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Introduction: A subset of people with Parkinson's disease (PD) develop dementia faster than others. We aimed to profile PD cognitive subtypes at risk of dementia based on their rate of cognitive decline. Method: Latent class mixed models stratified subtypes in Parkinson's Progression Markers Initiative (PPMI) (N = 770) and ICICLE-PD (N = 212) datasets based on their decline in the Montreal Cognitive Assessment over at least 4 years. Baseline demographic and cognitive data at diagnosis were compared between subtypes to determine their clinical profile. Results: Four subtypes were identified: two with stable cognition, one with steady decline, and one with rapid decline. Performance on Judgement of Line Orientation, but not category fluency, was associated with a steady decline in the PPMI dataset, and deficits in category fluency, but not visuospatial function, were associated with a steady decline in the ICICLE-PD dataset. Discussion: People with PD susceptible to cognitive decline demonstrate unique clinical profiles at diagnosis, although this differed between cohorts. Highlights: Four cognitive subtypes were revealed in two Parkinson's disease samples.Unique profiles of cognitive impairment were related to cognitive decline.Judgement of Line Orientation/category fluency predictive of steady decline.Global deficits related to rapid cognitive decline and increased dementia risk.
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Sleep disturbances are common in individuals with posttraumatic stress disorder. Exercise interventions are a promising approach in the treatment of sleep disorders, but little is known about the efficacy of exercise interventions for sleep disturbances associated with posttraumatic stress disorder. A total of 40 individuals with posttraumatic stress disorder were randomized to six sessions of either high-intensity interval training or low-to-moderate-intensity training, administered within 12 days. Sleep quality was assessed over 24 days from baseline to post with the Pittsburgh Sleep Quality Index, a sleep log, and a waist-worn actigraphy. Analyses revealed that, regardless of group allocation, Pittsburgh Sleep Quality Index score improved significantly by 2.28 points for high-intensity interval training and 1.70 points for low-to-moderate-intensity training (d = 0.56 for high-intensity interval training; 0.49 for low-to-moderate-intensity training) over time, while there were no significant changes in any sleep log or actigraphy measure. Analysis of a subsample of those affected by clinically significant sleep disturbances (n = 24) revealed a significant time effect with no difference between exercise interventions: Pittsburgh Sleep Quality Index improved significantly by 2.65 points for high-intensity interval training and 2.89 points for low-to-moderate-intensity training (d = 0.53 for high-intensity interval training; 0.88 for low-to-moderate-intensity training), and actigraphy measure of wake after sleep onset was reduced significantly by 14.39 minutes for high-intensity interval training and 6.96 minutes for low-to-moderate-intensity training (d = 0.47 for high-intensity interval training; 0.11 for low-to-moderate-intensity training) from baseline to post. In our pilot study, we found an improvement in sleep quality from pre- to post-assessment. There were no significant differences between exercise groups. Further studies are needed to investigate whether the found time effects reflect the exercise intervention or unrelated factors.
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Background: Acute DeBakey type I aortic dissection is associated with high morbidity and mortality. Little is known regarding the role of leukocyte trajectory in prognosis. Methods: We included adult acute DeBakey type I aortic dissection patients with emergency frozen elephant trunk and total arch replacement in 2 cardiovascular centers (2020-2022). We used latent class mixed model to group patients according to their leukocyte patterns from hospital admission to the first 5 days after surgery. We investigated the association of leukocyte trajectory and 30-day and latest follow-up mortality (October 31, 2023), exploratorily analyzing the effects of ulinastatin treatment on outcome. Results: Of 255 patients included, 3 distinct leukocyte trajectories were identified: 196 in group I (decreasing trajectory), 34 in group II (stable trajectory), and 25 in group III (rising trajectory). Overall, 30-day mortality was 25 (9.8%), ranging from 8.2% (16/196) in group I, 8.8% (3/34) in group II, to 24.0% (6/25) in group III (P for trend = .036). Group III was associated with higher mortality both at 30 days (adjusted hazard ratio, 3.260; 95% CI, 1.071-9.919; P = .037) and at the last follow-up (adjusted hazard ratio, 2.840; 95% CI, 1.098-7.345; P = .031) compared with group I. Conclusions: Distinct and clinically relevant groups can be identified by analyzing leukocyte trajectories, and a rising trajectory was associated with higher short-term and midterm mortality.
