Effects of Changes in Surface Water Regime and/or Land Use on the Vertical Distribution of Water Available for Wetland Vegetation: Dynamic Model of the Zone of Aeration (Part 1 of Completion Report for Project A-023-ARK)

A mathematical model by Green, simulating one-dimensional vertical ground-water movement in unsaturated soils of the prairie region of Kansas, has been adapted for use in a wetlands environment typified by the wetlands forest of Eastern Arkansas. The model consists of two second-order, non-linear, partial differential equations and an algorithm for their numerical solution. The original model was extended to include functions for seasonal changes in transpiration and for drainage of excess precipitation. Before the addition of the two functions, the model reliability was limited to one growth season

Effects of Changes in Surface Water Regime and/or Land Use on the Vertical Distribution of Water Available for Wetland Vegetation: Dynamic Model of the Zone of Aeration (Appendix to Part 1 of Completion Report for Project A-023-ARK)

Appendix to Part 1 of Completion Report for Project A-023-AR

Portable Environmental Data Logger and Sensor

An instrumentation and recording package and several transducers were constructed and used to collect data on the environmental parameters thought to affect wetland vegetation growth and reproduction. These parameters were temperature, humidity, wind velocity, depth of water table, and amount of surface water. The data were collected four times a day and recorded on a magnetic cassette tape that could record for as long as 90 days. The tapes were read and the data were converted to engineering units by a microcomputer-based instrument constructed for that purpose

Effects of Changes in Surface Water Regime and/or Land Use on the Vertical Distribution of Water Available for Wetland Vegetation: Portable Environmental Data Logger and Sensors (Part II of Completion Report for Project A-023-ARK)

An instrumentation and recording package and several transducers were constructed and used to collect data on the environmental parameters thought to affect wetland vegetation growth and reproduction. These parameters were temperature, humidity, wind velocity, depth of water table, and amount of surface water. The data were collected four times a day and recorded on a magnetic cassette tape that could record for as long as 90 days. The tapes were read and the data were converted to engineering units by a microcomputer-based instrument constructed for that purpose

Dynamic Model of the Zone of Aeration

A mathematical model by Green (1), simulating one-dimensional vertical ground-water movement in unsaturated soils of the prairie region of Kansas, has been adapted for use in a wetlands environment typified by the wetlands forest of Eastern Arkansas. The model consists of two second-order, non-linear, partial differential equations and an algorithm for their numerical solution. The original model was extended to include functions for seasonal changes in transpiration and for drainage of excess precipitation. Before the addition of the two functions, the model reliability was limited to one growth season. With the mathematical model presented in this work it is possible to study interactions between hydrologic changes and the long term vegetative changes. The model potentially is a versatile management tool which could be used to help predict the environmental impact of proposed flood control projects

Evolution of Music by Public Choice

Music evolves as composers, performers, and consumers favor some musical variants over others. To investigate the role of consumer selection, we constructed a Darwinian music engine consisting of a population of short audio loops that sexually reproduce and mutate. This population evolved for 2,513 generations under the selective influence of 6,931 consumers who rated the loops’ aesthetic qualities. We found that the loops quickly evolved into music attributable, in part, to the evolution of aesthetically pleasing chords and rhythms. Later, however, evolution slowed. Applying the Price equation, a general description of evolutionary processes, we found that this stasis was mostly attributable to a decrease in the fidelity of transmission. Our experiment shows how cultural dynamics can be explained in terms of competing evolutionary forces

Improved alignment quality by combining evolutionary information, predicted secondary structure and self-organizing maps

BACKGROUND: Protein sequence alignment is one of the basic tools in bioinformatics. Correct alignments are required for a range of tasks including the derivation of phylogenetic trees and protein structure prediction. Numerous studies have shown that the incorporation of predicted secondary structure information into alignment algorithms improves their performance. Secondary structure predictors have to be trained on a set of somewhat arbitrarily defined states (e.g. helix, strand, coil), and it has been shown that the choice of these states has some effect on alignment quality. However, it is not unlikely that prediction of other structural features also could provide an improvement. In this study we use an unsupervised clustering method, the self-organizing map, to assign sequence profile windows to "structural states" and assess their use in sequence alignment. RESULTS: The addition of self-organizing map locations as inputs to a profile-profile scoring function improves the alignment quality of distantly related proteins slightly. The improvement is slightly smaller than that gained from the inclusion of predicted secondary structure. However, the information seems to be complementary as the two prediction schemes can be combined to improve the alignment quality by a further small but significant amount. CONCLUSION: It has been observed in many studies that predicted secondary structure significantly improves the alignments. Here we have shown that the addition of self-organizing map locations can further improve the alignments as the self-organizing map locations seem to contain some information that is not captured by the predicted secondary structure

