Multiscale metabolic modeling of C4 plants: connecting nonlinear genome-scale models to leaf-scale metabolism in developing maize leaves

C4 plants, such as maize, concentrate carbon dioxide in a specialized compartment surrounding the veins of their leaves to improve the efficiency of carbon dioxide assimilation. Nonlinear relationships between carbon dioxide and oxygen levels and reaction rates are key to their physiology but cannot be handled with standard techniques of constraint-based metabolic modeling. We demonstrate that incorporating these relationships as constraints on reaction rates and solving the resulting nonlinear optimization problem yields realistic predictions of the response of C4 systems to environmental and biochemical perturbations. Using a new genome-scale reconstruction of maize metabolism, we build an 18000-reaction, nonlinearly constrained model describing mesophyll and bundle sheath cells in 15 segments of the developing maize leaf, interacting via metabolite exchange, and use RNA-seq and enzyme activity measurements to predict spatial variation in metabolic state by a novel method that optimizes correlation between fluxes and expression data. Though such correlations are known to be weak in general, here the predicted fluxes achieve high correlation with the data, successfully capture the experimentally observed base-to-tip transition between carbon-importing tissue and carbon-exporting tissue, and include a nonzero growth rate, in contrast to prior results from similar methods in other systems. We suggest that developmental gradients may be particularly suited to the inference of metabolic fluxes from expression data.Comment: 57 pages, 14 figures; submitted to PLoS Computational Biology; source code available at http://github.com/ebogart/fluxtools and http://github.com/ebogart/multiscale_c4_sourc

Teacher Evaluation and Classroom Practice: Teacher Perceptions in Northeast Tennessee

The purpose of this quantitative study was to investigate the perceptions of K-12 teachers as they relate to the implementation of the Tennessee Educator Acceleration (TEAM) evaluation framework. Survey links were sent to 1,115 K-12 teachers from 4 Northeast Tennessee school districts. The survey achieved a 24% return rate for a total of 270 participants. The research evaluated K-12 teachers’ overall perceptions of the TEAM evaluation framework, their perceptions of changes to their lesson planning processes, their perceptions of changes in the use of instructional strategies in their classrooms, and their perceptions of changes in the amount of time needed to prepare lessons for instruction since the implementation of the TEAM evaluation framework. Data sources analyzed consisted of an online survey design using a 5-point Likert-type scale. There were 4 research questions included in this research each with a corresponding null hypothesis. Each research question was analyzed with a series of single sample t-tests with mid-point of the scale (3.0) as the test value representing neutrality. All data were analyzed at the .05 level of significance. Findings from the data indicated a significant difference in perceptions of teachers in 3 of 4 areas. First the planning process for their lessons was reported to be more structured and focused on the evaluation rubric. Next, the instructional strategies used in their lessons were reported as more focused on higher order thinking skills. And finally the time required to plan instruction had increased since the implementation of the TEAM framework

Numerical semigroups via projections and via quotients

We examine two natural operations to create numerical semigroups. We say that a numerical semigroup $\mathcal S$ is $k$-normalescent if it is the projection of the set of integer points in a $k$-dimensional cone, and we say that $\mathcal S$ is a $k$-quotient if it is the quotient of a numerical semigroup with $k$ generators. We prove that all $k$-quotients are $k$-normalescent, and although the converse is false in general, we prove that the projection of the set of integer points in a cone with $k$ extreme rays (possibly lying in a dimension smaller than $k$) is a $k$-quotient. The discrete geometric perspective of studying cones is useful for studying $k$-quotients: in particular, we use it to prove that the sum of a $k_1$-quotient and a $k_2$-quotient is a $(k_1+k_2)$-quotient. In addition, we prove several results about when a numerical semigroup is not $k$-normalescent

Linguistic Markers of Influence in Informal Interactions

There has been a long standing interest in understanding `Social Influence' both in Social Sciences and in Computational Linguistics. In this paper, we present a novel approach to study and measure interpersonal influence in daily interactions. Motivated by the basic principles of influence, we attempt to identify indicative linguistic features of the posts in an online knitting community. We present the scheme used to operationalize and label the posts with indicator features. Experiments with the identified features show an improvement in the classification accuracy of influence by 3.15%. Our results illustrate the important correlation between the characteristics of the language and its potential to influence others.Comment: 10 pages, Accepted in NLP+CSS workshop for ACL (Association for Computational Linguistics) 201

