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From Sequencing to Conservation: Genomic Analysis of Three Sportfish Species in West Virginia
Get PDFSituated in the Appalachian Mountains, one of the oldest mountain ranges on Earth, West Virginia waters boast rich ichthyofauna including native sportfish species walleye (Sander vitreus), largemouth bass (Micropterus nigricans), and muskellunge (Esox masquinongy). These three species are all native to the contemporary Ohio River watershed and play a major recreational and ecological role in local fisheries. Ecologically, all three species are apex predators and play a key role in ecosystems by directly influencing local fish assemblages. As dominant apex predators all three species are highly sought after in recreational fisheries with largemouth bass being the most targeted freshwater fish among anglers in the United States, and muskellunge earning the nickname “fish of 10,000 casts” due its elusiveness to angler harvest. Genetic investigations of these sportfish species in West Virginia are limited, with only a handful of previous studies available. Noting a lack of genetic insight into these valuable sportfish species, the West Virginia Division of Natural Resources (WVDNR) employed significant sampling of sportfish populations throughout the state to establish state-wide genomic baselines to direct future management directives. Using a genotype-by-sequencing double digest restriction-site associated DNA sequencing protocol, a total of 642 sportfish samples from over 25 populations across the state were sequenced to identify genetically distinct populations, elucidate the impact of stocking non-native ancestry into native populations, quantify genomic diversity, and establish a genetic baseline foundation for each of the three species that can be incorporated into future management directives. Sub-objectives were also investigated for largemouth bass and walleye. Due to angler interest in creating a trophy fishery, Florida bass (Micropterus salmoides) presence and prevalence throughout the state was investigated using a 16 single nucleotide polymorphism (SNP) panel that was previously found to be fixed between Florida bass and northern largemouth bass to identify potential candidate populations. A total of 856 largemouth bass from 31 populations across the state were genotyped, along with known Florida bass and putative F1 hybrids to validate the panel.
Marker-assisted restoration of native walleye has previously used two microsatellite loci found to be correlative with diagnostic mitochondrial haplotypes of the native Eastern Highlands strain and introduced Great Lake strain. Difficulty with the two loci that displayed questionable diagnostic capability resulted in the need to change protocols and utilize diagnostic SNPs. Walleye of known Great Lakes and Eastern Highlands strain origins were sequenced and 57 fixed SNPs between the two strains were identified, resulting in a currently employed 2 SNP protocol being developed. The resulting panel’s diagnostic capability was compared to the microsatellite protocol, verified in diagnostic capability by comparing the 2 SNP protocol strain assignment to assignment using 42 diagnostic SNPs, and strain assignment of 1,532 walleye from 17 sampling locations across the state was assessed to determine the prevalence of the Eastern Highlands strain. The 2 SNP panel was found to be far superior to the microsatellite panel, which was only able to identify 58% of native Eastern Highlands strain broodstock in a 2-year period. The 2 SNP panel was also found to be highly effective in ancestry assignment, with almost 90% of 181 previously native walleye displaying \u3e 80% probability of being pure Eastern Highlands strain ancestry. Among sampled populations, the Kanawha and New rivers both displayed the highest prevalence of the Eastern Highlands strain with the Ohio River showing a higher introgression of the Great Lakes strain in comparison. Along these lines, genomic investigations found that the Kanawha and New rivers were more genetically similar to each other than to Ohio River populations. Eastern Highlands walleye from the Kanawha and New rivers populations displayed an ancestry absent from Eastern Highlands Ohio River individuals, indicating a divergence from historic a baseline due to introgression of the Great Lakes strain in the Ohio River that has also been observed in previous research. Inbreeding coefficients (FIS) were observed to be highest upstream in the Kanawha Falls and New River populations, indicating potential management intervention is needed to alleviate this observed inbreeding via stocking of non-related Eastern Highlands strain individuals to mitigate further potential inbreeding. Summersville Lake also had the highest frequency of the minor allele of two SNPs found to be potentially undergoing selection, indicating potential lentic and riverine habitat selection within the Eastern Highlands strain. Management of walleye should include the continued use of broodstock screening, alleviation of potential inbreeding in the New River and Kanawha Falls populations and emphasis on conservation of the Kanawha River and New River to prevent divergence observed in the Ohio River. Additionally, given concerns over angler movement of fish into native watersheds, stocking of Florida Bass and their hybrids should likely be avoided in other watersheds of West Virginia.
