Latent Population Research Articles

The Impact of Test and Sample Characteristics on Model Selection and Classification Accuracy in the Multilevel Mixture IRT Model.

The standard item response theory (IRT) model assumption of a single homogenous population may be violated in real data. Mixture extensions of IRT models have been proposed to account for latent heterogeneous populations, but these models are not designed to handle multilevel data structures. Ignoring the multilevel structure is problematic as it results in lower-level units aggregated with higher-level units and yields less accurate results, because of dependencies in the data. Multilevel data structures cause such dependencies between levels but can be modeled in a straightforward way in multilevel mixture IRT models. An important step in the use of multilevel mixture IRT models is the fit of the model to the data. This fit is often determined based on relative fit indices. Previous research on mixture IRT models has shown that performances of these indices and classification accuracy of these models can be affected by several factors including percentage of class-variant items, number of items, magnitude and size of clusters, and mixing proportions of latent classes. As yet, no studies appear to have been reported examining these issues for multilevel extensions of mixture IRT models. The current study aims to investigate the effects of several features of the data on the accuracy of model selection and parameter recovery. Results are reported on a simulation study designed to examine the following features of the data: percentages of class-variant items (30, 60, and 90%), numbers of latent classes in the data (with from 1 to 3 latent classes at level 1 and 1 and 2 latent classes at level 2), numbers of items (10, 30, and 50), numbers of clusters (50 and 100), cluster size (10 and 50), and mixing proportions [equal (0.5 and 0.5) vs. non-equal (0.25 and 0.75)]. Simulation results indicated that multilevel mixture IRT models resulted in less accurate estimates when the number of clusters and the cluster size were small. In addition, mean Root mean square error (RMSE) values increased as the percentage of class-variant items increased and parameters were recovered more accurately under the 30% class-variant item conditions. Mixing proportion type (i.e., equal vs. unequal latent class sizes) and numbers of items (10, 30, and 50), however, did not show any clear pattern. Sample size dependent fit indices BIC, CAIC, and SABIC performed poorly for the smaller level-1 sample size. For the remaining conditions, the SABIC index performed better than other fit indices.

HIV-1 Latency and Latency Reversal: Does Subtype Matter?

Cells that are latently infected with HIV-1 preclude an HIV-1 cure, as antiretroviral therapy does not target this latent population. HIV-1 is highly genetically diverse, with over 10 subtypes and numerous recombinant forms circulating worldwide. In spite of this vast diversity, much of our understanding of latency and latency reversal is largely based on subtype B viruses. As such, most of the development of cure strategies targeting HIV-1 are solely based on subtype B. It is currently assumed that subtype does not influence the establishment or reactivation of latent viruses. However, this has not been conclusively proven one way or the other. A better understanding of the factors that influence HIV-1 latency in all viral subtypes will help develop therapeutic strategies that can be applied worldwide. Here, we review the latest literature on subtype-specific factors that affect viral replication, pathogenesis, and, most importantly, latency and its reversal.

A Bayesian Hierarchical Model for Related Densities by using Pólya Trees

Summary Bayesian hierarchical models are used to share information between related samples and to obtain more accurate estimates of sample level parameters, common structure and variation between samples. When the parameter of interest is the distribution or density of a continuous variable, a hierarchical model for continuous distributions is required. Various such models have been described in the literature using extensions of the Dirichlet process and related processes, typically as a distribution on the parameters of a mixing kernel. We propose a new hierarchical model based on the Pólya tree, which enables direct modelling of densities and enjoys some computational advantages over the Dirichlet process. The Pólya tree also enables more flexible modelling of the variation between samples, providing more informed shrinkage and permitting posterior inference on the dispersion function, which quantifies the variation between sample densities. We also show how the model can be extended to cluster samples in situations where the observed samples are believed to have been drawn from several latent populations.