Automatic discovery of cross-family sequence features associated with protein function

BACKGROUND: Methods for predicting protein function directly from amino acid sequences are useful tools in the study of uncharacterised protein families and in comparative genomics. Until now, this problem has been approached using machine learning techniques that attempt to predict membership, or otherwise, to predefined functional categories or subcellular locations. A potential drawback of this approach is that the human-designated functional classes may not accurately reflect the underlying biology, and consequently important sequence-to-function relationships may be missed. RESULTS: We show that a self-supervised data mining approach is able to find relationships between sequence features and functional annotations. No preconceived ideas about functional categories are required, and the training data is simply a set of protein sequences and their UniProt/Swiss-Prot annotations. The main technical aspect of the approach is the co-evolution of amino acid-based regular expressions and keyword-based logical expressions with genetic programming. Our experiments on a strictly non-redundant set of eukaryotic proteins reveal that the strongest and most easily detected sequence-to-function relationships are concerned with targeting to various cellular compartments, which is an area already well studied both experimentally and computationally. Of more interest are a number of broad functional roles which can also be correlated with sequence features. These include inhibition, biosynthesis, transcription and defence against bacteria. Despite substantial overlaps between these functions and their corresponding cellular compartments, we find clear differences in the sequence motifs used to predict some of these functions. For example, the presence of polyglutamine repeats appears to be linked more strongly to the "transcription" function than to the general "nuclear" function/location. CONCLUSION: We have developed a novel and useful approach for knowledge discovery in annotated sequence data. The technique is able to identify functionally important sequence features and does not require expert knowledge. By viewing protein function from a sequence perspective, the approach is also suitable for discovering unexpected links between biological processes, such as the recently discovered role of ubiquitination in transcription

An expression map for Anopheles gambiae

<p>Abstract</p> <p>Background</p> <p>Quantitative transcriptome data for the malaria-transmitting mosquito <it>Anopheles gambiae </it>covers a broad range of biological and experimental conditions, including development, blood feeding and infection. Web-based summaries of differential expression for individual genes with respect to these conditions are a useful tool for the biologist, but they lack the context that a visualisation of <it>all </it>genes with respect to <it>all </it>conditions would give. For most organisms, including <it>A. gambiae</it>, such a systems-level view of gene expression is not yet available.</p> <p>Results</p> <p>We have clustered microarray-based gene-averaged expression values, available from VectorBase, for 10194 genes over 93 experimental conditions using a self-organizing map. Map regions corresponding to known biological events, such as egg production, are revealed. Many individual gene clusters (nodes) on the map are highly enriched in biological and molecular functions, such as protein synthesis, protein degradation and DNA replication. Gene families, such as odorant binding proteins, can be classified into distinct functional groups based on their expression and evolutionary history. Immunity-related genes are non-randomly distributed in several distinct regions on the map, and are generally distant from genes with house-keeping roles. Each immunity-rich region appears to represent a distinct biological context for pathogen recognition and clearance (e.g. the humoral and gut epithelial responses). Several immunity gene families, such as peptidoglycan recognition proteins (PGRPs) and defensins, appear to be specialised for these distinct roles, while three genes with physically interacting protein products (LRIM1/APL1C/TEP1) are found in close proximity.</p> <p>Conclusions</p> <p>The map provides the first genome-scale, multi-experiment overview of gene expression in <it>A. gambiae </it>and should also be useful at the gene-level for investigating potential interactions. A web interface is available through the VectorBase website <url>http://www.vectorbase.org/</url>. It is regularly updated as new experimental data becomes available.</p