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Emotional states are fundamentally related to cognitive processes such as attention, perception, memory and learning, and they play a very important role in helping to assess daily challenges. Thus, we need tools that measure emotions in the context of Physical Education. In this study, we adapted and tested the factor structure of the Achievement Emotion Questionnaire - Short Version (AEQ-SV) in the Italian physical education context. We administered the AEQ-SV to 867 participants (M age = 16.43 years, SD = 1.23) enrolled in Italian physical education classes to analyze the questionnaire's psychometric properties using Exploratory Structural Equation Modeling (ESEM), an internal reliability analysis, and a criterion validity analysis. The AEQ-SV subscales consisted of eight emotions and 32 items. Reliability analyses revealed acceptable fit indices and adequate temporal stability. Linear regression analysis showed that positive emotions positively predicted academic performance, while negative emotions negatively predicted academic performance. These results are in line with research on the original scale, and these data support the use of the AEQ-SV in the Italian educational context.

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For personalized medicine, we propose a general method of evaluating the potential performance of an individualized treatment rule in future clinical applications with new patients. We focus on rules that choose the most beneficial treatment for the patient out of two active (nonplacebo) treatments, which the clinician will prescribe regularly to the patient after the decision. We develop a measure of the individualization potential (IP) of a rule. The IP compares the expected effectiveness of the rule in a future clinical individualization setting versus the effectiveness of not trying individualization. We illustrate our evaluation method by explaining how to measure the IP of a useful type of individualized rules calculated through a new parametric interaction model of data from parallel-group clinical trials with continuous responses. Our interaction model implies a structural equation model we use to estimate the rule and its IP. We examine the IP both theoretically and with simulations when the estimated individualized rule is put into practice in new patients. Our individualization approach was superior to outcome-weighted machine learning according to simulations. We also show connections with crossover and N-of-1 trials. As a real data application, we estimate a rule for the individualization of treatments for diabetic macular edema and evaluate its IP.

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We introduce a simple diagnostic test for assessing the overall or partial goodness of fit of a linear causal model with errors being independent of the covariates. In particular, we consider situations where hidden confounding is potentially present. We develop a method and discuss its capability to distinguish between covariates that are confounded with the response by latent variables and those that are not. Thus, we provide a test and methodology for partial goodness of fit. The test is based on comparing a novel higher-order least squares principle with ordinary least squares. In spite of its simplicity, the proposed method is extremely general and is also proven to be valid for high-dimensional settings. Supplementary materials for this article are available online.

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BACKGROUND: Generating synthetic patient data is crucial for medical research, but common approaches build up on black-box models which do not allow for expert verification or intervention. We propose a highly available method which enables synthetic data generation from real patient records in a privacy preserving and compliant fashion, is interpretable and allows for expert intervention. METHODS: Our approach ties together two established tools in medical informatics, namely OMOP as a data standard for electronic health records and Synthea as a data synthetization method. For this study, data pipelines were built which extract data from OMOP, convert them into time series format, learn temporal rules by 2 statistical algorithms (Markov chain, TARM) and 3 algorithms of causal discovery (DYNOTEARS, J-PCMCI+, LiNGAM) and map the outputs into Synthea graphs. The graphs are evaluated quantitatively by their individual and relative complexity and qualitatively by medical experts. RESULTS: The algorithms were found to learn qualitatively and quantitatively different graph representations. Whereas the Markov chain results in extremely large graphs, TARM, DYNOTEARS, and J-PCMCI+ were found to reduce the data dimension during learning. The MultiGroupDirect LiNGAM algorithm was found to not be applicable to the problem statement at hand. CONCLUSION: Only TARM and DYNOTEARS are practical algorithms for real-world data in this use case. As causal discovery is a method to debias purely statistical relationships, the gradient-based causal discovery algorithm DYNOTEARS was found to be most suitable.

