Hierarchical Model Research Articles

Reconsidering Household Food Insecurity: Assessing the Impact of Neighborhood and Household Characteristics

ABSTRACT Motivated by substantial public health implications of food insecurity, this study systematically reexamined its determinants beyond household disadvantages. Utilizing data from the Panel Study of Income Dynamics and American Community Survey, hierarchical linear modeling was employed. The findings underscored notable between-neighborhood variations in food insecurity after adjusting for household-specific characteristics, revealing residing in high-poverty or nonmetropolitan areas heightened the risk. Demographic disparities were evident, with black and Hispanic households experiencing elevated food insecurity, despite neighborhood racial composition. Additional household factors exacerbating food insecurity included financial constraints, limited education, vehicle inaccessibility, and single-living arrangements. These findings inform interventions in similar contexts.

Examining the Impact of Race and Poverty on the Relationship Between Purpose in Life and Functional Health: Insights from the HANDLS Study.

While research has shown a positive association between a higher sense of purpose in life and functional health, there is a gap in understanding its benefits for racially minoritized and low SES individuals. This study aimed to investigate the correlation between purpose in life and physical functional health in a diverse sample, hypothesizing that purpose in life would be negatively associated with functional difficulties, with potentially stronger associations in White and high SES groups. Data from the Healthy Aging in Neighborhoods of Diversity across the Life Span (HANDLS) study were utilized (166 participants, mean age 59.44 [SD = 8.28], 59.6% females, 65.06% Black participants, 40.36% below poverty). Purpose in life was measured by Ryff's Psychological Well-being Purpose in Life subscale. Functional health was measured by functional difficulties in mobility and daily living. Race (Black and White) and poverty status (above and below) were used as moderators to probe the purpose-functional health association using zero-inflated Poisson regression while adjusting for age, education, depressive symptomology, and previous functional difficulties in four hierarchical models. Results showed that purpose in life was negatively associated with functional difficulty, indicating fewer difficulties in mobility and daily activities among those with a high sense of purpose. While the association did not remain significant after including previous functional difficulty as a covariate in Model 4, suggesting that race may not be a consistent moderator, poverty status remained a consistent moderator. The association was stronger for individuals above the poverty level. These findings underscore the complex interplay between purpose in life, race, poverty status, and functional health, emphasizing the importance of considering socioeconomic factors in interventions aimed at eliminating functional health disparities among diverse adult populations.

Development of a probabilistic risk model for pharmaceuticals in the environment under population and wastewater treatment scenarios.

Preparing for future environmental pressures requires projections of how relevant risks will change over time. Current regulatory models of environmental risk assessment (ERA) of pollutants such as pharmaceuticals could be improved by considering the influence of global change factors (e.g., population growth) and by presenting uncertainty more transparently. In this article, we present the development of a prototype object-oriented Bayesian network (BN) for the prediction of environmental risk for six high-priority pharmaceuticals across 36 scenarios: current and three future population scenarios, combined with infrastructure scenarios, in three Norwegian counties. We compare the risk, characterized by probability distributions of risk quotients (RQs), across scenarios and pharmaceuticals. Our results suggest that RQs would be greatest in rural counties, due to the lower development of current wastewater treatment facilities, but that these areas consequently have the most potential for risk mitigation. This pattern intensifies under higher population growth scenarios. With this prototype, we developed a hierarchical probabilistic model and demonstrated its potential in forecasting the environmental risk of chemical stressors under plausible demographic and management scenarios, contributing to the further development of BNs for ERA. Integr Environ Assess Manag 2024;00:1-21. © 2024 The Authors. Integrated Environmental Assessment and Management published by Wiley Periodicals LLC on behalf of Society of Environmental Toxicology & Chemistry (SETAC).

Sequential Bayesian Phylogenetic Inference.

