Vattenkraftens ska förses med moderna miljövillkor i linje med miljöbalken. Ett av underlagen som föreslås vara nödvändigt inför prövningen är kännedom var det finns strömmande vatten och olika typer av habitat, något som inte finns i alla avrinningsområden. Arbetets syfte var därför att testa och utvärdera metoder som med begränsat underlag kan användas för att identifiera lax- och öringshabitat samt utreda sträckornas habitatskvaliteten.  För att genomföra detta genomfördes först en lutningsanalys där vattendragets strömsträckor identifierades genom att utifrån lantmäteriets höjdmodell, bestämma lutningen i vattendraget. Därefter bestämdes beskuggningsgraden vid strömsträckorna genom att studera flygbilder följt av parametrarna vattenhastighet, bredd och djup beräknades med MSB:s hydrauliska modell. På detta applicerades trout habitat score och laxhabitatklass, två bedömningssystem som används för att kvalitetsbestämma habitat utifrån fiskens habitatspreferenser för ovannämnda parametrar. Den predikterade habitatskvaliteten validerades sedan mot redan kända habitat som karterats i fält då vattendraget biotopkarterades.  Med lutningsanalysen identifierades strömsträckor där samtliga kända habitat som karterats som bra eller mycket bra identifierades. Metoden fungerade således bra för att identifiera strömmande vatten. Det finns dock svagheter eftersom metoden inte nödvändigtvis säger något direkt om strömsträckornas habitatskvalitet, men tillsammans med andra metoder så som lokalkännedom kan borde den vara till nytta att på ett enkelt sätt få en bra bild över utbredningen av vattendragets strömsträckor. När habitatskvaliteten beräknades vid strömsträckorna underskattades i de flesta fall. Vad detta beror på går inte att svara på utan vidare undersökning, men det finns mycket som pekar på att det är till följd av att den hydrauliska modellens rumsliga skala samt kalibrering. Det finns således stora osäkerheter med att använda MSB:s hydrauliska modell i habitatkarteringssyfte. / Hydropower is vital for Sweden. Not only does it provide Sweden with around 40% of the annual electricity production and supply the electric grid with system services, but the energy is also to be concerned as renewable with no greenhouse gas emissions. However, the hydropower plants and its dams create environmental problems for the aquatic life in the lake and streams. Moreover, little to no measures have been implemented as most powerplants come from a time where the concern and requirements for environmental mitigation measures were considered. Sweden’s hydropower is therefore to apply for new water permits in line with the Swedish environmental code, likely to result in many powerplants having to implement mitigation measures to lower their impact on the aquatic life. In this work, data on river habitats must compiled, however, the extent to which this data exists varies between different river basins. In river basins where the level of this kind of knowledge is low there is a need for cost-effective ways to investigate this matter.  The aim of the thesis was to test and evaluate methods that can be used to identify in stream habitats for trout and salmon parr and assess the quality of these habitats using data which is highly available for many river basins.  First a slope analysis was conducted in GIS, to identify rapids in the study area. The method is based on calculating the average slope along the river using the national terrain model. Once rapids were identified, aerial footage was studied to determine the rate of shadow cast on the watercourse and a free to use hydraulic model was used to calculate water velocity, depth, and top width at the rapid locations. Later, the above-mentioned parameters were combined using two methods, trout habitat score and salmon habitat class, to rate the rapids’ functionality as habitat for salmon and trout parr. Finally, the habitat location and quality of the identified rapids were validated against already known habitats, mapped with conventional habitat mapping methods.  Using the slope analysis, all known habitats were identified. However, there were a few uncertainties as several river stretches, not mapped as habitat, also were identified. These wrongly identified rapids were for the most part to be regarded as moderately flat and could be excluded using aerial photographs. When it comes to the predicted habitat quality, it can be concluded that it generally was underestimated when compared to the habitat quality from the habitat mapping. The reasons for this were not fully investigated in this thesis, however much points towards weaknesses related to the hydraulic model as it primarily was hydraulic parameters such as depth and top width that was underestimated.  The thesis conclude that the slope analysis is a strong tool when it comes to locating in stream habitats using sparse data and has good potential as a screening tool, but one must be aware of the method’s short comings. The hydraulic model on the other hand, shouldn’t be used in its original form to investigate habitat quality.

