Rama Chellappa, Naresh P. Cuntoor, Seong-Wook Joo, V. S. Subrahmanian, and Pavan Turaga
- Published in print:
- 2008
- Published Online:
- May 2008
- ISBN:
- 9780195188370
- eISBN:
- 9780199870462
- Item type:
- chapter
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/acprof:oso/9780195188370.003.0021
- Subject:
- Psychology, Cognitive Neuroscience
Event modeling systems provide a semantic interpretation of sequences of pixels that are captured by a video camera. The design of a practical system has to take into account the following three main ...
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Event modeling systems provide a semantic interpretation of sequences of pixels that are captured by a video camera. The design of a practical system has to take into account the following three main factors: low-level preprocessing limitations, computational and storage complexity of the event model, and user interaction. The hidden Markov model (HMM) and its variants have been widely used to model both speech and video signals. Computational efficiency of the Baum-Welch and the Viterbi algorithms has been a leading reason for the popularity of the HMM. Since the objective is to detect events in video sequences that are meaningful to humans, one might want to provide space in the design loop for a user who can specify events of interest. This chapter explores this using semantic approaches that not only use features extracted from raw video streams but also incorporate metadata and ontologies of activities. It presents three approaches for applications such as event recognition: anomaly detection, temporal segmentation, and ontology evaluation. The three approaches discussed are statistical methods based on HMMs, formal grammars, and ontologies. The effectiveness of these approaches is illustrated using video sequences captured both indoors and outdoors: the indoor UCF human action dataset, the TSA airport tarmac surveillance dataset, and the bank monitoring dataset.Less
Event modeling systems provide a semantic interpretation of sequences of pixels that are captured by a video camera. The design of a practical system has to take into account the following three main factors: low-level preprocessing limitations, computational and storage complexity of the event model, and user interaction. The hidden Markov model (HMM) and its variants have been widely used to model both speech and video signals. Computational efficiency of the Baum-Welch and the Viterbi algorithms has been a leading reason for the popularity of the HMM. Since the objective is to detect events in video sequences that are meaningful to humans, one might want to provide space in the design loop for a user who can specify events of interest. This chapter explores this using semantic approaches that not only use features extracted from raw video streams but also incorporate metadata and ontologies of activities. It presents three approaches for applications such as event recognition: anomaly detection, temporal segmentation, and ontology evaluation. The three approaches discussed are statistical methods based on HMMs, formal grammars, and ontologies. The effectiveness of these approaches is illustrated using video sequences captured both indoors and outdoors: the indoor UCF human action dataset, the TSA airport tarmac surveillance dataset, and the bank monitoring dataset.
David F. Hendry
- Published in print:
- 1995
- Published Online:
- November 2003
- ISBN:
- 9780198283164
- eISBN:
- 9780191596384
- Item type:
- chapter
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/0198283164.003.0008
- Subject:
- Economics and Finance, Econometrics
Linear dynamic systems are formulated for stationary and integrated data processes. Both open and closed systems are noted. General‐to‐specific modelling of the joint data‐density function is ...
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Linear dynamic systems are formulated for stationary and integrated data processes. Both open and closed systems are noted. General‐to‐specific modelling of the joint data‐density function is reviewed. A typology of linear dynamic systems is developed, extending that for individual dynamic equations. Alternative models of systems are derived from three operations on systems, namely, contemporaneous and intertemporal transforms, and conditioning. Methods for analysing econometric systems are discussed.Less
Linear dynamic systems are formulated for stationary and integrated data processes. Both open and closed systems are noted. General‐to‐specific modelling of the joint data‐density function is reviewed. A typology of linear dynamic systems is developed, extending that for individual dynamic equations. Alternative models of systems are derived from three operations on systems, namely, contemporaneous and intertemporal transforms, and conditioning. Methods for analysing econometric systems are discussed.
Bradley C. Love and Marc Tomlinson
- Published in print:
- 2010
- Published Online:
- May 2010
- ISBN:
- 9780199549221
- eISBN:
- 9780191724152
- Item type:
- chapter
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/acprof:oso/9780199549221.003.04
- Subject:
- Psychology, Cognitive Psychology, Developmental Psychology
This chapter reviews the relative merits of a variety of category learning models. These include rule-based models, prototype-based models, exemplar-based models, hybrid models, and multiple systems ...
