RepurposeDrugs: an interactive web-portal and predictive platform for repurposing mono- and combination therapies.

RepurposeDrugs (https://repurposedrugs.org/) is a comprehensive web-portal that combines a unique drug indication database with a machine learning (ML) predictor to discover new drug-indication associations for approved as well as investigational mono and combination therapies. The platform provides detailed information on treatment status, disease indications and clinical trials across 25 indication categories, including neoplasms and cardiovascular conditions. The current version comprises 4314 compounds (approved, terminated or investigational) and 161 drug combinations linked to 1756 indications/conditions, totaling 28 148 drug-disease pairs. By leveraging data on both approved and failed indications, RepurposeDrugs provides ML-based predictions for the approval potential of new drug-disease indications, both for mono- and combinatorial therapies, demonstrating high predictive accuracy in cross-validation. The validity of the ML predictor is validated through a number of real-world case studies, demonstrating its predictive power to accurately identify repurposing candidates with a high likelihood of future approval. To our knowledge, RepurposeDrugs web-portal is the first integrative database and ML-based predictor for interactive exploration and prediction of both single-drug and combination approval likelihood across indications. Given its broad coverage of indication areas and therapeutic options, we expect it accelerates many future drug repurposing projects.

The role of alpha oscillations in free- and goal-directed semantic associations.

Alpha oscillations are known to play a central role in several higher-order cognitive functions, especially selective attention, working memory, semantic memory, and creative thinking. Nonetheless, we still know very little about the role of alpha in the generation of more remote semantic associations, which is key to creative and semantic cognition. Furthermore, it remains unclear how these oscillations are shaped by the intention to "be creative," which is the case in most creativity tasks. We aimed to address these gaps in two experiments. In Experiment 1, we compared alpha oscillatory activity (using a method which distinguishes genuine oscillatory activity from transient events) during the generation of free associations which were more vs. less distant from a given concept. In Experiment 2, we replicated these findings and also compared alpha oscillatory activity when people were generating free associations versus associations with the instruction to be creative (i.e. goal-directed). We found that alpha was consistently higher during the generation of more distant semantic associations, in both experiments. This effect was widespread, involving areas in both left and right hemispheres. Importantly, the instruction to be creative seems to increase alpha phase synchronisation from left to right temporal brain areas, suggesting that intention to be creative changed the flux of information in the brain, likely reflecting an increase in top-down control of semantic search processes. We conclude that goal-directed generation of remote associations relies on top-down mechanisms compared to when associations are freely generated.

Intracranial Volume Is Driven by Both Genetics and Early Life Exposures: The SOL-INCA-MRI Study.

Intracranial volume (ICV) reflects maximal brain development and is associated with later-life cognitive abilities. We quantified ICV among first- and second-generation Hispanic and Latino adults from the Study of Latinos-Investigation of Cognitive Aging - MRI (SOL-INCA-MRI), estimated ICV heritability, and tested its associations with previously reported genetic variants, both individually and as a genetic risk score (GRS). We also estimated the association of ICV with early life environmental measures: nativity or age of immigration and parental education. The estimated heritability of ICV was 19% (95% CI, 0.1%-56%) in n=1781 unrelated SOL-INCA-MRI individuals. Four of 10 tested genetic variants were associated with ICV and an increase of 1 SD of the ICV-GRS was associated with an increase of 10.37 cm3 in the ICV (95% CI, 5.29-15.45). Compared to being born in the continental United States, immigrating to the United States at age 11 years or older was associated with 24 cm3 smaller ICV (95% CI, -39.97 to -8.06). Compared to both parents having less than high-school education, at least 1 parent completing high-school education was associated with 15.4 cm3 greater ICV (95% CI, 4.46-26.39). These data confirm the importance of early life health on brain development.

Environmental, socioeconomic, and health factors associated with gut microbiome species and strains in isolated Honduras villages.

Despite a growing interest in the gut microbiome of non-industrialized countries, data linking deeply sequenced microbiomes from such settings to diverse host phenotypes and situational factors remain uncommon. Using metagenomic data from a community-based cohort of 1,871 people from 19 isolated villages in the Mesoamerican highlands of western Honduras, we report associations between bacterial species and human phenotypes and factors. Among them, socioeconomic factors account for 51.44% of the total associations. Meta-analysis of species-level profiles across several datasets identified several species associated with body mass index, consistent with previous findings. Furthermore, the inclusion of strain-phylogenetic information modifies the overall relationship between the gut microbiome and the phenotypes, especially for some factors like household wealth (e.g., wealthier individuals harbor different strains of Eubacterium rectale). Our analysis suggests a role that gut microbiome surveillance can play in understanding broad features of individual and public health.

