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To test the Foulds and Bedford hierarchical model of personal illness, the Delusions-Symptoms-States Inventory (DSSI) was administered to 100 psychiatric in-patients. Ninety-six had symptom patterns compatible with the hierarchy. This is significantly more than expected on the basis merely of the number of DSSI items affirmed, and overall the results support the model.
Cancer cell lines (CLs) are widely used to study tumor biology and drug response, yet their translational relevance is often limited by inaccurate subtype annotations. Existing CL-tumor matching approaches are frequently constrained by flat classification schemes, weak subtype definitions, and the exclusion of normal tissue references, leading to potential confounding of tumor-specific and tissue-of-origin signals. To address these limitations, a hierarchical classification (HC) framework is presented in which CLs are aligned with patient tumors across biological resolutions, from organ to molecular subtype. Gene expression profiles from 802 CLs, 5,612 tumors from The Cancer Genome Atlas (TCGA) , and 8,939 non-cancerous tissues were integrated to separate oncogenic signals from tissue-specific signals. Node-specific features were selected using maximum relevance minimum redundancy, and balanced accuracies of 89% in cross-validation and 75%, and 80% on external datasets were achieved. Through the framework, 43 CLs were reassigned, and clinically relevant underrepresented subtypes were identified.
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Seventy-eight depressives were assessed on the Delusions-Symptoms-States Inventory. The results supported Fould's and Bedford's hierarchy model, in that nearly all patients fitted this model, and the relationship between delusions of Contrition and state of Depression was found to be an inclusive, non-reflexive one. Suggestions are made for extensions to this study.
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This expository paper describes two useful tools for the statistical analysis of processes that generate repeated measures and longitudinal data. The first tool is a graph for a visual description of dependency structures. The second tool is a stochastic relaxation method ('Gibbs sampling') for fitting hierarchical Bayes models. Graphs are concise and accessible summaries of stochastic models. Graphs aid communications between statistical and subject-matter scientists, during which formulations of scientific questions are modified. An uncluttered picture of the dependency structure of a model augments effectively its corresponding formulaic description. Stochastic relaxation is a computationally intense method that allows experimentation with broader classes of models than were previously thought feasible because of analytic intractability. Stochastic relaxation is intuitive and easily described to non-statisticians. Several sample graphs show how hierarchical Bayes models can use stochastic relaxation to obtain their fits. An example based on estimating drug shelf-life demonstrates some uses of graphs and stochastic relaxation compared with several frequentist growth curve analyses that use restricted maximum likelihood and generalized estimating equations approaches.
MOTIVATION: Understanding the genetic basis of drug-induced toxicity is crucial for drug development. In-silico analysis of toxicogenomics datasets facilitates early detection of toxicity biomarkers. However, existing tools struggle with the complex interdependencies among hierarchically structured variables, leading to inaccurate biomarker identification. To address this limitation, we developed a Hierarchical Linear Model (HLM) and implemented it in the R package ToxAssay, offering extensive functionality for comprehensive toxicity assessment. RESULTS: ToxAssay outperforms existing methods by improving biomarker detection and computational efficiency. Applied to glutathione depletion-induced toxicity, it prioritized 71 key genes and identified 26 core genes with high discriminative accuracy (AUC = 0.97) and strong cross-correlation (Pearson's r = 0.88) with external datasets. Additionally, our advance outcome pathway (AOP) analysis algorithm uncovered disease outcomes linked to glutathione depletion. These findings provide precise insights into the molecular mechanisms driving drug-induced toxicity. AVAILABILITY AND IMPLEMENTATION: ToxAssay is available as an open-source R package at https://github.com/Fun-Gene/toxassay.
Three models of the organization of the circadian timing system in multicellular animals are presented. Each can account for the observed internal synchronization of the various circadian rhythms within the organism and each is also compatible with the known responses of circadian systems to manipulations of environmental time cues. One is a single oscillatory system (Model I) while the other two are multioscillator systems arranged in a hierarchical (Model II) or nonhierarchical (Model III) manner. Experiments that test the predictions of the different models are reviewed. These indicate that the circadian timing system in mammals is organized as a multioscillator system with oscillating concentrations of chemical mediators (nervous or endocrine) internally synchronizing the various potentially-independent oscillators by an entrainment mechanism. However, as yet there is insufficient evidence to indicate whether the oscillators are arranged with a predominantly hierarchical (Model II) or nonhierarchical (Model III) organization.