With the combined use of the 16 SNP panel and sequencing of known Florida bass and putative F1 hybrids, no presence of Florida bass ancestry was found in any West Virginia largemouth bass populations. A total of four genetic ancestries were found in sampled populations, with the dominant Ohio River strain ancestry being found in every population. Two of the three genetically distinct ancestries observed occurred in non-native introduced largemouth bass populations indicating these ancestries and the one discovered in the native East Lynn Lake population are a result of stocking and not natural sub-structuring of the Ohio River strain. Stocking Florida bass into the Ohio River watershed would pose a threat to native genetic diversity and is not recommended based on current results. However, if a trophy Florida bass fishery is to be established, stocking of Florida bass into introduced non-native largemouth bass reservoirs could be potentially utilized that would both conserve native genetic diversity and satisfy recreational angling opportunity.
Muskellunge populations that had been previously stocked with muskellunge of New York origin including Stonewall Jackson Lake, East Lynn Lake, Kimsey Run Lake, and the Monongahela and Buckhannon rivers all displayed introgression of the New York strain. Individuals of native origin in these populations were more similar to each other and most differentiated from native populations that show no New York strain presence, indicating a genetic shift in native muskellunge in these populations. A genetically distinct population was found in the Little Kanawha River system, including North Bend Lake, a dammed reservoir of an upstream tributary North Fork Hughes River, indicating movement of muskellunge within the Little Kanawha River system. The Little Kanawha River system ancestry also displayed the highest genomic diversity metrics and highest inbreeding coefficients, highlighting the need to further research on this unique population through the use of telemetry studies to elucidate movement of muskellunge throughout the system and identifying distinct spawning sites
End-to-End Neural Network Based Optimal Control for Asymmetric Quadrotor UAS
No full textThis thesis presents the development and evaluation of a neural network-based optimal controller for asymmetrically loaded quadrotor unmanned aerial systems (UAS). Traditional control strategies such as PID are typically designed under symmetry assumptions and often degrade in performance when faced with significant loading asymmetries. To address this, a six-degree-of-freedom quadrotor model incorporating rotor dynamics and center-of-mass offsets was developed. A trajectory optimization framework using MATLAB’s fmincon solver generated over 50,000 energy-optimal trajectories across symmetric and asymmetric conditions. These were used to train a range of feedforward neural network architectures in a full-factorial study.The best-performing controller was identified, having five hidden layers with 250 neurons per hidden layer. This decision was based on validation error and ANOVA analysis. Simulation results demonstrated that, under symmetric conditions, the neural network controller outperformed a conventionally tuned PID controller with up to 80% reduction in settling time, 70% reduction in overshoot, and 91% reduction in undershoot, while increasing total energy consumption by only 2%. Under asymmetric loading, similar transient benefits were observed at a marginal cost in energy consumption of 3%. These findings showcase improved performance under neural network based control for asymmetrically loaded quadrotor UAS. Limitations of this work include its simulation-based evaluation, lack of formal stability guarantees, and absence of hardware deployment. Future work includes hardware-in-the-loop testing, dataset refinement, stability analysis, and expanded baseline comparisons to further evaluate and extend the proposed approach
Comparative Analysis of Safety, Accuracy, Comprehensiveness, and Organization in Endodontic Patient Education Material: American Association of Endodontics (AAE) vs. AI-Generated Content.
