Microbial Ecology of Pecorino Siciliano PDO Cheese Production Systems

Pecorino Siciliano PDO is a semi-hard cheese that is produced in wooden vats using raw sheep's milk and its associated autochthonous microbial community. In the present study, we evaluated the microbial ecology of the milk, curd and whey from five Pecorino Siciliano PDO-producing farms in Sicily using a combination of metagenomic and microbiological approaches. We present an overview of the species and strain-level diversity of dairy lactococcal and streptococcal isolates using established genotyping tools and compare the lactic acid bacterial populations present in samples from these farms. Whole genome sequences of representative isolates of Lactococcus spp. and Streptococcus thermophilus were elucidated and the genetic diversity of the strains was established through analysis of predicted phage-resistance systems and prophage-associated regions. The analysis revealed farm-specific dairy lactococcal and streptococcal isolates that possess diverse genotypic features including newly described phage-resistance systems

t-SNE Visualization of Large-Scale Neural Recordings

Electrophysiology is entering the era of big data. Multiple probes, each with hundreds to thousands of individual electrodes, are now capable of simultaneously recording from many brain regions. The major challenge confronting these new technologies is transforming the raw data into physiologically meaningful signals, that is, single unit spikes. Sorting the spike events of individual neurons from a spatiotemporally dense sampling of the extracellular electric field is a problem that has attracted much attention (Rey, Pedreira, & Quian Quiroga, 2015; Rossant et al., 2016) but is still far from solved. Current methods still rely on human input and thus become unfeasible as the size of the data sets grows exponentially. Here we introduce the -student stochastic neighbor embedding (t-SNE) dimensionality reduction method (Van der Maaten & Hinton, 2008) as a visualization tool in the spike sorting process. t-SNE embeds the -dimensional extracellular spikes ( = number of features by which each spike is decomposed) into a low- (usually two-) dimensional space. We show that such embeddings, even starting from different feature spaces, form obvious clusters of spikes that can be easily visualized and manually delineated with a high degree of precision. We propose that these clusters represent single units and test this assertion by applying our algorithm on labeled data sets from both hybrid (Rossant et al., 2016) and paired juxtacellular/extracellular recordings (Neto et al., 2016). We have released a graphical user interface (GUI) written in Python as a tool for the manual clustering of the t-SNE embedded spikes and as a tool for an informed overview and fast manual curation of results from different clustering algorithms. Furthermore, the generated visualizations offer evidence in favor of the use of probes with higher density and smaller electrodes. They also graphically demonstrate the diverse nature of the sorting problem when spikes are recorded with different methods and arise from regions with different background spiking statistics