CONSOLIDATION IN THE FRESH FLORIDA GRAPEFRUIT PACKING INDUSTRY

First-order, homogeneous, stationary Markov models are used to compare patterns of adjustment among Florida fresh grapefruit packers between the 1970/71-1983/84 and 1987/88-1999/00 periods, and 1970/71-1994/95 and 1995/95-1999/00 periods, respectively. Despite sweeping aggregate trends impacting fresh produce markets, there was insufficient evidence to identify significant differences in the patterns of adjustment in the packing sector during the later time periods. Comparisons with actual firm numbers are indicative of an industry near equilibrium. While individual firms enter and exit the different states, currently there are no profound structural changes pointing to major concentration.Industrial Organization,

Fresh Grapefruit Supply-Chain Adjustments: Consolidation in Produce Packing?

This study evaluates long-term structural adjustments in fresh grapefruit packing under aggregate market pressures, including those from retail concentration. While individual firms enter and exit, Markov-model results are indicative of an industry near equilibrium with little expectation of change in the distribution of firm sizes given existing patterns of sector adjustment. Estimation of Lorenz curves and corresponding Gini coefficients fully support Markov analysis findings. Lags in the packing sector adjustment process in the face of sweeping forces of change in fresh produce markets are likely to put this sector at a relative disadvantage within the supply chain.Crop Production/Industries,

TIME VARYING PARAMETERS IN THE DEMAND FOR HIGH FRUCTOSE CORN SYRUP

Rapid adoption of high fructose corn syrup and the changing demand for HFCS is measured in a time-varying framework. Demand changes are separated into structural and price competitiveness. Tests for parameter stability are made and simulations based on the estimates are used to show the industry dynamics.Demand and Price Analysis,

Robust Optimization of Biological Protocols

When conducting high-throughput biological experiments, it is often necessary to develop a protocol that is both inexpensive and robust. Standard approaches are either not cost-effective or arrive at an optimized protocol that is sensitive to experimental variations. Here, we describe a novel approach that directly minimizes the cost of the protocol while ensuring the protocol is robust to experimental variation. Our approach uses a risk-averse conditional value-at-risk criterion in a robust parameter design framework. We demonstrate this approach on a polymerase chain reaction protocol and show that our improved protocol is less expensive than the standard protocol and more robust than a protocol optimized without consideration of experimental variation

Slow light with integrated gain and large pulse delay

We demonstrate slow and stored light in Rb vapor with a combination of desirable features: minimal loss and distortion of the pulse shape, and large fractional delay (> 10). This behavior is enabled by: (i) a group index that can be controllably varied during light pulse propagation; and (ii) controllable gain integrated into the medium to compensate for pulse loss. Any medium with the above two characteristics should be able to realize similarly high-performance slow light.Comment: 5 pages, 4 figures; abstract is shortened, some typo correcte

Late Wisconsin and Holocene Geological, Botanical, and Archaeological History of the Orono, Maine Region

Guidebook for field trips in east-central and north-central Maine: 66th annual meeting October 12 and 13, 1974: Trip B-

A dynamic network of transcription in LPS-treated human subjects

<p>Abstract</p> <p>Background</p> <p>Understanding the transcriptional regulatory networks that map out the coordinated dynamic responses of signaling proteins, transcription factors and target genes over time would represent a significant advance in the application of genome wide expression analysis. The primary challenge is monitoring transcription factor activities over time, which is not yet available at the large scale. Instead, there have been several developments to estimate activities computationally. For example, Network Component Analysis (NCA) is an approach that can predict transcription factor activities over time as well as the relative regulatory influence of factors on each target gene.</p> <p>Results</p> <p>In this study, we analyzed a gene expression data set in blood leukocytes from human subjects administered with lipopolysaccharide (LPS), a prototypical inflammatory challenge, in the context of a reconstructed regulatory network including 10 transcription factors, 99 target genes and 149 regulatory interactions. We found that the computationally estimated activities were well correlated to their coordinated action. Furthermore, we found that clustering the genes in the context of regulatory influences greatly facilitated interpretation of the expression data, as clusters of gene expression corresponded to the activity of specific factors or more interestingly, factor combinations which suggest coordinated regulation of gene expression. The resulting clusters were therefore more biologically meaningful, and also led to identification of additional genes under the same regulation.</p> <p>Conclusion</p> <p>Using NCA, we were able to build a network that accounted for between 8–11% genes in the known transcriptional response to LPS in humans. The dynamic network illustrated changes of transcription factor activities and gene expressions as well as interactions of signaling proteins, transcription factors and target genes.</p

