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427 results for “Modularity”
Data from: Genetic basis of continuous variation in the levels and modular inheritance of pigmentation in cichlid fishes
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Data from: Effects of environmental disturbance on phenotypic variation: an integrated assessment of canalization, developmental stability, modularity and allometry in lizard head shape
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Data from: Analysis and visualization of H7 influenza using genomic, evolutionary and geographic information in a modular web service
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Modular chromosome rearrangements reveal parallel and nonparallel adaptation in a marine fish
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Comparing the strength of modular signal, and evaluating alternative modular hypotheses, using covariance ratio effect sizes with morphometric data
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Data from: Ontogenetic changes in the phenotypic integration and modularity of leaf functional traits
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Data from: Functional integration for enrolment constrains evolutionary variation of phacopid trilobites despite developmental modularity
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Data from: Cope’s Rule in a modular organism: directional evolution without an overarching macroevolutionary trend
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Modular prophage interactions driven by capsule serotype select for capsule loss under phage predation
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Data from: Constraint and opportunity: the genetic basis and evolution of modularity in the cichlid mandible
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Data from: Does nasal echolocation influence the modularity of the mammal skull?
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Adaptive radiation despite conserved modularity patterns in San Salvador Island Cyprinodon pupfishes and their hybrids
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Data from: Functional modularity and mechanical stress shape plastic responses during fish development
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Data from: The evolutionary origins of modularity
A central biological question is how natural organisms are so evolvable (capable of quickly adapting to new environments). A key driver of evolvability is the widespread modularity of biological networks--their organization as functional, sparsely connected subunits--but there is no consensus regarding why modularity itself evolved. While most hypotheses assume indirect selection for evolvability, here we demonstrate that the ubiquitous, direct selection pressure to reduce the cost of connections between network nodes causes the emergence of modular networks. Computational evolution experiments with selection pressures to maximize network performance and minimize connection costs yield networks that are significantly more modular and more evolvable than control experiments that only select for performance. These results will catalyze research in numerous disciplines, including neuroscience, genetics and harnessing evolution for engineering purposes.
Architectural Feature Re-Modularization for Software Product Line Evolution
<p>Extensive maintenance leads to the Software Product Line Architecture<br> (PLA) degradation over time. When there is the need of<br> evolving the Software Product Line (SPL) to include new features,<br> or move to a new platform, a degraded PLA requires considerable<br> effort to understand and modify, demanding expensive refactoring<br> activity. In the state of the art, search-based algorithms are used to<br> improve PLA at package level. However, recent studies have shown<br> that the most variability and implementation details of an SPL are<br> described in the level of classes. There is a gap between existing<br> approaches and existing practical needs. In this work, we extend<br> the current state of the art to deal with feature modularization in<br> the level of classes by introducing a new search operator and a set<br> of objective functions to deal with feature modularization in a finer<br> granularity of the architectural elements, namely at class level. We<br> evaluated the proposal in an exploratory study with a PLA widely<br> investigated and a real-world PLA. The results of quantitative and<br> qualitative analysis point out that our proposal provides solutions<br> to properly re-modularize features in a PLA, being preferred by<br> practitioners, in order to support the evolution of SPLs.</p>
Unconsciousness reconfigures modular brain network dynamics
<p>Time-dependent adjacency matrices per state of consciousness</p>
Data from: Modularity and rates of evolutionary change in a power-amplified prey capture system
The dynamic interplay among structure, function and phylogeny form a classic triad of influences on the patterns and processes of biological diversification. While these dynamics are widely recognized as important, quantitative analyses of their interactions have infrequently been applied to biomechanical systems. Here we analyze these factors using a fundamental biomechanical mechanism: power amplification. Power-amplified systems use springs and latches to generate extremely fast and powerful movements. This study focuses specifically on the power amplification mechanism in the fast raptorial appendages of mantis shrimp (Crustacea: Stomatopoda). Using geometric morphometric and phylogenetic comparative analyses, we measured evolutionary modularity and rates of morphological evolution of the raptorial appendage's biomechanical components. We found that "smashers" (hammer-shaped raptorial appendages) exhibit lower modularity and 10-fold slower rates of morphological change when compared to non-smashers (spear-shaped or undifferentiated appendages). The morphological and biomechanical integration of this system at a macro-evolutionary scale and the presence of variable rates of evolution reveal a balance between structural constraints, functional variation, and the developmental and genetic roles in evolutionary diversification.