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This chapter reviews the relative merits of a variety of category learning models. These include rule-based models, prototype-based models, exemplar-based models, hybrid models, and multiple systems models. All of these models have played a critical role in driving advances in theory and in the design of key experiments. The development of new models is informed by the failings of preceding models.Less
This chapter reviews the relative merits of a variety of category learning models. These include rule-based models, prototype-based models, exemplar-based models, hybrid models, and multiple systems models. All of these models have played a critical role in driving advances in theory and in the design of key experiments. The development of new models is informed by the failings of preceding models.
William A. Brock, David Finnoff, Ann P. Kinzig, Unai Pascual, Charles Perrings, John Tschirhart, and Anastasios Xepapadeas
- Published in print:
- 2009
- Published Online:
- September 2009
- ISBN:
- 9780199547951
- eISBN:
- 9780191720345
- Item type:
- chapter
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/acprof:oso/9780199547951.003.0019
- Subject:
- Biology, Ecology, Biodiversity / Conservation Biology
This chapter considers how economists model biodiversity in coupled social ecological systems, taking two polar cases along with a more general problem. Economists assume that all human decisions are ...
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This chapter considers how economists model biodiversity in coupled social ecological systems, taking two polar cases along with a more general problem. Economists assume that all human decisions are purposive: people are assumed to optimize some objective function subject to some set of initial conditions, to some set of resource constraints, and to the dynamics of the system being used. The chapter considers two polar cases and one intermediate case. One polar case involves the preservation of wilderness areas or protected parks in 'close to natural' states. A second involves the exploitation of ecosystems to produce foods, fuels and fibers. The intermediate case involves the management of ecosystems to achieve a balance between non-consumptive cultural services with consumptive provisioning services. While the constrained optimization technique applied in all three cases may be unfamiliar, the chapter tries to give the intuition behind it. It also provides a verbal description of each of the three model structures developed. In all cases the social and biogeophysical components of the coupled system are interdependent — connected through a series of feedback loops. Economists refer to such systems as 'general equilibrium systems'. That is, the dynamics of the system in some state are driven by a tendency towards the equilibrium corresponding to that state, and any perturbation has the potential to stimulate responses across the system.Less
This chapter considers how economists model biodiversity in coupled social ecological systems, taking two polar cases along with a more general problem. Economists assume that all human decisions are purposive: people are assumed to optimize some objective function subject to some set of initial conditions, to some set of resource constraints, and to the dynamics of the system being used. The chapter considers two polar cases and one intermediate case. One polar case involves the preservation of wilderness areas or protected parks in 'close to natural' states. A second involves the exploitation of ecosystems to produce foods, fuels and fibers. The intermediate case involves the management of ecosystems to achieve a balance between non-consumptive cultural services with consumptive provisioning services. While the constrained optimization technique applied in all three cases may be unfamiliar, the chapter tries to give the intuition behind it. It also provides a verbal description of each of the three model structures developed. In all cases the social and biogeophysical components of the coupled system are interdependent — connected through a series of feedback loops. Economists refer to such systems as 'general equilibrium systems'. That is, the dynamics of the system in some state are driven by a tendency towards the equilibrium corresponding to that state, and any perturbation has the potential to stimulate responses across the system.
David F. Hendry
- Published in print:
- 1995
- Published Online:
- November 2003
- ISBN:
- 9780198283164
- eISBN:
- 9780191596384
- Item type:
- chapter
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/0198283164.003.0011
- Subject:
- Economics and Finance, Econometrics
Linear system modelling is structured in 10 stages from the general to the specific. The dynamic statistical system is the maintained model, defined by the variables of interest, their distributions, ...
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Linear system modelling is structured in 10 stages from the general to the specific. The dynamic statistical system is the maintained model, defined by the variables of interest, their distributions, whether they are modelled or non‐modelled, and their lag polynomials. An econometric model is a (possibly) simultaneous‐equations entity, which is intended to isolate autonomous, parsimonious relationships based on economic theory. That model must adequately characterize the data evidence and account for the results in the congruent statistical system. Model formulation, identification, estimation (using an estimator generating equation), encompassing, and evaluation are considered.Less
Linear system modelling is structured in 10 stages from the general to the specific. The dynamic statistical system is the maintained model, defined by the variables of interest, their distributions, whether they are modelled or non‐modelled, and their lag polynomials. An econometric model is a (possibly) simultaneous‐equations entity, which is intended to isolate autonomous, parsimonious relationships based on economic theory. That model must adequately characterize the data evidence and account for the results in the congruent statistical system. Model formulation, identification, estimation (using an estimator generating equation), encompassing, and evaluation are considered.
