2014 Sonia Kovalevsky Math for Girls Day Evaluation Forms

Presenter, student and teacher evaluation forms for the 9th Annual Lincoln University Sonia Kovalevsky Math for Girls Day program flyer on April 25, 2014.

2012 Sonia Kovalevsky Math for Girls Day Report

Report for the 7th Annual Lincoln University Sonia Kovalevsky Math for Girls Day that was held on April 27th, 2012 from 8:00am to 2:00pm on the campus of Lincoln University in Jefferson City, MO.

2014 Sonia Kovalevsky Math for Girls Day Registration Form

Registration form for 9th Annual Lincoln University Sonia Kovalevsky Math for Girls Day on April 25, 2014.

2015 Sonia Kovalevsky Math for Girls Day Program

10th Annual Lincoln University Sonia Kovalevsky Math for Girls Day program on April 24, 2015.

Utilising Provenance to Enhance Social Computation

Postprint

SC-PROV: A Provenance Vocabulary for Social Computation

The research described here is supported by the award made by the RCUK Digital Economy programme to the dot.rural Digital Economy Hub; award reference: EP/G066051/1.

Computation, prediction, and experimental tests of fitness for bacteriophage T7 mutants with permuted genomes

We created a simulation based on experimental data from bacteriophage T7 that computes the developmental cycle of the wild-type phage and also of mutants that have an altered genome order. We used the simulation to compute the fitness of more than 105 mutants. We tested these computations by constructing and experimentally characterizing T7 mutants in which we repositioned gene 1, coding for T7 RNA polymerase. Computed protein synthesis rates for ectopic gene 1 strains were in moderate agreement with observed rates. Computed phage-doubling rates were close to observations for two of four strains, but significantly overestimated those of the other two. Computations indicate that the genome organization of wild-type T7 is nearly optimal for growth: only 2.8% of random genome permutations were computed to grow faster, the highest 31% faster, than wild type. Specific discrepancies between computations and observations suggest that a better understanding of the translation efficiency of individual mRNAs and the functions of qualitatively “nonessential” genes will be needed to improve the T7 simulation. In silico representations of biological systems can serve to assess and advance our understanding of the underlying biology. Iteration between computation, prediction, and observation should increase the rate at which biological hypotheses are formulated and tested.

Molecular computation: RNA solutions to chess problems

We have expanded the field of “DNA computers” to RNA and present a general approach for the solution of satisfiability problems. As an example, we consider a variant of the “Knight problem,” which asks generally what configurations of knights can one place on an n × n chess board such that no knight is attacking any other knight on the board. Using specific ribonuclease digestion to manipulate strands of a 10-bit binary RNA library, we developed a molecular algorithm and applied it to a 3 × 3 chessboard as a 9-bit instance of this problem. Here, the nine spaces on the board correspond to nine “bits” or placeholders in a combinatorial RNA library. We recovered a set of “winning” molecules that describe solutions to this problem.

Computation of Surface Electrical Potentials of Plant Cell Membranes : Correspondence to Published Zeta Potentials from Diverse Plant Sources

A Gouy-Chapman-Stern model has been developed for the computation of surface electrical potential (ψ0) of plant cell membranes in response to ionic solutes. The present model is a modification of an earlier version developed to compute the sorption of ions by wheat (Triticum aestivum L. cv Scout 66) root plasma membranes. A single set of model parameters generates values for ψ0 that correlate highly with published ζ potentials of protoplasts and plasma membrane vesicles from diverse plant sources. The model assumes ion binding to a negatively charged site (R− = 0.3074 μmol m−2) and to a neutral site (P0 = 2.4 μmol m−2) according to the reactions R− + IΖ ⇌ RIΖ−1 and P0 + IΖ ⇌ PIΖ, where IΖ represents an ion of charge Ζ. Binding constants for the negative site are 21,500 m−1 for H+, 20,000 m−1 for Al3+, 2,200 m−1 for La3+, 30 m−1 for Ca2+ and Mg2+, and 1 m−1 for Na+ and K+. Binding constants for the neutral site are 1/180 the value for binding to the negative site. Ion activities at the membrane surface, computed on the basis of ψ0, appear to determine many aspects of plant-mineral interactions, including mineral nutrition and the induction and alleviation of mineral toxicities, according to previous and ongoing studies. A computer program with instructions for the computation of ψ0, ion binding, ion concentrations, and ion activities at membrane surfaces may be requested from the authors.

