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Research from undergraduate and K-12 environments suggests that providing meaningful support for Community College students requires faculty and staff to engage in ongoing self-reflection, peer support, and a commitment to research-based pedagogical shifts. Currently, California Community College (CCC) faculty and staff get very few of these opportunities. This study aims to address this issue through an intervention designed to provide opportunities for CCC faculty and staff to be part of a flexible, coherent professional learning community. The intervention is part of an NSF-funded research and development project at a community college in central California designated as a Hispanic Serving Institution (HSI), with the goal of providing faculty and staff with tools and processes to support students toward higher retention and success in STEM by facilitating “micro-internships.” Micro-internships are designed to reduce inequalities inherent in the traditional internship paradigm by providing access to professional and research skills for students who do not have the opportunities and/or confidence to participate in a more traditional full-length internship. Conversations with participants showed how they embraced the design principles that were negotiated as a project team, and how one or more of the study interventions had played a powerful role in supporting their learning and development toward certain pedagogical shifts. Our results highlight the power of providing spaces such as summer workshops and ongoing “community of practice” meetings for collaboration, professional learning, and peer-to-peer mentorship. 

Thousands of endoparasitoid wasp species in the families Braconidae and Ichneumonidae harbor domesticated endogenous viruses (DEVs) in their genomes. This study focuses on ichneumonid DEVs, named ichnoviruses (IVs). Large quantities of DNA-containing IV virions are produced in ovary calyx cells during the pupal and adult stages of female wasps. Females parasitize host insects by injecting eggs and virions into the body cavity. After injection, virions rapidly infect host cells which is followed by expression of IV genes that promote the successful development of wasp offspring. IV genomes consist of two components: proviral segment loci that serve as templates for circular dsDNAs that are packaged into capsids, and genes from an ancestral virus that produce virions. In this study, we generated a chromosome-scale genome assembly forHyposoter didymatorthat harborsH. didymatorichnovirus (HdIV). We identified a total of 67 HdIV loci that are amplified in calyx cells during the wasp pupal stage. We then focused on an HdIV gene,U16, which is transcribed in calyx cells during the initial stages of replication. Sequence analysis indicated that U16 contains a conserved domain in primases from select other viruses. Knockdown ofU16by RNA interference inhibited virion morphogenesis in calyx cells. Genome-wide analysis indicatedU16knockdown also inhibited amplification of HdIV loci in calyx cells. Altogether, our results identified several previously unknown HdIV loci, demonstrated that all HdIV loci are amplified in calyx cells during the pupal stage, and showed that U16 is required for amplification and virion morphogenesis. 

A graph homomorphism is a map between two graphs that preserves adjacency relations. We consider the problem of sampling a random graph homomorphism from a graph into a large network. We propose two complementary MCMC algorithms for sampling random graph homomorphisms and establish bounds on their mixing times and the concentration of their time averages. Based on our sampling algorithms, we propose a novel framework for network data analysis that circumvents some of the drawbacks in methods based on independent and neighborhood sampling. Various time averages of the MCMC trajectory give us various computable observables, including well-known ones such as homomorphism density and average clustering coefficient and their generalizations. Furthermore, we show that these network observables are stable with respect to a suitably renormalized cut dis- tance between networks. We provide various examples and simulations demonstrating our framework through synthetic networks. We also demonstrate the performance of our frame- work on the tasks of network clustering and subgraph classification on the Facebook100 dataset and on Word Adjacency Networks of a set of classic novels. 

A graph homomorphism is a map between two graphs that preserves adjacency relations. We consider the problem of sampling a random graph homomorphism from a graph into a large network. We propose two complementary MCMC algorithms for sampling random graph homomorphisms and establish bounds on their mixing times and the concentration of their time averages. Based on our sampling algorithms, we propose a novel framework for network data analysis that circumvents some of the drawbacks in methods based on independent and neighborhood sampling. Various time averages of the MCMC trajectory give us various computable observables, including well-known ones such as homomorphism density and average clustering coefficient and their generalizations. Furthermore, we show that these network observables are stable with respect to a suitably renormalized cut dis- tance between networks. We provide various examples and simulations demonstrating our framework through synthetic networks. We also demonstrate the performance of our frame- work on the tasks of network clustering and subgraph classification on the Facebook100 dataset and on Word Adjacency Networks of a set of classic novels. 