Tim Shallice
- Published in print:
- 2002
- Published Online:
- May 2009
- ISBN:
- 9780195134971
- eISBN:
- 9780199864157
- Item type:
- chapter
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/acprof:oso/9780195134971.003.0017
- Subject:
- Neuroscience, Behavioral Neuroscience, Molecular and Cellular Systems
This chapter focuses on two supervisory processes: the top-down modulation of lower-level schemas and checking or monitoring processes. It argues that there are processes that come into play ...
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This chapter focuses on two supervisory processes: the top-down modulation of lower-level schemas and checking or monitoring processes. It argues that there are processes that come into play specifically in nonroutine situations. This is the position initially adopted in Norman and Shallice. From an expert system AI perspective (the domino model), one would expect there to be a set of subprocesses that are computationally very different and have different specific roles but which also combine in coping with nonroutine situations. This is the approach taken earlier from a cognitive neuroscience perspective by the Mark II Supervisory System model. These subprocesses are localized in different parts of the prefrontal cortex.Less
This chapter focuses on two supervisory processes: the top-down modulation of lower-level schemas and checking or monitoring processes. It argues that there are processes that come into play specifically in nonroutine situations. This is the position initially adopted in Norman and Shallice. From an expert system AI perspective (the domino model), one would expect there to be a set of subprocesses that are computationally very different and have different specific roles but which also combine in coping with nonroutine situations. This is the approach taken earlier from a cognitive neuroscience perspective by the Mark II Supervisory System model. These subprocesses are localized in different parts of the prefrontal cortex.
Luc Bauwens, Michel Lubrano, and Jean-François Richard
- Published in print:
- 2000
- Published Online:
- September 2011
- ISBN:
- 9780198773122
- eISBN:
- 9780191695315
- Item type:
- chapter
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/acprof:oso/9780198773122.003.0009
- Subject:
- Economics and Finance, Econometrics
This chapter aims to review how Bayesian inference can be applied to some of the so-called systems of equations models. These models can be defined in several forms including multivariate regression ...
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This chapter aims to review how Bayesian inference can be applied to some of the so-called systems of equations models. These models can be defined in several forms including multivariate regression models, vector autoregressive (VAR) models, simultaneous equation models (SEM), and systems of seemingly unrelated regression equation (SURE) models. This chapter analyses VAR models which are formally equivalent to multivariate regression models and suggests that VAR models can be either open or closed depending on whether exogenous variables are included or not.Less
This chapter aims to review how Bayesian inference can be applied to some of the so-called systems of equations models. These models can be defined in several forms including multivariate regression models, vector autoregressive (VAR) models, simultaneous equation models (SEM), and systems of seemingly unrelated regression equation (SURE) models. This chapter analyses VAR models which are formally equivalent to multivariate regression models and suggests that VAR models can be either open or closed depending on whether exogenous variables are included or not.
Pierre Jacob and Frédérique de Vignemont
- Published in print:
- 2010
- Published Online:
- January 2011
- ISBN:
- 9780199551118
- eISBN:
- 9780191594960
- Item type:
- chapter
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/acprof:oso/9780199551118.003.0008
- Subject:
- Psychology, Cognitive Psychology
The ‘two-visual’ systems hypothesis has recently come under attack regarding its proposed functional dichotomy between vision-for-action and vision-for-perception as well as for the limited ...
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The ‘two-visual’ systems hypothesis has recently come under attack regarding its proposed functional dichotomy between vision-for-action and vision-for-perception as well as for the limited interaction it allows between visual awareness and processing in the dorsal stream. Schenk (2006) questions the rigid functional dichotomy between vision-for-perception and vision-for-action, arguing that the dual model of vision is best accounted for in terms of a dissociation between egocentric and allocentric spatial coordinate systems. Wallhagen (2007) argues that there is no evidence to claim that the processing in the dorsal stream cannot underlie visual awareness. This chapter offers a response to both challenges and disentangles the contribution of two separable factors to the two-visual systems model, namely, how spatial information is coded and the relation between consciousness and processing in the ventral and dorsal streams respectively.Less
The ‘two-visual’ systems hypothesis has recently come under attack regarding its proposed functional dichotomy between vision-for-action and vision-for-perception as well as for the limited interaction it allows between visual awareness and processing in the dorsal stream. Schenk (2006) questions the rigid functional dichotomy between vision-for-perception and vision-for-action, arguing that the dual model of vision is best accounted for in terms of a dissociation between egocentric and allocentric spatial coordinate systems. Wallhagen (2007) argues that there is no evidence to claim that the processing in the dorsal stream cannot underlie visual awareness. This chapter offers a response to both challenges and disentangles the contribution of two separable factors to the two-visual systems model, namely, how spatial information is coded and the relation between consciousness and processing in the ventral and dorsal streams respectively.
