Participation in a High-Structure General Chemistry Course Increases Student Sense of Belonging and Persistence to Organic Chemistry.

A parallel series of general chemistry courses for Life Science Majors was created in an effort to support students and improve general chemistry outcomes. We created a two-quarter enhanced general chemistry course series that is not remedial, but instead implements several evidence-based teaching practices including Process Oriented Guided Inquiry Learning (POGIL), Peer-Led Team Learning (PLTL), and the Learning Assistant (LA) model. We found that students who took enhanced general chemistry had higher persistence to the subsequent first organic chemistry course, and performed equally well in the organic course compared to their peers who took standard general chemistry. Students in the first enhanced general chemistry course also reported significantly higher belonging, although we were unable to determine if increased belonging was associated with the increased persistence to organic chemistry. Rather we found that the positive association between taking the enhanced general chemistry course and persistence to organic chemistry was mediated by higher grades received in the enhanced general chemistry course. Our findings highlight the responsibility we have as educators to carefully consider the pedagogical practices we use, in addition to how we assign student grades.

The Benefits of Participating in a Learning Assistant Program on the Metacognitive Awareness and Motivation of Learning Assistants.

Learning assistant (LA) programs train undergraduate students to foster peer discussion and facilitate active-learning activities in undergraduate science, technology, engineering, and mathematics (STEM) classes. Students who take courses that are supported by LAs demonstrate better conceptual understanding, lower failure rates, and higher satisfaction with the course. There is less work, however, on the impact that participating in LA programs has on the LAs themselves. The current study implements a pretest-posttest design to assess changes in LAs' metacognition and motivation to succeed in STEM across their first and second quarters as an LA. Our findings suggest that participating in this program may help LAs become more reflective learners, as was demonstrated by an increase in their scores on the Metacognitive Awareness Inventory (MAI) after the first quarter. LAs also showed increases on the Intrinsic Motivation and Self-Efficacy subscales of the Science Motivation Questionnaire. Students who participated in the program for an additional quarter continued to show increases in their MAI scores and maintained the gains that were observed in their motivation. Taken together, this work suggests that, in addition to benefiting the learner, LA programs may have positive impacts on the LAs themselves.

Modeling the Emergence of Antibiotic Resistance in the Environment: an Analytical Solution for the Minimum Selection Concentration.

Environmental antibiotic risk management requires an understanding of how subinhibitory antibiotic concentrations contribute to the spread of resistance. We develop a simple model of competition between sensitive and resistant bacterial strains to predict the minimum selection concentration (MSC), the lowest level of antibiotic at which resistant bacteria are selected. We present an analytical solution for the MSC based on the routinely measured MIC, the selection coefficient (sc) that expresses fitness differences between strains, the intrinsic net growth rate, and the shape of the bacterial growth dose-response curve with antibiotic or metal exposure (the Hill coefficient [κ]). We calibrated the model by optimizing the Hill coefficient to fit previously reported experimental growth rate difference data. The model fit varied among nine compound-taxon combinations examined but predicted the experimentally observed MSC/MIC ratio well (R2 ≥ 0.95). The shape of the antibiotic response curve varied among compounds (0.7 ≤ κ ≤ 10.5), with the steepest curve being found for the aminoglycosides streptomycin and kanamycin. The model was sensitive to this antibiotic response curve shape and to the sc, indicating the importance of fitness differences between strains for determining the MSC. The MSC can be >1 order of magnitude lower than the MIC, typically by the factor scκ This study provides an initial quantitative depiction and a framework for a research agenda to examine the growing evidence of selection for resistant bacterial communities at low environmental antibiotic concentrations.

Implementation of a Learning Assistant Program Improves Student Performance on Higher-Order Assessments.

Learning assistant (LA) programs have been implemented at a range of institutions, usually as part of a comprehensive curricular transformation accompanied by a pedagogical switch to active learning. While this shift in pedagogy has led to increased student learning gains, the positive effect of LAs has not yet been distinguished from that of active learning. To determine the effect that LAs would have beyond a student-centered instructional modality that integrated active learning, we introduced an LA program into a large-enrollment introductory molecular biology course that had already undergone a pedagogical transformation to a highly structured, flipped (HSF) format. We used questions from a concept test (CT) and exams to compare student performance in LA-supported HSF courses with student performance in courses without LAs. Students in the LA-supported course did perform better on exam questions common to both HSF course modalities but not on the CT. In particular, LA-supported students' scores were higher on common exam questions requiring higher-order cognitive skills, which LAs were trained to foster. Additionally, underrepresented minority (URM) students particularly benefited from LA implementation. These findings suggest that LAs may provide additional learning benefits to students beyond the use of active learning, especially for URM students.

Characterizing the burden of disease of particulate matter for life cycle impact assessment.

Fine particulate air pollution (PM2.5) is a major environmental contributor to human burden of disease and therefore an important component of life cycle impact assessments. An accurate PM2.5 characterization factor, i.e., the impact per kg of PM2.5 emitted, is critical to estimating "cradle-to-grave" human health impacts of products and processes. We developed and assessed new characterization factors (disability-adjusted life years (DALY)/kgPM2.5 emitted), or the products of dose-response factors (deaths/kgPM2.5 inhaled), severity factors (DALY/death) and intake fractions (kgPM2.5 inhaled/kgPM2.5 emitted). In contrast to previous health burden estimates, we calculated age-specific concentration- and dose-response factors using baseline data, from 63 U.S. metropolitan areas, consistent with the U.S. study population used to derive the relative risk. We also calculated severity factors using 2010 Global Burden of Disease data. Multiplying the revised PM2.5 dose-responses, severity factors and intake fractions yielded new PM2.5 characterization factors that are higher than previous factors for primary PM2.5 but lower for secondary PM2.5 due to NOx. Multiplying the concentration-response and severity factors by 2005 ambient PM2.5 concentrations yielded an annual U.S. burden of 2,000,000 DALY, slightly lower than previous U.S. estimates. The annual U.S. health burden estimated from PM emissions and characterization factors was 2.2 times higher.