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This paper aims to evaluate the statistical association between exposure to air pollution and forced expiratory volume in the first second (FEV1) in both asthmatic and non-asthmatic children and teenagers, in which the response variable FEV1 was repeatedly measured on a monthly basis, characterizing a longitudinal experiment. Due to the nature of the data, an robust linear mixed model (RLMM), combined with a robust principal component analysis (RPCA), is proposed to handle the multicollinearity among the covariates and the impact of extreme observations (high levels of air contaminants) on the estimates. The Huber and Tukey loss functions are considered to obtain robust estimators of the parameters in the linear mixed model (LMM). A finite sample size investigation is conducted under the scenario where the covariates follow linear time series models with and without additive outliers (AO). The impact of the time-correlation and the outliers on the estimates of the fixed effect parameters in the LMM is investigated. In the real data analysis, the robust model strategy evidenced that RPCA exhibits three principal component (PC), mainly related to relative humidity (Hmd), particulate matter with a diameter smaller than 10 µm (PM10) and particulate matter with a diameter smaller than 2.5 µm (PM2.5).
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It remains uncertain whether causal structure prediction can improve comprehension in Chinese sentences and whether the position of the headword mediates the prediction effect. We conducted an experiment to explore the effect of causal prediction and headword position in Chinese sentence reading. Participants were asked to read sentences containing causal connectives with their eye movements recorded. In the experiment, we manipulated the causal structure of the sentence and the position of the headword. We found a promoting effect of causal structure on first-pass reading time and a hindering impact on total reading time. However, the effect was not mediated by the headword position. The results show that causal syntactic prediction facilitated early-stage processing and increased the integration cost in the late stage of Chinese sentence processing. These findings also support the constraint-based approach, which suggests an isolation between semantic and syntactic processing.
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Compreensão , Movimentos Oculares , Leitura , Semântica , Humanos , Movimentos Oculares/fisiologia , Compreensão/fisiologia , Feminino , Masculino , Adulto Jovem , China , Idioma , Adulto , População do Leste AsiáticoRESUMO
MicroRNA-seq data is produced by aligning small RNA sequencing reads of different miRNA transcript isoforms, called isomiRs, to known microRNAs. Aggregation to microRNA-level counts discards information and violates core assumptions of differential expression (DE) methods developed for mRNA-seq data. We establish miRglmm, a DE method for microRNA-seq data, that uses a generalized linear mixed model of isomiR-level counts, facilitating detection of miRNA with differential expression or differential isomiR usage. We demonstrate that miRglmm outperforms current DE methods in estimating DE for miRNA, whether or not there is significant isomiR variability, and simultaneously provides estimates of isomiR-level DE.
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This paper explores learned-context neural networks. It is a multi-task learning architecture based on a fully shared neural network and an augmented input vector containing trainable task parameters. The architecture is interesting due to its powerful task adaption mechanism, which facilitates a low-dimensional task parameter space. Theoretically, we show that a scalar task parameter is sufficient for universal approximation of all tasks, which is not necessarily the case for more common architectures. Empirically it is shown that, for homogeneous tasks, the dimension of the task parameter may vary with the complexity of the tasks, but a small task parameter space is generally viable. The task parameter space is found to be well-behaved, which simplifies workflows related to updating models as new data arrives, and learning new tasks with the shared parameters are frozen. Additionally, the architecture displays robustness towards datasets where tasks have few data points. The architecture's performance is compared to similar neural network architectures on ten datasets, with competitive results.
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During the drug development process, testing potency plays an important role in the quality assessment required for the manufacturing and marketing of biologics. Due to multiple operational and biological factors, higher variability is usually observed in bioassays compared with physicochemical methods. In this paper, we discuss different sources of bioassay variability and how this variability can be statistically estimated. In addition, we propose an algorithm to estimate the variability of reportable results associated with different numbers of runs and their corresponding OOS rates under a given specification. Numerical experiments are conducted on multiple assay formats to elucidate the empirical distribution of bioassay variability.
