Using Avida-ED for Teaching and Learning About Evolution in Undergraduate Introductory Biology Courses

Evolution is a complex subject that requires knowledge of basic biological concepts and the ability to connect them across multiple scales of time, space, and biological organization. Avida-ED is a digital evolution educational software environment designed for teaching and learning about evolution and the nature of science in undergraduate biology courses. This study describes our backward design approach to developing an instructional activity using Avida-ED for teaching and learning about evolution in a large-enrollment introductory biology course. Using multiple assessment instruments, we measured student knowledge and understanding of key principles of natural selection before and after instruction on evolution (including the Avida-ED activity). Assessment analysis revealed significant post-instruction learning gains, although certain evolutionary principles (most notably those including genetics concepts, such as the genetic origin of variation) remained particularly difficult for students, even after instruction. Students, however, demonstrated a good grasp of the genetic component of the evolutionary process in the context of a problem on Avida-ED. We propose that: (a) deep understanding of evolution requires complex systems thinking skills, such as connecting concepts across multiple levels of biological organization, and (b) well designed use of Avida-ED holds the potential to help learners build a meaningful and transferable understanding of the evolutionary process. An erratum to this article can be found at     

Goal functions,orientors and indicators (GoFOrIt's) in ecology. Application and functional aspects – Strengths and weaknesses

For more than two millenniums it has been almost impossible to address the issue of teleology in Western world science. Meanwhile, after the introduction of a systems view in ecology it has been clearly demonstrated that when attempting to understand nature as ecosystems, we deal with structures organized in a hierarchy and with a very high complexity. Many unexpected properties emerge from the constituent components and the interactions among them. The processes result in an intricate behaviour that can only be understood and explained within the realm of some degree of goal oriented behaviour of the systems. The use of the term goal function proper implies that a full teleological perspective is included in our perception of the systems. However, lately we have tended use more vague concepts like orientors and indicators. This paper argues that such a division makes sense, only we need in the future to be more clear and consistent in our use of the concepts. The term goal functions should be reserved to functions that are given final extremum states alone. Orientors should be used for localized directional behaviour in time and space. Indicators should be used where isolated time/space information exists. It may be used for monitoring changes in systems without giving any directional cause to development. The exact role among the concepts of being either a goal function, orientor or indicator is not always clearly defined. In many cases because the concepts are often used outside their original domains. Three types of original domains are clearly distinguishable, a biotic, a network and a thermodynamical direction. The strengths and weaknesses within the areas are discussed. Biotic concepts share the advantage of being close to established biological views and traditional ecology. They tell little about functionality, directionality and consequences of interactions. This is opposed to the two other directions that find their background in network analysis, information theory, and various domains of thermodynamics. As derived from physics the latter two areas achieve some scientific credibility, but suffer from problems of definition already inherent in the scientific sub-disciplines. Concerning the acceptance of ecosystem theory to ecologists it is also clear that approaches close to traditional biology such as the energetics of eco-physiology and diversity are much more accepted than any of the others leading to an overwhelming number of isolated and non-aggregated indices in the area of indicators. Indices that demand multidisciplinary insights such as goal functions and orientors are found to be less popular and deserve to receive much more attention in the future.     

EcoPred: an educational individual based model to explain biological control,a case study within an arable land

ABSTRACT

Individual based models (IBMs) are up-to-date tools both in research and educational areas. Here we introduce an IBM built on NetLogo platform that simulates a top-down trophic cascade controlled by the pressure exerted by two model predators (web-building spiders and ground runner spiders) on a model pest (the olive fruit fly) within a hypothetical agricultural landscape (the olive crop). EcoPred is an IBM that aims to be an educational tool that can help teachers to explain concepts related to ecology in a modern, enjoyable and comprehensive way. EcoPred reflects the changes on a fly population within a simulated olive crop according to (1) the mortality rate caused by the predation of two spider species and energy loss, (2) the energy gain by feeding on flowers and (3) the reproduction rate in olive trees. The model was tested with 26 students achieving very good results in terms of acceptance and interest on the learning method. EcoPred can be used for educational purposes with 16 year old students and older to explain ecological concepts such as trophic level, species interactions, limiting factor and biological control in an interactive way simultaneously introducing students to biology oriented programming languages.     

