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Hydraulic fracturing is used to extract oil and natural gas from low permeability formations. The potential of fluids migrating from depth through adjacent wellbores and through the production wellbore was investigated using statistical modeling and predic-tive classifiers. The probability of a hydraulic fracturing well becoming hydraulically connected to an adjacent well in the Marcellus shale of New York was determined to be between 0.00% and 3.45% at the time of the study. This means that the chance of an in-duced fracture from hydraulic fracturing intersecting an existing well is highly dependent on the area of increased permeability caused by fracturing. The chance of intersecting an existing well does not mean that fluid will flow upwards; for upward migration to occur, a pathway must exist and a pressure gradient is required to drive flow, with the exception of gas flow caused by buoyancy. Predictive classifiers were employed on a dataset of wells in Alberta Canada to identify well characteristics most associated to fluid migration along the production well. The models, specifically a random forest, were able to identify pathways better than random guessing with 78% of wells in the data set identified cor-rectly. Magnetic resonance imaging (MRI) was used to visualize and quantify contami-nant transport in a soil column using a full body scanner. T1 quantification was used to determine the concentration of a contaminant surrogate in the form of Magnevist, an MRI contrast agent. Imaging showed a strong impact from density driven convection when the density difference between the two fluids was small (0.3%). MRI also identified a buildup of contrast agent concentration at the interface between a low permeability ground silica and higher permeability AFS 50-70 testing sand when density driven con-vection was eliminated.

The overarching goal of the dissertation is to illustrate the relevance of learner corpus research to the field of second language acquisition (SLA). The possibility that learner corpora can be useful in mainstream SLA research has a significant implication given that they have not been systematically explored in relation to SLA theories. The thesis contributes to building a methodological framework to utilize learner corpora beneficially to SLA and argues that learner corpus research contributes to other disciplines. This is achieved by a series of case studies that quantitatively analyze individual variation and the role of native language (L1) in second language (L2) development of English grammatical morphemes and explain the findings with existing SLA theories. The dissertation investigates the L2 development of morphemes based on two largescale learner corpora. It first reviews the literature and points out that the L2 acquisition order of English grammatical morphemes that has been believed universal in SLA research may, in fact, vary across the learners with different L1 backgrounds and that individual differences in morpheme studies have been relatively neglected in previous literature. The present research, thus, provides empirical evidence testing the universality of the order and the extent of individual differences. In the first study, the thesis investigates L1 influence on the L2 acquisition order of six English grammatical morphemes across seven L1 groups and five proficiency levels. Data drawn from approximately 12,000 essays from the Cambridge Learner Corpus establish clear L1 influence on this issue. The study also reveals that learners without the equivalent morpheme in L1 tend to achieve an accuracy level of below 90% with respect to the morpheme even at the highest proficiency level, and that morphemes requiring learners to learn to pay attention to the relevant distinctions in their acquisition show a stronger effect of L1 than those which only require new form-meaning mappings. The findings are interpreted under the framework of thinking-for-speaking proposed by Dan Slobin. Following the first study, the dissertation exploits EF-Cambridge Open Language Database (EFCamDat) and analyzes the developmental patterns of morphemes, L1 influence on the patterns, and the extent to which individual variation is observed in the development. Based on approximately 140,000 essays written by 46,700 learners of 10 L1 groups across a wide range of proficiency levels, the study found that (i) certain developmental patterns of accuracy are observed irrespective of target morphemes, (ii) inverted U-shaped development is rare irrespective of morphemes, (iii) proficiency influences the within-learner developmental patterns of morphemes, (iv) the developmental patterns at least slightly vary depending on morphemes, and (v) significant individual variation is observed in absolute accuracy, the accuracy difference between morphemes, and the rate of development. The findings are interpreted with dynamic systems theory (DST), a theory of development that has recently been applied to SLA research. The thesis further examines whether any systematic relationship is observed between the developmental patterns of morphemes. Although DST expects that their development is interlinked, the study did not find any strong relationships between the developmental patterns. However, it revealed a weak supportive relationship in the developmental pattern between articles and plural -s. That is, within individual learners, when the accuracy of articles increases, the accuracy of plural -s tends to increase as well, and vice versa.

