A Bayesian Analysis of a Cognitive-Behavioral Therapy Intervention for High-Risk People on Probation

This analysis employs a Bayesian framework to estimate the impact of a Cognitive-Behavioral Therapy (CBT) intervention on the recidivism of high-risk people under community supervision. The study relies on the reanalysis of experimental datal using a Bayesian logistic regression model. In doing so, new estimates of programmatic impact were produced using weakly informative Cauchy priors and the Hamiltonian Monte Carlo method. The Bayesian analysis indicated that CBT reduced the prevalence of new charges for total, non-violent, property, and drug crimes. However, the effectiveness of the CBT program varied meaningfully depending on the participant's age. The probability of the successful reduction of drug offenses was high only for younger individuals (<26 years old), while there was an impact on property offenses only for older individuals (>26 years old). In general, the probability of the successful reduction of new charges was higher for the older group of people on probation. Generally, this study demonstrates that Bayesian analysis can complement the more commonplace Null Hypothesis Significance Test (NHST) analysis in experimental research by providing practically useful probability information. Additionally, the specific findings of the reestimation support the principles of risk-needs responsivity and risk-stratified community supervision and align with related findings, though important differences emerge. In this case, the Bayesian estimations suggest that the effect of the intervention may vary for different types of crime depending on the age of the participants. This is informative for the development of evidence-based correctional policy and effective community supervision programming.

ROMI: a randomized two-stage basket trial design to optimize doses for multiple indications

Optimizing doses for multiple indications is challenging. The pooled approach of finding a single optimal biological dose (OBD) for all indications ignores that dose-response or dose-toxicity curves may differ between indications, resulting in varying OBDs. Conversely, indication-specific dose optimization often requires a large sample size. To address this challenge, we propose a Randomized two-stage basket trial design that Optimizes doses in Multiple Indications (ROMI). In stage 1, for each indication, response and toxicity are evaluated for a high dose, which may be a previously obtained maximum tolerated dose, with a rule that stops accrual to indications where the high dose is unsafe or ineffective. Indications not terminated proceed to stage 2, where patients are randomized between the high dose and a specified lower dose. A latent-cluster Bayesian hierarchical model is employed to borrow information between indications, while considering the potential heterogeneity of OBD across indications. Indication-specific utilities are used to quantify response-toxicity trade-offs. At the end of stage 2, for each indication with at least one acceptable dose, the dose with highest posterior mean utility is selected as optimal. Two versions of ROMI are presented, one using only stage 2 data for dose optimization and the other optimizing doses using data from both stages. Simulations show that both versions have desirable operating characteristics compared to designs that either ignore indications or optimize dose independently for each indication.

Circuit change in neonatal and pediatric extracorporeal membrane oxygenation is associated with adverse outcomes

INTRODUCTION

Extracorporeal membrane oxygenation (ECMO) circuits may be changed during the run for multiple reasons; however, these circuit changes may be associated with adverse events. Predictors for undergoing a circuit change (CC) and their outcomes remain unclear. We hypothesized that neonatal and pediatric CC correlates with increased morbidity and mortality.

METHODS

Pediatric and neonatal patients who underwent one ECMO run lasting <30 days at a tertiary children's hospital from 2011 through 2017 were retrospectively reviewed. Bivariate regression analysis evaluated factors associated with ECMO mortality and morbidity. LASSO logistic regression models identified independent risk factors for undergoing a CC. p < .05 was significant.

RESULTS

One hundred 85 patients were included; 137 (74%) underwent no CC, while 48 (26%) underwent one or more. Undergoing a CC was associated with longer ECMO duration (p < .001), higher blood transfusion volumes (p < .001), increased hemorrhagic complications (p < .001) and increased mortality (p = .002). Increased platelet (p = .001) and FFP (p = .016) transfusion volumes at any time while on ECMO were independent factors associated with undergoing a CC.

CONCLUSIONS

Changing the circuit during the ECMO run occurs frequently and may be associated with poorer outcomes. Understanding the outcomes and predictors for CC may guide management protocols for more efficient circuit changes given its important association with overall outcomes.

