logical errors in Spencer's work; such errors were alluded

Introduction to the Symposium "Leading Students and Faculty to Quantitative Biology through Active Learning".

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Waldrop, Lindsay D; Miller, Laura A

2015-11-01

The broad aim of this symposium and set of associated papers is to motivate the use of inquiry-based, active-learning teaching techniques in undergraduate quantitative biology courses. Practical information, resources, and ready-to-use classroom exercises relevant to physicists, mathematicians, biologists, and engineers are presented. These resources can be used to address the lack of preparation of college students in STEM fields entering the workforce by providing experience working on interdisciplinary and multidisciplinary problems in mathematical biology in a group setting. Such approaches can also indirectly help attract and retain under-represented students who benefit the most from "non-traditional" learning styles and strategies, including inquiry-based, collaborative, and active learning. © The Author 2015. Published by Oxford University Press on behalf of the Society for Integrative and Comparative Biology. All rights reserved. For permissions please email: journals.permissions@oup.com.

Training the Millennial learner through experiential evolutionary scaffolding: implications for clinical supervision in graduate education programs.

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Venne, Vickie L; Coleman, Darrell

2010-12-01

They are the Millennials--Generation Y. Over the next few decades, they will be entering genetic counseling graduate training programs and the workforce. As a group, they are unlike previous youth generations in many ways, including the way they learn. Therefore, genetic counselors who teach and supervise need to understand the Millennials and explore new ways of teaching to ensure that the next cohort of genetic counselors has both skills and knowledge to represent our profession well. This paper will summarize the distinguishing traits of the Millennial generation as well as authentic learning and evolutionary scaffolding theories of learning that can enhance teaching and supervision. We will then use specific aspects of case preparation during clinical rotations to demonstrate how incorporating authentic learning theory into evolutionary scaffolding results in experiential evolutionary scaffolding, a method that potentially offers a more effective approach when teaching Millennials. We conclude with suggestions for future research.

Theoretical Approaches in Evolutionary Ecology: Environmental Feedback as a Unifying Perspective.

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Lion, Sébastien

2018-01-01

Evolutionary biology and ecology have a strong theoretical underpinning, and this has fostered a variety of modeling approaches. A major challenge of this theoretical work has been to unravel the tangled feedback loop between ecology and evolution. This has prompted the development of two main classes of models. While quantitative genetics models jointly consider the ecological and evolutionary dynamics of a focal population, a separation of timescales between ecology and evolution is assumed by evolutionary game theory, adaptive dynamics, and inclusive fitness theory. As a result, theoretical evolutionary ecology tends to be divided among different schools of thought, with different toolboxes and motivations. My aim in this synthesis is to highlight the connections between these different approaches and clarify the current state of theory in evolutionary ecology. Central to this approach is to make explicit the dependence on environmental dynamics of the population and evolutionary dynamics, thereby materializing the eco-evolutionary feedback loop. This perspective sheds light on the interplay between environmental feedback and the timescales of ecological and evolutionary processes. I conclude by discussing some potential extensions and challenges to our current theoretical understanding of eco-evolutionary dynamics.

A formative evaluation of a high school blended learning biology course

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Nellman, Stephen William

As growing student populations continue to tax the resources of public high schools, administrators are constantly looking for ways to address the needs of all students. One option for increasing the number of students in a classroom without sacrificing quality of instruction is to use "blended learning". Blended learning is defined by Marsh et al. (2003, p.2) as a situation where "face-to-face and distance education delivery methods and resources are merged". In such a course, students receive the benefits of classroom-based instruction, while also benefiting from several aspects of distance learning. This is especially true for science courses that rely heavily on both hands-on labs and various multimedia. The purpose of this study was a formative evaluation of a high school blended learning biology course, focusing on a genetics unit. The research question addressed by the study was "Will participants increase their domain knowledge and problem-solving skills after instruction in a high school level blended distance learning biology course? Also investigated was if higher levels of self-regulation skills were correlated to higher levels of content-understanding and problem-solving. The study was composed of a pilot study and a main study. Participants were students in an urban Southern California public high school biology course. Classroom instruction was from a single instructor, and online content was managed using the "Moodle" course management system. Participants were assessed for their gains in genetics content-understanding, genetics problem-solving skills (Punnett squares), and self-regulation. Additionally, participant reactions to the blended instruction model were surveyed. Results indicated that significant increases (pself-regulation skills were not shown to be significantly correlated to increased content-understanding, or problem-solving skills. Participants reacted positively to the blended model, suggesting that it be used more often in their

Misrepresentations of evolutionary psychology in sex and gender textbooks.

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Winegard, Benjamin M; Winegard, Bo M; Deaner, Robert O

2014-05-20

Evolutionary psychology has provoked controversy, especially when applied to human sex differences. We hypothesize that this is partly due to misunderstandings of evolutionary psychology that are perpetuated by undergraduate sex and gender textbooks. As an initial test of this hypothesis, we develop a catalog of eight types of errors and document their occurrence in 15 widely used sex and gender textbooks. Consistent with our hypothesis, of the 12 textbooks that discussed evolutionary psychology, all contained at least one error, and the median number of errors was five. The most common types of errors were "Straw Man," "Biological Determinism," and "Species Selection." We conclude by suggesting improvements to undergraduate sex and gender textbooks.

ELeaRNT: Evolutionary Learning of Rich Neural Network Topologies

National Research Council Canada - National Science Library

Matteucci, Matteo

2006-01-01

In this paper we present ELeaRNT an evolutionary strategy which evolves rich neural network topologies in order to find an optimal domain specific non linear function approximator with a good generalization performance...

Epistemological Predictors of "Self Efficacy on Learning Biology" and "Test Anxiety Related to Evaluation of Learning on Biology" for Pre-Service Elementary Teachers

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Koksal, Mustafa Serdar

2011-01-01

The degree to which pre-service teachers learn biology is related to both motivational factors of self-regulation and factors regarding epistemological beliefs. At the same time, self-regulation and epistemological beliefs are also associated with one another. Based on this relationship, the purpose of this study was to investigate the…

The curiously long absence of cooking in evolutionary thought.

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Wrangham, R

2016-06-01

Beran et al. (2015, p. 1) characterized the idea that "cooked food was integral in human evolution" as a "long-held hypothesis" favored by Darwin and Engels. In fact, however, although Darwin and Engels considered the use of cooked food to be an important influence on behavior and society, neither of them suggested that its effects were evolutionary in the sense of affecting biology. Explicit discussion of the possible evolutionary impacts of cooking did not begin until the twentieth century.

Enhancing Higher Order Thinking Skills In A Marine Biology Class Through Problem-Based Learning

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Richard M. Magsino

2014-10-01

Full Text Available The purpose of this research was to examine students' perspectives of their learning in marine biology in the collaborative group context of Problem-based Learning (PBL. Students’ higher order thinking skills (HOTS using PBL involves the development of their logical thinking and reasoning abilities which stimulates their curiosity and associative thinking. This study aimed to investigate how critical thinking skills, particularly analysis, synthesis and evaluation were enhanced in a marine biology class through PBL. Qualitative research approach was used to examine student responses in a questionnaire involving 10 open-ended questions that target students’ HOTS on a problem presented in a marine biology class for BS Biology students. Using axial coding as a qualitative data analysis technique by which grounded theory can be performed, the study was able to determine how students manifest their higher reasoning abilities when confronted with a marine biology situation. Results show student responses yielding affirmative remarks on the 10 questions intended to know their level of analysis (e.g., analyzing, classifying, inferring, discriminating and relating or connecting, synthesis (e.g., synthesizing and collaborating, and evaluation (e.g., comparing, criticizing, and convincing of information from the presented marine biology problem. Consequently, students were able to effectively design experiments to address the presented issue through problem-based learning. Results of the study show that PBL is an efficient instructional strategy embedded within a conventional curriculum used to develop or enhance critical thinking in marine biology.

Student Perceptions of the Cell Biology Laboratory Learning Environment in Four Undergraduate Science Courses in Spain

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De Juan, Joaquin; Pérez-Cañaveras, Rosa M.; Segovia, Yolanda; Girela, Jose Luis; Martínez-Ruiz, Noemi; Romero-Rameta, Alejandro; Gómez-Torres, Maria José; Vizcaya-Moreno, M. Flores

2016-01-01

Cell biology is an academic discipline that organises and coordinates the learning of the structure, function and molecular composition of cells in some undergraduate biomedical programs. Besides course content and teaching methodologies, the laboratory environment is considered a key element in the teaching of and learning of cell biology. The…

The Adult Learning Open University Determinants (ALOUD) study: Biological and psychological factors associated with learning performance in adult distance education

NARCIS (Netherlands)

Neroni, Joyce; Gijselaers, Jérôme; Kirschner, Paul A.; De Groot, Renate

2017-01-01

Learning is crucial for everyone. The association between biological (eg, sleep, nutrition) and psychological factors (eg, test anxiety, goal orientation) and learning performance has been well established for children, adolescents and college students in traditional education. Evidence for these

Teaching About "Brain and Learning" in High School Biology Classes: Effects on Teachers' Knowledge and Students' Theory of Intelligence.

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Dekker, Sanne; Jolles, Jelle

2015-01-01

This study evaluated a new teaching module about "Brain and Learning" using a controlled design. The module was implemented in high school biology classes and comprised three lessons: (1) brain processes underlying learning; (2) neuropsychological development during adolescence; and (3) lifestyle factors that influence learning performance. Participants were 32 biology teachers who were interested in "Brain and Learning" and 1241 students in grades 8-9. Teachers' knowledge and students' beliefs about learning potential were examined using online questionnaires. Results indicated that before intervention, biology teachers were significantly less familiar with how the brain functions and develops than with its structure and with basic neuroscientific concepts (46 vs. 75% correct answers). After intervention, teachers' knowledge of "Brain and Learning" had significantly increased (64%), and more students believed that intelligence is malleable (incremental theory). This emphasizes the potential value of a short teaching module, both for improving biology teachers' insights into "Brain and Learning," and for changing students' beliefs about intelligence.

Social Learning Strategies: Bridge-Building between Fields.

Science.gov (United States)

Kendal, Rachel L; Boogert, Neeltje J; Rendell, Luke; Laland, Kevin N; Webster, Mike; Jones, Patricia L

2018-07-01

While social learning is widespread, indiscriminate copying of others is rarely beneficial. Theory suggests that individuals should be selective in what, when, and whom they copy, by following 'social learning strategies' (SLSs). The SLS concept has stimulated extensive experimental work, integrated theory, and empirical findings, and created impetus to the social learning and cultural evolution fields. However, the SLS concept needs updating to accommodate recent findings that individuals switch between strategies flexibly, that multiple strategies are deployed simultaneously, and that there is no one-to-one correspondence between psychological heuristics deployed and resulting population-level patterns. The field would also benefit from the simultaneous study of mechanism and function. SLSs provide a useful vehicle for bridge-building between cognitive psychology, neuroscience, and evolutionary biology. Copyright © 2018. Published by Elsevier Ltd.

The evolutionary landscape of antifolate resistance in Plasmodium ...

Indian Academy of Sciences (India)

Permanent link: https://www.ias.ac.in/article/fulltext/jgen/090/02/0187-0190. Keywords. malaria; drug resistance; dihydrofolate reductase; pyrimethamine; chlorcycloguanil; antifolates. Author Affiliations. Marna S. Costanzo1 Daniel L. Hartl1. Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, ...

Is evolutionary biology becoming too politically correct? A reflection on the scala naturae, phylogenetically basal clades, anatomically plesiomorphic taxa, and 'lower' animals.

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Diogo, Rui; Ziermann, Janine M; Linde-Medina, Marta

2015-05-01

, or the strepsirrhines and lemurs within the Primates, for instance. This review will contribute to improving our understanding of these broad evolutionary issues and of the evolution of the vertebrate Bauplans, and hopefully will stimulate future phylogenetic, evolutionary and developmental studies of these clades. © 2014 The Authors. Biological Reviews © 2014 Cambridge Philosophical Society.

Evolutionary Explanations for Antibiotic Resistance in Daily Press, Online Websites and Biology Textbooks in Sweden

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Bohlin, Gustav; Höst, Gunnar E.

2015-01-01

The present study explores the extent and precision of evolutionary explanations for antibiotic resistance in communication directed toward the Swedish public. Bacterial resistance develops through evolutionary mechanisms and knowledge of these helps to explain causes underlying the growing prevalence of resistant strains, as well as important…

Testing the ecological consequences of evolutionary change using elements.

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Jeyasingh, Punidan D; Cothran, Rickey D; Tobler, Michael

2014-02-01

Understanding the ecological consequences of evolutionary change is a central challenge in contemporary biology. We propose a framework based on the ˜25 elements represented in biology, which can serve as a conduit for a general exploration of poorly understood evolution-to-ecology links. In this framework, known as ecological stoichiometry, the quantity of elements in the inorganic realm is a fundamental environment, while the flow of elements from the abiotic to the biotic realm is due to the action of genomes, with the unused elements excreted back into the inorganic realm affecting ecological processes at higher levels of organization. Ecological stoichiometry purposefully assumes distinct elemental composition of species, enabling powerful predictions about the ecological functions of species. However, this assumption results in a simplified view of the evolutionary mechanisms underlying diversification in the elemental composition of species. Recent research indicates substantial intraspecific variation in elemental composition and associated ecological functions such as nutrient excretion. We posit that attention to intraspecific variation in elemental composition will facilitate a synthesis of stoichiometric information in light of population genetics theory for a rigorous exploration of the ecological consequences of evolutionary change.

On the Evolutionary Bases of Consumer Reinforcement

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Nicholson, Michael; Xiao, Sarah Hong

2010-01-01

This article locates consumer behavior analysis within the modern neo-Darwinian synthesis, seeking to establish an interface between the ultimate-level theorizing of human evolutionary psychology and the proximate level of inquiry typically favored by operant learning theorists. Following an initial overview of the central tenets of neo-Darwinism,…

Reconstructing Causal Biological Networks through Active Learning.

Directory of Open Access Journals (Sweden)

Hyunghoon Cho

Full Text Available Reverse-engineering of biological networks is a central problem in systems biology. The use of intervention data, such as gene knockouts or knockdowns, is typically used for teasing apart causal relationships among genes. Under time or resource constraints, one needs to carefully choose which intervention experiments to carry out. Previous approaches for selecting most informative interventions have largely been focused on discrete Bayesian networks. However, continuous Bayesian networks are of great practical interest, especially in the study of complex biological systems and their quantitative properties. In this work, we present an efficient, information-theoretic active learning algorithm for Gaussian Bayesian networks (GBNs, which serve as important models for gene regulatory networks. In addition to providing linear-algebraic insights unique to GBNs, leading to significant runtime improvements, we demonstrate the effectiveness of our method on data simulated with GBNs and the DREAM4 network inference challenge data sets. Our method generally leads to faster recovery of underlying network structure and faster convergence to final distribution of confidence scores over candidate graph structures using the full data, in comparison to random selection of intervention experiments.

Applications of evolutionary computation in image processing and pattern recognition

CERN Document Server

Cuevas, Erik; Perez-Cisneros, Marco

2016-01-01

This book presents the use of efficient Evolutionary Computation (EC) algorithms for solving diverse real-world image processing and pattern recognition problems. It provides an overview of the different aspects of evolutionary methods in order to enable the reader in reaching a global understanding of the field and, in conducting studies on specific evolutionary techniques that are related to applications in image processing and pattern recognition. It explains the basic ideas of the proposed applications in a way that can also be understood by readers outside of the field. Image processing and pattern recognition practitioners who are not evolutionary computation researchers will appreciate the discussed techniques beyond simple theoretical tools since they have been adapted to solve significant problems that commonly arise on such areas. On the other hand, members of the evolutionary computation community can learn the way in which image processing and pattern recognition problems can be translated into an...