The ideal approach to Bayesian phylogenetic inference is to estimate all parameters of interest jointly in a single hierarchical model. However, this is often not feasible in practice due to the high computational cost. Instead, phylogenetic pipelines generally consist of sequential analyses, whereby a single point estimate from a given analysis is used as input for the next analysis (e.g., a single multiple sequence alignment is used to estimate a gene tree). In this framework, uncertainty is not propagated from step to step, which can lead to inaccurate or spuriously confident results. Here, we formally develop and test a sequential inference approach for Bayesian phylogenetic inference, which uses importance sampling to generate observations for the next step of an analysis pipeline from the posterior distribution produced in the previous step. Our sequential inference approach presented here not only accounts for uncertainty between analysis steps, but also allows for greater flexibility in software choice (and hence model availability) and can be computationally more efficient than the traditional joint inference approach when multiple models are being tested. We show that our sequential inference approach is identical in practice to the joint inference approach only if sufficient information in the data is present (a narrow posterior distribution) and/or sufficiently many importance samples are used. Conversely, we show that the common practice of using a single point estimate can be biased, e.g., a single phylogeny estimate to transform an unrooted phylogeny into a time-calibrated phylogeny. We demonstrate the theory of sequential Bayesian inference using both a toy example and an empirical case study of divergence-time estimation in insects using a relaxed clock model from transcriptome data. In the empirical example, we estimate three posterior distributions of branch lengths from the same data (DNA character matrix with a GTR+Γ+I substitution model, an amino acid data matrix with empirical substitution models, and an amino acid data matrix with the PhyloBayes CAT-GTR model). Finally, we apply three different node-calibration strategies and show that divergence-time estimates are affected by both the data source and underlying substitution process to estimate branch lengths as well as the node-calibration strategies. Thus, our new sequential Bayesian phylogenetic inference provides the opportunity to efficiently test different approaches for divergence time estimation, including branch-length estimation from other software.

Evaluating human landing catches as a measure of mosquito biting and the importance of considering additional modes of action

Entomological evaluations of vector control tools often use human landing catches (HLCs) as a standard measure of a direct human-vector contact. However, some tools have additional characteristics, such as mortality, and HLCS are not sensitive for measuring other effects beyond landing inhibition. Therefore, additional measures may need to be considered when evaluating these tools for public health use. This study has two main aims (1) the evaluate the accuracy of HLCs as a proxy for feeding and (2) to compare the predicted reduction in vectorial capacity when we do and do not consider these additional characteristics. To achieve this, we analyse previously published semi-field data from an experiment which used HLCs and another where mosquitoes were allowed to feed in the presence of different dosages of the volatile pyrethroid spatial repellent, transfluthrin. We compare results for two mathematical models: one which only considers the reduction in feeding effect and one which also considers mortality before and after feeding (using data gathered by the aspiration of mosquitoes after the semi-field feeding/landing period and 24 h survival monitoring). These Bayesian hierarchical models are parameterised using Bayesian inference. We observe that, for susceptible mosquitoes, reduction in landing is underestimated by HLCs. For knockdown resistant mosquitoes the relationship is less clear; with HLCs sometimes appearing to overestimate this characteristic. We find HLCs tend to under-predict the relative reduction in vectorial capacity in susceptible mosquitoes while over-predicting this impact in knockdown-resistant mosquitoes. Models without secondary effects have lower predicted relative reductions in vectorial capacities. Overall, this study highlights the importance of considering additional characteristics to reduction in biting of volatile pyrethroid spatial repellents. We recommend that these are considered when evaluating novel vector control tools.

Defining hybrid hierarchical models in πHYFLOW

This paper presents πHyFlow, a modular approach to the process interaction worldview (PI). Traditionally, PI supports a set of interacting processes without enabling modular and hierarchical model definition. πHyFlow basic models define a dynamic set of processes while also enabling a modular interface for supporting model composition. πHyFlow allows an exact representation of continuous signals based on the concepts of dense outputs, and generalized sampling. πHyFlow ability to support accurate models of hybrid systems is presented through a DC-DC converter (DCC) based on a resistor–capacitor (RC) electrical circuit. The DCC output voltage can be modeled by a first-order differential equation that is solved using an exponential time differencing (ETD) integrator. The DCC network model uses a digital on-off controller and an ETD. Simulation results are generated by πHyFlow++, a C++ implementation of the formalism.

