Stan: A Probabilistic Programming Language

Five-Week, Low-Intensity Blood Flow Restriction Rowing Improves V̇ o2 max in Elite Rowers

ABSTRACT

Held, S, Rappelt, L, Rein, R, Deutsch, J-P, Wiedenmann, T, and Donath, L. Five-week, low-intensity, blood flow restriction rowing improves V̇ o2 max in elite rowers. J Strength Cond Res 38(6): e299-e303, 2024-This controlled intervention study examined the effects of low-intensity rowing with blood flow restriction (BFR) on maximal oxygen uptake (V̇ o2 max), peak power output during ramp testing (PPO), and 2000-m time trial performance (P2k). Eleven, highly elite, male rowers (22.1 ± 1.6 years; 92.6 ± 3.8 kg; 1.93 ± 0.04 m; 7.9. ± 2.2 years rowing experience; 20.4 ± 2.0 h·w -1 training volume; 11.9 ± 1.1 session per week) trained 5 weeks without BFR (Base) followed by a 5-week BFR intervention period. BFR of the lower limb was applied through customized elastic wraps. BFR took place 3 times a week (accumulated net pBFR: 60 min·wk -1 ; occlusion per session: 2 times 10 min·session -1 ) and was used exclusively at low intensities (<2 mmol·L -1 ). V̇ o2 max, PPO, and P2k were examined before, between, and after both intervention periods. Bayesian's credible intervals revealed relevantly increased V̇ o2 max +0.30 L·min -1 (95% credible interval: +0.00 to +0.61 L·min -1 ) adaptations through BFR. By contrast, PPO +14 W (-6 to +34 W) and P2k -5 W (-14 to +3 W) were not noticeably affected by the BFR intervention. This study revealed that 15 sessions of BFR application with a cumulative total BFR load of 5 h over a 5-week macrocycle increased V̇ o2 max remarkably. Thus, pBFR might serve as a promising tool to improve aerobic capacity in highly trained elite rowers.

Losses disguised as wins evoke the reward positivity event-related potential in a simulated machine gambling task

Electronic gambling machines include a suite of design characteristics that may contribute to gambling-related harms and require more careful attention of regulators and policymakers. One strategy that has contributed to these concerns is the presentation of "losses disguised as wins" (LDWs), a type of salient losing outcome in which a gambling payout is less than the amount wagered (i.e., a net loss), but is nonetheless accompanied by the celebratory audio-visual stimuli that typically accompany a genuine win. These events could thereby be mistaken for gains, or otherwise act as a reward signal, reinforcing persistent gambling, despite being a loss. This study aimed to determine whether LDWs evoke a reward positivity component in a task modeled on slot machine gambling. A prominent account of the reward positivity event-related potential suggests that it is evoked during the positive appraisal of task-related feedback, relative to neutral or negative events, or that it is evoked by neural systems that implement the computation of a positive reward prediction error. We recruited 32 individuals from university recruitment pools and asked them to engage in a simple gambling task designed to mimic key features of a slot machine design. The reward positivity was identified using temporospatial principal components analysis. Results indicated a more positive reward positivity following LDWs relative to clear losses, consistent with the theory that LDWs contribute to positive reinforcement of continued gambling, despite being net losses.

Upstream land use with microbial downstream consequences: Iron and humic substances link to Legionella spp

Intensified land use can disturb water quality, potentially increasing the abundance of bacterial pathogens, threatening public access to clean water. This threat involves both direct contamination of faecal bacteria as well as indirect factors, such as disturbed water chemistry and microbiota, which can lead to contamination. While direct contamination has been well described, the impact of indirect factors is less explored, despite the potential of severe downstream consequences on water supply. To assess direct and indirect downstream effects of buildings, farms, pastures and fields on potential water sources, we studied five Swedish lakes and their inflows. We analysed a total of 160 samples in a gradient of anthropogenic activity spanning four time points, including faecal and water-quality indicators. Through species distribution modelling, Random Forest and network analysis using 16S rRNA amplicon sequencing data, our findings highlight that land use indirectly impacts lakes via inflows. Land use impacted approximately one third of inflow microbiota taxa, in turn impacting ∼20-50 % of lake taxa. Indirect effects via inflows were also suggested by causal links between e.g. water colour and lake bacterial taxa, where this influenced the abundance of several freshwater bacteria, such as Polynucleobacter and Limnohabitans. However, it was not possible to identify direct effects on the lakes based on analysis of physiochemical- or microbial parameters. To avoid potential downstream consequences on water supply, it is thus important to consider possible indirect effects from upstream land use and inflows, even when no direct effects can be observed on lakes. Legionella (a genus containing bacterial pathogens) illustrated potential consequences, since the genus was particularly abundant in inflows and was shown to increase by the presence of pastures, fields, and farms. The approach presented here could be used to assess the suitability of lakes as alternative raw water sources or help to mitigate contaminations in important water catchments. Continued broad investigations of stressors on the microbial network can identify indirect effects, avoid enrichment of pathogens, and help secure water accessibility.

The efficacy of Chinese herbal drugs for adults with angina pectoris: Bayesian network meta-analysis of 331 RCTs involving 36,467 individuals

ETHNOPHARMACOLOGICAL RELEVANCE

Hundreds of randomized controlled trials (RCT) on Chinese herbal drugs (CHDs) including Shexiang baoxin pill (BXP), compound Danshen dripping pill (DSP), compound Danshen tablet (DST), Suxiao jiuxin pill (JXP), Naoxintong capsule (NXT), Tongxinluo capsule (TXL), and Di'ao xinxuekang capsule (XXK) and conventional chemical drugs, such as isosorbide dinitrate (ISDN), for angina pectoris are available but have not been evaluated by a PRISMA-compliant network meta-analysis (NMA).

AIM OF THE STUDY

This study aimed to compare the efficacy of nine anti-anginal drugs through NMA on RCTs.

METHODS

RCTs of drug treatment for adult patients with angina pectoris for improvements in symptoms and electrocardiography were retrieved. Odds ratios and 95% credible intervals were computed to measure effect sizes. RCT quality was evaluated with the Cochrane risk of bias tool. Evidence synthesis was performed with Bayesian NMA. Essential analyses including subgroup analysis, sensitivity analysis, meta-regression analysis, publication bias analysis, and ranking analysis were conducted to assess the robustness of efficacies. Evidence strength was assessed with the GRADE approach.

RESULTS

A total of 331 RCTs with 36,467 participants were eligible. The overall quality of all included RCTs was low. Overall efficacy estimates from different approaches of evidential synthesis found that BXP, TXL, and DSP were more efficacious than DST and ISDN. Essential analyses indicated consistent efficacy estimates, insignificant publication bias, and corroborative ranking results. The overall GRADE evidence strength was low.

CONCLUSION

This comprehensive Bayesian NMA found BXP, TXL, and DSP to be the top three candidates among the seven tested CHDs for treating adults suffering from angina pectoris. However, the quality and the evidence strength of eligible RCTs were low. Further high-quality RCTs with more outcome measures and their NMAs are warranted.

REGISTRATION

PROSPERO CRD42014007035.

A novel Bayesian generative approach for estimating tumor dynamics from published studies

Tumor growth inhibition (TGI) modeling attempts to describe the time course changes in tumor size for patients undergoing cancer therapy. TGI models present several advantages over traditional exposure-response models that are based explicitly on clinical end points and have become a popular tool in the pharmacometrics community. Unfortunately, the data required to fit TGI models, namely longitudinal tumor measurements, are sparse or often not available in literature or publicly accessible databases. On the contrary, common end points such as progression-free survival (PFS) and objective response rate (ORR) are directly derived from longitudinal tumor measurements and are routinely published. To this end, a Bayesian generative model relating underlying tumor dynamics to summary PFS and ORR data is introduced to learn TGI model parameters using only published summary data. The parameterized model can describe the tumor dynamics, quantify treatment effect, and account for differences in the study population. The utility of this model is shown by applying it to several published studies, and learned parameters are combined to simulate an in silico trial of a novel combination therapy in a novel setting.

