TREEPLAN� - A Genetic Evaluation System for Forest Trees

The TREEPLAN� genetic evaluation system is designed specifically for the efficient and accurate prediction of breeding and other genetic values in trees. TREEPLAN� uses the preferred statistical method of best linear unbiased prediction (BLUP) using an individual tree additive genetic effect. Although BLUP methods are well developed theoretically, other software is suitable only for breeding value estimation and prediction on small and/or highly structured (balanced) data sets. Packages such as ASREML and SAS have hardware and software limitations that make them unsuitable for routine prediction on large data sets with complex pedigree structures and overlapping generations. TREEPLAN� fits a reduced individual tree model for purposes of efficiency. TREEPLAN� can model multiple genetic groups, handle clonal data, fit multi-trait models with more than 50 traits, accommodate heterogeneous variances, fit site specific statistical and genetic models, and also weights information across environments (accounts for genotype by environment interaction) and time (allows for age:age correlations). The Southern Tree Breeding Association (STBA) is routinely using TREEPLAN� for genetic evaluation in Australian tree improvement programs for Pinus radiata, Eucalyptus globulus and E. nitens. TREEPLAN� has allowed data across generations and years to be combined in a multi-trait analysis to produce single lists of breeding values for each trait and environment combination. TREEPLAN� is easy to use and has the �industrial strength� to handle large amounts of unbalanced data with the complex pedigree structures that are usually associated with national or regional tree improvement programs. TREEPLAN� is fully integrated with a web based data management system that efficiently handles data and pedigree information. The analytical power and flexibility of the TREEPLAN� system has made routine genetic evaluation in trees a straightforward process.Papers and abstracts from the 27th Southern Forest Tree Improvement Conference held at Oklahoma State University in Stillwater, Oklahoma on June 24-27, 2003

Network Archaeology: Uncovering Ancient Networks from Present-day Interactions

Often questions arise about old or extinct networks. What proteins interacted in a long-extinct ancestor species of yeast? Who were the central players in the Last.fm social network 3 years ago? Our ability to answer such questions has been limited by the unavailability of past versions of networks. To overcome these limitations, we propose several algorithms for reconstructing a network's history of growth given only the network as it exists today and a generative model by which the network is believed to have evolved. Our likelihood-based method finds a probable previous state of the network by reversing the forward growth model. This approach retains node identities so that the history of individual nodes can be tracked. We apply these algorithms to uncover older, non-extant biological and social networks believed to have grown via several models, including duplication-mutation with complementarity, forest fire, and preferential attachment. Through experiments on both synthetic and real-world data, we find that our algorithms can estimate node arrival times, identify anchor nodes from which new nodes copy links, and can reveal significant features of networks that have long since disappeared.Comment: 16 pages, 10 figure

On consensus biomarker selection

<p>Abstract</p> <p>Background</p> <p>Recent development of mass spectrometry technology enabled the analysis of complex peptide mixtures. A lot of effort is currently devoted to the identification of biomarkers in human body fluids like serum or plasma, based on which new diagnostic tests for different diseases could be constructed. Various biomarker selection procedures have been exploited in recent studies. It has been noted that they often lead to different biomarker lists and as a consequence, the patient classification may also vary.</p> <p>Results</p> <p>Here we propose a new approach to the biomarker selection problem: to apply several competing feature ranking procedures and compute a consensus list of features based on their outcomes. We validate our methods on two proteomic datasets for the diagnosis of ovarian and prostate cancer.</p> <p>Conclusion</p> <p>The proposed methodology can improve the classification results and at the same time provide a unified biomarker list for further biological examinations and interpretation.</p

Histopathological evidence of invasive gastric mucormycosis after transarterial chemoembolization and liver transplantation

We describe a case of a 62-year-old diabetic woman with hepatocellular carcinoma due to chronic hepatitis B virus infection. Two weeks after orthotopic liver transplantation, endoscopy for massive upper gastrointestinal bleeding revealed a large necrotic area in the gastric fundus. The patient underwent emergency resection. Histopathologically, angioinvasive mold infection compatible with mucormycosis was diagnosed in a large area of necrosis, mimicking an atypically localized gastric ulcer. Foreign bodies originating from transarterial chemoembolization (TACE) performed 7 and 8 months earlier and 40 days before transplantation were identified in the submucosal tissue. The patient was treated with liposomal amphotericin B (LAB) for 5 weeks, followed by 7 weeks of posaconazole. Follow-up biopsies after 1 and 5 months confirmed successful treatment. Review of the radiological images of the TACE procedure showed that some of the TACE material had been diverted to the stomach via an accessory gastric branch originating from the left hepatic artery. TACE agents may be associated with chronic, refractory gastroduodenal ulcers. We hypothesize that the ischemic lesion was first colonized with presumed Mucorales mold and invasive growth was promoted by the posttransplantation immunosuppression. Careful exploration of extrahepatic collaterals during TACE may prevent this complication

The Imperative to Share Clinical Study Reports: Recommendations from the Tamiflu Experience

Peter Doshi and colleagues describe their experience trying and failing to access clinical study reports from the manufacturer of Tamiflu and challenge industry to defend their current position of RCT data secrecy