Vicarious Versus Traditional Learning in Biology: A Case of Sexually ...

African Journals Online (AJOL)

The purpose of this study was to compare between learning sexually transmitted infections in Biology by observation and traditional classroom lecture method ... The study found that observational method was more effective and preferred by students as compared to traditional lecture method ... AJOL African Journals Online.

Testing evolutionary convergence on Europa

Energy Technology Data Exchange (ETDEWEB)

Chela-Flores, Julian [Instituto de Estudios Avanzados, Caracas (Venezuela); [Abdus Salam International Centre for Theoretical Physics, Trieste (Italy)

2002-11-01

A major objective in solar system exploration is the insertion of appropriate biology-oriented experiments in future missions. We discuss various reasons for suggesting that this type of research be considered a high priority for feasibility studies and, subsequently, for technological development of appropriate melters and submersibles. Based on numerous examples, we argue in favour of the assumption that Darwin's theory is valid for the evolution of life anywhere in the universe. We have suggested how to obtain preliminary insights into the question of the distribution of life in the universe. Universal evolution of intelligent behaviour is at the end of an evolutionary pathway, in which evolution of ion channels in the membrane of microorganisms occurs in its early stages. Further, we have argued that a preliminary test of this conjecture is feasible with experiments on the Europan surface or ocean, involving evolutionary biosignatures (ion channels). This aspect of the exploration for life in the solar system should be viewed as a complement to the astronomical approach for the search of evidence of the later stages of the evolutionary pathways towards intelligent behaviour. (author)

[Evolutionary medicine: A new look on health and disease].

Science.gov (United States)

Bauduer, F

2017-03-01

Evolutionary medicine represents an innovative approach deriving from evolutionary biology. It includes the initial Darwin's view, its actualization in the light of progresses in genetics and also dissident theories (i.e. non gene-based) particularly epigenetics. This approach enables us to reconsider the pathophysiology of numerous diseases, as for instance, infection, and our so-called diseases of civilization especially obesity, type 2 diabetes, allergy or cancer. Evolutionary medicine may also improve our knowledge regarding inter-individual variation in susceptibility to disease or drugs. Furthermore, it points out the impact of our behaviors and environment on the genesis of a series of diseases. Copyright © 2016 Société Nationale Française de Médecine Interne (SNFMI). Published by Elsevier SAS. All rights reserved.

EvoBuild: A Quickstart Toolkit for Programming Agent-Based Models of Evolutionary Processes

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Wagh, Aditi; Wilensky, Uri

2018-04-01

Extensive research has shown that one of the benefits of programming to learn about scientific phenomena is that it facilitates learning about mechanisms underlying the phenomenon. However, using programming activities in classrooms is associated with costs such as requiring additional time to learn to program or students needing prior experience with programming. This paper presents a class of programming environments that we call quickstart: Environments with a negligible threshold for entry into programming and a modest ceiling. We posit that such environments can provide benefits of programming for learning without incurring associated costs for novice programmers. To make this claim, we present a design-based research study conducted to compare programming models of evolutionary processes with a quickstart toolkit with exploring pre-built models of the same processes. The study was conducted in six seventh grade science classes in two schools. Students in the programming condition used EvoBuild, a quickstart toolkit for programming agent-based models of evolutionary processes, to build their NetLogo models. Students in the exploration condition used pre-built NetLogo models. We demonstrate that although students came from a range of academic backgrounds without prior programming experience, and all students spent the same number of class periods on the activities including the time students took to learn programming in this environment, EvoBuild students showed greater learning about evolutionary mechanisms. We discuss the implications of this work for design research on programming environments in K-12 science education.

Evolutionary Computing Methods for Spectral Retrieval

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Terrile, Richard; Fink, Wolfgang; Huntsberger, Terrance; Lee, Seugwon; Tisdale, Edwin; VonAllmen, Paul; Tinetti, Geivanna

2009-01-01

A methodology for processing spectral images to retrieve information on underlying physical, chemical, and/or biological phenomena is based on evolutionary and related computational methods implemented in software. In a typical case, the solution (the information that one seeks to retrieve) consists of parameters of a mathematical model that represents one or more of the phenomena of interest. The methodology was developed for the initial purpose of retrieving the desired information from spectral image data acquired by remote-sensing instruments aimed at planets (including the Earth). Examples of information desired in such applications include trace gas concentrations, temperature profiles, surface types, day/night fractions, cloud/aerosol fractions, seasons, and viewing angles. The methodology is also potentially useful for retrieving information on chemical and/or biological hazards in terrestrial settings. In this methodology, one utilizes an iterative process that minimizes a fitness function indicative of the degree of dissimilarity between observed and synthetic spectral and angular data. The evolutionary computing methods that lie at the heart of this process yield a population of solutions (sets of the desired parameters) within an accuracy represented by a fitness-function value specified by the user. The evolutionary computing methods (ECM) used in this methodology are Genetic Algorithms and Simulated Annealing, both of which are well-established optimization techniques and have also been described in previous NASA Tech Briefs articles. These are embedded in a conceptual framework, represented in the architecture of the implementing software, that enables automatic retrieval of spectral and angular data and analysis of the retrieved solutions for uniqueness.

Using information and communication technology (ICT) to the maximum: learning and teaching biology with limited digital technologies

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Van Rooy, Wilhelmina S.

2012-04-01

Background: The ubiquity, availability and exponential growth of digital information and communication technology (ICT) creates unique opportunities for learning and teaching in the senior secondary school biology curriculum. Digital technologies make it possible for emerging disciplinary knowledge and understanding of biological processes previously too small, large, slow or fast to be taught. Indeed, much of bioscience can now be effectively taught via digital technology, since its representational and symbolic forms are in digital formats. Purpose: This paper is part of a larger Australian study dealing with the technologies and modalities of learning biology in secondary schools. Sample: The classroom practices of three experienced biology teachers, working in a range of NSW secondary schools, are compared and contrasted to illustrate how the challenges of limited technologies are confronted to seamlessly integrate what is available into a number of molecular genetics lessons to enhance student learning. Design and method: The data are qualitative and the analysis is based on video classroom observations and semi-structured teacher interviews. Results: Findings indicate that if professional development opportunities are provided where the pedagogy of learning and teaching of both the relevant biology and its digital representations are available, then teachers see the immediate pedagogic benefit to student learning. In particular, teachers use ICT for challenging genetic concepts despite limited computer hardware and software availability. Conclusion: Experienced teachers incorporate ICT, however limited, in order to improve the quality of student learning.

Evolutionary genetic analyses of MEF2C gene: implications for learning and memory in Homo sapiens.

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Kalmady, Sunil V; Venkatasubramanian, Ganesan; Arasappa, Rashmi; Rao, Naren P

2013-02-01

MEF2C facilitates context-dependent fear conditioning (CFC) which is a salient aspect of hippocampus-dependent learning and memory. CFC might have played a crucial role in human evolution because of its advantageous influence on survival of species. In this study, we analyzed 23 orthologous mammalian gene sequences of MEF2C gene to examine the evidence for positive selection on this gene in Homo sapiens using Phylogenetic Analysis by Maximum Likelihood (PAML) and HyPhy software. Both PAML Bayes Empirical Bayes (BEB) and HyPhy Fixed Effects Likelihood (FEL) analyses supported significant positive selection on 4 codon sites in H. sapiens. Also, haplotter analysis revealed significant ongoing positive selection on this gene in Central European population. The study findings suggest that adaptive selective pressure on this gene might have influenced human evolution. Further research on this gene might unravel the potential role of this gene in learning and memory as well as its pathogenetic effect in certain hippocampal disorders with evolutionary basis like schizophrenia. Copyright © 2012 Elsevier B.V. All rights reserved.

Rethinking biology instruction: The application of DNR-based instruction to the learning and teaching of biology

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Maskiewicz, April Lee

Educational studies report that secondary and college level students have developed only limited understandings of the most basic biological processes and their interrelationships from typical classroom experiences. Furthermore, students have developed undesirable reasoning schemes and beliefs that directly affect how they make sense of and account for biological phenomena. For these reasons, there exists a need to rethink instructional practices in biology. This dissertation discusses how the principles of Harel's (1998, 2001) DNR-based instruction in mathematics could be applied to the teaching and learning of biology. DNR is an acronym for the three foundational principles of the system: Duality, Necessity, and Repeated-reasoning. This study examines the application of these three principles to ecology instruction. Through clinical and teaching interviews, I developed models of students' existing ways of understanding in ecology and inferred their ways of thinking. From these models a hypothetical learning trajectory was developed for 16 college level freshmen enrolled in a 10-week ecology teaching experiment. Through cyclical, interpretive analysis I documented and analyzed the evolution of the participants' progress. The results provide empirical evidence to support the claim that the DNR principles are applicable to ecology instruction. With respect to the Duality Principle, helping students develop specific ways of understanding led to the development of model-based reasoning---a way of thinking and the cognitive objective guiding instruction. Through carefully structured problem solving tasks, the students developed a biological understanding of the relationship between matter cycling, energy flow, and cellular processes such as photosynthesis and respiration, and used this understanding to account for observable phenomena in nature. In the case of intellectual necessity, the results illuminate how problem situations can be developed for biology learners

Do we need an extended evolutionary synthesis?

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Pigliucci, Massimo

2007-12-01

The Modern Synthesis (MS) is the current paradigm in evolutionary biology. It was actually built by expanding on the conceptual foundations laid out by its predecessors, Darwinism and neo-Darwinism. For sometime now there has been talk of a new Extended Evolutionary Synthesis (EES), and this article begins to outline why we may need such an extension, and how it may come about. As philosopher Karl Popper has noticed, the current evolutionary theory is a theory of genes, and we still lack a theory of forms. The field began, in fact, as a theory of forms in Darwin's days, and the major goal that an EES will aim for is a unification of our theories of genes and of forms. This may be achieved through an organic grafting of novel concepts onto the foundational structure of the MS, particularly evolvability, phenotypic plasticity, epigenetic inheritance, complexity theory, and the theory of evolution in highly dimensional adaptive landscapes.

Teaching Tree-Thinking to Undergraduate Biology Students.

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Meisel, Richard P

2010-07-27

Evolution is the unifying principle of all biology, and understanding how evolutionary relationships are represented is critical for a complete understanding of evolution. Phylogenetic trees are the most conventional tool for displaying evolutionary relationships, and "tree-thinking" has been coined as a term to describe the ability to conceptualize evolutionary relationships. Students often lack tree-thinking skills, and developing those skills should be a priority of biology curricula. Many common student misconceptions have been described, and a successful instructor needs a suite of tools for correcting those misconceptions. I review the literature on teaching tree-thinking to undergraduate students and suggest how this material can be presented within an inquiry-based framework.

Ecological and evolutionary impacts of changing climatic variability.

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Vázquez, Diego P; Gianoli, Ernesto; Morris, William F; Bozinovic, Francisco

2017-02-01

While average temperature is likely to increase in most locations on Earth, many places will simultaneously experience higher variability in temperature, precipitation, and other climate variables. Although ecologists and evolutionary biologists widely recognize the potential impacts of changes in average climatic conditions, relatively little attention has been paid to the potential impacts of changes in climatic variability and extremes. We review the evidence on the impacts of increased climatic variability and extremes on physiological, ecological and evolutionary processes at multiple levels of biological organization, from individuals to populations and communities. Our review indicates that climatic variability can have profound influences on biological processes at multiple scales of organization. Responses to increased climatic variability and extremes are likely to be complex and cannot always be generalized, although our conceptual and methodological toolboxes allow us to make informed predictions about the likely consequences of such climatic changes. We conclude that climatic variability represents an important component of climate that deserves further attention. © 2015 Cambridge Philosophical Society.

Racial classification in the evolutionary sciences: a comparative analysis.

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Billinger, Michael S

2007-01-01

Human racial classification has long been a problem for the discipline of anthropology, but much of the criticism of the race concept has focused on its social and political connotations. The central argument of this paper is that race is not a specifically human problem, but one that exists in evolutionary thought in general. This paper looks at various disciplinary approaches to racial or subspecies classification, extending its focus beyond the anthropological race concept by providing a comparative analysis of the use of racial classification in evolutionary biology, genetics, and anthropology.

WORMHOLE: Novel Least Diverged Ortholog Prediction through Machine Learning

Science.gov (United States)

Sutphin, George L.; Mahoney, J. Matthew; Sheppard, Keith; Walton, David O.; Korstanje, Ron

2016-01-01

The rapid advancement of technology in genomics and targeted genetic manipulation has made comparative biology an increasingly prominent strategy to model human disease processes. Predicting orthology relationships between species is a vital component of comparative biology. Dozens of strategies for predicting orthologs have been developed using combinations of gene and protein sequence, phylogenetic history, and functional interaction with progressively increasing accuracy. A relatively new class of orthology prediction strategies combines aspects of multiple methods into meta-tools, resulting in improved prediction performance. Here we present WORMHOLE, a novel ortholog prediction meta-tool that applies machine learning to integrate 17 distinct ortholog prediction algorithms to identify novel least diverged orthologs (LDOs) between 6 eukaryotic species—humans, mice, zebrafish, fruit flies, nematodes, and budding yeast. Machine learning allows WORMHOLE to intelligently incorporate predictions from a wide-spectrum of strategies in order to form aggregate predictions of LDOs with high confidence. In this study we demonstrate the performance of WORMHOLE across each combination of query and target species. We show that WORMHOLE is particularly adept at improving LDO prediction performance between distantly related species, expanding the pool of LDOs while maintaining low evolutionary distance and a high level of functional relatedness between genes in LDO pairs. We present extensive validation, including cross-validated prediction of PANTHER LDOs and evaluation of evolutionary divergence and functional similarity, and discuss future applications of machine learning in ortholog prediction. A WORMHOLE web tool has been developed and is available at http://wormhole.jax.org/. PMID:27812085

WORMHOLE: Novel Least Diverged Ortholog Prediction through Machine Learning.

Directory of Open Access Journals (Sweden)

George L Sutphin

2016-11-01

Full Text Available The rapid advancement of technology in genomics and targeted genetic manipulation has made comparative biology an increasingly prominent strategy to model human disease processes. Predicting orthology relationships between species is a vital component of comparative biology. Dozens of strategies for predicting orthologs have been developed using combinations of gene and protein sequence, phylogenetic history, and functional interaction with progressively increasing accuracy. A relatively new class of orthology prediction strategies combines aspects of multiple methods into meta-tools, resulting in improved prediction performance. Here we present WORMHOLE, a novel ortholog prediction meta-tool that applies machine learning to integrate 17 distinct ortholog prediction algorithms to identify novel least diverged orthologs (LDOs between 6 eukaryotic species-humans, mice, zebrafish, fruit flies, nematodes, and budding yeast. Machine learning allows WORMHOLE to intelligently incorporate predictions from a wide-spectrum of strategies in order to form aggregate predictions of LDOs with high confidence. In this study we demonstrate the performance of WORMHOLE across each combination of query and target species. We show that WORMHOLE is particularly adept at improving LDO prediction performance between distantly related species, expanding the pool of LDOs while maintaining low evolutionary distance and a high level of functional relatedness between genes in LDO pairs. We present extensive validation, including cross-validated prediction of PANTHER LDOs and evaluation of evolutionary divergence and functional similarity, and discuss future applications of machine learning in ortholog prediction. A WORMHOLE web tool has been developed and is available at http://wormhole.jax.org/.