<p><strong>Background:</strong> A better application of evidence-based available therapies and optimal patient care are suggested to have a positive association with patient outcomes for cardiovascular disease (CVD) patients. Electronic integration of care tested in the Computerization of Medical Practices for the Enhancement of Therapeutic Effectiveness (COMPETE) Π study showed that a shared electronic decision-support system to support the primary care of diabetes improved the process of care and some clinical markers of the quality of diabetes care. On the basis of COMPETE Π trial, COMPETE Ш study showed that older adults at increased risk of cardiovascular events, if connected with their family physicians and other providers via an electronic network sharing an intensive, individualized cardiovascular tracking, advice and support program, enhanced their process of care – using a process composite score to lower their cardiovascular risk more than those in conventional care. However, results of the effect of intervention on composite process and clinical outcomes were not similar – there was no significant effect on clinical outcomes.</p> <p><strong>Objectives:</strong> Our objectives were to investigate the robustness of the results based the commonly used statistical models using COMPETE III dataset and explore the validity of the surrogate process composite score using a correlation analysis between the clinical outcomes and process composite score.</p> <p><strong>Methods:</strong> Generalized estimating equations (GEE) were used as a primary statistical model in this study. Three patient-level statistical methods (simple linear regression, fixed-effects regression, and mixed-effects regression) and two center-level statistical approaches (center-level fixed-effects model and center-level random-effects model) were compared to reference GEE model in terms of the robustness of the results – magnitude, direction and statistical significance of the estimated effects on the change of process composite score / on-target clinical composite score. GEE was also used to investigate thecorrelation between the clinical outcomes and surrogate process composite scores.</p> <p><strong>Results:</strong> All six statistical models used in this study produced robust estimates of intervention effect. No significant association between cardiovascular events and on-target clinical composite score and individual component of on-target clinical composite score were found between the intervention group and control group. However, blood pressure, LDL cholesterol, and psychosocial index are significant predictors of cardiovascular events. Process composite score can both predict the cardiovascular events and clinical improvement, but the results were not statistically significant- possibly due to the small number of events. However, the process composite score was significantly associated with the on-target clinical composite score.</p> <p><strong>Conclusions:</strong> We concluded that all five analytic models yielded similar robust estimation of intervention effect comparing to the reference GEE model. The relatively smaller estimate effects in the center-level fixed-effects model suggest that the within-center variation should be considered in the analysis of multicenter RCTs. Process composite score may serve as a good predictor for CVD outcomes.</p> / Master of Science (MSc)

Polygenic risk scores are scores used in precision medicine in order to assess an individual's risk of having a certain quantitative trait based on his or her genetics. Previous works have shown that machine learning, namely Gradient Boosted Regression Trees, can be successfully applied to calibrate the weights of the risk score to improve its predictive power in a target population. Neural networks are a very powerful class of machine learning algorithms that have demonstrated success in various elds of genetics, and in this work, we examined the predictive power of a polygenic risk score that uses neural networks to perform the weight calibration. Using a single neural network, we were able to obtain prediction R2 of 0.234 and 0.074 for height and BMI, respectively. We further experimented with changing the dimension of the input features, using ensembled models, and varying the number of splits used to train the models in order to obtain a nal prediction R2 of 0.242 for height and 0.0804 for BMI, achieving a relative improvement of 1.26% in prediction R2 for height. Furthermore, we performed extensive analysis of the behaviour of the neural network-calibrated weights. In our analysis, we highlighted several potential drawbacks of using neural networks, as well as machine learning algorithms in general when performing the weight calibration, and o er several suggestions for improving the consistency and performance of machine learning-calibrated weights for future research. / Thesis / Master of Science (MSc)

