Ring Exchange and Correlated Fermions

The concept of exchange in strongly-correlated fermions is reviewed with emphasis on the generalization of the Heisenberg pair exchange to higher order n-particle permutations. The "frustration" resulting from competing ferromagnetic three-spin exchange and antiferromagnetic two- and four-spin exchanges is illustrated on a two-dimensional model system: solid 3He films. Recent experimental results proving the presence of four-spin exchange interactions in the CuO2 plaquettes of high Tc cuprates are reported.Comment: Invited talk at ISSP9 Conference Tokyo, Japan, November 2004 10 page

Lunar regolith bagging system

A design of a lunar regolith bag and bagging system is described. The bags of regolith are to be used for construction applications on the lunar surface. The machine is designed to be used in conjunction with the lunar SKITTER currently under development. The bags for this system are 1 cu ft volume and are made from a fiberglass composite weave. The machinery is constructed mostly from a boron/aluminum composite. The machine can fill 120 bags per hour and work for 8 hours a day. The man hours to machine hours ratio to operate the machine is .5/8

Identification And Management Of Pregnancy‐Related Low Back Pain

Back pain is a common complaint of women during pregnancy. It is frequently dismissed as trivial and inevitable. This article gives an overview of recent research on pregnancy‐related back pain that documents the impact of this pain on women's lives, during and beyond the childbearing year. It argues for a more active approach to the prevention and management of back pain during pregnancy. Two common back pain types, lumbar pain and posterior pelvic pain, are described and basic management techniques for the woman and her primary caregiver are suggested. Red flag findings that necessitate specialist referral are also highlighted, as are suggestions for further research.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/90043/1/j.1542-2011.1998.tb03313.x.pd

Progression Of Diseases Caused By The Oyster Parasites, Perkinsus Marinus And Haplosporidium Nelsoni, In Crassostrea Virginica On Constructed Intertidal Reefs

The progression of diseases caused by the oyster parasites Perkinsus marinus and Haplosporidium nelsoni were evaluated by periodic sampling (May 1994-December 1995) of eastern oysters Crassostrea virginica on an artificial reef located in the Piankatank River, Virginia. The infections observed were recorded as a function of: (1) prevalence and intensity; (2) oyster size and age; and (3) depth below mean low water at which the host oyster was found on the reef. Only a very small number of oysters were infected with the two species of pathogens on the oyster reef during the first 11 months of Life. In the second year of oyster life. epizootiological patterns of disease development followed temperature and salinity trends. Oysters at residence depths less than or equal to 45 cm below mean low water exhibited significantly (P \u3c 0.0001) lower prevalence and intensity of infections than oysters at depths greater than or equal to 90 cm. In contrast, oysters at residence depths greater than or equal to 90 cm had significantly higher growth rates (P \u3c 0.05) than those at less than or equal to 45 cm. However, size differences were not significant (P \u3e 0.05) at the end of the study. Results from this study may be used in managing oyster fisheries on natural or artificial reefs

GOFFA: Gene Ontology For Functional Analysis – A FDA Gene Ontology Tool for Analysis of Genomic and Proteomic Data

BACKGROUND: Gene Ontology (GO) characterizes and categorizes the functions of genes and their products according to biological processes, molecular functions and cellular components, facilitating interpretation of data from high-throughput genomics and proteomics technologies. The most effective use of GO information is achieved when its rich and hierarchical complexity is retained and the information is distilled to the biological functions that are most germane to the phenomenon being investigated. RESULTS: Here we present a FDA GO tool named Gene Ontology for Functional Analysis (GOFFA). GOFFA first ranks GO terms in the order of prevalence for a list of selected genes or proteins, and then it allows the user to interactively select GO terms according to their significance and specific biological complexity within the hierarchical structure. GOFFA provides five interactive functions (Tree view, Terms View, Genes View, GO Path and GO TreePrune) to analyze the GO data. Among the five functions, GO Path and GO TreePrune are unique. The GO Path simultaneously displays the ranks that order GOFFA Tree Paths based on statistical analysis. The GO TreePrune provides a visual display of a reduced GO term set based on a user's statistical cut-offs. Therefore, the GOFFA visual display can provide an intuitive depiction of the most likely relevant biological functions. CONCLUSION: With GOFFA, the user can dynamically interact with the GO data to interpret gene expression results in the context of biological plausibility, which can lead to new discoveries or identify new hypotheses. AVAILABILITY: GOFFA is available through ArrayTrack software

Investigation of reproducibility of differentially expressed genes in DNA microarrays through statistical simulation