On the Problem of Man in Psychology

Very few works in our literature have been devoted to human ethology and social ethology, particularly of children. Psychological-pedagogical approaches predominate; these are based on an assessment of verbal information and on the results of experimental or educational inputs. But such studies should rest on knowledge of the distinctive features of a person's immediate behavior, and these can be ascertained solely through objective study, in which every directed input is precluded.     

Integrated bio-entity network: a system for biological knowledge discovery

A significant part of our biological knowledge is centered on relationships between biological entities (bio-entities) such as proteins, genes, small molecules, pathways, gene ontology (GO) terms and diseases. Accumulated at an increasing speed, the information on bio-entity relationships is archived in different forms at scattered places. Most of such information is buried in scientific literature as unstructured text. Organizing heterogeneous information in a structured form not only facilitates study of biological systems using integrative approaches, but also allows discovery of new knowledge in an automatic and systematic way. In this study, we performed a large scale integration of bio-entity relationship information from both databases containing manually annotated, structured information and automatic information extraction of unstructured text in scientific literature. The relationship information we integrated in this study includes protein-protein interactions, protein/gene regulations, protein-small molecule interactions, protein-GO relationships, protein-pathway relationships, and pathway-disease relationships. The relationship information is organized in a graph data structure, named integrated bio-entity network (IBN), where the vertices are the bio-entities and edges represent their relationships. Under this framework, graph theoretic algorithms can be designed to perform various knowledge discovery tasks. We designed breadth-first search with pruning (BFSP) and most probable path (MPP) algorithms to automatically generate hypotheses--the indirect relationships with high probabilities in the network. We show that IBN can be used to generate plausible hypotheses, which not only help to better understand the complex interactions in biological systems, but also provide guidance for experimental designs.     

The BioLexicon: a large-scale terminological resource for biomedical text mining

Background

Due to the rapidly expanding body of biomedical literature, biologists require increasingly sophisticated and efficient systems to help them to search for relevant information. Such systems should account for the multiple written variants used to represent biomedical concepts, and allow the user to search for specific pieces of knowledge (or events) involving these concepts, e.g., protein-protein interactions. Such functionality requires access to detailed information about words used in the biomedical literature. Existing databases and ontologies often have a specific focus and are oriented towards human use. Consequently, biological knowledge is dispersed amongst many resources, which often do not attempt to account for the large and frequently changing set of variants that appear in the literature. Additionally, such resources typically do not provide information about how terms relate to each other in texts to describe events.

Results

This article provides an overview of the design, construction and evaluation of a large-scale lexical and conceptual resource for the biomedical domain, the BioLexicon. The resource can be exploited by text mining tools at several levels, e.g., part-of-speech tagging, recognition of biomedical entities, and the extraction of events in which they are involved. As such, the BioLexicon must account for real usage of words in biomedical texts. In particular, the BioLexicon gathers together different types of terms from several existing data resources into a single, unified repository, and augments them with new term variants automatically extracted from biomedical literature. Extraction of events is facilitated through the inclusion of biologically pertinent verbs (around which events are typically organized) together with information about typical patterns of grammatical and semantic behaviour, which are acquired from domain-specific texts. In order to foster interoperability, the BioLexicon is modelled using the Lexical Markup Framework, an ISO standard.

Conclusions

The BioLexicon contains over 2.2 M lexical entries and over 1.8 M terminological variants, as well as over 3.3 M semantic relations, including over 2 M synonymy relations. Its exploitation can benefit both application developers and users. We demonstrate some such benefits by describing integration of the resource into a number of different tools, and evaluating improvements in performance that this can bring.     