PathogenMIPer: a tool for the design of molecular inversion probes to detect multiple pathogens

BACKGROUND: Here we describe PathogenMIPer, a software program for designing molecular inversion probe (MIP) oligonucleotides for use in pathogen identification and detection. The software designs unique and specific oligonucleotide probes targeting microbial or other genomes. The tool tailors all probe sequence components (including target-specific sequences, barcode sequences, universal primers and restriction sites) and combines these components into ready-to-order probes for use in a MIP assay. The system can harness the genetic variability available in an entire genome in designing specific probes for the detection of multiple co-infections in a single tube using a MIP assay. RESULTS: PathogenMIPer can accept sequence data in FASTA file format, and other parameter inputs from the user through a graphical user interface. It can design MIPs not only for pathogens, but for any genome for use in parallel genomic analyses. The software was validated experimentally by applying it to the detection of human papilloma virus (HPV) as a model system, which is associated with various human malignancies including cervical and skin cancers. Initial tests of laboratory samples using the MIPs developed by the PathogenMIPer to recognize 24 different types of HPVs gave very promising results, detecting even a small viral load of single as well as multiple infections (Akhras et al, personal communication). CONCLUSION: PathogenMIPer is a software for designing molecular inversion probes for detection of multiple target DNAs in a sample using MIP assays. It enables broader use of MIP technology in the detection through genotyping of pathogens that are complex, difficult-to-amplify, or present in multiple subtypes in a sample

Knowledge-Based Reconstruction of mRNA Transcripts with Short Sequencing Reads for Transcriptome Research

While most transcriptome analyses in high-throughput clinical studies focus on gene level expression, the existence of alternative isoforms of gene transcripts is a major source of the diversity in the biological functionalities of the human genome. It is, therefore, essential to annotate isoforms of gene transcripts for genome-wide transcriptome studies. Recently developed mRNA sequencing technology presents an unprecedented opportunity to discover new forms of transcripts, and at the same time brings bioinformatic challenges due to its short read length and incomplete coverage for the transcripts. In this work, we proposed a computational approach to reconstruct new mRNA transcripts from short sequencing reads with reference information of known transcripts in existing databases. The prior knowledge helped to define exon boundaries and fill in the transcript regions not covered by sequencing data. This approach was demonstrated using a deep sequencing data set of human muscle tissue with transcript annotations in RefSeq as prior knowledge. We identified 2,973 junctions, 7,471 exons, and 7,571 transcripts not previously annotated in RefSeq. 73% of these new transcripts found supports from UCSC Known Genes, Ensembl or EST transcript annotations. In addition, the reconstructed transcripts were much longer than those from de novo approaches that assume no prior knowledge. These previously un-annotated transcripts can be integrated with known transcript annotations to improve both the design of microarrays and the follow-up analyses of isoform expression. The overall results demonstrated that incorporating transcript annotations from genomic databases significantly helps the reconstruction of novel transcripts from short sequencing reads for transcriptome research

Significance Analysis of Time Course Microarray Experiments

Characterizing the genome-wide dynamic regulation of gene expression is important and will be of much interest in the future. However, there is currently no established method for identifying differentially expressed genes in a time course study. Here we propose a significance method for analyzing time course microarray studies that can be applied to the typical types of comparisons and sampling schemes. This method is applied to two studies on humans. In one study, genes are identified that show differential expression over time in response to in vivo endotoxin administration. Using our method 7409 genes are called significant at a 1% FDR level, whereas several existing approaches fail to identify any genes. In another study, 417 genes are identified at a 10% FDR level that show expression changing with age in the kidney cortex. Here it is also shown that as many as 47% of the genes change with age in a manner more complex than simple exponential growth or decay. The methodology proposed here has been implemented in the freely distributed and open-source EDGE software package