Bayesian Meta-Analysis of Health State Utility Values: A Tutorial with a Practical Application in Heart Failure

Researchers incorporate health state utility values as inputs to inform economic models. However, for a particular health state or condition, multiple utility values derived from different studies typically exist and a single study is often insufficient to represent the best available source of utility needed to inform policy decisions. The purpose of this paper is to provide an introductory guidance for conducting Bayesian meta-analysis of health state utility values to generate a single parameter input for economic evaluation, using R. The tutorial is illustrated using data from a systematic review of health state utilities of patients with heart failure, with 21 studies that reported utilities measured using the EuroQol-5D (EQ-5D). Explanations, key considerations and suggested readings are provided for each step of the tutorial, adhering to a clear workflow for conducting Bayesian meta-analysis: (1) setting-up the data; (2) employing methods to impute missing standard deviations; (3) defining the priors; (4) fitting the model; (5) diagnosing model convergence; (6) interpreting the results; and (7) performing sensitivity analyses. The posterior distributions for the pooled effect size (i.e. mean health state utility) and between-study heterogeneity are discussed and interpreted in light of the data, priors and models used. We hope that this tutorial will foster interest in Bayesian methods and their applications in the meta-analysis of utilities.

Tool use increases mechanical foraging success and tooth health in southern sea otters (Enhydra lutris nereis)

Although tool use may enhance resource utilization, its fitness benefits are difficult to measure. By examining longitudinal data from 196 radio-tagged southern sea otters (Enhydra lutris nereis), we found that tool-using individuals, particularly females, gained access to larger and/or harder-shelled prey. These mechanical advantages translated to reduced tooth damage during food processing. We also found that tool use diminishes trade-offs between access to different prey, tooth condition, and energy intake, all of which are dependent on the relative prey availability in the environment. Tool use allowed individuals to maintain energetic requirements through the processing of alternative prey that are typically inaccessible with biting alone, suggesting that this behavior is a necessity for the survival of some otters in environments where preferred prey are depleted.

Impacts of human mobility on the citywide transmission dynamics of 18 respiratory viruses in pre- and post-COVID-19 pandemic years

Many studies have used mobile device location data to model SARS-CoV-2 dynamics, yet relationships between mobility behavior and endemic respiratory pathogens are less understood. We studied the effects of population mobility on the transmission of 17 endemic viruses and SARS-CoV-2 in Seattle over a 4-year period, 2018-2022. Before 2020, visits to schools and daycares, within-city mixing, and visitor inflow preceded or coincided with seasonal outbreaks of endemic viruses. Pathogen circulation dropped substantially after the initiation of COVID-19 stay-at-home orders in March 2020. During this period, mobility was a positive, leading indicator of transmission of all endemic viruses and lagging and negatively correlated with SARS-CoV-2 activity. Mobility was briefly predictive of SARS-CoV-2 transmission when restrictions relaxed but associations weakened in subsequent waves. The rebound of endemic viruses was heterogeneously timed but exhibited stronger, longer-lasting relationships with mobility than SARS-CoV-2. Overall, mobility is most predictive of respiratory virus transmission during periods of dramatic behavioral change and at the beginning of epidemic waves.

Seasonal occurrence and individual variability of bull sharks, Carcharhinus leucas, in a marine reserve of the southwestern Gulf of California

Background

Studying how the bull sharks aggregate and how they can be driven by life history traits such as reproduction, prey availability, predator avoidance and social interaction in a National Park such as Cabo Pulmo, is key to understand and protect the species.

Methods

The occurrence variability of 32 bull sharks tracked with passive acoustic telemetry were investigated via a hierarchical logistic regression model, with inference conducted in a Bayesian framework, comparing sex, and their response to temperature and chlorophyll.

Results

Based on the fitted model, occurrence probability varied by sex and length. Juvenile females had the highest values, whereas adult males the lowest. A strong seasonality or day of the year was recorded, where sharks were generally absent during September-November. However, some sharks did not show the common pattern, being detected just for a short period. This is one of the first studies where the Bayesian framework is used to study passive acoustic telemetry proving the potential to be used in further studies.

Electrophysiological and behavioral differences of general and food-specific inhibitory control in people with different levels of intuitive eating

Intuitive eating influences health-related behaviors (including calorie and nutritional intake) that are modulated by inhibitory control, producing implications for physical, mental, and emotional health. However, little is known about the relationship between intuitive eating habits and inhibitory control. Therefore, we tested intuitive eating's influence on measures of general and food-related inhibitory control using behavioral and event-related potentials (N2 and P3 components). We included 40 healthy participants: 23 had a higher level of intuitive eating, and 17 had a lower level. They participated in food-specific go/no-go and general go/no-go tasks for which we recorded electroencephalogram data. As expected, in the food-specific go/no-go task, the P3 component amplitude in the lower intuitive eating group was significantly larger than in the higher intuitive eating group; there were no significant between-group differences in the N2 amplitudes or behavioral measures. Moreover, there were no ERP or behavioral difference between groups in the general go/no-go task. Further research is needed to understand the role of positive eating behaviors in food-specific inhibitory control.

Effect of Fatty Acid Unsaturation on the Intermolecular Weak Interaction at the Low-Rank Coal-Water Interface: Density Functional Theory Calculations and Experimental Study

The study on the effect of fatty acid saturation on low-rank coal (LRC) flotation is still limited. In this investigation, density functional theory (DFT) combined with Zeta potential and Fourier transform infrared spectroscopy (FTIR) was used to study the mechanism of intermolecular weak interaction at the LRC-water interface of fatty acids (decanoic acid (DA), undecylenic acid (UA), and phenyl propionic acid (PA)) with different saturations and different dodecane (D) composition hydrocarbon oil-fatty acid mixed collectors (D-DA, D-UA, D-PA). The findings demonstrated that the hydrogen bond interaction and electrostatic interaction between the UA/PA with unsaturated bonded carbon chains and the LRC molecular fragments/water molecules were stronger than DA without a saturated bond carbon chain, and UA/PA strengthened its interaction with water molecules on the whole, even PA molecules would preferentially interact with water molecules. The unsaturated bond had a minimal impact on the adsorption of the LRC hydrophobic site, and the strength of the hydrogen bond between the mixed collector and LRC is D-DA > D-UA > D-PA. In the actual flotation process, the strong hydrogen bonding and electrostatic interaction between UA/PA and water molecules weaken the collection performance of the mixed collector D-UA/D-PA for LRC, which also confirmed the research results of DFT, FTIR, and Zeta.

Designing a Bayesian adaptive clinical trial to evaluate novel mechanical ventilation strategies in acute respiratory failure using integrated nested Laplace approximations

BACKGROUND

Adaptive trials usually require simulations to determine values for design parameters, demonstrate error rates, and establish the sample size. We designed a Bayesian adaptive trial comparing ventilation strategies for patients with acute hypoxemic respiratory failure using simulations. The complexity of the analysis would usually require computationally expensive Markov Chain Monte Carlo methods but this barrier to simulation was overcome using the Integrated Nested Laplace Approximations (INLA) algorithm to provide fast, approximate Bayesian inference.

METHODS

We simulated two-arm Bayesian adaptive trials with equal randomization that stratified participants into two disease severity states. The analysis used a proportional odds model, fit using INLA. Trials were stopped based on pre-specified posterior probability thresholds for superiority or futility, separately for each state. We calculated the type I error and power across 64 scenarios that varied the probability thresholds and the initial minimum sample size before commencing adaptive analyses. Two designs that maintained a type I error below 5%, a power above 80%, and a feasible mean sample size were evaluated further to determine the optimal design.

RESULTS

Power generally increased as the initial sample size and the futility threshold increased. The chosen design had an initial recruitment of 500 and a superiority threshold of 0.9925, and futility threshold of 0.95. It maintained high power and was likely to reach a conclusion before exceeding a feasible sample size.

CONCLUSIONS

We designed a Bayesian adaptive trial to evaluate novel strategies for ventilation using the INLA algorithm to efficiently evaluate a wide range of designs through simulation.

Long-distance migrations and seasonal movements of meagre (Argyrosomus regius), a large coastal predator, along the Iberian Peninsula coast

BACKGROUND

The meagre, Argyrosomus regius, is a large coastal predatory fish inhabiting waters from the north-eastern Atlantic and Mediterranean Sea, where it is targeted by commercial and recreational fisheries. Previous genetic studies have found an unexpectedly high population differentiation not only between the Atlantic and the Mediterranean, but also along the Atlantic coast. However, the reasons underpinning this genetic barrier remained unclear. Likewise, even though the species is amongst the world's largest marine teleosts, knowledge about its movement ecology and migratory behaviour remains notably scarce, and primarily reliant on fisheries-dependent data.

METHODS

In this study, we used a combination of acoustic telemetry and pop-up satellite archival tags to investigate the movements of 22 adult meagre (70-143 cm total length) along the Southwestern coast of the Iberian Peninsula.