The evolution of emergent computation.

A simple evolutionary process can discover sophisticated methods for emergent information processing in decentralized spatially extended systems. The mechanisms underlying the resulting emergent computation are explicated by a technique for analyzing particle-based logic embedded in pattern-forming systems. Understanding how globally coordinated computation can emerge in evolution is relevant both for the scientific understanding of natural information processing and for engineering new forms of parallel computing systems.

Rapid local synchronization of action potentials: toward computation with coupled integrate-and-fire neurons.

The collective behavior of interconnected spiking nerve cells is investigated. It is shown that a variety of model systems exhibit the same short-time behavior and rapidly converge to (approximately) periodic firing patterns with locally synchronized action potentials. The dynamics of one model can be described by a downhill motion on an abstract energy landscape. Since an energy landscape makes it possible to understand and program computation done by an attractor network, the results will extend our understanding of collective computation from models based on a firing-rate description to biologically more realistic systems with integrate-and-fire neurons.

A Young Child's Anxiety and Math Performance: Enhancing Cognitive Behavioral Intervention with Conjoint Behavioral Consultation

Partnering with families, school personnel, and community resources is an important step to supporting the child and family, especially when children might suffer from debilitating anxiety concerns. However, little research examines the impact of anxiety on math performance for young children participating in school-based interventions enhanced by family components. The following research questions were addressed in the study: 1a) Will a young child with elevated levels of anxiety show a decrease in anxiety symptoms with a Cognitive Behavioral framework intervention program for children? 1b) Will anxiety be reduced with the addition of a Conjoint Behavioral Consultation with the family and teacher? 2a) Will a young child show an increase in math performance after participation in a Cognitive Behavioral framework intervention program for children? 2b) Will math performance be increased with the addition of a Conjoint Behavioral Consultation with the family and teacher? A single-subject staggered baseline across situations intervention study addressed whether the Coping Cat, an evidenced-based child-focused intervention now widely used in schools and clinics to treat childhood anxiety, combined with family and school consultation will decrease elevated anxiety levels and improve math performance in an elementary-aged student. The objective was to support mental health development and math performance with an eight-year-old, female elementary student through a collaborative effort of stakeholders in the student's life. Baseline data was collected with repeated measures of anxiety and math performance, and was compared to two intervention phases: first, a child-focused intervention and second, a family and school consultation. The study tested the theory that the Cognitive Behavioral intervention and Conjoint Behavioral Consultation intervention will influence, positively, the anxiety levels and math performance for an elementary-aged student. Results indicate that the child participant with elevated levels of anxiety showed a reduction in symptoms with the introduction of a Cognitive Behavioral framework intervention when compared to her baseline data. The participant showed further reduction in symptoms across the school and home settings with the implementation of Conjoint Behavioral Consultation when compared to baseline and the first intervention phase. Math performance began to increase with the introduction of the Cognitive Behavioral intervention, and continued to improve with the implementation of the Conjoint Behavioral Consultation. Findings suggest that consultation should begin immediately when an intervention is implemented in order to enhance outcomes.

Sequential and parallel synchronous alternating iterative methods

The so-called parallel multisplitting nonstationary iterative Model A was introduced by Bru, Elsner, and Neumann [Linear Algebra and its Applications 103:175-192 (1988)] for solving a nonsingular linear system Ax = b using a weak nonnegative multisplitting of the first type. In this paper new results are introduced when A is a monotone matrix using a weak nonnegative multisplitting of the second type and when A is a symmetric positive definite matrix using a P -regular multisplitting. Also, nonstationary alternating iterative methods are studied. Finally, combining Model A and alternating iterative methods, two new models of parallel multisplitting nonstationary iterations are introduced. When matrix A is monotone and the multisplittings are weak nonnegative of the first or of the second type, both models lead to convergent schemes. Also, when matrix A is symmetric positive definite and the multisplittings are P -regular, the schemes are also convergent.