An assessment of the species diversity and disease potential of Pythium communities in Europe

Pythium sensu lato (s.l.) is a genus of parasitic oomycetes that poses a serious threat to agricultural production worldwide, but their severity is often neglected because little knowledge about them is available. Using an internal transcribed spacer (ITS) amplicon-based-metagenomics approach, we investigate the occurrence, abundance, and diversity of Pythium spp. s.l. in 127 corn fields of 11 European countries from the years 2019 to 2021. We also identify 73 species, with up to 20 species in a single soil sample, and the prevalent species, which show high species diversity, varying disease potential, and are widespread in most countries. Further, we show species-species co-occurrence patterns considering all detected species and link species abundance to soil parameter using the LUCAS topsoil dataset. Infection experiments with recovered isolates show that Pythium s.l. differ in disease potential, and that effective interference with plant hormone networks suppressing JA (jasmonate)-mediated defenses is an essential component of the virulence mechanism of Pythium s.l. species. This study provides a valuable dataset that enables deep insights into the structure and species diversity of Pythium s.l. communities in European corn fields and knowledge for better understanding plant-Pythium interactions, facilitating the development of an effective strategy to cope with this pathogen.

Association Between Periconceptional Diet Quality and Hypertensive Disorders of Pregnancy: The Japan Environment and Children's Study

BACKGROUND

Little is known about the relationship of healthy diets, which are widely recommended to prevent diseases in general populations, with the risk of hypertensive disorders of pregnancy (HDP), particular among non-Western populations with different dietary habits. We aimed to investigate the association between periconceptional diet quality and the risk of HDP among pregnant Japanese women.

METHODS AND RESULTS

Dietary intake over 1 year before the first trimester of pregnancy was assessed using a validated, self-administered food frequency questionnaire among 81 113 pregnant Japanese women who participated in a prospective cohort of the Japan Environment and Children's Study. Overall diet quality was assessed by the Balanced Diet Score (BDS) based on adherence to the country-specific dietary guidelines and the Dietary Approaches to Stop Hypertension (DASH) score. Cases of HDP were identified by medical record transcription. The association between diet quality and HDP risk was examined using Bayesian logistic regression models with monotonic effects. We identified 2383 (2.9%) cases of HDP. A higher BDS was associated with a lower risk of HDP. When comparing the highest with the lowest quintile of the BDS, the adjusted odds ratio (aOR) of HDP was 0.83 (95% credible interval [CrI], 0.73-0.94). The DASH score and HDP risk were inversely associated in a monotonic dose-response manner (aOR per 1-quintile increase in the DASH score, 0.92 [95% CrI, 0.89-0.95]).

CONCLUSIONS

A high-quality diet, which is recommended for disease prevention in general populations, before conception may also reduce the risk of HDP among pregnant Japanese women.

Bayesian statistics for clinical research

Frequentist and Bayesian statistics represent two differing paradigms for the analysis of data. Frequentism became the dominant mode of statistical thinking in medical practice during the 20th century. The advent of modern computing has made Bayesian analysis increasingly accessible, enabling growing use of Bayesian methods in a range of disciplines, including medical research. Rather than conceiving of probability as the expected frequency of an event (purported to be measurable and objective), Bayesian thinking conceives of probability as a measure of strength of belief (an explicitly subjective concept). Bayesian analysis combines previous information (represented by a mathematical probability distribution, the prior) with information from the study (the likelihood function) to generate an updated probability distribution (the posterior) representing the information available for clinical decision making. Owing to its fundamentally different conception of probability, Bayesian statistics offers an intuitive, flexible, and informative approach that facilitates the design, analysis, and interpretation of clinical trials. In this Review, we provide a brief account of the philosophical and methodological differences between Bayesian and frequentist approaches and survey the use of Bayesian methods for the design and analysis of clinical research.

A Bayesian pharmacokinetics integrated phase I-II design to optimize dose-schedule regimes

The schedule of administering a drug has profound impact on the toxicity and efficacy profiles of the drug through changing its pharmacokinetics (PK). PK is an innate and indispensable component of the dose-schedule optimization. Motivated by this, we propose a Bayesian PK integrated dose-schedule finding (PKIDS) design to identify the optimal dose-schedule regime by integrating PK, toxicity, and efficacy data. Based on the causal pathway that dose and schedule affect PK, which in turn affects efficacy and toxicity, we jointly model the three endpoints by first specifying a Bayesian hierarchical model for the marginal distribution of the longitudinal dose-concentration process. Conditional on the drug concentration in plasma, we jointly model toxicity and efficacy as a function of the concentration. We quantify the risk-benefit of regimes using utility-continuously updating the estimates of PK, toxicity, and efficacy based on interim data-and make adaptive decisions to assign new patients to appropriate dose-schedule regimes via adaptive randomization. The simulation study shows that the PKIDS design has desirable operating characteristics.