Effectiveness of computer-assisted learning in biology teaching in primary schools in Serbia

Directory of Open Access Journals (Sweden)

Županec Vera

2013-01-01

Full Text Available The paper analyzes the comparative effectiveness of Computer-Assisted Learning (CAL and the traditional teaching method in biology on primary school pupils. A stratified random sample consisted of 214 pupils from two primary schools in Novi Sad. The pupils in the experimental group learned the biology content (Chordate using CAL, whereas the pupils in the control group learned the same content using traditional teaching. The research design was the pretest-posttest equivalent groups design. All instruments (the pretest, the posttest and the retest contained the questions belonging to three different cognitive domains: knowing, applying, and reasoning. Arithmetic mean, standard deviation, and standard error were analyzed using the software package SPSS 14.0, and t-test was used in order to establish the difference between the same statistical indicators. The analysis of results of the post­test and the retest showed that the pupils from the CAL group achieved significantly higher quantity and quality of knowledge in all three cognitive domains than the pupils from the traditional group. The results accomplished by the pupils from the CAL group suggest that individual CAL should be more present in biology teaching in primary schools, with the aim of raising the quality of biology education in pupils. [Projekat Ministarstva nauke Republike Srbije, br. 179010: Quality of Educational System in Serbia in the European Perspective

Characterization and evolutionary analysis of tributyltin-binding protein and pufferfish saxitoxin and tetrodotoxin-binding protein genes in toxic and nontoxic pufferfishes.

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Hashiguchi, Y; Lee, J M; Shiraishi, M; Komatsu, S; Miki, S; Shimasaki, Y; Mochioka, N; Kusakabe, T; Oshima, Y

2015-05-01

Understanding the evolutionary mechanisms of toxin accumulation in pufferfishes has been long-standing problem in toxicology and evolutionary biology. Pufferfish saxitoxin and tetrodotoxin-binding protein (PSTBP) is involved in the transport and accumulation of tetrodotoxin and is one of the most intriguing proteins related to the toxicity of pufferfishes. PSTBPs are fusion proteins consisting of two tandem repeated tributyltin-binding protein type 2 (TBT-bp2) domains. In this study, we examined the evolutionary dynamics of TBT-bp2 and PSTBP genes to understand the evolution of toxin accumulation in pufferfishes. Database searches and/or PCR-based cDNA cloning in nine pufferfish species (6 toxic and 3 nontoxic) revealed that all species possessed one or more TBT-bp2 genes, but PSTBP genes were found only in 5 toxic species belonging to genus Takifugu. These toxic Takifugu species possessed two or three copies of PSTBP genes. Phylogenetic analysis of TBT-bp2 and PSTBP genes suggested that PSTBPs evolved in the common ancestor of Takifugu species by repeated duplications and fusions of TBT-bp2 genes. In addition, a detailed comparison of Takifugu TBT-bp2 and PSTBP gene sequences detected a signature of positive selection under the pressure of gene conversion. The complicated evolutionary dynamics of TBT-bp2 and PSTBP genes may reflect the diversity of toxicity in pufferfishes. © 2015 European Society For Evolutionary Biology. Journal of Evolutionary Biology © 2015 European Society For Evolutionary Biology.

ESL students learning biology: The role of language and social interactions

Science.gov (United States)

Jaipal, Kamini

This study explored three aspects related to ESL students in a mainstream grade 11 biology classroom: (1) the nature of students' participation in classroom activities, (2) the factors that enhanced or constrained ESL students' engagement in social interactions, and (3) the role of language in the learning of science. Ten ESL students were observed over an eight-month period in this biology classroom. Data were collected using qualitative research methods such as participant observation, audio-recordings of lessons, field notes, semi-structured interviews, short lesson recall interviews and students' written work. The study was framed within sociocultural perspectives, particularly the social constructivist perspectives of Vygotsky (1962, 1978) and Wertsch (1991). Data were analysed with respect to the three research aspects. Firstly, the findings showed that ESL students' preferred and exhibited a variety of participation practices that ranged from personal-individual to socio-interactive in nature. Both personal-individual and socio-interactive practices appeared to support science and language learning. Secondly, the findings indicated that ESL students' engagement in classroom social interactions was most likely influenced by the complex interactions between a number of competing factors at the individual, interpersonal and community/cultural levels (Rogoff, Radziszewska, & Masiello, 1995). In this study, six factors that appeared to enhance or constrain ESL students' engagement in classroom social interactions were identified. These factors were socio-cultural factors, prior classroom practice, teaching practices, affective factors, English language proficiency, and participation in the research project. Thirdly, the findings indicated that language played a significant mediational role in ESL students' learning of science. The data revealed that the learning of science terms and concepts can be explained by a functional model of language that includes: (1

The evolutionary ecology of clonally propagated domesticated plants.

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McKey, Doyle; Elias, Marianne; Pujol, Benoît; Duputié, Anne

2010-04-01

While seed-propagated crops have contributed many evolutionary insights, evolutionary biologists have often neglected clonally propagated crops. We argue that widespread notions about their evolution under domestication are oversimplified, and that they offer rich material for evolutionary studies. The diversity of their wild ancestors, the diverse ecologies of the crop populations themselves, and the intricate mix of selection pressures, acting not only on the parts harvested but also on the parts used by humans to make clonal propagules, result in complex and diverse evolutionary trajectories under domestication. We examine why farmers propagate some plants clonally, and discuss the evolutionary dynamics of sexual reproduction in clonal crops. We explore how their mixed clonal/sexual reproductive systems function, based on the sole example studied in detail, cassava (Manihot esculenta). Biotechnology is now expanding the number of clonal crops, continuing the 10 000-yr-old trend to increase crop yields by propagating elite genotypes. In an era of rapid global change, it is more important than ever to understand how the adaptive potential of clonal crops can be maintained. A key component of strategies for preserving this adaptive potential is the maintenance of mixed clonal/sexual systems, which can be achieved by encouraging and valuing farmer knowledge about the sexual reproductive biology of their clonal crops.

Evolution of egg coats: linking molecular biology and ecology.

Science.gov (United States)

Shu, Longfei; Suter, Marc J-F; Räsänen, Katja

2015-08-01

One central goal of evolutionary biology is to explain how biological diversity emerges and is maintained in nature. Given the complexity of the phenotype and the multifaceted nature of inheritance, modern evolutionary ecological studies rely heavily on the use of molecular tools. Here, we show how molecular tools help to gain insight into the role of egg coats (i.e. the extracellular structures surrounding eggs and embryos) in evolutionary diversification. Egg coats are maternally derived structures that have many biological functions from mediating fertilization to protecting the embryo from environmental hazards. They show great molecular, structural and functional diversity across species, but intraspecific variability and the role of ecology in egg coat evolution have largely been overlooked. Given that much of the variation that influences egg coat function is ultimately determined by their molecular phenotype, cutting-edge molecular tools (e.g. proteomics, glycomics and transcriptomics), combined with functional assays, are needed for rigorous inferences on their evolutionary ecology. Here, we identify key research areas and highlight emerging molecular techniques that can increase our understanding of the role of egg coats in the evolution of biological diversity, from adaptation to speciation. © 2015 John Wiley & Sons Ltd.

The four cornerstones of Evolutionary Toxicology.

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Bickham, John W

2011-05-01

Evolutionary Toxicology is the study of the effects of chemical pollutants on the genetics of natural populations. Research in Evolutionary Toxicology uses experimental designs familiar to the ecotoxicologist with matched reference and contaminated sites and the selection of sentinel species. It uses the methods of molecular genetics and population genetics, and is based on the theories and concepts of evolutionary biology and conservation genetics. Although it is a relatively young field, interest is rapidly growing among ecotoxicologists and more and more field studies and even controlled laboratory experiments are appearing in the literature. A number of population genetic impacts have been observed in organisms exposed to pollutants which I refer to here as the four cornerstones of Evolutionary Toxicology. These include (1) genome-wide changes in genetic diversity, (2) changes in allelic or genotypic frequencies caused by contaminant-induced selection acting at survivorship loci, (3) changes in dispersal patterns or gene flow which alter the genetic relationships among populations, and (4) changes in allelic or genotypic frequencies caused by increased mutation rates. It is concluded that population genetic impacts of pollution exposure are emergent effects that are not necessarily predictable from the mode of toxicity of the pollutant. Thus, to attribute an effect to a particular contaminant requires a careful experimental design which includes selection of appropriate reference sites, detailed chemistry analyses of environmental samples and tissues, and the use of appropriate biomarkers to establish exposure and effect. This paper describes the field of Evolutionary Toxicology and discusses relevant field studies and their findings. © Springer Science+Business Media, LLC 2011

Investigating the Relationship between Instructors' Use of Active-Learning Strategies and Students' Conceptual Understanding and Affective Changes in Introductory Biology: A Comparison of Two Active-Learning Environments.

Science.gov (United States)

Cleveland, Lacy M; Olimpo, Jeffrey T; DeChenne-Peters, Sue Ellen

2017-01-01

In response to calls for reform in undergraduate biology education, we conducted research examining how varying active-learning strategies impacted students' conceptual understanding, attitudes, and motivation in two sections of a large-lecture introductory cell and molecular biology course. Using a quasi-experimental design, we collected quantitative data to compare participants' conceptual understanding, attitudes, and motivation in the biological sciences across two contexts that employed different active-learning strategies and that were facilitated by unique instructors. Students participated in either graphic organizer/worksheet activities or clicker-based case studies. After controlling for demographic and presemester affective differences, we found that students in both active-learning environments displayed similar and significant learning gains. In terms of attitudinal and motivational data, significant differences were observed for two attitudinal measures. Specifically, those students who had participated in graphic organizer/worksheet activities demonstrated more expert-like attitudes related to their enjoyment of biology and ability to make real-world connections. However, all motivational and most attitudinal data were not significantly different between the students in the two learning environments. These data reinforce the notion that active learning is associated with conceptual change and suggests that more research is needed to examine the differential effects of varying active-learning strategies on students' attitudes and motivation in the domain. © 2017 L. M. Cleveland et al. CBE—Life Sciences Education © 2017 The American Society for Cell Biology. This article is distributed by The American Society for Cell Biology under license from the author(s). It is available to the public under an Attribution–Noncommercial–Share Alike 3.0 Unported Creative Commons License (http://creativecommons.org/licenses/by-nc-sa/3.0).

2007 Microbial Population Biology (July 22-26, 2007)

Energy Technology Data Exchange (ETDEWEB)

Anthony M. Dean

2008-04-01

Microbial Population Biology covers a diverse range of cutting edge issues in the microbial sciences and beyond. Firmly founded in evolutionary biology and with a strongly integrative approach, past meetings have covered topics ranging from the dynamics and genetics of adaptation to the evolution of mutation rate, community ecology, evolutionary genomics, altruism, and epidemiology. This meeting is never dull: some of the most significant and contentious issues in biology have been thrashed out here. We anticipate the 2007 meeting being no exception. The final form of the 2007 meeting is yet to be decided, but the following topics are likely to be included: evolutionary emergence of infectious disease and antibiotic resistance, genetic architecture and implications for the evolution of microbial populations, ageing in bacteria, biogeography, evolution of symbioses, the role of microbes in ecosystem function, and ecological genomics.

Associative learning alone is insufficient for the evolution and maintenance of the human mirror neuron system.

Science.gov (United States)

Oberman, Lindsay M; Hubbard, Edward M; McCleery, Joseph P

2014-04-01

Cook et al. argue that mirror neurons originate from associative learning processes, without evolutionary influence from social-cognitive mechanisms. We disagree with this claim and present arguments based upon cross-species comparisons, EEG findings, and developmental neuroscience that the evolution of mirror neurons is most likely driven simultaneously and interactively by evolutionarily adaptive psychological mechanisms and lower-level biological mechanisms that support them.

Squamate hatchling size and the evolutionary causes of negative offspring size allometry.

Science.gov (United States)

Meiri, S; Feldman, A; Kratochvíl, L

2015-02-01

Although fecundity selection is ubiquitous, in an overwhelming majority of animal lineages, small species produce smaller number of offspring per clutch. In this context, egg, hatchling and neonate sizes are absolutely larger, but smaller relative to adult body size in larger species. The evolutionary causes of this widespread phenomenon are not fully explored. The negative offspring size allometry can result from processes limiting maximal egg/offspring size forcing larger species to produce relatively smaller offspring ('upper limit'), or from a limit on minimal egg/offspring size forcing smaller species to produce relatively larger offspring ('lower limit'). Several reptile lineages have invariant clutch sizes, where females always lay either one or two eggs per clutch. These lineages offer an interesting perspective on the general evolutionary forces driving negative offspring size allometry, because an important selective factor, fecundity selection in a single clutch, is eliminated here. Under the upper limit hypotheses, large offspring should be selected against in lineages with invariant clutch sizes as well, and these lineages should therefore exhibit the same, or shallower, offspring size allometry as lineages with variable clutch size. On the other hand, the lower limit hypotheses would allow lineages with invariant clutch sizes to have steeper offspring size allometries. Using an extensive data set on the hatchling and female sizes of > 1800 species of squamates, we document that negative offspring size allometry is widespread in lizards and snakes with variable clutch sizes and that some lineages with invariant clutch sizes have unusually steep offspring size allometries. These findings suggest that the negative offspring size allometry is driven by a constraint on minimal offspring size, which scales with a negative allometry. © 2014 European Society For Evolutionary Biology. Journal of Evolutionary Biology © 2014 European Society For Evolutionary

Holistic Darwinism: the new evolutionary paradigm and some implications for political science.

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Corning, Peter A

2008-03-01

Holistic Darwinism is a candidate name for a major paradigm shift that is currently underway in evolutionary biology and related disciplines. Important developments include (1) a growing appreciation for the fact that evolution is a multilevel process, from genes to ecosystems, and that interdependent coevolution is a ubiquitous phenomenon in nature; (2) a revitalization of group selection theory, which was banned (prematurely) from evolutionary biology over 30 years ago (groups may in fact be important evolutionary units); (3) a growing respect for the fact that the genome is not a "bean bag" (in biologist Ernst Mayr's caricature), much less a gladiatorial arena for competing selfish genes, but a complex, interdependent, cooperating system; (4) an increased recognition that symbiosis is an important phenomenon in nature and that symbiogenesis is a major source of innovation in evolution; (5) an array of new, more advanced game theory models, which support the growing evidence that cooperation is commonplace in nature and not a rare exception; (6) new research and theoretical work that stresses the role of nurture in evolution, including developmental processes, phenotypic plasticity, social information transfer (culture), and especially the role of behavioral innovations as pacemakers of evolutionary change (e.g., niche construction theory, which is concerned with the active role of organisms in shaping the evolutionary process, and gene-culture coevolution theory, which relates especially to the dynamics of human evolution); (7) and, not least, a broad effort to account for the evolution of biological complexity--from major transition theory to the "Synergism Hypothesis." Here I will briefly review these developments and will present a case for the proposition that this paradigm shift has profound implications for the social sciences, including specifically political theory, economic theory, and political science as a discipline. Interdependent superorganisms, it

Evolutionary medicine: update on the relevance to family practice.

Science.gov (United States)

Naugler, Christopher T

2008-09-01

To review the relevance of evolutionary medicine to family practice and family physician training. Articles were located through a MEDLINE search, using the key words evolution, Darwin, and adaptation. Most references presented level III evidence (expert opinion), while a minority provided level II evidence (epidemiologic studies). Evolutionary medicine deals with the interplay of biology and the environment in the understanding of human disease. Yet medical schools have virtually ignored the need for family physicians to have more than a cursory knowledge of this topic. A review of the main trends in this field most relevant to family practice revealed that a basic knowledge of evolutionary medicine might help in explaining the causation of diseases to patients. Evolutionary medicine has also proven key to explaining the reasons for the development of antibiotic resistance and has the potential to explain cancer pathogenesis. As an organizing principle, this field also has potential in the teaching of family medicine. Evolutionary medicine should be studied further and incorporated into medical training and practice. Its practical utility will be proven through the generation of testable hypotheses and their application in relation to disease causation and possible prevention.