Clinicopathological study of otomycosis in a tertiary hospital in South-west Nigeria.

Background: Otomycosis is common in environments with hot, humid weather, and it may be challenging to manage. Objectives: To profile common clinical presentations, the pathogenic fungi, the treatment modalities with responses, and explore clinical factors associated with having positive fungal culture in Otomycosis. Methods: Retrospective review of patients with Otomycosis. Demographic and clinical parameters, otoscopic findings and mycological study results were recorded. The treatment modalities used and treatment response were summarized. Comparative statistical analyses of associated factors to positive fungal culture were performed with Chi square test, and Student's t-test, using SPSS version 22.0. Results: Total of 71 patients with M: F=1:1.8, mean age 38.5±19.8 years. Average duration of symptoms was 5.4 ±4.6 weeks; common presenting complaint was itchy ear (33.8%). Majority of patients (85.9%) had unilateral ear involvement, 50.0% applied ototopic medications before presentation, 8.5% had multiple co-morbidities. 20 patients had positive fungal culture results; common fungal isolate was Aspergillus niger 9 (45.0%).Clinical factors associated with positive culture of fungus were age, non-previous use of ototopic drugs, and presence of co-morbidity. The most common treatment was local ear debridement and use of topical antifungal creams. Majority (91.5%) of the patients responded with resolution of fungal infection. Complications rate was 8.4%. Conclusions: Otomycosis commonly present with itchy ears, the pathogenic fungi commonly being Aspergillus species. The factors associated with positive fungal culture were age, non-usage of ototopic agents and presence of co-morbidity. Treatment modality used was local debridement and topical antifungal agents, which produced favourable response in most patients.

NSAID medication mediates the causal effect of genetically predicted major depressive disorder on falls: Evidence from a Mendelian randomization study.

BACKGROUND: Increasing evidence supports that depression including major depressive disorder (MDD) is associated with an increased risk of falls. However, some studies suggest no association between MDD and falls. Therefore, the specific causal relationship whereby MDD affects the risk of falls remains elusive, and the potential mediators are unclear. METHODS: Summary-level data for MDD and falls were collected from the Genome-wide association studies (GWAS) in this study. Mendelian randomization (MR) and multivariable MR (MVMR) analyses were performed to evaluate the causal associations between MDD and falls. A Two-step MR analysis was employed to analyze the mediating effect of nonsteroidal anti-inflammatory drugs (NSAIDs) on the causal association between MDD and the risk of falls. RESULTS: Using the inverse-variance weighted (IVW) method, genetically predicted MDD was associated with an increased risk of falls (ß = 0.15, SE = 0.034; P = 1.61E-5). MVMR and two-step MR analyses demonstrated that MDD was a causal determinant of increased falls independent of body mass index (BMI), smoking initiation, and alcohol consumption and that this causal relationship was mediated by NSAID medication. LIMITATIONS: Extracted GWAS summary statistics are from European ancestry. Stratified analyses by sex and age were not included in our study. Therefore, it is unclear whether the results are the same for other ethnic groups, genders, and ages. CONCLUSIONS: Our results demonstrate that MDD is independently associated with an increased risk of falls, in which NSAIDs mediate the association. This study suggests that avoiding the use of NSAIDs may reduce the risk of falls in patients diagnosed with MDD.

A deep learning framework for predicting disease-gene associations with functional modules and graph augmentation.

BACKGROUND: The exploration of gene-disease associations is crucial for understanding the mechanisms underlying disease onset and progression, with significant implications for prevention and treatment strategies. Advances in high-throughput biotechnology have generated a wealth of data linking diseases to specific genes. While graph representation learning has recently introduced groundbreaking approaches for predicting novel associations, existing studies always overlooked the cumulative impact of functional modules such as protein complexes and the incompletion of some important data such as protein interactions, which limits the detection performance. RESULTS: Addressing these limitations, here we introduce a deep learning framework called ModulePred for predicting disease-gene associations. ModulePred performs graph augmentation on the protein interaction network using L3 link prediction algorithms. It builds a heterogeneous module network by integrating disease-gene associations, protein complexes and augmented protein interactions, and develops a novel graph embedding for the heterogeneous module network. Subsequently, a graph neural network is constructed to learn node representations by collectively aggregating information from topological structure, and gene prioritization is carried out by the disease and gene embeddings obtained from the graph neural network. Experimental results underscore the superiority of ModulePred, showcasing the effectiveness of incorporating functional modules and graph augmentation in predicting disease-gene associations. This research introduces innovative ideas and directions, enhancing the understanding and prediction of gene-disease relationships.