Research on the influencing factors of Chinese agricultural brand competitiveness based on DEMATEL-ISM

Agricultural products are pivotal to the national economy, and a comprehensive analysis of brand competitiveness significantly contributes to the support of agricultural structural adjustment and modernization. Focusing on the Yangtze River Delta region of China, this study develops an evaluation index system encompassing four dimensions: core brand competitiveness, brand management, market competitiveness, and innovation in branding. Utilizing a DEMATEL-ISM model, this research elucidates the intrinsic relationships among factors that influence brand competitiveness, resulting in a four-tier hierarchical model. The analysis delineates key factors at superficial, intermediate, and profound levels that influence brand competitiveness. Notably, regional production bases, along with innovations in brand technology and systems, emerge as profound influencers. Drawing on these findings, the study recommends strategies to enhance production foundations, accurately define development trajectories, spearhead technological innovation to foster collective reform efforts, and advocate for institutional advancements to bolster healthy brand growth.

Congestion Number Analysis of Cross-Flow Dynamics: Experimental Data and Simulations

We recently proposed the "Congestion Number" (CN) as a metricto evaluate the state of a pedestrian crowd. Such metric, whose definition is based on the gradient of the rotor of the crowd velocity field, appears to provide additional information with respect to traditional metrics based on pedestrian density and flow. We also published two works on the dynamics of orthogonally crossing pedestrian flows under different density regimes. In the first manuscript we analysed experimental data by using traditionalobservables such as density, velocity and relative position between pedestrians, along with less explored ones such as body orientation. In the second one we proposed a hierarchy of simulation models to reproduce the cross-flow dynamics, and used the aforementioned observables to compare such models. Based on theoretical considerations and analysis of real world data, we believe the crossing flow setting to be a good arena to test the CN metric, and in this work we perform a CN analysis on the empirical and simulation data. Results show that simulation models, which reproduced almost perfectly the density time dependence of the pedestrian crowd, fail to reproduce the CN one. Actually, models "outperform" the pedestrian crowd when analysed using CN. These preliminary results suggest that the CN metric may provide useful information not only in crowd assessment but also in model evaluation.

How Much Retrieval Ability Is in Originality?

ABSTRACTCreative fluency and originality are pivotal indicators of creative potential. Both have been embedded in hierarchical intelligence models as part of the ability to retrieve information from long‐term memory; an ability that is often measured with indicators of retrieval fluency. Creative fluency and retrieval fluency, both expressed by the count of correct responses, are procedurally highly similar. This raises the question how creative fluency and originality are related with retrieval fluency and how both are predicted by other cognitive abilities. In a multivariate study (N = 320), we found that retrieval fluency is very strongly related with creative fluency (r = .87) and substantially related with originality (r = .59). A combined fluency factor still fitted the data well. Cognitive abilities accounted for 63% variance in fluency and 47% variance in originality. After controlling for established cognitive abilities, latent variables for fluency and originality were unrelated with one another. This suggests that the procedural proximity of the ability to fluently generate either information from long‐term memory or ad‐hoc solutions to unusual tasks and the ability to come up with original ideas needs reconsideration. Locating originality below an overarching retrieval factor is contradicted by the present data.

Eveningness chronotype influences social functioning by deteriorating depressive symptoms in remitted patients with schizophrenia

ABSTRACT Sleep and circadian rhythm disruption (SCRD) is common in schizophrenia patients, who also typically experience impaired social functioning. While various factors influence social functioning in schizophrenia, the specific impact of sleep and circadian rhythm disruption remains unclear. This study aimed to investigate the connection between chronotype and social functioning in remitted schizophrenia patients, examining the mediating roles of depression and sleep quality. The study included 185 patients diagnosed with schizophrenia based on DSM-5 criteria. After categorizing the patients into morningness, eveningness, or intermediate chronotypes using the Morningness-Eveningness Questionnaire(MEQ), they were assessed with the Positive and Negative Syndrome Scale(PANSS), Calgary Depression Scale for Schizophrenia(CDSS), Personal and Social Performance Scale(PSPS) and Pittsburgh Sleep Quality Index(PSQI). The eveningness chronotype group showed higher CDSS and PSQI scores and lower PWBS and PSPS-Total scores than the other groups (p < 0.05). A hierarchical linear regression model assessed MEQ, PSQI, and CDSS scores’ effects on PSPS total scores. MEQ scores’ significance diminished when CDSS scores were included. Eveningness chronotype, particularly with increased depressive symptoms, negatively impacts social functioning in remitted schizophrenia patients.These findings contribute to the understudied area of chronotype in schizophrenia and its impact on social functioning, including its interaction with sleep..