Numerical Solution of Diabetes Mellitus Model without Genetic Factors with Treatment using Runge Kutta Method

This study discusses the numerical solution of the model analysis of diabetes mellitus without genetic factors with treatment using the fourth order Runge-Kutta. The mathematical model in the form of a differential equations system that includes S (Susceptible), E (Exposed), I (Infected), and IT (Infected with Treatment) which has been simplified into the total population class (N), the latent population (E), the patient population without treatment (I) and the patient population with treatment (IT) as the initial value and the value of A, μ, δ1, δ2, α, β as parameters were resolved numerically using the fourth order Runge-Kutta method and performed as many iteration with interval time or h = 0,01 years. The data taken in Makassar City for each class population. Initial and parameters values are subtituted into numerical solutions to the model which are then simulated using Maple, indicating that at t = 5 years the magnitude of the entire population class (N) = 195.216, the latent population (E) = 31, the patient population without treatment (I) = 4.836 and the patient population with treatment (IT) = 1.454. The results concluded that the magnitude of the value for the rate of each class of population in the next five years decreased due to the population dead and/or move the next class of the population.

Strength of T cell signaling regulates HIV-1 replication and establishment of latency.

A major barrier to curing HIV-1 is the long-lived latent reservoir that supports re-emergence of HIV-1 upon treatment interruption. Targeting this reservoir will require mechanistic insights into the establishment and maintenance of HIV-1 latency. Whether T cell signaling at the time of HIV-1 infection influences productive replication or latency is not fully understood. We used a panel of chimeric antigen receptors (CARs) with different ligand binding affinities to induce a range of signaling strengths to model differential T cell receptor signaling at the time of HIV-1 infection. Stimulation of T cell lines or primary CD4+ T cells expressing chimeric antigen receptors supported HIV-1 infection regardless of affinity for ligand; however, only signaling by the highest affinity receptor facilitated HIV-1 expression. Activation of chimeric antigen receptors that had intermediate and low binding affinities did not support provirus transcription, suggesting that a minimal signal is required for optimal HIV-1 expression. In addition, strong signaling at the time of infection produced a latent population that was readily inducible, whereas latent cells generated in response to weaker signals were not easily reversed. Chromatin immunoprecipitation showed HIV-1 transcription was limited by transcriptional elongation and that robust signaling decreased the presence of negative elongation factor, a pausing factor, by more than 80%. These studies demonstrate that T cell signaling influences HIV-1 infection and the establishment of different subsets of latently infected cells, which may have implications for targeting the HIV-1 reservoir.

One-step adaptive markov random field for structured compressive sensing

Recently, Markov random fields (MRFs) have gained much success in sparse signal recovery. One of the challenges is to adaptively estimate the MRF parameters from a few compressed measurements in compressive sensing (CS). To address this problem, a recently developed method proposes to estimate the MRF parameters based on the point estimation of sparse signals. However, the point estimation cannot depict the statistical uncertainty of the latent sparse signal, which can result in inaccurate parameters estimation; thus, limiting the ultimate performance. In this study, we propose a one-step MRF based CS that estimates the MRF parameters from the given measurements through solving a maximum marginal likelihood (MML) problem. Since the marginal likelihood is obtained from averaging over the latent sparse signal population, it offers better generalization over all the latent sparse signals than the point estimation. To solve the MML problem effectively, we approximate the MRF distribution by the product of two simpler distributions, which enables to produce closed-form solutions for all unknown variables with low computational cost. Extensive experiments on a synthetic and three real-world datasets demonstrate the effectiveness of the proposed method in recovery accuracy, noise tolerance, and runtime.

Population dynamics of the copepod invader Oithona davisae in the Black Sea

During 2013-2016 in Sevastopol Bay, seasonal and interannual dynamics of the population density, age and sex composition, fecundity, and mortality of males and females of Oithona davisae were studied. A maximum population number (up to 554 ind × 10$^{3}$ m$^{-3}$) was registered at the end of summer?end of autumn. In this period, the nauplii and early copepodite stages constituted up to 95% of the Oithona davisae population. During 1-2.5 cold winter?spring months, the population consisted only of females, who were not numerous. The phase of latent population development was recorded from April until July-August, with a high share of ovigerous females, maximum clutch size, and high egg production rate but low population density, which may be due to predator pressure from lobate ctenophores. The average share of adult males amounted to 32% at the end of spring and 27% at the beginning of winter, while the share of dead males reached maximum values (20.7%) during the winter degradation period for the population. On the basis of the studied successions of the population's developmental stages, from ovigerous females through nauplii and early copepodites to adults, the number of generations produced by Oithona davisae during the reproductive period (10-11) was determined.