Spatial analysis of toxic emissions in LCA: a sub-continental nested USEtox model with freshwater archetypes.

This paper develops continent-specific factors for the USEtox model and analyses the accuracy of different model architectures, spatial scales and archetypes in evaluating toxic impacts, with a focus on freshwater pathways. Inter-continental variation is analysed by comparing chemical fate and intake fractions between sub-continental zones of two life cycle impact assessment models: (1) the nested USEtox model parameterized with sub-continental zones and (2) the spatially differentiated IMPACTWorld model with 17 interconnected sub-continental regions. Substance residence time in water varies by up to two orders of magnitude among the 17 zones assessed with IMPACTWorld and USEtox, and intake fraction varies by up to three orders of magnitude. Despite this variation, the nested USEtox model succeeds in mimicking the results of the spatially differentiated model, with the exception of very persistent volatile pollutants that can be transported to polar regions. Intra-continental variation is analysed by comparing fate and intake fractions modelled with the a-spatial (one box) IMPACT Europe continental model vs. the spatially differentiated version of the same model. Results show that the one box model might overestimate chemical fate and characterisation factors for freshwater eco-toxicity of persistent pollutants by up to three orders of magnitude for point source emissions. Subdividing Europe into three archetypes, based on freshwater residence time (how long it takes water to reach the sea), improves the prediction of fate and intake fractions for point source emissions, bringing them within a factor five compared to the spatial model. We demonstrated that a sub-continental nested model such as USEtox, with continent-specific parameterization complemented with freshwater archetypes, can thus represent inter- and intra-continental spatial variations, whilst minimizing model complexity.

Intake fraction for particulate matter: recommendations for life cycle impact assessment.

Particulate matter (PM) is a significant contributor to death and disease globally. This paper summarizes the work of an international expert group on the integration of human exposure to PM into life cycle impact assessment (LCIA), within the UNEP/SETAC Life Cycle Initiative. We review literature-derived intake fraction values (the fraction of emissions that are inhaled), based on emission release height and "archetypal" environment (indoor versus outdoor; urban, rural, or remote locations). Recommended intake fraction values are provided for primary PM(10-2.5) (coarse particles), primary PM(2.5) (fine particles), and secondary PM(2.5) from SO(2), NO(x), and NH(3). Intake fraction values vary by orders of magnitude among conditions considered. For outdoor primary PM(2.5), representative intake fraction values (units: milligrams inhaled per kilogram emitted) for urban, rural, and remote areas, respectively, are 44, 3.8, and 0.1 for ground-level emissions, versus 26, 2.6, and 0.1 for an emission-weighted stack height. For outdoor secondary PM, source location and source characteristics typically have only a minor influence on the magnitude of the intake fraction (exception: intake fraction values can be an order of magnitude lower for remote-location emission than for other locations). Outdoor secondary PM(2.5) intake fractions averaged over respective locations and stack heights are 0.89 (from SO(2)), 0.18 (NO(x)), and 1.7 (NH(3)). Estimated average intake fractions are greater for primary PM(10-2.5) than for primary PM(2.5) (21 versus 15), owing in part to differences in average emission height (lower, and therefore closer to people, for PM(10-2.5) than PM(2.5)). For indoor emissions, typical intake fraction values are ∼1000-7000. This paper aims to provide as complete and consistent an archetype framework as possible, given current understanding of each pollutant. Values presented here facilitate incorporating regional impacts into LCIA for human health damage from PM.

Assessing regional intake fractions in North America.

This paper develops the IMPACT North America model, a spatially resolved multimedia, multi-pathway, fate, exposure and effect model that includes indoor and urban compartments. IMPACT North America allows geographic differentiation of population exposure of toxic emissions for comparative risk assessment and life cycle impact assessment within U.S. and Canada. It looks at air, water, soil, sediment and vegetation media, and divides North America into several hundred zones. It is nested within a single world box to account for emissions leaving North America. It is a multi-scale model, covering three different spatial scales--indoor, urban and regional--in all zones in North America. Model results are evaluated against monitored emissions and concentrations of benzo(a)pyrene, 2,3,7,8-TCDD and mercury. Most of the chemical concentrations predicted by the model fall within two orders of magnitude of the monitored data. The model shows that urban intake fractions are one order of magnitude higher than rural intake fractions. The model application and importance is demonstrated by a case study on spatially-distributed emissions over the life cycle of diesel fuel. Depending on population densities and agricultural intensities, intake fractions can vary by eight orders of magnitudes, and even limited indoor emissions can lead to intakes comparable to those from outdoor emissions. To accurately assess these variations in intake fraction, we require the essential three original features described in the present paper: i) inclusion of the continental model within a world box for persistent pollutants, ii) addition of an urban box for short- and medium-lived substances (for grid size larger than 100 km), and iii) assess indoor emissions. This model can therefore be used to screen chemicals and assess regionalized intake fractions within North America for population-based human exposure assessment, life cycle impact assessment, and comparative risk assessment. The model can be downloaded at http://www.impactmodeling.org.