416 research outputs found
Letters between Charlotte D. Seaver and William Kerr\u27s secretary
Letters concerning application for a position in the Domestic Science Department at Utah Agricultural College as well as Charlotte\u27s resume
Micro-bias and macro-performance
We use agent-based modeling to investigate the effect of conservatism and
partisanship on the efficiency with which large populations solve the density
classification task--a paradigmatic problem for information aggregation and
consensus building. We find that conservative agents enhance the populations'
ability to efficiently solve the density classification task despite large
levels of noise in the system. In contrast, we find that the presence of even a
small fraction of partisans holding the minority position will result in
deadlock or a consensus on an incorrect answer. Our results provide a possible
explanation for the emergence of conservatism and suggest that even low levels
of partisanship can lead to significant social costs.Comment: 11 pages, 5 figure
Improving automated model reconstruction across phylogenetically diverse genome-scale metabolic models
info:eu-repo/semantics/publishedVersio
High-throughput comparison, functional annotation, and metabolic modeling of plant genomes using the PlantSEED resource
The increasing number of sequenced plant genomes is placing new demands on the methods applied to analyze, annotate, and model these genomes. Today's annotation pipelines result in inconsistent gene assignments that complicate comparative analyses and prevent efficient construction of metabolic models. To overcome these problems, we have developed the PlantSEED, an integrated, metabolism-centric database to support subsystems-based annotation and metabolic model reconstruction for plant genomes. PlantSEED combines SEED subsystems technology, first developed for microbial genomes, with refined protein families and biochemical data to assign fully consistent functional annotations to orthologous genes, particularly those encoding primary metabolic pathways. Seamless integration with its parent, the prokaryotic SEED database, makes PlantSEED a unique environment for cross-kingdom comparative analysis of plant and bacterial genomes. The consistent annotations imposed by PlantSEED permit rapid reconstruction and modeling of primary metabolism for all plant genomes in the database. This feature opens the unique possibility of model-based assessment of the completeness and accuracy of gene annotation and thus allows computational identification of genes and pathways that are restricted to certain genomes or need better curation. We demonstrate the PlantSEED system by producing consistent annotations for 10 reference genomes. We also produce a functioning metabolic model for each genome, gapfilling to identify missing annotations and proposing gene candidates for missing annotations. Models are built around an extended biomass composition representing the most comprehensive published to date. To our knowledge, our models are the first to be published for seven of the genomes analyzed
Affordances, constraints and information flows as ‘leverage points’ in design for sustainable behaviour
Copyright @ 2012 Social Science Electronic PublishingTwo of Donella Meadows' 'leverage points' for intervening in systems (1999) seem particularly pertinent to design for sustainable behaviour, in the sense that designers may have the scope to implement them in (re-)designing everyday products and services. The 'rules of the system' -- interpreted here to refer to affordances and constraints -- and the structure of information flows both offer a range of opportunities for design interventions to in fluence behaviour change, and in this paper, some of the implications and possibilities are discussed with reference to parallel concepts from within design, HCI and relevant areas of psychology
High-throughput Comparison, Functional Annotation, and Metabolic Modeling of Plant Genomes using the PlantSEED Resource
There is a growing demand for genome-scale metabolic reconstructions for plants, fueled by the need to understand the metabolic basis of crop yield and by progress in genome and transcriptome sequencing. Methods are also required to enable the interpretation of plant transcriptome data to study how cellular metabolic activity varies under different growth conditions or even within different organs, tissues, and developmental stages. Such methods depend extensively on the accuracy with which genes have been mapped to the biochemical reactions in the plant metabolic pathways. Errors in these mappings lead to metabolic reconstructions with an inflated number of reactions and possible generation of unreliable metabolic phenotype predictions. Here we introduce a new evidence-based genome-scale metabolic reconstruction of maize, with significant improvements in the quality of the gene-reaction associations included within our model. We also present a new approach for applying our model to predict active metabolic genes based on transcriptome data. This method includes a minimal set of reactions associated with low expression genes to enable activity of a maximum number of reactions associated with high expression genes. We apply this method to construct an organ-specific model for the maize leaf, and tissue specific models for maize embryo and endosperm cells. We validate our models using fluxomics data for the endosperm and embryo, demonstrating an improved capacity of our models to fit the available fluxomics data. All models are publicly available via the DOE Systems Biology Knowledgebase and PlantSEED, and our new method is generally applicable for analysis transcript profiles from any plant, paving the way for further in silico studies with a wide variety of plant genomes
Improved Evidence-based Genome-scale Metabolic Models for Maize Leaf, Embryo, and Endosperm
There is a growing demand for genome-scale metabolic reconstructions for plants, fueled by the need to understand the metabolic basis of crop yield and by progress in genome and transcriptome sequencing. Methods are also required to enable the interpretation of plant transcriptome data to study how cellular metabolic activity varies under different growth conditions or even within different organs, tissues, and developmental stages. Such methods depend extensively on the accuracy with which genes have been mapped to the biochemical reactions in the plant metabolic pathways. Errors in these mappings lead to metabolic reconstructions with an inflated number of reactions and possible generation of unreliable metabolic phenotype predictions. Here we introduce a new evidence-based genome-scale metabolic reconstruction of maize, with significant improvements in the quality of the gene-reaction associations included within our model. We also present a new approach for applying our model to predict active metabolic genes based on transcriptome data. This method includes a minimal set of reactions associated with low expression genes to enable activity of a maximum number of reactions associated with high expression genes. We apply this method to construct an organ-specific model for the maize leaf, and tissue specific models for maize embryo and endosperm cells. We validate our models using fluxomics data for the endosperm and embryo, demonstrating an improved capacity of our models to fit the available fluxomics data. All models are publicly available via the DOE Systems Biology Knowledgebase and PlantSEED, and our new method is generally applicable for analysis transcript profiles from any plant, paving the way for further in silico studies with a wide variety of plant genomes
