Examining changes in sexual lifestyles in Britain between 1990-2010: a latent class analysis approach

BACKGROUND

Understanding sexual lifestyles and how they change over time is important for determining the likelihood of sexual health outcomes. Standard descriptive and regression methods are limited in their ability to capture multidimensional concepts such as sexual lifestyles. Latent Class Analysis (LCA) is a mixture modelling method that generates a categorical latent variable to derive homogenous groups from a heterogeneous population. Our study investigates (1) the potential of LCA to assess change over time in sexual lifestyles and (2) how quantifying this change using LCA compares to previous findings using standard approaches.

METHODS

Probability-sampled data from three rounds of the National Survey of Sexual Attitudes and Lifestyle (Natsal) were used, restricted to sexually active participants (i.e., those reporting sexual partners in the past year) aged 16-44 years (N1990 = 11,738; N2000 = 9,690; N2010 = 8,397). An LCA model was built from four variables: number of sexual partners (past year), number of partners without a condom (past year), age at first sex and self-perceived HIV risk. Covariates included age, ethnicity, educational attainment, same-sex attraction, and marital status. Multinomial regression analyses and Chi-Squared tests were used to investigate change over time in the size of each class.

RESULTS

We successfully used a LCA approach to examine change in sexual lifestyle over time. We observed a statistically significant increase between 1990 and 2010 in the proportion of men (χ2 = 739.49, p < 0.01) and women (χ2 = 1270.43, p < 0.01) in a latent class associated with reporting 2 or more partners in the last year, relatively high probabilities of reporting condomless sex partners, greater self-perceived HIV risk, and a high probability of first sex before age 16 years, increasing from 19.5% to 31.1% (men) and 9.9% to 22.1% (women).

CONCLUSION

Our results indicate the viability of LCA models to assess change over time for complex behavioural phenomena. They align with previous findings, namely changing sexual lifestyles in Britain in recent decades, partnership number driving class assignment, and significant sex differences in sexual lifestyles. This approach can be used to extend previous LCA models (e.g., to investigate the impact of COVID-19 on sexual lifestyles) and to support empirical evidence of change over time, facilitating more nuanced public health policy.

Harmonization of CSF and imaging biomarkers in Alzheimer's disease: Need and practical applications for genetics studies and preclinical classification

In Alzheimer's disease (AD) research, cerebrospinal fluid (CSF) Amyloid beta (Aβ), Tau and pTau are the most accepted and well validated biomarkers. Several methods and platforms exist to measure those biomarkers, leading to challenges in combining data across studies. Thus, there is a need to identify methods that harmonize and standardize these values. We used a Z-score based approach to harmonize CSF and amyloid imaging data from multiple cohorts and compared GWAS results using this approach with currently accepted methods. We also used a generalized mixture model to calculate the threshold for biomarker-positivity. Based on our findings, our normalization approach performed as well as meta-analysis and did not lead to any spurious results. In terms of dichotomization, cutoffs calculated with this approach were very similar to those reported previously. These findings show that the Z-score based harmonization approach can be applied to heterogeneous platforms and provides biomarker cut-offs consistent with the classical approaches without requiring any additional data.

In-situ and airborne hyperspectral data for detecting agricultural activities in a dense forest landscape

Maintaining rich biodiversity and being a habitat and resource for humans, tropical forests are one of the most important global biomes. These forest ecosystems have been experiencing a host of unregulated anthropogenic activities including illegal tourism, and shifting cultivation. The presence of human-habitats in the restricted zones of forest ecosystems is a direct indicator of the human activities that may accelerate deterioration of forest quality by area and tree species composition. Remote sensing data have been extensively used for mapping forest types, and biophysical characterization at various spatial scales. Several remote sensing datasets from multispectral, hyperspectral and LIDAR sensors are available for developing and validating a host of methodologies for remote sensing application in forestry. However, quantifying the quality of forest stands and detecting potential threats from the sporadic and small-scale human activities requires sub-pixel level remote sensing data analysis methods such as, spectral mixture modelling. Generally, most of the studies employ pixel-level supervised learning-based analysis techniques to detect infrastructure and settlements. However, if the settlements are smaller than the ground sampling distance and are under the canopy, pixel-based techniques are not suitable. Reinvigorated with progressive availability of hyperspectral imagery, spectral mixture modelling based sub-pixel image analysis is gaining prominence in the contemporary remote sensing application development. However, there is a paucity of high-resolution hyperspectral imagery and associated ground truth spectral measurements for assessing various methodological approaches on studies related to anthropogenic activities and forest disturbance. Most of the studies have relied upon simulating and synthesising the hyperspectral imagery and its associated ground truth spectra for implementation of methods and algorithms. This article presents a distinct dataset of high-resolution hyperspectral imagery and associated ground truth spectra of various vegetable crops acquired over a tropical forest ecosystem. The dataset is valuable for research on developing new discrimination models of forest and cultivated vegetation, classification methods, spectral matching analysis techniques, and sub-pixel target detection methods.