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Growing evidence implicates systemic inflammation in the loss of structural brain integrity in natural ageing and disorder development. Chronic stress and glucocorticoid exposure can potentiate inflammatory processes and may also be linked to neuronal atrophy, particularly in the hippocampus and the human neocortex. To improve understanding of emerging maladaptive interactions between stress and inflammation, this study examined evidence for glucocorticoid- and inflammation-mediated neurodegeneration in healthy mid-aged adults. N = 169 healthy adults (mean age = 39.4, 64.5% female) were sampled from the general population in the context of the ReSource Project. Stress, inflammation and neuronal atrophy were quantified using physiological indices of chronic stress (hair cortisol (HCC) and cortisone (HEC) concentration), systemic inflammation (interleukin-6 (IL-6), high-sensitive C-reactive protein (hs-CRP)), the systemic inflammation index (SII), hippocampal volume (HCV) and cortical thickness (CT) in regions of interest. Structural equation models were used to examine evidence for pathways from stress and inflammation to neuronal atrophy. Model fit indices indicated good representation of stress, inflammation, and neurological data through the constructed models (CT model: robust RMSEA = 0.041, robust χ2 = 910.90; HCV model: robust RMSEA <0.001, robust χ2 = 40.95). Among inflammatory indices, only the SII was positively associated with hair cortisol as one indicator of chronic stress (ß = 0.18, p < 0.05). Direct and indirect pathways from chronic stress and systemic inflammation to cortical thickness or hippocampal volume were non-significant. In exploratory analysis, the SII was inversely related to mean cortical thickness. Our results emphasize the importance of considering the multidimensionality of systemic inflammation and chronic stress, with various indicators that may represent different aspects of the systemic reaction. We conclude that inflammation and glucocorticoid-mediated neurodegeneration indicated by IL-6 and hs-CRP and HCC and HEC may only emerge during advanced ageing and disorder processes, still the SII could be a promising candidate for detecting associations between inflammation and neurodegeneration in younger and healthy samples. Future work should examine these pathways in prospective longitudinal designs, for which the present investigation serves as a baseline.

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The current manuscript presents the convergence of the Dimensional Assessment of Personality Pathology (DAPP-BQ), using its short form the DAPP-90, and the Five-Factor Personality Inventory for International Classification of Diseases (ICD-11), the FFiCD, in the context of the five-factor personality model and the categorical approach of personality disorders (PDs). The current manuscript compares the predictive validity of both the FFiCD and the DAPP-90 regarding personality disorder scales and clusters. Results demonstrate a very high and meaningful convergence between the DAPP-90 and the FFiCD personality pathology models and a strong alignment with the FFM. The DAPP-90 and the FFiCD also present an almost identical predictive power of PDs. The DAPP-90 accounts for between 18% and 47%, and the FFiCD between 21% and 47% of PDs adjusted variance. It is concluded that both DAPP-90 and FFiCD questionnaires measure strongly similar pathological personality traits that could be described within the frame of the FFM. Additionally, both questionnaires predict a very similar percentage of the variance of personality disorders.

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PURPOSE: To evaluate the direct and indirect pathways between cavitated carious lesions and oral health-related quality of life (OHRQoL) during the transition from childhood to adolescence. METHODS: A prospective cohort study followed an initial sample of 639 children (one to five years old), in southern Brazil for ten years. In addition to the baseline (T1), two subsequent reassessments were conducted after seven (T2) and ten years (T3). OHRQoL was measured through the Early Childhood Oral Health Impact Scale (ECOHIS) and Child Perceptions Questionnaire (CPQ8-10 and CPQ11-14), according to the age group. Dental caries was evaluated using the International Caries Detection and Assessment System (ICDAS) in the three waves. Socioeconomic variables and other factors related to oral health (frequency of toothbrushing and toothache) were also collected. Structural equation modeling was used to estimate the direct and indirect effects among the variables over ten years. RESULTS: A total of 449 and 429 children were reevaluated at T2 and T3 (positive response rates of 70.3% and 67.1%, respectively). Dental caries in T1 and T2 directly predicted the occurrence of a worse OHRQoL in the respective follow-up periods. Dental caries at T3 indirectly predicted a worse OHRQoL (T3), via toothache. Dental caries at T1 and T2 directly predicted the occurrence of dental caries at T3, while a worse OHRQoL at T2 directly predicted a worse OHRQoL at T3. CONCLUSION: Dental caries negatively impacts, directly and indirectly, the OHRQoL from early childhood to adolescence.

