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To study the relative importance of inbreeding depression and the loss of adaptive diversity in determining the extinction risk of small populations, we carried out an experiment in which we crossed and self-fertilized founder plants from a single, large population of shore campion (Silene littorea Brot.). We used the seeds these plants produced to colonize 18 new locations within the distribution area of the species. The reintroduced populations were of three kinds: inbred and genetically homogeneous, each made up of selfed seed from a single plant; inbred and mixed, made up of a mixture of selfed seeds from all founder plants; and outbred and mixed, made up of a mixture of seeds obtained in outcrosses between the founders. We compared the inbred homogeneous populations with the inbred mixed to measure the effect of genetic diversity among individuals and the inbred mixed with the outbred mixed to measure the effect of inbreeding. Reintroduction success was seriously limited by inbreeding, whereas it was not affected by genetic diversity. This observation and the nonsignificant interaction between family and reintroduction location for individual plant characters suggest that the fixation of overall deleterious genes causing inbreeding depression posed a more serious threat to the short-term survival of the populations than the loss of genes involved in genotype and environment interactions. Thus, reintroduction success was related to adaptive diversity. Preventing such fixation might be the most important consideration in the genetic management and conservation of shore campion populations.

Step clinal transitions in inherited character(s) between genetically distinct populations are usually referred to as hybrid zones. An example is found in the population of the intertidal snail Littorina saxatilis in Galicia (NW Spain). We studied the shape of the overall fitness surface for sexual selection in this hybrid zone, and the position of hybrids and pure morphs on this surface. We found that sexual divergent selection acted on a combination of phenotypic traits separating the pure morphs, and therefore that sexual selection contributed to morph differentiation. The average fitness of hybrids as a group was not significantly different from that of the pure morphs, but they did show divergent sexual selection in some traits. These results are in agreement with a model of divergent selection favouring both the pure morph as well as those hybrids most resembling each morph. The finding of divergent selection is remarkable because quadratic selection gradients are usually weak in nature.

BackgroundIn the last few years, the Non-negative Matrix Factorization ( NMF ) technique has gained a great interest among the Bioinformatics community, since it is able to extract interpretable parts from high-dimensional datasets. However, the computing time required to process large data matrices may become impractical, even for a parallel application running on a multiprocessors cluster.In this paper, we present NMF-mGPU, an efficient and easy-to-use implementation of the NMF algorithm that takes advantage of the high computing performance delivered by Graphics-Processing Units ( GPUs ). Driven by the ever-growing demands from the video-games industry, graphics cards usually provided in PCs and laptops have evolved from simple graphics-drawing platforms into high-performance programmable systems that can be used as coprocessors for linear-algebra operations. However, these devices may have a limited amount of on-board memory, which is not considered by other NMF implementations on GPU.Results NMF-mGPU is based on CUDA ( Compute Unified Device Architecture ), the NVIDIA’s framework for GPU computing. On devices with low memory available, large input matrices are blockwise transferred from the system’s main memory to the GPU’s memory, and processed accordingly. In addition, NMF-mGPU has been explicitly optimized for the different CUDA architectures. Finally, platforms with multiple GPUs can be synchronized through MPI ( Message Passing Interface ). In a four-GPU system, this implementation is about 120 times faster than a single conventional processor, and more than four times faster than a single GPU device (i.e., a super-linear speedup).ConclusionsApplications of GPUs in Bioinformatics are getting more and more attention due to their outstanding performance when compared to traditional processors. In addition, their relatively low price represents a highly cost-effective alternative to conventional clusters. In life sciences, this results in an excellent opportunity to facilitate the daily work of bioinformaticians that are trying to extract biological meaning out of hundreds of gigabytes of experimental information. NMF-mGPU can be used “out of the box” by researchers with little or no expertise in GPU programming in a variety of platforms, such as PCs, laptops, or high-end GPU clusters. NMF-mGPU is freely available at https://github.com/bioinfo-cnb/bionmf-gpu.