Estimation of invasive Group B Streptococcus disease risk in young infants from case-control serological studies

Background

Group B Streptococcus (GBS) infections are a major cause of invasive disease (IGbsD) in young infants and cause miscarriage and stillbirths. Immunization of pregnant women against GBS in addition to intrapartum antibiotic prophylaxis could prevent disease. Establishing accurate serological markers of protection against IGbsD could enable use of efficient clinical trial designs for vaccine development and licensure, without needing to undertake efficacy trials in prohibitively large number of mother-infant dyads. The association of maternal naturally acquired serotype-specific anti-capsular antibodies (IgG) against serotype-specific IGbsD in their infants has been studied in case-control studies. The statistical models used so far to estimate IGbsD risk from these case-control studies assumed that the antibody concentrations measured sharing the same disease status are sampled from the same population, not allowing for differences between mothers colonised by GBS and mothers also potentially infected (e.g urinary tract infection or chorioamnionitis) by GBS during pregnancy. This distinction is relevant as infants born from infected mothers with occult medical illness may be exposed to GBS prior to the mother developing antibodies measured in maternal or infant sera.

Methods

Unsupervised mixture model averaging (MMA) is proposed and applied here to accurately estimate infant IGbsD risk from case-control study data in presence or absence of antibody concentration subgroups potentially associated to maternal GBS carriage or infection. MMA estimators are compared to non-parametric disease risk estimators in simulation studies and by analysis of two published GBS case-control studies.

Results

MMA provides more accurate relative risk estimates under a broad range of data simulation scenarios and more accurate absolute disease risk estimates when the proportion of IGbsD cases with high antibody levels is not ignorable. MMA estimates of the relative and absolute disease risk curves are more amenable to clinical interpretation compared to non-parametric estimates with no detectable overfitting of the data. Antibody concentration thresholds predictive of protection from infant IGbsD estimated by MMA from maternal and infant sera are consistent with non-parametric estimates.

Conclusions

MMA is a flexible and robust method for design, accurate analysis and clinical interpretation of case-control studies estimating relative and absolute IGbsD risk from antibody concentrations measured at or after birth.

Latent profiles of autism symptoms in children and adolescents with Down syndrome

BACKGROUND

Down syndrome (DS) is associated with elevated rates of autism spectrum disorder (ASD) and autism symptomatology. To better characterise heterogeneity in ASD symptomatology in DS, profiles of caregiver-reported ASD symptoms were modelled for children and adolescents with DS.

METHODS

Participants (n = 125) were recruited through several multi-site research studies on cognition and language in DS. Using the Social Responsiveness Scale-2 (SRS-2; Constantino and Gruber 2012), two latent profile analyses (LPA) were performed, one on the broad composite scores of social communication and interaction and restricted interests and repetitive behaviour, and a second on the four social dimensions of social communication, social motivation, social awareness, and social cognition.

RESULTS

A three-profile model was the best fit for both analyses, with each analysis yielding a low ASD symptom profile, an elevated or mixed ASD symptom profile and a high ASD symptom profile. Associations were observed between profile probability scores and IQ, the number of co-occurring biomedical conditions reported, sex, and SRS-2 form.