Musicians' pursuit of expertise-related goals is characterised by strategic regulation of functional and counterproductive affect

Background

Emotion regulation is an important part of effective goal pursuit. Functional accounts of emotion regulation suggest that the attainment of challenging goals may be supported by regulating emotions which promote utilitarian over hedonic outcomes. When pursuing the challenging, long-term goal of acquiring expert musical skills and knowledge, musicians may wish to prioritise whichever emotions are most conducive to attaining this goal, even if those emotions are not necessarily positive.

Methods

Via an online questionnaire, musicians (N = 421) answered questions concerning their musical experience and their expertise-related practice goals. They also reported how strongly they experienced different emotions during practice, and how strongly they desired to either increase or decrease the intensity of those same emotions. Data were analysed using inferential frequentist statistics and Bayesian mixed effects models. Evidence ratios (ER) > 19 were considered strong evidence in favour of an effect.

Results

Our analysis showed that musicians experienced and desired strong levels of positive emotions in their practice. In addition, they reported greater desire to intensify positive compared to negative emotions [paired t (420) = 58.13, p < 0.001]. Our Bayesian mixed effects model provided strong evidence that greater desire to intensify anger increased the probability that an observation derived from a musician with stronger expertise-related goals [Est = 0.70; Odds (Est > 0) > 9,999]. In addition to anger, higher levels of expertise-related goals were increasingly predicted by less strong desire to intensify guilt and gloom and greater desire to reduce downheartedness (all ER > 19).

Discussion

Overall, musicians had a strong, general desire to intensify positive emotions during their musical practice. However, musicians with higher levels of expertise-related goals increasingly indicated a nuanced approach regarding how they desired to regulate certain negative emotions. Findings suggest that musicians engage in selective and sophisticated emotion regulation behaviour that aligns with their long-term commitment to develop musical expertise. They may prioritise emotions which may be functionally beneficial, whilst avoiding emotions which may be counterproductive or undermine efforts. Findings from this study contribute to our understanding of expertise-related, domain-specific emotion regulation behaviour and may inform the design of prioritised musical practice strategies.

Frailty and decisional regret after elective noncardiac surgery: a multicentre prospective cohort study

BACKGROUND

Frailty is associated with morbidity and mortality after surgery. The association of frailty with decisional regret is poorly defined. Our objective was to estimate the association of preoperative frailty with decisional regret status in the year after surgery.

METHODS

We conducted a secondary analysis of a prospective, multicentre cohort study of patients aged ≥65 years who underwent elective noncardiac surgery. Decisional regret about having undergone surgery was ascertained at 30, 90, and 365 (primary time point) days after surgery using a 3-point ordinal scale. Bayesian ordinal logistic regression was used to estimate the association of frailty with decisional regret, adjusted for surgery type, age, sex, and mental health conditions. Subgroup and sensitivity analyses were conducted.

RESULTS

We identified 669 patients; 293 (43.8%) lived with frailty. At 365 days after surgery, the unadjusted odds ratio (OR) associating frailty with greater decisional regret was 2.21 (95% credible interval [CrI] 0.98-5.09; P(OR>1)=0.97), which was attenuated after confounder adjustment (adjusted OR 1.68, 95% CrI 0.84-3.36; P(OR>1)=0.93). Similar results were estimated at 30 and 90 days. Additional adjustment for baseline comorbidities and disability score substantially altered the OR at 365 days (0.89, 95% CrI 0.37-2.12; P(OR>1)=0.39). There was a high probability that surgery type was an effect modifier (non-orthopaedic: OR 1.90, 95% CrI 1.00-3.59; P(OR>1)=0.98); orthopaedic: OR 0.87, 95% CrI 0.41-1.91; P(OR>1)=0.36).

CONCLUSIONS

Among older surgical patients, there appears to be a complex association with frailty and decisional regret, with substantial heterogeneity based on assumed causal pathways and surgery type. Future studies are required to untangle the complex interplay between these factors.

