Abstract. Air quality forecasting requires atmospheric weather models to generate accurate meteorological conditions, one of which is the development of the planetary boundary layer (PBL). An important contributor to the development of the PBL is the land-air exchange captured in the energy budget as well as turbulence parameters. Standard and surface energy variables were modeled using the fifth-generation Penn State/National Center for Atmospheric Research mesoscale model (MM5), version 3.6.1, and the Weather Research and Forecasting (WRF) model, version 3.5.1, and compared to measurements for a southeastern Texas coastal region. The study period was 28 August-1 September 2006. It also included a frontal passage.The results of the study are ambiguous. Although WRF does not perform as well as MM5 in predicting PBL heights, it better simulates energy budget and most of the general variables. Both models overestimate incoming solar radiation, which implies a surplus of energy that could be redistributed in either the partitioning of the surface energy variables or in some other aspect of the meteorological modeling not examined here. The MM5 model consistently had much drier conditions than the WRF model, which could lead to more energy available to other parts of the meteorological system. On the clearest day of the study period, MM5 had increased latent heat flux, which could lead to higher evaporation rates and lower moisture in the model. However, this latent heat disparity between the two models is not visible during any other part of the study. The observed frontal passage affected the performance of most of the variables, including the radiation, flux, and turbulence variables, at times creating dramatic differences in the r 2 values.

Abstract. Air quality forecasting requires atmospheric weather models to generate accurate meteorological conditions, one of which is the development of the planetary boundary layer (PBL). An important contributor to the development of the PBL is the land-air exchange captured in the energy budget as well as turbulence parameters. Standard and surface energy variables were modeled using the fifth-generation Penn State/National Center for Atmospheric Research mesoscale model (MM5), version 3.6.1, and the Weather Research and Forecasting (WRF) model, version 3.2.1, and compared to measurements for a southeastern Texas coastal region. The study period was 28 August–1 September 2006. It also included a frontal passage. The results of the study are ambiguous. Although WRF does not perform as well as MM5 in predicting PBL heights, it better simulates most of the general and energy budget variables. Both models overestimate incoming solar radiation, which implies a surplus of energy that could be redistributed in either the partitioning of the surface energy variables or in some other aspect of the meteorological modeling not examined here. The MM5 model consistently had much drier conditions than the WRF model, which could lead to more energy available to other parts of the meteorological system. On the clearest day of the study period MM5 had increased latent heat flux, which could lead to higher evaporation rates and lower moisture in the model. However, this latent heat disparity between the two models is not visible during any other part of the study. The observed frontal passage affected the performance of most of the variables, including the radiation, flux, and turbulence variables, at times creating dramatic differences in the r2 values.

This paper describes the continuing and evolving relationship between the Writing in the Discipline Program in the University of Houston Writing Center and the Cullen College of Engineering. This specific project is an intervention into a sophomore design course in mechanical engineering that took place for the first time in the fall 2004. The paper will describe the development of the course-specific workshops and the establishment of a "draft review" process utilizing a peer Writing Consultant. Student surveys were used to assess the effectiveness of the new process. The student response was positive, but a few students resisted the implementation of a significant writing component into a "design" class. Only minor modifications were implemented as the intervention continues for this spring semester.

2The antibiotic trimethoprim targets the bacterial dihydrofolate reductase enzyme and 3 subsequently affects the entire folate network. We present an expanded mathematical model of 4 trimethoprim's action on the Escherichia coli folate network that greatly improves upon Kwon et 5 al. (2008). The improvement upon the Kwon Model lends greater insight into the effects of 6 trimethoprim at higher resolution and accuracy. More importantly, the presented mathematical 7 model enables drug target discovery in a way the earlier model could not. Using the improved 8 mathematical model as a scaffold, we use parameter optimization to search for new drug targets 9 that replicate the effect of trimethoprim. We present the model and model-scaffold strategy as 10 an efficient route for drug target discovery. 1112 Introduction 13 Antibiotic resistance 14 Antibiotic resistance is a major health policy concern. New and resistant forms of 15 common infections such as tuberculosis necessitate urgent drug development efforts [1][2][3].16 Strategies such as discovering new bacterial communication networks and inhibiting these 17 networks are a popular avenue for drug discovery yet are very expensive in both time and 18 resources. Utilizing math models to gain a greater level of insight into the mechanisms of known 19 drugs may offer opportunities for drug development, using well-studied and richly described 20 pathways that already show weakness to chemical intervention to search for new potential 21 targets [4]. The presented work models the mechanism of a common antibiotic, trimethoprim, at 2 22 the biological information layer at which it functions, the metabolic network level. This is done 23 not only to study the mechanism of trimethoprim but to find alternatives to it by attempting to 24 replicate its effect on the folate network, which is known to be critical to cell function. The 25 presented mathematical model improves on previous work by providing a higher resolution 26 model of Trimethoprim's effect on E. coli. Without a highly detailed model of the bacterial folate 27 network and trimethoprim's effect on it, the presented strategy of drug target discovery would 28 not be possible. 29 The folate network and trimethoprim 30 The folate network is a traditional therapeutic target for both cancerous and bacterial 31 cells due to the integral role folates play in cell division [5, 6]. The folate network provides and 32 accepts one-carbon units for the biosynthesis of amino acids and metabolites such as S-adenosyl 33 methionine (SAM), the universal methyl group donor [7-9]. The antibiotic trimethoprim (TM) 34 inhibits the activity of bacterial dihydrofolate reductase (DHFR), an enzyme that converts 35 dihydrofolate (DHF) to tetrahydrofolate (THF, Fig 1). DHFR inhibition causes a spike in DHF. DHF 36 in turn inhibits folypolyglutamate gamma synthetase (FPGS), the enzyme tasked with adding 37 glutamates to THF and its derivatives [10, 11]. Because folate-catalyzed conversions of one-38 carbon units are sensitive to glutamation lev...