Timothy C. Bates
- Published in print:
- 2012
- Published Online:
- May 2012
- ISBN:
- 9780199600496
- eISBN:
- 9780191739187
- Item type:
- chapter
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/acprof:oso/9780199600496.003.0052
- Subject:
- Neuroscience, Development, Behavioral Neuroscience
Neuroscience indicates that children's minds are made up of multiple genetically and psychologically distinct mechanisms underlying learning; that children differ in the functioning of these systems; ...
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Neuroscience indicates that children's minds are made up of multiple genetically and psychologically distinct mechanisms underlying learning; that children differ in the functioning of these systems; and that much of this difference is genetic is origin and general in nature, with limits to brain plasticity. This chapter presents a systems framework linking these basic biological bases to the lifespan thriving which school is intended to foster. The systems model suggests that the principal outcome of neuroscience research will be to focus attention on how non-neuroscientific choices impact learning: teacher selection and training, teaching methods, and curriculum. The chapter concludes by suggesting that much of education is based on different answers to the research questions posed here than those flowing from current neuroscience. To maximize the capability of all children, it is important to achieve consilience between what we know about the mind and what educationalists do.Less
Neuroscience indicates that children's minds are made up of multiple genetically and psychologically distinct mechanisms underlying learning; that children differ in the functioning of these systems; and that much of this difference is genetic is origin and general in nature, with limits to brain plasticity. This chapter presents a systems framework linking these basic biological bases to the lifespan thriving which school is intended to foster. The systems model suggests that the principal outcome of neuroscience research will be to focus attention on how non-neuroscientific choices impact learning: teacher selection and training, teaching methods, and curriculum. The chapter concludes by suggesting that much of education is based on different answers to the research questions posed here than those flowing from current neuroscience. To maximize the capability of all children, it is important to achieve consilience between what we know about the mind and what educationalists do.
Monique Ernst
- Published in print:
- 2011
- Published Online:
- May 2011
- ISBN:
- 9780199600434
- eISBN:
- 9780191725623
- Item type:
- chapter
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/acprof:oso/9780199600434.003.0019
- Subject:
- Psychology, Cognitive Psychology, Developmental Psychology
This chapter focuses on a heuristic neural systems model of motivated behaviour. This model provides hypotheses for mechanisms underlying changes in behaviour across development and psychopathology. ...
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This chapter focuses on a heuristic neural systems model of motivated behaviour. This model provides hypotheses for mechanisms underlying changes in behaviour across development and psychopathology. The fractal triadic model (FTM) posits that goal-directed behaviour results from the interaction among three nodes of behavioural control. These three functional nodes are centred on the amygdala, striatum, and medial prefrontal cortex, which contribute to avoidance, approach, and modulation, respectively. They feed two distinct neural circuits: one that is modulated primarily by appetitive stimuli and serves approach behaviour, and one that is modulated primarily by aversive stimuli and serves avoidance behaviour. The behavioural output results from the integration of the information that is processed by these two neural circuits and is submitted to the control of the supervisory node. Such organization of three functional nodes subserving two neural circuits relies on the well-described structural and functional heterogeneity of these nodes. In addition, asynchrony in the maturational trajectories not only among the nodes, but also among the subunits of these nodes, is the central principle that underlies the typical behavioural changes seen in adolescence. Functional neuroimaging research is beginning to examine ontogenic changes in neural responses to reward-related processes that can further inform this heuristic model. The chapter addresses the major points mentioned above and ends with selected questions proposed as priority for future research.Less
This chapter focuses on a heuristic neural systems model of motivated behaviour. This model provides hypotheses for mechanisms underlying changes in behaviour across development and psychopathology. The fractal triadic model (FTM) posits that goal-directed behaviour results from the interaction among three nodes of behavioural control. These three functional nodes are centred on the amygdala, striatum, and medial prefrontal cortex, which contribute to avoidance, approach, and modulation, respectively. They feed two distinct neural circuits: one that is modulated primarily by appetitive stimuli and serves approach behaviour, and one that is modulated primarily by aversive stimuli and serves avoidance behaviour. The behavioural output results from the integration of the information that is processed by these two neural circuits and is submitted to the control of the supervisory node. Such organization of three functional nodes subserving two neural circuits relies on the well-described structural and functional heterogeneity of these nodes. In addition, asynchrony in the maturational trajectories not only among the nodes, but also among the subunits of these nodes, is the central principle that underlies the typical behavioural changes seen in adolescence. Functional neuroimaging research is beginning to examine ontogenic changes in neural responses to reward-related processes that can further inform this heuristic model. The chapter addresses the major points mentioned above and ends with selected questions proposed as priority for future research.