Molecular dynamics simulations of glycoclusters and glycodendrimers

Protein-carbohydrate recognition plays a crucial role in a wide range of biological processes, required both for normal physiological functions and the onset of disease. Nature uses multivalency in carbohydrate-protein interactions as a strategy to overcome the low affinity found for singular binding of an individual saccharide epitope to a single carbohydrate recognition domain of a lectin. To mimic the complex multi-branched oligosaccharides found in glycoconjugates, which form the structural basis of multivalent carbohydrate-protein interactions, so-called glycoclusters and glycodendrimers have been designed to serve as high-affinity ligands of the respective receptor proteins. To allow a rational design of glycodendrimer-type molecules with regard to the receptor structures involved in carbohydrate recognition, a deeper knowledge of the dynamics of such molecules is desirable. Most glycodendrimers have to be considered highly flexible molecules with their conformational preferences most difficult to elucidate by experimental methods. Longtime molecular dynamics (MD) simulations with inclusion of explicit solvent molecules are suited to explore the conformational space accessible to glycodendrimers. Here, a detailed geometric and conformational analysis of 15 glycodendrimers and glycoclusters has been accomplished, which differ with regard to their core moieties, spacer characteristics and the type of terminal carbohydrate units. It is shown that the accessible conformational space depends strongly on the structural features of the core and spacer moieties and even on the type of terminating sugars. The obtained knowledge about possible spatial distributions of the sugar epitopes exposed on the investigated hyperbranched neoglycoconjugates is detailed for all examples and forms important information for the interpretation and prediction of affinity data, which can be deduced from biological testing of these multivalent neoglycoconjugates.     

From researcher to farmer: The use of extension programs to transfer biological control technology in developed countries

Effective use of biological control by the pest manager requires knowledge of the biologies of the pests and natural enemies, and their interactions with their environment and agronomic practices. Manufacturers provide information for products such as microbial pesticides and entomophagous arthropods used in augmentative biological control. However, information about process-oriented methods such as classical (importation) biological control and conservation of natural enemies is not often available to the farmer. Governmental extension programs are one method for providing practical biological control information, but availability in developed countries varies considerably. Examples of transfer of biological control information are provided for New Zealand, Canada, and Australia. In the United States, the Extension Service, a branch of the U.S. Department of Agriculture, provides partial funding and coordination for pest management educational programs conducted at the national, regional, state and local levels. In a twelve-state region of the North Central United States, university extension and research entomologists have developed a coordinated program to educate county extension personnel, farmers, and private consultants about the use of biological controls in pest management. The details of this model program are discussed. The paper concludes with a discussion of the educational constraints that must be overcome to successfully increase the adoption of biological control.     

Exploring and linking biomedical resources through multidimensional semantic spaces

BACKGROUND: The semantic integration of biomedical resources is still a challenging issue which is required for effective information processing and data analysis. The availability of comprehensive knowledge resources such as biomedical ontologies and integrated thesauri greatly facilitates this integration effort by means of semantic annotation, which allows disparate data formats and contents to be expressed under a common semantic space. In this paper, we propose a multidimensional representation for such a semantic space, where dimensions regard the different perspectives in biomedical research (e.g., population, disease, anatomy and protein/genes). RESULTS: This paper presents a novel method for building multidimensional semantic spaces from semantically annotated biomedical data collections. This method consists of two main processes: knowledge and data normalization. The former one arranges the concepts provided by a reference knowledge resource (e.g., biomedical ontologies and thesauri) into a set of hierarchical dimensions for analysis purposes. The latter one reduces the annotation set associated to each collection item into a set of points of the multidimensional space. Additionally, we have developed a visual tool, called 3D-Browser, which implements OLAP-like operators over the generated multidimensional space. The method and the tool have been tested and evaluated in the context of the Health-e-Child (HeC) project. Automatic semantic annotation was applied to tag three collections of abstracts taken from PubMed, one for each target disease of the project, the Uniprot database, and the HeC patient record database. We adopted the UMLS Meta-thesaurus 2010AA as the reference knowledge resource. CONCLUSIONS: Current knowledge resources and semantic-aware technology make possible the integration of biomedical resources. Such an integration is performed through semantic annotation of the intended biomedical data resources. This paper shows how these annotations can be exploited for integration, exploration, and analysis tasks. Results over a real scenario demonstrate the viability and usefulness of the approach, as well as the quality of the generated multidimensional semantic spaces.     