Genetic parameters for growth, wood density and pulp yield in Eucalyptus globulus

Genetic variation and co-variation among the key pulpwood selection traits for Eucalyptus globulus were estimated for a range of sites in Portugal, with the aim of improving genetic parameters used to predict breeding values and correlated response to selection. The trials comprised clonally replicated full-sib families (eight trials) and unrelated clones (17 trials), and exhibited varying levels of pedigree connectivity. The traits studied were stem diameter at breast height, Pilodyn penetration (an indirect measure of wood basic density) and near infrared reflectance predicted pulp yield. Univariate and multivariate linear mixed models were fitted within and across sites, and estimates of additive genetic, total genetic, environmental and phenotypic variances and covariances were obtained. All traits studied exhibited significant levels of additive genetic variation. The average estimated within-site narrowsense heritability was 0.19±0.03 for diameter and 0.29± 0.03 for Pilodyn penetration, and the pooled estimate for predicted pulp yield was 0.42±0.14. When they could be tested, dominance and epistatic effects were generally not statistically significant, although broad-sense heritability estimates were slightly higher than narrow-sense heritability estimates. Averaged across trials, positive additive (0.64±0.08), total genetic (0.58±0.04), environmental (0.38±0.03) and phenotypic (0.43±0.02) correlation estimates were consistently obtained between diameter and Pilodyn penetration. This data argues for at least some form of pleiotropic relationship between these two traits and that selection for fast growth will adversely affect wood density in this population. Estimates of the across-site genetic correlations for diameter and Pilodyn penetration were high, indicating that the genotype by environment interaction is low across the range of sites tested. This result supports the use of single aggregated selection criteria for growth and wood density across planting environments in Portugal, as opposed to having to select for performance in different environment

Simulated Evolution of Protein-Protein Interaction Networks with Realistic Topology

We model the evolution of eukaryotic protein-protein interaction (PPI) networks. In our model, PPI networks evolve by two known biological mechanisms: (1) Gene duplication, which is followed by rapid diversification of duplicate interactions. (2) Neofunctionalization, in which a mutation leads to a new interaction with some other protein. Since many interactions are due to simple surface compatibility, we hypothesize there is an increased likelihood of interacting with other proteins in the target protein’s neighborhood. We find good agreement of the model on 10 different network properties compared to high-confidence experimental PPI networks in yeast, fruit flies, and humans. Key findings are: (1) PPI networks evolve modular structures, with no need to invoke particular selection pressures. (2) Proteins in cells have on average about 6 degrees of separation, similar to some social networks, such as human-communication and actor networks. (3) Unlike social networks, which have a shrinking diameter (degree of maximum separation) over time, PPI networks are predicted to grow in diameter. (4) The model indicates that evolutionarily old proteins should have higher connectivities and be more centrally embedded in their networks. This suggests a way in which present-day proteomics data could provide insights into biological evolution

Protein Networks as Logic Functions in Development and Cancer

Many biological and clinical outcomes are based not on single proteins, but on modules of proteins embedded in protein networks. A fundamental question is how the proteins within each module contribute to the overall module activity. Here, we study the modules underlying three representative biological programs related to tissue development, breast cancer metastasis, or progression of brain cancer, respectively. For each case we apply a new method, called Network-Guided Forests, to identify predictive modules together with logic functions which tie the activity of each module to the activity of its component genes. The resulting modules implement a diverse repertoire of decision logic which cannot be captured using the simple approximations suggested in previous work such as gene summation or subtraction. We show that in cancer, certain combinations of oncogenes and tumor suppressors exert competing forces on the system, suggesting that medical genetics should move beyond cataloguing individual cancer genes to cataloguing their combinatorial logic

A Switch in the Control of Growth of the Wing Imaginal Disks of Manduca sexta

Background: Insulin and ecdysone are the key extrinsic regulators of growth for the wing imaginal disks of insects. In vitro tissue culture studies have shown that these two growth regulators act synergistically: either factor alone stimulates only limited growth, but together they stimulate disks to grow at a rate identical to that observed in situ. It is generally thought that insulin signaling links growth to nutrition, and that starvation stops growth because it inhibits insulin secretion. At the end of larval life feeding stops but the disks continue to grow, so at that time disk growth has become uncoupled from nutrition. We sought to determine at exactly what point in development this uncoupling occurs. Methodology: Growth and cell proliferation in the wing imaginal disks and hemolymph carbohydrate concentrations were measured at various stages in the last larval instar under experimental conditions of starvation, ligation, rescue, and hormone treatment. Principal Findings: Here we show that in the last larval instar of M. sexta, the uncoupling of nutrition and growth occurs as the larva passes the critical weight. Before this time, starvation causes a decline in hemolymph glucose and trehalose and a cessation of wing imaginal disks growth, which can be rescued by injections of trehalose. After the critical weight the trehalose response to starvation disappears, and the expression of insulin becomes decoupled from nutrition. After the critical weight the wing disks loose their sensitivity to repression by juvenile hormone, and factors from the abdomen, bu