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Pairwise likelihood is a limited-information method widely used to estimate latent variable models, including factor analysis of categorical data. It can often avoid evaluating high-dimensional integrals and, thus, is computationally more efficient than relying on the full likelihood. Despite its computational advantage, the pairwise likelihood approach can still be demanding for large-scale problems that involve many observed variables. We tackle this challenge by employing an approximation of the pairwise likelihood estimator, which is derived from an optimization procedure relying on stochastic gradients. The stochastic gradients are constructed by subsampling the pairwise log-likelihood contributions, for which the subsampling scheme controls the per-iteration computational complexity. The stochastic estimator is shown to be asymptotically equivalent to the pairwise likelihood one. However, finite-sample performance can be improved by compounding the sampling variability of the data with the uncertainty introduced by the subsampling scheme. We demonstrate the performance of the proposed method using simulation studies and two real data applications.

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We provide a framework for motivating and diagnosing the functional form in the structural part of nonlinear or linear structural equation models when the measurement model is a correctly specified linear confirmatory factor model. A mathematical population-based analysis provides asymptotic identification results for conditional expectations of a coordinate of an endogenous latent variable given exogenous and possibly other endogenous latent variables, and theoretically well-founded estimates of this conditional expectation are suggested. Simulation studies show that these estimators behave well compared to presently available alternatives. Practically, we recommend the estimator using Bartlett factor scores as input to classical non-parametric regression methods.

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BACKGROUND: The elderly, especially those with physical disabilities, often encounter barriers that prevent them from accessing outdoor activities. Their perceptions of the convenience of accessing outdoor activities may be influenced by various factors including their health, the social context, and/or planned behavior. This study aimed to develop predictive models that identify the principal determinants of perceived convenience among this demographic, and it also examined the disparities observed between genders. METHODS: This was a cross-sectional survey of 1216 community-dwelling older people with physical disabilities in rural China. Grounded on the rehabilitation concepts and the theory of planned behavior, structural equation models integrated health and social behavior factors were constructed to predict perceived convenience of accessing outdoor activities. The standardized coefficients explained the contributions of various factors to the variance. RESULTS: The final structural models demonstrated good fit for both female and male participants. Perceptions of the convenience of accessing outdoor activities among both women and men were directly impacted by their physical functioning and their intention to participate, and indirectly by medical expenditure, subjective norms, pain, and role limitation in emotional interactions. Positive mental health was more influential for women, while men were more influenced by subjective norms. CONCLUSIONS: Structural equation models have effectively predicted the self-reported convenience of accessing outdoor activities, underscoring the importance of functional and behavioral rehabilitation. Furthermore, gender-sensitive rehabilitation programs are advised to promote engagement in outdoor activities among elderly individuals with physical disabilities.

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The public acceptance of evolution remains a contentious issue in the United States. Numerous investigations have used national cross-sectional studies to examine the factors associated with the acceptance or rejection of evolution. This analysis uses a 33-year longitudinal study that followed the same 5000 public-school students from grade 7 through midlife (ages 45-48) and is the first to do so in regard to evolution. A set of structural equation models demonstrate the complexity and changing nature of influences over these three decades. Parents and local influences are strong during the high school years. The combination of post-secondary education and occupational and family choices demonstrate that the 15 years after high school are the switchyards of life.

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Anaerobic ammonium oxidation (ANAMMOX)-mediated system is a cost-effective green nitrogen removal process. However, there are few examples of successful application of this advanced wastewater denitrification process in wastewater treatment plants, and the understanding of how to implement anaerobic ammonia oxidation process in full-scale is still limited. In this study, it was found that the abundance of anaerobic ammonia-oxidizing bacteria (AnAOB) in the two livestock wastewater plants named J1 and J2, respectively, showed diametrically opposed trends of waxing and waning with time. The microbial communities of the activated sludge in the two plants at different time were sampled and analyzed by high-throughput sequencing of 16S rRNA genes. Structural equation models (SEMs) were used to reveal the key factors affecting the realization of the ANAMMOX. Changes in the concentration of dissolved oxygen and C/N had a significant effect on the relative abundance of anaerobic ammonia oxidation bacteria (AnAOB). The low concentration of DO (0.2∼0.5 mg/L) could inhibit the activity of nitrifying bacteria (NOB) to achieve partial oxidation of ammonia nitrogen and provide sufficient substrate for the growth of AnAOB, similar to the CANON (Completely Autotrophic Nitrogen removal Over Nitrite). Unlike CANON, heterotrophic denitrification is also a particularly critical part of the livestock wastewater treatment, and a suitable C/N of about 0.6 could reduce the competition risk of heterotrophic microorganisms to AnAOB and ensure a good ecological niche for AnAOB. Based on the results of 16S rRNA and microbial co-occurrence networks, it was discovered that microorganisms in the sludge not only had a richer network interaction, but also achieved a mutually beneficial symbiotic interaction network among denitrifying bacteria (Pseudomonas sp., Terrimonas sp., Dokdonella sp.), AnAOB (Candidatus Brocadia sp.) at DO of 0.2∼0.5 mg/L and C/N of 0.6. Among the top 20 in abundance of genus level, AnAOB had a high relative abundance of 27.66%, followed by denitrifying bacteria of 3.67%, AOB of 0.64% and NOB of 0.26%, which is an essential indicator for the emergence of an AnAOB-dominated nitrogen removal cycle. In conclusion, this study highlights the importance of dissolved oxygen and C/N regulation by analyzing the mechanism of ANAMMOX sludge extinction and growth in two plants under anthropogenic regulation of AnAOB in full-scale wastewater treatment systems.

