AutoMLST: an automated web server for generating multi-locus species trees highlighting natural product potential

Understanding the evolutionary background of a bacterial isolate has applications for a wide range of research. However generating an accurate species phylogeny remains challenging. Reliance on 16S rDNA for species identification currently remains popular. Unfortunately, this widespread method suffers from low resolution at the species level due to high sequence conservation. Currently, there is now a wealth of genomic data that can be used to yield more accurate species designations via modern phylogenetic methods and multiple genetic loci. However, these often require extensive expertise and time. The Automated Multi-Locus Species Tree (autoMLST) was thus developed to provide a rapid ‘one-click’ pipeline to simplify this workflow at: https://automlst.ziemertlab.com. This server utilizes Multi-Locus Sequence Analysis (MLSA) to produce high-resolution species trees; this does not preform multi-locus sequence typing (MLST), a related classification method. The resulting phylogenetic tree also includes helpful annotations, such as species clade designations and secondary metabolite counts to aid natural product prospecting. Distinct from currently available web-interfaces, autoMLST can automate selection of reference genomes and out-group organisms based on one or more query genomes. This enables a wide range of researchers to perform rigorous phylogenetic analyses more rapidly compared to manual MLSA workflows.

Archiving Aggregates of Individually Created Digital Content: Lessons from Archiving the Occupy Movement

With the ubiquity of digital phones and cameras, our memory institutions will need to ingest an increasingly large number of born-digital media, much of it coming from personal archives. The high volume of this material will force collections to find smart ways to automate appraisal/selection, metadata assignment, and standardization of formats, or to convince contributors to do so. This article reports on ideas and methods developed by the group Activist Archivists to automate selection, metadata collection, and standardization of the born-digital media documenting the Occupy movement. Techniques include employing automatic time/date/location stamping, analyzing social media sites, producing guidelines for contributors, and asking for community involvement in selecting the material perceived as having the most enduring value.

Optimal Symbol Set Selection: A Semiautomated Procedure

A new model of the visual search process is developed which can improve the design of large symbol sets such as those used by nuclear power plant personnel, air traffic controllers, and battlefield troops. An experiment was conducted to determine whether the new, componential model or an already existing, discriminability model better explains visual search behavior. The results were consistent with the componential model. We show how to use the componential model to help automate selection of the optimal symbol set (i.e., the symbol set that minimizes the average time to find a target).