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Background: Paranoia is a spectrum of fear-related experiences that spans diagnostic categories and is influenced by social and cognitive factors. The extent to which social media and other types of media use are associated with paranoia remains unclear. Objective: We aimed to examine associations between media use and paranoia at the within- and between-person levels. Methods: Participants were 409 individuals diagnosed with schizophrenia spectrum or bipolar disorder. Measures included sociodemographic and clinical characteristics at baseline, followed by ecological momentary assessments (EMAs) collected 3 times daily over 30 days. EMA evaluated paranoia and 5 types of media use: social media, television, music, reading or writing, and other internet or computer use. Generalized linear mixed models were used to examine paranoia as a function of each type of media use and vice versa at the within- and between-person levels. Results: Of the 409 participants, the following subgroups reported at least 1 instance of media use: 261 (63.8%) for using social media, 385 (94.1%) for watching TV, 292 (71.4%) for listening to music, 191 (46.7%) for reading or writing, and 280 (68.5%) for other internet or computer use. Gender, ethnoracial groups, educational attainment, and diagnosis of schizophrenia versus bipolar disorder were differentially associated with the likelihood of media use. There was a within-person association between social media use and paranoia: using social media was associated with a subsequent decrease of 5.5% (fold-change 0.945, 95% CI 0.904-0.987) in paranoia. The reverse association, from paranoia to subsequent changes in social media use, was not statistically significant. Other types of media use were not significantly associated with paranoia. Conclusions: This study shows that social media use was associated with a modest decrease in paranoia, perhaps reflecting the clinical benefits of social connection. However, structural disadvantage and individual factors may hamper the accessibility of media activities, and the mental health correlates of media use may further vary as a function of contents and contexts of use.
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Transtorno Bipolar , Avaliação Momentânea Ecológica , Transtornos Paranoides , Esquizofrenia , Mídias Sociais , Humanos , Feminino , Masculino , Transtorno Bipolar/psicologia , Transtorno Bipolar/epidemiologia , Adulto , Esquizofrenia/epidemiologia , Esquizofrenia/diagnóstico , Mídias Sociais/estatística & dados numéricos , Pessoa de Meia-Idade , Transtornos Paranoides/psicologia , Transtornos Paranoides/epidemiologiaRESUMO
The increasing burden of Alzheimer's disease (AD) emphasizes the need for effective diagnostic and therapeutic strategies. Despite available treatments targeting amyloid beta (Aß) plaques, disease-modifying therapies remain elusive. Early detection of mild cognitive impairment (MCI) patients at risk for AD conversion is crucial, especially with anti-Aß therapy. While plasma biomarkers hold promise in differentiating AD from MCI, evidence on predicting cognitive decline is lacking. This study's objectives were to evaluate whether plasma protein biomarkers could predict both cognitive decline in non-demented individuals and the conversion to AD in patients with MCI. This study was conducted as part of the Korean Longitudinal Study on Cognitive Aging and Dementia (KLOSCAD), a prospective, community-based cohort. Participants were based on plasma biomarker availability and clinical diagnosis at baseline. The study included MCI (n = 50), MCI-to-AD (n = 21), and cognitively unimpaired (CU, n = 40) participants. Baseline plasma concentrations of six proteins-total tau (tTau), phosphorylated tau at residue 181 (pTau181), amyloid beta 42 (Aß42), amyloid beta 40 (Aß40), neurofilament light chain (NFL), and glial fibrillary acidic protein (GFAP)-along with three derivative ratios (pTau181/tTau, Aß42/Aß40, pTau181/Aß42) were analyzed to predict cognitive decline over a six-year follow-up period. Baseline protein biomarkers were stratified into tertiles (low, intermediate, and high) and analyzed using a linear mixed model (LMM) to predict longitudinal cognitive changes. In addition, Kaplan-Meier analysis was performed to discern whether protein biomarkers could predict AD conversion in the MCI subgroup. This prospective cohort study revealed that plasma NFL may predict longitudinal declines in Mini-Mental State Examination (MMSE) scores. In participants categorized as amyloid positive, the NFL biomarker demonstrated predictive performance for both MMSE and total scores of the Korean version of the Consortium to Establish a Registry for Alzheimer's Disease Assessment Packet (CERAD-TS) longitudinally. Additionally, as a baseline predictor, GFAP exhibited a significant association with cross-sectional cognitive impairment in the CERAD-TS measure, particularly in amyloid positive participants. Kaplan-Meier curve analysis indicated predictive performance of NFL, GFAP, tTau, and Aß42/Aß40 on MCI-to-AD conversion. This study suggests that plasma GFAP in non-demented participants may reflect baseline cross-sectional CERAD-TS scores, a measure of global cognitive function. Conversely, plasma NFL may predict longitudinal decline in MMSE and CERAD-TS scores in participants categorized as amyloid positive. Kaplan-Meier curve analysis suggests that NFL, GFAP, tTau, and Aß42/Aß40 are potentially robust predictors of future AD conversion.