Associations with other cancer-related biomarkers might contribute to poor outcomes in RAS-altered, younger patients with colorectal cancer.

Colorectal cancer (CRC) is a common cancer in younger adults. In patients undergoing liver resection with RAS-altered CRCs, there is evidence suggesting younger patients have worse outcomes than older patients. To explain this pattern, differences in associations between RAS status and other cancer-related biomarkers in tumors from younger versus older patients with CRC were evaluated in a cohort of 925 patients with CRC, 277 (30.0%) of whom were ≤50 years old, and 454 (49.1%) who had RAS-altered tumors. For 3 biomarkers, RNF43, APC, and microsatellite instability (MSI), the association with RAS status was significantly modified by age after adjustment for multiple testing. Specifically, younger patients with RAS-altered tumors were more likely to be MSI-high, RNF43 mutated, and APC wild type. These differences might contribute to the observed pattern of diminished survival in younger versus older patients with CRC with RAS-mutated tumors undergoing liver metastasis resection.

Divergent impacts of fertilization regimes on below-ground prokaryotic and eukaryotic communities in the Tibetan Plateau.

Chemical nutrient amendment by human activities can lead to environmental impacts contributing to global biodiversity loss. However, the comprehensive understanding of how below- and above-ground biodiversity shifts under fertilization regimes in natural ecosystems remains elusive. Here, we conducted a seven-year field experiment (2011-2017) and examined the effects of different fertilization on plant biodiversity and soil belowground (prokaryotic and eukaryotic) communities in the alpine meadow of the Tibetan Plateau, based on data collected in 2017. Our results indicate that nitrogen addition promoted total plant biomass but reduced the plant species richness. Conversely, phosphorus enrichment did not promote plant biomass and exhibited an unimodal pattern with plant richness. In the belowground realm, distinct responses of soil prokaryotic and eukaryotic communities were observed under fertilizer application. Specifically, soil prokaryotic diversity decreased with nitrogen enrichment, correlating with shifts in soil pH. Similarly, soil eukaryotic diversity decreased with increased phosphorous inputs, aligning with the equilibrium between soil available and total phosphorus. We also established connections between these soil organism communities with above-ground plant richness and biomass. Overall, our study contributes to a better understanding of the sustainable impacts of human-induced nutrient enrichment on the natural environment. Future research should delve deeper into the long-term effects of fertilization on soil health and ecosystem functioning, aiming to achieve a balance between agricultural productivity and environmental conservation.

EMCMDA: predicting miRNA-disease associations via efficient matrix completion.

Abundant researches have consistently illustrated the crucial role of microRNAs (miRNAs) in a wide array of essential biological processes. Furthermore, miRNAs have been validated as promising therapeutic targets for addressing complex diseases. Given the costly and time-consuming nature of traditional biological experimental validation methods, it is imperative to develop computational methods. In the work, we developed a novel approach named efficient matrix completion (EMCMDA) for predicting miRNA-disease associations. First, we calculated the similarities across multiple sources for miRNA/disease pairs and combined this information to create a holistic miRNA/disease similarity measure. Second, we utilized this biological information to create a heterogeneous network and established a target matrix derived from this network. Lastly, we framed the miRNA-disease association prediction issue as a low-rank matrix-complete issue that was addressed via minimizing matrix truncated schatten p-norm. Notably, we improved the conventional singular value contraction algorithm through using a weighted singular value contraction technique. This technique dynamically adjusts the degree of contraction based on the significance of each singular value, ensuring that the physical meaning of these singular values is fully considered. We evaluated the performance of EMCMDA by applying two distinct cross-validation experiments on two diverse databases, and the outcomes were statistically significant. In addition, we executed comprehensive case studies on two prevalent human diseases, namely lung cancer and breast cancer. Following prediction and multiple validations, it was evident that EMCMDA proficiently forecasts previously undisclosed disease-related miRNAs. These results underscore the robustness and efficacy of EMCMDA in miRNA-disease association prediction.

Part-set cuing effects in spatial memory: the role of interitem associations.

