The Roy Adaptation Model (3rd Edition)
Facunda Ganesh
She was a Robert Wood Johnson postdoctoral fellow at the University of California, San Francisco, from 1983 to 1985 as a clinical nurse scholar in neuroscience. During this time, she researched nursing interventions for cognitive recovery in head injuries and the influence of nursing models on clinical decision making.
Based on Roy, humans are holistic beings that are in constant interaction with their environment. Humans use a system of adaptation, both innate and acquired, to respond to the environmental stimuli they experience. Human systems can be individuals or groups, such as families, organizations, and the whole global community.
As one of the weaknesses of the theory that applying it is time-consuming, applying the model to emergencies requiring quick action is difficult to complete, the individual might have completed the whole adaptation process without the benefit of having a complete assessment for thorough nursing interventions.
Unlike Levine, although the latter tackled adaptation, Roy focused on the whole adaptive system itself. Each concept was linked with the coping mechanisms of every individual in the process of adapting.
The climate system is dynamic affecting all aspects of our health, wealth, security and competitiveness. The climate has, is and will continue to change and change means adaptation. But which future adaptation pathway to choose is often a difficult and complex question with many options, collateral effects, investments and many partners. Adaptation science has a key and fundamental role in this process to help decision-makers choose the right pathway forward.
This document presents and summarizes 35 selected adaptation models for climate change. The Compendium is a contribution to the Nairobi Work Program and provides scientific and technical information on adaptation models for climate change.
The mechanisms triggering the human immunodeficiency virus type I (HIV-1) to switch the coreceptor usage from CCR5 to CXCR4 during the course of infection are not entirely understood. While low CD4+ T cell counts are associated with CXCR4 usage, a predominance of CXCR4 usage with still high CD4+ T cell counts remains puzzling. Here, we explore the hypothesis that viral adaptation to the human leukocyte antigen (HLA) complex, especially to the HLA class II alleles, contributes to the coreceptor switch. To this end, we sequence the viral gag and env protein with corresponding HLA class I and II alleles of a new cohort of 312 treatment-naive, subtype C, chronically-infected HIV-1 patients from South Africa. To estimate HLA adaptation, we develop a novel computational approach using Bayesian generalized linear mixed models (GLMMs). Our model allows to consider the entire HLA repertoire without restricting the model to pre-learned HLA-polymorphisms. In addition, we correct for phylogenetic relatedness of the viruses within the model itself to account for founder effects. Using our model, we observe that CXCR4-using variants are more adapted than CCR5-using variants (p-value = 1.34e-2). Additionally, adapted CCR5-using variants have a significantly lower predicted false positive rate (FPR) by the geno2pheno[coreceptor] tool compared to the non-adapted CCR5-using variants (p-value = 2.21e-2), where a low FPR is associated with CXCR4 usage. Consequently, estimating HLA adaptation can be an asset in predicting not only coreceptor usage, but also an approaching coreceptor switch in CCR5-using variants. We propose the usage of Bayesian GLMMs for modeling virus-host adaptation in general.
Viral control via treatment is currently our only counter mechanism against HIV-1 with no practicable cure nor a vaccine at hand. In treatment-naive patients, host immune responses denote the only counter-mechanism. HLA adaptation and coreceptor usage of HIV-1 play a major role on the capability of the host immune responses to control the virus. The interplay between both factors, however, has remained unexplored so far. Assessing the degree of viral HLA adaptation is challenging due to the exceptional genetic diversity of both the HLA complex and HIV-1. Therefore, current approaches constrain the adaptation prediction to a set of p-value selected HLA-polymorphism candidates. The selection of these candidates, however, requires extensive external large-scale population-based experiments that are not always available for the population of interest, especially not for newly emerging viruses. In this work, we present a novel computational approach using Bayesian generalized linear mixed models (GLMMs) that enables not only to predict the adaptation to the complete HLA profile of a patient, but also to handle phylogenetic-dependencies of the variants within the model directly. Using this light-weight approach for modeling (any) virus-host adaptation, we show that HLA adaptation is associated with coreceptor usage.