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Doença de Alzheimer , Peptídeos beta-Amiloides , Biomarcadores , Disfunção Cognitiva , Proteínas tau , Humanos , Disfunção Cognitiva/sangue , Disfunção Cognitiva/diagnóstico , Biomarcadores/sangue , Doença de Alzheimer/sangue , Doença de Alzheimer/diagnóstico , Masculino , Feminino , Idoso , Estudos Longitudinais , Peptídeos beta-Amiloides/sangue , Proteínas tau/sangue , Pessoa de Meia-Idade , Progressão da Doença , Proteínas de Neurofilamentos/sangue , Proteína Glial Fibrilar Ácida/sangue , Estudos ProspectivosRESUMO
This paper presents a model specification for group comparisons regarding a functional trend over time within a trial and learning across a series of trials in intensive binary longitudinal eye-tracking data. The functional trend and learning effects are modeled using by-variable smooth functions. This model specification is formulated as a generalized additive mixed model, which allowed for the use of the freely available mgcv package (Wood in Package 'mgcv.' https://cran.r-project.org/web/packages/mgcv/mgcv.pdf , 2023) in R. The model specification was applied to intensive binary longitudinal eye-tracking data, where the questions of interest concern differences between individuals with and without brain injury in their real-time language comprehension and how this affects their learning over time. The results of the simulation study show that the model parameters are recovered well and the by-variable smooth functions are adequately predicted in the same condition as those found in the application.
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Groundwater contamination by nitrate and sulfate in mining areas is a significant challenge. Consequently, the inputs sources of these contaminants and their evolution have received considerable attention, with the knowledge gained critical for improved management of water quality. This study integrated data on multiple stable isotopes and water chemistry data and a Bayesian isotope mixing model to investigate the relative contributions of inputs sources of sulfate and nitrate sources to bodies of water in a karst mining area in southwest China. The outcomes indicated that hydrochemical component in the water bodies of the study area is mainly derived from the dissolution of silicate rocks, carbonate rocks and sulfate minerals as well as the oxidation of sulfides. The human and agricultural wastewater, soil nitrogen, and fertilizers were the predominant inputs sources of nitrate to the mine water environment; the predominant inputs sources of sulfide were mineral oxidation, evaporite dissolution, atmospheric deposition, and sewage. Groundwater is mainly recharged from atmospheric precipitation, and surface water is closely hydraulically connected to groundwater. Nitrogen and oxygen isotope composition and water chemistry indicative of nitrification dominate the nitrogen cycle in the study area. The oxidation of pyrite and bacterial sulfate reduction (SRB) had no significant impact on the stable isotopes of groundwater. The results of this study demonstrate the inputs of different sources to nitrate and sulfate in karst mines and associated transformation processes. The results of this study can assist in the conservation of groundwater quality in mining areas and can act as a reference for future related studies.
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This study proposes a systematic approach to address ergonomic factors, including physical, environmental and psychosocial aspects, in solving assembly line balancing problems. A three-stage framework is developed, starting with determining weights for ergonomic risk assessment methods using the interval-valued spherical fuzzy analytical hierarchy process. In the second stage, a fuzzy logic model for integrated ergonomic risk assessment is constructed based on these weights, and the integrated ergonomic risk score is determined. In the third stage, a mathematical model is formulated to minimise the cycle time while balancing the ergonomic risk level. A case study conducted in a wire harness factory validated the effectiveness of the proposed approach, showing a 10-11% improvement in line efficiency and a 12-25% enhancement in ergonomic risk balancing performance. These findings underscore the potential benefits of implementing this approach, which can significantly improve occupational safety and overall performance.
This article presents a practical and systematic approach for enhancing ergonomic conditions in assembly lines. The proposed approach aims to balance the ergonomic risk level while minimising the cycle time by considering physical, environmental and psychosocial risk factors. A case study conducted in a wire harness factory demonstrated significant improvements in balancing ergonomic risks, highlighting the real-world applicability of this research.
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INTRODUCTION: Systemic inflammation and endothelial dysfunction are potentially modifiable factors implicated in Alzheimer's disease (AD), which offer potential therapeutic targets to slow disease progression. METHODS: We investigated the relationship between baseline circulating levels of inflammatory (TNF-α, IL-1ß) and endothelial cell markers (VCAM-1, ICAM-1, E-selectin) and 18-month cognitive decline (ADAS-cog12) in 266 mild-to-moderate AD patients from the NILVAD study. We employed individual growth models to examine associations, potential mediation, and interaction effects while adjusting for confounders. RESULTS: The average increase in ADAS-cog12 scores over all patients was 8.1 points in 18 months. No significant association was found between the markers and the rate of cognitive decline. Mediation analysis revealed no mediating role for endothelial cell markers, and interaction effects were not observed. DISCUSSION: Our results do not support the role of systemic inflammation or endothelial dysfunction in progression in persons with AD.