Construction and Implementation of Content-Based National Music Retrieval Model Under Deep Learning

This research mainly studies the construction and implementation of the content-based folk music retrieval model. Firstly, it studies the music automatic annotation method based on deep learning, and then proposes the tag conditional random field music automatic annotation method, and then constructs the music annotation depth neural network model combining a variety of music representation and attention mechanism. Finally, it analyzes the proposed folk music retrieval model the effectiveness of the cable model is verified and its performance is evaluated. The results show that in Glu module, Glu blocks had better performance in music annotation, and the music annotation results of each index in music hierarchical sequence modeling are better, which ensures the effectiveness of music annotation. Compared with other algorithms, the AUC tag score of the proposed method is the highest, which is 0.913; it can better model the mapping relationship between the audio features of music input to the text tag and has higher scores on all evaluation indicators.

Effects of spruce budworm defoliation on in-stream algal production and carbon use by food webs

Insect defoliation is a widespread cause of forest disturbance in Canada, and it has the potential to alter terrestrial carbon contributions to stream consumers and thus affect stream ecosystems. Although defoliation by spruce budworms ( Choristoneura fumiferana) is considered a natural disturbance with importance to forest succession, the magnitude and duration of outbreaks are increasing, and there is a growing need to understand their downstream consequences. To examine this, algal productivity and autochthonous (in-stream) energy supporting macroinvertebrate and fish consumers (using δ13C, δ15N) were examined in 12 streams in the Gaspé Peninsula, Québec, Canada, that ranged in watershed defoliation from spruce budworm. Hierarchical partitioning models were used to assess the contributions of watershed and stream variables to these endpoints. Total seasonal algal production ranged fourfold among sites, and cumulative defoliation was the strongest predictor. Consumers from streams with greater defoliation (cumulative or annual) had lower reliance on autochthonous carbon, despite increased algal production, and this effect was greater for brook trout than macroinvertebrates. These findings demonstrate for the first time that insect outbreaks—which alter forest structure and function—also similarly affect downstream aquatic ecosystems by causing changes in the production and contributions of carbon sources. This suggests that insect control strategies and forest management decisions should also consider implications for downstream aquatic ecosystems.

AsteRIa enables robust interaction modeling between chromatin modifications and epigenetic readers.

Chromatin, the nucleoprotein complex consisting of DNA and histone proteins, plays a crucial role in regulating gene expression by controlling access to DNA. Chromatin modifications are key players in this regulation, as they help to orchestrate DNA transcription, replication, and repair. These modifications recruit epigenetic 'reader' proteins, which mediate downstream events. Most modifications occur in distinctive combinations within a nucleosome, suggesting that epigenetic information can be encoded in combinatorial chromatin modifications. A detailed understanding of how multiple modifications cooperate in recruiting such proteins has, however, remained largely elusive. Here, we integrate nucleosome affinity purification data with high-throughput quantitative proteomics and hierarchical interaction modeling to estimate combinatorial effects of chromatin modifications on protein recruitment. This is facilitated by the computational workflow asteRIa which combines hierarchical interaction modeling, stability-based model selection, and replicate-consistency checks for astable estimation of Robust Interactions among chromatin modifications. asteRIa identifies several epigenetic reader candidates responding to specific interactions between chromatin modifications. For the polycomb protein CBX8, we independently validate our results using genome-wide ChIP-Seq and bisulphite sequencingdatasets. We provide the first quantitative framework for identifying cooperative effects of chromatin modifications on protein binding.