Data Availability: The NGS sequences from the study cohort are available under the BioProject ID PRJNA810303 ( =PRJNA810303) with the following corresponding BioSample Accession IDs: SAMN26242168 - SAMN26241863 and SAMN28728524 - SAMN28728529 ( =bioproject_biosample_all&from_uid=810303). The generated consensus nucleotide sequences are provided on Zenodo at Due to privacy reasons, the HLA information cannot be published. Consequently, we cannot publish the trained models as the HLA information can be exposed thereby. The corresponding Ethics Committee can be reached at -us0715-8777.aspx. A minimal data set including the estimated adaptation scores for all presented data sets is available on Zenodo at All code not compromising the privacy concerns, including the complete call to train and build the multinomial Bayesian generalized linear mixed models, is provided at GitHub at TOOL.git to be used as template. To ensure reproducibility, we have used the workflow manager Snakemake 5.4.5 (10.12688/f1000research.29032.2) and the Anaconda Software Distribution ( ) for the training and prediction pipeline. We have used the R Language and Environment for Statistical Computing, Version 3.5.1 ( -project.org/) for modeling and analyses.
To jointly model HLA class I and class II adaptation, we develop a novel computational approach. In detail, the adaptation of a particular amino acid in a viral sequence to the host HLA profile is inferred using phylogeny-corrected, multinomial, Bayesian generalized linear mixed models (GLMMs). Without the need for an additional model, GLMMs allow to correct for phylogenetic relatedness of the variants directly by modeling the between-subject correlation as a group-level effect. Using a Bayesian setting allows to learn feature importance directly within the model by applying the horseshoe prior on all HLA class I and class II alleles of the data set and without the need for additional preselection steps or a large amount of data. The horseshoe prior is used in sparse model settings to shrink the majority of the coefficients to zero by having the point mass at zero and symmetric fat tails [46].
We divide the newly sequenced study cohort of 312 samples based on a CD4+ T cell count cutoff of 500 cells/mm3 into a chronic_highCD4 data set (n = 38) and a chronic_lowCD4 count data set (n = 274). High CD4+ T cell count indicates a stronger immune system. Since infection duration is not known for the patients, a high CD4+ T cell count might indicate that the patients have been infected for a shorter time (less chronic). Moreover, a virus is assumed to be less adapted to a host with a strong immune system compared to a host with a weak immune system. Thus, the adaptation model is only trained on the chronic_lowCD4 data set. In addition, we create an artificial data set (random) based on the chronic_lowCD4 data set, where the HLA alleles per HLA gene and haplotype have been randomized 100 times. HLA adaptation for this random data set is predicted with models based on the chronic_lowCD4 data set as well. For further validation of the adaptation model, we estimate HIV-1 adaptation of publicly available cohort of acutely-infected HIV-1 patients (n = 23) from the Los Alamos HIV sequence database ( ). The acute data set comprised the p24 sequence as well as the HLA I information of 23 patients with the following accession numbers GQ275453, GQ275750, GQ275852, GQ275894, KM192425, KM192440, KM192471, KM192536, KM192566, KM192640, KM192653, KM192674, KM192686, KM192702, KM192762, KM192844, KM192856, KM192870, KM192884, KM192912, KM192942, KM192970, KM192998. Since only the HLA I profile was available, we build an adaptation model based only on the HLA I profile for this purpose (n = 274).
In the following, we formalize the per-site model and the final adaptation score. Afterwards, we present the selection process of the frequent sites. Since each per-site model is built using Bayesian GLMMs, we provide a brief introduction to Bayesian GLMMs and their benefit over classical GLMMs and phylogeny-corrected LMMs. In addition, we provide a section on the model specification for each per-site model.
The host immune system is represented by the HLA alleles of the HLA I and HLA II genes. The HLA profile of an individual consists in our case of six (homozygous in all genes) to 12 (heterozygous in all genes) different HLA alleles. Let H represent the set of all possible HLA I and HLA II alleles. A particular HLA profile h is encoded as a binary vector with zeros everywhere, apart from the positions corresponding to the HLA alleles of the HLA profile. Note, thereby homozygosity is not modeled.
Assuming independence among sites and relevance of only frequent sites, the conditional probability over the sequence s can be decomposed to the product over the conditional probabilities over all m frequent sites sj (per-site model):(2)
We model the conditional probabilities for site adaptation (see Eqs 2 and 3) using separate multinomial Bayesian generalized linear mixed models (GLMMs). GLMMs are tailored for data with non-normal response distributions and dependency structures in the observations by combining the properties of generalized linear models (GLMs) [51, 52] and linear mixed models (LMMs). While GLMs model non-normal response distributions (such as binomial) via link functions of the means (e.g. logistic regression), LMMs enable to model not only population-level effects but also group-level effects assuming dependency structures in the samples. Mathematically, GLMMs have the following form excluding the residuals (ϵ) [53]:(4)where Y is the response variable, β and u the coefficients for the population and group-level effects, respectively, X and Z the corresponding design matrices and g(x) a link function relating the response Y to the linear predictor η. Thus, between-subject correlations, like the phylogenetic relatedness of some viruses, can be modeled as a group-level effect.
582128177f