The embarrassment of riches: atmospheric deposition of nitrogen and community and ecosystem processes

Anthropogenic sources of nitrogen have exceeded, and will continue to exceed, annual inputs of nitrogen produced by natural processes. Nitrogen enrichment may in plant tissue chemistry and microbial decomposition processes, as well as affecting rates of herbivory, all of which may be expected to result in changes in plant species assemblages Individual concepts, such as nitrogen saturation and critical load, used to describe the effects of enrichment on soil, community, ecosystm processes and species assemblages, cannot accomodate easily the range of interactions and different environmental processes. A number of approaches need to be used in tandem. Major gaps in knowledge are rates of transfer of anthropogenic nitrogen within and between different ecosystem and how these rates affect population dynamic of individual species and trophic relationships. Without this information, predictions of biological effects of enrichment are difficult to make.     

Integrating recent advances in neuroscience into undergraduate neuroscience and physiology courses

Neuroscience has enjoyed tremendous growth over the past 20 years, including a substantial increase in the number of neuroscience departments, programs, and courses at the undergraduate level. To meet the need of new neuroscience courses, there has also been growth in the number of introductory neuroscience textbooks designed for undergraduates. However, textbooks typically trail current knowledge by five to ten years, especially in neuroscience where our understanding is increasing rapidly. Consequently, it is often important to supplement neuroscience and physiology textbooks with information about recent findings in neuroscience. To design supplementary educational material, it is essential first to identify the educational objectives of the program and the characteristics of the learners, which can differ dramatically between undergraduate and graduate or professional students. Four principles that may serve the selection and design of supplementary material for undergraduate neuroscience and physiology courses are that (1) material must be interesting to the undergraduates, (2) material should reinforce previously learned concepts, (3) students must be adequately prepared, and (4) the teacher and student must have sufficient appropriate resources.     

Rapid Identification of Chemical Genetic Interactions in Saccharomyces cerevisiae

Determining the mode of action of bioactive chemicals is of interest to a broad range of academic, pharmaceutical, and industrial scientists. Saccharomyces cerevisiae, or budding yeast, is a model eukaryote for which a complete collection of ~6,000 gene deletion mutants and hypomorphic essential gene mutants are commercially available. These collections of mutants can be used to systematically detect chemical-gene interactions, i.e. genes necessary to tolerate a chemical. This information, in turn, reports on the likely mode of action of the compound. Here we describe a protocol for the rapid identification of chemical-genetic interactions in budding yeast. We demonstrate the method using the chemotherapeutic agent 5-fluorouracil (5-FU), which has a well-defined mechanism of action. Our results show that the nuclear TRAMP RNA exosome and DNA repair enzymes are needed for proliferation in the presence of 5-FU, which is consistent with previous microarray based bar-coding chemical genetic approaches and the knowledge that 5-FU adversely affects both RNA and DNA metabolism. The required validation protocols of these high-throughput screens are also described.     

Espaces de représentation et psychopathologie

A subjective world can be conceptualized as a “representational space” — that is, as a universe displayed from memory, in which the subject “is sailing”. Recent cognitive theory provides some conceptual tools for describing such a space in a relevant way for clinical purposes. The construction of a representational space is based on mental representations: (a) analogical representations, which display a content in working memory; (b) symbolic representations, which code and link up analogical representations to form a represented world. The dynamics of representations and affects is ruled by a principle of unification of representational space. The topology of a representational space depends on objective and subjective constraints which cause some “folds” and limit the display of such a space. The interaction between an event and a subjective memory can be analyzed within this framework, so that the concepts of trauma and defence processes can be defined. Clinical syndromes are defensive configurations that tend to close the representational space.     