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INTRODUCTION: We investigated the association of inflammatory mechanisms with markers of Alzheimer's disease (AD) pathology and rates of cognitive decline in the AD spectrum. METHODS: We studied 296 cases from the Deutsches Zentrum für Neurodegenerative Erkrankungen Longitudinal Cognitive Impairment and Dementia Study (DELCODE) cohort, and an extension cohort of 276 cases of the Alzheimer's Disease Neuroimaging Initiative study. Using Bayesian confirmatory factor analysis, we constructed latent factors for synaptic integrity, microglia, cerebrovascular endothelial function, cytokine/chemokine, and complement components of the inflammatory response using a set of inflammatory markers in cerebrospinal fluid. RESULTS: We found strong evidence for an association of synaptic integrity, microglia response, and cerebrovascular endothelial function with a latent factor of AD pathology and with rates of cognitive decline. We found evidence against an association of complement and cytokine/chemokine factors with AD pathology and rates of cognitive decline. DISCUSSION: Latent factors provided access to directly unobservable components of the neuroinflammatory response and their association with AD pathology and cognitive decline.

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A sense of Purpose in Life is an important aspect of ageing well which is related to older adult's social relationships. Social network types and the different sources of support they provide are theorized here as a pathway to maintaining a sense of purpose as we age. The study utilized a population sample from the 2016 and 2020 NZ Health, Work, and Retirement (NZHWR) longitudinal survey waves; N = 2869 (mean age of 65.82 years (SD = 6.40). A structural equation model investigated the relationship between Social Network Type and Purpose in Life and the mediating role of Social Support. The final model had a good fit to the data and explained 33.7% of the variance in Purpose in Life. Private, Self-Contained and Family Dependent network types (2016) were negatively related to Purpose in Life 4 years later (2020). Support from sense of Attachment, Reassurance of Worth, Reliable Alliance and Social Integration played a mediating role between Social Network type and Purpose in Life. These findings highlight the importance of social networks in maintaining a sense of purpose among older adults and highlight pathways for the types of networks, and kinds of social support they offer, which contribute to a sense of purpose in life. They underscore the importance of social support for the well-being of older adults and highlight the need to consider the quality and type of social networks and support they provide, when designing interventions to enhance well-being.