Rowland H. Davis
- Published in print:
- 2003
- Published Online:
- April 2010
- ISBN:
- 9780195154368
- eISBN:
- 9780199893935
- Item type:
- chapter
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/acprof:oso/9780195154368.003.0019
- Subject:
- Biology, Biochemistry / Molecular Biology
This chapter begins with a description of the attributes of model organisms. It then reviews some of the choices of model organisms discussed in the preceding chapters. It argues that molecular ...
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This chapter begins with a description of the attributes of model organisms. It then reviews some of the choices of model organisms discussed in the preceding chapters. It argues that molecular biology and its model microbes are simply parts of a large science to which they have contributed understanding and unification. Much new knowledge has originated almost explosively in molecular biology, but within a few short years of the ascendancy of prokaryotic genetics, the rest of biology has contributed most of the questions. In their married state, the new and the old biology have developed a partnership that has swept us back into the study of diversity and complexity, now approachable with new tools.Less
This chapter begins with a description of the attributes of model organisms. It then reviews some of the choices of model organisms discussed in the preceding chapters. It argues that molecular biology and its model microbes are simply parts of a large science to which they have contributed understanding and unification. Much new knowledge has originated almost explosively in molecular biology, but within a few short years of the ascendancy of prokaryotic genetics, the rest of biology has contributed most of the questions. In their married state, the new and the old biology have developed a partnership that has swept us back into the study of diversity and complexity, now approachable with new tools.
Rowland H. Davis
- Published in print:
- 2003
- Published Online:
- April 2010
- ISBN:
- 9780195154368
- eISBN:
- 9780199893935
- Item type:
- book
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/acprof:oso/9780195154368.001.0001
- Subject:
- Biology, Biochemistry / Molecular Biology
This book explains the role of simple biological model systems in the growth of molecular biology. Essentially, the whole history of molecular biology is presented here, tracing the work in ...
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This book explains the role of simple biological model systems in the growth of molecular biology. Essentially, the whole history of molecular biology is presented here, tracing the work in bacteriophages in E. coli, the role of other prokaryotic systems, and also the protozoan and algal models — Paramecium and Chlamydomonas, primarily — and the move into eukaryotes with the fungal systems Neurospora, Aspergillus, and yeast. Each model was selected for its appropriateness for asking a given class of questions, and each spawned its own community of investigators. Some individuals made the transition to a new model over time, and remnant communities of investigators continue to pursue questions in all these models, as the cutting edge of molecular biological research flows onward from model to model, and onward into higher organisms and, ultimately, mouse and man.Less
This book explains the role of simple biological model systems in the growth of molecular biology. Essentially, the whole history of molecular biology is presented here, tracing the work in bacteriophages in E. coli, the role of other prokaryotic systems, and also the protozoan and algal models — Paramecium and Chlamydomonas, primarily — and the move into eukaryotes with the fungal systems Neurospora, Aspergillus, and yeast. Each model was selected for its appropriateness for asking a given class of questions, and each spawned its own community of investigators. Some individuals made the transition to a new model over time, and remnant communities of investigators continue to pursue questions in all these models, as the cutting edge of molecular biological research flows onward from model to model, and onward into higher organisms and, ultimately, mouse and man.
Rowland H. Davis
- Published in print:
- 2003
- Published Online:
- April 2010
- ISBN:
- 9780195154368
- eISBN:
- 9780199893935
- Item type:
- chapter
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/acprof:oso/9780195154368.003.0001
- Subject:
- Biology, Biochemistry / Molecular Biology
Natural philosophers and biologists of the past sought to make sense of the many types of plants and creatures that, if not made for humans, were at least created by imaginative gods. As the ...
