Production Costs in Atlantic Fresh Fish Processing

Production costs for fresh Atlantic groundfish and scallop processing are examined using direct observation, linear regression analysis, and cost accounting. Assuming that management chooses a production technique where marginal costs are constant over a wide range of production due to management's expectation of predictable and unpredictable variation in product demand and exvessel supply, estimates of marginal cost for nonfish inputs from linear regression results and from cost accounting are compared. Also, regression results for physical yield from fish inputs are compared to estimates from the U.S. Department of Commerce. The similarity in results between these independent forms of estimation supports the maintained hypothesis of constant marginal cost over a wide range of production.Demand and Price Analysis, Environmental Economics and Policy, Food Consumption/Nutrition/Food Safety, Production Economics, Resource /Energy Economics and Policy, Risk and Uncertainty,

Applications of Machine Learning for Predicting Selection Outcomes in Antibody Phage Display

Antibodies form an essential component of the adaptive immune system, but they also have important scientific and clinical applications. These applications exploit the proven ability of antibodies to bind strongly and specifically to nearly any biomolecular target (e.g. protein) of interest. To produce antibodies for scientific and clinical applications, researchers can use a wet-lab technique called antibody phage display. Antibody phage display starts with a library of diverse antibody fragments and selects and amplifies those fragments that bind to the target. Antibody phage display combined with next-generation sequencing (NGS) technology has the potential to yield greater insight into the selection process. Machine learning is an area of artificial intelligence uniquely suited to recognizing patterns in large datasets, like those produced by NGS. The research goals of this thesis were to (1) train machine learning models to predict the selection of antibody fragments in antibody phage display using only the sequence of the fragment; (2) validate the ability of the trained models to generalize to different experiments; and (3) reverse engineer the trained models to gain greater insight into the learned patterns and the selection process. Antibody phage display data produced by the Geyer lab (University of Saskatchewan, SK) using two libraries called F and S was used to train a set of machine learning models: naive Bayes network (NB), linear model (LM), artificial neural network (ANN), support vector machine (SVM) with a radial basis function kernel (RBF-SVM), a SVM with a string kernel (SSK-SVM), and a random forest (RF). In addition, key parameters of the RBF- and SSK-SVM were tuned using a gridsearch. The trained models were then used to predict which antibody-displaying phage would be observed after the 5th round of panning, and their prediction accuracy on this data was used to help select models for subsequent analyses. The models selected were the RBF- and SSK-SVM. To achieve the second research goal, data originating from library F was used to train the two SVMs while library S data was used to test them. Finally, the two SVM models trained on library F were deconstructed to understand what features of the input correspond to negative predictions, and what features correspond to positive predictions. The ANN, SVMs, and RF models had the best average classification accuracy (81.5%), but of this group, there was not one classifier that performed significantly better than the others. These classifiers could be used to help non-experts select clones from either library F or S for further wet-lab analyses. The SVMs trained on library F and tested on library S achieved an average classification accuracy of 66.7%, significantly better than would be achieved by relying on chance. These two SVMs could be used to help non-experts select clones for further wet-lab analyses, provided the library being used is not too different from library S. Finally, deconstructing the SVMs trained on library F yielded insight into the basis for their predictions. The predictions of the RBF-SVM were found to be highly dependent on the molecular weight of the relevant binding region (i.e. CDRH3)

REMOVING THE FENCE?: A MIXED-METHODS STUDY OF COREQUISITE COURSES, FACULTY VALIDATION, AND EQUITY FOR MEN OF COLOR IN COMMUNITY COLLEGE ENGLISH