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The composition of soil organic carbon and its stability mechanism are the key to understanding the terrestrial carbon sink capacity. The stability of soil organic carbon in a karst ecosystem greatly affects the soil carbon fixation capacity. In order to understand the impact of human activities on the stability of soil organic carbon in karst areas, the karst valley area of Zhongliang Mountain in Chongqing was selected as an example, and soil samples of four typical land use modes (mixed forest, bamboo forest, grassland, and cultivated land) were collected in layers to analyze the total organic carbon (TOC) and heavy fraction organic carbon (HFOC). The distribution characteristics of light fraction organic carbon (LFOC), labile organic carbon (LOC), and recalcitrant organic carbon (ROC) were analyzed quantitatively by using a structural equation model to provide basic data for soil carbon sink assessment and soil quality protection in karst areas. The results showed that the organic carbon components under different land use patterns in karst areas had obvious surface accumulation, and the content of organic carbon components in the surface layer was 1.2 times that in the bottom layer. Except for LFOC, the content of other organic carbon components was the highest in the mixed forest, followed by that in the bamboo forest and wasteland, with the lowest in cultivated land. Mixed forest ω(TOC) content was the highest, 42.5 g·kg-1, followed by that of bamboo forest (36.6 g·kg-1) and grassland (18.7 g·kg-1), and cultivated land content was the lowest, 13.4 g·kg-1. The soil organic carbon content of cultivated land was 68.5%, 63.5%, and 28.3% lower than that of mixed forest, bamboo forest, and grassland, respectively. Mixed forest had the highest content of ω(HFOC), 21 g·kg-1, followed by those of bamboo forest (20.9 g·kg-1), grassland (18.2 g·kg-1), and cultivated land (13.5 g·kg-1). The mixed forest ω(LOC) content was the highest, 16.3 g·kg-1, followed by those of bamboo forest (14.9 g·kg-1), grassland (11.5 g·kg-1), and cultivated land (5.3 g·kg-1). Mixed forest ω (ROC) content was the highest, 25.7 g·kg-1, followed by those of bamboo forest (21.6 g·kg-1), grassland (15.9 g·kg-1), and cultivated land (10.3 g·kg-1). The bamboo forest land ω(LFOC) content was 15.9 g·kg-1, followed by those of mixed forest (13.9 g·kg-1), grassland (7.3 g·kg-1), and cultivated land (4.9 g·kg-1). The recalcitrant organic carbon index (ROCI) was used to indicate the stability of soil organic carbon. The variation range of ROCI was 33.9%-64.5%, of which the highest was mixed forest (64.5%-66.3%), and the lowest was cultivated land (33.8%-39.6%). The ROCI of mixed forest, bamboo forest, and grassland were 1.8 times, 1.6 times, and 1.4 times that of cultivated land, respectively. Karst area ω (inert organic carbon) content and ROCI showed that human agricultural activities caused the reduction in soil organic carbon content and the destruction of soil physical structure, resulting in the accelerated decomposition and turnover rate of soil organic matter. The most important factor affecting soil stability in karst areas was soil pH. Tillage activities caused soil pH to rise, reduced soil microbial activity, and were not conducive to the accumulation of the inert organic carbon and soil organic carbon pool in the soil.

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Human activities simultaneously alter nutrient levels, habitat structure, and levels of parasitism. These activities likely have individual and joint impacts on food webs. Furthermore, there is particular concern that nutrient additions and changes to habitat structure might exacerbate the size of epidemics and impacts on host density. We used a well-studied zooplankton-fungus host-parasite system and experimental whole water column enclosures to factorially manipulate nutrient levels, habitat structure (specifically: mixing), and presence of parasites. Nutrient addition increased infection prevalence, density of infected hosts, and total host density. We hypothesized that nutrients, mixing, and parasitism were linked in multiple ways, including via their combined effects on phytoplankton (resource) abundance, and we used structural equation modeling to disentangle these pathways. In the absence of the parasite, both nutrients and mixing increased abundance of phytoplankton, whereas host density was negatively related to phytoplankton abundance, suggesting a mixture of bottom-up and top-down control of phytoplankton. In the presence of the parasite, nutrients still increased phytoplankton abundance but mixing no longer did, and there was no longer a significant relationship between host density and phytoplankton. This decoupling of host-resource dynamics may have resulted from reduced grazing due to illness-mediated changes in feeding behavior. Overall, our results show that the impact of one human activity (e.g., altered habitat structure) might depend on other human impacts (e.g., parasite introduction). Fortunately, carefully designed experiments and analyses can help tease apart these multifaceted relationships, allowing us to understand how human activities alter food webs, including interactions between hosts and their parasites and resources.

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Introduction: The objective of this study was to investigate the bi-directional associations between experienced and witnessed gender-based harassment (GBH) on the one hand, and depressive symptoms and psychological treatment on the other, in an occupational setting. GBH are behaviors that derogate, demean, or humiliate an individual based on his or her gender. Methods: The analyses were based on data from the Swedish Longitudinal Occupational Survey of Health at 2018 (T1) and 2020 (T2), including 6,679 working participants (60.3% women) with a majority in the age range of 45-64. Using cross-lagged structural equational models, we analyzed experienced and witnessed GBH in relation to depressive symptoms and having received psychological treatment (talked to a counselor or psychological professional) over time. Results: Our results showed that neither experienced nor witnessed GBH was prospectively associated with depressive symptoms or psychological treatment over two years. Both higher levels of depressive symptoms (ß = 0.002, p ≤ 0.001) and having received psychological treatment (ß = 0.013, p = 0.027) weakly predicted experiences of GBH over time. Having received psychological treatment was furthermore weakly associated with witnessed GBH (ß = 0.019, p = 0.012). Discussion: In conclusion, the hypothesized associations between exposure to GBH and mental health outcomes were not statistically significant, while a weak reverse association was noted. More research addressing bidirectional associations between GBH and mental health outcomes are needed.