Part-set cuing facilitation and impairment effects are rarely found in spatial memory, which is a challenge to the theories of part-set cuing effects based on lexical stimulus. This study aims to investigate whether there part-set cuing facilitation and impairment effects are present in spatial memory by constructing two types of memory scenes with high and low degrees of interitem associations, achieved by manipulating the presentation of miniatures. This study examined the effects of different part-set cues on free recall, recognition, and reconstruction tasks. The results of two experiments revealed that matrix cues impaired the performance of three recall tasks in memory scenes with a high degree of interitem associations, and scene cues facilitated the reconstruction performance (Experiment 1). Conversely, in memory scenes with a low degree of interitem associations, the impairment effect of matrix cues was not observed in the three recall tasks, but scene cues still facilitated the reconstruction performance (Experiment 2). These findings supported the retrieval strategy disruption hypothesis, the two-mechanism and the multi-mechanism accounts, demonstrating the significance of interitem associations in spatial memory. Furthermore, the results provided direct evidence for the importance of the encoding-retrieval strategy matching principle in spatial memory tasks.

Genetic evidence for the causal relationships between migraine, dementia, and longitudinal brain atrophy.

BACKGROUND: Migraine is a neurological disease with a significant genetic component and is characterized by recurrent and prolonged episodes of headache. Previous epidemiological studies have reported a higher risk of dementia in migraine patients. Neuroimaging studies have also shown structural brain atrophy in regions that are common to migraine and dementia. However, these studies are observational and cannot establish causality. The present study aims to explore the genetic causal relationship between migraine and dementia, as well as the mediation roles of brain structural changes in this association using Mendelian randomization (MR). METHODS: We collected the genome-wide association study (GWAS) summary statistics of migraine and its two subtypes, as well as four common types of dementia, including Alzheimer's disease (AD), vascular dementia, frontotemporal dementia, and Lewy body dementia. In addition, we collected the GWAS summary statistics of seven longitudinal brain measures that characterize brain structural alterations with age. Using these GWAS, we performed Two-sample MR analyses to investigate the causal effects of migraine and its two subtypes on dementia and brain structural changes. To explore the possible mediation of brain structural changes between migraine and dementia, we conducted a two-step MR mediation analysis. RESULTS: The MR analysis demonstrated a significant association between genetically predicted migraine and an increased risk of AD (OR = 1.097, 95% CI = [1.040, 1.158], p = 7.03 × 10- 4). Moreover, migraine significantly accelerated annual atrophy of the total cortical surface area (-65.588 cm2 per year, 95% CI = [-103.112, -28.064], p = 6.13 × 10- 4) and thalamic volume (-9.507 cm3 per year, 95% CI = [-15.512, -3.502], p = 1.91 × 10- 3). The migraine without aura (MO) subtype increased the risk of AD (OR = 1.091, 95% CI = [1.059, 1.123], p = 6.95 × 10- 9) and accelerated annual atrophy of the total cortical surface area (-31.401 cm2 per year, 95% CI = [-43.990, -18.811], p = 1.02 × 10- 6). The two-step MR mediation analysis revealed that thalamic atrophy partly mediated the causal effect of migraine on AD, accounting for 28.2% of the total effect. DISCUSSION: This comprehensive MR study provided genetic evidence for the causal effect of migraine on AD and identified longitudinal thalamic atrophy as a potential mediator in this association. These findings may inform brain intervention targets to prevent AD risk in migraine patients.

Association Between Gestational Weeks, Initial Maternal Perception of Fetal Movement, and Individual Interoceptive Differences in Pregnant Women: Cross-Sectional Study.

BACKGROUND: Interoception encompasses the conscious awareness of homeostasis in the body. Given that fetal movement awareness is a component of interoception in pregnant women, the timing of initial detection of fetal movement may indicate individual differences in interoceptive sensitivity. OBJECTIVE: The aim of this study is to determine whether the association between the gestational week of initial movement awareness and interoception can be a convenient evaluation index for interoception in pregnant women. METHODS: A cross-sectional study was conducted among 32 pregnant women aged 20 years or older at 22-29 weeks of gestation with stable hemodynamics in the Obstetric Outpatient Department. Interoception was assessed using the heartbeat-counting task, with gestational weeks at the first awareness of fetal movement recorded via a questionnaire. Spearman rank correlation was used to compare the gestational weeks at the first awareness of fetal movement and heartbeat-counting task scores. RESULTS: A significant negative correlation was found between the gestational weeks at the first fetal movement awareness and heartbeat-counting task performance among all participants (r=-0.43, P=.01) and among primiparous women (r=-0.53, P=.03) but not among multiparous women. CONCLUSIONS: Individual differences in interoception appear to correlate with the differences observed in the timing of the first awareness of fetal movement.