Chemotherapy resistance and stemness in mitotically quiescent human breast cancer cells identified by fluorescent dye retention

Metastatic recurrence in breast cancer is a major cause of mortality and often occurs many years after removal of the primary tumour. This process is driven by the reactivation of disseminated tumour cells that are characterised by mitotic quiescence and chemotherapeutic resistance. The ability to reliably isolate and characterise this cancer cell population is critical to enable development of novel therapeutic strategies for prevention of breast cancer recurrence. Here we describe the identification and characterisation of a sub-population of slow-cycling tumour cells in the MCF-7 and MDA-MB-231 human breast cancer cell lines based on their ability to retain the lipophilic fluorescent dye Vybrant® DiD for up to six passages in culture. Vybrant® DiD-retaining (DiD+) cells displayed significantly increased aldehyde dehydrogenase activity and exhibited significantly reduced sensitivity to chemotherapeutic agents compared to their rapidly dividing, Vybrant® DiD-negative (DiD−) counterparts. In addition, DiD+ cells were exclusively capable of initiating population re-growth following withdrawal of chemotherapy. The DiD+ population displayed only partial overlap with the CD44+CD24−/low cell surface protein marker signature widely used to identify breast cancer stem cells, but was enriched for CD44+CD24+ cells. Real-time qPCR profiling revealed differential expression of epithelial-to-mesenchymal transition and stemness genes between DiD+ and DiD− populations. This is the first demonstration that both MCF-7 and MDA-MB-231 human breast cancer lines contain a latent therapy-resistant population of slow-cycling cells capable of initiating population regrowth post-chemotherapy. Our data support that label-retaining cells can serve as a model for identification of molecular mechanisms driving tumour cell quiescence and de novo chemoresistance and that further characterisation of this prospective tumour-reinitiating population could yield novel therapeutic targets for elimination of the cells responsible for breast cancer recurrence.

Stochastic Dynamics of the Latently Infected Cell Reservoir During HIV Infection.

The presence of cells latently infected with HIV is currently considered to be a major barrier to viral eradication within a patient. Here, we consider birth-death-immigration models for the latent cell population in a single patient, and present analytical results for the size of this population in the absence of treatment. We provide results both at steady state (viral set point), and during the non-equilibrium setting of early infection. We obtain semi-analytic results showing how latency-reversing drugs might be expected to affect the size of the latent pool over time. We also analyze the probability of rare mutant viral strains joining the latent cell population, allowing for steady-state and dynamic viral populations within the host.

Distribution-free models for latent mixed population responses in a longitudinal setting with missing data.

Many biomedical and psychosocial studies involve population mixtures, which consist of multiple latent subpopulations. Because group membership cannot be observed, standard methods do not apply when differential treatment effects need to be studied across subgroups. We consider a two-group mixture in which membership of latent subgroups is determined by structural zeroes of a zero-inflated count variable and propose a new approach to model treatment differences between latent subgroups in a longitudinal setting. It has also been incorporated with the inverse probability weighted method to address data missingness. As the approach builds on the distribution-free functional response models, it requires no parametric distribution model and thereby provides a robust inference. We illustrate the approach with both real and simulated data.

A unified genetic association test robust to latent population structure for a count phenotype.

Confounding caused by latent population structure in genome-wide association studies has been a big concern despite the success of genome-wide association studies at identifying genetic variants associated with complex diseases. In particular, because of the growing interest in association mapping using count phenotype data, it would be interesting to develop a testing framework for genetic associations that is immune to population structure when phenotype data consist of count measurements. Here, I propose a solution for testing associations between single nucleotide polymorphisms and a count phenotype in the presence of an arbitrary population structure. I consider a classical range of models for count phenotype data. Under these models, a unified test for genetic associations that protects against confounding was derived. An algorithm was developed to efficiently estimate the parameters that are required to fit the proposed model. I illustrate the proposed approach using simulation studies and an empirical study. Both simulated and real-data examples suggest that the proposed method successfully corrects population structure.

