Polyploidy is remarkably common in the plant kingdom and polyploidization is a major driving force for plant genome evolution. Polyploids may contain genomes from different parental species (allopolyploidy) or include multiple sets of the same genome (autopolyploidy). Genetic and epigenetic changes associated with allopolyploidization have been a major research subject in recent years. However, we know little about the genetic impact imposed by autopolyploidization. We developed a synthetic autopolyploid series in potato (Solanum phureja) that includes one monoploid (1x) clone, two diploid (2x) clones, and one tetraploid (4x) clone. Cell size and organ thickness were positively correlated with the ploidy level. However, the 2x plants were generally the most vigorous and the 1x plants exhibited less vigor compared to the 2x and 4x individuals. We analyzed the transcriptomic variation associated with this autopolyploid series using a potato cDNA microarray containing $9000 genes. Statistically significant expression changes were observed among the ploidies for $10% of the genes in both leaflet and root tip tissues. However, most changes were associated with the monoploid and were within the twofold level. Thus, alteration of ploidy caused subtle expression changes of a substantial percentage of genes in the potato genome. We demonstrated that there are few genes, if any, whose expression is linearly correlated with the ploidy and can be dramatically changed because of ploidy alteration. Polyploids originate from either sexual reproduction via 2n gametes or somatic chromosome doubling. By traditional definition, there are two forms of polyploidy: allopolyploidy and autopolyploidy. These terms are often used to imply the mode of polyploid formation, but more accurately describe the degree of similarity between the subgenomes in polyploids. Allopolyploids have distinct subgenomes and typically originate from interspecific hybridization between divergent progenitor species. Autopolyploids have (nearly) identical subgenomes and typically originate from intraspecific hybridization (or self-fertilization through 2n gametes) or somatic chromosome doubling. Allo-and autopolyploids have traditionally been distinguished by modes of chromosome pairing and inheritance, with allopolyploids exhibiting bivalent pairing and disomic inheritance and autopolyploids exhibiting multivalent pairing and polysomic inheritance.A number of well-known polyploid plants of agricultural interest are classical allopolyploids, which include important crops such as bread wheat (2n ¼ 6x ¼ 42) and cotton (2n ¼ 4x ¼ 56). Studies of genetic and epigenetic changes associated with polyploidization have been focused mostly on newly synthesized allopolyploid materials 1 Present address:

Efficacy of Gd-DTPA and Gd-DOTA as GdNCT agents is predicted to be low, due to the insufficient number of tumor cell nuclei incorporating Gd. Although multiple administration schedules in vivo might induce Gd penetration into more tumor cell nuclei, a search for new Gd compounds with higher nuclear affinity is warranted before planning GdNCT in animal models or clinical trials.

Statistical analysis of max-stable processes used to model spatial extremes has been limited by the difficulty in calculating the joint likelihood function. This precludes all standard likelihood-based approaches, including Bayesian approaches. In this paper we present a Bayesian approach through the use of approximate Bayesian computing. This circumvents the need for a joint likelihood function by instead relying on simulations from the (unavailable) likelihood. This method is compared with an alternative approach based on the composite likelihood. When estimating the spatial dependence of extremes, we demonstrate that approximate Bayesian computing can provide estimates with a lower mean square error than the composite likelihood approach, though at an appreciably higher computational cost.We also illustrate the performance of the method with an application to US temperature data to estimate the risk of crop loss due to an unlikely freeze event.

Purpose: We present preclinical data showing the in vitro intranuclear uptake of motexafin gadolinium by glioblastoma multiforme cells, which could serve as a prelude to the future development of radiosensitizing techniques, such as gadolinium synchrotron stereotactic radiotherapy (GdSSR), a new putative treatment for glioblastoma multiforme. Experimental Design: In this approach, administration of a tumor-seeking Gd-containing compound would be followed by stereotactic external beam radiotherapy with 51-keV photons from a synchrotron source. At least two criteria must be satisfied before this therapy can be established: Gd must accumulate in cancer cells and spare the normal tissue; Gd must be present in almost all the cancer cell nuclei. We address the in vitro intranuclear uptake of motexafin gadolinium in this article.We analyzed the Gd distribution with subcellular resolution in four human glioblastoma cell lines, using three independent methods: two novel synchrotron spectromicroscopic techniques and one confocal microscopy.We present in vitro evidence that the majority of the cell nuclei take up motexafin gadolinium, a drug that is known to selectively reach glioblastoma multiforme. Results: With all three methods, we found Gd in at least 90% of the cell nuclei. The results are highly reproducible across different cell lines. The present data provide evidence for further studies, with the goal of developing GdSSR, a process that will require further in vivo animal and future clinical studies.

Occupancy models are widely used in camera trap studies to analyze species presence, abundance, and geographic distribution, among other important ecological quantities. These models account for imperfect detection using a latent variable to distinguish between true presence/absence and observed detection of a species. Under certain experimental setups, parameter estimation in a latent variable framework can be challenging. Several studies have issued guidelines on the number of independent replicated observations (surveys) needed for each unchanging occupancy field (season) to ensure reliable estimation. In this paper, we present a spatio-temporal occupancy model, and show through a simulation study that it can be fit to data obtained from a single survey per season, so long as the number of seasons is sufficiently large. We include an application using camera-trap data on the Thomson's gazelle in the Serengeti in Tanzania.