Background: South Asians are at an increased risk of developing dysglycemia during and after pregnancy. In pregnant women, dysglycemia often develops in the form of gestational diabetes mellitus (GDM), which may predispose their newborns to adverse health outcomes through abnormal cord blood insulin levels. However, reasons for the elevated risk of dysglycemia in South Asians have not been extensively studied. Genetic factors may contribute to the heritability of GDM and abnormal cord blood insulin levels in South Asians. Objectives: The objectives of this thesis were to test the association of: 1) A type 2 diabetes polygenic risk score with GDM in South Asian pregnant women from the South Asian Birth Cohort (START); 2) maternal and newborn insulin-based polygenic risk scores with cord blood insulin and glucose/insulin ratio in South Asian newborns from START Methods: Three polygenic risk scores were created to test their association with participant data (N=1012) from START. GDM was defined using cut-offs established by the Born in Bradford cohort of South Asian women. The type 2 diabetes polygenic risk score was created in 832 START mothers and included 35,274 independent variants. The maternal and newborn insulin-based polygenic risk scores were created in 604 START newborns and included 1128017 independent variants. Univariate and multiple logistic and linear regression models were used to test the associations between the polygenic risk scores and dysglycemia outcomes. Results: The type 2 diabetes polygenic risk score was associated with GDM in both univariate (OR: 2.00, 95% CI: 1.46-2.75, P<0.001), and multivariable models (OR: 1.81, 95% CI: 1.30-2.53, P<0.001). The maternal insulin-based polygenic risk score was not associated with cord blood insulin or cord glucose/insulin ratio. However, the newborn insulin-based polygenic risk score was associated with cord blood insulin in a multivariable model adjusted for maternal insulin-based polygenic risk score (β = 0.036, 95% CI: 0.002 – 0.069; P=0.038 among other factors. Conclusion: A type 2 diabetes polygenic risk score and a newborn insulin-based polygenic risk score may be associated with maternal and newborn dysglycemia. / Thesis / Master of Science (MSc) / Background: South Asians are approximately two times more at risk for developing gestational diabetes mellitus (GDM) compared to white Caucasians. Genetic factors may contribute to this elevated risk. Polygenic risk scores (PRSs), which combine the effects of multiple disease loci and variants associated with the disease into one variable could be useful in further understanding how GDM develops in South Asians. Methods: Data from the South Asian Birth Cohort (START) was used to test the association of three PRSs with the outcomes of interest. Results: The type 2 diabetes PRS was independently associated with GDM. The insulin-based maternal PRS was not associated with cord blood insulin but the insulin-based newborn PRS was independently associated with cord blood insulin. However, neither the insulin-based maternal nor newborn PRS was associated with cord blood glucose/insulin ratio. Conclusion: The PRSs suggests a possible genetic component, which contributes to abnormal glycemic status development in South Asian mothers and their newborns.

Yes / Background: National guidelines emphasize healthy eating to promote wellbeing and prevention of non-communicable diseases. The perceived healthiness of food is determined by many factors affecting food intake. A positive perception of healthy eating has been shown to be associated with greater diet quality. Internet-based methodologies allow contact with large populations. Our present study aims to design and a short nutritional perception questionnaire, to be used as a screening tool for assessing nutritional status, and to predict an optimal level of personalisation in nutritional advice delivered via the Internet. Methods: Data from all participants who were screened and then enrolled into the Food4Me proof-of-principle study (n=2369) were used to determine the optimal items for inclusion in a novel screening tool, the Nutritional Perception Screening Questionnaire-9 (NPSQ9). Exploratory and confirmatory factor analyses were performed on anthropometric and biochemical data and on dietary indices acquired from participants who had completed the Food4Me dietary intervention (n=1153). Baseline and intervention data were analysed using linear regression and linear mixed regression, respectively. Results: A final model with 9 NPSQ items was validated against the dietary intervention data. NPSQ9 scores were inversely associated with BMI (β=-0.181, p<0.001) and waist circumference (Β=-0.155, p<0.001), and positively associated with total carotenoids (β=0.198, p<0.001), omega-3 fatty acid index (β=0.155, p<0.001), Healthy Eating Index (HEI) (β=0.299, p<0.001) and Mediterranean Diet Score (MDS) (β=0. 279, p<0.001). Findings from the longitudinal intervention study showed a greater reduction in BMI and improved dietary indices among participants with lower NPSQ9 scores. Conclusions: Healthy eating perceptions and dietary habits captured by the NPSQ9 score, based on 9 questionnaire items, were associated with reduced body weight and improved diet quality. Likewise, participants with a lower score achieved greater health improvements than those with higher scores, in response to personalised advice, suggesting that NPSQ9 may be used for early evaluation of nutritional status and to tailor nutritional advice. / European Union’s Seventh Framework Programme for 23 research, technological development and demonstration (grant agreement no. 265494). "la Caixa" Banking Foundation through a grant.