Use of Galvanic Skin Responses,Salivary Biomarkers,and Self-reports to Assess Undergraduate Student Performance During a Laboratory Exam Activity

Typically, self-reports are used in educational research to assess student response and performance to a classroom activity. Yet, addition of biological and physiological measures such as salivary biomarkers and galvanic skin responses are rarely included, limiting the wealth of information that can be obtained to better understand student performance. A laboratory protocol to study undergraduate students'' responses to classroom events (e.g., exams) is presented. Participants were asked to complete a representative exam for their degree. Before and after the laboratory exam session, students completed an academic achievement emotions self-report and an interview that paralleled these questions when participants wore a galvanic skin sensor and salivary biomarkers were collected. Data collected from the three methods resulted in greater depth of information about students'' performance when compared to the self-report. The work can expand educational research capabilities through more comprehensive methods for obtaining nearer to real-time student responses to an examination activity.     

Generic Information Can Retrieve Known Biological Associations: Implications for Biomedical Knowledge Discovery

Motivation

Weighted semantic networks built from text-mined literature can be used to retrieve known protein-protein or gene-disease associations, and have been shown to anticipate associations years before they are explicitly stated in the literature. Our text-mining system recognizes over 640,000 biomedical concepts: some are specific (i.e., names of genes or proteins) others generic (e.g., ‘Homo sapiens’). Generic concepts may play important roles in automated information retrieval, extraction, and inference but may also result in concept overload and confound retrieval and reasoning with low-relevance or even spurious links. Here, we attempted to optimize the retrieval performance for protein-protein interactions (PPI) by filtering generic concepts (node filtering) or links to generic concepts (edge filtering) from a weighted semantic network. First, we defined metrics based on network properties that quantify the specificity of concepts. Then using these metrics, we systematically filtered generic information from the network while monitoring retrieval performance of known protein-protein interactions. We also systematically filtered specific information from the network (inverse filtering), and assessed the retrieval performance of networks composed of generic information alone.

Results

Filtering generic or specific information induced a two-phase response in retrieval performance: initially the effects of filtering were minimal but beyond a critical threshold network performance suddenly drops. Contrary to expectations, networks composed exclusively of generic information demonstrated retrieval performance comparable to unfiltered networks that also contain specific concepts. Furthermore, an analysis using individual generic concepts demonstrated that they can effectively support the retrieval of known protein-protein interactions. For instance the concept “binding” is indicative for PPI retrieval and the concept “mutation abnormality” is indicative for gene-disease associations.

Conclusion

Generic concepts are important for information retrieval and cannot be removed from semantic networks without negative impact on retrieval performance.     

Concept mapping One-Carbon Metabolism to model future ontologies for nutrient–gene–phenotype interactions

Advances in the development of bioinformatic tools continue to improve investigators’ ability to interrogate, organize, and derive knowledge from large amounts of heterogeneous information. These tools often require advanced technical skills not possessed by life scientists. User-friendly, low-barrier-to-entry methods of visualizing nutrigenomics information are yet to be developed. We utilized concept mapping software from the Institute for Human and Machine Cognition to create a conceptual model of diet and health-related data that provides a foundation for future nutrigenomics ontologies describing published nutrient–gene/polymorphism–phenotype data. In this model, maps containing phenotype, nutrient, gene product, and genetic polymorphism interactions are visualized as triples of two concepts linked together by a linking phrase. These triples, or “knowledge propositions,” contextualize aggregated data and information into easy-to-read knowledge maps. Maps of these triples enable visualization of genes spanning the One-Carbon Metabolism (OCM) pathway, their sequence variants, and multiple literature-mined associations including concepts relevant to nutrition, phenotypes, and health. The concept map development process documents the incongruity of information derived from pathway databases versus literature resources. This conceptual model highlights the importance of incorporating information about genes in upstream pathways that provide substrates, as well as downstream pathways that utilize products of the pathway under investigation, in this case OCM. Other genes and their polymorphisms, such as TCN2 and FUT2, although not directly involved in OCM, potentially alter OCM pathway functionality. These upstream gene products regulate substrates such as B12. Constellations of polymorphisms affecting the functionality of genes along OCM, together with substrate and cofactor availability, may impact resultant phenotypes. These conceptual maps provide a foundational framework for development of nutrient–gene/polymorphism–phenotype ontologies and systems visualization.     

