Compact mono-energetic photon sources are sought for active interrogation systems to detect shielded special nuclear materials in, for example, cargo containers, trucks and other vehicles. A prototype gamma interrogation source has been designed and built that utilizes the 11B(p,gamma)12C reaction to produce 12 MeV gamma-rays which are near the peak of the photofission cross section. In particular, the 11B(p,gamma)12C resonance at 163 kV allows the production of gammas at low proton acceleration voltages, thus keeping the design of a gamma generator comparatively small and simple. A coaxial design has been adopted with a toroidal-shaped plasma chamber surrounding a cylindrical gamma production target. The plasma discharge is driven by a 2 MHz rf-power supply (capable up to 50 kW) using a circular rf-antenna. Permanent magnets embedded in the walls of the plasma chamber generate a multi-cusp field that confines the plasma and allows higher plasma densities and lower gas pressures. About 100 proton beamlets are extracted through a slotted plasma electrode towards the target at the center of the device that is at a negative 180 kV. The target consists of LaB6 tiles that are brazed to a water-cooled cylindrical structure. The generator is designed to operate at 500 Hz with 20 mu s long pulses, and a 1percent duty factor by pulsing the ion source rf-power. A first-generation coaxial gamma source has been built for low duty factor experiments and testing.

The transcriptional regulatory network (TRN) of E. coli MG1655 contains thousands of regulatory interactions between transcription factors, sigma factors and promoter sequences of genes. The membership of a regulon, in other words whether a gene is regulated by a transcription factor, has a basis in the gene’s promoter. To determine the TRN, there are two primary families of methods: bottom-up identification of binding sites via methods such as ChIP-seq experiments, and top-down inference of the TRN via expression analysis methods such as independent component analysis (ICA). In this work, the promoter sequence of each gene was utilized to predict regulons with machine learning. Certain promoter features, such as TF binding site motif scores, DNA shape features, and sigma factor-related features were found to be essential to predict whether a gene is regulated by particular transcription factors. ICA and ChIP TRNs were compared to investigate factors underlying their difference, and two case studies were carried out. An FNR case study showed that ICA regulons fractionate a large ChIP regulon into several regulation types. An ArcA case study demonstrated that the ICA TRN captures diversity in binding sites’ architecture. In general, through comparison, ICA TRN extracts genes of strong regulation activity from ChIP TRN. We then expanded this analysis to understand differences in the regulons of multiple strains of E. coli. A pan-regulon of Fur was reconstructed with unique, accessory and core regulons annotated. We found that genes in the core regulon have stronger regulation activity than genes in the unique regulon. Additionally, it was also found that the motif score and helix twist of DNA sequence were both significant indicators of Fur ChIP-exo peak heights, represented by S/N ratios. This study was a meaningful application of machine learning on biological problems that probed biophysical factors underlying omics data, giving directions for the genome design in synthetic biology aiming to control the phenotype by TRN tuning.