A machine learning algorithm for the automatic classification of Phytophthora infestans genotypes into clonal lineages

Premise

The prompt categorization of Phytophthora infestans isolates into described clonal lineages is a key tool for the management of its associated disease, potato late blight. New isolates of this pathogen are currently classified by comparing their microsatellite genotypes with characterized clonal lineages, but an automated classification tool would greatly improve this process. Here, we developed a flexible machine learning-based classifier for P. infestans genotypes.

Methods

The performance of different machine learning algorithms in classifying P. infestans genotypes into its clonal lineages was preliminarily evaluated with decreasing amounts of training data. The four best algorithms were then evaluated using all collected genotypes.

Results

mlpML, cforest, nnet, and AdaBag performed best in the preliminary test, correctly classifying almost 100% of the genotypes. AdaBag performed significantly better than the others when tested using the complete data set (Tukey HSD P < 0.001). This algorithm was then implemented in a web application for the automated classification of P. infestans genotypes, which is freely available at https://github.com/cpatarroyo/genotypeclas.

Discussion

We developed a gradient boosting-based tool to automatically classify P. infestans genotypes into its clonal lineages. This could become a valuable resource for the prompt identification of clonal lineages spreading into new regions.

Drought tolerance and species abundance mediate dry season negative density dependence in a tropical forest

Conspecific negative density dependence (CNDD) is thought to be a key process in maintaining plant diversity. However, the strength of CNDD is highly variable in space and time as well as among species, and correlates of this variation that might help to understand and explain it remain largely unquantified. Using Bayesian hierarchical models, we took advantage of 10-year seedling monitoring data that were collected annually in every dry and rainy season in a seasonal tropical forest. We quantified the interspecific variation in the strength of CNDD and its temporal variation. We also examined potential correlates of this interspecific and temporal variation, including species functional traits (such as drought-tolerant traits, defense-related traits, and recourse acquisition traits) and species abundances. In the dry season, we found a negative relationship between the density of neighboring conspecific seedlings on seedling survival, while in the rainy season, there was a negative relationship between the density of neighboring conspecific adults on seedling survival. In addition, we found that interspecific variation in CNDD was related to drought-tolerant traits in the dry season but not in the rainy season. Across years, we found that drought-intolerant species suffer less CNDD during the dry seasons that have higher rainfall, whereas drought-tolerant species suffer less CNDD when the dry season has lower rainfall. We also found that rare species suffered stronger CNDD in the dry season. Overall, our study highlights that CNDD is highly variable among species and through time, necessitating a deeper appreciation of the environmental and functional contexts of CNDD and their interactions.

Identification of autoantibodies as potential non-invasive biomarkers for intrahepatic cholangiocarcinoma

Intrahepatic cholangiocarcinoma (iCCA) presents a challenging diagnosis due to its nonspecific early clinical manifestations, often resulting in late-stage detection and high mortality. Diagnosing iCCA is further complicated by its limited accuracy, often necessitating multiple invasive procedures for precise identification. Despite carbohydrate antigen 19-9 (CA19-9) having been investigated and employed for iCCA diagnosis, it demonstrates modest diagnostic performance. Consequently, the identification of novel biomarkers with improved sensitivity and specificity remains an imperative yet formidable task. Autoantibodies, as early indicators of the immune response against cancer, offer a promising avenue for enhancing diagnostic accuracy. Our study aimed to identify non-invasive blood-based autoantibody biomarkers capable of distinguishing iCCA patients from healthy individuals (CTRs). We profiled autoantibodies in 26 serum samples (16 iCCAs and 10 CTRs) using protein microarrays containing 1622 functional proteins. Leveraging machine learning techniques, we identified a signature composed of three autoantibody biomarkers (NDE1, PYCR1, and VIM) in conjunction with CA19-9 for iCCA detection. This combined signature demonstrated superior diagnostic performance with an AUC of 96.9%, outperforming CA19-9 alone (AUC: 83.8%). These results suggest the potential of autoantibody biomarkers to develop a complementary non-invasive diagnostic utility for routine iCCA screening.