A common ericoid shrub modulates the diversity and structure of fungal communities across an arbuscular to ectomycorrhizal tree dominance gradient.

Differences between arbuscular (AM) and ectomycorrhizal (EcM) trees strongly influence forest ecosystem processes, in part through their impact on saprotrophic fungal communities. Ericoid mycorrhizal (ErM) shrubs likely also impact saprotrophic communities given that they can shape nutrient cycling by slowing decomposition rates and intensifying nitrogen limitation. We investigated the depth distributions of saprotrophic and EcM fungal communities in paired subplots with and without a common understory ErM shrub, mountain laurel (Kalmia latifolia L.), across an AM to EcM tree dominance gradient in a temperate forest by analyzing soils from the organic, upper mineral (0-10 cm), and lower mineral (cumulative depth of 30 cm) horizons. The presence of K. latifolia was strongly associated with the taxonomic and functional composition of saprotrophic and ectomycorrhizal communities. Saprotrophic richness was consistently lower in the Oa horizon when this ErM shrub species was present. However, in AM tree dominated plots, the presence of the ErM shrub was associated with a higher relative abundance of saprotrophs. Given that EcM trees suppress both the diversity and relative abundance of saprotrophic communities, our results suggest that separate consideration of ErM shrubs and EcM trees may be necessary when assessing the impacts of plant mycorrhizal associations on belowground communities.

Leveraging the adolescent brain cognitive development study to improve behavioral prediction from neuroimaging in smaller replication samples.

Neuroimaging is a popular method to map brain structural and functional patterns to complex human traits. Recently published observations cast doubt upon these prospects, particularly for prediction of cognitive traits from structural and resting state functional magnetic resonance imaging (MRI). We leverage baseline data from thousands of children in the Adolescent Brain Cognitive DevelopmentSM Study to inform the replication sample size required with univariate and multivariate methods across different imaging modalities to detect reproducible brain-behavior associations. We demonstrate that by applying multivariate methods to high-dimensional brain imaging data, we can capture lower dimensional patterns of structural and functional brain architecture that correlate robustly with cognitive phenotypes and are reproducible with only 41 individuals in the replication sample for working memory-related functional MRI, and ~ 100 subjects for structural and resting state MRI. Even with 100 random re-samplings of 100 subjects in discovery, prediction can be adequately powered with 66 subjects in replication for multivariate prediction of cognition with working memory task functional MRI. These results point to an important role for neuroimaging in translational neurodevelopmental research and showcase how findings in large samples can inform reproducible brain-behavior associations in small sample sizes that are at the heart of many research programs and grants.

The emergence and organizational choices of patient advocacy associations: Evidence from the Italian context.

BACKGROUND: Within many advanced healthcare systems, patient advocacy associations are gaining a more relevant role in healthcare policy decisions. These associations provide help, representation, and voice to patients and citizens. However, although their role is important, a paucity of research exists analyzing their foundation dynamics, including their organizational decision, from a managerial perspective. To fill this knowledge gap, we formulated two research questions to investigate the motivations underlying the foundation of patient advocacy associations and the institutional pressures influencing the changes that have occurred in these associations throughout their evolution. METHODS: Using a semi-structured questionnaire developed as part of a larger research project, we collected data about 31 patient advocacy associations operating within the Italian National Health Service. We employed qualitative analysis to examine the associations' birth and evolution as well as the motivations and influences driving change within the sampled organizations. RESULTS: Questionnaire responses provided information about the patient advocacy associations' histories and highlighted their close links with the Italian National Health Service. Our results indicated the motivations for the associations' establishment could be grouped into five categories: support at the system level, patient protection and support, actions to increase awareness, actions for improving patients' health and quality of life, and dissemination of knowledge. We also identified the frequency and nature of the changes within the associations and explored the institutional pressures that influenced these organizational changes. CONCLUSIONS: The results can be interpreted considering the organizational theories that focus on organizations' establishment decisions and how external pressures impact organizations' dynamics and evolution.

Network enrichment significance testing in brain-phenotype association studies.