The passage of California’s AB 705 in 2017 mandated that community colleges drastically reimagine their English course offerings in an effort to increase student throughput and eliminate equity gaps. This typically meant replacing traditional remedial coursework and placement with corequisite models of remediation, wherein students took transfer-level courses with built-in concurrent remedial support. The purpose of this mixed-methods study was to explore the relationship between these structural changes and non-traditional relational success markers, namely faculty validation, especially for male Black and Latino students in English at a large urban California community college. The quantitative phase was a survey of over 1,000 students to measure the amount of faculty validation they received from their English instructors; the qualitative phase consisted of nine interviews with Black and Latino men to discover and understand the most salient validating faculty practices. The quantitative portion of the study found that on average, male Black and Latino students reported significantly higher levels of faculty validation in corequisite courses than in traditional courses, and that higher levels of faculty validation significantly predicted higher course grades in both course models. The qualitative portion of the study showed that high faculty validation typically resulted in course success, was often more prevalent in corequisite courses, and manifested itself most saliently in faculty individualizing instruction, providing clear feedback and support on student work and assignments, and maintaining high expectations

The Impact of Own, Rival and Market Effects on Real Estate Private Equity Fund Performance

Real estate private equity has become an increasingly favored asset class for institutional investors. This topic is important to academic researchers and industry professionals because it constitutes a large part of the global economy. This research paper will lay out a brief background of the real estate private equity industry and will explore the factors affecting real estate private equity fund performance through the lens of three factors: own effects, rival effects and market effects. The findings and implications from the above analysis will be examined and opined upon. This field is particularly interesting because relatively little research has been done on the real estate private equity landscape, given the limited data that is publically available. The majority of research has been focused on public real estate equities, as it composes a larger portion of the overall economy and is accessible to both professional and retail investors

Clonally diverse T cell homeostasis is maintained by a common program of cell-cycle control

Lymphopenia induces T cells to undergo cell divisions as part of a homeostatic response mechanism. The clonal response to lymphopenia is extremely diverse, and it is unknown whether this heterogeneity represents distinct mechanisms of cell-cycle control or whether a common mechanism can account for the diversity. We addressed this question by combining in vivo and mathematical modeling of lymphopenia-induced proliferation (LIP) of two distinct T cell clonotypes. OT-I T cells undergo rapid LIP accompanied by differentiation that superficially resembles Ag-induced proliferation, whereas F5 T cells divide slowly and remain naive. Both F5 and OT-I LIP responses were most accurately described by a single stochastic division model where the rate of cell division was exponentially decreased with increasing cell numbers. The model successfully identified key biological parameters of the response and accurately predicted the homeostatic set point of each clone. Significantly, the model was successful in predicting interclonal competition between OT-I and F5 T cells, consistent with competition for the same resource(s) required for homeostatic proliferation. Our results show that diverse and heterogenous clonal T cell responses can be accounted for by a single common model of homeostasis

Spline-based modelling of trends in the force of HIV infection, with application to the UNAIDS Estimation and Projection Package

Objective: We previously developed a flexible specification of the UNAIDS Estimation and Projection Package (EPP) that relied on splines to generate time-varying values for the force of infection parameter. Here, we test the feasibility of this approach for concentrated HIV/AIDS epidemics with very sparse data and compare two methods for making short-term future projections with the spline-based model. Methods: Penalised B-splines are used to model the average infection risk over time within the EPP 2011 modelling framework, which includes antiretroviral treatment effects and CD4 cell count progression, and is fit to sentinel surveillance prevalence data with a Bayesian algorithm. We compare two approaches for future projections: (1) an informative prior related to equilibrium prevalence and (2) a random walk formulation. Results: The spline-based model produced plausible fits across a range of epidemics, which included 87 subpopulations from 14 countries with concentrated epidemics and 75 subpopulations from 33 countries with generalised epidemics. The equilibrium prior and random walk approaches to future projections yielded similar prevalence estimates, and both performed well in tests of out-of-sample predictive validity for prevalence. In contrast, in some cases the two approaches varied substantially in estimates of incidence, with the random walk formulation avoiding extreme changes in incidence. Conclusions: A spline-based approach to allowing the force of infection parameter to vary over time within EPP 2011 is robust across a diverse array of epidemics, including concentrated ones with limited surveillance data. Future work on the EPP model should consider the impact that different modelling approaches have on estimates of HIV incidence