Factors affecting maximal oxygen uptake in prepubertal children: a systematic review and meta-analysis

In pre-pubertal children the factors affecting maximal oxygen uptake have yet to be fully understood. Therefore, the purpose of this analysis is to present cardiorespiratory fitness in prepubertal boys and girls and to determine if there are any differences based on sex, exercise testing modality or if maximal or peak oxygen consumption metrics are used. PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines were followed. For statistical analysis, multilevel models grounded in Bayesian principles were used. Selected studies obtained: maximal effort during the test, peak or maximal oxygen consumption (V̇O2) values, boys and girls (sex specific groups only) age under 11, cycle ergometry or treadmill, pre-intervention or no intervention data. In boys using cycle ergometry, 118 studies were included in the analysis, in boys using treadmill 115, in girls using cycle ergometry 83 and in girls using treadmill testing 95 study entries were included. As children get older, their cardiorespiratory fitness increases (P ≈ 100%). Studies with participants having smaller body mass have higher V̇O2 relative to body mass values (P ≈ 100%). Boys have higher V̇O2 values than girls (P ≈ 100%). Studies using treadmill reported higher values than those using cycle ergometer (P ≈ 100%). Regarding the influence of measurement method (max vs. peak) on V̇O2 values we did not find significant differences. In conclusion, we present reference values for cardiorespiratory fitness in prepubertal boys and girls using cycle ergometry or treadmill. Prepubertal boys have higher cardiorespiratory fitness than girls and using treadmill testing might be a preferred method to cycle ergometry, especially in older children. Maximal or peak oxygen consumption metrics might be used interchangeably in prepubertal children.

Bayesian sparse regression for exposure-response analyses of efficacy and safety endpoints to justify the clinical dose of valemetostat for adult T-cell leukemia/lymphoma

Valemetostat is an oral inhibitor of enhancer of zeste homolog (EZH) 2 and EZH1 approved in Japan for the treatment of adult T-cell leukemia/lymphoma (ATLL). To support the approved daily dose of 200 mg and inform dose adjustments in patients with ATLL, Bayesian exposure-response analyses were conducted using data from two clinical trials. The analyses included two efficacy endpoints, overall response by central and investigator assessments in patients with ATLL (n = 38, 150-200 mg), and six safety endpoints in patients with non-Hodgkin lymphoma (n = 102, 150-300 mg), which included grade ≥3 laboratory values for anemia, absolute neutrophil count decreased, and platelet count decreased; any grade ≥3 treatment-emergent adverse event (TEAE); and dose reductions and dose interruptions due to TEAEs. A slightly positive relationship was observed between unbound exposure and efficacy endpoints. A steeper relationship was observed in safety endpoints, compared with efficacy. Candidate covariate effects, except intercepts of the baseline laboratory values, were regularized via spike and slab priors in a Bayesian framework; only the laboratory values for corresponding hematologic TEAEs were shown to be of substantial impact. The target exposure range was established by defining a modified region of practical equivalence (184-887 ng·h/mL), which was expected to provide satisfactory efficacy and acceptable safety within the range of available exposure data. The simulated exposure range considering inter-individual variability showed that 200 mg could reach target exposure in the overall population and across subpopulations of interest, supporting the use of valemetostat 200 mg in patients with ATLL.

A new exposure protocol adapted for wild bees reveals species-specific impacts of the sulfoximine insecticide sulfoxaflor

Wild bees are crucial pollinators of flowering plants and concerns are rising about their decline associated with pesticide use. Interspecific variation in wild bee response to pesticide exposure is expected to be related to variation in their morphology, physiology, and ecology, though there are still important knowledge gaps in its understanding. Pesticide risk assessments have largely focussed on the Western honey bee sensitivity considering it protective enough for wild bees. Recently, guidelines for Bombus terrestris and Osmia bicornis testing have been developed but are not yet implemented at a global scale in pesticide risk assessments. Here, we developed and tested a new simplified method of pesticide exposure on wild bee species collected from the field in Belgium. Enough specimens of nine species survived in a laboratory setting and were exposed to oral and topical acute doses of a sulfoximine insecticide. Our results confirm significant variability among wild bee species. We show that Osmia cornuta is more sensitive to sulfoxaflor than B. terrestris, whereas Bombus hypnorum is less sensitive. We propose hypotheses on the mechanisms explaining interspecific variations in sensitivity to pesticides. Future pesticide risk assessments of wild bees will require further refinement of protocols for their controlled housing and exposure.