Data from: High-dimensional variance partitioning reveals the modular genetic basis of adaptive divergence in gene expression during reproductive character displacement
Although adaptive change is usually associated with complex changes in phenotype, few genetic investigations have been conducted of adaptations that involve sets of high dimensional traits. Microarrays have supplied high-dimensional descriptions of gene expression, and phenotypic change resulting from adaptation often results in large-scale changes in gene expression. We demonstrate how genetic analysis of large-scale changes in gene expression generated during adaptation can be accomplished by determining by high-dimensional variance partitioning within classical genetic experimental designs. A microarray experiment conducted on a panel of recombinant inbred lines (RILs) generated from two populations of Drosophila serrata that have diverged in response to natural selection, revealed genetic divergence in 10.6% of 3762 gene products examined. Over 97% of the genetic divergence in transcript abundance was explained by only 12 genetic modules. The two most important modules, explaining 50% of the genetic variance in transcript abundance, were genetically correlated with the morphological traits that are known to be under selection. The expression of three candidate genes from these two important genetic modules was assessed in an independent experiment using qRT-PCR on 430 individuals from the panel of RILs, and confirmed the genetic association between transcript abundance and morphological traits under selection.
Data from: Evaluating modularity in morphometric data: challenges with the RV coefficient and a new test measure
Modularity describes the case where patterns of trait covariation are unevenly dispersed across traits. Specifically, trait correlations are high and concentrated within subsets of variables (modules), but the correlations between traits across modules are relatively weaker. For morphometric data sets, hypotheses of modularity are commonly evaluated using the RV coefficient, an association statistic used in a wide variety of fields. In this article, I explore the properties of the RV coefficient using simulated data sets. Using data drawn from a normal distribution where the data were neither modular nor integrated in structure, I show that the RV coefficient is adversely affected by attributes of the data (sample size and the number of variables) that do not characterize the covariance structure between sets of variables. Thus, with the RV coefficient, patterns of modularity or integration in data are confounded with trends generated by sample size and the number of variables, which limits biological interpretations and renders comparisons of RV coefficients across data sets uninformative. As an alternative, I propose the covariance ratio (CR) for quantifying modular structure and show that it is unaffected by sample size or the number of variables. Further, statistical tests based on the CR exhibit appropriate type I error rates and display higher statistical power relative to the RV coefficient when evaluating modular data. Overall, these findings demonstrate that the RV coefficient does not display statistical characteristics suitable for reliable assessment of hypotheses of modular or integrated structure and therefore should not be used to evaluate these patterns in morphological data sets. By contrast, the covariance ratio meets these criteria and provides a useful alternative method for assessing the degree of modular structure in morphological data.
Data from: Neural modularity helps organisms evolve to learn new skills without forgetting old
A long-standing goal in artificial intelligence is creating agents that can learn a variety of different skills for different problems. In the artificial intelligence subfield of neural networks, a barrier to that goal is that when agents learn a new skill they typically do so by losing previously acquired skills, a problem called catastrophic forgetting. That occurs because, to learn the new task, neural learning algorithms change connections that encode previously acquired skills. How networks are organized critically affects their learning dynamics. In this paper, we test whether catastrophic forgetting can be reduced by evolving modular neural networks. Modularity intuitively should reduce learning interference between tasks by separating functionality into physically distinct modules in which learning can be selectively turned on or off. Modularity can further improve learning by having a reinforcement learning module separate from sensory processing modules, allowing learning to happen only in response to a positive or negative reward. In this paper, learning takes place via neuromodulation, which allows agents to selectively change the rate of learning for each neural connection based on environmental stimuli (e.g. to alter learning in specific locations based on the task at hand). To produce modularity, we evolve neural networks with a cost for neural connections. We show that this connection cost technique causes modularity, confirming a previous result, and that such sparsely connected, modular networks have higher overall performance because they learn new skills faster while retaining old skills more and because they have a separate reinforcement learning module. Our results suggest (1) that encouraging modularity in neural networks may help us overcome the long-standing barrier of networks that cannot learn new skills without forgetting old ones, and (2) that one benefit of the modularity ubiquitous in the brains of natural animals might be to alleviate the problem of catastrophic forgetting.
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.