In 1990 a new organizational structure based on decentralization and team leadership, where the leader (usually a doctor) is responsible for the final decision, was introduced at the regional and university hospital of Tromsø. This structure replaces the traditional dual structure of leadership where the leaders (a doctor and a nurse) did not share responsibility for the whole department. In order to analyze organizational practices after the reform we constructed three different organization models of the hospital: the hierarchical model, the professional model and the workshop model. Of five teams, one functioned hierarchically, three resembled the professional model, and the fifth came close to the workshop model. The leader of the hierarchical team behaves autocratically and the employees are dissatisfied. In the three remaining teams conditions have changed very little compared with the situation before the reorganization. In the workshop team decisions are reached jointly. This team functions in an innovative way. Even though the new organizational structure has quite divergent consequences and some leaders have problems, the majority of the hospital employees support the new structure.
A hierarchical model of the mind is required for a more integrated understanding of psychic conflict. At a higher developmental level, the hierarchical model includes the tripartite model, and at a lower level it includes an object-relations model. Psychic conflicts may be classified into object relations conflicts and structural conflicts. The object-relations class of psychic conflict covers the phase of psychic development prior to id-ego-superego differentiation. The earlier psychoanalytic writings tended to ascribe all kinds of symptoms, conflicts, and disorders to structural conflicts. Logical and empirical evidence against the universality of structural conflicts in various disorders and symptoms, even psychoneurotic symptoms, has been summarized and discussed.
In order to describe the information processing mechanisms of a neural system a basic building block (sub-network), consisting of a group of interacting neural elements, is introduced. By interconnecting such units to give an integrated structure, a hierarchically-organised processing chain is formed. The properties of such a system are shown to provide a logical description of information processing which links high level events with underlying neural mechanisms.
Technology assessment of an imaging method such as magnetic resonance (MR) is a complicated concept that includes aspects of epidemiology, biostatistics, clinical efficacy determination, outcomes assessment, and knowledge of the technical and medical bases of the imaging method under study. To enhance understanding of the interrelations of the different aspects of technology assessment, a hierarchical model is proposed. This extends from the basic imaging physics domain through clinical applications in diagnosis and treatment decisions to patient outcomes and ultimately societal considerations. This overview paper presents the conceptual continuum of the hierarchical model, and then describes the interrelationships among efficacy, cost effectiveness, and outcomes research as components embedded within the context of technology assessment. It also points out how scientific quality of research in MR imaging assessment can be enhanced through improved research design that takes into account basic concepts of the model.
A stochastic computer model was developed to simulate individual pigs in a hierarchical breeding system. The bioeconomic model was designed as a tool to facilitate the evaluation of selection, culling, and management strategies for a three-tiered breeding structure. Events such as mating, farrowing, and selection occurred weekly. Variables included number of pigs born alive, survival rate from birth to weaning, average daily gain and backfat at 110 kg, number of pigs weaned, feed per gain, days from weaning to 110 kg, age at puberty, and growth rate and weight of sows and service boars. Also included were probabilities of conception, return to estrus by week, survival, involuntary culling, male infertility, and unacceptable conformation. Variables important for selection were determined by breeding value, individual and maternal heterosis, parity, size of birth litter, sex, age of dam, genetic and environmental relationships between variables, and common litter, permanent, and random environmental effects. Variables derived from selection variables were computed by regression using phenotypic relationships between all variables. Also, a random environmental effect was added to predicted performance. Means and variances of variables differed between genetic lines. Production costs included feed, non-feed operating, fixed, and replacement stock costs. Income included market animals, culls, and replacements sold to lower tiers. Effects of changes in backfat on market value and sow maintenance feed costs were not modeled. An example is given to illustrate model output.
Nucleotide recoding RNA sequencing methods (NR-seq; TimeLapse-seq, SLAM-seq, TUC-seq, etc.) are powerful approaches for assaying transcript population dynamics. In addition, these methods have been extended to probe a host of regulated steps in the RNA life cycle. Current bioinformatic tools significantly constrain analyses of NR-seq data. To address this limitation, we developed EZbakR (https://github.com/isaacvock/EZbakR), an R package to facilitate a more comprehensive set of NR-seq analyses, and fastq2EZbakR (https://github.com/isaacvock/fastq2EZbakR), a Snakemake pipeline for flexible preprocessing of NR-seq datasets, collectively referred to as the EZbakR suite. Together, these tools generalize many aspects of the NR-seq analysis workflow. The fastq2EZbakR pipeline can assign reads to a diverse set of genomic features (e.g., genes, exons, splice junctions), and EZbakR can perform analyses on any combination of these features. EZbakR extends standard NR-seq mutational modeling to support multi-label analyses (e.g., s4U and s6G dual labeling), and implements an improved hierarchical model to better account for transcript-to-transcript variance in metabolic label incorporation. EZbakR also generalizes dynamical systems modeling of NR-seq data to support analyses of premature mRNA processing and flow between subcellular compartments. Finally, EZbakR implements flexible and well-powered comparative analyses of all estimated parameters via design matrix-specified generalized linear modeling. The EZbakR suite will thus allow researchers to make full, effective use of NR-seq data.