Get PDFComparative Analysis of Safety, Accuracy, Comprehensiveness, and Organization in Endodontic Patient Education Material: American Association of Endodontics (AAE) vs. AI-Generated Content. Fabiola Salgado Reyes, DMD Introduction: With patients increasingly using AI chatbots like ChatGPT4o and Copilot, it is essential to assess whether these tools can effectively support patient understanding of complex endodontic procedures. AI-generated responses may occasionally provide inaccurate, unsafe, disorganized, or content that is not comprehended. The purpose of this study is to examine if dental experts and patients can determine if patient education materials (PEMS) were 1) generated by AI; 2) medically safe; 3) accurate; 4) can be comprehended; and 5) organized. Secondarily, the PEMS were evaluated for their readability. Methods: AAE PEMs were compared to two AI-generated PEMs (ChatGPT4o and Copilot Premium accessed via subscription from Nov 13–Dec 14, 2024) by dental experts and patients. Content included information on diagnosis, symptoms, risk factors, treatment, and prognosis. Each dental expert evaluated all three PEM. Each patient receiving endodontic therapy at West Virginia School of Dentistry who agreed to participate evaluated one randomized PEM. Both groups evaluated the PEMs for safety, accuracy, comprehensibility, and organization (0–4 scale). They also determined if each PEM was AI generated or human generated. PEMs were also evaluated for readability. Results: Dental experts did not distinguish a quality difference in safety, accuracy, organization, or comprehensibility among the 3 PEM (p\u3e0.05). Patients could not distinguish between AI and human generated PEM (p=0.666). However, Copilot PEM consistently was scored higher by patients in safety, accuracy, and comprehension (p \u3c 0.05). Conclusion: AI-generated PEMs, especially from Copilot, demonstrate strong potential as accurate, readable, and well-received alternatives to traditional materials, supporting their integration into clinical patient education
Three Essays on the Economics of Crime and Education
Get PDFThe first chapter investigates the labor market impacts of the Clean Slate Law, a policy designed to reduce barriers to entry by automating the sealing of criminal records. Using IPUMS CPS data from six states, I employ a difference-in-differences framework with individual fixed effects and an event study design to identify changes in employment outcomes. I find that the Clean Slate policy increases employment by 0.33 percentage points and weekly hours worked by 0.44 hours (approximately 24 minutes). Full-time employment rises, while self-employment declines by 0.12 percentage points, suggesting a shift from informal to more stable, formal jobs. These employment gains are primarily driven by individuals entering the labor market from unemployment, rather than by existing workers increasing their hours or shifting from part-time to full-time roles, indicating that effects operate mainly along the extensive margin. Additional analysis reveals the largest gains are found among men aged 25–34 and those without a college degree, with blue-collar industries such as manufacturing, construction, and food services showing the most significant gains.
The second chapter, coauthored with Bryan McCannon, explores the long-term consequences of being involved in the criminal justice system when young. Utilizing data from the National Longitudinal Survey of Youth, we employ Coarsened Exact Matching to mitigate selection effects. We consider teenagers who self-report being criminally engaged in 1997 and identify samples of those arrested and those not arrested who are observationally similar. We show that by 2019 being criminal-justice-involved corresponds with less employment and lower incomes. Additionally, we explore a potential mechanism human capital accumulation. We find that criminal justice involvement coincides with an increase in the likelihood of not completing high school and reduces the total number of years of education obtained. Further, human capital interruption explains most of the later-in-life income differences.
The third chapter examines the impact of an Illinois disciplinary reform policy on school suspension practices. By making high suspension rates publicly visible, the policy introduces a form of social sanction that pressures districts to adjust behavior through reputational incentives. Senate Bill 100 requires public flagging of school districts in the top 20\% for out-of-school suspension rates. Using administrative data and a sharp regression discontinuity design around the 80th percentile cutoff, I estimate the causal effect of being flagged as a high-suspension district. I find that public identification increases next-year suspension rates by 3.2 percentage points. The effect is concentrated among first-time flagged districts, with no change following mandated reporting requirements after three consecutive years. Districts just below the threshold appear to reduce suspensions to avoid bein
Leveraging Physiological Signal Activity and Self-Report Data to Assess Students’ Trust In “My Friendly Mind” App and Its Impact on Their Mental Health Knowledge: A Mixed-Method Phase 1 Clinical Trial Focusing on Depression and Attention Deficit Hyperactivity Disorder from Human Factors Standpoint.
