The SPF27 Homologue Num1 Connects Splicing and Kinesin 1-Dependent Cytoplasmic Trafficking in Ustilago maydis

The conserved NineTeen protein complex (NTC) is an integral subunit of the spliceosome and required for intron removal during pre-mRNA splicing. The complex associates with the spliceosome and participates in the regulation of conformational changes of core spliceosomal components, stabilizing RNA-RNA- as well as RNA-protein interactions. In addition, the NTC is involved in cell cycle checkpoint control, response to DNA damage, as well as formation and export of mRNP-particles. We have identified the Num1 protein as the homologue of SPF27, one of NTC core components, in the basidiomycetous fungus Ustilago maydis. Num1 is required for polarized growth of the fungal hyphae, and, in line with the described NTC functions, the num1 mutation affects the cell cycle and cell division. The num1 deletion influences splicing in U. maydis on a global scale, as RNA-Seq analysis revealed increased intron retention rates. Surprisingly, we identified in a screen for Num1 interacting proteins not only NTC core components as Prp19 and Cef1, but several proteins with putative functions during vesicle-mediated transport processes. Among others, Num1 interacts with the motor protein Kin1 in the cytoplasm. Similar phenotypes with respect to filamentous and polar growth, vacuolar morphology, as well as the motility of early endosomes corroborate the genetic interaction between Num1 and Kin1. Our data implicate a previously unidentified connection between a component of the splicing machinery and cytoplasmic transport processes. As the num1 deletion also affects cytoplasmic mRNA transport, the protein may constitute a novel functional interconnection between the two disparate processes of splicing and trafficking

The Emergence and Survival of \u3cem\u3eDigitaria eriantha\u3c/em\u3e and \u3cem\u3eChloris gayana\u3c/em\u3e Seedlings on Mine Tailings Planted with Coated and Non-Coated Seed

The importance of vegetation in the process of rehabilitation and stabilisation of mined land is becoming more critical as the size of the affected areas and the impact on urban development increases. Successful establishment of vegetation on these areas are complicated by adverse physical and chemical properties of the growth mediums. These include; soil compaction, acidity, salinity and heavy metal contamination, extreme temperatures, low soil water contents and soil erosion (Oncel et al. 2000; Turner et al. 2006; Aken et al. 2007). Many of these soil/substrate conditions mentioned limit the establishment of vegetation from seed. Seed coating technologies have become a possible solution to address difficult seeding challenges to ultimately facilitate more successful establishment of vegetation in these hostile environments (Turner et al. 2006). From humble beginnings seed coating has developed into technologies that can be used to ameliorate the root zone to an extent by chemically changing the environment, aerating the root zone or by improving the seedlings health through the inoculation of seedling environment with beneficial micro-organisms (Harman 1991; Thrall et al. 2005; Turner et al. 2006). These specific attributes are not always clear and environmental specialists do not always know they have access to technologies that can change the micro-environment of a seedling

Bayesian model-based age classification using small mammal body mass and capture dates

Accurate age determination is a fundamental prerequisite for demographic studies as well as population monitoring efforts that provide information for management and conservation. Yet, common age determination methods suffer from low accuracy rates, impose additional handling and time costs on animals and biologists, or rely on invasive techniques such as tooth extraction. We introduce an alternative, mixture modeling approach for age determination that exploits mammalian growth patterns to classify newly encountered animals as juveniles or adults, and present an example analysis that classifies Allegheny woodrats based solely on their capture dates and mass at capture, in combination with data from known adults. We also introduce and validate a simulation-based heuristic to evaluate potential classification accuracy when no known-age test cases are available. In the Allegheny woodrat example, the mixture model achieved a 90–92% accuracy rate (heuristic range: 89–94%), far better than the 36–43% achieved with a fixed mass criterion, and comparable to accuracies reported for other species using more data-intensive, multivariate classification techniques. The model can be extended to classify multiple age groups, estimate chronological age, or further improve accuracy by including additional morphometric measures