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Natural philosophers and biologists of the past sought to make sense of the many types of plants and creatures that, if not made for humans, were at least created by imaginative gods. As the Renaissance yielded enlightenment, the classification of organisms and a study of their structures embodied a new attempt to subdue the mystery of life. The effort was satisfying in confirming the systematic intentions of the designer. But as the last millennium progressed, the urge simply to recognize and sort plants and animals gave way to an ambition to understand their origins, functions, and diversity. Detached from constraints of authority by the examples of Galileo and Newton, the early explorers of the living domain began to ask empirical questions buried for centuries, latent in the human imagination. This chapter explores the choice and use of organisms in answering these questions. The organisms now living on earth are so diverse that one must ask how biologists made such choices. It looks at the organisms that brought biology to the beginning of the 20th century, when particular models began to guide experimental research and for a time limited our appreciation of organismic diversity. In doing so, it shows how very recent our understanding of living things really is, and why we saw such an acceleration of biological research in the 20th century.Less
Natural philosophers and biologists of the past sought to make sense of the many types of plants and creatures that, if not made for humans, were at least created by imaginative gods. As the Renaissance yielded enlightenment, the classification of organisms and a study of their structures embodied a new attempt to subdue the mystery of life. The effort was satisfying in confirming the systematic intentions of the designer. But as the last millennium progressed, the urge simply to recognize and sort plants and animals gave way to an ambition to understand their origins, functions, and diversity. Detached from constraints of authority by the examples of Galileo and Newton, the early explorers of the living domain began to ask empirical questions buried for centuries, latent in the human imagination. This chapter explores the choice and use of organisms in answering these questions. The organisms now living on earth are so diverse that one must ask how biologists made such choices. It looks at the organisms that brought biology to the beginning of the 20th century, when particular models began to guide experimental research and for a time limited our appreciation of organismic diversity. In doing so, it shows how very recent our understanding of living things really is, and why we saw such an acceleration of biological research in the 20th century.
Rand D. Conger, Hairong Song, Gary D. Stockdale, Emilio Ferrer, Keith F. Widaman, and Ana M. Cauce
- Published in print:
- 2012
- Published Online:
- May 2012
- ISBN:
- 9780199736546
- eISBN:
- 9780199932443
- Item type:
- chapter
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/acprof:oso/9780199736546.003.0017
- Subject:
- Psychology, Developmental Psychology, Clinical Child Psychology / School Psychology
This chapter presents an important extension of the family system model for understanding risk and resilience in adolescent development by considering the unique cultural and economic context of ...
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This chapter presents an important extension of the family system model for understanding risk and resilience in adolescent development by considering the unique cultural and economic context of Mexican-origin families. In their model, the authors studied the interplay of economic stress, interparental conflict, and parent-child relations as they affect child competence. The authors add to their well-replicated family stress model considerations that are specific to Mexican-origin families in the United States, including the common experiences of poverty, residence in high-risk neighborhoods, and exposure to racial discrimination. At each stage of the model, these culture-specific factors act as potentiators of the processes by which economic strain leads to disruptions in parent's emotional functioning, increases in interparental conflict, negative parenting, and less competent youth development.Less
This chapter presents an important extension of the family system model for understanding risk and resilience in adolescent development by considering the unique cultural and economic context of Mexican-origin families. In their model, the authors studied the interplay of economic stress, interparental conflict, and parent-child relations as they affect child competence. The authors add to their well-replicated family stress model considerations that are specific to Mexican-origin families in the United States, including the common experiences of poverty, residence in high-risk neighborhoods, and exposure to racial discrimination. At each stage of the model, these culture-specific factors act as potentiators of the processes by which economic strain leads to disruptions in parent's emotional functioning, increases in interparental conflict, negative parenting, and less competent youth development.
Rowland H. Davis
- Published in print:
- 2003
- Published Online:
- April 2010
- ISBN:
- 9780195154368
- eISBN:
- 9780199893935
- Item type:
- chapter
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/acprof:oso/9780195154368.003.0011
- Subject:
- Biology, Biochemistry / Molecular Biology
The previous 50 years had been devoted in large part to discovering the nature of the gene through studies of its function, mutation, and recombination. What was there for geneticists to learn after ...
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The previous 50 years had been devoted in large part to discovering the nature of the gene through studies of its function, mutation, and recombination. What was there for geneticists to learn after Watson and Crick's famous discovery? The answer lies in the transformation of genetics from its preoccupation with the identity of the gene to the use of genetics as an analytical tool. Genetic rationales would probe the properties of DNA in the living system and would continue to extend themselves into the analysis of many complex structures and processes. Genetics began to lose its identity as a separate field as it invaded almost every area of biological science. This chapter explores the major trends of investigation with prokaryotes mainly in the years 1953-65, focusing on the use of model organisms that have been considered so far in particular programs.Less
The previous 50 years had been devoted in large part to discovering the nature of the gene through studies of its function, mutation, and recombination. What was there for geneticists to learn after Watson and Crick's famous discovery? The answer lies in the transformation of genetics from its preoccupation with the identity of the gene to the use of genetics as an analytical tool. Genetic rationales would probe the properties of DNA in the living system and would continue to extend themselves into the analysis of many complex structures and processes. Genetics began to lose its identity as a separate field as it invaded almost every area of biological science. This chapter explores the major trends of investigation with prokaryotes mainly in the years 1953-65, focusing on the use of model organisms that have been considered so far in particular programs.