No full textMental health issues have become a significant global public health concern, especially among younger generations. The growing number of mental health challenges, combined with limited access to quality care, makes the problem even worse. Studies reveal that over 70% of individuals worldwide in need of mental health services do not receive appropriate care. Digital health technologies have the potential to enhance mental health services by making them more accessible and affordable. Despite the increasing popularity of mental health mobile applications (mHealth), there remains a lack of robust evidence of their effectiveness and the level of user trust, particularly in areas such as depression and attention deficit hyperactivity disorder (ADHD).
This dissertation aims to deepen understanding of user engagement, trust, effectiveness, and overall perception in digital mental health apps. To address this goal, four key research questions were developed. The first research question explores the effectiveness of evidence-based mental health applications through a literature review. The second question examines user acceptance and knowledge improvement resulting from the use of the mental health app. The third question focuses on quantifying users’ physiological responses when interacting with AI-generated versus doctor-generated depression screening outcomes. The fourth question focuses on quantifying objective measures to assess users’ trust in repeated decision-making tasks of AI-generated depression treatment recommendations.
We followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines to address Research Question 1. To investigate Research Questions 2 and Research Questions 3, we designed and developed a web-based mental health application, My Friendly Mind, and implemented a complex mixed-methods Phase 1 clinical trial design. This experimental framework incorporated eye tracking and heart rate recordings, pre- and post-intervention surveys, and qualitative interviews. To address Research Question 4, we developed a task-based experimental module in Qualtrics focused on mental health scenarios and treatment recommendations. This intervention followed the same Phase 1 clinical trial framework to capture neurophysiological responses during trust-based decision-making.
We applied the Wilcoxon signed-rank test, Partial Least Squares Structural Equation Modeling (PLS-SEM), and thematic analysis to analyze data for Research Question 2. To address Research Question 3, we used the Mann-Whitney U test and Wilcoxon signed-rank test to analyze pupil diameter using MATLAB and employed Python-based models to predict trust using Galvanic Skin Response (GSR) signals. For Research Question 4, we conducted EEG signal analysis in MATLAB and used repeated-measures ANOVA to examine brain responses.
The novelty of this dissertation is to use objective measures to quantify the association between physiological signal activity and user trust in AI-generated depression diagnosis and treatment recommendations. The findings from this research will guide future app designs that enhance effectiveness, user acceptance, and address the nuances of user trust in AI within mental healthcare
Post-Training Quantization of Generative and Discriminative LSTM Text Classifiers: A Study of Calibration, Class Balance, and Robustness
No full textText classification plays a pivotal role in edge computing applications like industrial monitoring, health diagnostics, and smart assistants, where low latency and high accuracy are both key requirements. Generative classifiers, in particular, have been shown to exhibit robustness to out-of-distribution and noisy data, which is an extremely critical consideration for deployment in such real-time edge environments. However, deploying such models on edge devices faces computational and memory constraints. Post Training Quantization (PTQ) reduces model size and compute costs without retraining, making it ideal for edge deployment. In this work, we present a comprehensive comparative study of generative and discriminative Long Short Term Memory (LSTM)-based text classification models with PTQ using the Brevitas quantization library. We evaluate both types of classifier models across multiple bitwidths and assess their robustness under regular and noisy input conditions. We find that while discriminative classifiers remain robust, generative ones are more sensitive to bitwidth, calibration data used during PTQ, and input noise during quantized inference. We study the influence of class imbalance in calibration data for both types of classifiers, comparing scenarios with evenly and unevenly distributed class samples including their effect on weight adjustments and activation profiles during PTQ. Using test statistics derived from nonparametric hypothesis testing, we identify that using class imbalanced data during calibration introduces insufficient weight adaptation at lower bitwidths for generative LSTM classifiers, thereby leading to degraded performance. This study underscores the role of calibration data in PTQ and when generative classifiers succeed or fail under noise, aiding deployment in edge environments
Evaluating Osmia cornifrons as an Effective Pollinator for Agricultural Crops: An Analysis of Hair Morphology and Sex-Ratio Distorting Bacterium, Wolbachia
No full textEffective pollination relies not only on the presence of pollinators but also on their morphological adaptations and reproductive biology. In this study, I investigated both the external hair morphology and the intracellular bacterial infection status of Osmia cornifrons, a solitary bee species increasingly used in orchard pollination due to its efficiency and early seasonal activity. Using Scanning Electron Microscopy (SEM), I characterized the distribution and structure of two major hair types present on the bee\u27s body: branched and unbranched. Branched hairs were found widely distributed across the head, thorax, dorsal abdomen, and legs, and appeared to function primarily in incidental pollen collection. In contrast, unbranched hairs were found concentrated on the metasomal sterna and the inner surfaces of the legs and appear specialized for storing pollen for nest provisioning. Pollen viability assays confirmed that pollen grains collected from both hair types remained viable, as evidenced by pollen tube formation, but unbranched hairs were able to collect and deposit significantly more.
