Platelet‐Rich Plasma Increases Growth and Motility of Adipose Tissue‐Derived Mesenchymal Stem Cells and Controls Adipocyte Secretory Function

Adipose tissue‐derived mesenchymal stem cells (Ad‐MSC) and platelet derivatives have been used alone or in combination to achieve regeneration of injured tissues. We have tested the effect of platelet‐rich plasma (PRP) on Ad‐MSC and adipocyte function. PRP increased Ad‐MSC viability, proliferation rate and G1‐S cell cycle progression, by at least 7‐, 2‐, and 2.2‐fold, respectively, and reduced caspase 3 cleavage. Higher PRP concentrations or PRPs derived from individuals with higher platelet counts were more effective in increasing Ad‐MSC growth. PRP also accelerated cell migration by at least 1.5‐fold. However, PRP did not significantly affect mature adipocyte viability, differentiation and expression levels of PPAR‐γ and AP‐2 mRNAs, while it increased leptin production by 3.5‐fold. Interestingly, PRP treatment of mature adipocytes also enhanced the release of Interleukin (IL)‐6, IL‐8, IL‐10, Interferon‐γ, and Vascular Endothelial Growth Factor. Thus, data are consistent with a stimulatory effect of platelet derivatives on Ad‐MSC growth and motility. Moreover, PRP did not reduce mature adipocyte survival and increased the release of pro‐angiogenic factors, which may facilitate tissue regeneration processes. © 2015 The Authors. Journal of Cellular Biochemistry Published by Wiley Periodicals, Inc. J. Cell. Biochem. 116: 2408–2418, 2015. © 2015 The Authors. Journal of Cellular Biochemistry Published by Wiley Periodicals, Inc.     

Extending networks of protected areas to optimize connectivity and population growth rate

Protected areas (PAs) are recognized as the flagship tool to offset biodiversity loss on Earth. Spatial conservation planning seeks optimal designs of PAs that meet multiple targets such as biodiversity representation and population persistence. Since connectivity between PAs is a fundamental requirement for population persistence, several methods have been developed to include connectivity into PA design algorithms. Among these, the eigenvalue decomposition of the connectivity matrix allows for identifying clusters of strongly connected sites and selecting the sites contributing the most to population persistence. So far, this method was only suited to optimize an entire network of PAs without considering existing PAs in the new design. However, a more cost‐effective and realistic approach is to optimize the design of an extended network to improve its connectivity and thus population persistence. Here, we develop a flexible algorithm based on eigenvalue decomposition of connectivity matrices to extend existing networks of PAs while optimizing connectivity and population growth rate. We also include a splitting algorithm to improve cluster identification. The new algorithm accounts for the change in connectivity due to the increased biological productivity often observed in existing PAs. We illustrate the potential of our algorithm by proposing an extension of the network of ～100 Mediterranean marine PAs to reach the targeted 10% surface area protection from the current 1.8%. We identify differences between the clean slate scenario, where all sites are available for protection, irrespective of their current protection status, and the scenario where existing PAs are forced to be included into the optimized solution. By integrating this algorithm to existing multi‐objective and multi‐specific algorithms of PA selection, the demographic effects of connectivity can be explicitly included into conservation planning.     

Evidence for strong genetic structure in European populations of the little owl Athene noctua

The little owl Athene noctua is a widespread species in Europe. This mainly sedentary owl experienced reduction in population sizes in some areas due to habitat loss and modification of the landscape. To assess the genetic structure of the populations of western and central Europe, we analysed 333 specimens from 15 geographical areas at 13 microsatellite loci. Statistical analyses and Bayesian clustering procedures detected two major genetically distinct clusters, the first distributed from Portugal to the Czech Republic and the second from the Balkans to Italy. The second cluster was further split into three groups, located in Italy, Sardinia and the Balkans. These groups match four previously‐described mtDNA haplogroups, and probably originated from the isolation of little owl populations in Sardinia and in three glacial refugia (Iberia, south Italy and Balkans) during the ice ages. High genetic admixture was recorded in central and northern Europe, probably as a consequence of the expansion from the refugia during interglacial. The main colonization route originated from the Iberian Peninsula towards central and northern Europe. Contact zones with colonization events from Italy and the Balkans were detected respectively in northern Italy and central Europe. Genetic indices show the existence of moderate levels of genetic variability throughout Europe, although evidence of recent evolutionary bottlenecks was found in some populations. Estimation of migration rates and approximate Bayesian computations highlighted the most likely phylogeographical scenario for the current distribution of little owl populations.     

Odourant dominance in olfactory mixture processing: what makes a strong odourant?

The question of how animals process stimulus mixtures remains controversial as opposing views propose that mixtures are processed analytically, as the sum of their elements, or holistically, as unique entities different from their elements. Overshadowing is a widespread phenomenon that can help decide between these alternatives. In overshadowing, an individual trained with a binary mixture learns one element better at the expense of the other. Although element salience (learning success) has been suggested as a main explanation for overshadowing, the mechanisms underlying this phenomenon remain unclear. We studied olfactory overshadowing in honeybees to uncover the mechanisms underlying olfactory-mixture processing. We provide, to our knowledge, the most comprehensive dataset on overshadowing to date based on 90 experimental groups involving more than 2700 bees trained either with six odourants or with their resulting 15 binary mixtures. We found that bees process olfactory mixtures analytically and that salience alone cannot predict overshadowing. After normalizing learning success, we found that an unexpected feature, the generalization profile of an odourant, was determinant for overshadowing. Odourants that induced less generalization enhanced their distinctiveness and became dominant in the mixture. Our study thus uncovers features that determine odourant dominance within olfactory mixtures and allows the referring of this phenomenon to differences in neural activity both at the receptor and the central level in the insect nervous system.