Development of FPGA-based multi-sensor excitation low voltage (MSELV) chassis at Jefferson Lab.

The design and development of the sensor excitation and read back chassis was driven by the requirements for the monitoring and control of two conduction-cooled superconducting magnets in Hall B for the 12 GeV accelerator upgrade. The torus and solenoid superconducting magnets require extensive instrumentation. Sensor selection was accomplished by applying Jefferson Lab's (JLab) risk mitigation process, which employed a failure modes and effects analysis approach. The goal was to accommodate all sensor types for monitoring and control and to develop a generic multisensor excitation low voltage chassis that would be used across both magnet systems with a reduced set of functions. The chassis has been deployed in experimental Hall B at JLab and has been performing successfully since July 2016.

Parallel mapping of genotypes to phenotypes contributing to overall biological fitness.

Laboratory selection is a powerful approach for engineering new traits in metabolic engineering applications. This approach is limited because determining the genetic basis of improved strains can be difficult using conventional methods. We have recently reported a new method that enables the measurement of fitness for all clones contained within comprehensive genomic libraries, thus enabling the genome-scale mapping of fitness altering genes. Here, we demonstrate a strategy for relating these measurements to the individual phenotypes selected for in a particular environment. We first provide a mathematical framework for decomposing fitness into selectable phenotypes. We then employed this framework to predict that single-batch selections would enrich primarily for library clones with increased growth rate, serial-batch would enrich for a broad collection of clones enhanced via a combination of increased growth rate and/or reduced lag times, and that overlap among selected clones would be minimal. We used the SCalar Analysis of Library Enrichments (SCALEs) method to test these predictions. We mapped all genomic regions for which increased copy number conferred a selective advantage to Escherichia coli when cultured via single- or serial-batch in the presence of 1-naphthol. We identified a surprisingly large collection (163 total) of tolerance regions, including all previously identified solvent tolerance genes in E. coli. We show that the majority of the identified regions were unique to the different selection strategies examined and that such differences were indeed due to differences among enriched clones in growth rate and lag times over the solvent concentrations examined. The combination of a framework for decomposing overall fitness into selectable phenotypes along with a genome-scale method for mapping genes to such phenotypes lays the groundwork for improving the rational design of laboratory selections.