In addition to morphological analysis, I assessed the presence of the endosymbiotic bacterium Wolbachia, which is common in arthropods and known to manipulate host reproduction to create more females and thus more quickly fix the infection in the population. I screened 12 individuals from an O. cornifrons population located at the West Virginia University Organic Farm in Morgantown, WV. DNA was extracted from the abdominal tissue of 9 females and 3 males and subjected to PCR using Wolbachia-specific primers. Positive amplification was observed in 75% of tested individuals (6/9 females and 3/3 males). The relatively high infection rate and presence in both sexes suggest that Wolbachia may be established or nearing fixation in this population. Given its ability to induce parthenogenesis in haplodiploid species, the presence of Wolbachia may help explain recent observations of shifting sex ratios in this population, which historically has been male-biased.
Taken together, our findings may help contribute to a deeper understanding of the functional morphology and reproductive dynamics of O. cornifrons. These insights have important implications for the management and optimization of this species in agricultural systems, particularly in orchards requiring high-efficiency pollination and stable bee populations. Further research is warranted to explore the evolutionary origin of Wolbachia in North American populations and to assess its long-term effects on population structure, reproductive success, and pollination efficacy
Developing Consensus-based Methods for the Examination and Interpretation of Contemporary Vehicle, Architectural, and Portable Electronic Device Glasses by micro-X-Ray Fluorescence Spectrometry
No full textGlass is a trace material that is commonly encountered during investigations of violent crimes. When glass is recovered at crime scenes, it can be used to establish links between suspects, victims, and the scene itself. The most discriminatory form of analysis for glass evidence is elemental analysis, and micro-X-ray fluorescence spectrometry (µXRF) is becoming an increasingly common technique used for this purpose. Recent advances in µXRF technology, such as the introduction of silicon drift detectors (SDD) and improved polycapillary optics are promising in enhancing the capabilities for the examination of glass evidence in forensic investigations. However, along with these advances comes a need to adapt to the new technology. For example, it becomes necessary to reevaluate the standard analytical methods and comparison criteria for the latest instrumentation and improve objectivity in interpreting spectral data. Additionally, the ubiquity of cellular phones in everyday life has made them increasingly prominent in crimes. Still, the current body of research surrounding this type of glass is limited, necessitating the development of methods before it can be incorporated into forensic services.
This research aimed to address these gaps in the forensic glass analysis discipline by establishing an extensive µXRF database of glass from various sources, including vehicles, residential and commercial buildings, and cellular devices. The µXRF technique was tested through the analysis of extremely thin fragments of glass that are routinely observed in forensic casework and contemporary formulations and coatings of architectural and portable electronic device (PED) glasses. This study also proposed the development of a spectral similarity metric to increase objectivity and enable greater collaboration and data sharing among law enforcement agencies.