Technology and geometry: Fostering young children's geometrical concepts through a research-based robotic coding program

AbstractGeometric concepts are fundamental to early geometry education, and developmentally appropriate practices are crucial for teaching them to young children. Robotic coding is an effective tool in many areas of early mathematics and has significant potential in teaching geometric concepts. This study aimed to test the impact of a research-based robotic coding program on young children's understanding of geometric concepts. A quasi-experimental design was used with two intervention groups. The study involved 52 5-year-old children from a public kindergarten. The intervention-1 group (n = 18) received a research-based robotic coding program, while the intervention-2 group (n = 16) received a research-based no-coding program. The non-intervention group (n = 18) attended their regular program. Data on participants' understanding of geometrical concepts was collected through individual interviews using a geometric shapes classification test. Hierarchical linear modeling (HLM) was utilized to assess the effectiveness of two interventions. The results showed that both interventions had a significant effect with the robotic program being more effective. Additionally, follow-up tests indicated that both interventions had a lasting effect on the children's understanding of geometrical concepts. The study highlighted the potential of incorporating robotic coding and relevant research in fostering young children's geometrical development.

Lower-Body Power, Body Composition, Speed, and Agility Performance among Youth Soccer Players

Speed and agility have been described as crucial abilities for soccer players. The purpose of this study was to analyze, in detail, the variance in speed and agility tasks explained by lower-body power after controlling for age and body composition. The participants were 96 male soccer players aged 16.1 ± 1.6 years. Body composition (stature, body fat percentage—BF%, body mass, and fat-free mass—FFM), lower-body power (countermovement jump—CMJ, and squat jump—SJ), speed (5-, 10-, and 35 m sprints), and agility (t-test) were assessed. Among body composition parameters, BF% presented the highest number of significant relationships with speed and agility, with the strength of correlations ranging from small (5 m sprint, r = 0.25) to large (35 m sprint, r = 0.52). The strongest correlation coefficient emerged between FFM and the 35 m sprint (r = −0.65). Significant correlations were found between vertical jump performance and the 35 m sprint (CMJ: r = −0.68; SJ: r = −0.69), followed by the t-test (CMJ: r = −0.35; SJ: r = −0.47). The hierarchical multiple regression model could explain 22% to 67% of the variance observed in agility scores and speed. BF% remained the most statistically significant negative predictor of all regression models. The CMJ remained a statistically significant positive predictor of the 35 m sprint (β = −0.581, p ≤ 0.01) after controlling for age and body composition. Integrating programs targeting lower-body power might be important to enhance speed and agility performance in youth soccer. On the other hand, future research based on multidisciplinary approaches to investigate the effects of nutritional strategies in reducing or preventing gains in BF% is still needed, which remained a significant predictor of sprint and agility performance in the final models.

Effectiveness of a Behavioral Activation Intervention for Peripartum Women with Opioid Use Disorder.

Pregnant women with opioid use disorder show elevated rates of comorbid mental health problems, both of which are associated with negative health outcomes for mothers and children. There is substantial evidence supporting the benefits of treatment of perinatal opioid use disorder, as well as perinatal depression and anxiety, but there are gaps in knowledge about the effectiveness of perinatal behavioral health interventions in the context of co-occurring substance use disorder. The current study seeks to address this gap by examining outcomes of a behavioral activation treatment in a group of peripartum women with opioid use disorder (N = 68). Behavioral activation has shown promise in treating co-occurring depression and substance use problems. The intervention was delivered as part of an integrated care treatment model, in which patients received co-located obstetric, substance use, and mental health care in a hospital-based clinic. Hierarchical linear modeling was used to assess change in symptoms over time. Results suggest that the group behavioral activation intervention was associated with reduced depression and anxiety symptoms, demonstrated by significant reductions in PHQ-9 and GAD-7 scores over the course of treatment. Moreover, there were indications that increased attendance was associated with further reductions in depressive symptoms. Results contribute to understanding the effectiveness of behavioral activation in the context of peripartum opioid use disorder. Findings also add to the evidence supporting integrated care models and offer a potential blueprint for improving outcomes and reducing barriers to care in this population.

Native American college students in counseling: Results from a large-scale, multisite effectiveness study.