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The i-DREAMS project established a 'Safety Tolerance Zone (STZ)' to maintain operators within safe boundaries through real-time and post-trip interventions, based on the crucial role of the human element in driving behavior. This paper aims to model the inter-relationship among driving task complexity, operator and vehicle coping capacity, and crash risk. Towards that aim, data from 80 drivers, who participated in a naturalistic driving experiment carried out in three countries (i.e., Belgium, Germany, and Portugal), resulting in a dataset of approximately 19,000 trips were collected and analyzed. The exploratory analysis included the development of Generalized Linear Models (GLMs) and the choice of the most appropriate variables associated with the latent variables "task complexity" and "coping capacity" that are to be estimated from the various indicators. In addition, Structural Equation Models (SEMs) were used to explore how the model variables were interrelated, allowing for both direct and indirect relationships to be modeled. Comparisons on the performance of such models, as well as a discussion on behaviors and driving patterns across different countries and transport modes, were also provided. The findings revealed a positive relationship between task complexity and coping capacity, indicating that as the difficulty of the driving task increased, the driver's coping capacity increased accordingly, (i.e., higher ability to manage and adapt to the challenges posed by more complex tasks). The integrated treatment of task complexity, coping capacity, and risk can improve the behavior and safety of all travelers, through the unobtrusive and seamless monitoring of behavior. Thus, authorities should utilize a data system oriented towards collecting key driving insights on population level to plan mobility and safety interventions, develop incentives for road users, optimize enforcement, and enhance community building for safe traveling.

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To be effective in the prevention of adolescent problem gambling, it is fundamental to enhance knowledge about the antecedents of gambling problem severity and the mechanisms through which these dimensions are related to problematic gambling behavior. This study aimed at testing how selected cognitive (correct knowledge of gambling and gambling-related cognitive distortions) and affective (positive economic perception of gambling and expectation and enjoyment and arousal towards gambling) variables are related to gambling frequency and gambling problem severity. Problem gambling was conceptualized as Gambling Disorder symptoms according to the last edition of the DSM. Participants were 447 Italian high school students (68% males, mean age = 16.8, SD = 0.84). Structural Equation Models (SEMs) conducted with adolescent gamblers attested two indirect effects from knowledge to problem gambling: One through gambling-related cognitive distortions and one through gambling frequency. Overall, results confirmed that adolescent problem gambling is a complex phenomenon explained by multiple and different factors. Practical implications for preventive efforts are discussed.

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Measuring trace element concentrations in tissue can be a valuable approach to monitor animal health status. Temporal variation in the absorption, transport, and storage of elements between different tissues can, however, complicate the assessment of element-health relationships. Here, we measured concentrations of selected essential (copper (Cu), zinc (Zn), selenium (Se)) and non-essential (arsenic (As), cadmium (Cd), lead (Pb)) trace elements within blood, liver, kidney, and hair of fallow deer (Dama dama; N=20) and red deer (Cervus elaphus; N=21). Using multivariate regression and structural equation models, we estimated direct and indirect linkages between tissue-specific trace element profiles and long- (body condition) and short-term (serum protein biomarkers for acute inflammation, infection, and malnutrition) health indicators. Trace element concentrations varied markedly and were weakly correlated among tissues, with the exception of Se. After accounting for sex- and site-differences in trace element concentrations, body condition of red deer was directly, and positively, associated to trace element status in liver and hair, but not in kidney. For both deer species, trace element status in blood was directly linked to serum protein status with an indirect positive association to deer body condition. For fallow deer, no direct association between trace element status and body condition was detected in any of the tissues, possibly because of elemental homeostasis, and because all individuals were in good clinical health. This study shows that hair can serve as an effective, non-invasive, biomarker in deer health assessments, yet, to fully uncover trace element-health relationships a variety of sample matrices is preferred.