Eliciting informal specifications from scientific modelers for evaluation and debugging

Professional software engineers have an arsenal of techniques such as unit testing and assertions to check their specifications, but these techniques require tools, motivation, experience and training that programmers without professional software engineering training may not have. As a result, professionals in other fields, such as scientific modelers, face greater hurdles in debugging and validating the programs they write. This thesis introduces the concept of "evaluation abstractions" as a framework for tool designers to think about this kind of support. Evaluation abstractions are the patterns of data in program traces and outputs that programmers examine in order to evaluate software behavior. The thesis provides two intellectual contributions aimed at helping tool designers: (1) A theory of evaluation abstraction support (EAST) that describes at a granular scale the factors contributing to a modeler's decision to use or not use an evaluation abstraction support feature; (2) a new user-centered design methodology, Natural Programming Plus (NP+), specialized for the design of interactive languages aimed at experienced users, in a way that allows for validation early in the process. Using EAST and NP+ I built and evaluated an evaluation abstraction support tool for cognitive modelers (psychologists who study human cognition by writing simulations of cognition), with features that (1) elicit and persist a database of a modeler's evaluation abstractions, in a piecemeal, just-in-time fashion as their questions about model behavior arise, and (2) use the modeler's unique set of evaluation abstractions to structure visualizations, listings, and regression tests, as the modeler continues to maintain and develop the project. Using this tool modelers were able to repeatedly answer questions about model behavior that would have been time-consuming and error-prone to check in state-of-the-art cognitive modeling tools. This dissertation includes formative investigation of modelers' evaluation abstractions, iterative development and testing of interaction designs for elicitation and use of evaluation abstractions, a description of a domain-specific language for representing and transforming evaluation abstractions, and two summative studies showing the usability and generalizability of the technique

Large Language Models (GPT) Struggle to Answer Multiple-Choice Questions about Code

We analyzed effectiveness of three generative pre-trained transformer (GPT) models in answering multiple-choice question (MCQ) assessments, often involving short snippets of code, from introductory and intermediate programming courses at the postsecondary level. This emerging technology stirs countless discussions of its potential uses (e.g., exercise generation, code explanation) as well as misuses in programming education (e.g., cheating). However, the capabilities of GPT models and their limitations to reason about and/or analyze code in educational settings have been under-explored. We evaluated several OpenAI's GPT models on formative and summative MCQ assessments from three Python courses (530 questions). We found that MCQs containing code snippets are not answered as successfully as those that only contain natural language. While questions requiring to fill-in a blank in the code or completing a natural language statement about the snippet are handled rather successfully, MCQs that require analysis and/or reasoning about the code (e.g., what is true/false about the snippet, or what is its output) appear to be the most challenging. These findings can be leveraged by educators to adapt their instructional practices and assessments in programming courses, so that GPT becomes a valuable assistant for a learner as opposed to a source of confusion and/or potential hindrance in the learning process.Comment: 12 page

Can Generative Pre-trained Transformers (GPT) Pass Assessments in Higher Education Programming Courses?

We evaluated the capability of generative pre-trained transformers (GPT), to pass assessments in introductory and intermediate Python programming courses at the postsecondary level. Discussions of potential uses (e.g., exercise generation, code explanation) and misuses (e.g., cheating) of this emerging technology in programming education have intensified, but to date there has not been a rigorous analysis of the models' capabilities in the realistic context of a full-fledged programming course with diverse set of assessment instruments. We evaluated GPT on three Python courses that employ assessments ranging from simple multiple-choice questions (no code involved) to complex programming projects with code bases distributed into multiple files (599 exercises overall). Further, we studied if and how successfully GPT models leverage feedback provided by an auto-grader. We found that the current models are not capable of passing the full spectrum of assessments typically involved in a Python programming course (<70% on even entry-level modules). Yet, it is clear that a straightforward application of these easily accessible models could enable a learner to obtain a non-trivial portion of the overall available score (>55%) in introductory and intermediate courses alike. While the models exhibit remarkable capabilities, including correcting solutions based on auto-grader's feedback, some limitations exist (e.g., poor handling of exercises requiring complex chains of reasoning steps). These findings can be leveraged by instructors wishing to adapt their assessments so that GPT becomes a valuable assistant for a learner as opposed to an end-to-end solution.Comment: 7 pages. arXiv admin note: text overlap with arXiv:2303.0803