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Joint association analysis of multiple traits with multiple genetic variants can provide insight into genetic architecture and pleiotropy, improve trait prediction, and increase power for detecting association. Furthermore, some traits are naturally high-dimensional, e.g., images, networks, or longitudinally measured traits. Assessing significance for multitrait genetic association can be challenging, especially when the sample has population sub-structure and/or related individuals. Failure to adequately adjust for sample structure can lead to power loss and inflated type 1 error, and commonly used methods for assessing significance can work poorly with a large number of traits or be computationally slow. We developed JASPER, a fast, powerful, robust method for assessing significance of multitrait association with a set of genetic variants, in samples that have population sub-structure, admixture, and/or relatedness. In simulations, JASPER has higher power, better type 1 error control, and faster computation than existing methods, with the power and speed advantage of JASPER increasing with the number of traits. JASPER is potentially applicable to a wide range of association testing applications, including for multiple disease traits, expression traits, image-derived traits, and microbiome abundances. It allows for covariates, ascertainment, and rare variants and is robust to phenotype model misspecification. We apply JASPER to analyze gene expression in the Framingham Heart Study, where, compared to alternative approaches, JASPER finds more significant associations, including several that indicate pleiotropic effects, most of which replicate previous results, while others have not previously been reported. Our results demonstrate the promise of JASPER for powerful multitrait analysis in structured samples.
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Pleiotropia Genética , Humanos , Estudo de Associação Genômica Ampla/métodos , Fenótipo , Expressão Gênica/genética , Simulação por Computador , Modelos Genéticos , Locos de Características Quantitativas , Polimorfismo de Nucleotídeo ÚnicoRESUMO
BACKGROUND: Heatstroke (HS), associated with the early activation of the coagulation system and frequently presenting with thrombocytopenia, poses a significant healthcare challenge. Understanding the relationship of nadir platelet count (PLT) within 24 h for adverse outcomes in HS patients is crucial for optimizing management strategies. METHODS: This retrospective cohort study, conducted in six tertiary care hospitals, involved patients diagnosed with HS and admitted to the emergency departments. The primary and secondary outcomes included in-hospital mortality and various acute complications, respectively, with logistic regression models utilized for assessing associations between nadir PLT and outcomes. The PLT count change curve was described using a generalized additive mixed model (GAMM), with additional analyses involving body temperature (BT) at 2 h also conducted. RESULTS: Of the 152 patients included, 19 (12.5%) died in-hospital. The median nadir PLT within 24 h was 99.5 (58.8-145.0)*10^9/L. Notably, as a continuous variable (10*10^9/L), nadir PLT was significantly associated with in-hospital mortality (OR 0.76; 95% CI 0.64-0.91; P = 0.003) and other adverse outcomes like acute kidney and liver injury, even after adjustment for confounders. GAMM revealed a more rapid and significant PLT decline in the non-survival group over 24 h, with differential PLT dynamics also observed based on BT at 2 h. CONCLUSIONS: Nadir PLT within 24 h were tied to in-hospital mortality and various adverse outcomes in HS patients. Early effective cooling measures demonstrated a positive impact on these associations, underscoring their importance in patient management.
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Golpe de Calor , Mortalidade Hospitalar , Humanos , Estudos Retrospectivos , Contagem de Plaquetas , Feminino , Masculino , Golpe de Calor/sangue , Golpe de Calor/mortalidade , Golpe de Calor/terapia , Golpe de Calor/complicações , Pessoa de Meia-Idade , Prognóstico , Idoso , Trombocitopenia/sangue , Serviço Hospitalar de Emergência , AdultoRESUMO
BACKGROUND: Improved knowledge of factors that influence treatment engagement could help treatment providers and systems better engage patients. The present study used machine learning to explore associations between individual- and neighborhood-level factors, and SUD treatment engagement. METHODS: This was a secondary analysis of the Global Appraisal of Individual Needs (GAIN) dataset and United States Census Bureau data utilizing random forest machine learning and generalized linear mixed modelling. Our sample (N = 15,873) included all people entering SUD treatment at GAIN sites from 2006 to 2012. Predictors included an array of demographic, psychosocial, treatment-specific, and clinical measures, as well as environment-level measures for the neighborhood in which patients received treatment. RESULTS: Greater odds of treatment engagement were predicted by adolescent age and psychiatric comorbidity, and at the neighborhood-level, by low unemployment and high population density. Lower odds of treatment engagement were predicted by Black/African American race, and at the neighborhood-level by high rate of public assistance and high income inequality. Regardless of the degree of treatment engagement, individuals receiving treatment in areas with high unemployment, alcohol sale outlet concentration, and poverty had greater substance use and related problems at baseline. Although these differences reduced with treatment and over time, disparities remained. CONCLUSIONS: Neighborhood-level factors appear to play an important role in SUD treatment engagement. Regardless of whether individuals engage with treatment, greater loading on social determinants of health such as unemployment, alcohol sale outlet density, and poverty in the therapeutic landscape are associated with worse SUD treatment outcomes.
