Iowa State University

About: Iowa State University is a education organization based out in Ames, Iowa, United States. It is known for research contribution in the topics: Population & Gene. The organization has 50151 authors who have published 107716 publications receiving 3355909 citations. The organization is also known as: Iowa State University of Science and Technology & Iowa State College.

A 3347-Locus Genetic Recombination Map of Sequence-Tagged Sites Reveals Features of Genome Organization, Transmission and Evolution of Cotton (Gossypium)

Abstract: We report genetic maps for diploid (D) and tetraploid (AtDt) Gossypium genomes composed of sequence-tagged sites (STS) that foster structural, functional, and evolutionary genomic studies. The maps include, respectively, 2584 loci at 1.72-cM (600 kb) intervals based on 2007 probes (AtDt) and 763 loci at 1.96-cM (500 kb) intervals detected by 662 probes (D). Both diploid and tetraploid cottons exhibit negative crossover interference; i.e., double recombinants are unexpectedly abundant. We found no major structural changes between Dt and D chromosomes, but confirmed two reciprocal translocations between At chromosomes and several inversions. Concentrations of probes in corresponding regions of the various genomes may represent centromeres, while genome-specific concentrations may represent heterochroma- tin. Locus duplication patterns reveal all 13 expected homeologous chromosome sets and lend new support to the possibility that a more ancient polyploidization event may have predated the A-D divergence of 6-11 million years ago. Identification of SSRs within 312 RFLP sequences plus direct mapping of 124 SSRs and exploration for CAPS and SNPs illustrate the "portability" of these STS loci across populations and detection systems useful for marker-assisted improvement of the world's leading fiber crop. These data provide new insights into polyploid evolution and represent a foundation for assembly of a finished sequence of the cotton genome.

Mental Simulation of Causality

Abstract: We propose that people imagine alternatives to reality (counterfactuals) in assessing the causal role of a prior event. This process of mental simulation (Kahneman & Tversky, 1982) is used to derive novel predictions about the effects of default events on causal attribution. A default event is the alternative event that most readily comes to mind when a factual event is mentally mutated. The factual event is judged to be causal to the extent that its default undoes the outcome. In Experiment 1, a woman was described as having died from an allergic reaction to a meal ordered by her boss. When the boss was described as having considered another meal without the allergic ingredient, people were more likely to mutate his decision and his causal role in the death was judged to be greater than when the alternative meal was also said to have the allergic ingredient. In Experiment 2, a paraplegic couple was described as having died in an auto accident after having been denied a cab ride. People perceived the cabby's refusal to take the couple as a stronger cause of the deaths when his taking the couple would have undone the accident than when it would have not have. We conclude that an adequate theory of causal judgment requires an understanding of these counterfactual simulations.

Randomized trial of brief family interventions for general populations: adolescent substance use outcomes 4 years following baseline.

Abstract: This study examined the long-term substance use outcomes of 2 brief interventions designed for general population families of young adolescents. Thirty-three public schools were randomly assigned to 3 conditions: the 5-session Preparing for the Drug Free Years Program, the 7-session Iowa Strengthening Families Program, and a minimal contact control condition. The pretest involved 667 6th graders and their families. Assessments included multiple measures of initiation and current use of alcohol, tobacco, and marijuana. Pretest data were collected in the 6th grade and the reported follow-up data were collected in the 10th grade. Significant intervention-control differences in initiation and current use were found for both interventions. It is concluded that brief family skills-training interventions designed for general populations have the potential to reduce adolescent substance use and thus have important public health implications.

Predicting RNA-Protein Interactions Using Only Sequence Information

Abstract: RNA-protein interactions (RPIs) play important roles in a wide variety of cellular processes, ranging from transcriptional and post-transcriptional regulation of gene expression to host defense against pathogens. High throughput experiments to identify RNA-protein interactions are beginning to provide valuable information about the complexity of RNA-protein interaction networks, but are expensive and time consuming. Hence, there is a need for reliable computational methods for predicting RNA-protein interactions. We propose RPISeq, a family of classifiers for predicting R NA-p rotein i nteractions using only seq uence information. Given the sequences of an RNA and a protein as input, RPIseq predicts whether or not the RNA-protein pair interact. The RNA sequence is encoded as a normalized vector of its ribonucleotide 4-mer composition, and the protein sequence is encoded as a normalized vector of its 3-mer composition, based on a 7-letter reduced alphabet representation. Two variants of RPISeq are presented: RPISeq-SVM, which uses a Support Vector Machine (SVM) classifier and RPISeq-RF, which uses a Random Forest classifier. On two non-redundant benchmark datasets extracted from the Protein-RNA Interface Database (PRIDB), RPISeq achieved an AUC (Area Under the Receiver Operating Characteristic (ROC) curve) of 0.96 and 0.92. On a third dataset containing only mRNA-protein interactions, the performance of RPISeq was competitive with that of a published method that requires information regarding many different features (e.g., mRNA half-life, GO annotations) of the putative RNA and protein partners. In addition, RPISeq classifiers trained using the PRIDB data correctly predicted the majority (57-99%) of non-coding RNA-protein interactions in NPInter-derived networks from E. coli, S. cerevisiae, D. melanogaster, M. musculus, and H. sapiens. Our experiments with RPISeq demonstrate that RNA-protein interactions can be reliably predicted using only sequence-derived information. RPISeq offers an inexpensive method for computational construction of RNA-protein interaction networks, and should provide useful insights into the function of non-coding RNAs. RPISeq is freely available as a web-based server at http://pridb.gdcb.iastate.edu/RPISeq/ .