<div>Reproductive efficiency in sows and boars affects the profitability of swine production systems. However, breeding stock selection is primarily based on progeny performance traits such as feed efficiency, growth rate, carcass characteristics, physical appearance, and structure, especially for terminal sire lines, with less emphasis on reproduction. While maternal sire lines are co-selected for reproductive traits including birth litter size, number weaned, weaning weight, and wean to estrus interval, currently, there is no single test predictive of fertility, and thus subfertile males and sub-fertile or even infertile females enter the swine breeding herds (Oh et al., 2006b; Safranski, 2008). Thus, to maximize economic returns and swine production efficiency there is a need for a biomarker to identify boars and gilts with the greatest reproductive potential before admittance into the breeding herd. The overall aim of the described studies was to determine if biomarkers of fertility of boars and gilts could be identified in biological samples taken prior to or just after animals entering the breeding herds using high throughput omic screening tools resulting from recent advancements in mass spectrometry.</div><div>Current semen evaluation techniques only identify boars with fertility issues associated with sperm motility, morphology, and concentration. We know that seminal plasma proteins are essential for proper sperm function and play an important role in fertilization. Therefore, we hypothesized that fertility differences could be reflected in the seminal plasma proteome profiles. A fertility index was created from 110 boars with data on total born and farrowing rate following 50 single-sired matings. Thirty-two of the 110 boars were identified as having extreme phenotypes for total born and farrowing rate and were categorized into one of the following: high farrowing rate and high total born (HFHB; n=9), high farrowing rate with low total born (HFLB; n=10), low farrowing rate and low total born (LFLB; n=9), and low farrowing rate with high total born (LFHB; n=4). The seminal plasma proteins were isolated and measured using label-free liquid chromatography-tandem mass spectrometry (LC-MS/MS). There were 436 proteins measured in at least one sample across all animals, with 245 proteins considered for analysis (detected in samples of at least n=3 animals/phenotype). Of the 245 proteins, 56 were differentially abundant (P < 0.05) between the high fertility phenotype (HFHB) and at least one of the three subfertile groups. Proteins previously associated with fertility such as Porcine seminal protein I (PSP-I) and epididymis-specific alpha-mannosidase (MAN2B2) and free radical detoxification such as superoxide dismutase 1 (SOD1), peroxiredoxin 4 (PRDX4), and glutathione peroxidase 6 (GPX6) were more abundant in HFHB. Subfertile phenotypes had a greater abundance of blood microparticle proteins, biomarkers of inflammation, and lower inositol-1-monophosphatase (IMPA1), which regulates inositol production. Findings supported that seminal plasma protein profiles were distinct between boars with different fertility phenotypes and have the potential to predict boar reproductive performance.</div><div>The selection of replacement females for the sow herd is one of the most important facets in sow system management. However, selection of gilts for sow herd replacements is primarily based on how the animal appears such as feet and leg confirmation, the gilt’s underline, and parent past performance. This practice resulted in a high degree of variation in sow reproductive performance traits such as pigs per sow per year (PSY) and increased culling rates due to reproductive failure. In female swine, perinatal nutritional environment has been associated with their long-term fertility. The vaginal lipidome of 2 day and 14 day old gilts was found reflective of nutrition source, which suggests that perinatal nutrition affects the composition of reproductive tissues. Thus, it was hypothesized that the vaginal lipidome profiles of gilts at weaning would be reflective of fertility later in life. The first study aimed to find potential on-farm biomarkers that technicians could use to make selection decisions. Variables chosen as potential biomarkers have potential to influence or predict long-term fertility. Data were prospectively collected from 2146 gilts born on a commercial sow production facility and included birth and weaning weights, vulva length and width at 21 d postnatal (PN), birth and nursing litter size, days nursed, average daily gain from birth to weaning, and age at first estrus. Of the initial animals, 400 (17%) were selected for the sow herd, 353 remained after removing animals culled for non-reproductive reasons. Animals were assigned to 1 of 5 reproductive performance categories based on observation of estrus or pigs per sow per year (PSY) across two farrowings: High Fertility (HF; 23%; n=82; ≥26 PSY), Middle Fertility (MF2; 12%; n=43; 20-25 PSY), Low Fertility (MF3; 15%; n=54; <20 PSY), Infertile-Estrus (IFe; 10%; n= 36; estrus, no pregnancy), and Infertile-No Estrus (IFno; 39%; n=138; no estrus, no pregnancy). Generalized linear model analysis indicated vulva width (P=0.03) was related to PSY, however, it only explained 1.5% of the total variation in PSY. To determine if preweaning variables were predictive of gilt fertility outcome, animals were grouped as those that became pregnant (n=179) or not (n=174). Vulva width tended to be greater in fertile animals versus infertile (P=0.07). Binomial regression analysis revealed a positive relationship between vulva width and gilt fertility; however, this relationship is not strong enough to make sow herd selection decisions.</div><div>Because gilts are so phenotypically similar at weaning, we hypothesized that the biomarker predictive of fertility at this stage of selection might need a more sensitive means of detection. Therefore, we evaluated the vaginal lipid profiles from a subset of animals enrolled in the previous study that were the extremes of fertility phenotype: High Fertility (HF; n=28; ≥26 PSY) and Infertile (IF; n=34; no estrus, no pregnancy). Vaginal swabs of the anterior vagina were taken at 21 ± 4 d PN. Lipids were extracted from cellular material collected with swabs and analyzed using multiple reaction monitoring (MRM) profiling for lipidome analysis. Relative abundance of arachidonic acid (ARA, C20:4) and docosahexaenoic acid (DHA, C22:6) were lower (P<0.05) in IF gilts than HF gilts, whereas abundance of the free fatty acids cerotic (C26:0), ximenic (C26:1), and nonadecanoic (C19:0) acids were greater (P<0.05) in IF gilts. Additionally, eicosapentaenoic acid (C20:5), a precursor of prostaglandins, was also higher (P<0.05) in IF gilts.</div><div> Previous studies support that higher levels of arachidonic acid in vaginal lipidomes maybe a biomarker of colostrum intake, and thus provides further evidence for a relationship between fertility and the perinatal nutritional environment. The perspective of having a panel of lipids captured with vaginal swabs at weaning that can predict the reproductive efficiency of gilts shows promise and warrants future research in this area. Taken together, the experiments described above demonstrate that detection of infertile and subfertile animals before entering the breeding herd is possible and warrants further development and validation of diagnostic panels capable of doing so. </div><div><br></div>