RESULTS

Our results strongly suggest that the previously reported genetic differentiation is not maintained by limited adult dispersal/movement, as hypothesized. On the contrary, we documented some of the longest individual annual migrations ever recorded for a coastal teleost, up to > 2000 km, with frequent back-and-forth movements between the West and Southern Iberian coasts. Moreover, their detected regional movement patterns support the existence of a marked seasonal behavioural shift, with individuals being less active and moving to deeper waters during winter, and are consistent with spawning philopatry associated to their summer reproductive movements. Finally, we identified putative aggregation areas that may harbour important feeding/overwintering grounds.

CONCLUSIONS

These findings shed new light on the movement and behaviour patterns of meagre that may be of particular importance for the conservation and spatial management of this species throughout its range, and open the door to further research on functional connectivity.

A Preliminary Study of Voluntary Cough Motor Performance and Learning With Skill Training and Biofeedback

PURPOSE

Sensorimotor cough skill training (CST) has been shown to improve cough strength, as well as facilitate changes during training (i.e., motor performance) and generalization to untrained tasks (i.e., motor learning). However, there is a gap in our understanding of the effects of voluntary CST (without sensory stimuli) on motor performance and learning. Furthermore, the contribution of physiologic factors, such as lung volume, a driver of cough strength in healthy adults, and treatment-specific factors, such as biofeedback, remains unexamined.

METHOD

Twenty individuals with Parkinson's disease (PD) completed pre- and post-CST single voluntary, sequential voluntary, and reflex cough testing. Participants were randomized to biofeedback or no biofeedback groups. They completed one CST session involving 25 trials of voluntary coughs, with the treatment target set 25% above baseline peak flow. Participants were instructed to "cough hard" to exceed the target. In the biofeedback group, participants received direct visualization of the target line in real time.

RESULTS

Cough peak flow showed positive improvements in motor performance (β = .02; 95% credible interval [CI]: 0.01, 0.03) and learning (β = .26; 95% CI: 0.03, 0.47). Changes in lung volume from pre- to post-CST did not predict treatment response. No differences in treatment response were detected between the biofeedback groups.

CONCLUSIONS

A single session of voluntary CST improved voluntary cough motor performance and learning. Although lung volume increased during CST, changes to lung volume did not predict treatment response. These findings demonstrate the potential of voluntary CST to improve motor performance and motor learning among individuals with PD and cough dysfunction.

SUPPLEMENTAL MATERIAL AND OPEN SCIENCE FORM

https://doi.org/10.23641/asha.25447444.

Statistical modeling of acute and chronic pain patient-reported outcomes obtained from ecological momentary assessment

ABSTRACT

Ecological momentary assessment (EMA) allows for the collection of participant-reported outcomes (PROs), including pain, in the normal environment at high resolution and with reduced recall bias. Ecological momentary assessment is an important component in studies of pain, providing detailed information about the frequency, intensity, and degree of interference of individuals' pain. However, there is no universally agreed on standard for summarizing pain measures from repeated PRO assessment using EMA into a single, clinically meaningful measure of pain. Here, we quantify the accuracy of summaries (eg, mean and median) of pain outcomes obtained from EMA and the effect of thresholding these summaries to obtain binary clinical end points of chronic pain status (yes/no). Data applications and simulations indicate that binarizing empirical estimators (eg, sample mean, random intercept linear mixed model) can perform well. However, linear mixed-effect modeling estimators that account for the nonlinear relationship between average and variability of pain scores perform better for quantifying the true average pain and reduce estimation error by up to 50%, with larger improvements for individuals with more variable pain scores. We also show that binarizing pain scores (eg, <3 and ≥3) can lead to a substantial loss of statistical power (40%-50%). Thus, when examining pain outcomes using EMA, the use of linear mixed models using the entire scale (0-10) is superior to splitting the outcomes into 2 groups (<3 and ≥3) providing greater statistical power and sensitivity.

Vegetation structure drives mosquito community composition in UK's largest managed lowland wetland

PURPOSE

The rising burden of mosquito-borne diseases in Europe extends beyond urban areas, encompassing rural and semi-urban regions near managed and natural wetlands evidenced by recent outbreaks of Usutu and West Nile viruses. While wetland management policies focus on biodiversity and ecosystem services, few studies explore the impact on mosquito vectors.

METHODS

Our research addresses this gap, examining juvenile mosquito and aquatic predator communities in 67 ditch sites within a South England coastal marsh subjected to different wetland management tiers. Using joint distribution models, we analyse how mosquito communities respond to abiotic and biotic factors influenced by wetland management.

RESULTS

Of the 12 mosquito species identified, Culiseta annulata (Usutu virus vector) and Culex pipiens (Usutu and West Nile virus vector) constitute 47% of 6825 larval mosquitoes. Abundant predators include Coleoptera (water beetles) adults, Corixidae (water boatmen) and Zygoptera (Damselfy) larvae. Models reveal that tier 3 management sites (higher winter water levels, lower agricultural intensity) associated with shade and less floating vegetation are preferred by specific mosquito species. All mosquito species except Anopheles maculipennis s.l., are negatively impacted by potential predators. Culiseta annulata shows positive associations with shaded and turbid water, contrary to preferences of Corixidae predators.

CONCLUSIONS

Tier 3 areas managed for biodiversity, characterised by higher seasonal water levels and reduced livestock grazing intensity, provide favourable habitats for key mosquito species that are known vectors of arboviruses, such as Usutu and West Nile. Our findings emphasise the impact of biodiversity-focused wetland management, altering mosquito breeding site vegetation to enhance vector suitability. Further exploration of these trade-offs is crucial for comprehending the broader implications of wetland management.

Diffusion tensor imaging analysis along the perivascular space in the UK biobank

BACKGROUND

The recently discovered glymphatic system may support the removal of neurotoxic proteins, mainly during sleep, that are associated with neurodegenerative diseases such as Alzheimer's and Parkinson's Disease. Diffusion tensor image analysis along the perivascular space (DTI-ALPS) has been suggested as a method to index the health of glymphatic system (with higher values indicating a more intact glymphatic system). Indeed, in small-scale studies the DTI-ALPS index has been shown to correlate with age, cognitive health, and sleep, and is higher in females than males.

OBJECTIVE

To determine whether these relationships are stable we replicated previous findings associating the DTI-ALPS index with demographic, sleep-related, and cognitive markers in a large sample of participants from the UK Biobank.

METHODS

We calculated the DTI-ALPS index in UK Biobank participants (n = 17723). Using Bayesian and Frequentist analysis approaches, we replicate previously reported relationships between the DTI-ALPS index.

RESULTS

We found the predicted associations between the DTI-ALPS index and age, longest uninterrupted sleep window (LUSWT) on a typical night, cognitive performance, and sex. However, these effects were substantially smaller than those found in previous studies. Parameter estimates from this study may be used as priors in subsequent studies using a Bayesian approach. These results suggest that the DTI-ALPS index is consistently, and therefore predictably, associated with demographics, LUWST, and cognition.

CONCLUSION

We propose that the metric, calculated for the first time in a large-scale, population-based cohort, is a stable measure, but one for which stronger links to glymphatic system function are needed before it can be used to understand the relationships between glymphatic system function and health outcomes reported in the UK Biobank.

A Bayesian approach for exploring person × environment interaction within the environmental sensitivity meta-framework

BACKGROUND

For investigating the individual-environment interplay and individual differences in response to environmental exposures as captured by models of environmental sensitivity including Diathesis-stress, Differential Susceptibility, and Vantage Sensitivity, over the last few years, a series of statistical guidelines have been proposed. However, available solutions suffer of computational problems especially relevant when sample size is not sufficiently large, a common condition in observational and clinical studies.

METHOD

In the current contribution, we propose a Bayesian solution for estimating interaction parameters via Monte Carlo Markov Chains (MCMC), adapting Widaman et al. (Psychological Methods, 17, 2012, 615) Nonlinear Least Squares (NLS) approach.

RESULTS

Findings from an applied exemplification and a simulation study showed that with relatively big samples both MCMC and NLS estimates converged on the same results. Conversely, MCMC clearly outperformed NLS, resolving estimation problems and providing more accurate estimates, particularly with small samples and greater residual variance.

CONCLUSIONS

As the body of research exploring the interplay between individual and environmental variables grows, enabling predictions regarding the form of interaction and the extent of effects, the Bayesian approach could emerge as a feasible and readily applicable solution to numerous computational challenges inherent in existing frequentist methods. This approach holds promise for enhancing the trustworthiness of research outcomes, thereby impacting clinical and applied understanding.