Connecting proximate mechanisms and evolutionary patterns: pituitary gland size and mammalian life history.

Science.gov (United States)

Kamilar, J M; Tecot, S R

2015-11-01

At the proximate level, hormones are known to play a critical role in influencing the life history of mammals, including humans. The pituitary gland is directly responsible for producing several hormones, including those related to growth and reproduction. Although we have a basic understanding of how hormones affect life history characteristics, we still have little knowledge of this relationship in an evolutionary context. We used data from 129 mammal species representing 14 orders to investigate the relationship between pituitary gland size and life history variation. Because pituitary gland size should be related to hormone production and action, we predicted that species with relatively large pituitaries should be associated with fast life histories, especially increased foetal and post-natal growth rates. Phylogenetic analyses revealed that total pituitary size and the size of the anterior lobe of the pituitary significantly predicted a life history axis that was correlated with several traits including body mass, and foetal and post-natal growth rates. Additional models directly examining the association between relative pituitary size and growth rates produced concordant results. We also found that relative pituitary size variation across mammals was best explained by an Ornstein-Uhlenbeck model of evolution, suggesting an important role of stabilizing selection. Our results support the idea that the size of the pituitary is linked to life history variation through evolutionary time. This pattern is likely due to mediating hormone levels but additional work is needed. We suggest that future investigations incorporating endocrine gland size may be critical for understanding life history evolution. © 2015 European Society For Evolutionary Biology. Journal of Evolutionary Biology © 2015 European Society For Evolutionary Biology.

Moglichkeiten und Grenzen eines evolutionaren Paradigmas in der Erziehungswissenschaft (Possibilities and Limits of an Evolutionary Paradigm in Educational Science).

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Nipkow, Karl Ernst

2002-01-01

Describes a sectoral and paradigmatic approach to evolutionary research. Argues that an evolutionary paradigm does not exist. Examines the socio-biological approach and that of a system-theoretical oriented general evolutionary theory. Utilizes the topics of cooperation, delimitation, and indoctrination to explain more promising ways of adoption.…

Understanding variation in human fertility: what can we learn from evolutionary demography?

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Sear, Rebecca; Lawson, David W; Kaplan, Hillard; Shenk, Mary K

2016-04-19

Decades of research on human fertility has presented a clear picture of how fertility varies, including its dramatic decline over the last two centuries in most parts of the world. Why fertility varies, both between and within populations, is not nearly so well understood. Fertility is a complex phenomenon, partly physiologically and partly behaviourally determined, thus an interdisciplinary approach is required to understand it. Evolutionary demographers have focused on human fertility since the 1980s. The first wave of evolutionary demographic research made major theoretical and empirical advances, investigating variation in fertility primarily in terms of fitness maximization. Research focused particularly on variation within high-fertility populations and small-scale subsistence societies and also yielded a number of hypotheses for why fitness maximization seems to break down as fertility declines during the demographic transition. A second wave of evolutionary demography research on fertility is now underway, paying much more attention to the cultural and psychological mechanisms underpinning fertility. It is also engaging with the complex, multi-causal nature of fertility variation, and with understanding fertility in complex modern and transitioning societies. Here, we summarize the history of evolutionary demographic work on human fertility, describe the current state of the field, and suggest future directions. © 2016 The Author(s).

Designing synthetic networks in silico: a generalised evolutionary algorithm approach.

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Smith, Robert W; van Sluijs, Bob; Fleck, Christian

2017-12-02

Evolution has led to the development of biological networks that are shaped by environmental signals. Elucidating, understanding and then reconstructing important network motifs is one of the principal aims of Systems & Synthetic Biology. Consequently, previous research has focused on finding optimal network structures and reaction rates that respond to pulses or produce stable oscillations. In this work we present a generalised in silico evolutionary algorithm that simultaneously finds network structures and reaction rates (genotypes) that can satisfy multiple defined objectives (phenotypes). The key step to our approach is to translate a schema/binary-based description of biological networks into systems of ordinary differential equations (ODEs). The ODEs can then be solved numerically to provide dynamic information about an evolved networks functionality. Initially we benchmark algorithm performance by finding optimal networks that can recapitulate concentration time-series data and perform parameter optimisation on oscillatory dynamics of the Repressilator. We go on to show the utility of our algorithm by finding new designs for robust synthetic oscillators, and by performing multi-objective optimisation to find a set of oscillators and feed-forward loops that are optimal at balancing different system properties. In sum, our results not only confirm and build on previous observations but we also provide new designs of synthetic oscillators for experimental construction. In this work we have presented and tested an evolutionary algorithm that can design a biological network to produce desired output. Given that previous designs of synthetic networks have been limited to subregions of network- and parameter-space, the use of our evolutionary optimisation algorithm will enable Synthetic Biologists to construct new systems with the potential to display a wider range of complex responses.

Evolution of learned strategy choice in a frequency-dependent game.

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Katsnelson, Edith; Motro, Uzi; Feldman, Marcus W; Lotem, Arnon

2012-03-22

In frequency-dependent games, strategy choice may be innate or learned. While experimental evidence in the producer-scrounger game suggests that learned strategy choice may be common, a recent theoretical analysis demonstrated that learning by only some individuals prevents learning from evolving in others. Here, however, we model learning explicitly, and demonstrate that learning can easily evolve in the whole population. We used an agent-based evolutionary simulation of the producer-scrounger game to test the success of two general learning rules for strategy choice. We found that learning was eventually acquired by all individuals under a sufficient degree of environmental fluctuation, and when players were phenotypically asymmetric. In the absence of sufficient environmental change or phenotypic asymmetries, the correct target for learning seems to be confounded by game dynamics, and innate strategy choice is likely to be fixed in the population. The results demonstrate that under biologically plausible conditions, learning can easily evolve in the whole population and that phenotypic asymmetry is important for the evolution of learned strategy choice, especially in a stable or mildly changing environment.

Learning from evolutionary optimisation: what are toughening mechanisms good for in dentine, a nonrepairing bone tissue?

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Zaslansky, Paul; Currey, John D; Fleck, Claudia

2016-09-12

The main mass of material found in teeth is dentine, a bone-like tissue, riddled with micron-sized tubules and devoid of living cells. It provides support to the outer wear-resistant layer of enamel, and exhibits toughening mechanisms which contribute to crack resistance. And yet unlike most bone tissues, dentine does not remodel and consequently any accumulated damage does not 'self repair'. Because damage containment followed by tissue replacement is a prime reason for the crack-arresting microstructures found in most bones, the occurrence of toughening mechanisms without the biological capability to repair is puzzling. Here we consider the notion that dentine might be overdesigned for strength, because it has to compensate for the lack of cell-mediated healing mechanisms. Based on our own and on literature-reported observations, including quasistatic and fatigue properties, dentine design principles are discussed in light of the functional conditions under which teeth evolved. We conclude that dentine is only slightly overdesigned for everyday cyclic loading because usual mastication stresses may come close to its endurance strength. The in-built toughening mechanisms constitute an evolutionary benefit because they prevent catastrophic failure during rare overload events, which was probably very advantageous in our hunter gatherer ancestor times. From a bio-inspired perspective, understanding the extent of evolutionary overdesign might be useful for optimising biomimetic structures used for load bearing.

An introduction to deep learning on biological sequence data: examples and solutions.

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Jurtz, Vanessa Isabell; Johansen, Alexander Rosenberg; Nielsen, Morten; Almagro Armenteros, Jose Juan; Nielsen, Henrik; Sønderby, Casper Kaae; Winther, Ole; Sønderby, Søren Kaae

2017-11-15

Deep neural network architectures such as convolutional and long short-term memory networks have become increasingly popular as machine learning tools during the recent years. The availability of greater computational resources, more data, new algorithms for training deep models and easy to use libraries for implementation and training of neural networks are the drivers of this development. The use of deep learning has been especially successful in image recognition; and the development of tools, applications and code examples are in most cases centered within this field rather than within biology. Here, we aim to further the development of deep learning methods within biology by providing application examples and ready to apply and adapt code templates. Given such examples, we illustrate how architectures consisting of convolutional and long short-term memory neural networks can relatively easily be designed and trained to state-of-the-art performance on three biological sequence problems: prediction of subcellular localization, protein secondary structure and the binding of peptides to MHC Class II molecules. All implementations and datasets are available online to the scientific community at https://github.com/vanessajurtz/lasagne4bio. skaaesonderby@gmail.com. Supplementary data are available at Bioinformatics online. © The Author (2017). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com

Flotillin-1 is an evolutionary-conserved memory-related protein up-regulated in implicit and explicit learning paradigms.

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Monje, Francisco J; Divisch, Isabella; Demit, Marvie; Lubec, Gert; Pollak, Daniela D

2013-06-01

Studies of synaptic plasticity using the marine mollusk Aplysia californica as model system have been successfully used to identify proteins involved in learning and memory. The importance of molecular elements regulated by the learning- related neurotransmitter serotonin in Aplysia can then be explored in rodent models and finally tested for their relevance for human physiology and pathology. Herein, 2-DE gel-based electrophoresis has been used to investigate protein level changes after treatment with serotonin in Aplysia abdominal ganglia. Twenty-one proteins have been found to be regulated by serotonin, and protein level changes of actin depolymerizing factor (ADF), deleted in azoospermia associated protein (DAZAP-1), and Flotillin-1 have been verified by Western blotting. Flotillin-1, a member of the flotillin/reggie family of scaffolding proteins, has been previously found to be involved in neuritic branching and synapse formation in hippocampal neurons in vitro. However, its importance for hippocampal- dependent learning and memory in the mouse has not been examined. Here, elevated levels of Flotillin-1 in hippocampal tissue of mice trained in the Morris water maze confirmed the relevance of Flotillin-1 for memory-related processes in a mammalian system. Thus, a translational approach-from invertebrates to rodents-led to the identification of Flotillin-1 as evolutionary-conserved memory-related protein.

Evolutionary history of the thicket rats (genus Grammomys) mirrors the evolution of African forests since late Miocene

Czech Academy of Sciences Publication Activity Database

Bryja, J.; Šumbera, R.; Kerbis Peterhans, J. C.; Aghová, T.; Bryjová, A.; Mikula, Ondřej; Nicolas, V.; Denys, C.; Verheyen, E.

2017-01-01

Roč. 44, č. 1 (2017), s. 182-194 ISSN 0305-0270 Institutional support: RVO:67985904 Keywords : Arvicanthini * coastal forests * late Miocene Subject RIV: EA - Cell Biology OBOR OECD: Biology (theoretical, mathematical, thermal, cryobiology, biological rhythm), Evolutionary biology Impact factor: 4.248, year: 2016

Incorporating Development Into Evolutionary Psychology

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David F. Bjorklund

2016-09-01

Full Text Available Developmental thinking is gradually becoming integrated within mainstream evolutionary psychology. This is most apparent with respect to the role of parenting, with proponents of life history theory arguing that cognitive and behavioral plasticity early in life permits children to select different life history strategies, with such strategies being adaptive solutions to different fitness trade-offs. I argue that adaptations develop and are based on the highly plastic nature of infants’ and children’s behavior/cognition/brains. The concept of evolved probabilistic cognitive mechanisms is introduced, defined as information processing mechanisms evolved to solve recurrent problems faced by ancestral populations that are expressed in a probabilistic fashion in each individual in a generation and are based on the continuous and bidirectional interaction over time at all levels of organization, from the genetic through the cultural. Early perceptual/cognitive biases result in behavior that, when occurring in a species-typical environment, produce continuous adaptive changes in behavior (and cognition, yielding adaptive outcomes. Examples from social learning and tool use are provided, illustrating the development of adaptations via evolved probabilistic cognitive mechanisms. The integration of developmental concepts into mainstream evolutionary psychology (and evolutionary concepts into mainstream developmental psychology will provide a clearer picture of what it means to be human.

Traditional Versus Online Biology Courses: Connecting Course Design and Student Learning in an Online Setting.

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Biel, Rachel; Brame, Cynthia J

2016-12-01

Online courses are a large and growing part of the undergraduate education landscape, but many biology instructors are skeptical about the effectiveness of online instruction. We reviewed studies comparing the effectiveness of online and face-to-face (F2F) undergraduate biology courses. Five studies compared student performance in multiple course sections at community colleges, while eight were smaller scale and compared student performance in particular biology courses at a variety of types of institutions. Of the larger-scale studies, two found that students in F2F sections outperformed students in online sections, and three found no significant difference; it should be noted, however, that these studies reported little information about course design. Of the eight smaller scale studies, six found no significant difference in student performance between the F2F and online sections, while two found that the online sections outperformed the F2F sections. In alignment with general findings about online teaching and learning, these results suggest that well-designed online biology courses can be effective at promoting student learning. Three recommendations for effective online instruction in biology are given: the inclusion of an online orientation to acclimate students to the online classroom; student-instructor and student-student interactions facilitated through synchronous and asynchronous communication; and elements that prompt student reflection and self-assessment. We conclude that well-designed online biology courses can be as effective as their traditional counterparts, but that more research is needed to elucidate specific course elements and structures that can maximize online students' learning of key biology skills and concepts.

Traditional Versus Online Biology Courses: Connecting Course Design and Student Learning in an Online Setting

Directory of Open Access Journals (Sweden)

Rachel Biel

2016-12-01

Full Text Available Online courses are a large and growing part of the undergraduate education landscape, but many biology instructors are skeptical about the effectiveness of online instruction. We reviewed studies comparing the effectiveness of online and face-to-face (F2F undergraduate biology courses. Five studies compared student performance in multiple course sections at community colleges, while eight were smaller scale and compared student performance in particular biology courses at a variety of types of institutions. Of the larger-scale studies, two found that students in F2F sections outperformed students in online sections, and three found no significant difference; it should be noted, however, that these studies reported little information about course design. Of the eight smaller scale studies, six found no significant difference in student performance between the F2F and online sections, while two found that the online sections outperformed the F2F sections. In alignment with general findings about online teaching and learning, these results suggest that well-designed online biology courses can be effective at promoting student learning. Three recommendations for effective online instruction in biology are given: the inclusion of an online orientation to acclimate students to the online classroom; student-instructor and student-student interactions facilitated through synchronous and asynchronous communication; and elements that prompt student reflection and self-assessment. We conclude that well-designed online biology courses can be as effective as their traditional counterparts, but that more research is needed to elucidate specific course elements and structures that can maximize online students’ learning of key biology skills and concepts.

EPISTEMOLOGY IN THE CONTEXT OF THE MODERN BIOLOGICAL EVOLUTIONAL PARADIGM

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О. S. Tokovenko

2014-12-01

Full Text Available The purpose of the work is finding the reasonability of using bio-evolutionary paradigm for researching ratio morphic cognitive activity. Methodology. Methodological grounds consist of the original principles and conceptual apparatus of evolutionary epistemology. Scientific novelty. The article identifies opportunities for using of biological and evolutionary paradigm to study the peculiarities of ratiomorphic cognitive backgrounds and their influence on the formation and development of human knowledge. Conclusions. The article concludes that together with the idea of hyper cycles (feedback loop with mutual transmission of information in cognitive process the concept of ratiomorphic cognitive backgrounds, as well as attempts to examine cognitive processes based on the scientific criteria (empirical verification, explanatory power and ability to predict should be certainly considered as a positive contribution to the development of evolutionary epistemology into modern epistemological research. However, it is also indicated the fact of narrowing the heuristic possibilities of this epistemological direction because of excessive metaphor of bio-evolutionary paradigm. Further development of evolutionary epistemological research is considered in shifting the emphasis from biological and evolutionary towards cultural and evolutionary paradigm.