Effects of perceptions of forest change and intergroup competition on community-based conservation behaviors

Approximately one quarter of the earth's population directly harvests natural resources to meet their daily needs. These individuals are disproportionately required to alter their behaviors in response to increasing climatic variability and global biodiversity loss. Much of the ever-ambitious global conservation agenda relies on the voluntary uptake of conservation behaviors in such populations. Thus, it is critical to understand how such individuals perceive environmental change and use conservation practices as a tool to protect their well-being. We developed a participatory mapping activity to elicit spatially explicit perceptions of forest change and its drivers across 43 mangrove-dependent communities in Pemba, Tanzania. We administered this activity along with a questionnaire regarding conservation preferences and behaviors to 423 individuals across those 43 communities. We analyzed these data with a set of Bayesian hierarchical statistical models. Perceived cover loss in 50% of a community's mangrove area drove individuals to decrease proposed limits on fuelwood bundles from 2.74 (forest perceived as intact) to 2.37 if participants believed resultant gains in mangrove cover would not be stolen by outsiders. Conversely, individuals who believed their community mangrove forests were at high risk of theft loosened their proposed harvest limits from 1.26 to 2.75 bundles of fuelwood in response to the same perceived forest decline. High rates of intergroup competition and mangrove loss were thus driving a self-reinforcing increase in unsustainable harvesting preferences in community forests in this system. This finding demonstrates a mechanism by which increasing environmental decline may cause communities to forgo conservation practices, rather than adopt them, as is often assumed in much community-based conservation planning. However, we also found that when effective boundaries were present, individuals were willing to limit their own harvests to stem such perceived decline.

Episodic-semantic linkage for $1000: New semantic knowledge is more strongly coupled with episodic memory in trivia experts

Some people exhibit impressive memory for a wide array of semantic knowledge. What makes these trivia experts better able to learn and retain novel facts? We hypothesized that new semantic knowledge may be more strongly linked to its episodic context in trivia experts. We designed a novel online task in which 132 participants varying in trivia expertise encoded "exhibits" of naturalistic facts with related photos in one of two "museums." Afterward, participants were tested on cued recall of facts and recognition of the associated photo and museum. Greater trivia expertise predicted higher cued recall for novel facts. Critically, trivia experts but not non-experts showed superior fact recall when they remembered both features (photo and museum) of the encoding context. These findings illustrate enhanced links between episodic memory and new semantic learning in trivia experts, and show the value of studying trivia experts as a special population that can shed light on the mechanisms of memory.

Fetal and Maternal Factors Predictive of Primary Cesarean Delivery at Term in a Low-Risk Population: NICHD Fetal Growth Studies-Singletons

OBJECTIVE

 This study aimed to examine associations of fetal biometric and amniotic fluid measures with intrapartum primary cesarean delivery (PCD) and develop prediction models for PCD based on ultrasound parameters and maternal factors.

STUDY DESIGN

 Secondary analysis of the National Institute of the Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD) Fetal Growth Studies-singleton cohort (2009-2013) including patients with uncomplicated pregnancies and intent to deliver vaginally at ≥370/7 weeks. The estimated fetal weight, individual biometric parameters, fetal asymmetry measurements, and amniotic fluid single deepest vertical pocket assessed at the final scan (mean 37.5 ± 1.9 weeks) were categorized as <10th, 10th to 90th (reference), and >90th percentiles. Logistic regression analyses examined the association between the ultrasound measures and PCD. Fetal and maternal SuperLearner prediction algorithms were constructed for the full and nulliparous cohorts.

RESULTS

 Of the 1,668 patients analyzed, 249 (14.9%) had PCD. The fetal head circumference, occipital-frontal diameter, and transverse abdominal diameter >90th percentile (adjusted odds ratio [aOR] = 2.50, 95% confidence interval [95% CI]: 1.39, 4.51; aOR = 1.86, 95% CI: 1.02, 3.40; and aOR = 2.13, 95% CI: 1.16, 3.89, respectively) were associated with PCD. The fetal model demonstrated poor ability to predict PCD in the full cohort and in nulliparous patients (area under the receiver-operating characteristic curve [AUC] = 0.56, 95% CI: 0.52, 0.61; and AUC = 0.54, 95% CI: 0.49, 0.60, respectively). Conversely, the maternal model had better predictive capability overall (AUC = 0.79, 95% CI: 0.75, 0.82) and in the nulliparous subgroup (AUC = 0.72, 95% CI: 0.67, 0.77). Models combining maternal/fetal factors performed similarly to the maternal model (AUC = 0.78, 95% CI: 0.75, 0.82 in full cohort, and AUC = 0.71, 95% CI: 0.66, 0.76 in nulliparas).