Progression subtypes in Parkinson's disease identified by a data-driven multi cohort analysis

The progression of Parkinson's disease (PD) is heterogeneous across patients, affecting counseling and inflating the number of patients needed to test potential neuroprotective treatments. Moreover, disease subtypes might require different therapies. This work uses a data-driven approach to investigate how observed heterogeneity in PD can be explained by the existence of distinct PD progression subtypes. To derive stable PD progression subtypes in an unbiased manner, we analyzed multimodal longitudinal data from three large PD cohorts and performed extensive cross-cohort validation. A latent time joint mixed-effects model (LTJMM) was used to align patients on a common disease timescale. Progression subtypes were identified by variational deep embedding with recurrence (VaDER). In each cohort, we identified a fast-progressing and a slow-progressing subtype, reflected by different patterns of motor and non-motor symptoms progression, survival rates, treatment response, features extracted from DaTSCAN imaging and digital gait assessments, education, and Alzheimer's disease pathology. Progression subtypes could be predicted with ROC-AUC up to 0.79 for individual patients when a one-year observation period was used for model training. Simulations demonstrated that enriching clinical trials with fast-progressing patients based on these predictions can reduce the required cohort size by 43%. Our results show that heterogeneity in PD can be explained by two distinct subtypes of PD progression that are stable across cohorts. These subtypes align with the brain-first vs. body-first concept, which potentially provides a biological explanation for subtype differences. Our predictive models will enable clinical trials with significantly lower sample sizes by enriching fast-progressing patients.

Genetic Variation in Male Aggression Is Influenced by Genotype of Prior Social Partners in Drosophila melanogaster

AbstractSocial behaviors can be influenced by the genotypes of interacting individuals through indirect genetic effects (IGEs) and can also display developmental plasticity. We investigated how developmental IGEs, which describe the effects of a prior social partner's genotype on later behavior, can influence aggression in male Drosophila melanogaster. We predicted that developmental IGEs cannot be estimated by simply extending the effects of contextual IGEs over time and instead have their own unique effects on behavior. On day 1 of the experiment, we measured aggressive behavior in 15 genotypic pairings (n = 600 males). On day 2, each of the males was paired with a new opponent, and aggressive behavior was again measured. We found contextual IGEs on day 1 of the experiment and developmental IGEs on day 2 of the experiment: the influence of the day 1 partner's genotype on the focal individual's day 2 behavior depended on the genotypic identity of both the day 1 partner and the focal male. Importantly, the developmental IGEs in our system produced fundamentally different dynamics than the contextual IGEs, as the presence of IGEs was altered over time. These findings represent some of the first empirical evidence demonstrating developmental IGEs, a first step toward incorporating developmental IGEs into our understanding of behavioral evolution.

Robustly estimating the demographic contribution of immigration: Simulation, sensitivity analysis and seals

Identifying important demographic drivers of population dynamics is fundamental for understanding life-history evolution and implementing effective conservation measures. Integrated population models (IPMs) coupled with transient life table response experiments (tLTREs) allow ecologists to quantify the contributions of demographic parameters to observed population change. While IPMs can estimate parameters that are not estimable using any data source alone, for example, immigration, the estimated contribution of such parameters to population change is prone to bias. Currently, it is unclear when robust conclusions can be drawn from them. We sought to understand the drivers of a rebounding southern elephant seal population on Marion Island using the IPM-tLTRE framework, applied to count and mark-recapture data on 9500 female seals over nearly 40 years. Given the uncertainty around IPM-tLTRE estimates of immigration, we also aimed to investigate the utility of simulation and sensitivity analyses as general tools for evaluating the robustness of conclusions obtained in this framework. Using a Bayesian IPM and tLTRE analysis, we quantified the contributions of survival, immigration and population structure to population growth. We assessed the sensitivity of our estimates to choice of multivariate priors on immigration and other vital rates. To do so we make a novel application of Gaussian process priors, in comparison with commonly used shrinkage priors. Using simulation, we assessed our model's ability to estimate the demographic contribution of immigration under different levels of temporal variance in immigration. The tLTRE analysis suggested that adult survival and immigration were the most important drivers of recent population growth. While the contribution of immigration was sensitive to prior choices, the estimate was consistently large. Furthermore, our simulation study validated the importance of immigration by showing that our estimate of its demographic contribution is unlikely to result as a biased overestimate. Our results highlight the connectivity between distant populations of southern elephant seals, illustrating that female dispersal can be important in regulating the abundance of local populations even when natal site fidelity is high. More generally, we demonstrate how robust ecological conclusions may be obtained about immigration from the IPM-tLTRE framework, by combining sensitivity analysis and simulation.

Lower versus higher blood pressure targets in comatose patients resuscitated from out-of-hospital cardiac arrest-Protocol for a secondary Bayesian analysis of the box trial

BACKGROUND

The management of blood pressure targets during intensive care after out-of-hospital cardiac arrest (OHCA) remains a topic of debate. The blood Pressure and Oxygenation Targets After OHCA (BOX) trial explored the efficacy of two different blood pressure targets in 789 patients during intensive care after OHCA. In the primary frequentist analysis, no statistically significant differences were found for neurological outcome after 90 days.

METHODS

This protocol outlines secondary Bayesian analyses of 365-day all-cause mortality and two secondary outcomes: neurological outcome after 365 days, and plasma neuron-specific enolase, a biomarker of brain injury, after 48 h. We will employ adjusted Bayesian logistic and linear regressions, presenting results as relative and absolute differences with 95% confidence intervals. We will use weakly informative priors for the primary analyses, and skeptical and evidence-based priors (where available) in sensitivity analyses. Exact probabilities for any benefit/harm will be presented for all outcomes, along with probabilities of clinically important benefit/harm (risk differences larger than 2%-points absolute) and no clinically important differences for the binary outcomes. We will assess whether heterogeneity of treatment effects on mortality is present according to lactate at admission, time to return of spontaneous circulation, primary shockable rhythm, age, hypertension, and presence of ST-elevation myocardial infarction.

DISCUSSION

This secondary analysis of the BOX trial aim to complement the primary frequentist analysis by quantifying the probabilities of beneficial or harmful effects of different blood pressure targets. This approach seeks to provide clearer insights for researchers and clinicians into the effectiveness of these blood pressure management strategies in acute medical conditions, particularly focusing on mortality, neurological outcomes, and neuron-specific enolase.

Hotspots of biogeochemical activity linked to aridity and plant traits across global drylands

Perennial plants create productive and biodiverse hotspots, known as fertile islands, beneath their canopies. These hotspots largely determine the structure and functioning of drylands worldwide. Despite their ubiquity, the factors controlling fertile islands under conditions of contrasting grazing by livestock, the most prevalent land use in drylands, remain virtually unknown. Here we evaluated the relative importance of grazing pressure and herbivore type, climate and plant functional traits on 24 soil physical and chemical attributes that represent proxies of key ecosystem services related to decomposition, soil fertility, and soil and water conservation. To do this, we conducted a standardized global survey of 288 plots at 88 sites in 25 countries worldwide. We show that aridity and plant traits are the major factors associated with the magnitude of plant effects on fertile islands in grazed drylands worldwide. Grazing pressure had little influence on the capacity of plants to support fertile islands. Taller and wider shrubs and grasses supported stronger island effects. Stable and functional soils tended to be linked to species-rich sites with taller plants. Together, our findings dispel the notion that grazing pressure or herbivore type are linked to the formation or intensification of fertile islands in drylands. Rather, our study suggests that changes in aridity, and processes that alter island identity and therefore plant traits, will have marked effects on how perennial plants support and maintain the functioning of drylands in a more arid and grazed world.