MENINGKATKAN MINAT BELAJAR BIOLOGI MENGGUNAKAN PEMBELAJARAN CTL (Contextual Teaching and Learning PADA SISWA DI KELAS VII-B MTs NEGERI PURWOKERTO

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Teguh Julianto

2010-09-01

Full Text Available Improving Student’s Interest in Biology lesson by using contextual teaching and learning (CLT methods is n action research study which had an aim to improve student’s interest in biology. The indicator of student’s interest covers the student’s diligence in learning process, active in following teaching and learning process, active in doing a task, the facility and the sources of learning. The result of this researched showed that there were an improvement of students learning interest. The percentage result in cycle I was 39.5%, in cycle 2 was 72.67% and in Cycle 3 was 80.92%. The improvement of students learning interests gave a positive effect toward the students achievement. The students learning achievement in cycle 1 I was 39%, in cycle II was 82% and in cycle III was 93%. In conclusion Contextual Teaching and Learning (CLT can improve the students learning interest in Biology at the Second Grade Students of MTs Negeri Purwokerto. Key words : Improving, Student’s learning interest, Contextual Teaching and Learning (CTL

The Effectiveness of KNOS-KGS Learning Model to Improve Generic Science Skill and Biology Student Learning Outcomes SMA PGRI 1 Banjarmasin

OpenAIRE

Nefianthi, Rezky

2015-01-01

This study aims to determine the effectiveness of the model KNOS-KGS, to improve the learning outcomes of Biology at the ecosystem material in class X SMA PGRI 1 Banjarmasin. This research is a classroom action research, conducted in two cycles. Each cycle is done with two meetings. This study was conducted on 25 students in class X1. The research data is the result of student learning that consists of cognitive learning outcomes such as pretest and posttest. Affective learning outcomes such ...

Evolutionary Games with Randomly Changing Payoff Matrices

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Yakushkina, Tatiana; Saakian, David B.; Bratus, Alexander; Hu, Chin-Kun

2015-06-01

Evolutionary games are used in various fields stretching from economics to biology. In most of these games a constant payoff matrix is assumed, although some works also consider dynamic payoff matrices. In this article we assume a possibility of switching the system between two regimes with different sets of payoff matrices. Potentially such a model can qualitatively describe the development of bacterial or cancer cells with a mutator gene present. A finite population evolutionary game is studied. The model describes the simplest version of annealed disorder in the payoff matrix and is exactly solvable at the large population limit. We analyze the dynamics of the model, and derive the equations for both the maximum and the variance of the distribution using the Hamilton-Jacobi equation formalism.

Coping with complexity: machine learning optimization of cell-free protein synthesis.

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Caschera, Filippo; Bedau, Mark A; Buchanan, Andrew; Cawse, James; de Lucrezia, Davide; Gazzola, Gianluca; Hanczyc, Martin M; Packard, Norman H

2011-09-01

Biological systems contain complex metabolic pathways with many nonlinearities and synergies that make them difficult to predict from first principles. Protein synthesis is a canonical example of such a pathway. Here we show how cell-free protein synthesis may be improved through a series of iterated high-throughput experiments guided by a machine-learning algorithm implementing a form of evolutionary design of experiments (Evo-DoE). The algorithm predicts fruitful experiments from statistical models of the previous experimental results, combined with stochastic exploration of the experimental space. The desired experimental response, or evolutionary fitness, was defined as the yield of the target product, and new experimental conditions were discovered to have ∼ 350% greater yield than the standard. An analysis of the best experimental conditions discovered indicates that there are two distinct classes of kinetics, thus showing how our evolutionary design of experiments is capable of significant innovation, as well as gradual improvement. Copyright © 2011 Wiley Periodicals, Inc.

Development links psychological causes to evolutionary explanations.

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Fedyk, Mark; Kushnir, Tamar

2014-04-01

Our conscious abilities are learned in environments that have evolved to support them. This insight provides an alternative way of framing Huang & Bargh's (H&B's) provocative hypothesis. To understand the conflict between unconscious goals and consciousness, we can study the emergence of conscious thought and control in childhood. These developmental processes are also central to the best available current evolutionary theories.

Biology and Economics: Metaphors that Economists usually take from Biology

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Danny García Callejas

2007-10-01

Full Text Available Adam Smith, Alfred Marshall, Stanley Jevons, Karl Marx, Francois Quesnay and Joseph Schumpeter all have at least one thing in common: they used biological metaphors when speaking about economics. Nonetheless, today, this relation subsists and biology and economics are viewed as complementary sciences that have a lot to gain from joint research in fields like: evolutionary economics, economic growth, cognitive economics and environmental and ecological economics, among others. This paper, divided in four sections, will show this conclusion and explain that biology and economics are more sisters than strangers

The evolutionary basis of human social learning.

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Morgan, T J H; Rendell, L E; Ehn, M; Hoppitt, W; Laland, K N

2012-02-22

Humans are characterized by an extreme dependence on culturally transmitted information. Such dependence requires the complex integration of social and asocial information to generate effective learning and decision making. Recent formal theory predicts that natural selection should favour adaptive learning strategies, but relevant empirical work is scarce and rarely examines multiple strategies or tasks. We tested nine hypotheses derived from theoretical models, running a series of experiments investigating factors affecting when and how humans use social information, and whether such behaviour is adaptive, across several computer-based tasks. The number of demonstrators, consensus among demonstrators, confidence of subjects, task difficulty, number of sessions, cost of asocial learning, subject performance and demonstrator performance all influenced subjects' use of social information, and did so adaptively. Our analysis provides strong support for the hypothesis that human social learning is regulated by adaptive learning rules.

The concept of ageing in evolutionary algorithms: Discussion and inspirations for human ageing.

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Dimopoulos, Christos; Papageorgis, Panagiotis; Boustras, George; Efstathiades, Christodoulos

2017-04-01

This paper discusses the concept of ageing as this applies to the operation of Evolutionary Algorithms, and examines its relationship to the concept of ageing as this is understood for human beings. Evolutionary Algorithms constitute a family of search algorithms which base their operation on an analogy from the evolution of species in nature. The paper initially provides the necessary knowledge on the operation of Evolutionary Algorithms, focusing on the use of ageing strategies during the implementation of the evolutionary process. Background knowledge on the concept of ageing, as this is defined scientifically for biological systems, is subsequently presented. Based on this information, the paper provides a comparison between the two ageing concepts, and discusses the philosophical inspirations which can be drawn for human ageing based on the operation of Evolutionary Algorithms. Copyright © 2017 Elsevier B.V. All rights reserved.

Individual-based modeling of ecological and evolutionary processes

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DeAngelis, Donald L.; Mooij, Wolf M.

2005-01-01

Individual-based models (IBMs) allow the explicit inclusion of individual variation in greater detail than do classical differential-equation and difference-equation models. Inclusion of such variation is important for continued progress in ecological and evolutionary theory. We provide a conceptual basis for IBMs by describing five major types of individual variation in IBMs: spatial, ontogenetic, phenotypic, cognitive, and genetic. IBMs are now used in almost all subfields of ecology and evolutionary biology. We map those subfields and look more closely at selected key papers on fish recruitment, forest dynamics, sympatric speciation, metapopulation dynamics, maintenance of diversity, and species conservation. Theorists are currently divided on whether IBMs represent only a practical tool for extending classical theory to more complex situations, or whether individual-based theory represents a radically new research program. We feel that the tension between these two poles of thinking can be a source of creativity in ecology and evolutionary theory.

Advanced Level Biology Teachers' Attitudes towards Assessment and Their Engagement in Assessment for Learning

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Bramwell-Lalor, Sharon; Rainford, Marcia

2015-01-01

This paper reports on a Mixed Methods study involving an investigation into the attitudes of advanced level biology teachers towards assessment and describes the teachers' experiences while being engaged in Assessment for Learning (AfL) practices such as sharing of learning objectives and peer- and self-assessment. Quantitative data were collected…

Wolbachia: Evolutionary novelty in a rickettsial bacteria

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Anderson Cort L

2001-11-01

Full Text Available Abstract Background Although closely related, the alpha-proteobacteria Wolbachia and the Rickettsiacae (Rickettsia and Ehrlichia, employ different evolutionary life history strategies. Wolbachia are obligate endocellular symbionts that infect an extraordinary host range and, in contrast to the infectious and pathogenic Rickettsia and Ehrlichia, profoundly influence host reproductive biology. Results Phylogenies of the Rickettsia, Ehrlichia, and Wolbachia were independently inferred from 16S rDNA sequences and GroEL amino acid sequences. Topologies inferred from both sets of sequence data were consistent with one another, and both indicate the genus Wolbachia shared a common ancestor most recently with Ehrlichia. These two genera are a sister group to the genus Rickettsia. Mapping biological properties onto this phylogeny reveals that manipulation of host reproduction, characteristic of Wolbachia strains, is a derived characteristic. This evolutionary novelty is accompanied by the loss of the ability to infect vertebrate hosts. Conclusions Because of the contrasting transmission strategies employed by each, Wolbachia is expected to maximize efficiency of vertical transmission, while Ehrlichia and Rickettsia will optimize horizontal transfer of infection. Wolbachia manipulation of host reproduction could thus be viewed as strategy employed by this bacterium to foster its own propagation via vertical transmission.

An Integrated Qualitative and Quantitative Biochemical Model Learning Framework Using Evolutionary Strategy and Simulated Annealing.

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Wu, Zujian; Pang, Wei; Coghill, George M

2015-01-01

Both qualitative and quantitative model learning frameworks for biochemical systems have been studied in computational systems biology. In this research, after introducing two forms of pre-defined component patterns to represent biochemical models, we propose an integrative qualitative and quantitative modelling framework for inferring biochemical systems. In the proposed framework, interactions between reactants in the candidate models for a target biochemical system are evolved and eventually identified by the application of a qualitative model learning approach with an evolution strategy. Kinetic rates of the models generated from qualitative model learning are then further optimised by employing a quantitative approach with simulated annealing. Experimental results indicate that our proposed integrative framework is feasible to learn the relationships between biochemical reactants qualitatively and to make the model replicate the behaviours of the target system by optimising the kinetic rates quantitatively. Moreover, potential reactants of a target biochemical system can be discovered by hypothesising complex reactants in the synthetic models. Based on the biochemical models learned from the proposed framework, biologists can further perform experimental study in wet laboratory. In this way, natural biochemical systems can be better understood.

Kant and Hegel's Responses to Hume's Skepticism Concerning Causality: An Evolutionary Epistemological Perspective

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Adam Christian Scarfe

2012-05-01

Full Text Available According to Hume, determinations of necessary causal connection are without empirical warrant, but, as he maintains, the concept of causality qua necessary connection is indispensable to human beings, having survival value for them, a claim which points to the biological significance of this concept. In contrast to Hume, Kant argues that the causal principle qua necessary connection belongs to the a priori conceptual framework by which rational beings constitute their experience and render the world intelligible. In “Kant’s Doctrine of the A Priori in Light of Contemporary Biology” (1941 / 1962 evolutionary epistemologist Konrad Lorenz sought to adapt Kant’s philosophy to contemporary biology by arguing that the a priori concepts of the understanding can be interpreted as comprising a biologically inherited framework, yet one that is provisional and in flux. Such an evolutionary interpretation of both Hume and Kant’s perspectives of the lacuna concerning causality brings the ideas of these thinkers closer together. Kant himself used suggestive analogies between the major epistemological positions concerning the origin of the a priori concepts of the understanding and the major biological theories of his time concerning the generation and development of organisms. Nevertheless, Kant would probably be reluctant to embrace such an evolutionarily-oriented conception of the categories, given his descriptions of them as self-thought, a priori first principles having a purely intellectual origin, belonging as a very condition for the possibility of the experience of rational beings in general, and as neither the product of a process of development, nor subject to one. This paper shows how Hegel’s emphasis on the dialectical progression of the logical Concept (Begriff can help to ground Lorenz’s evolutionary neo-Kantianism. Toward the end of the paper, I discuss the evolutionary relevance of skepticism and critical thinking in this

Evolutionary Medicine and Future of Humanity: Will Evolution Have the Final Word?

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Maciej Henneberg

2013-06-01

Full Text Available Evolutionary medicine in its classical form assumes that since cultural evolution is faster than biological evolution, ailments of modern people are a result of mismatch between adaptations to the past environments and current situations. A core principle is that we, humans, having evolved for millions of years in a specific natural environment (environment of evolutionary adaptation EEA are biologically adapted to this past environment and the ancient lifestyle. This adaptation to the past produces major mismatch of our bodies with the present, highly anthropic and thus “artificial” living conditions. This article provides two areas of possible future evolution, diet and physical activity levels which have been dramatically altered in industrialised societies. Consequently, micro-evolution is an on-going process.

Active Learning outside the Classroom: Implementation and Outcomes of Peer-Led Team-Learning Workshops in Introductory Biology

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Kudish, Philip; Shores, Robin; McClung, Alex; Smulyan, Lisa; Vallen, Elizabeth A.; Siwicki, Kathleen K.

2016-01-01

Study group meetings (SGMs) are voluntary-attendance peer-led team-learning workshops that supplement introductory biology lectures at a selective liberal arts college. While supporting all students' engagement with lecture material, specific aims are to improve the success of underrepresented minority (URM) students and those with weaker…

Biological lifestyle factors in adult distance education: predicting cognitive and learning performance

NARCIS (Netherlands)

Gijselaers, Jérôme

2015-01-01

Gijselaers, H. J. M. (2015, 20 October). Biological lifestyle factors in adult distance education: predicting cognitive and learning performance. Presentation given for the inter-faculty Data Science group at the Open University of the Netherlands, Heerlen, The Netherlands.

Investigating evolutionary constraints on the detection of threatening stimuli in preschool children.

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Zsido, Andras N; Deak, Anita; Losonci, Adrienn; Stecina, Diana; Arato, Akos; Bernath, Laszlo

2018-04-01

Numerous objects and animals could be threatening, and thus, children learn to avoid them early. Spiders and syringes are among the most common targets of fears and phobias of the modern word. However, they are of different origins: while the former is evolutionary relevant, the latter is not. We sought to investigate the underlying mechanisms that make the quick detection of such stimuli possible and enable the impulse to avoid them in the future. The respective categories of threatening and non-threatening targets were similar in shape, while low-level visual features were controlled. Our results showed that children found threatening cues faster, irrespective of the evolutionary age of the cues. However, they detected non-threatening evolutionary targets faster than non-evolutionary ones. We suggest that the underlying mechanism may be different: general feature detection can account for finding evolutionary threatening cues quickly, while specific features detection is more appropriate for modern threatening stimuli. Copyright © 2018 Elsevier B.V. All rights reserved.

Advanced Reactor Licensing: Experience with Digital I&C Technology in Evolutionary Plants

Energy Technology Data Exchange (ETDEWEB)

Wood, RT

2004-09-27

This report presents the findings from a study of experience with digital instrumentation and controls (I&C) technology in evolutionary nuclear power plants. In particular, this study evaluated regulatory approaches employed by the international nuclear power community for licensing advanced l&C systems and identified lessons learned. The report (1) gives an overview of the modern l&C technologies employed at numerous evolutionary nuclear power plants, (2) identifies performance experience derived from those applications, (3) discusses regulatory processes employed and issues that have arisen, (4) captures lessons learned from performance and regulatory experience, (5) suggests anticipated issues that may arise from international near-term deployment of reactor concepts, and (6) offers conclusions and recommendations for potential activities to support advanced reactor licensing in the United States.

How cultural evolutionary theory can inform social psychology and vice versa.