abstract: The typical engineering curriculum has become less effective in training construction professionals because of the evolving construction industry needs. The latest National Science Foundation and the National Academies report indicate that industry-valued skills are changing. The Associated General Contractors of America recently stated that contractors expect growth in all sectors; however, companies are worried about the supply of skilled professionals. Workforce development has been of a growing interest in the construction industry, and this study approaches it by conducting an exploratory analysis applied to students that have completed a mandatory internship as part of their construction program at Arizona State University, in the School of Sustainable Engineering and the Built Environment. Data is collected from surveys, including grades by a direct evaluator from the company reflecting each student’s performance based on recent Student Learning Objectives. Preliminary correlations are computed between scores received on the 15 metrics in the survey and the final industry suggested grade. Based on the factors identified as highest predictors: ingenuity and creativity, punctuality and attendance, and initiative; a prognostic model of student performance in the construction industry is generated. With regard to graduate employability, student performance in the industry and human predispositions are also tested in order to evaluate their contribution to the generated model. The study finally identifies threats to validity and opportunities presented in a dynamic learning environment presented by internships. Results indicate that measuring student performance during internships in the construction industry creates challenges for the evaluator from the host company. Scoring definitions are introduced to standardize the evaluators’ grading based on observations of student behavior. 12 questions covering more Student Learning Objectives identified by the industry are added to the survey, potentially improving the reliability of the predictive model. / Dissertation/Thesis / Doctoral Dissertation Construction Management 2019

Thesis advisor: Henry Braun / The State NAEP program only reports the mean achievement estimate of a subgroup within a given state if it samples at least 62 students who identify with the subgroup. Since some subgroups of students constitute small proportions of certain states’ general student populations, these low-incidence groups of students are seldom sufficiently sampled to meet this rule-of-62 requirement. As a result, education researchers and policymakers are frequently left without a full understanding of how states are supporting the learning and achievement of different subgroups of students.Using grade 8 mathematics results in 2015, this dissertation addresses the problem by comparing the performance of three different techniques in predicting mean subgroup achievement on NAEP. The methodology involves simulating scenarios in which subgroup samples greater or equal to 62 are treated as not available for calculating mean achievement estimates. These techniques comprise an adaptation of Multivariate Imputation by Chained Equations (MICE), a common form of Small Area Estimation known as the Fay-Herriot model (FH), and a Cross-Survey analysis approach that emphasizes flexibility in model specification, referred to as Flexible Cross-Survey Analysis (FLEX CS) in this study. Data used for the prediction study include public-use state-level estimates of mean subgroup achievement on NAEP, restricted-use student-level achievement data on NAEP, public-use state-level administrative data from Education Week, the Common Core of Data, the U.S. Census Bureau, and public-use district-level achievement data in NAEP-referenced units from the Stanford Education Data Archive. To evaluate the accuracy of the techniques, a weighted measure of Mean Absolute Error and a coverage indicator quantify differences between predicted and target values. To evaluate whether a technique could be recommended for use in practice, accuracy measures for each technique are compared to benchmark values established as markers of successful prediction based on results from a simulation analysis with example NAEP data. Results indicate that both the FH and FLEX CS techniques may be suitable for use in practice and that the FH technique is particularly appealing. However, before definitive recommendations are made, the analyses from this dissertation should be conducted employing math achievement data from other years, as well as data from NAEP Reading. / Thesis (PhD) — Boston College, 2021. / Submitted to: Boston College. Lynch School of Education. / Discipline: Educational Research, Measurement and Evaluation.