Influence of microarchitecture on stressed volume and mechanical fatigue behaviour of equine subchondral bone

Fractures of the equine metacarpophalangeal (MCP) joint are among the most common and fatal injuries experienced by racehorses. These bone injuries are a direct result of repetitive, high intensity loading of the skeleton during racing and training and there is consensus that they represent a mechanical fatigue phenomenon. Existing work has found the fatigue life of bone to be strongly determined by bone microarchitecture and the resulting stressed volume (i.e., the volume of bone stressed above assumed yield). The purpose of this study was to quantify the influence of bone microarchitecture on the mechanical fatigue behaviour of equine subchondral bone from the MCP joint across a wide variety of sample types. Forty-eight subchondral bone samples were prepared from the third metacarpal (MC3) and proximal phalanx (P1) of 8 horses and subsequently imaged using high resolution micro-computed tomography (μCT) to quantify microarchitectural features of interest, including bone volume fraction, tissue mineral density, pore size, pore spacing, and pore number. Samples were cyclically loaded in compression to a stress of 70 MPa, and fatigue life was defined as the number of cycles until failure. Finite element models were created from the μCT images and used to quantify stressed volume. Based on the expected log point-wise predictive density, stressed volume was a strong predictor of fatigue life in both the MC3 and P1. A regional analysis indicated fatigue life was more strongly associated with bone volume fraction in the superficial (r2 = 0.32, p < 0.001) and middle (r2 = 0.70, p < 0.001) regions of the subchondral bone, indicating the prominent role that the cortical plate played in the fatigue resistance of equine subchondral bone. By improving our understanding of the variance in fatigue life measurements, this research helps clarify the underlying mechanisms of the mechanical fatigue process and provides a basic understanding of subchondral bone injuries in the equine fetlock joint.

Ictal Central Apnea Is Predictive of Mesial Temporal Seizure Onset: An Intracranial Investigation

OBJECTIVE

Ictal central apnea (ICA) is a semiological sign of focal epilepsy, associated with temporal and frontal lobe seizures. In this study, using qualitative and quantitative approaches, we aimed to assess the localizational value of ICA. We also aimed to compare ICA clinical utility in relation to other seizure semiological features of focal epilepsy.

METHODS

We analyzed seizures in patients with medically refractory focal epilepsy undergoing intracranial stereotactic electroencephalographic (SEEG) evaluations with simultaneous multimodal cardiorespiratory monitoring. A total of 179 seizures in 72 patients with reliable artifact-free respiratory signal were analyzed.

RESULTS

ICA was seen in 55 of 179 (30.7%) seizures. Presence of ICA predicted a mesial temporal seizure onset compared to those without ICA (odds ratio = 3.8, 95% confidence interval = 1.3-11.6, p = 0.01). ICA specificity was 0.82. ICA onset was correlated with increased high-frequency broadband gamma (60-150Hz) activity in specific mesial or basal temporal regions, including amygdala, hippocampus, and fusiform and lingual gyri. Based on our results, ICA has an almost 4-fold greater association with mesial temporal seizure onset zones compared to those without ICA and is highly specific for mesial temporal seizure onset zones. As evidence of symptomatogenic areas, onset-synchronous increase in high gamma activity in mesial or basal temporal structures was seen in early onset ICA, likely representing anatomical substrates for ICA generation.

INTERPRETATION

ICA recognition may help anatomoelectroclinical localization of clinical seizure onset to specific mesial and basal temporal brain regions, and the inclusion of these regions in SEEG evaluations may help accurately pinpoint seizure onset zones for resection. ANN NEUROL 2024;95:998-1008.

P-hacking in meta-analyses: A formalization and new meta-analytic methods

As traditionally conceived, publication bias arises from selection operating on a collection of individually unbiased estimates. A canonical form of such selection across studies (SAS) is the preferential publication of affirmative studies (i.e., those with significant, positive estimates) versus nonaffirmative studies (i.e., those with nonsignificant or negative estimates). However, meta-analyses can also be compromised by selection within studies (SWS), in which investigators "p-hack" results within their study to obtain an affirmative estimate. Published estimates can then be biased even conditional on affirmative status, which comprises the performance of existing methods that only consider SAS. We propose two new analysis methods that accommodate joint SAS and SWS; both analyze only the published nonaffirmative estimates. First, we propose estimating the underlying meta-analytic mean by fitting "right-truncated meta-analysis" (RTMA) to the published nonaffirmative estimates. This method essentially imputes the entire underlying distribution of population effects. Second, we propose conducting a standard meta-analysis of only the nonaffirmative studies (MAN); this estimate is conservative (negatively biased) under weakened assumptions. We provide an R package (phacking) and website (metabias.io). Our proposed methods supplement existing methods by assessing the robustness of meta-analyses to joint SAS and SWS.

Body size shapes song in honeyeaters

Birdsongs are among the most distinctive animal signals. Their evolution is thought to be shaped simultaneously by habitat structure and by the constraints of morphology. Habitat structure affects song transmission and detectability, thus influencing song (the acoustic adaptation hypothesis), while body size and beak size and shape necessarily constrain song characteristics (the morphological constraint hypothesis). Yet, support for the acoustic adaptation and morphological constraint hypotheses remains equivocal, and their simultaneous examination is infrequent. Using a phenotypically diverse Australasian bird clade, the honeyeaters (Aves: Meliphagidae), we compile a dataset consisting of song, environmental, and morphological variables for 163 species and jointly examine predictions of these two hypotheses. Overall, we find that body size constrains song frequency and pace in honeyeaters. Although habitat type and environmental temperature influence aspects of song, that influence is indirect, likely via effects of environmental variation on body size, with some evidence that elevation constrains the evolution of song peak frequency. Our results demonstrate that morphology has an overwhelming influence on birdsong, in support of the morphological constraint hypothesis, with the environment playing a secondary role generally via body size rather than habitat structure. These results suggest that changing body size (a consequence of both global effects such as climate change and local effects such as habitat transformation) will substantially influence the nature of birdsong.

A multi-organ map of the human immune system across age, sex and ethnicity

Understanding tissue biology's heterogeneity is crucial for advancing precision medicine. Despite the centrality of the immune system in tissue homeostasis, a detailed and comprehensive map of immune cell distribution and interactions across human tissues and demographics remains elusive. To fill this gap, we harmonised data from 12,981 single-cell RNA sequencing samples and curated 29 million cells from 45 anatomical sites to create a comprehensive compositional and transcriptional healthy map of the healthy immune system. We used this resource and a novel multilevel modelling approach to track immune ageing and test differences across sex and ethnicity. We uncovered conserved and tissue-specific immune-ageing programs, resolved sex-dependent differential ageing and identified ethnic diversity in clinically critical immune checkpoints. This study provides a quantitative baseline of the immune system, facilitating advances in precision medicine. By sharing our immune map, we hope to catalyse further breakthroughs in cancer, infectious disease, immunology and precision medicine.

Acupuncture versus tricyclic antidepressants in the prophylactic treatment of tension-type headaches: an indirect treatment comparison meta-analysis

BACKGROUND

Acupuncture showed better improvement than sham acupuncture in reducing attack frequency of tension-type headache (TTH), but its effectiveness relative to first-line drugs for TTH is unknown, which impedes the recommendation of acupuncture for patients who are intolerant to drugs for TTH. We aimed to estimate the relative effectiveness between acupuncture and tricyclic antidepressants (TCAs) through indirect treatment comparison (ITC) meta-analysis.

METHODS

We searched Ovid Medline, Embase, and Cochrane Library from database inception until April 13, 2023. Randomized controlled trials of TCAs or acupuncture in the prevention of TTH in adults were included. The primary outcome was headache frequency. The secondary outcomes were headache intensity, responder rate, and adverse event rate. Bayesian random-effect models were used to perform ITC meta-analysis, and confidence of evidence was evaluated by using the GRADE approach.

RESULTS

A total of 34 trials involving 4426 participants were included. Acupuncture had similar effect with TCAs in decreasing TTH frequency (amitriptyline: mean difference [MD] -1.29, 95% CI -5.28 to 3.02; amitriptylinoxide: MD -0.05, 95% CI -6.86 to 7.06) and reducing TTH intensity (amitriptyline: MD 2.35, 95% CI -1.20 to 5.78; clomipramine: MD 1.83, 95% CI -4.23 to 8.20). Amitriptyline had a higher rate of adverse events than acupuncture (OR 4.73, 95% CI 1.42 to 14.23).

CONCLUSION

Acupuncture had similar effect as TCAs in reducing headache frequency of TTH, and acupuncture had a lower adverse events rate than amitriptyline, as shown by very low certainty of evidence.

Reservoir displacement by an invasive rodent reduces Lassa virus zoonotic spillover risk

The black rat (Rattus rattus) is a globally invasive species that has been widely introduced across Africa. Within its invasive range in West Africa, R. rattus may compete with the native rodent Mastomys natalensis, the primary reservoir host of Lassa virus, a zoonotic pathogen that kills thousands annually. Here, we use rodent trapping data from Sierra Leone and Guinea to show that R. rattus presence reduces M. natalensis density within the human dwellings where Lassa virus exposure is most likely to occur. Further, we integrate infection data from M. natalensis to demonstrate that Lassa virus zoonotic spillover risk is lower at sites with R. rattus. While non-native species can have numerous negative effects on ecosystems, our results suggest that R. rattus invasion has the indirect benefit of decreasing zoonotic spillover of an endemic pathogen, with important implications for invasive species control across West Africa.