Functional networks often guide our interpretation of spatial maps of brain-phenotype associations. However, methods for assessing enrichment of associations within networks of interest have varied in terms of both scientific rigor and underlying assumptions. While some approaches have relied on subjective interpretations, others have made unrealistic assumptions about spatial properties of imaging data, leading to inflated false positive rates. We seek to address this gap in existing methodology by borrowing insight from a method widely used in genetics research for testing enrichment of associations between a set of genes and a phenotype of interest. We propose network enrichment significance testing (NEST), a flexible framework for testing the specificity of brain-phenotype associations to functional networks or other sub-regions of the brain. We apply NEST to study enrichment of associations with structural and functional brain imaging data from a large-scale neurodevelopmental cohort study.

Implicit measures of suicide vulnerability: Investigating suicide-related information-processing biases and a deficit in behavioral impulse control in a high-risk sample and healthy controls.

OBJECTIVE: Relevant implicit markers of suicidal thoughts and behaviors (STBs) have only been studied in isolation with mixed evidence. This is the first study that investigated a suicide attentional bias, a death-identity bias and a deficit in behavioral impulsivity in a high-risk sample and healthy controls. METHOD: We administered the Death Implicit Association Test, the Modified Suicide Stroop Task, and a Go/No-Go Task to inpatient suicide ideators (n = 42), suicide attempters (n = 40), and community controls (n = 61). RESULTS: Suicide ideators and attempters showed a suicide attentional bias and a death-identity bias compared to healthy controls. Ideators and attempters did not differ in these implicit information-processing biases. Notably, only attempters were more behaviorally impulsive compared to controls; however, ideators and attempters did not significantly differ in behavioral impulsivity. Moreover, implicit scores were positively intercorrelated in the total sample. CONCLUSION: In line with the Cognitive Model of Suicide, ideators and attempters display suicide-related information processing biases, which can be considered as implicit cognitive markers of suicide vulnerability. Furthermore, attempters have elevated levels of behavioral impulsiveness. These results are highly relevant in the context of crisis intervention strategies and warrant further research.

PGCNMDA: Learning node representations along paths with graph convolutional network for predicting miRNA-disease associations.

Identifying miRNA-disease associations (MDAs) is crucial for improving the diagnosis and treatment of various diseases. However, biological experiments can be time-consuming and expensive. To overcome these challenges, computational approaches have been developed, with Graph Convolutional Network (GCN) showing promising results in MDA prediction. The success of GCN-based methods relies on learning a meaningful spatial operator to extract effective node feature representations. To enhance the inference of MDAs, we propose a novel method called PGCNMDA, which employs graph convolutional networks with a learning graph spatial operator from paths. This approach enables the generation of meaningful spatial convolutions from paths in GCN, leading to improved prediction performance. On HMDD v2.0, PGCNMDA obtains a mean AUC of 0.9229 and an AUPRC of 0.9206 under 5-fold cross-validation (5-CV), and a mean AUC of 0.9235 and an AUPRC of 0.9212 under 10-fold cross-validation (10-CV), respectively. Additionally, the AUC of PGCNMDA also reaches 0.9238 under global leave-one-out cross-validation (GLOOCV). On HMDD v3.2, PGCNMDA obtains a mean AUC of 0.9413 and an AUPRC of 0.9417 under 5-CV, and a mean AUC of 0.9419 and an AUPRC of 0.9425 under 10-CV, respectively. Furthermore, the AUC of PGCNMDA also reaches 0.9415 under GLOOCV. The results show that PGCNMDA is superior to other compared methods. In addition, the case studies on pancreatic neoplasms, thyroid neoplasms and leukemia show that 50, 50 and 48 of the top 50 predicted miRNAs linked to these diseases are confirmed, respectively. It further validates the effectiveness and feasibility of PGCNMDA in practical applications.

Can I still eat this? Using implicit and explicit measures to explore consumer behavior toward food products with date labels.

This study investigates implicit and explicit attitudes toward products before and beyond the best-before date (BBD) using an Implicit Association Test and an online questionnaire. Moreover, we test whether consumer perception of and behavior toward products beyond the BBD can be manipulated using a priming task. We use a three-group between-subjects design where respondents had to recall either a frugal, a wasteful, or an unrelated behavior. Results show that consumers have negative implicit associations with products beyond the BBD. Reduced health and safety perceptions, consumers' strategies to determine edibility, and general risk perception of products beyond the BBD predict consumption of these products. While recalling a frugal behavior does not have significant effects, recalling a wasteful behavior prior to evaluating products beyond the BBD leads to a decrease in the perceived safety and healthfulness of these products.