Titel: Koldioxidutsläpp och Finansiell Prestanda inom Energisektorn   Nivå: Examensarbete på Grundnivå (kandidatexamen) i ämnet företagsekonomi    Författare: Jesper Nilsson &amp; David Wahlqvist   Handledare:  Jan Svanberg   Datum: 2024 - Maj    Syfte: Syftet med studien är att undersöka hur förändringen i koldioxidutsläpp från företag inom energisektorn påverkar deras finansiella prestationer. Genom att fokusera på koldioxidutsläpp som en indikator på miljöprestationer belyses dess effekter på marknadsvärde och företagens ekonomiska prestation.   Metod: Kvantitativ data hämtades för 50 publika energiföretag i Europa för åren 2020–2022 från databasen Refinitiv Eikon. Datamaterialet analyserades sedan med hjälp av Pearsons korrelationstest, VIF-test samt multipla regressionsanalyser genom statistikprogrammet IBM SPSS.    Resultat och slutsats: Resultaten från multipel regressionsanalys visar att ökningar i koldioxidutsläpp (ΔCO2) korrelerar svagt positivt med ROA och ROE, vilket indikerar en förbättring i dessa finansiella nyckeltal. Detta resultat är motsatt till den ursprungliga hypotesen, som förväntade en negativ korrelation. Koefficienten för ΔCO2 var 0,091 för ROA och 0,254 för ROE, båda statistiskt signifikanta. För Tobins Q var sambandet mellan ΔCO2 och finansiell prestation inte signifikant. Dessa oväntade fynd kan delvis förklaras av de ekonomiska effekterna av miljöinvesteringar, som på kort sikt kan öka kostnaderna utan att omedelbart öka finansiella vinster.   Studiens bidrag: Studien ifrågasätter rådande antaganden genom att identifiera en svag positiv korrelation mellan ökade CO2-utsläpp och ekonomisk prestanda. Den understryker vikten av sektorspecifik forskning och ger nya insikter som kan hjälpa beslutsfattare, företagsledare och investerare att bättre bedöma risker och möjligheter inom energisektorn.    Förslag till framtida forskning: Framtida forskning bör utföra mer omfattande longitudinella studier för att förstå de långsiktiga effekterna av förändringar i CO2-utsläpp på finansiell prestanda inom energisektorn. Det vore också intressant att jämföra detta samband med andra sektorer för att se om liknande mönster uppträder. Genom att inkludera fler ekonomiska och miljömässiga faktorer kan forskare få en mer detaljerad bild av hur dessa faktorer samspelar och påverkar företagens prestanda.    Nyckelord: Koldioxidutsläpp, finansiell prestation, energisektorn, ESG-score, ROA, ROE, Tobins Q / Title: Carbon dioxide Emissions and Financial Performance in the Energy sector   Level: Student thesis, final assignment for bachelor’s degree in business administration    Author:  Jesper Nilsson &amp; David Wahlqvist   Supervisor:  Jan Svanberg   Date: 2024 - May    Aim: The purpose of the study is to investigate how changes in carbon emissions from companies in the energy sector affect their financial performance. By focusing on carbon emissions as an indicator of environmental performance, the effects on market value and the companies' economic performance can be highlighted.   Method: Quantitative data was obtained for 50 public energy companies in Europe for the years 2020-2022 from the Refinitiv Eikon database. The data was then analyzed using Pearson’s correlation test, VIF-test, and multiple regression analyses through the statistical program IBM SPSS.    Result and conclusion: The results from the multiple regression analysis show that increases in carbon emissions (ΔCO2) correlates weakly positively with ROA and ROE, indicating an improvement in these financial key figures. This result is contrary to the initial hypothesis, which expected a negative correlation. The coefficient for ΔCO2 was 0.091 for ROA and 0.254 for ROE, both statistically significant. For Tobin’s Q, the relationship between ΔCO2 and financial performance was not significant. These unexpected findings can partly be explained by the economic effects of environmental investments, which in the short term can increase costs without immediately translating into financial gains.   Contribution of the thesis: The study questions prevailing assumptions by identifying a weak positive correlation between increased CO2 emissions and economic performance. It underscores the importance of sector-specific research and provides new insights that can help policymakers, business leaders, and investors better assess risks and opportunities within the energy sector.    Suggestions for future research: Future research should conduct more comprehensive longitudinal studies to understand the long-term effects of changes in CO2 emissions on financial performance within the energy sector. It would also be interesting to compare this relationship with other sectors to see if similar patterns emerge. By including a broader set of economic and environmental factors, researchers can gain a more nuanced and detailed understanding of how these factors interact and impact companies' performance.    Keywords: Carbon emissions, financial performance, energy sector, ESG-score, ROA, ROE, Tobin’s Q

