Phenological shift along an elevational gradient and dispersal of pollen and seeds maintain a hybrid zone between two cherry tree species

Hybrid zones between species with staggered reproductive phenology sometimes occur along elevational gradients. The maintenance of such hybrid zones may depend on elevational shift in phenology and vertical dispersal of pollen and seeds. In a hybrid zone of Cerasus leveilleana (Cl) and C. sargentii (Cs), 111 adults, 372 embryos of 65 mothers, and 133 juveniles were sampled across elevations of 900–1,400 m in central Japan. Using the hybrid index estimated from nuclear microsatellites, the samples were assigned to Cl, Cs or their hybrid taxon. Cs was in higher elevations than Cl with an overlapping range, and their hybrids were in intermediary elevations. Flowering periods were earlier in Cs than in Cl with little overlap at the same elevation and were intermediate in their hybrids. Flowering periods were delayed in higher elevations and overlapped between Cl and Cs in different elevations. Fruiting periods were slightly earlier in Cs than in Cl but were largely overlapped. Effective pollinators and seed dispersers were common among the taxa. Changes in the hybrid index from mothers to their embryos were larger in hybrid mothers than in Cl or Cs mothers, indicating mating of hybrids with diverse taxa. In Cs, juveniles were more abundant in lower elevations than embryos, indicating downhill seed dispersal. Proportions of hybrids were 19% in embryos, 17% in juveniles and 12% in adults. The findings suggest that the hybrid zone is maintained by interspecific pollination between different elevations, backcrossing with hybrids at similar elevations, vertical seed dispersal and mild selection against hybrids.     

Factors involved in the adherence of Candida albicans and Candida tropicalis to protein-adsorbed surfaces

The adherence of Candida albicans and C. tropicalis to protein-adsorbed surfaces was investigated with surface-modified glass slides to which serum or salivary proteins were covalently bound. A specific adherence like a ligand-receptor interaction was observed between C. albicans and mucin- or salivary protein-immobilized glass slides. This interaction was eliminated by deglycosylation of the slides, suggesting that the receptor may be an oligosaccharide(s) contained mucin or saliva. A similar specific interaction was also observed between C. tropicalis and fibrinogen-immobilized glass surfaces. When the numbers of adherent cells to deglycosylated protein-immobilized glass glides were plotted against zeta potentials and contact angles of these protein-immobilized glass slides, a significant correaltion was observed between the numbers of adherent cells and zeta potentials in the case of C. albicans (r = –0.87), whereas a significant correlation was observed between cell numbers and contact angles (r = 0.82) in the case of C. tropicalis. These results suggest that the forces governing the adherence of fungi to pellicle in dentures may vary depending upon the surface properties of fungi and substrate.     

A new look at state-space models for neural data

State space methods have proven indispensable in neural data analysis. However, common methods for performing inference in state-space models with non-Gaussian observations rely on certain approximations which are not always accurate. Here we review direct optimization methods that avoid these approximations, but that nonetheless retain the computational efficiency of the approximate methods. We discuss a variety of examples, applying these direct optimization techniques to problems in spike train smoothing, stimulus decoding, parameter estimation, and inference of synaptic properties. Along the way, we point out connections to some related standard statistical methods, including spline smoothing and isotonic regression. Finally, we note that the computational methods reviewed here do not in fact depend on the state-space setting at all; instead, the key property we are exploiting involves the bandedness of certain matrices. We close by discussing some applications of this more general point of view, including Markov chain Monte Carlo methods for neural decoding and efficient estimation of spatially-varying firing rates.