CONCLUSIONS

Characterising heterogeneity in ASD symptom profiles can inform more personalised supports in this population, and implications for potential therapeutic approaches for individuals with DS are discussed.

mixtur: An R package for designing, analysing, and modelling continuous report visual short-term memory studies

Visual short-term memory (vSTM) is often measured via continuous-report tasks whereby participants are presented with stimuli that vary along a continuous dimension (e.g., colour) with the goal of memorising the stimulus features. At test, participants are probed to recall the feature value of one of the memoranda in a continuous manner (e.g., by clicking on a colour wheel). The angular deviation between the participant response and the true feature value provides an estimate of recall precision. Two prominent models of performance on such tasks are the two- and three-component mixture models (Bays et al., Journal of Vision, 9(10), Article 7, 2009; Zhang and Luck, Nature, 453(7192), 233–235, 2008). Both models decompose participant responses into probabilistic mixtures of: (1) responses to the true target value based on a noisy memory representation; (2) random guessing when memory fails. In addition, the three-component model proposes (3) responses to a non-target feature value (i.e., binding errors). Here we report the development of mixtur, an open-source package written for the statistical programming language R that facilitates the fitting of the two- and three-component mixture models to continuous report data. We also conduct simulations to develop recommendations for researchers on trial numbers, set sizes, and memoranda similarity, as well as parameter recovery and model recovery. In the Discussion, we discuss how mixtur can be used to fit the slots and the slots-plus-averaging models, as well as how mixtur can be extended to fit explanatory models of visual short-term memory. It is our hope that mixtur will lower the barrier of entry for utilising mixture modelling.

Distinct health-related risk profiles among middle-aged and older adults with risky alcohol use from the Danish general population

BACKGROUND

Knowledge is lacking on distinct health-related risk profiles among the substantial group of middle-aged and older adults with risky alcohol use (AU). Such profiles could inform the planning of interventions and prevention.

AIMS

To 1) identify distinct health-related profiles based on different types of health-related functioning limitations and distress and 2) assess associations between these profiles and age, sex, and health-relevant behaviors (e.g., smoking).

METHODS

Cross-sectional nation-wide Danish health survey with n = 6630 adults aged 55-64 and n = 7605 aged 65-74 with at least risky AU (>84 g ethanol/week in women, >168 in men). Health-related risk profiles were identified with Latent Class Analysis (LCA). Multinomial regression was applied for the association between risk profiles and auxiliary variables.

RESULTS

A six-class LCA solution was found among 55-64 year-olds (classes: 'Normative' [61%], 'Distress' [6%], 'Mental health limitations [5%]', 'Pain-related distress [10%]', 'Broad limitations and pain distress [7%]', 'High overall burden' [11%]) and a five-class solution among 65-74 year-olds. Most classes were comparable across age groups. The 'Distress'-class characterized by pain-distress, tiredness-distress, and sleep-related distress (6%) only showed in the younger group. In both age groups, auxiliary covariates (high-risk AU, possible alcohol use disorder, weekly smoking) were positively associated with problematic profile membership (vs. normative class membership).

CONCLUSION

Middle-aged and older adults with risky AU have distinct health-related profiles relevant for the form and content of prevention and interventions. Despite their distinct features, almost all problematic health profiles warrant careful attention regarding high-risk AU and probable alcohol use disorder.

Semi-automatic segmentation from intrinsically-registered 18F-FDG-PET/MRI for treatment response assessment in a breast cancer cohort: comparison to manual DCE-MRI

Objectives

To investigate the reliability of simultaneous positron emission tomography and magnetic resonance imaging (PET/MRI)-derived biomarkers using semi-automated Gaussian mixture model (GMM) segmentation on PET images, against conventional manual tumor segmentation on dynamic contrast-enhanced (DCE) images.

Materials and methods

Twenty-four breast cancer patients underwent PET/MRI (following 18F-fluorodeoxyglucose (18F-FDG) injection) at baseline and during neoadjuvant treatment, yielding 53 data sets (24 untreated, 29 treated). Two-dimensional tumor segmentation was performed manually on DCE–MRI images (manual DCE) and using GMM with corresponding PET images (GMM–PET). Tumor area and mean apparent diffusion coefficient (ADC) derived from both segmentation methods were compared, and spatial overlap between the segmentations was assessed with Dice similarity coefficient and center-of-gravity displacement.

Results

No significant differences were observed between mean ADC and tumor area derived from manual DCE segmentation and GMM–PET. There were strong positive correlations for tumor area and ADC derived from manual DCE and GMM–PET for untreated and treated lesions. The mean Dice score for GMM–PET was 0.770 and 0.649 for untreated and treated lesions, respectively.

Discussion

Using PET/MRI, tumor area and mean ADC value estimated with a GMM–PET can replicate manual DCE tumor definition from MRI for monitoring neoadjuvant treatment response in breast cancer.