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Mesoudi, Alex

2009-10-01

Cultural evolutionary theory is an interdisciplinary field in which human culture is viewed as a Darwinian process of variation, competition, and inheritance, and the tools, methods, and theories developed by evolutionary biologists to study genetic evolution are adapted to study cultural change. It is argued here that an integration of the theories and findings of mainstream social psychology and of cultural evolutionary theory can be mutually beneficial. Social psychology provides cultural evolution with a set of empirically verified microevolutionary cultural processes, such as conformity, model-based biases, and content biases, that are responsible for specific patterns of cultural change. Cultural evolutionary theory provides social psychology with ultimate explanations for, and an understanding of the population-level consequences of, many social psychological phenomena, such as social learning, conformity, social comparison, and intergroup processes, as well as linking social psychology with other social science disciplines such as cultural anthropology, archaeology, and sociology.

Multi-objective evolutionary algorithms for fuzzy classification in survival prediction.

Science.gov (United States)

Jiménez, Fernando; Sánchez, Gracia; Juárez, José M

2014-03-01

This paper presents a novel rule-based fuzzy classification methodology for survival/mortality prediction in severe burnt patients. Due to the ethical aspects involved in this medical scenario, physicians tend not to accept a computer-based evaluation unless they understand why and how such a recommendation is given. Therefore, any fuzzy classifier model must be both accurate and interpretable. The proposed methodology is a three-step process: (1) multi-objective constrained optimization of a patient's data set, using Pareto-based elitist multi-objective evolutionary algorithms to maximize accuracy and minimize the complexity (number of rules) of classifiers, subject to interpretability constraints; this step produces a set of alternative (Pareto) classifiers; (2) linguistic labeling, which assigns a linguistic label to each fuzzy set of the classifiers; this step is essential to the interpretability of the classifiers; (3) decision making, whereby a classifier is chosen, if it is satisfactory, according to the preferences of the decision maker. If no classifier is satisfactory for the decision maker, the process starts again in step (1) with a different input parameter set. The performance of three multi-objective evolutionary algorithms, niched pre-selection multi-objective algorithm, elitist Pareto-based multi-objective evolutionary algorithm for diversity reinforcement (ENORA) and the non-dominated sorting genetic algorithm (NSGA-II), was tested using a patient's data set from an intensive care burn unit and a standard machine learning data set from an standard machine learning repository. The results are compared using the hypervolume multi-objective metric. Besides, the results have been compared with other non-evolutionary techniques and validated with a multi-objective cross-validation technique. Our proposal improves the classification rate obtained by other non-evolutionary techniques (decision trees, artificial neural networks, Naive Bayes, and case

On Reciprocal Causation in the Evolutionary Process.

Science.gov (United States)

Svensson, Erik I

2018-01-01

might have been underestimated by critics of contemporary evolutionary biology.

Atypical biological motion kinematics are represented by complementary lower-level and top-down processes during imitation learning.

Science.gov (United States)

Hayes, Spencer J; Dutoy, Chris A; Elliott, Digby; Gowen, Emma; Bennett, Simon J

2016-01-01

Learning a novel movement requires a new set of kinematics to be represented by the sensorimotor system. This is often accomplished through imitation learning where lower-level sensorimotor processes are suggested to represent the biological motion kinematics associated with an observed movement. Top-down factors have the potential to influence this process based on the social context, attention and salience, and the goal of the movement. In order to further examine the potential interaction between lower-level and top-down processes in imitation learning, the aim of this study was to systematically control the mediating effects during an imitation of biological motion protocol. In this protocol, we used non-human agent models that displayed different novel atypical biological motion kinematics, as well as a control model that displayed constant velocity. Importantly the three models had the same movement amplitude and movement time. Also, the motion kinematics were displayed in the presence, or absence, of end-state-targets. Kinematic analyses showed atypical biological motion kinematics were imitated, and that this performance was different from the constant velocity control condition. Although the imitation of atypical biological motion kinematics was not modulated by the end-state-targets, movement time was more accurate in the absence, compared to the presence, of an end-state-target. The fact that end-state targets modulated movement time accuracy, but not biological motion kinematics, indicates imitation learning involves top-down attentional, and lower-level sensorimotor systems, which operate as complementary processes mediated by the environmental context. Copyright © 2015 Elsevier B.V. All rights reserved.

The Effect of Cooperative Learning Model Script and Think-Pair-Share to Critical Thinking Skills, Social Attitude and Learning Outcomes Cognitive Biology of multiethnic High School Students

Directory of Open Access Journals (Sweden)

Didimus Tanah Boleng

2015-03-01

Full Text Available Pengaruh Model Pembelajaran Cooperative Script dan Think-Pair-Share terhadap Keterampilan Berpikir Kritis, Sikap Sosial, dan Hasil Belajar Kognitif Biologi Siswa SMA Multietnis   Abstract: Biological learning process with multiethnic students requires a learning models which allow students to work independently, to work together in small groups, and to share with other groups. The purpose of this study was to determine the effect of learning models, ethnicity, and the interaction of learning model and ethnic on critical thinking skills, social attitudes, and cognitive achievement. This quasi experimental study was conducted in 11th grade of Natural Science Class Highschool students with six ethnicaly and Junior Highschool National score groups consisted of 132 samples. The results of Covarian Analysis showed that the learning models significantly affected the social attitudes and increased the critical thinking skills and cognitive achievement. Ethnicity significantly affected the social attitudes and cognitive achievement. Interaction of learning models and ethnicity significantly affected students social attitudes. Key Words: cooperative script, think-pair-share, critical thinking skills, social attitudes, biology cognitive achievement, multiethnic students Abstrak: Pengelolaan proses pembelajaran biologi pada siswa multietnis memerlukan model pembelajaran yang memungkinkan siswa bekerja mandiri, bekerja sama dalam kelompok kecil, dan berbagi dengan kelompok lain. Tujuan penelitian ini untuk mengetahui pengaruh model pembelajaran, etnis, serta interaksi model pembelajaran dan etnis terhadap keterampilan berpikir kritis, sikap sosial, dan hasil belajar kognitif biologi siswa. Penelitian eksperimen semu ini dilakukan di kelas XI IPA SMA dengan sampel sebanyak 132 orang siswa terbagi dalam enam kelas yang homogen berdasarkan etnis dan nilai ujian nasional SMP siswa. Hasil analisis data dengan menggunakan Analisis Kovarian menunjukkan bahwa model

Evolutionary fuzzy ARTMAP neural networks for classification of semiconductor defects.

Science.gov (United States)

Tan, Shing Chiang; Watada, Junzo; Ibrahim, Zuwairie; Khalid, Marzuki

2015-05-01

Wafer defect detection using an intelligent system is an approach of quality improvement in semiconductor manufacturing that aims to enhance its process stability, increase production capacity, and improve yields. Occasionally, only few records that indicate defective units are available and they are classified as a minority group in a large database. Such a situation leads to an imbalanced data set problem, wherein it engenders a great challenge to deal with by applying machine-learning techniques for obtaining effective solution. In addition, the database may comprise overlapping samples of different classes. This paper introduces two models of evolutionary fuzzy ARTMAP (FAM) neural networks to deal with the imbalanced data set problems in a semiconductor manufacturing operations. In particular, both the FAM models and hybrid genetic algorithms are integrated in the proposed evolutionary artificial neural networks (EANNs) to classify an imbalanced data set. In addition, one of the proposed EANNs incorporates a facility to learn overlapping samples of different classes from the imbalanced data environment. The classification results of the proposed evolutionary FAM neural networks are presented, compared, and analyzed using several classification metrics. The outcomes positively indicate the effectiveness of the proposed networks in handling classification problems with imbalanced data sets.

Conceptions of Memorizing and Understanding in Learning, and Self-Efficacy Held by University Biology Majors

Science.gov (United States)

Lin, Tzu-Chiang; Liang, Jyh-Chong; Tsai, Chin-Chung

2015-01-01

This study aims to explore Taiwanese university students' conceptions of learning biology as memorizing or as understanding, and their self-efficacy. To this end, two questionnaires were utilized to survey 293 Taiwanese university students with biology-related majors. A questionnaire for measuring students' conceptions of memorizing and…

A Deep Learning Framework for Robust and Accurate Prediction of ncRNA-Protein Interactions Using Evolutionary Information.

Science.gov (United States)

Yi, Hai-Cheng; You, Zhu-Hong; Huang, De-Shuang; Li, Xiao; Jiang, Tong-Hai; Li, Li-Ping

2018-06-01

The interactions between non-coding RNAs (ncRNAs) and proteins play an important role in many biological processes, and their biological functions are primarily achieved by binding with a variety of proteins. High-throughput biological techniques are used to identify protein molecules bound with specific ncRNA, but they are usually expensive and time consuming. Deep learning provides a powerful solution to computationally predict RNA-protein interactions. In this work, we propose the RPI-SAN model by using the deep-learning stacked auto-encoder network to mine the hidden high-level features from RNA and protein sequences and feed them into a random forest (RF) model to predict ncRNA binding proteins. Stacked assembling is further used to improve the accuracy of the proposed method. Four benchmark datasets, including RPI2241, RPI488, RPI1807, and NPInter v2.0, were employed for the unbiased evaluation of five established prediction tools: RPI-Pred, IPMiner, RPISeq-RF, lncPro, and RPI-SAN. The experimental results show that our RPI-SAN model achieves much better performance than other methods, with accuracies of 90.77%, 89.7%, 96.1%, and 99.33%, respectively. It is anticipated that RPI-SAN can be used as an effective computational tool for future biomedical researches and can accurately predict the potential ncRNA-protein interacted pairs, which provides reliable guidance for biological research. Copyright © 2018 The Author(s). Published by Elsevier Inc. All rights reserved.

Massively parallel evolutionary computation on GPGPUs

CERN Document Server

Tsutsui, Shigeyoshi

2013-01-01

Evolutionary algorithms (EAs) are metaheuristics that learn from natural collective behavior and are applied to solve optimization problems in domains such as scheduling, engineering, bioinformatics, and finance. Such applications demand acceptable solutions with high-speed execution using finite computational resources. Therefore, there have been many attempts to develop platforms for running parallel EAs using multicore machines, massively parallel cluster machines, or grid computing environments. Recent advances in general-purpose computing on graphics processing units (GPGPU) have opened u

Modern Biology

OpenAIRE

ALEKSIC, Branko

2014-01-01

The purpose of this course is to learn the philosophy, principles, and techniques of modern biology. The course is particularly designed for those who have not learned biology previously or whose major is other than biology, and who may think that they do not need to know any biology at all. The topics are covered in a rather general, overview manner, but certain level of diligence in grasping concepts and memorizing the terminology is expected.

Information fluency for undergraduate biology majors: applications of inquiry-based learning in a developmental biology course.

Science.gov (United States)

Gehring, Kathleen M; Eastman, Deborah A

2008-01-01

Many initiatives for the improvement of undergraduate science education call for inquiry-based learning that emphasizes investigative projects and reading of the primary literature. These approaches give students an understanding of science as a process and help them integrate content presented in courses. At the same time, general initiatives to promote information fluency are being promoted on many college and university campuses. Information fluency refers to discipline-specific processing of information, and it involves integration of gathered information with specific ideas to form logical conclusions. We have implemented the use of inquiry-based learning to enhance and study discipline-specific information fluency skills in an upper-level undergraduate Developmental Biology course. In this study, an information literacy tutorial and a set of linked assignments using primary literature analysis were integrated with two inquiry-based laboratory research projects. Quantitative analysis of student responses suggests that the abilities of students to identify and apply valid sources of information were enhanced. Qualitative assessment revealed a set of patterns by which students gather and apply information. Self-assessment responses indicated that students recognized the impact of the assignments on their abilities to gather and apply information and that they were more confident about these abilities for future biology courses and beyond.

Technology Integration in Science Education: A Study of How Teachers Use Modern Learning Technologies in Biology Classrooms

Science.gov (United States)

Gnanakkan, Dionysius Joseph

2017-01-01

This multiple case-study investigated how high school biology teachers used modern learning technologies (probes, interactive simulations and animations, animated videos) in their classrooms and why they used the learning technologies. Another objective of the study was to assess whether the use of learning technologies alleviated misconceptions…

An evolutionary ecology of individual differences

Science.gov (United States)

Dall, Sasha R. X.; Bell, Alison M.; Bolnick, Daniel I.; Ratnieks, Francis L. W.

2014-01-01

Individuals often differ in what they do. This has been recognised since antiquity. Nevertheless, the ecological and evolutionary significance of such variation is attracting widespread interest, which is burgeoning to an extent that is fragmenting the literature. As a first attempt at synthesis, we focus on individual differences in behaviour within populations that exceed the day-to-day variation in individual behaviour (i.e. behavioural specialisation). Indeed, the factors promoting ecologically relevant behavioural specialisation within natural populations are likely to have far-reaching ecological and evolutionary consequences. We discuss such individual differences from three distinct perspectives: individual niche specialisations, the division of labour within insect societies and animal personality variation. In the process, while recognising that each area has its own unique motivations, we identify a number of opportunities for productive ‘crossfertilisation’ among the (largely independent) bodies of work. We conclude that a complete understanding of evolutionarily and ecologically relevant individual differences must specify how ecological interactions impact the basic biological process (e.g. Darwinian selection, development and information processing) that underpin the organismal features determining behavioural specialisations. Moreover, there is likely to be covariation amongst behavioural specialisations. Thus, we sketch the key elements of a general framework for studying the evolutionary ecology of individual differences. PMID:22897772

Modeling of biological intelligence for SCM system optimization.

Science.gov (United States)

Chen, Shengyong; Zheng, Yujun; Cattani, Carlo; Wang, Wanliang

2012-01-01

This article summarizes some methods from biological intelligence for modeling and optimization of supply chain management (SCM) systems, including genetic algorithms, evolutionary programming, differential evolution, swarm intelligence, artificial immune, and other biological intelligence related methods. An SCM system is adaptive, dynamic, open self-organizing, which is maintained by flows of information, materials, goods, funds, and energy. Traditional methods for modeling and optimizing complex SCM systems require huge amounts of computing resources, and biological intelligence-based solutions can often provide valuable alternatives for efficiently solving problems. The paper summarizes the recent related methods for the design and optimization of SCM systems, which covers the most widely used genetic algorithms and other evolutionary algorithms.

Modeling of Biological Intelligence for SCM System Optimization

Directory of Open Access Journals (Sweden)

Shengyong Chen

2012-01-01

Full Text Available This article summarizes some methods from biological intelligence for modeling and optimization of supply chain management (SCM systems, including genetic algorithms, evolutionary programming, differential evolution, swarm intelligence, artificial immune, and other biological intelligence related methods. An SCM system is adaptive, dynamic, open self-organizing, which is maintained by flows of information, materials, goods, funds, and energy. Traditional methods for modeling and optimizing complex SCM systems require huge amounts of computing resources, and biological intelligence-based solutions can often provide valuable alternatives for efficiently solving problems. The paper summarizes the recent related methods for the design and optimization of SCM systems, which covers the most widely used genetic algorithms and other evolutionary algorithms.

Modeling of Biological Intelligence for SCM System Optimization

Science.gov (United States)

Chen, Shengyong; Zheng, Yujun; Cattani, Carlo; Wang, Wanliang

2012-01-01

This article summarizes some methods from biological intelligence for modeling and optimization of supply chain management (SCM) systems, including genetic algorithms, evolutionary programming, differential evolution, swarm intelligence, artificial immune, and other biological intelligence related methods. An SCM system is adaptive, dynamic, open self-organizing, which is maintained by flows of information, materials, goods, funds, and energy. Traditional methods for modeling and optimizing complex SCM systems require huge amounts of computing resources, and biological intelligence-based solutions can often provide valuable alternatives for efficiently solving problems. The paper summarizes the recent related methods for the design and optimization of SCM systems, which covers the most widely used genetic algorithms and other evolutionary algorithms. PMID:22162724

Improving processes through evolutionary optimization.