The rodent preputial gland

Researches published in the last 2 years have advanced direct evidence for regarding the rodent preputial gland as a scent organ, confirming a speculation made at least as long ago as 1725. The gland is the source of olfactory stimuli which mediate behavioural responses in social and sexual interactions between conspecifics. Although the gland has received little direct attention from investigators, a considerable amount of information on it is available. Most of this was gained ancilliary to other, and very diverse, research objectives so that the literature is very scattered. These references are collated to present a comprehensive review of knowledge of the biology of the rodent preputial gland. Theoretical concepts of population structure and regulation, involving density-dependent factors, require a sensory mechanism to activate the physiological effects attributed to social interactions. The preputial gland, as a source of olfactory cues, has all the biological characteristics required of such a sensory modulator.     

Digital technology to support students’ socioscientific reasoning about environmental issues

Scientific expertise and outcomes often give rise to controversy. An educational response that equips students to take part in such discussions is the teaching of socially acute questions (SAQs). With SAQs, the understanding of uncertainty, risk and how knowledge is developed is central. This study explores the way in which students from different disciplines and different continents are brought together via a digital platform to explore SAQs about environmental issues (a green algae outbreak linked to release of fertilisers along the coast of Brittany; the construction of a desalination plant near Melbourne to produce freshwater; and changes in meat consumption on a global scale, with regard to population projections in 2050). We have developed frameworks for looking at the quality of the collective reasoning and at the nature of students’ interactions, so that we can analyse the organisation of the learning communities and the building of collegial expertise. The results show that interdisciplinary discussions, especially on an international scale, foster the understanding of complex situations. In this paper, we discuss the modalities of one didactic scenario to enhance critical thinking and collaborative work, and to provide space for learners to support argumentation.     

Neurocognitive insights on conceptual knowledge and its breakdown

Conceptual knowledge reflects our multi-modal ‘semantic database’. As such, it brings meaning to all verbal and non-verbal stimuli, is the foundation for verbal and non-verbal expression and provides the basis for computing appropriate semantic generalizations. Multiple disciplines (e.g. philosophy, cognitive science, cognitive neuroscience and behavioural neurology) have striven to answer the questions of how concepts are formed, how they are represented in the brain and how they break down differentially in various neurological patient groups. A long-standing and prominent hypothesis is that concepts are distilled from our multi-modal verbal and non-verbal experience such that sensation in one modality (e.g. the smell of an apple) not only activates the intramodality long-term knowledge, but also reactivates the relevant intermodality information about that item (i.e. all the things you know about and can do with an apple). This multi-modal view of conceptualization fits with contemporary functional neuroimaging studies that observe systematic variation of activation across different modality-specific association regions dependent on the conceptual category or type of information. A second vein of interdisciplinary work argues, however, that even a smorgasbord of multi-modal features is insufficient to build coherent, generalizable concepts. Instead, an additional process or intermediate representation is required. Recent multidisciplinary work, which combines neuropsychology, neuroscience and computational models, offers evidence that conceptualization follows from a combination of modality-specific sources of information plus a transmodal ‘hub’ representational system that is supported primarily by regions within the anterior temporal lobe, bilaterally.     

Autonomous learning based on cost assumptions: theoretical studies and experiments in robot control

Autonomous learning techniques are based on experience acquisition. In most realistic applications, experience is time-consuming: it implies sensor reading, actuator control and algorithmic update, constrained by the learning system dynamics. The information crudeness upon which classical learning algorithms operate make such problems too difficult and unrealistic. Nonetheless, additional information for facilitating the learning process ideally should be embedded in such a way that the structural, well-studied characteristics of these fundamental algorithms are maintained. We investigate in this article a more general formulation of the Q-learning method that allows for a spreading of information derived from single updates towards a neighbourhood of the instantly visited state and converges to optimality. We show how this new formulation can be used as a mechanism to safely embed prior knowledge about the structure of the state space, and demonstrate it in a modified implementation of a reinforcement learning algorithm in a real robot navigation task.