Tissue and cellular spatiotemporal dynamics in colon aging

Tissue structure and molecular circuitry in the colon can be profoundly impacted by systemic age-related effects, but many of the underlying molecular cues remain unclear. Here, we built a cellular and spatial atlas of the colon across three anatomical regions and 11 age groups, encompassing ~1,500 mouse gut tissues profiled by spatial transcriptomics and ~400,000 single nucleus RNA-seq profiles. We developed a new computational framework, cSplotch, which learns a hierarchical Bayesian model of spatially resolved cellular expression associated with age, tissue region, and sex, by leveraging histological features to share information across tissue samples and data modalities. Using this model, we identified cellular and molecular gradients along the adult colonic tract and across the main crypt axis, and multicellular programs associated with aging in the large intestine. Our multi-modal framework for the investigation of cell and tissue organization can aid in the understanding of cellular roles in tissue-level pathology.

Estimating SARS-CoV-2 infection probabilities with serological data and a Bayesian mixture model

The individual results of SARS-CoV-2 serological tests measured after the first pandemic wave of 2020 cannot be directly interpreted as a probability of having been infected. Plus, these results are usually returned as a binary or ternary variable, relying on predefined cut-offs. We propose a Bayesian mixture model to estimate individual infection probabilities, based on 81,797 continuous anti-spike IgG tests from Euroimmun collected in France after the first wave. This approach used serological results as a continuous variable, and was therefore not based on diagnostic cut-offs. Cumulative incidence, which is necessary to compute infection probabilities, was estimated according to age and administrative region. In France, we found that a "negative" or a "positive" test, as classified by the manufacturer, could correspond to a probability of infection as high as 61.8% or as low as 67.7%, respectively. "Indeterminate" tests encompassed probabilities of infection ranging from 10.8 to 96.6%. Our model estimated tailored individual probabilities of SARS-CoV-2 infection based on age, region, and serological result. It can be applied in other contexts, if estimates of cumulative incidence are available.

Bayesian Mechanistic Inference, Statistical Mechanics, and a New Era for Monte Carlo

On the one hand, much of computational chemistry is concerned with "bottom-up" calculations which elucidate observable behavior starting from exact or approximated physical laws, a paradigm exemplified by typical quantum mechanical calculations and molecular dynamics simulations. On the other hand, "top down" computations aiming to formulate mathematical models consistent with observed data, e.g., parametrizing force fields, binding or kinetic models, have been of interest for decades but recently have grown in sophistication with the use of Bayesian inference (BI). Standard BI provides an estimation of parameter values, uncertainties, and correlations among parameters. Used for "model selection," BI can also distinguish between model structures such as the presence or absence of individual states and transitions. Fortunately for physical scientists, BI can be formulated within a statistical mechanics framework, and indeed, BI has led to a resurgence of interest in Monte Carlo (MC) algorithms, many of which have been directly adapted from or inspired by physical strategies. Certain MC algorithms─notably procedures using an "infinite temperature" reference state─can be successful in a 5-20 parameter BI context which would be unworkable in molecular spaces of 103 coordinates and more. This Review provides a pedagogical introduction to BI and reviews key aspects of BI through a physical lens, setting the computations in terms of energy landscapes and free energy calculations and describing promising sampling algorithms. Statistical mechanics and basic probability theory also provide a reference for understanding intrinsic limitations of Bayesian inference with regard to model selection and the choice of priors.

Estimating the effects of temperature on transmission of the human malaria parasite, Plasmodium falciparum

Despite concern that climate change could increase the human risk to malaria in certain areas, the temperature dependency of malaria transmission is poorly characterized. Here, we use a mechanistic model fitted to experimental data to describe how Plasmodium falciparum infection of the African malaria vector, Anopheles gambiae, is modulated by temperature, including its influences on parasite establishment, conversion efficiency through parasite developmental stages, parasite development rate, and overall vector competence. We use these data, together with estimates of the survival of infected blood-fed mosquitoes, to explore the theoretical influence of temperature on transmission in four locations in Kenya, considering recent conditions and future climate change. Results provide insights into factors limiting transmission in cooler environments and indicate that increases in malaria transmission due to climate warming in areas like the Kenyan Highlands, might be less than previously predicted.

Modeling children's moral development in postwar Taiwan through naturalistic observations preserved in historical texts

A core issue in the interdisciplinary study of human morality is its ontogeny in diverse cultures, but systematic, naturalistic data in specific cultural contexts are rare to find. This study conducts a novel analysis of 213 children's socio-moral behavior in a historical, non-Western, rural setting, based on a unique dataset of naturalistic observations from the first field research on Han Chinese children. Using multilevel multinomial modeling, we examined a range of proactive behaviors in 0-to-12-year-old children's peer cooperation and conflict in an entire community in postwar Taiwan. We modeled the effects of age, sex, kinship, and behavioral roles, and revealed complex interactions between these four variables in shaping children's moral development. We discovered linkages between coercive and non-coercive behaviors as children strategically negotiated leadership dynamics. We identified connections between prosocial and aggressive behaviors, illuminating the nuances of morality in real life. Our analysis also revealed gendered patterns and age-related trends that deviated from cultural norms and contradicted popular assumptions about Chinese family values. These findings highlight the importance of naturalistic observations in cultural contexts for understanding how we become moral persons. This re-analysis of historically significant fieldnotes also enriches the interdisciplinary study of child development across societies.

Bayesian Regression Facilitates Quantitative Modeling of Cell Metabolism

This paper presents Maud, a command-line application that implements Bayesian statistical inference for kinetic models of biochemical metabolic reaction networks. Maud takes into account quantitative information from omics experiments and background knowledge as well as structural information about kinetic mechanisms, regulatory interactions, and enzyme knockouts. Our paper reviews the existing options in this area, presents a case study illustrating how Maud can be used to analyze a metabolic network, and explains the biological, statistical, and computational design decisions underpinning Maud.

Boosting or inhibiting - how semantic-pragmatic and syntactic cues affect prosodic prominence relations in German

In this exploratory study, we investigate the influence of several semantic-pragmatic and syntactic factors on prosodic prominence production in German, namely referential and lexical newness/givenness, grammatical role, and position of a referential target word within a sentence. Especially in terms of the probabilistic distribution of accent status (nuclear, prenuclear, deaccentuation) we find evidence for an additive influence of the discourse-related and syntactic cues, with lexical newness and initial sentence position showing the strongest boosting effects on a target word's prosodic prominence. The relative strength of the initial position is found in nearly all prosodic factors investigated, both discrete (such as the choice of accent type) and gradient (e.g., scaling of the Tonal Center of Gravity and intensity). Nevertheless, the differentiation of prominence relations is information-structurally less important in the beginning of an utterance than near the end: The prominence of the final object relative to the surrounding elements, especially the verbal component, is decisive for the interpretation of the sentence. Thus, it seems that a speaker adjusts locally important prominence relations (object vs. verb in sentence-final position) in addition to a more global, rhythmically determined distribution of prosodic prominences across an utterance.

Imaging the top of the Earth's inner core: a present-day flow model

Despite considerable progress in seismology, mineral physics, geodynamics, paleomagnetism, and mathematical geophysics, Earth's inner core structure and evolution remain enigmatic. One of the most significant issues is its thermal history and the current thermal state. Several hypotheses involving a thermally-convecting inner core have been proposed: a simple, high-viscosity, translational mode, or a classical, lower-viscosity, plume-style convection. Here, we use state-of-the-art seismic imaging to probe the outermost shell of the inner core for its isotropic compressional speed and compare it with recently developed attenuation maps. The pattern emerging in the resulting tomograms is interpreted with recent data on the viscosity of iron as the inner core surface manifestation of a thermally-driven flow, with a positive correlation among compressional speed and attenuation and temperature. Although the outer-core convection controls the heat flux across the inner core boundary, the internally driven inner-core convection is a plausible model that explains a range of observations for the inner core, including distinct anisotropy in the innermost inner core.