Science.gov (United States)

Clancy, Thomas R

2011-09-01

As systems evolve over time, their natural tendency is to become increasingly more complex. Studies on complex systems have generated new perspectives on management in social organizations such as hospitals. Much of this research appears as a natural extension of the cross-disciplinary field of systems theory. This is the 18th in a series of articles applying complex systems science to the traditional management concepts of planning, organizing, directing, coordinating, and controlling. In this article, I discuss methods to optimize complex healthcare processes through learning, adaptation, and evolutionary planning.

A Survey on Evolutionary Algorithm Based Hybrid Intelligence in Bioinformatics

Directory of Open Access Journals (Sweden)

Shan Li

2014-01-01

Full Text Available With the rapid advance in genomics, proteomics, metabolomics, and other types of omics technologies during the past decades, a tremendous amount of data related to molecular biology has been produced. It is becoming a big challenge for the bioinformatists to analyze and interpret these data with conventional intelligent techniques, for example, support vector machines. Recently, the hybrid intelligent methods, which integrate several standard intelligent approaches, are becoming more and more popular due to their robustness and efficiency. Specifically, the hybrid intelligent approaches based on evolutionary algorithms (EAs are widely used in various fields due to the efficiency and robustness of EAs. In this review, we give an introduction about the applications of hybrid intelligent methods, in particular those based on evolutionary algorithm, in bioinformatics. In particular, we focus on their applications to three common problems that arise in bioinformatics, that is, feature selection, parameter estimation, and reconstruction of biological networks.

Application of Machine Learning to Proteomics Data: Classification and Biomarker Identification in Postgenomics Biology

Science.gov (United States)

Swan, Anna Louise; Mobasheri, Ali; Allaway, David; Liddell, Susan

2013-01-01

Abstract Mass spectrometry is an analytical technique for the characterization of biological samples and is increasingly used in omics studies because of its targeted, nontargeted, and high throughput abilities. However, due to the large datasets generated, it requires informatics approaches such as machine learning techniques to analyze and interpret relevant data. Machine learning can be applied to MS-derived proteomics data in two ways. First, directly to mass spectral peaks and second, to proteins identified by sequence database searching, although relative protein quantification is required for the latter. Machine learning has been applied to mass spectrometry data from different biological disciplines, particularly for various cancers. The aims of such investigations have been to identify biomarkers and to aid in diagnosis, prognosis, and treatment of specific diseases. This review describes how machine learning has been applied to proteomics tandem mass spectrometry data. This includes how it can be used to identify proteins suitable for use as biomarkers of disease and for classification of samples into disease or treatment groups, which may be applicable for diagnostics. It also includes the challenges faced by such investigations, such as prediction of proteins present, protein quantification, planning for the use of machine learning, and small sample sizes. PMID:24116388

Effect of Process-Oriented Guided-Inquiry Learning on Non-majors Biology Students' Understanding of Biological Classification

Science.gov (United States)

Wozniak, Breann M.

The purpose of this study was to examine the effect of process-oriented guided-inquiry learning (POGIL) on non-majors college biology students' understanding of biological classification. This study addressed an area of science instruction, POGIL in the non-majors college biology laboratory, which has yet to be qualitatively and quantitatively researched. A concurrent triangulation mixed methods approach was used. Students' understanding of biological classification was measured in two areas: scores on pre and posttests (consisting of 11 multiple choice questions), and conceptions of classification as elicited in pre and post interviews and instructor reflections. Participants were Minnesota State University, Mankato students enrolled in BIOL 100 Summer Session. One section was taught with the traditional curriculum (n = 6) and the other section in the POGIL curriculum (n = 10) developed by the researcher. Three students from each section were selected to take part in pre and post interviews. There were no significant differences within each teaching method (p familiar animal categories and aquatic habitats, unfamiliar organisms, combining and subdividing initial groupings, and the hierarchical nature of classification. The POGIL students were the only group to surpass these challenges after the teaching intervention. This study shows that POGIL is an effective technique at eliciting students' misconceptions, and addressing these misconceptions, leading to an increase in student understanding of biological classification.

Coming Out in Class: Challenges and Benefits of Active Learning in a Biology Classroom for LGBTQIA Students.

Science.gov (United States)

Cooper, Katelyn M; Brownell, Sara E

As we transition our undergraduate biology classrooms from traditional lectures to active learning, the dynamics among students become more important. These dynamics can be influenced by student social identities. One social identity that has been unexamined in the context of undergraduate biology is the spectrum of lesbian, gay, bisexual, transgender, queer, intersex, and asexual (LGBTQIA) identities. In this exploratory interview study, we probed the experiences and perceptions of seven students who identify as part of the LGBTQIA community. We found that students do not always experience the undergraduate biology classroom to be a welcoming or accepting place for their identities. In contrast to traditional lectures, active-learning classes increase the relevance of their LGBTQIA identities due to the increased interactions among students during group work. Finally, working with other students in active-learning classrooms can present challenges and opportunities for students considering their LGBTQIA identity. These findings indicate that these students' LGBTQIA identities are affecting their experience in the classroom and that there may be specific instructional practices that can mitigate some of the possible obstacles. We hope that this work can stimulate discussions about how to broadly make our active-learning biology classes more inclusive of this specific population of students. © 2016 K. M. Cooper and S. E. Brownell. CBE—Life Sciences Education © 2016 The American Society for Cell Biology. This article is distributed by The American Society for Cell Biology under license from the author(s). It is available to the public under an Attribution–Noncommercial–Share Alike 3.0 Unported Creative Commons License (http://creativecommons.org/licenses/by-nc-sa/3.0).

Learning Achievement Packages in Sciences-Biology: Cell Theory, Mitosis, Magnification, Wounds.

Science.gov (United States)

Solis, Juan D.

This publication presents four science curriculum units designed to meet the learning problems of students with special language handicaps. The materials are written in both English and Spanish, and deal with topics in biology suitable for students in grades 7 through 11. All four units were classroom tested during 1970-1972 in the Calexico…

An introduction to Deep learning on biological sequence data - Examples and solutions

DEFF Research Database (Denmark)

Jurtz, Vanessa Isabell; Johansen, Alexander Rosenberg; Nielsen, Morten

2017-01-01

Deep neural network architectures such as convolutional and long short-term memory networks have become increasingly popular as machine learning tools during the recent years. The availability of greater computational resources, more data, new algorithms for training deep models and easy to use....... Here, we aim to further the development of deep learning methods within biology by providing application examples and ready to apply and adapt code templates. Given such examples, we illustrate how architectures consisting of convolutional and long short-term memory neural networks can relatively...

Student Interpretations of Phylogenetic Trees in an Introductory Biology Course

Science.gov (United States)

Dees, Jonathan; Momsen, Jennifer L.; Niemi, Jarad; Montplaisir, Lisa

2014-01-01

Phylogenetic trees are widely used visual representations in the biological sciences and the most important visual representations in evolutionary biology. Therefore, phylogenetic trees have also become an important component of biology education. We sought to characterize reasoning used by introductory biology students in interpreting taxa…

The effects of stress and sex on selection, genetic covariance, and the evolutionary response.

Science.gov (United States)

Holman, L; Jacomb, F

2017-10-01

The capacity of a population to adapt to selection (evolvability) depends on whether the structure of genetic variation permits the evolution of fitter trait combinations. Selection, genetic variance and genetic covariance can change under environmental stress, and males and females are not genetically independent, yet the combined effects of stress and dioecy on evolvability are not well understood. Here, we estimate selection, genetic (co)variance and evolvability in both sexes of Tribolium castaneum flour beetles under stressful and benign conditions, using a half-sib breeding design. Although stress uncovered substantial latent heritability, stress also affected genetic covariance, such that evolvability remained low under stress. Sexual selection on males and natural selection on females favoured a similar phenotype, and there was positive intersex genetic covariance. Consequently, sexual selection on males augmented adaptation in females, and intralocus sexual conflict was weak or absent. This study highlights that increased heritability does not necessarily increase evolvability, suggests that selection can deplete genetic variance for multivariate trait combinations with strong effects on fitness, and tests the recent hypothesis that sexual conflict is weaker in stressful or novel environments. © 2017 European Society For Evolutionary Biology. Journal of Evolutionary Biology © 2017 European Society For Evolutionary Biology.

Evolutionary bottlenecks in brackish water habitats drive the colonization of fresh water by stingrays.

Science.gov (United States)

Kirchhoff, K N; Hauffe, T; Stelbrink, B; Albrecht, C; Wilke, T

2017-08-01

Species richness in freshwater bony fishes depends on two main processes: the transition into and the diversification within freshwater habitats. In contrast to bony fishes, only few cartilaginous fishes, mostly stingrays (Myliobatoidei), were able to colonize fresh water. Respective transition processes have been mainly assessed from a physiological and morphological perspective, indicating that the freshwater lifestyle is strongly limited by the ability to perform osmoregulatory adaptations. However, the transition history and the effect of physiological constraints on the diversification in stingrays remain poorly understood. Herein, we estimated the geographic pathways of freshwater colonization and inferred the mode of habitat transitions. Further, we assessed habitat-related speciation rates in a time-calibrated phylogenetic framework to understand factors driving the transition of stingrays into and the diversification within fresh water. Using South American and Southeast Asian freshwater taxa as model organisms, we found one independent freshwater colonization event by stingrays in South America and at least three in Southeast Asia. We revealed that vicariant processes most likely caused freshwater transition during the time of major marine incursions. The habitat transition rates indicate that brackish water species switch preferably back into marine than forth into freshwater habitats. Moreover, our results showed significantly lower diversification rates in brackish water lineages, whereas freshwater and marine lineages exhibit similar rates. Thus, brackish water habitats may have functioned as evolutionary bottlenecks for the colonization of fresh water by stingrays, probably because of the higher variability of environmental conditions in brackish water. © 2017 European Society For Evolutionary Biology. Journal of Evolutionary Biology © 2017 European Society For Evolutionary Biology.

Using Photographs as Case Studies to Promote Active Learning in Biology

Science.gov (United States)

Krauss, David A.; Salame, Issa I.; Goodwyn, Lauren N.

2010-01-01

If a picture is worth a thousand words, think about how long it takes your students to read a thousand words. Case studies are effective and stimulating ways to teach a variety of subjects, including the biological sciences. In learning the details of a particular case, students develop skills in both deductive and inductive reasoning, hypothesis…

In Darwin's Footsteps: An On and Off-Campus Approach to Teaching Evolutionary Theory and Animal Behavior

Science.gov (United States)

Gillie, Lynn; Bizub, Anne L.

2012-01-01

The study of evolutionary theory and fieldwork in animal behavior is enriched when students leave the classroom so they may test their abilities to think and act like scientists. This article describes a course on evolutionary theory and animal behavior that blended on campus learning with field experience in the United States and in Ecuador and…

Telomere biology in healthy aging and disease

NARCIS (Netherlands)

Oeseburg, Hisko; de Boer, Rudolf A.; van Gilst, Wiek H.; van der Harst, Pim

Aging is a biological process that affects most cells, organisms and species. Telomeres have been postulated as a universal biological clock that shortens in parallel with aging in cells. Telomeres are located at the end of the chromosomes and consist of an evolutionary conserved repetitive

A brief introduction to continuous evolutionary optimization

CERN Document Server

Kramer, Oliver

2014-01-01

Practical optimization problems are often hard to solve, in particular when they are black boxes and no further information about the problem is available except via function evaluations. This work introduces a collection of heuristics and algorithms for black box optimization with evolutionary algorithms in continuous solution spaces. The book gives an introduction to evolution strategies and parameter control. Heuristic extensions are presented that allow optimization in constrained, multimodal, and multi-objective solution spaces. An adaptive penalty function is introduced for constrained optimization. Meta-models reduce the number of fitness and constraint function calls in expensive optimization problems. The hybridization of evolution strategies with local search allows fast optimization in solution spaces with many local optima. A selection operator based on reference lines in objective space is introduced to optimize multiple conflictive objectives. Evolutionary search is employed for learning kernel ...

Influence of Web-Aided Cooperative Learning Environment on Motivation and on Self-Efficacy Belief in Biology Teaching

Science.gov (United States)

Hevedanli, Murat

2015-01-01

The purpose of this study is to investigate the influence of the web-aided cooperative learning environment on biology preservice teachers' motivation and on their self-efficacy beliefs in biology teaching. The study was carried out with 30 biology preservice teachers attending a state university in Turkey. In the study, the pretest-posttest…

Evolutionary thinking in microeconomic models: prestige bias and market bubbles.

Directory of Open Access Journals (Sweden)

Adrian Viliami Bell

Full Text Available Evolutionary models broadly support a number of social learning strategies likely important in economic behavior. Using a simple model of price dynamics, I show how prestige bias, or copying of famed (and likely successful individuals, influences price equilibria and investor disposition in a way that exacerbates or creates market bubbles. I discuss how integrating the social learning and demographic forces important in cultural evolution with economic models provides a fruitful line of inquiry into real-world behavior.

An Evolutionary Approach to the Climate Change Negotiation Game

Energy Technology Data Exchange (ETDEWEB)

Courtois, P. [CIRED and University of Paris, Paris (France); Pereau, J.C. [OEP, University of Marne-la-Vallee, Marne-la-Vallee (France); Tazdait, T. [CIRED and OEP, University of Marne-la-Vallee, Marne-la-Vallee (France)

2001-10-01

We describe in this paper an evolutionary game theoretic model aiming at representing the climate change negotiation. The model is used to examine the outcome of climate change negotiations in a framework which seeks to closely represent negotiation patterns. Evolutionary setting allows us to consider a decision making structure characterised by agents with bounded knowledge practising mimics and learning from past events and strategies. We show on that framework that a third significant alternative to the binary coordination-defection strategies needs to be considered: a unilateral commitment as precautionary strategy. As a means to widen cooperation, we examine the influence of linking environmental and trade policies via the implementation of a trade penalty on non cooperative behaviours.

An Evolutionary Approach to the Climate Change Negotiation Game

International Nuclear Information System (INIS)

Courtois, P.; Pereau, J.C.; Tazdait, T.

2001-10-01

We describe in this paper an evolutionary game theoretic model aiming at representing the climate change negotiation. The model is used to examine the outcome of climate change negotiations in a framework which seeks to closely represent negotiation patterns. Evolutionary setting allows us to consider a decision making structure characterised by agents with bounded knowledge practising mimics and learning from past events and strategies. We show on that framework that a third significant alternative to the binary coordination-defection strategies needs to be considered: a unilateral commitment as precautionary strategy. As a means to widen cooperation, we examine the influence of linking environmental and trade policies via the implementation of a trade penalty on non cooperative behaviours

ERC analysis: web-based inference of gene function via evolutionary rate covariation.