Long-term trajectories of depressive symptoms in old age: Relationships with sociodemographic and health-related factors

BACKGROUND

This study aimed at depicting the course of depression symptoms over the old age, with a special interest in a) uncovering its relationships with sociodemographic and health-related factors; b) analysing its predictive role on healthy-ageing outcomes later in life.

METHODS

The sample comprised 8317 older adults (46.02% men) from the English Longitudinal Study of Ageing. Robust structural equation modelling was used to identify symptom trajectories and their relationships with time-varying factors. Trajectory class and covariates were used to predict outcomes (quality of life, satisfaction with life, and daily living functioning) in a 2-year follow-up.

RESULTS

Three trajectory classes (so-called, normative, subclinical, chronic symptom trajectories) were identified for both sexes. Rising hearing difficulties and history of psychiatric problems were consistently associated with the chronic symptom trajectory. Lower education level, history of psychiatric problems, and increasing visual difficulties were connected with the subclinical trajectories. Finally, participants with either a subclinical or a chronic symptom trajectory showed worse outcomes than the remaining participants in the follow-up.

CONCLUSION

This study highlighted the presence of varying courses of depression symptoms (each showing some distinctive features from other another) over the old age, pointing to some relevant implications for clinical assessment and treatment prescription.

Opinion leadership types or continuous opinion leadership traits?

Opinion leadership is typically conceptualised as a continuous personality trait. However, many authors adhere to the view of qualitatively different opinion leadership types and apply arbitrary criteria to split continuous trait scores into two groups (i.e., opinion leaders vs. non-leaders). The present study is the first to empirically evaluate this approach. A sample of N = 3812 adults (67% women) was administered a validated opinion leadership scale. Finite mixture models examined whether the latent trait distribution can be represented by a set of discrete trait levels that reflected distinct opinion leadership types. The results did not give support to a discrete typology that distinguished leaders from non-leaders. Rather, opinion leadership was best characterised as a continuous trait.

Integrated single cell data analysis reveals cell specific networks and novel coactivation markers

BACKGROUND

Large scale single cell transcriptome profiling has exploded in recent years and has enabled unprecedented insight into the behavior of individual cells. Identifying genes with high levels of expression using data from single cell RNA sequencing can be useful to characterize very active genes and cells in which this occurs. In particular single cell RNA-Seq allows for cell-specific characterization of high gene expression, as well as gene coexpression.

RESULTS

We offer a versatile modeling framework to identify transcriptional states as well as structures of coactivation for different neuronal cell types across multiple datasets. We employed a gamma-normal mixture model to identify active gene expression across cells, and used these to characterize markers for olfactory sensory neuron cell maturity, and to build cell-specific coactivation networks. We found that combined analysis of multiple datasets results in more known maturity markers being identified, as well as pointing towards some novel genes that may be involved in neuronal maturation. We also observed that the cell-specific coactivation networks of mature neurons tended to have a higher centralization network measure than immature neurons.

CONCLUSION

Integration of multiple datasets promises to bring about more statistical power to identify genes and patterns of interest. We found that transforming the data into active and inactive gene states allowed for more direct comparison of datasets, leading to identification of maturity marker genes and cell-specific network observations, taking into account the unique characteristics of single cell transcriptomics data.

Attention and memory protection: Interactions between retrospective attention cueing and interference

Visual working memory (VWM) and attention have a number of features in common, but despite extensive research it is still unclear how the two interact. Can focused attention improve VWM precision? Can it protect VWM from interference? Here we used a partial-report, continuous-response orientation memory task to examine how attention and interference affect different aspects of VWM and how they interact with one another. Both attention and interference were orthogonally manipulated during the retention interval. Attention was manipulated by presenting informative retro-cues, whereas interference was manipulated by introducing a secondary interfering task. Mixture-model analyses revealed that retro-cues, compared to uninformative cues, improved all aspects of performance: Attention increased recall precision and decreased guessing rate and swap-errors (reporting a wrong item in memory). Similarly, performing a secondary task impaired all aspects of the VWM task. In particular, an interaction between retro-cue and secondary task interference was found primarily for swap-errors. Together these results suggest that both the quantity and quality of VWM representations are sensitive to attention cueing and interference modulations, and they highlight the role of attention in protecting the feature-location associations needed to access the correct items in memory.