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Aprendizado de Máquina , Características de Residência , Determinantes Sociais da Saúde , Transtornos Relacionados ao Uso de Substâncias , Humanos , Transtornos Relacionados ao Uso de Substâncias/terapia , Transtornos Relacionados ao Uso de Substâncias/epidemiologia , Masculino , Feminino , Adulto , Adolescente , Estados Unidos/epidemiologia , Adulto Jovem , Pessoa de Meia-Idade , Fatores Socioeconômicos , Desemprego/estatística & dados numéricos , Aceitação pelo Paciente de Cuidados de Saúde/estatística & dados numéricos , Aceitação pelo Paciente de Cuidados de Saúde/psicologia , Transtornos Mentais/terapia , Transtornos Mentais/epidemiologia , Pobreza , Fatores EtáriosRESUMO
The main objective of mapping quantitative trait loci (QTL) and genome-wide association studies (GWAS) is to identify and locate QTLs on the genome. Estimating the sizes of QTL is equally important as identifying the QTLs. The size of a QTL is often measured by the QTL variance, or the proportion of phenotypic variance explained by the QTL, known as the QTL heritability. The reported QTL heritability is biased upward for small-sized QTLs estimated from small samples, especially in GWAS with a very small P-value threshold accommodating to Bonferroni correction for multiple tests. The phenomenon is called the Beavis effect. Methods of correcting the Beavis effect have been developed for additive effect models. Corresponding methods are not available for QTLs with more than one effect, such as QTLs including dominance and other genetic effects. In this study, we developed explicit formulas for estimating the variances and heritability for QTL with multiple effects. We also developed a method to remove nuisance parameters via an annihilator matrix. Finally, biases in estimated QTL variances caused by the Beavis effect are investigated and corrected. The new method is demonstrated by analyzing the 1000 grain weight (KGW) trait in a hybrid rice population.
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Estudo de Associação Genômica Ampla , Modelos Genéticos , Oryza , Locos de Características Quantitativas , Oryza/genética , Estudo de Associação Genômica Ampla/métodos , Variação Genética , Fenótipo , Mapeamento Cromossômico/métodosRESUMO
Wild boar (Sus scrofa), an abundant species across Europe, is often subjected to management in agro-ecosystems in order to control population size, or to scare them away from agricultural fields to safeguard crop yields. Wild boar management can benefit from a better understanding on changes in its space use across the diel cycle (i.e., diel space use) in relation to variable hunting pressures or other factors. Here, we estimate wild boar diel space use in an agro-ecosystem in central Belgium during four consecutive "growing seasons" (i.e., April-September). To achieve this, we fit generalized additive mixed models (GAMMs) to camera trap data of wild boar aggregated over 1-h periods. Our results reveal that wild boar are predominantly nocturnal in all of the hunting management zones in Meerdaal, with activity peaks around sunrise and sunset. Hunting events in our study area tend to take place around sunrise and sunset, while non-lethal human activities occur during sunlight hours. Our GAMM reveals that wild boar use different areas throughout the diel cycle. During the day, wild boar utilized areas in the centre of the forest, possibly to avoid human activities during daytime. During the night, they foraged near (or in) agricultural fields. A post hoc comparison of space use maps of wild boar in Meerdaal revealed that their diurnal and nocturnal space use were uncorrelated. We did not find sufficient evidence to prove that wild boar spatiotemporally avoid hunters. Finally, our work reveals the potential of GAMMs to model variation in space across 24-h periods from camera trap data, an application that will be useful to address a range of ecological questions. However, to test the robustness of this approach we advise that it should be compared against telemetry-based methods to derive diel space use.