This project generated a physical and digital database comprising over 160 sources of contemporary soda-lime and aluminosilicate glasses with known origins of the manufacturing sources. These sources were used to create more than 2,700 glass fragments, which were all analyzed using µXRF instrumentation, and a subset was compared across multiple laboratories. The within- and between-source variations were evaluated to provide revised recommendations to the ASTM E 2926-25 Standard Test Method for Forensic Comparison of Glass Using µXRF, regarding the collection, sampling, analysis, and interpretation of glass when using µXRF with SDD detectors. The spectral comparison comprised spectral overlay, comparison intervals of selected elemental ratios, and the Similarity Contrast Angle Ratio (SCAR). The use of a modified 3s comparison criterion for soda-lime glass resulted in false exclusion rates of less than 3% and false inclusion rates of less than 0.5%. A modified 5s comparison criterion was necessary for aluminosilicate glass, resulting in false exclusion rates of less than 4% and false inclusion rates of less than 0.2%. The use of the SCAR quantitative metric also showed promise in evaluating spectral similarity, with misclassification rates of less than 3%. Since the µXRF with SDD offered improved sensitivity and precision, it enabled the analysis of smaller samples and faster acquisition times. The µXRF technique was stress-tested through the analysis of thin soda-lime glass fragments, ranging in thickness from 10 to 50 µm, to simulate cases where only minute fragments are recovered. The most substantial finding of this study was that modern µXRFs are suitable for analyzing glass as small as 10 µm, thereby expanding capabilities in casework. However, when using thin fragments, heavier elements were more challenging to detect in thinner fragments than in their full-thickness counterparts. Since these elements are the most discriminatory in soda-lime glass, forensic scientists must exercise caution when evaluating this type of sample size.
An interlaboratory study was also conducted to evaluate the performance of different µXRF instruments with eight participants across the United States. This study showed remarkable performance in discriminating between sets of different glass sources and correctly associating glass sets from a common source through spectral overlay comparisons. The use of a modified 3s comparison criterion for element ratio comparisons resulted in zero false inclusions and a false exclusion rate of less than 5%. Regardless of instrument configuration, same-source comparisons yielded low SCAR values, close to one. In contrast, different-source comparisons mostly resulted in SCAR values much greater than one, with the higher the value, the more distinctive the elemental profiles of the compared items. The level of agreement in SCAR values among participants indicated that this quantitative metric could be used to support the analyst’s opinion in a more objective manner, opening up the opportunity to share µXRF data among participants with less influence on the instrumental configurations employed. Furthermore, SCAR provided a promising proxy for the weight of the evidence when used as an input for calculating score likelihood ratios (SLRs).
This study also expanded the forensic examination of glass to modern PED glass. The findings demonstrated that the chemical and optical properties of PED glass are substantially different from those of typical soda-lime glass, requiring modifications in the methodologies. For example, the refractive index values of PED glass fell outside of the typical calibration ranges of silicon oils A, B, or C used for soda-lime and borosilicate glass. A modified method using a mixture of A-B and B-C oils is proposed here as a modification to ASTM E1967 Standard Test Method for the Automated Determination of Refractive Index of Glass Samples Using the Oil Immersion Method and a Phase Contrast Microscope. Moreover, due to inherent variations observed across a cell phone pane, specific recommendations for RI and elemental µXRF analysis are presented here for sampling, analysis, and data comparison criteria. It is recommended that 30 measurements from at least 10 fragments be collected to properly characterize the refractive index of a known sample, thereby reducing error rates. For refractive index measurements, the false exclusion and inclusion rates were less than 3 % and 9 %, respectively, when using a range overlap criterion. For µXRF measurements, using a modified 5s comparison criterion for element ratio comparisons resulted in low false exclusion and false inclusion rates (less than 4.0 % and 0.5 %, respectively). In this dataset of 45 phones from six manufacturers, the combination of refractive index and µXRF yielded a 99.9 % discrimination of glass originating from different sources. The SCAR metric was also effective in interpreting data from aluminosilicate glasses. The study provides insights into the informative elements to characterize PED glass and differentiate it from soda-lime glass.
