Reconstructing a Z' Lagrangian using the LHC and low-energy data

We study the potential of the LHC and future low-energy experiments to precisely measure the underlying model parameters of a new Z' boson. We emphasize the complimentary information obtained from both on- and off-peak LHC dilepton data, from the future Q-weak measurement of the weak charge of the proton, and from a proposed measurement of parity violation in low-energy Moller scattering. We demonstrate the importance of off-peak LHC data and Q-weak for removing sign degeneracies between Z' couplings that occur if only on-peak LHC data is studied. A future precision measurement of low-energy Moller scattering can resolve a scaling degeneracy between quark and lepton couplings that remains after analyzing LHC dilepton data, permitting an extraction of the individual Z' couplings rather than combinations of them. We study how precisely Z' properties can be extracted for LHC integrated luminosities ranging from a few inverse femtobarns to super-LHC values of an inverse attobarn. For the several example cases studied with M_Z'=1.5 TeV, we find that coupling combinations can be determined with relative uncertainties reaching 30% with 30 fb^-1 of integrated luminosity, while 50% is possible with 10 fb^-1. With SLHC luminosities of 1 ab^-1, we find that products of quark and lepton couplings can be probed to 10%.Comment: 36 pages, 17 figure

New Directions in Subband Coding

Two very different subband coders are described. The first is a modified dynamic bit-allocation-subband coder (D-SBC) designed for variable rate coding situations and easily adaptable to noisy channel environments. It can operate at rates as low as 12 kb/s and still give good quality speech. The second coder is a 16-kb/s waveform coder, based on a combination of subband coding and vector quantization (VQ-SBC). The key feature of this coder is its short coding delay, which makes it suitable for real-time communication networks. The speech quality of both coders has been enhanced by adaptive postfiltering. The coders have been implemented on a single AT&T DSP32 signal processo

Statistical modeling for selecting housekeeper genes

There is a need for statistical methods to identify genes that have minimal variation in expression across a variety of experimental conditions. These 'housekeeper' genes are widely employed as controls for quantification of test genes using gel analysis and real-time RT-PCR. Using real-time quantitative RT-PCR, we analyzed 80 primary breast tumors for variation in expression of six putative housekeeper genes (MRPL19 (mitochondrial ribosomal protein L19), PSMC4 (proteasome (prosome, macropain) 26S subunit, ATPase, 4), SF3A1 (splicing factor 3a, subunit 1, 120 kDa), PUM1 (pumilio homolog 1 (Drosophila)), ACTB (actin, beta) and GAPD (glyceraldehyde-3-phosphate dehydrogenase)). We present appropriate models for selecting the best housekeepers to normalize quantitative data within a given tissue type (for example, breast cancer) and across different types of tissue samples

The TIGR Gene Indices: clustering and assembling EST and known genes and integration with eukaryotic genomes

Although the list of completed genome sequencing projects has expanded rapidly, sequencing and analysis of expressed sequence tags (ESTs) remain a primary tool for discovery of novel genes in many eukaryotes and a key element in genome annotation. The TIGR Gene Indices (http://www.tigr.org/tdb/tgi) are a collection of 77 species-specific databases that use a highly refined protocol to analyze gene and EST sequences in an attempt to identify and characterize expressed transcripts and to present them on the Web in a user-friendly, consistent fashion. A Gene Index database is constructed for each selected organism by first clustering, then assembling EST and annotated cDNA and gene sequences from GenBank. This process produces a set of unique, high-fidelity virtual transcripts, or tentative consensus (TC) sequences. The TC sequences can be used to provide putative genes with functional annotation, to link the transcripts to genetic and physical maps, to provide links to orthologous and paralogous genes, and as a resource for comparative and functional genomic analysis

Correction: Statistical modeling for selecting housekeeper genes

A correction to Statistical modeling for selecting housekeeper genes by Aniko Szabo, Charles M Perou, Mehmet Karaca, Laurent Perreard, John F Quackenbush, and Philip S Bernard. Genome Biology 2004, 5:R5

Alaskan mammoth expeditions

p. 87-130, [9] leaves of plates (3 folded) : ill., maps ; 24 cm.Introduction -- Itinerary -- Fox Gulch, Klondike District -- The Palisades, Yukon River -- Nome coastal plain -- Keewalik River. Alder Creek. Native Gulch -- Eschscholtz Bay -- Fossil remains found imbedded in the historic bluff -- Remarks on the occurrence of fossils and recent bones on the shores of Eschscholtz Bay -- Ah-weeng-nuk River -- Buckland River -- Hotham Inlet and Selawik Lake -- Summary and conclusions. List of Pleistocene mammals."Literature on the Pleistocene mammals of, and their occurrence in, Alaska and the Klondike region, Canada": p. 128-130

Predicting growth rates of adult working boars in a commercial boar stud

There is almost no information on ideal growth rates for adult boars, but estimates can be made if the relationship between boar weight and age is known. Therefore, this study was aimed to predict growth rates in adult working boars in a commercial boar stud. A total of 214 adult working boars from two genetic lines in a commercial boar stud were individually weighed on a platform scale. Age of the boar was recorded at the time of weighing. A regression equation to predict boar weight as a function of age was developed by using PROC REG of SAS. The model was used to predict BW on a daily basis, and ADG was derived as the difference between two predicted BW values. Factorial estimates of daily ME requirement and feeding rates were determined. The energy requirement for weight gain was computed by using the predicted ADG as a guide in setting target weight gains. Results showed a positive curvilinear response (P\u3c0.01) to describe the relationship between boar weight and age. Predicted ADG decreased in a curvilinear manner as the boars aged. In conclusion, on-farm growth rates can be predicted effectively by relating weight with age, taken from a representative number of boars in a given farm population. These data can then be used to develop farm specific feeding programs or to set different growth curves for experimental purposes.; Swine Day, 2006, Kansas State University, Manhattan, KS, 200

Interpreting microarray experiments via co-expressed gene groups analysis

International audienceMicroarray technology produces vast amounts of data by measuring simultaneously the expression levels of thousands of genes under hundreds of biological conditions. Nowadays, one of the principal challenges in bioinformatics is the interpretation of huge data using different sources of information. We propose a novel data analysis method named CGGA (Co-expressed Gene Groups Analysis) that automatically finds groups of genes that are functionally enriched, i.e. have the same functional annotations, and are co- expressed. CGGA automatically integrates the information of microarrays, i.e. gene expression profiles, with the functional annotations of the genes obtained by the genome-wide information sources such as Gene Ontology (GO)1. By applying CGGA to well-known microarray experiments, we have identified the principal functionally enriched and co-expressed gene groups, and we have shown that this approach enhances and accelerates the interpretation of DNA microarray experiments