D-106 Chromatin Functional States Correlate with the Reversal of Latently HIV-1 Infected Primary CD4+ T cells

Current antiretroviral therapies (ART) do not allow for the eradication of the human immunodeficiency virus (HIV) due to the presence of latent proviruses in rare, long-lived resting CD4+ T cells. The main research efforts to eliminate the viral reservoir are focused on the use of latency reversing agents (LRAs) to force the reactivation of the latent provirus, while maintaining ART to prevent de novo infections. Subsequently, reactivation of HIV expression would kill reservoir cells via viral cytopathic effects and/or immune clearance (“shock and kill” strategy). So far, no LRAs tested in clinical trials have succeeded in reducing the reservoir. Furthermore, there is limited understanding as to why some latent proviruses are being induced, while others are not. We used an improved dual-fluorescent HIV reporter (GKO), which distinguishes productively infected cells from the latent population, to investigate the efficacy of the “shock and kill” strategy. In addition, GKO provides a unique opportunity to (1) explore the impact of HIV integration site specificity on the fate of the infection, and to (2) characterize the inducible subpopulation of latently infected cells, since it allows the isolation of inducible latent and non-inducible latent populations from the productively infected majority. We first showed that our patients' data was consistent with previously published studies. However, we found that at most 5% of GKO latent proviruses were reactivated. Moreover, the analysis of HIV-1 integration sites from productively, non-inducible and inducible latently infected populations reveals heterogeneity within the latent infections. In contrast to non-inducible latent infections, the integration sites of inducible latent proviruses have similar features to those of productive proviruses, thus demonstrating a prominent role for the site of integration and its chromatin context for the fate of the initial infection as well as for latency reversal. Our study shows an important roadblock for the “shock and kill” approach to reservoir eradication. Differences between inducible and permanently latent reservoir cells suggest that complete reservoir reactivation and eradication with LRAs may prove impossible, and that a multipronged “functional cure” approach may be necessary.

The spread and prediction of Ebola virus

Ebola virus is a potent infectious disease virus that can cause Ebola haemorrhagic fever caused by human and primate. It has high mortality and easy infectivity to form a great obstacle to the steady development of human society. The profound understanding of the virus is particularly important harm. In this paper, a number of mathematical models are established to solve this problem. The software is used to analyze and predict the propagation of Ebola virus. The residual analysis is used to test the model. Finally, the effects of various control measures on controlling the epidemic are analyzed. In order to solve the problem, we will establish the infectious disease model to dynamically describe the spread of the virus in the 'virtual orangutan population'. Considering that the latent population is analyzed in this question, we will improve the model. Join the latent group (), and the migrants are divided into self-healing () and the dead (), to establish a suitable solution to this problem model. According to the relevant data given in the title, differential equations were established. For the second question, this question involves the one-way transmission of the virus across the species, so we can improve the model, on the basis of human contact with orangutans infected groups, the establishment of a one-way model to solve this problem. On the basis of the problem one, the differential equation is established, the model is predicted and tested. In the case of question 3, the number of human susceptible groups is much higher than that of the orangutan infection group by comparing the relevant data with the increase of the cure rate to 80% after the intervention of the outside experts. Therefore, the original data of human populations from experts can be ignored. Since then the virus spreads within a single species, the differential equation can be established according to the model in question 1 and the data values in the virtual human population are predicted. For question 4, the effect of the measures such as the strict enforcement of the various epidemic control measures and the improvement of the drug effect on the control of the epidemic are analyzed by comparing the above-mentioned models with the control measures.