Altogether, this study significantly expanded the knowledge base in the forensic analysis and interpretation of glass using modern µXRF technology with SDD detectors. The combination of the largest existing digital database of µXRF spectral data and interlaboratory testing enabled a comprehensive evaluation of error rates, providing a foundation for recommendations on necessary adaptations to existing standard test methods and the creation of new methods for examining newer formulations, such as aluminosilicate glasses from mobile devices
Beyond the Byproduct: Exploring Approaches for the Sustainable Valorization of Underutilized Food Streams
No full textCommercial food processing creates side-streams that are often underutilized yet hold significant potential for valorization into value-added products for consumer use. This study investigated and compared potential protein recovery and lipid removal potential from buckwheat (BW; Fagopyrum esculentum Moench), brewer’s spent grain (BSG), and coffee silverskin (CSS) using a one-step solvent defatting and a pH-shift protein extraction method. The extraction efficiency yields of extracted protein from CSS were significantly lower than all other samples (p \u3c 0.05). The defatted BW had the highest extracted protein content of all samples (p \u3c 0.05). The sizes of the proteins in the starting materials were observed using LDS-PAGE. These sizes were consistent with published values and were mostly unchanged by defatting or the pH-shift process. The presence of 13S globulins and 2S albumins was confirmed under reducing conditions. Protein extracted from all native samples had lower total free essential amino acids than the starting materials. Native CSS and extracted protein followed this trend, but in defatted CSS and respective extracted protein samples, the extracted proteins had higher free essential amino acid content than the starting materials, sometimes of twofold or greater (p \u3c 0.05). After defatting, the percentage of lipids recovered compared to the original lipid content of the samples was measured. The crude fat content of native and hexane-defatted BW flour was higher than BW flour defatted with ethanol (p \u3c 0.05). The lipid extraction efficiency of all samples ranged from 4.6 ± 1.26%–43.20 ± 14.21%. The mass of the native and defatted starting materials were recorded at each step of processing, with reasonable losses assessed. The major non-polar lipid classes in each starting material were separated using thin-layer chromatography. All samples also displayed the presence of polar lipids. When comparing starting materials, BW and BSG was the most effective for protein extraction, and BSG has the most promise for lipid removal and potential for upcycling. All the examined starting materials show promise for use in food supplementation, especially as native flours. These products are plentiful and considered underutilized or side-stream products
Adolescent Attachment and Anxiety: Examining Implicit Emotion Beliefs and Emotion Regulation as Mediators
Get PDFAnxiety is a growing issue in adolescents, with teens reporting worse and more severe symptoms in recent years (Kajastus et al., 2023; Solmi et al., 2022). There are several risk factors for developing anxiety symptoms, including cognitive, behavioral, and social factors. Effective emotion regulation (ER) has been shown to be helpful in decreasing anxiety symptom levels (Arbulu et al., 2023; Cho et al., 2019). A better understanding of precursors of ER could aid in prevention of anxiety. ER may stem from our personal beliefs about emotions (Ford et al., 2018; Schroder et al., 2015; Tamir et al., 2007) and to the attachment style one has with their parents (Blissett et al., 2006; Henschel et al., 2020). The present study examined the relationships between parental attachment, beliefs about emotions, and ER in relation to anxiety symptom levels in adolescents. This study tests several mediational pathways between these variables. It is hypothesized that differences in parental attachment will lead to different beliefs about emotions, which will influence ER and anxiety levels. Results indicated significant individual paths between variables (i.e., higher avoidant attachment with parents predicted greater emotion expression control beliefs, higher beliefs emotions should be controlled predicted greater reappraisal, greater use of reappraisal predicted lower anxiety). However, no significant indirect effects were found through the hypothesized paths. The findings of this study further research on emotion beliefs and is novel in its approach to include parental attachment as a predictor, particularly in an adolescent sample. Implications of these findings could include a different route in targeted interventions - specifically ones targeted at focusing on beliefs about the controllability of emotions, which could provide more effective application use of ER strategies like reappraisal