Thesis advisor: Kenneth Williams / Due to overuse and misuse of antibiotics, the global threat of antibiotic resistance is a growing crisis. Three critical issues surrounding antibiotic resistance are the lack of rapid testing, treatment failure, and evolution of resistance. However, with new technology facilitating data collection and powerful statistical learning advances, our understanding of the bacterial stress response to antibiotics is rapidly expanding. With a recent influx of omics data, it has become possible to develop powerful computational methods that make the best use of growing systems-level datasets. In this work, I present several such approaches that address the three challenges around resistance. While this body of work was motivated by the antibiotic resistance crisis, the approaches presented here favor generalization, that is, applicability beyond just one context. First, I present ShinyOmics, a web-based application that allow visualization, sharing, exploration and comparison of systems-level data. An overview of transcriptomics data in the bacterial pathogen Streptococcus pneumoniae led to the hypothesis that stress-susceptible strains have more chaotic gene expression patterns than stress-resistant ones. This hypothesis was supported by data from multiple strains, species, antibiotics and non-antibiotic stress factors, leading to the development of a transcriptomic entropy based, general predictor for bacterial fitness. I show the potential utility of this predictor in predicting antibiotic susceptibility phenotype, and drug minimum inhibitory concentrations, which can be applied to bacterial isolates from patients in the near future. Predictors for antibiotic susceptibility are of great value when there is large phenotypic variability across isolates from the same species. Phenotypic variability is accompanied by genomic diversity harbored within a species. I address the genomic diversity by developing BFClust, a software package that for the first time enables pan-genome analysis with confidence scores. Using pan-genome level information, I then develop predictors of essential genes unique to certain strains and predictors for genes that acquire adaptive mutations under prolonged stress exposure. Genes that are essential offer attractive drug targets, and those that are essential only in certain strains would make great targets for very narrow-spectrum antibiotics, potentially leading the way to personalized therapies in infectious disease. Finally, the prediction of adaptive outcome can lead to predictions of future cross-resistance or collateral sensitivities. Overall, this body of work exemplifies how computational methods can complement the increasingly rapid data generation in the lab, and pave the way to the development of more effective antibiotic stewardship practices. / Thesis (PhD) — Boston College, 2020. / Submitted to: Boston College. Graduate School of Arts and Sciences. / Discipline: Biology.

The main goal of this thesis is to analyse the fluctuation of the employees in a well established Czech manufacturing company. Due to the GDPR regulations, the underlying company is kept anonymised in this thesis. The data were transformed into longitudinal data and the GEE methodology was used for the analysis of the fluctuation. In the first chapter, an introduction to the problem and a short description of the data is provided. The second chapter contains some theoretical description of the GEE methodology and the QIC information criterion. In the third chapter, multiple models for a binary and multinomial response are fitted to the data and their results are described in detail. This allows us to describe the influence of various factors to the fluctuation of the employees in the underlying company. 1

Postprandial characterization of patients with type 1 diabetes is crucial for the development of an automatic glucose control system (Artificial Pancreas). Uncertainty sources within the patient, and variability of the glucose response between patients, are a challenge for individual patients model identification leading to poor predictability with current methods. Also, continuous glucose monitors, which have been the springboard for research towards a domiciliary artificial pancreas, still introduce large measurement errors, greatly complicating the characterization of the patient. In this thesis, individual model identification characterizing intra-patient variability from domiciliary data is addressed. First, literature models are reviewed. Next, we investigate the collection of data, and how can it be improved using optimal experiment design. Data gathering improvement is later applied to an ambulatory clinical protocol implemented at the Hospital Clínic Universitari de València, and data are collected from twelve patients following a set of mixed meal studies. With regard to the uncertainty of the glucose monitors, two continuous glucose monitoring devices are analyzed and statistically modeled. The models of these devices are used for in silico simulations and the analysis of identification methods. Identification using intervals models is then performed, showing an inherent capability for characterization of both the patient and the related uncertainty. First an in silico study is conducted in order to assess the feasibility of the identifications. Then, model identification is addressed from real patient data, increasing the complexity of the problem. As conclusion a new method for interval model identification is developed and successfully validated from clinical data. / Laguna Sanz, AJ. (2014). Uncertainty in Postprandial Model Identification in type 1 Diabetes [Tesis doctoral]. Editorial Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/37191 / Alfresco

From creation to its degradation, the RNA molecule is the action field of many binding proteins with different roles in regulation and RNA metabolism. Since these proteins are involved in a large number of processes, a variety of diseases are related to abnormalities occurring within the binding mechanisms. One of the experimental methods for detecting the binding sites of these proteins is PAR-CLIP built on the next generation sequencing technology. Due to its size and intrinsic noise, PAR-CLIP data analysis requires appropriate pre-processing and thorough statistical analysis. The present work has two main goals. First, to develop a modular pipeline for preprocessing PAR-CLIP data and extracting necessary signals for further analysis. Second, to devise a novel statistical model in order to carry out inference about presence of protein binding sites based on the signals extracted in the pre-processing step.