Genome-Wide Likelihood Ratio Tests under Heterogeneity

The commonly used statistical methods in medical research generally assume patients arise from one homogeneous population. However, the existence and importance of significant heterogeneity have been widely documented. It is well known that common and complex human diseases usually have heterogeneous disease etiology, which often involves interplay of multiple genetic and environmental factors, leading to latent population substructure. Genome-wide association studies (GWAS) is a useful tool to uncover genetic association with disease of interest, while linkage analysis is a commonly used method to identify statistical association between the inheritance of a human disease and inheritance of marker loci that are in linkage with disease causing loci. We propose a likelihood ratio test for genome-wide linkage analysis under genetic heterogeneity using family data. We derive a closed-form formula for the LRT test statistic and provide explicit asymptotic null distribution. The closed form asymptotic distribution allows easy determination of the asymptotic p-values. Our extensive simulation studies indicate that the proposed test has proper type I error and good power under genetic heterogeneity. In order to simplify application of the proposed method for non-statisticians, we develop an R package gLRTH to implement the proposed LRT for genome-wide linkage analysis as well as Qian and Shao’s LRT for GWAS under heterogeneity. The newly developed open source R package gLRTH is available at CRAN.

Understanding the demographic drivers of realized population growth rates.

Identifying the demographic parameters (e.g., reproduction, survival, dispersal) that most influence population dynamics can increase conservation effectiveness and enhance ecological understanding. Life table response experiments (LTRE) aim to decompose the effects of change in parameters on past demographic outcomes (e.g., population growth rates). But the vast majority of LTREs and other retrospective population analyses have focused on decomposing asymptotic population growth rates, which do not account for the dynamic interplay between population structure and vital rates that shape realized population growth rates (λt=Nt+1/Nt) in time-varying environments. We provide an empirical means to overcome these shortcomings by merging recently developed "transient life-table response experiments" with integrated population models (IPMs). IPMs allow for the estimation of latent population structure and other demographic parameters that are required for transient LTRE analysis, and Bayesian versions additionally allow for complete error propagation from the estimation of demographic parameters to derivations of realized population growth rates and perturbation analyses of growth rates. By integrating available monitoring data for Lesser Scaup over 60yr, and conducting transient LTREs on IPM estimates, we found that the contribution of juvenile female survival to long-term variation in realized population growth rates was 1.6 and 3.7 times larger than that of adult female survival and fecundity, respectively. But a persistent long-term decline in fecundity explained 92% of the decline in abundance between 1983 and 2006. In contrast, an improvement in adult female survival drove the modest recovery in Lesser Scaup abundance since 2006, indicating that the most important demographic drivers of Lesser Scaup population dynamics are temporally dynamic. In addition to resolving uncertainty about Lesser Scaup population dynamics, the merger of IPMs with transient LTREs will strengthen our understanding of demography for many species as we aim to conserve biodiversity during an era of non-stationary global change.

A Bayesian latent mixture model approach to assessing performance in stock-flow reasoning

AbstractPeople often perform poorly on stock-flow reasoning tasks, with many (but not all) participants appearing to erroneously match the accumulation of the stock to the inflow – a response pattern attributed to the use of a “correlation heuristic”. Efforts to improve understanding of stock-flow systems have been limited by the lack of a principled approach to identifying and measuring individual differences in reasoning strategies. We present a principled inferential method known as Hierarchical Bayesian Latent Mixture Models (HBLMMs) to analyze stock-flow reasoning. HBLMMs use Bayesian inference to classify different patterns of responding as coming from multiple latent populations. We demonstrate the usefulness of this approach using a dataset from a stock-flow drawing task which compared performance in a problem presented in a climate change context, a problem in a financial context, and a problem in which the financial context was used as an analogy to assist understanding in the climate problem. The hierarchical Bayesian model showed that the proportion of responses consistent with the “correlation heuristic” was lower in the financial context and financial analogy context than in the pure climate context. We discuss the benefits of HBLMMs and implications for the role of contexts and analogy in improving stock-flow reasoning.

