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The Student Pathways in Engineering and Computing for Transfers (SPECTRA) program is anticipated to provide a streamlined academic pathway for transfer students from 2-year programs within South Carolina into Clemson University, and deliver programming to aid their academic success and social integration. To achieve this, the faculty intended to solidify cohorts of students at two community/technical colleges (Spartanburg Community College and Trident Technical College) and then support that cohort as they transitioned together into Clemson University. This paper provides an overview of the larger SPECTRA program and a deeper dive into the role of the graduate teaching assistants (‘fellows’). Specifically, we will provide an overview of: (1) changes between initial program vision and adjustments from this vision during initial implementation, (2) recruitment processes and application requirements for the graduate teaching fellowship, (3) the framework for development of undergraduate research courses taught by fellows, (4) mentorship web for fellows on the research university campus and technical/community college locations, (5) the lessons learned from semi structured programmatic exit interviews of matriculated fellows, and (6) design for additional professional programming for scholars at the community/technical college locations by the fellows. 

Poeciliopsis jackschultzi sp. nov., is described based on seven specimens (17.9–26.7 mm SL) from the Río Concepción (also known as Río Magdalena), Sonora, Mexico. The new species belongs to the Leptorhaphis species group and can be distinguished from other members of this group by features of the skeleton and colouration. The new species is sympatric with P. occidentalis, a hybridogenetic all-female biotype P. monacha-occidentalis, and hybrids between P. monacha-occidentalis females and P. jackschultzi males. The distribution of P. jackschultzi is highly restricted, and the main habitat, spring-fed marshy streams and pools, is susceptible to loss and degradation in a desert environment with increasing human water demand. 

Abstract The Pandora Software Development Kit and algorithm libraries perform reconstruction of neutrino interactions in liquid argon time projection chamber detectors. Pandora is the primary event reconstruction software used at the Deep Underground Neutrino Experiment, which will operate four large-scale liquid argon time projection chambers at the far detector site in South Dakota, producing high-resolution images of charged particles emerging from neutrino interactions. While these high-resolution images provide excellent opportunities for physics, the complex topologies require sophisticated pattern recognition capabilities to interpret signals from the detectors as physically meaningful objects that form the inputs to physics analyses. A critical component is the identification of the neutrino interaction vertex. Subsequent reconstruction algorithms use this location to identify the individual primary particles and ensure they each result in a separate reconstructed particle. A new vertex-finding procedure described in this article integrates a U-ResNet neural network performing hit-level classification into the multi-algorithm approach used by Pandora to identify the neutrino interaction vertex. The machine learning solution is seamlessly integrated into a chain of pattern-recognition algorithms. The technique substantially outperforms the previous BDT-based solution, with a more than 20% increase in the efficiency of sub-1 cm vertex reconstruction across all neutrino flavours.