This dissertation investigates novel statistical approaches for causal effect estimation in observational settings, where controlled experimentation is infeasible and confounding is the main hurdle in estimating causal effect. As such, deconfounding constructs the main subject of this dissertation, that is (i) to restore the covariate balance between treatment groups and (ii) to attenuate spurious correlations in training data to derive valid causal conclusions that generalize. By incorporating ideas from representation learning, adversarial matching, generative causal estimation, and invariant risk modeling, this dissertation establishes a causal framework that balances the covariate distribution in latent representation space to yield individualized estimations, and further contributes novel perspectives on causal effect estimation based on invariance principles. The dissertation begins with a systematic review and examination of classical propensity score based balancing schemes for population-level causal effect estimation, presented in Chapter 2. Three causal estimands that target different foci in the population are considered: average treatment effect on the whole population (ATE), average treatment effect on the treated population (ATT), and average treatment effect on the overlap population (ATO). The procedure is demonstrated in a naturalistic driving study (NDS) to evaluate the causal effect of cellphone distraction on crash risk. While highlighting the importance of adopting causal perspectives in analyzing risk factors, discussions on the limitations in balance efficiency, robustness against high-dimensional data and complex interactions, and the need for individualization are provided to motivate subsequent developments. Chapter 3 presents a novel generative Bayesian causal estimation framework named Balancing Variational Neural Inference of Causal Effects (BV-NICE). Via appealing to the Robinson factorization and a latent Bayesian model, a novel variational bound on likelihood is derived, explicitly characterized by the causal effect and propensity score. Notably, by treating observed variables as noisy proxies of unmeasurable latent confounders, the variational posterior approximation is re-purposed as a stochastic feature encoder that fully acknowledges representation uncertainties. To resolve the imbalance in representations, BV-NICE enforces KL-regularization on the respective representation marginals using Fenchel mini-max learning, justified by a new generalization bound on the counterfactual prediction accuracy. The robustness and effectiveness of this framework are demonstrated through an extensive set of tests against competing solutions on semi-synthetic and real-world datasets. In recognition of the reliability issue when extending causal conclusions beyond training distributions, Chapter 4 argues ascertaining causal stability is the key and introduces a novel procedure called Risk Invariant Causal Estimation (RICE). By carefully re-examining the relationship between statistical invariance and causality, RICE cleverly leverages the observed data disparities to enable the identification of stable causal effects. Concretely, the causal inference objective is reformulated under the framework of invariant risk modeling (IRM), where a population-optimality penalty is enforced to filter out un-generalizable effects across heterogeneous populations. Importantly, RICE allows settings where counterfactual reasoning with unobserved confounding or biased sampling designs become feasible. The effectiveness of this new proposal is verified with respect to a variety of study designs on real and synthetic data. In summary, this dissertation presents a flexible causal inference framework that acknowledges the representation uncertainties and data heterogeneities. It enjoys three merits: improved balance to complex covariate interactions, enhanced robustness to unobservable latent confounders, and better generalizability to novel populations. / Doctor of Philosophy / Reasoning cause and effect is the innate ability of a human. While the drive to understand cause and effect is instinct, the rigorous reasoning process is usually trained through the observation of countless trials and failures. In this dissertation, we embark on a journey to explore various principles and novel statistical approaches for causal inference in observational studies. Throughout the dissertation, we focus on the causal effect estimation which answers questions like ``what if" and ``what could have happened". The causal effect of a treatment is measured by comparing the outcomes corresponding to different treatment levels of the same unit, e.g. ``what if the unit is treated instead of not treated?". The challenge lies in the fact that i) a unit only receives one treatment at a time and therefore it is impossible to directly compare outcomes of different treatment levels; ii) comparing the outcomes across different units may involve bias due to confounding as the treatment assignment potentially follows a systematic mechanism. Therefore, deconfounding constructs the main hurdle in estimating causal effects. This dissertation presents two parallel principles of deconfounding: i) balancing, i.e., comparing difference under similar conditions; ii) contrasting, i.e., extracting invariance under heterogeneous conditions. Chapter 2 and Chapter 3 explore causal effect through balancing, with the former systematically reviews a classical propensity score weighting approach in a conventional