A Bayesian approach to estimating COVID-19 incidence and infection fatality rates

Naive estimates of incidence and infection fatality rates (IFR) of coronavirus disease 2019 suffer from a variety of biases, many of which relate to preferential testing. This has motivated epidemiologists from around the globe to conduct serosurveys that measure the immunity of individuals by testing for the presence of SARS-CoV-2 antibodies in the blood. These quantitative measures (titer values) are then used as a proxy for previous or current infection. However, statistical methods that use this data to its full potential have yet to be developed. Previous researchers have discretized these continuous values, discarding potentially useful information. In this article, we demonstrate how multivariate mixture models can be used in combination with post-stratification to estimate cumulative incidence and IFR in an approximate Bayesian framework without discretization. In doing so, we account for uncertainty from both the estimated number of infections and incomplete deaths data to provide estimates of IFR. This method is demonstrated using data from the Action to Beat Coronavirus erosurvey in Canada.

Characterizing quantile-varying covariate effects under the accelerated failure time model

An important task in survival analysis is choosing a structure for the relationship between covariates of interest and the time-to-event outcome. For example, the accelerated failure time (AFT) model structures each covariate effect as a constant multiplicative shift in the outcome distribution across all survival quantiles. Though parsimonious, this structure cannot detect or capture effects that differ across quantiles of the distribution, a limitation that is analogous to only permitting proportional hazards in the Cox model. To address this, we propose a general framework for quantile-varying multiplicative effects under the AFT model. Specifically, we embed flexible regression structures within the AFT model and derive a novel formula for interpretable effects on the quantile scale. A regression standardization scheme based on the g-formula is proposed to enable the estimation of both covariate-conditional and marginal effects for an exposure of interest. We implement a user-friendly Bayesian approach for the estimation and quantification of uncertainty while accounting for left truncation and complex censoring. We emphasize the intuitive interpretation of this model through numerical and graphical tools and illustrate its performance through simulation and application to a study of Alzheimer's disease and dementia.

Control adjustment costs limit goal flexibility: Empirical evidence and a computational account

A cornerstone of human intelligence is the ability to flexibly adjust our cognition and behavior as our goals change. For instance, achieving some goals requires efficiency, while others require caution. Adapting to these changing goals require corresponding adjustments in cognitive control (e.g., levels of attention, response thresholds). However, adjusting our control to meet new goals comes at a cost: we are better at achieving a goal in isolation than when transitioning between goals. The source of these control adjustment costs remains poorly understood, and the bulk of our understanding of such costs comes from settings in which participants transition between discrete task sets, rather than performance goals. Across four experiments, we show that adjustments in continuous control states incur a performance cost, and that a dynamical systems model can explain the source of these costs. Participants performed a single cognitively demanding task under varying performance goals (e.g., to be fast or to be accurate). We modeled control allocation to include a dynamic process of adjusting from one's current control state to a target state for a given performance goal. By incorporating inertia into this adjustment process, our model accounts for our empirical findings that people under-shoot their target control state more (i.e., exhibit larger adjustment costs) when (a) goals switch rather than remain fixed over a block (Study 1); (b) target control states are more distant from one another (Study 2); (c) less time is given to adjust to the new goal (Study 3); and (d) when anticipating having to switch goals more frequently (Study 4). Our findings characterize the costs of adjusting control to meet changing goals, and show that these costs can emerge directly from cognitive control dynamics. In so doing, they shed new light on the sources of and constraints on flexibility in human goal-directed behavior.

Effectiveness of a Web-Based Individual Coping and Alcohol Intervention Program for Children of Parents With Alcohol Use Problems: Randomized Controlled Trial

BACKGROUND

Children whose parents have alcohol use problems are at an increased risk of several negative consequences, such as poor school performance, an earlier onset of substance use, and poor mental health. Many would benefit from support programs, but the figures reveal that only a small proportion is reached by existing support. Digital interventions can provide readily accessible support and potentially reach a large number of children. Research on digital interventions aimed at this target group is scarce. We have developed a novel digital therapist-assisted self-management intervention targeting adolescents whose parents had alcohol use problems. This program aims to strengthen coping behaviors, improve mental health, and decrease alcohol consumption in adolescents.

OBJECTIVE

This study aims to examine the effectiveness of a novel web-based therapist-assisted self-management intervention for adolescents whose parents have alcohol use problems.

METHODS

Participants were recruited on the internet from social media and websites containing health-related information about adolescents. Possible participants were screened using the short version of the Children of Alcoholics Screening Test-6. Eligible participants were randomly allocated to either the intervention group (n=101) or the waitlist control group (n=103), and they were unblinded to the condition. The assessments, all self-assessed, consisted of a baseline and 2 follow-ups after 2 and 6 months. The primary outcome was the Coping With Parents Abuse Questionnaire (CPAQ), and secondary outcomes were the Center for Epidemiological Studies Depression Scale, Alcohol Use Disorders Identification Test (AUDIT-C), and Ladder of Life (LoL).

RESULTS

For the primary outcome, CPAQ, a small but inconclusive treatment effect was observed (Cohen d=-0.05 at both follow-up time points). The intervention group scored 38% and 46% lower than the control group on the continuous part of the AUDIT-C at the 2- and 6-month follow-up, respectively. All other between-group comparisons were inconclusive at either follow-up time point. Adherence was low, as only 24% (24/101) of the participants in the intervention group completed the intervention.

CONCLUSIONS

The findings were inconclusive for the primary outcome but demonstrate that a digital therapist-assisted self-management intervention may contribute to a reduction in alcohol consumption. These results highlight the potential for digital interventions to reach a vulnerable, hard-to-reach group of adolescents but underscore the need to develop more engaging support interventions to increase adherence.

TRIAL REGISTRATION

ISRCTN Registry ISRCTN41545712; https://www.isrctn.com/ISRCTN41545712?q=ISRCTN41545712.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

RR2-10.1186/1471-2458-12-35.

Paradigmatic and syntagmatic effects of information status on prosodic prominence - evidence from an interactive web-based production experiment in German

In this paper, we investigate how information status is encoded paradigmatically and syntagmatically via prosodic prominence in German. In addition, we consider individual variability in the production of prominence. To answer our research questions, we collected controlled yet ecologically valid speech by applying an innovative recording paradigm. Participants were asked to perform an interactive reading task in collaboration with an interlocutor remotely via video calls. Results indicate that information status is encoded paradigmatically via the F0 contour, while syntagmatic effects are subtle and depend on the acoustic parameter used. Individual speakers differ primarily in their strength of encoding and secondarily in the type of parameters employed. While the paradigmatic effects we observe are in line with previous findings, our syntagmatic findings support two contradictory ideas, a balancing effect and a radiating effect. Along with the findings at the individual level, this study thus allows for new insights regarding the redundant and relational nature of prosodic prominence.

A Model-Based Approach to the Disentanglement and Differential Treatment of Engaged and Disengaged Item Omissions

Item omissions in large-scale assessments may occur for various reasons, ranging from disengagement to not being capable of solving the item and giving up. Current response-time-based classification approaches allow researchers to implement different treatments of item omissions presumably going back to different mechanisms. These approaches, however, are limited in that they require a clear-cut decision on the underlying missingness mechanism and do not allow to take the uncertainty in classification into account. We present a response-time-based model-based mixture modeling approach that overcomes this limitation. The approach (a) facilitates disentangling item omissions stemming from disengagement from those going back to solution behavior, (b) considers the uncertainty in omission classification, (c) allows for omission mechanisms to vary on the item-by-examinee level, (d) supports investigating person and item characteristics associated with different types of omission behavior, and (e) gives researchers flexibility in deciding on how to handle different types of omissions. The approach exhibits good parameter recovery under realistic research conditions. We illustrate the approach on data from the Programme for the International Assessment of Adult Competencies 2012 and compare it against previous classification approaches for item omissions.

