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Death Model (death + model)

Distribution by Scientific Domains

Mathematics and Statistics	40%
Life Sciences	40%

Selected Abstracts

Recent insights into R gene evolution

MOLECULAR PLANT PATHOLOGY, Issue 5 2006
JOHN M. MCDOWELL
SUMMARY Plants are under strong evolutionary pressure to maintain surveillance against pathogens. Resistance (R) gene-dependent recognition of pathogen avirulence (Avr) determinants plays a major role in plant defence. Here we highlight recent insights into the molecular mechanisms and selective forces that drive the evolution of NB-LRR (nucleotide binding-leucine-rich repeat) resistance genes. New implications for models of R gene evolution have been raised by demonstrations that R proteins can detect cognate Avr proteins indirectly by ,guarding' virulence targets, and by evidence that R protein signalling is regulated by intramolecular interactions between different R functional domains. Comparative genomic surveys of NB-LRR diversity in different species have revealed ancient NB-LRR lineages that are unequally represented among plant taxa, consistent with a Birth and Death Model of evolution. The physical distribution of NB-LRRs in plant genomes indicates that tandem and segmental duplication are important factors in R gene proliferation. The majority of R genes reside in clusters, and the frequency of recombination between clustered genes can vary strikingly, even within a single cluster. Biotic and abiotic factors have been shown to increase the frequency of recombination in reporter transgene-based assays, suggesting that external stressors can affect genome stability. Fitness penalties have been associated with some R genes, and population studies have provided evidence for maintenance of ancient R allelic diversity by balancing selection. The available data suggest that different R genes can follow strikingly distinct evolutionary trajectories, indicating that it will be difficult to formulate universally applicable models of R gene evolution. [source]

Nonparametric Estimation for the Three-Stage Irreversible Illness,Death Model

BIOMETRICS, Issue 3 2000
Somnath Datta
Summary. In this paper, we present new nonparametric estimators of the stage-occupation probabilities in the three-stage irreversible illness-death model. These estimators use a fractional risk set and a reweighting approach and are valid under stage-dependent censoring. Using a simulated data set, we compare the behavior of our estimators with previously proposed estimators. We also apply our estimators to data on time to Pneumocystis pneumonia and death obtained from an AIDS cohort study. [source]

Yeast Programmed Cell Death: An Intricate Puzzle

IUBMB LIFE, Issue 3 2005
P. Ludovico
Abstract Yeasts as eukaryotic microorganisms with simple, well known and tractable genetics, have long been powerful model systems for studying complex biological phenomena such as the cell cycle or vesicle fusion. Until recently, yeast has been assumed as a cellular 'clean room' to study the interactions and the mechanisms of action of mammalian apoptotic regulators. However, the finding of an endogenous programmed cell death (PCD) process in yeast with an apoptotic phenotype has turned yeast into an 'unclean' but even more powerful model for apoptosis research. Yeast cells appear to possess an endogenous apoptotic machinery including its own regulators and pathway(s). Such machinery may not exactly recapitulate that of mammalian systems but it represents a simple and valuable model which will assist in the future understanding of the complex connections between apoptotic and non-apoptotic mammalian PCD pathways. Following this line of thought and in order to validate and make the most of this promising cell death model, researchers must undoubtedly address the following issues: what are the crucial yeast PCD regulators? How do they play together? What are the cell death pathways shared by yeast and mammalian PCD? Solving these questions is currently the most pressing challenge for yeast cell death researchers.IUBMB Life, 57: 129-135, 2005 [source]

Kidney Injury Molecule-1 is an Early Noninvasive Indicator for Donor Brain Death-Induced Injury Prior to Kidney Transplantation

AMERICAN JOURNAL OF TRANSPLANTATION, Issue 8 2009
W. N. Nijboer
With more marginal deceased donors affecting graft viability, there is a need for specific parameters to assess kidney graft quality at the time of organ procurement in the deceased donor. Recently, kidney injury molecule-1 (Kim-1) was described as an early biomarker of renal proximal tubular damage. We assessed Kim-1 in a small animal brain death model as an early and noninvasive marker for donor-derived injury related to brain death and its sequelae, with subsequent confirmation in human donors. In rat kidney, real-time PCR revealed a 46-fold Kim-1 gene upregulation after 4 h of brain death. In situ hybridization showed proximal tubular Kim-1 localization, which was confirmed by immunohistochemistry. Also, Luminex assay showed a 6.6-fold Kim-1 rise in urine after 4 h of brain death. In human donors, 2.5-fold kidney injury molecule-1 (KIM-1) gene upregulation and 2-fold higher urine levels were found in donation after brain death (DBD) donors compared to living kidney donors. Multiple regression analysis showed that urinary KIM-1 at brain death diagnosis was a positive predictor of recipient serum creatinine, 14 days (p < 0.001) and 1 year (p < 0.05) after kidney transplantation. In conclusion, we think that Kim-1 is a promising novel marker for the early, organ specific and noninvasive detection of brain death-induced donor kidney damage. [source]

A Versatile Birth,Death Model Applicable to Four Distinct Problems

AUSTRALIAN & NEW ZEALAND JOURNAL OF STATISTICS, Issue 1 2004
J. Gani
Summary This paper revisits a simple birth,death model which arises in slightly different forms in four distinct stochastic problems. These are the barbershop queue, coupon collecting, vocabulary usage and geological dating. Discrete and continuous time Markov chains are used to characterize these problems. Somewhat different questions are posed for each particular case, and practical results are derived for each process. The paper concludes with some comments on the versatility of this applied probability model. [source]