Identifying Unique Neighborhood Characteristics to Guide Health Planning for Stroke and Heart Attack: Fuzzy Cluster and Discriminant Analyses Approaches
Socioeconomic, demographic, and geographic factors are known determinants of stroke and myocardial infarction (MI) risk. Clustering of these factors in neighborhoods needs to be taken into consideration during planning, prioritization and implementation of health programs intended to reduce disparities. Given the complex and multidimensional nature of these factors, multivariate methods are needed to identify neighborhood clusters of these determinants so as to better understand the unique neighborhood profiles. This information is critical for evidence-based health planning and service provision. Therefore, this study used a robust multivariate approach to classify neighborhoods and identify their socio-demographic characteristics so as to provide information for evidence-based neighborhood health planning for stroke and MI.The study was performed in East Tennessee Appalachia, an area with one of the highest stroke and MI risks in USA. Robust principal component analysis was performed on neighborhood (census tract) socioeconomic and demographic characteristics, obtained from the US Census, to reduce the dimensionality and influence of outliers in the data. Fuzzy cluster analysis was used to classify neighborhoods into Peer Neighborhoods (PNs) based on their socioeconomic and demographic characteristics. Nearest neighbor discriminant analysis and decision trees were used to validate PNs and determine the characteristics important for discrimination. Stroke and MI mortality risks were compared across PNs. Four distinct PNs were identified and their unique characteristics and potential health needs described. The highest risk of stroke and MI mortality tended to occur in less affluent PNs located in urban areas, while the suburban most affluent PNs had the lowest risk.Implementation of this multivariate strategy provides health planners useful information to better understand and effectively plan for the unique neighborhood health needs and is important in guiding resource allocation, service provision, and policy decisions to address neighborhood health disparities and improve population health
Expression of Distal-less, dachshund, and optomotor blind in Neanthes arenaceodentata (Annelida, Nereididae) does not support homology of appendage-forming mechanisms across the Bilateria
The similarity in the genetic regulation of
arthropod and vertebrate appendage formation has been
interpreted as the product of a plesiomorphic gene
network that was primitively involved in bilaterian
appendage development and co-opted to build appendages
(in modern phyla) that are not historically related
as structures. Data from lophotrochozoans are needed to
clarify the pervasiveness of plesiomorphic appendage forming
mechanisms. We assayed the expression of three
arthropod and vertebrate limb gene orthologs, Distal-less
(Dll), dachshund (dac), and optomotor blind (omb), in
direct-developing juveniles of the polychaete Neanthes
arenaceodentata. Parapodial Dll expression marks premorphogenetic
notopodia and neuropodia, becoming restricted
to the bases of notopodial cirri and to ventral
portions of neuropodia. In outgrowing cephalic appendages,
Dll activity is primarily restricted to proximal
domains. Dll expression is also prominent in the brain. dac
expression occurs in the brain, nerve cord ganglia, a pair
of pharyngeal ganglia, presumed interneurons linking a
pair of segmental nerves, and in newly differentiating
mesoderm. Domains of omb expression include the brain,
nerve cord ganglia, one pair of anterior cirri, presumed
precursors of dorsal musculature, and the same pharyngeal
ganglia and presumed interneurons that express dac.
Contrary to their roles in outgrowing arthropod and
vertebrate appendages, Dll, dac, and omb lack comparable
expression in Neanthes appendages, implying independent
evolution of annelid appendage development. We infer
that parapodia and arthropodia are not structurally or
mechanistically homologous (but their primordia might
be), that Dll’s ancestral bilaterian function was in sensory
and central nervous system differentiation, and that
locomotory appendages possibly evolved from sensory
outgrowths
Genomic Organization and Expression Demonstrate Spatial and Temporal Hox Gene Colinearity in the Lophotrochozoan Capitella sp. I
Hox genes define regional identities along the anterior–posterior axis in many animals. In a number of species, Hox genes are clustered in the genome, and the relative order of genes corresponds with position of expression in the body. Previous Hox gene studies in lophotrochozoans have reported expression for only a subset of the Hox gene complement and/or lack detailed genomic organization information, limiting interpretations of spatial and temporal colinearity in this diverse animal clade. We studied expression and genomic organization of the single Hox gene complement in the segmented polychaete annelid Capitella sp. I. Total genome searches identified 11 Hox genes in Capitella, representing 11 distinct paralog groups thought to represent the ancestral lophotrochozoan complement. At least 8 of the 11 Capitella Hox genes are genomically linked in a single cluster, have the same transcriptional orientation, and lack interspersed non-Hox genes. Studying their expression by situ hybridization, we find that the 11 Capitella Hox genes generally exhibit spatial and temporal colinearity. With the exception of CapI-Post1, Capitella Hox genes are all expressed in broad ectodermal domains during larval development, consistent with providing positional information along the anterior–posterior axis. The anterior genes CapI-lab, CapI-pb, and CapI-Hox3 initiate expression prior to the appearance of segments, while more posterior genes appear at or soon after segments appear. Many of the Capitella Hox genes have either an anterior or posterior expression boundary coinciding with the thoracic–abdomen transition, a major body tagma boundary. Following metamorphosis, several expression patterns change, including appearance of distinct posterior boundaries and restriction to the central nervous system. Capitella Hox genes have maintained a clustered organization, are expressed in the canonical anterior–posterior order found in other metazoans, and exhibit spatial and temporal colinearity, reflecting Hox gene characteristics that likely existed in the protostome–deuterostome ancestor
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