CONCLUSION

 Although a few fetal biometric parameters were associated with PCD, the fetal prediction model had low performance. In contrast, the maternal model had a fair-to-good ability to predict PCD.

KEY POINTS

· Fetal HC >90th percentile was associated with cesarean delivery.. · Fetal parameters did not effectively predict PCD.. · Maternal factors were more predictive of PCD.. · Maternal/fetal and maternal models performed similarly.. · Prediction models had lower performance in nulliparas..

Desensitizing Anxiety Through Imperceptible Change: Feasibility Study on a Paradigm for Single-Session Exposure Therapy for Fear of Public Speaking

BACKGROUND

Exposure therapy (ET) for anxiety disorders involves introducing the participant to an anxiety-provoking situation over several treatment sessions. Each time, the participant is exposed to a higher anxiety-provoking stimulus; for example, in the case of fear of heights, the participant would successively experience being at a greater height. ET is effective, and its counterpart, virtual reality (VR) exposure therapy (VRET), where VR substitutes real-world exposure, is equally so. However, ET is time-consuming, requiring several sessions.

OBJECTIVE

This study aimed to compare the results of single-session exposure with those of traditional VRET with regard to reducing public speaking anxiety.

METHODS

We introduced a paradigm concerned with public speaking anxiety where the VR exposure occurred in a single session while the participant interacted with a virtual therapist. Over time, the therapist transformed into an entire audience with almost imperceptible changes. We carried out a feasibility study with 45 participants, comparing 3 conditions: single-session exposure (n=16, 36%); conventional multiple-session exposure (n=14, 31%), where the same content was delivered in successive segments over 5 sessions; and a control group (n=15, 33%), who interacted with a single virtual character to talk about everyday matters. A week later, the participants were required to speak on a stage in front of a large audience in VR.

RESULTS

Across most of the series of conventional public speaking anxiety measures, the single-session exposure was at least as effective in reducing anxiety as the multiple-session exposure, and both these conditions were better than the control condition. The 12-item Personal Report of Confidence as a Speaker was used to measure public speaking anxiety levels, where higher values indicated more anxiety. Using a Bayesian model, the posterior probabilities of improvement compared to a high baseline were at least 1.7 times greater for single- and multiple-session exposures compared to the control group. The State Perceived Index of Competence was used as a measure of anticipatory anxiety for speaking on a stage in front of a large audience, where lower values indicated higher anxiety. The probabilities of improvement were just over 4 times greater for single- and multiple-session exposures compared to the control group for a low baseline and 489 (single) and 53 (multiple) times greater for a middle baseline.

CONCLUSIONS

Overall, the results of this feasibility study show that for moderate public speaking anxiety, the paradigm of gradual change in a single session is worth following up with further studies with more severe levels of anxiety and a larger sample size, first with a randomized controlled trial with nonpatients and subsequently, if the outcomes follow those that we have found, with a full clinical trial with patients.

Copy number losses of oncogenes and gains of tumor suppressor genes generate common driver mutations

Cancer driver genes can undergo positive selection for various types of genetic alterations, including gain-of-function or loss-of-function mutations and copy number alterations (CNA). We investigated the landscape of different types of alterations affecting driver genes in 17,644 cancer exomes and genomes. We find that oncogenes may simultaneously exhibit signatures of positive selection and also negative selection in different gene segments, suggesting a method to identify additional tumor types where an oncogene is a driver or a vulnerability. Next, we characterize the landscape of CNA-dependent selection effects, revealing a general trend of increased positive selection on oncogene mutations not only upon CNA gains but also upon CNA deletions. Similarly, we observe a positive interaction between mutations and CNA gains in tumor suppressor genes. Thus, two-hit events involving point mutations and CNA are universally observed regardless of the type of CNA and may signal new therapeutic opportunities. An analysis with focus on the somatic CNA two-hit events can help identify additional driver genes relevant to a tumor type. By a global inference of point mutation and CNA selection signatures and interactions thereof across genes and tissues, we identify 9 evolutionary archetypes of driver genes, representing different mechanisms of (in)activation by genetic alterations.