data setting and the latter presents a novel generative Bayesian framework named Balancing Variational Neural Inference of Causal Effects(BV-NICE) for high-dimensional, complex, and noisy observational data. It incorporates the advance deep learning techniques of representation learning, adversarial learning, and variational inference. The robustness and effectiveness of the proposed framework are demonstrated through an extensive set of experiments. Chapter 4 extracts causal effect through contrasting, emphasizing that ascertaining stability is the key of causality. A novel causal effect estimating procedure called Risk Invariant Causal Estimation(RICE) is proposed that leverages the observed data disparities to enable the identification of stable causal effects. The improved generalizability of RICE is demonstrated through synthetic data with different structures, compared with state-of-art models. In summary, this dissertation presents a flexible causal inference framework that acknowledges the data uncertainties and heterogeneities. By promoting two different aspects of causal principles and integrating advance deep learning techniques, the proposed framework shows improved balance for complex covariate interactions, enhanced robustness for unobservable latent confounders, and better generalizability for novel populations.

Although previous studies on structural equation modeling (SEM) have indicated that the second-order latent growth model (SOLGM) is a more appropriate approach to longitudinal intervention effects, its application still requires researchers to collect at least three-wave data (e.g. randomized pretest, posttest, and follow-up design). However, in some circumstances, researchers can only collect two-wave data for resource limitations. With only two-wave data, the SOLGM can not be identified and researchers often choose alternative SEM models to fit two-wave data. Recent studies show that the two-wave longitudinal common factor model (2W-LCFM) and latent change score model (2W-LCSM) can perform well for comparing latent change between groups. However, there still lacks empirical evidence about how accurately these two-wave models can estimate the group effects of latent change obtained by three-wave SOLGM (3W-SOLGM). The main purpose of this dissertation, therefore, is trying to examine to what extent the fixed effects of the tree-wave SOLGM can be recovered from the parameter estimates of the two-wave LCFM and LCSM given different simulation conditions. Fundamentally, the supplementary study (study 2) using three-wave LCFM was established to help justify the logistics of different model comparisons in our main study (study 1). The data generating model in both studies is 3W-SOLGM and there are in total 5 simulation factors (sample size, group differences in intercept and slope, the covariance between the slope and intercept, size of time-specific residual, change the pattern of time-specific residual). Three main types of evaluation indices were used to assess the quality of estimation (bias/relative bias, standard error, and power/type I error rate). The results in the supplementary study show that the performance of 3W-LCFM and 3W-LCSM are equivalent, which further justifies the different models' comparison in the main study. The point estimates for the fixed effect parameters obtained from the two-wave models are unbiased or identical to the ones from the three-wave model. However, using two-wave models could reduce the estimation precision and statistical power when the time-specific residual variance is large and changing pattern is heteroscedastic (non-constant). Finally, two real datasets were used to illustrate the simulation results. / Doctor of Philosophy / To collect and analyze the longitudinal data is a very important approach to understand the phenomenon of development in the real world. Ideally, researchers who are interested in using a longitudinal framework would prefer collecting data at more than two points in time because it can provide a deeper understanding of the developmental processes. However, in real scenarios, data may only be collected at two-time points. With only two-wave data, the second-order latent growth model (SOLGM) could not be used. The current dissertation compared the performance of two-wave models (longitudinal common factor model and latent change score model) with the three-wave SOLGM in order to better understand how the estimation quality of two-wave models could be comparable to the tree-wave model. The results show that on average, the estimation from two-wave models is identical to the ones from the three-wave model. So in real data analysis with only one sample, the point estimate by two-wave models should be very closed to that of the three-wave model. But this estimation may not be as accurate as it is obtained by the three-wave model when the latent variable has large variability in the first or last time point. This latent variable is more likely to exist as a statelike construct in the real world. Therefore, the current study could provide a reference framework for substantial researchers who could only have access to two-wave data but are still interested in estimating the growth effect that supposed to obtain by three-wave SOLGM.