There is a large body of research exploring therapeutic effectiveness for racially or ethnically minoritized college students. Prior literature highlights the unique mental health and academic challenges faced by Native American students in higher education; however, there is a paucity of research examining the effectiveness of counseling for Native American college students. The present study examined the effectiveness of counseling on psychological and academic distress among Native American college students, comparing their initial distress and rate of change to White students in counseling. Using naturalistic data from a large practice-research network spanning 2015-2019, we employed hierarchical linear modeling to evaluate the effect of race on psychological distress (N = 9,621) and academic distress (N = 9,643) scores during treatment. Results revealed that all clients demonstrated a significant decrease in both types of distress over the course of treatment. Native American and White clients presented to counseling with similar levels of psychological distress. However, Native American clients experienced more change and at a faster rate on psychological distress symptoms compared to White clients. On academic distress, Native American clients began and concluded counseling with higher levels of distress while experiencing a similar amount of change at a similar rate in their reduction of academic distress over the course of treatment. The study findings provide unique insight on the outcomes of treatment-seeking Native students by demonstrating a significant positive response to counseling, as well as novel comparisons between Native and White students receiving services within college counseling settings. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Hierarchical modelling of variance components makes analysis of resolvable incomplete block designs more efficient

The standard approach to variance component estimation in linear mixed models for alpha designs is the residual maximum likelihood (REML) method. One drawback of the REML method in the context of incomplete block designs is that the block variance may be estimated as zero, which can compromise the recovery of inter-block information and hence reduce the accuracy of treatment effects estimation. Due to the development of statistical and computational methods, there is an increasing interest in adopting hierarchical approaches to analysis. In order to increase the precision of the analysis of individual trials laid out as alpha designs, we here make a proposal to create an objectively informed prior distribution for variance components for replicates, blocks and plots, based on the results of previous (historical) trials. We propose different modelling approaches for the prior distributions and evaluate the effectiveness of the hierarchical approach compared to the REML method, which is classically used for analysing individual trials in two-stage approaches for multi-environment trials.

An exploratory study of the relationship between pulse transit time and blood pressure based on causal inference

Aim: We propose to examine the causal relationship between the noninvasive features represented by pulse transit time (PTT) and blood pressure (BP), with the aim of mitigating the impact of confounding factor(s) and thus improving the performance of BP estimation. Methods: We identified the causal graph of BP and the important noninvasive features extracted from electrocardiogram (ECG) and photoplethysmogram (PPG) via fast causal inference (FCI) algorithm, with the orientations of the edges in the causal graph being determined by the causal generative neural networks (CGNN) algorithm. With the knowledge obtained from the causal graph, we further used hierarchical regression model to estimate BP, and validated the proposed method on 17 subjects. Results: We found that the obtained causal graph was almost consistent with the prior knowledge, and heart rate (HR) was one of the main confounding factors of PTT and BP. Incorporating HR into the hierarchical regression model to eliminate its confounding effect on the PTT-based BP estimation, the mean value of SBP and DBP estimation was improved by 1.27 and 1.89 mmHg, respectively, and the mean absolute difference (MAD) was improved by 2.28 and 3.60 mmHg, respectively. Conclusion: Causal inference-based method has the potential to clarify the causal relationship between BP and related wearable noninvasive features, which can further shed light on developing new methods for cuffless BP with acceptable accuracy.

Challenges faced by women entrepreneurs in rural India: A mixed-method study on perception

Women entrepreneurs confront myriad issues related to economic, social, familial, market environments, and regulatory challenges in the rural Indian setting. These issues must be identified and characterized for designing policies/strategies to promote women's entrepreneurship. Accordingly, the key objective of this research is to identify and discuss challenges faced by women entrepreneurs in India, with a particular focus on rural areas of Rajasthan. Furthermore, the present research also investigates the inter-relationships between the challenges to determine the impact potential of individual challenges and issues. A mixed-method approach is adopted in the present article. Initially, a questionnaire-based survey was conducted to identify significant challenges and barriers confronted by women entrepreneurs in the selected region. A total of 120 responses were collected based on a purposive sampling procedure. The significant issues are further characterized using Interpretive Structural Modeling and MICMAC analysis. The research discloses that a lack of cooperation from male family members, weak bargaining power, self-esteem issues, psychological barriers, lack of government support, absence of adequate regulatory policies, problems in attracting customers, corruption, and other market behaviors as the major challenges faced by women entrepreneurs. The article also reports the impact potential of these challenges through a hierarchical model.