Microsatellites Reveal Genetic Homogeneity among Outbreak Populations of Crown-of-Thorns Starfish (Acanthaster cf. solaris) on Australia’s Great Barrier Reef

Specific patterns in the initiation and spread of reef-wide outbreaks of crown-of-thorns starfish are important, both to understand potential causes (or triggers) of outbreaks and to develop more effective and highly targeted management and containment responses. Using analyses of genetic diversity and structure (based on 17 microsatellite loci), this study attempted to resolve the specific origin for recent outbreaks of crown-of-thorns on Australia’s Great Barrier Reef (GBR). We assessed the genetic structure amongst 2705 starfish collected from 13 coral reefs in four regions that spanned ~1000 km of the GBR. Our results indicate that populations sampled across the full length of the GBR are genetically homogeneous (G’ST = −0.001; p = 0.948) with no apparent genetic structure between regions. Approximate Bayesian computational analyses suggest that all sampled populations had a common origin and that current outbreaking populations of crown-of-thorns starfish (CoTS) in the Swains are not independent of outbreak populations in the northern GBR. Despite hierarchical sampling and large numbers of CoTS genotyped from individual reefs and regions, limited genetic structure meant we were unable to determine a putative source population for the current outbreak of CoTS on the GBR. The very high genetic homogeneity of sampled populations and limited evidence of inbreeding indicate rapid expansion in population size from multiple, undifferentiated latent populations.

What Can the Duration of Discovered Cartels Tell Us About the Duration of All Cartels?

Estimates of average cartel duration and the annual probability of cartel death are based on data for discovered cartels. It is recognised that these estimates could be biased because the population of discovered cartels may not be a representative sample of the latent population of cartels. This article constructs a birth‐death‐discovery process to investigate the source and direction of possible biases. Bayesian inference is used to provide bounds on the extent of the bias and deliver an improved set of beliefs on the probability of cartel death.

CONTINUOUS-TIME SEMI-MARKOV INFERENCE OF BIOMETRIC LAWS ASSOCIATED WITH A LONG-TERM CARE INSURANCE PORTFOLIO

AbstractUnlike the mortality risk on which actuaries have been working for more than a century, the long-term care (LTC) risk is relatively new and as of today hardly mastered. Semi-Markov processes have been identified as an adequate tool to study this risk. Nevertheless, access to data is limited and the associated literature still scarce. Insurers mainly use discrete time methods directly inspired from the study of mortality in order to build experience tables. Those methods however are not perfectly suited for the study of competing risk situations. This article provides a theoretical framework to estimate biometric laws associated with a LTC insurance portfolio. The presented method relies on a continuous-time semi-Markov model with three states: autonomy, disability and death. The process describing the state of disability is defined through its transition intensities. We provide a formula to infer the mortality of autonomous people from the mortality of the whole portfolio, on which we have more reliable knowledge. We then propose a parametric expression for the remaining intensities of the model. In particular, incidence in LTC is described by a logistic formula. Under the assumption that the disabled population is a mixture of two latent populations with respect to the category of pathology that caused LTC, we show that the resulting intensity of mortality in LTC takes a very peculiar form and depends on time spent in the LTC state. Estimation of parameters relies on the maximum likelihood method. Our parametric approach, while inducing model uncertainty, eliminates issues related to segmentation in age categories, smoothing or extrapolation at higher ages and thus proves very convenient for the practitioner. Finally, we provide an application using data from a real LTC insurance portfolio.

Dynamic N-occupancy models: estimating demographic rates and local abundance from detection-nondetection data.

Occupancy modeling is a widely used analytical technique for assessing species distributions and range dynamics. However, occupancy analyses frequently ignore variation in abundance of occupied sites, even though site abundances affect many of the parameters being estimated (e.g., extinction, colonization, detection probability). We introduce a new model ("dynamic N-occupancy") capable of providing accurate estimates of local abundance, population gains (reproduction/immigration), and apparent survival probabilities while accounting for imperfect detection using only detection/nondetection data. Our model utilizes heterogeneity in detection based on variations in site abundances to estimate latent demographic rates via a dynamic N-mixture modeling framework. We validate our model using simulations across a wide range of values and examine the data requirements, including the number of years and survey sites needed, for unbiased and precise estimation of parameters. We apply our model to estimate spatiotemporal heterogeneity in abundances of barred owls (Strix varia) within a recently invaded region in Oregon (USA). Estimates of apparent survival and population gains are consistent with those from a nearby radio-tracking study and elucidate how barred owl abundances have increased dramatically over time. The dynamic N-occupancy model greatly improves inferences on individual-level population processes from occupancy data by explicitly modeling the latent population structure.