This dissertation documents efforts to evaluate metabolic variables and the fecal microbiome in adult horses fed grass hay. In the first study, eight Arabian geldings limit-fed an 18% vs. 12% non-structural carbohydrate (NSC) hays in a cross-over design during two 28-day periods were included to evaluate the influence of grass hay NSC on serum insulin and plasma glucose concentrations. Serum insulin concentrations was higher in geldings fed the 18% NSC hay; however, this difference was only detected on day 7 and none of the geldings developed hyperinsulinemia. Blood glucose concentrations did not differ between hay groups. The second and third studies were extensions of the first and were conducted to use denaturing gradient gel electrophoresis (DGGE) and real-time PCR in evaluating the effect of forage carbohydrates on equine fecal bacteria diversity and abundance, respectively. Fecal microbiomes were similar (80.5-87.9%) between geldings. The abundance of bacteria belonging to the Firmicutes phylum increased (p = 0.02) in the feces of geldings fed 12% NSC hay (mean 8.06 range [8.03-8.11] log10 copies/g feces) compared to the feces of the same geldings when fed the 18% NSC hay (7.97 [7.97-7.98] log₁₀ copies/g feces). The Firmicutes (43.7%), Verrucomicrobia (4.1%), Proteobacteria (3.8%), and Bacteroidetes (3.7%) phyla dominated the fecal microbiomes. This work was the first to report the presence of the Actinobacteria, Cyanobacteria, and TM7 phyla in the equine fecal or gut microbiome. There was a high abundance (38%) of unclassified bacterial sequences in the gelding fecal microbiome. In the fourth study, 5 overweight adult mixed-breed mares and 5 adult mixed-breed mares in moderate condition, limit-fed a grass hay, were used to evaluate the effect of body condition on diet digestibility, plasma and fecal volatile fatty acid (VFA) concentrations, and fecal bacterial abundance. Hay, fecal, and blood samples were taken daily for 4 days after a 10 day adaptation period. A difference in hay digestibility, fecal VFA concentration, or bacterial abundance was not detected between overweight mares and mares in moderate condition. Plasma acetate, a product of microbial fermentation of fiber, was higher in the overweight mare group. / Ph. D.

This dissertation presents a quantitative study of the effects of multiple aspects of working conditions on teacher effectiveness as measured by value-added scores and student perceptions of teaching. The data were derived from the 2009-2010 Teacher Working Condition Survey and Student Perception Survey in Measures of Effective Teaching (MET) Project. Using the structural equation modeling and other related methods, several models of teacher effectiveness were estimated. The results supported that instruction and classroom related working conditions at school played important role in effective teaching and student achievement gains in English language arts and mathematics. It was found that, after controlling for teachers' education degree and experience, instructional practice support had significant effect on teachers' value-added scores. Moreover, Classroom autonomy and support for student conduct management were found to have indirect effect on teacher value-added score mediated through the students' perceptions of teaching. In addition, student perceptions of teaching was found to be significantly worse in high-need schools than schools serving fewer minority students or students from low-incoming families, but teacher value-added score was not significantly different between the high versus low needs schools. The findings of the study significantly contributed to a better understanding of the effects of working environment and how these are related to teacher performance. The study has both theoretical and practical significance; it provided critical evidence that can be used by policy makers to promote teachers' performance, especially in high-needs schools. / Ph. D.