A new computational methodology, termed ‘PeleLM-FDF’ is developed and utilised for high fidelity large eddy simulation (LES) of complex turbulent combustion systems. This methodology is constructed via a hybrid scheme combining the Eulerian PeleLM base flow solver with the Lagrangian Monte Carlo simulator of the filtered density func- tion (FDF) for the subgrid scale reactive scalars. The resulting methodology is capable of simulating some of the most intricate physics of complex turbulence-combustion interactions. This is demonstrated by LES of a non-premixed CO/H2 temporally evolv- ing jet flame. The chemistry is modelled via a skeletal kinetics model, and the results are appraised via a posteriori comparisons against direct numerical simulation (DNS) data of the same flame. Excellent agreements are observed for the time evolution of various statistics of the thermo-chemical quantities, including the manifolds of the multi-scalar mixing. The new methodology is capable of capturing the complex phe- nomena of flame-extinction and re-ignition at a 1/512 of the computational cost of the DNS. The high fidelity and the computational affordability of the new PeleLM-FDF solver warrants its consideration for LES of practical turbulent combustion systems. 

Experimental data about gene functions curated from the primary literature have enormous value for research scientists in understanding biology. Using the Gene Ontology (GO), manual curation by experts has provided an important resource for studying gene function, especially within model organisms. Unprecedented expansion of the scientific literature and validation of the predicted proteins have increased both data value and the challenges of keeping pace. Capturing literature-based functional annotations is limited by the ability of biocurators to handle the massive and rapidly growing scientific literature. Within the community-oriented wiki framework for GO annotation called the Gene Ontology Normal Usage Tracking System (GONUTS), we describe an approach to expand biocuration through crowdsourcing with undergraduates. This multiplies the number of high-quality annotations in international databases, enriches our coverage of the literature on normal gene function, and pushes the field in new directions. From an intercollegiate competition judged by experienced biocurators, Community Assessment of Community Annotation with Ontologies (CACAO), we have contributed nearly 5,000 literature-based annotations. Many of those annotations are to organisms not currently well-represented within GO. Over a 10-year history, our community contributors have spurred changes to the ontology not traditionally covered by professional biocurators. The CACAO principle of relying on community members to participate in and shape the future of biocuration in GO is a powerful and scalable model used to promote the scientific enterprise. It also provides undergraduate students with a unique and enriching introduction to critical reading of primary literature and acquisition of marketable skills. 

n this research, a method is examined by which the behavior of continuous carbon fiber rein-forced additive manufacturing may be simulated using Finite Element Analysis. This technique is used in a simulated tensile test experiment in which the findings are compared to results determined from theoretical calculations according to the Rule of Mixtures method and from existing mechanical testing results. Four different fiber reinforcement configurations are examined with fiber volume fractions ranging from 4% to 32%. It was found that for fiber vol-ume fractions of 11%, the simulation results closely match those predicted theoretically by the Rule of Mixtures as well as the mechanical testing results published in existing research. Lower fiber volume fractions near 4% yield less accurate results, with a 20% error due to the fact that the anisotropic behavior of the polymer matrix is the dominant material trait. Simulation of higher volume fractions near 32% closely approximate theoretical predictions, however neither the theoretical results nor the simulation results accurately reflect real world mechanical testing, indicating that nonideal condition factors such as the effect of micro-voids between the start and end of the fiber reinforcements play a significant role in the overall strength of the material. Thus, for fiber volume fractions near 11%, this simulation method can accurately be used to predict the behavior of end-use components, but more study must be done to increase simulation accuracy in low and high fiber volume fractions. 

We present useful connections between the finite difference and the finite element methods for a model boundary value problem. We start from the observation that, in the finite element context, the interpolant of the solution in one dimension coincides with the finite element approximation of the solution. This result can be viewed as an extension of the Green function formula for the solution at the continuous level. We write the finite difference and the finite element systems such that the two corresponding linear systems have the same stiffness matrices and compare the right hand side load vectors for the two methods. Using evaluation of the Green function, a formula for the inverse of the stiffness matrix is extended to the case of non-uniformly distributed mesh points. We provide an error analysis based on the connection between the two methods and estimate the energy norm of the difference of the two solutions. Interesting extensions to the 2D case are provided.