Recent publications have raised concerns about the reliability of microarray technology because of the lack of reproducibility of differentially expressed genes (DEGs) from highly similar studies across laboratories and platforms. The rat toxicogenomics study of the MicroArray Quality Control (MAQC) project empirically revealed that the DEGs selected using a fold change (FC)-based criterion were more reproducible than those derived solely by statistical significance such as P-value from a simple t-tests. In this study, we generate a set of simulated microarray datasets to compare gene selection/ranking rules, including P-value, FC and their combinations, using the percentage of overlapping genes between DEGs from two similar simulated datasets as the measure of reproducibility. The results are supportive of the MAQC's conclusion on that DEG lists are more reproducible across laboratories and platforms when FC-based ranking coupled with a nonstringent P-value cutoff is used for gene selection compared with selection based on P-value based ranking method. We conclude that the MAQC recommendation should be considered when reproducibility is an important study objective

Selecting a single model or combining multiple models for microarray-based classifier development? – A comparative analysis based on large and diverse datasets generated from the MAQC-II project

<p>Abstract</p> <p>Background</p> <p>Genomic biomarkers play an increasing role in both preclinical and clinical application. Development of genomic biomarkers with microarrays is an area of intensive investigation. However, despite sustained and continuing effort, developing microarray-based predictive models (i.e., genomics biomarkers) capable of reliable prediction for an observed or measured outcome (i.e., endpoint) of unknown samples in preclinical and clinical practice remains a considerable challenge. No straightforward guidelines exist for selecting a single model that will perform best when presented with unknown samples. In the second phase of the MicroArray Quality Control (MAQC-II) project, 36 analysis teams produced a large number of models for 13 preclinical and clinical endpoints. Before external validation was performed, each team nominated one model per endpoint (referred to here as 'nominated models') from which MAQC-II experts selected 13 'candidate models' to represent the best model for each endpoint. Both the nominated and candidate models from MAQC-II provide benchmarks to assess other methodologies for developing microarray-based predictive models.</p> <p>Methods</p> <p>We developed a simple ensemble method by taking a number of the top performing models from cross-validation and developing an ensemble model for each of the MAQC-II endpoints. We compared the ensemble models with both nominated and candidate models from MAQC-II using blinded external validation.</p> <p>Results</p> <p>For 10 of the 13 MAQC-II endpoints originally analyzed by the MAQC-II data analysis team from the National Center for Toxicological Research (NCTR), the ensemble models achieved equal or better predictive performance than the NCTR nominated models. Additionally, the ensemble models had performance comparable to the MAQC-II candidate models. Most ensemble models also had better performance than the nominated models generated by five other MAQC-II data analysis teams that analyzed all 13 endpoints.</p> <p>Conclusions</p> <p>Our findings suggest that an ensemble method can often attain a higher average predictive performance in an external validation set than a corresponding “optimized” model method. Using an ensemble method to determine a final model is a potentially important supplement to the good modeling practices recommended by the MAQC-II project for developing microarray-based genomic biomarkers.</p

Phonon Assisted Multimagnon Optical Absorption and Long Lived Two-Magnon States in Undoped Lamellar Copper Oxides

We calculate the effective charge for multimagnon infrared (IR) absorption assisted by phonons in the parent insulating compounds of cuprate superconductors and the spectra for two-magnon absorption using interacting spin-wave theory. Recent measured bands in the mid IR [Perkins et al. Phys. Rev. Lett. {\bf 71} 1621 (1993)] are interpreted as involving one phonon plus a two-magnon virtual bound state, and one phonon plus higher multimagnon absorption processes. The virtual bound state consists of a narrow resonance occurring when the magnon pair has total momentum close to $(\pi,0)$.Comment: 4 page

Using Decision Forest to Classify Prostate Cancer Samples on the Basis of SELDI-TOF MS Data: Assessing Chance Correlation and Prediction Confidence

Class prediction using “omics” data is playing an increasing role in toxicogenomics, diagnosis/prognosis, and risk assessment. These data are usually noisy and represented by relatively few samples and a very large number of predictor variables (e.g., genes of DNA microarray data or m/z peaks of mass spectrometry data). These characteristics manifest the importance of assessing potential random correlation and overfitting of noise for a classification model based on omics data. We present a novel classification method, decision forest (DF), for class prediction using omics data. DF combines the results of multiple heterogeneous but comparable decision tree (DT) models to produce a consensus prediction. The method is less prone to overfitting of noise and chance correlation. A DF model was developed to predict presence of prostate cancer using a proteomic data set generated from surface-enhanced laser deposition/ionization time-of-flight mass spectrometry (SELDI-TOF MS). The degree of chance correlation and prediction confidence of the model was rigorously assessed by extensive cross-validation and randomization testing. Comparison of model prediction with imposed random correlation demonstrated biologic relevance of the model and the reduction of overfitting in DF. Furthermore, two confidence levels (high and low confidences) were assigned to each prediction, where most misclassifications were associated with the low-confidence region. For the high-confidence prediction, the model achieved 99.2% sensitivity and 98.2% specificity. The model also identified a list of significant peaks that could be useful for biomarker identification. DF should be equally applicable to other omics data such as gene expression data or metabolomic data. The DF algorithm is available upon request