YEAST SUCCESSION IN THE AMAZON FRUIT PARAHANCORNIA-AMAPA AS RESOURCE PARTITIONING AMONG DROSOPHILA SPP

The succession of yeasts colonizing the fallen ripe amapa fruit, from Parahancornia. amapa, aas examined. The occupation of the substrate depended on both the competitive interactions of yeast species, such as the production of killer toxins, and the selective dispersion by the drosophilid guild of the amapa fruit. The yeast community associated with this Amazon fruit differed from those isolated from other fruits in the same forest. The physiological profile of these yeasts was mostly restricted to the assimilation of a few simple carbon sources, mainly L-sorbose, D-glycerol, DL-lactate, cellobiose, and salicin. Common fruit-associated yeasts of the genera Kloeckera and Hanseniaspora, Candida guilliermondii, and Candida krusei colonized fruits during the first three days after the fruit fell. These yeasts were dispersed and served as food for the invader Drosophila malerkotliana. The resident flies of the Drosophila willistoni group fed selectively on patches of yeasts colonizing fruits 3 to 10 days after the fruit fell. The killer toxin-producing yeasts Pichia kluyveri var. kluyveri and Candida fructus were probably involved in the exclusion of some species during the intermediate stages of fruit deterioration. An increase in pH, inhibiting toxin activity and the depletion of simple sugars, may have promoted an increase in yeast diversity in the later stages of decomposition. The yeast succession provided a patchy environment for the drosophilids sharing this ephemeral substrate.61124251425

Environmental metabarcoding reveals contrasting belowground and aboveground fungal communities from poplar at a Hg phytomanagement site

Characterization of microbial communities in stressful conditions at a field level is rather scarce, especially when considering fungal communities from aboveground habitats. We aimed at characterizing fungal communities from different poplar habitats at a Hg-contaminated phytomanagement site by using Illumina-based sequencing, network analysis approach, and direct isolation of Hg-resistant fungal strains. The highest diversity estimated by the Shannon index was found for soil communities, which was negatively affected by soil Hg concentration. Among the significant correlations between soil operational taxonomic units (OTUs) in the co-occurrence network, 80% were negatively correlated revealing dominance of a pattern of mutual exclusion. The fungal communities associated with Populus roots mostly consisted of OTUs from the symbiotic guild, such as members of the Thelephoraceae, thus explaining the lowest diversity found for root communities. Additionally, root communities showed the highest network connectivity index, while rarely detected OTUs from the Glomeromycetes may have a central role in the root network. Unexpectedly high richness and diversity were found for aboveground habitats, compared to the root habitat. The aboveground habitats were dominated by yeasts from the Lalaria, Davidiella, and Bensingtonia genera, not detected in belowground habitats. Leaf and stem habitats were characterized by few dominant OTUs such as those from the Dothideomycete class producing mutual exclusion with other OTUs. Aureobasidium pullulans, one of the dominating OTUs, was further isolated from the leaf habitat, in addition to Nakazawaea populi species, which were found to be Hg resistant. Altogether, these findings will provide an improved point of reference for microbial research on inoculation-based programs of tailings dumps

Step by step: Early detection of diseases using an intelligent floor

The development of sensor technologies in smart homes helps to increase user comfort or to create safety through the recognition of emergency situations. For example, lighting in the home can be controlled or an emergency call can be triggered if sensors hidden in the floor detect a fall of a person. It makes sense to also use these technologies regarding prevention and early detection of diseases. By detecting deviations and behavioral changes through long-term monitoring of daily life activities it is possible to identify physical or cognitive diseases. In this work, we first examine in detail the existing possibilities to recognize the activities of daily life and the capability of such a system to conclude from the given data on illnesses. Then we propose a model for the use of floor-based sensor technology to help diagnose diseases and behavioral changes by analyzing the time spent in bed as well as the walking speed of users. Finally, we show that the system can be used in a real environment