Which is the most convenient database model considering specific applications? The goal of this paper is to try to answer this question by the use of a chemical example. Examples of requests describe the problems of insertion, deletion, and updating; these requests are analyzed for the hierarchical model and are expressed in a relational language defined by the authors and in Socrate for the network model.
ObjectiveThis work establishes a methodological framework for sources of pilot distraction and interruptions in a structured model that can be used as a tool for cockpit design/procedure assessment.BackgroundPilots must complete complex tasks, and distractions can impair performance and lead to errors that can cause aircraft accidents. Although various cockpit distractors are examined individually, there is no integrated approach.MethodDistraction and interruption sources were identified through a literature review and confirmed/extended by interviews with airline pilots. Associated weights were determined through pairwise comparisons, yielding a hierarchical model using the Analytic Hierarchy Process.Results26 sources of pilot distraction and interruptions were quantified and categorized into four categories: communication, head-down time, responding to abnormal conditions & unexpected situations, and searching for traffic.ConclusionA taxonomic structure for assessment is achieved with the top 5 sources identified as communications, technical interruptions, experience in type, environmental factors, operational irregularities, and airspace high terrain, accounting for 63.07%.ApplicationThe structured system is a flexible assessment scale that provides a taxonomic framework for airline risk management, supports future research, and cockpit design efforts.
BACKGROUND: Patients with severe mental disorders (SMD) are at substantially elevated risk for metabolic syndrome (MetS), contributing to excess cardiovascular morbidity and premature mortality. Adverse childhood experiences (ACEs) have been associated with dysregulation of metabolic pathways, yet their contribution to MetS risk in SMD remains poorly understood. OBJECTIVE: This study aimed to investigate the association between ACEs and MetS in outpatients with bipolar disorder (BD) and schizophrenia (SZ) in clinical remission and to identify independent and incremental predictors of MetS using a hierarchical analytical framework. METHODS: This cross-sectional study included 140 outpatients with SMD (96 with BD and 44 with SZ) in clinical remission, recruited from a university hospital in Eastern Turkey. MetS was defined according to NCEP-ATP III criteria, and ACEs were assessed using the Turkish version of the Adverse Childhood Experiences Scale (ACE-TR). Hierarchical and multivariable logistic regression analyses were performed to examine factors associated with MetS. RESULTS: MetS was highly prevalent in this sample (46.4%). ACE-TR total score was independently and consistently associated with MetS across all hierarchical models (odds ratio [OR] range: 1.68-1.77), with each one-unit increase conferring approximately 71% higher odds in the fully adjusted model (OR = 1.71; 95% confidence interval [CI] 1.26-2.32; P = 0.001). The number of hospitalizations was the only other independently associated variable (OR = 1.19; 95% CI 1.02-1.39). Sexual abuse (16.9% vs. 2.7%; P = 0.004), emotional neglect (63.1% vs. 30.7%; P < 0.001), and physical neglect (30.8% vs. 14.7%; P = 0.022) were significantly more prevalent in the MetS group. ACE-TR total score was positively correlated with waist circumference and triglyceride levels. CONCLUSION: The strong and consistent association between ACEs and MetS underscores the importance of trauma-informed care models in psychiatric practice, where metabolic comorbidity remains a leading cause of premature mortality.
This paper reviews methods for mapping geographical variation in disease incidence and mortality. Recent results in Bayesian hierarchical modelling of relative risk are discussed. Two approaches to relative risk estimation, along with the related computational procedures, are described and compared. The first is an empirical Bayes approach that uses a technique of penalized log-likelihood maximization; the second approach is fully Bayesian, and uses an innovative stochastic simulation technique called the Gibbs sampler. We chose to map geographical variation in breast cancer and Hodgkin's disease mortality as observed in all the health care districts of Sardinia, to illustrate relevant problems, methods and techniques.