Science.gov (United States)

Wolfe, Nicholas W; Clark, Nathan L

2015-12-01

The recent explosion of comparative genomics data presents an unprecedented opportunity to construct gene networks via the evolutionary rate covariation (ERC) signature. ERC is used to identify genes that experienced similar evolutionary histories, and thereby draws functional associations between them. The ERC Analysis website allows researchers to exploit genome-wide datasets to infer novel genes in any biological function and to explore deep evolutionary connections between distinct pathways and complexes. The website provides five analytical methods, graphical output, statistical support and access to an increasing number of taxonomic groups. Analyses and data at http://csb.pitt.edu/erc_analysis/ nclark@pitt.edu. © The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

A Vision and Change Reform of Introductory Biology Shifts Faculty Perceptions and Use of Active Learning

Science.gov (United States)

Auerbach, Anna Jo; Schussler, Elisabeth

2017-01-01

Increasing faculty use of active-learning (AL) pedagogies in college classrooms is a persistent challenge in biology education. A large research-intensive university implemented changes to its biology majors’ two-course introductory sequence as outlined by the Vision and Change in Undergraduate Biology Education final report. One goal of the curricular reform was to integrate core biological concepts and competencies into the courses using AL pedagogical approaches. The purpose of this study was to observe the instructional practices used by faculty (N = 10) throughout the 3-year process of reform to determine whether the use of AL strategies (including student collaboration) increased, given that it can maximize student learning gains. Instructors participated in yearly interviews to track any change in their perceptions of AL instruction. Instructors increased their average use of AL by 12% (group AL by 8%) of total class time throughout the 3-year study. Interviews revealed that instructors shifted their definitions of AL and talked more about how to assess student learning over the 3 years of the project. Collaboration, feedback, and time may have been important factors in the reform, suggesting that small shifts over time can accumulate into real change in the classroom. PMID:29146663

Evolving cell models for systems and synthetic biology.

Science.gov (United States)

Cao, Hongqing; Romero-Campero, Francisco J; Heeb, Stephan; Cámara, Miguel; Krasnogor, Natalio

2010-03-01

This paper proposes a new methodology for the automated design of cell models for systems and synthetic biology. Our modelling framework is based on P systems, a discrete, stochastic and modular formal modelling language. The automated design of biological models comprising the optimization of the model structure and its stochastic kinetic constants is performed using an evolutionary algorithm. The evolutionary algorithm evolves model structures by combining different modules taken from a predefined module library and then it fine-tunes the associated stochastic kinetic constants. We investigate four alternative objective functions for the fitness calculation within the evolutionary algorithm: (1) equally weighted sum method, (2) normalization method, (3) randomly weighted sum method, and (4) equally weighted product method. The effectiveness of the methodology is tested on four case studies of increasing complexity including negative and positive autoregulation as well as two gene networks implementing a pulse generator and a bandwidth detector. We provide a systematic analysis of the evolutionary algorithm's results as well as of the resulting evolved cell models.

Hybrid fitness, adaptation and evolutionary diversification: lessons learned from Louisiana Irises.

Science.gov (United States)

Arnold, M L; Ballerini, E S; Brothers, A N

2012-03-01

Estimates of hybrid fitness have been used as either a platform for testing the potential role of natural hybridization in the evolution of species and species complexes or, alternatively, as a rationale for dismissing hybridization events as being of any evolutionary significance. From the time of Darwin's publication of The Origin, through the neo-Darwinian synthesis, to the present day, the observation of variability in hybrid fitness has remained a challenge for some models of speciation. Yet, Darwin and others have reported the elevated fitness of hybrid genotypes under certain environmental conditions. In modern scientific terminology, this observation reflects the fact that hybrid genotypes can demonstrate genotype × environment interactions. In the current review, we illustrate the development of one plant species complex, namely the Louisiana Irises, into a 'model system' for investigating hybrid fitness and the role of genetic exchange in adaptive evolution and diversification. In particular, we will argue that a multitude of approaches, involving both experimental and natural environments, and incorporating both manipulative analyses and surveys of natural populations, are necessary to adequately test for the evolutionary significance of introgressive hybridization. An appreciation of the variability of hybrid fitness leads to the conclusion that certain genetic signatures reflect adaptive evolution. Furthermore, tests of the frequency of allopatric versus sympatric/parapatric divergence (that is, divergence with ongoing gene flow) support hybrid genotypes as a mechanism of evolutionary diversification in numerous species complexes.

A critical analysis of the new biology and the biological revolution: their impact - from medicine to evolution.

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Dev, Sukhendu B

2010-01-01

In this article, I critically analyze the impact of the new biology and the biological revolution. I argue that indiscriminate use of the words such as 'interdisciplinary,' 'integrative,' and 'revolution' has caused only confusion when applied to biology. The recent debate, especially after the exploding field of systems biology, has brought back the controversy whether molecular biology is reductionist or holistic. I look at the issues involved critically. I discuss the problem of defining the word 'gene' and argue that recent attempts to redefine the central dogma of molecular biology about the information flow from DNA to RNA to protein are not justified. I support my view with comments from the scientist who discovered RNA splicing. Several aspects of evo-devo, a new branch of biology, are discussed. I give examples from this evolution-developmental biology to show how some of Darwin's inspired guesses have had resounding victory when it was found that specific genes during embryonic development of the Galapagos finches decided the size and shape of their beaks. I discuss the recent publications which show that the conditions in the island, such as wet to dry to wet season, can bring about evolutionary changes from year to year. Thus it is essential to monitor both short and long-term evolutionary changes to get the full picture of evolution.

Converging biology, economics and social science in fisheries research –lessons learned

DEFF Research Database (Denmark)

Haapasaari, Päivi Elisabet; Kulmala, Soile; Kuikka, Sakari

2011-01-01

of the Baltic salmon stocks, using the Bayesian networks. It enabled the analysis of the outcomes of different management measures from biological, social and economic perspectives. The synthesis was the final output of a learning process of eight years. We reflect how and what kind of interdisciplinarity...... between natural scientists, economists and social scientists grew from the need to better understand complexity related to the salmon fisheries in the Baltic Sea, what we learned about the fishery, and what we learned about interdisciplinary collaboration.......It has been acknowledged that natural sciences cannot provide an adequate basis for the management of complex environmental problems. The scientific knowledge base has to be expanded towards a more holistic direction by incorporating social and economic issues. Besides this, the multifaceted...

Frontiers in mathematical biology

CERN Document Server

1994-01-01

Volume 100, which is the final volume of the LNBM series serves to commemorate the acievements in two decades of this influential collection of books in mathematical biology. The contributions, by the leading mathematical biologists, survey the state of the art in the subject, and offer speculative, philosophical and critical analyses of the key issues confronting the field. The papers address fundamental issues in cell and molecular biology, organismal biology, evolutionary biology, population ecology, community and ecosystem ecology, and applied biology, plus the explicit and implicit mathematical challenges. Cross-cuttting issues involve the problem of variation among units in nonlinear systems, and the related problems of the interactions among phenomena across scales of space, time and organizational complexity.

The island model for parallel implementation of evolutionary algorithm of Population-Based Incremental Learning (PBIL) optimization

International Nuclear Information System (INIS)

Lima, Alan M.M. de; Schirru, Roberto

2000-01-01

Genetic algorithms are biologically motivated adaptive systems which have been used, with good results, for function optimization. The purpose of this work is to introduce a new parallelization method to be applied to the Population-Based Incremental Learning (PBIL) algorithm. PBIL combines standard genetic algorithm mechanisms with simple competitive learning and has ben successfully used in combinatorial optimization problems. The development of this algorithm aims its application to the reload optimization of PWR nuclear reactors. Tests have been performed with combinatorial optimization problems similar to the reload problem. Results are compared to the serial PBIL ones, showing the new method's superiority and its viability as a tool for the nuclear core reload problem solution. (author)

Investigating Flipped Learning: Student Self-Regulated Learning, Perceptions, and Achievement in an Introductory Biology Course

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Sletten, Sarah Rae

2017-06-01

In flipped classrooms, lectures, which are normally delivered in-class, are assigned as homework in the form of videos, and assignments that were traditionally assigned as homework, are done as learning activities in class. It was hypothesized that the effectiveness of the flipped model hinges on a student's desire and ability to adopt a self-directed learning style. The purpose of this study was twofold; it aimed at examining the relationship between two variables—students' perceptions of the flipped model and their self-regulated learning (SRL) behaviors—and the impact that these variables have on achievement in a flipped class. For the study, 76 participants from a flipped introductory biology course were asked about their SRL strategy use and perceptions of the flipped model. SRL strategy use was measured using a modified version of the Motivated Strategies for Learning Questionnaire (MSLQ; Wolters et al. 2005), while the flipped perceptions survey was newly derived. Student letter grades were collected as a measure of achievement. Through regression analysis, it was found that students' perceptions of the flipped model positively predict students' use of several types of SRL strategies. However, the data did not indicate a relationship between student perceptions and achievement, neither directly nor indirectly, through SRL strategy use. Results suggest that flipped classrooms demonstrate their successes in the active learning sessions through constructivist teaching methods. Video lectures hold an important role in flipped classes, however, students may need to practice SRL skills to become more self-directed and effectively learn from them.

Advanced Reactor Licensing: Experience with Digital I and C Technology in Evolutionary Plants

International Nuclear Information System (INIS)

Wood, RT

2004-01-01

This report presents the findings from a study of experience with digital instrumentation and controls (I and C) technology in evolutionary nuclear power plants. In particular, this study evaluated regulatory approaches employed by the international nuclear power community for licensing advanced l and C systems and identified lessons learned. The report (1) gives an overview of the modern l and C technologies employed at numerous evolutionary nuclear power plants, (2) identifies performance experience derived from those applications, (3) discusses regulatory processes employed and issues that have arisen, (4) captures lessons learned from performance and regulatory experience, (5) suggests anticipated issues that may arise from international near-term deployment of reactor concepts, and (6) offers conclusions and recommendations for potential activities to support advanced reactor licensing in the United States

Multiscale structure in eco-evolutionary dynamics

Science.gov (United States)

Stacey, Blake C.

In a complex system, the individual components are neither so tightly coupled or correlated that they can all be treated as a single unit, nor so uncorrelated that they can be approximated as independent entities. Instead, patterns of interdependency lead to structure at multiple scales of organization. Evolution excels at producing such complex structures. In turn, the existence of these complex interrelationships within a biological system affects the evolutionary dynamics of that system. I present a mathematical formalism for multiscale structure, grounded in information theory, which makes these intuitions quantitative, and I show how dynamics defined in terms of population genetics or evolutionary game theory can lead to multiscale organization. For complex systems, "more is different," and I address this from several perspectives. Spatial host--consumer models demonstrate the importance of the structures which can arise due to dynamical pattern formation. Evolutionary game theory reveals the novel effects which can result from multiplayer games, nonlinear payoffs and ecological stochasticity. Replicator dynamics in an environment with mesoscale structure relates to generalized conditionalization rules in probability theory. The idea of natural selection "acting at multiple levels" has been mathematized in a variety of ways, not all of which are equivalent. We will face down the confusion, using the experience developed over the course of this thesis to clarify the situation.

Nature vs Nurture: Effects of Learning on Evolution

Science.gov (United States)

Nagrani, Nagina

In the field of Evolutionary Robotics, the design, development and application of artificial neural networks as controllers have derived their inspiration from biology. Biologists and artificial intelligence researchers are trying to understand the effects of neural network learning during the lifetime of the individuals on evolution of these individuals by qualitative and quantitative analyses. The conclusion of these analyses can help develop optimized artificial neural networks to perform any given task. The purpose of this thesis is to study the effects of learning on evolution. This has been done by applying Temporal Difference Reinforcement Learning methods to the evolution of Artificial Neural Tissue controller. The controller has been assigned the task to collect resources in a designated area in a simulated environment. The performance of the individuals is measured by the amount of resources collected. A comparison has been made between the results obtained by incorporating learning in evolution and evolution alone. The effects of learning parameters: learning rate, training period, discount rate, and policy on evolution have also been studied. It was observed that learning delays the performance of the evolving individuals over the generations. However, the non zero learning rate throughout the evolution process signifies natural selection preferring individuals possessing plasticity.

Evolutionary explanations in medical and health profession courses: are you answering your students' "why" questions?

Directory of Open Access Journals (Sweden)

Malyango Avelin A

2005-05-01

Full Text Available Abstract Background Medical and pre-professional health students ask questions about human health that can be answered in two ways, by giving proximate and evolutionary explanations. Proximate explanations, most common in textbooks and classes, describe the immediate scientifically known biological mechanisms of anatomical characteristics or physiological processes. These explanations are necessary but insufficient. They can be complemented with evolutionary explanations that describe the evolutionary processes and principles that have resulted in human biology we study today. The main goal of the science of Darwinian Medicine is to investigate human disease, disorders, and medical complications from an evolutionary perspective. Discussion This paper contrasts the differences between these two types of explanations by describing principles of natural selection that underlie medical questions. Thus, why is human birth complicated? Why does sickle cell anemia exist? Why do we show symptoms like fever, diarrhea, and coughing when we have infection? Why do we suffer from ubiquitous age-related diseases like arteriosclerosis, Alzheimer's and others? Why are chronic diseases like type II diabetes and obesity so prevalent in modern society? Why hasn't natural selection eliminated the genes that cause common genetic diseases like hemochromatosis, cystic fibrosis, Tay sachs, PKU and others? Summary In giving students evolutionary explanations professors should underscore principles of natural selection, since these can be generalized for the analysis of many medical questions. From a research perspective, natural selection seems central to leading hypotheses of obesity and type II diabetes and might very well explain the occurrence of certain common genetic diseases like cystic fibrosis, hemochromatosis, Tay sachs, Fragile X syndrome, G6PD and others because of their compensating advantages. Furthermore, armed with evolutionary explanations, health care

Evolutionary image simplification for lung nodule classification with convolutional neural networks.

Science.gov (United States)

Lückehe, Daniel; von Voigt, Gabriele

2018-05-29

Understanding decisions of deep learning techniques is important. Especially in the medical field, the reasons for a decision in a classification task are as crucial as the pure classification results. In this article, we propose a new approach to compute relevant parts of a medical image. Knowing the relevant parts makes it easier to understand decisions. In our approach, a convolutional neural network is employed to learn structures of images of lung nodules. Then, an evolutionary algorithm is applied to compute a simplified version of an unknown image based on the learned structures by the convolutional neural network. In the simplified version, irrelevant parts are removed from the original image. In the results, we show simplified images which allow the observer to focus on the relevant parts. In these images, more than 50% of the pixels are simplified. The simplified pixels do not change the meaning of the images based on the learned structures by the convolutional neural network. An experimental analysis shows the potential of the approach. Besides the examples of simplified images, we analyze the run time development. Simplified images make it easier to focus on relevant parts and to find reasons for a decision. The combination of an evolutionary algorithm employing a learned convolutional neural network is well suited for the simplification task. From a research perspective, it is interesting which areas of the images are simplified and which parts are taken as relevant.

Indirect genetics effects and evolutionary constraint: an analysis of social dominance in red deer, Cervus elaphus.

Science.gov (United States)

Wilson, A J; Morrissey, M B; Adams, M J; Walling, C A; Guinness, F E; Pemberton, J M; Clutton-Brock, T H; Kruuk, L E B

2011-04-01

By determining access to limited resources, social dominance is often an important determinant of fitness. Thus, if heritable, standard theory predicts mean dominance should evolve. However, dominance is usually inferred from the tendency to win contests, and given one winner and one loser in any dyadic contest, the mean proportion won will always equal 0.5. Here, we argue that the apparent conflict between quantitative genetic theory and common sense is resolved by recognition of indirect genetic effects (IGEs). We estimate selection on, and genetic (co)variance structures for, social dominance, in a wild population of red deer Cervus elaphus, on the Scottish island of Rum. While dominance is heritable and positively correlated with lifetime fitness, contest outcomes depend as much on the genes carried by an opponent as on the genotype of a focal individual. We show how this dependency imposes an absolute evolutionary constraint on the phenotypic mean, thus reconciling theoretical predictions with common sense. More generally, we argue that IGEs likely provide a widespread but poorly recognized source of evolutionary constraint for traits influenced by competition. © 2011 The Authors. Journal of Evolutionary Biology © 2011 European Society For Evolutionary Biology.

The evolutionary origin and significance of Menopause