Enricherator: A Bayesian Method for Inferring Regularized Genome-wide Enrichments from Sequencing Count Data

A pervasive question in biological research studying gene regulation, chromatin structure, or genomics is where, and to what extent, does a signal of interest arise genome-wide? This question is addressed using a variety of methods relying on high-throughput sequencing data as their final output, including ChIP-seq for protein-DNA interactions,1 GapR-seq for measuring supercoiling,2 and HBD-seq or DRIP-seq for R-loop positioning.3,4 Current computational methods to calculate genome-wide enrichment of the signal of interest usually do not properly handle the count-based nature of sequencing data, they often do not make use of the local correlation structure of sequencing data, and they do not apply any regularization of enrichment estimates. This can result in unrealistic estimates of the true underlying biological enrichment of interest, unrealistically low estimates of confidence in point estimates of enrichment (or no estimates of confidence at all), unrealistic gyrations in enrichment estimates at very close (<10 bp) genomic loci due to noise inherent in sequencing data, and in a multiple-hypothesis testing problem during interpretation of genome-wide enrichment estimates. We developed a tool called Enricherator to infer genome-wide enrichments from sequencing count data. Enricherator uses the variational Bayes algorithm to fit a generalized linear model to sequencing count data and to sample from the approximate posterior distribution of enrichment estimates (https://github.com/jwschroeder3/enricherator). Enrichments inferred by Enricherator more precisely identify known binding sites in cases where low coverage between binding sites leads to false-positive peak calls in these noisy regions of the genome; these benefits extend to published datasets.

Depression and thoughts of self-harm or suicide among gender and sexually diverse people in a regional Australian community

ISSUES ADDRESSED

There is a paucity of data regarding depression and thoughts of self-harm or suicide among gender and sexually diverse (GSD) people living within Australian regional/rural locations. This study aims to elucidate these issues and fill a critical gap.

METHODS

The sample included 91 GSD people from a regional community in South-West Queensland utilising the PHQ-9 to determine presence/severity of depression and self-harm/suicide ideation. These data were drawn from a larger health and wellbeing survey. Raw mean scores were calculated to determine prevalence/severity of clinical symptoms. Bayesian ordinal regression models were employed to analyse between-subgroup differences in depression and self-harm/suicide ideation.

RESULTS

Overall, 80.2% of GSD sample experienced depression (35.2% severe, 45.1% mild/moderate) and 41.8% experienced self-harm/suicide ideation in the past two-weeks. Trans and nonbinary people experienced higher levels of depressions than sexually diverse cisgender people. Pansexual and bisexual people experienced higher levels of depression than gay people. Trans people experienced higher prevalence of self-harm/suicide ideation than cisgender and nonbinary people, with no differences between sexuality subgroups.

CONCLUSIONS

These findings contribute to deeper and more nuanced insights regarding clinically salient depressive and self-harm/suicide ideation symptoms among trans, nonbinary, bisexual, pansexual and queer people in regional Australian communities, with the aim to ultimately reduce mental health prevalence, improve mental health outcomes and health promotion among GSD people. SO WHAT?: The current findings revealed GSD people experience high prevalence of depression and self-harm/suicide ideation indicating tailored mental health awareness-raising, training and health promotion is warranted to enhance psychological support.

Improved RNA stability estimation through Bayesian modeling reveals most Salmonella transcripts have subminute half-lives

RNA decay is a crucial mechanism for regulating gene expression in response to environmental stresses. In bacteria, RNA-binding proteins (RBPs) are known to be involved in posttranscriptional regulation, but their global impact on RNA half-lives has not been extensively studied. To shed light on the role of the major RBPs ProQ and CspC/E in maintaining RNA stability, we performed RNA sequencing of Salmonella enterica over a time course following treatment with the transcription initiation inhibitor rifampicin (RIF-seq) in the presence and absence of these RBPs. We developed a hierarchical Bayesian model that corrects for confounding factors in rifampicin RNA stability assays and enables us to identify differentially decaying transcripts transcriptome-wide. Our analysis revealed that the median RNA half-life in Salmonella in early stationary phase is less than 1 min, a third of previous estimates. We found that over half of the 500 most long-lived transcripts are bound by at least one major RBP, suggesting a general role for RBPs in shaping the transcriptome. Integrating differential stability estimates with cross-linking and immunoprecipitation followed by RNA sequencing (CLIP-seq) revealed that approximately 30% of transcripts with ProQ binding sites and more than 40% with CspC/E binding sites in coding or 3' untranslated regions decay differentially in the absence of the respective RBP. Analysis of differentially destabilized transcripts identified a role for ProQ in the oxidative stress response. Our findings provide insights into posttranscriptional regulation by ProQ and CspC/E, and the importance of RBPs in regulating gene expression.

Does precrastination explain why some observers are suboptimal in a visual search task?

How do we decide where to search for a target? Optimal search relies on first considering the relative informational value of different locations and then executing eye movements to the best options. However, many participants consistently move their eyes to locations that can be easily ascertained to neither contain the target nor provide new information about the target's location. Here, we asked whether this suboptimal search behaviour represents a specific example of a general tendency towards precrastination: starting sub-goals of a task before they are needed, and in so doing, spending longer time on doing the task than is necessary. To test this hypothesis, we asked 200 participants to do two tasks: retrieve two heavy buckets (one close and one far) and search for a line segment. Precrastination is defined as consistently picking up the closer bucket first, versus the more efficient strategy of picking up the farther bucket first. Search efficiency is the proportion of fixations directed to more cluttered regions of the search array. Based on the pilot data, we predicted an association of precrastination with inefficient search strategies. Personality inventories were also administered to identify stable characteristics associated with these strategies. In the final dataset, there was no clear association between search strategy and precrastination, nor did these correlate strongly with any of the personality measures collected. This article received in-principle acceptance (IPA) at Royal Society Open Science on 29 January 2020. The accepted Stage 1 version of the manuscript, not including results and discussion, may be found at https://osf.io/p2sjx. This preregistration was performed prior to data collection and analysis.

A host-microbiota interactome reveals extensive transkingdom connectivity

The myriad microorganisms that live in close association with humans have diverse effects on physiology, yet the molecular bases for these impacts remain mostly unknown1-3. Classical pathogens often invade host tissues and modulate immune responses through interactions with human extracellular and secreted proteins (the 'exoproteome'). Commensal microorganisms may also facilitate niche colonization and shape host biology by engaging host exoproteins; however, direct exoproteome-microbiota interactions remain largely unexplored. Here we developed and validated a novel technology, BASEHIT, that enables proteome-scale assessment of human exoproteome-microbiome interactions. Using BASEHIT, we interrogated more than 1.7 million potential interactions between 519 human-associated bacterial strains from diverse phylogenies and tissues of origin and 3,324 human exoproteins. The resulting interactome revealed an extensive network of transkingdom connectivity consisting of thousands of previously undescribed host-microorganism interactions involving 383 strains and 651 host proteins. Specific binding patterns within this network implied underlying biological logic; for example, conspecific strains exhibited shared exoprotein-binding patterns, and individual tissue isolates uniquely bound tissue-specific exoproteins. Furthermore, we observed dozens of unique and often strain-specific interactions with potential roles in niche colonization, tissue remodelling and immunomodulation, and found that strains with differing host interaction profiles had divergent interactions with host cells in vitro and effects on the host immune system in vivo. Overall, these studies expose a previously unexplored landscape of molecular-level host-microbiota interactions that may underlie causal effects of indigenous microorganisms on human health and disease.

Air Cleaners and Respiratory Infections in Schools: A Modeling Study Based on Epidemiologic, Environmental, and Molecular Data

Background

Using a multiple-measurement approach, we examined the real-world effectiveness of portable HEPA air filtration devices (air cleaners) in a school setting.

Methods

We collected data over 7 weeks during winter 2022/2023 in 2 Swiss secondary school classes: environmental (CO2, particle concentrations), epidemiologic (absences related to respiratory infections), audio (coughing), and molecular (bioaerosol and saliva samples). Using a crossover design, we compared particle concentrations, coughing, and risk of infection with and without air cleaners.

Results

All 38 students participated (age, 13-15 years). With air cleaners, mean particle concentration decreased by 77% (95% credible interval, 63%-86%). There were no differences in CO2 levels. Absences related to respiratory infections were 22 without air cleaners vs 13 with them. Bayesian modeling suggested a reduced risk of infection, with a posterior probability of 91% and a relative risk of 0.73 (95% credible interval, 0.44-1.18). Coughing also tended to be less frequent (posterior probability, 93%), indicating that fewer symptomatic students were in class. Molecular analysis detected mainly non-SARS-CoV-2 viruses in saliva (50/448 positive) but not in bioaerosols (2/105) or on the HEPA filters of the air cleaners (4/160). The molecular detection rate in saliva was similar with and without air cleaners. Spatiotemporal analysis of positive saliva samples identified several likely transmissions.

Conclusions

Air cleaners improved air quality and showed potential benefits in reducing respiratory infections. Airborne detection of non-SARS-CoV-2 viruses was rare, suggesting that these viruses may be more difficult to detect in the air. Future studies should examine the importance of close contact and long-range transmission and the cost-effectiveness of using air cleaners.