Axonal Dynamics of Excitatory and Inhibitory Neurons in Somatosensory Cortex

Electrophysiology-delivery of fluorescent viral vectors-and two-photon microscopy were used to demonstrate the rapidity of axonal restructuring of both excitatory and inhibitory neurons in rodent cortical layer II/III following alterations in sensory experience

What is the role of the nitrate reductase (euknr) gene in fungi that live in nitrate-free environments? A targeted gene knock-out study in Ampelomyces mycoparasites

Mycoparasitic fungi can be utilized as biocontrol agents (BCAs) of many plant pathogens. Deciphering the molecular mechanisms of mycoparasitism may improve biocontrol efficiency. This work reports the first functional genetic studies in Ampelomyces, widespread mycoparasites and BCAs of powdery mildew fungi, and a molecular genetic toolbox for future works. The nitrate reductase (euknr) gene was targeted to reveal the biological function of nitrate assimilation in Ampelomyces. These mycoparasites live in an apparently nitrate-free environment, i.e. inside the hyphae of powdery mildew fungi that lack any nitrate uptake and assimilation system. Homologous recombination-based gene knock-out (KO) was applied to eliminate the euknr gene using Agrobacterium tumefaciens-mediated transformation. Efficient KO of euknr was confirmed by PCR, and visible phenotype caused by loss of euknr was detected on media with different nitrogen sources. Mycoparasitic ability was not affected by knocking out euknr as a tested transformant readily parasitized Blumeria graminis and Podosphaera xanthii colonies on barley and cucumber, respectively, and the rate of mycoparasitism did not differ from the wild type. These results indicate that euknr is not involved in mycoparasitism. Dissimilatory processes, involvement in nitric oxide metabolism, or other, yet undiscovered processes may explain why a functional euknr is maintained in Ampelomyces

Connecting the data landscape of long-term ecological studies : The SPI-Birds data hub

The integration and synthesis of the data in different areas of science is drastically slowed and hindered by a lack of standards and networking programmes. Long-term studies of individually marked animals are not an exception. These studies are especially important as instrumental for understanding evolutionary and ecological processes in the wild. Furthermore, their number and global distribution provides a unique opportunity to assess the generality of patterns and to address broad-scale global issues (e.g. climate change). To solve data integration issues and enable a new scale of ecological and evolutionary research based on long-term studies of birds, we have created the SPI-Birds Network and Database ()-a large-scale initiative that connects data from, and researchers working on, studies of wild populations of individually recognizable (usually ringed) birds. Within year and a half since the establishment, SPI-Birds has recruited over 120 members, and currently hosts data on almost 1.5 million individual birds collected in 80 populations over 2,000 cumulative years, and counting. SPI-Birds acts as a data hub and a catalogue of studied populations. It prevents data loss, secures easy data finding, use and integration and thus facilitates collaboration and synthesis. We provide community-derived data and meta-data standards and improve data integrity guided by the principles of Findable, Accessible, Interoperable and Reusable (FAIR), and aligned with the existing metadata languages (e.g. ecological meta-data language). The encouraging community involvement stems from SPI-Bird's decentralized approach: research groups retain full control over data use and their way of data management, while SPI-Birds creates tailored pipelines to convert each unique data format into a standard format. We outline the lessons learned, so that other communities (e.g. those working on other taxa) can adapt our successful model. Creating community-specific hubs (such as ours, COMADRE for animal demography, etc.) will aid much-needed large-scale ecological data integration.Peer reviewe