Douglas A. Luke, Alexandra B. Morshed, Virginia R. McKay, and Todd B. Combs
- Published in print:
- 2017
- Published Online:
- November 2017
- ISBN:
- 9780190683214
- eISBN:
- 9780190683245
- Item type:
- chapter
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/oso/9780190683214.003.0010
- Subject:
- Public Health and Epidemiology, Public Health
As we have seen, numerous analysis and modeling tools that take into account the natural complexity of systems and dissemination and implementation processes are available, and the use of them is ...
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As we have seen, numerous analysis and modeling tools that take into account the natural complexity of systems and dissemination and implementation processes are available, and the use of them is increasing over time. This chapter summarizes the characteristics, potential insights, and limitations of each modeling approach. It is important to note that modeling from a systems perspective, like all modeling approaches, requires assumptions about variables to include (or exclude), and hypothesized relationships dictate the quality of the model and the utility of the results. As such, using theory and empirical data to inform model design is paramount. Systems thinking and methods remain underutilized in dissemination and implementation despite demonstrations of the utility of incorporating systems thinking and methods into dissemination and implementation studies.Less
As we have seen, numerous analysis and modeling tools that take into account the natural complexity of systems and dissemination and implementation processes are available, and the use of them is increasing over time. This chapter summarizes the characteristics, potential insights, and limitations of each modeling approach. It is important to note that modeling from a systems perspective, like all modeling approaches, requires assumptions about variables to include (or exclude), and hypothesized relationships dictate the quality of the model and the utility of the results. As such, using theory and empirical data to inform model design is paramount. Systems thinking and methods remain underutilized in dissemination and implementation despite demonstrations of the utility of incorporating systems thinking and methods into dissemination and implementation studies.
J. R. Krebs and G. Horn (eds)
- Published in print:
- 1991
- Published Online:
- March 2012
- ISBN:
- 9780198521983
- eISBN:
- 9780191688492
- Item type:
- book
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/acprof:oso/9780198521983.001.0001
- Subject:
- Psychology, Cognitive Psychology
The topics discussed in this volume have been chosen to represent studies in which both behavioural and neurobiological analysis have been emphasized. They include work on behavioural and neural ...
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The topics discussed in this volume have been chosen to represent studies in which both behavioural and neurobiological analysis have been emphasized. They include work on behavioural and neural aspects of imprinting, song learning in birds, and spatial memory of food-storing birds. There are also overviews of neural and behavioural aspects of classical conditioning, the role of the hippocampus in spatial behaviour in mammals, and studies of learning in invertebrate model systems. The volume is based on a Royal Society Discussion Meeting held in February 1990 and the chapters have been published in the Society's Philosophical Transactions series B.Less
The topics discussed in this volume have been chosen to represent studies in which both behavioural and neurobiological analysis have been emphasized. They include work on behavioural and neural aspects of imprinting, song learning in birds, and spatial memory of food-storing birds. There are also overviews of neural and behavioural aspects of classical conditioning, the role of the hippocampus in spatial behaviour in mammals, and studies of learning in invertebrate model systems. The volume is based on a Royal Society Discussion Meeting held in February 1990 and the chapters have been published in the Society's Philosophical Transactions series B.
Andrew Finney, Michael Hucka, Benjamin J. Bornstein, Sarah M. Keating, Bruce E. Shapiro, Joanne Matthews, Ben L. Kovitz, Maria J. Schilstra, Akira Funahashi, John Doyle, and Hiroaki Kitano
- Published in print:
- 2006
- Published Online:
- August 2013
- ISBN:
- 9780262195485
- eISBN:
- 9780262257060
- Item type:
- chapter
- Publisher:
- The MIT Press
- DOI:
- 10.7551/mitpress/9780262195485.003.0017
- Subject:
- Mathematics, Mathematical Biology
This chapter describes Systems Biology Markup Language (SBML), a format for representing models in a way that can be used by different software systems to communicate and exchange those models. By ...
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This chapter describes Systems Biology Markup Language (SBML), a format for representing models in a way that can be used by different software systems to communicate and exchange those models. By supporting SBML as an input and output format, different software tools can all operate on an identical representation of a model, removing opportunities for errors in translation and assuring a common starting point for analyses and simulations. The chapter also discusses some of the resources available for working with SBML as well as ongoing efforts in SBML’s continuing evolution.Less
This chapter describes Systems Biology Markup Language (SBML), a format for representing models in a way that can be used by different software systems to communicate and exchange those models. By supporting SBML as an input and output format, different software tools can all operate on an identical representation of a model, removing opportunities for errors in translation and assuring a common starting point for analyses and simulations. The chapter also discusses some of the resources available for working with SBML as well as ongoing efforts in SBML’s continuing evolution.
Rowland H. Davis
- Published in print:
- 2003
- Published Online:
- April 2010
- ISBN:
- 9780195154368
- eISBN:
- 9780199893935
- Item type:
- chapter
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/acprof:oso/9780195154368.003.0012
- Subject:
- Biology, Biochemistry / Molecular Biology
After the genetic code had been defined fully (by 1963), some believed that it was all over. This feeling signified only that the goals of many up to that time had been so narrowly conceived that ...
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After the genetic code had been defined fully (by 1963), some believed that it was all over. This feeling signified only that the goals of many up to that time had been so narrowly conceived that their attainment left nothing to aim for. For those who thought this way, continued study of these phenomena would consign them to gleaning the stubble of a freshly harvested terrain; the clean-up job of ordinary, journeyman scientists. For others, the Central Dogma and the explication of the genetic code were simply open doors to the real, newly appreciated, unique biochemical mechanisms of protein and nucleic acid synthesis, the broad, formal outline of these life processes now behind them. For others, observation and imagination would define problems previously undreamt of. Fortunately, tastes vary, and work continued in earnest, in many ways and on many fronts. This chapter considers the sweeping developments after 1963.Less
After the genetic code had been defined fully (by 1963), some believed that it was all over. This feeling signified only that the goals of many up to that time had been so narrowly conceived that their attainment left nothing to aim for. For those who thought this way, continued study of these phenomena would consign them to gleaning the stubble of a freshly harvested terrain; the clean-up job of ordinary, journeyman scientists. For others, the Central Dogma and the explication of the genetic code were simply open doors to the real, newly appreciated, unique biochemical mechanisms of protein and nucleic acid synthesis, the broad, formal outline of these life processes now behind them. For others, observation and imagination would define problems previously undreamt of. Fortunately, tastes vary, and work continued in earnest, in many ways and on many fronts. This chapter considers the sweeping developments after 1963.
Zoltan Szallasi, Jorg Stelling, and Vipul Periwal (eds)
- Published in print:
- 2006
- Published Online:
- August 2013
- ISBN:
- 9780262195485
- eISBN:
- 9780262257060
- Item type:
- book
- Publisher:
- The MIT Press
- DOI:
- 10.7551/mitpress/9780262195485.001.0001
- Subject:
- Mathematics, Mathematical Biology
Research in systems biology requires the collaboration of researchers from diverse backgrounds, including biology, computer science, mathematics, statistics, physics, and biochemistry. These ...
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Research in systems biology requires the collaboration of researchers from diverse backgrounds, including biology, computer science, mathematics, statistics, physics, and biochemistry. These collaborations, necessary because of the enormous breadth of background needed for research in this field, can be hindered by differing understandings of the limitations and applicability of techniques and concerns from different disciplines. The emerging area of systems level modeling in cellular biology has lacked a critical and thorough overview. The book provides the necessary critical comparison of concepts and approaches, with an emphasis on their possible applications. It presents key concepts and their theoretical background, including the concepts of robustness and modularity and their exploitation to study biological systems; the best-known modeling approaches, and their advantages and disadvantages; lessons from the application of mathematical models to the study of cellular biology; and available modeling tools and datasets, along with their computational limitations.Less
Research in systems biology requires the collaboration of researchers from diverse backgrounds, including biology, computer science, mathematics, statistics, physics, and biochemistry. These collaborations, necessary because of the enormous breadth of background needed for research in this field, can be hindered by differing understandings of the limitations and applicability of techniques and concerns from different disciplines. The emerging area of systems level modeling in cellular biology has lacked a critical and thorough overview. The book provides the necessary critical comparison of concepts and approaches, with an emphasis on their possible applications. It presents key concepts and their theoretical background, including the concepts of robustness and modularity and their exploitation to study biological systems; the best-known modeling approaches, and their advantages and disadvantages; lessons from the application of mathematical models to the study of cellular biology; and available modeling tools and datasets, along with their computational limitations.