Egg protein exposure estimation in risk assessment for Japanese food allergy labeling

To assess the risk of food allergies in foods processed under the Japanese food labeling system, estimating exposure to hidden allergens is necessary. We assessed exposure to egg protein in foods processed according to the Japanese food labeling system. First, we estimated the concentration distribution of egg protein by Bayesian methods using data from the literature and the measurement of food products with precautional declarations in the labeling margin. We then estimated the food-intake portion-size distribution under two scenarios: soft drink consumption as an example of single, high-intake consumption, and confections, which are frequently consumed by children, as a realistic example of low-intake consumption. Finally, we estimated the distribution of unexpected intake of egg proteins in the form of single consumption. The mean exposure to egg protein under the high-intake scenario was estimated to be 0.0164 mg for 1-15-year-olds, 0.0171 mg for 4-15-year-olds, 0.0181 mg for 7-15-year-olds, and ≥0.0188 mg for 16-year-olds. The mean exposure to egg protein under the low-intake scenario was estimated to be 0.0018 mg for 1-15-year-olds, 0.0019 mg for 4-15-year-olds, 0.0020 mg for 7-15-year-olds, and ≥0.0022 mg for 16-year-olds. Compared to the reference dose of 2.0 mg proposed by the Joint the Food and Agriculture Organization (FAO)/World Health Organization (WHO) Expert Committee, the risk of onset of food allergies due to egg protein contamination from foods without egg labeling is considered to be extremely low under the current Japanese food labeling system.

Four decades of data indicate that planted mangroves stored up to 75% of the carbon stocks found in intact mature stands

Mangroves' ability to store carbon (C) has long been recognized, but little is known about whether planted mangroves can store C as efficiently as naturally established (i.e., intact) stands and in which time frame. Through Bayesian logistic models compiled from 40 years of data and built from 684 planted mangrove stands worldwide, we found that biomass C stock culminated at 71 to 73% to that of intact stands ~20 years after planting. Furthermore, prioritizing mixed-species planting including Rhizophora spp. would maximize C accumulation within the biomass compared to monospecific planting. Despite a 25% increase in the first 5 years following planting, no notable change was observed in the soil C stocks thereafter, which remains at a constant value of 75% to that of intact soil C stock, suggesting that planting effectively prevents further C losses due to land use change. These results have strong implications for mangrove restoration planning and serve as a baseline for future C buildup assessments.

Hierarchical syntax model of music predicts theta power during music listening

Linguistic research showed that the depth of syntactic embedding is reflected in brain theta power. Here, we test whether this also extends to non-linguistic stimuli, specifically music. We used a hierarchical model of musical syntax to continuously quantify two types of expert-annotated harmonic dependencies throughout a piece of Western classical music: prolongation and preparation. Prolongations can roughly be understood as a musical analogue to linguistic coordination between constituents that share the same function (e.g., 'pizza' and 'pasta' in 'I ate pizza and pasta'). Preparation refers to the dependency between two harmonies whereby the first implies a resolution towards the second (e.g., dominant towards tonic; similar to how the adjective implies the presence of a noun in 'I like spicy … '). Source reconstructed MEG data of sixty-five participants listening to the musical piece was then analysed. We used Bayesian Mixed Effects models to predict theta envelope in the brain, using the number of open prolongation and preparation dependencies as predictors whilst controlling for audio envelope. We observed that prolongation and preparation both carry independent and distinguishable predictive value for theta band fluctuation in key linguistic areas such as the Angular, Superior Temporal, and Heschl's Gyri, or their right-lateralised homologues, with preparation showing additional predictive value for areas associated with the reward system and prediction. Musical expertise further mediated these effects in language-related brain areas. Results show that predictions of precisely formalised music-theoretical models are reflected in the brain activity of listeners which furthers our understanding of the perception and cognition of musical structure.

A Bayesian phase I-II clinical trial design to find the biological optimal dose on drug combination

In recent years, combined therapy shows expected treatment effect as they increase dose intensity, work on multiple targets and benefit more patients for antitumor treatment. However, dose -finding designs for combined therapy face a number of challenges. Therefore, under the framework of phase I-II, we propose a two-stage dose -finding design to identify the biologically optimal dose combination (BODC), defined as the one with the maximum posterior mean utility under acceptable safety. We model the probabilities of toxicity and efficacy by using linear logistic regression models and conduct Bayesian model selection (BMS) procedure to define the most likely pattern of dose-response surface. The BMS can adaptively select the most suitable model during the trial, making the results robust. We investigated the operating characteristics of the proposed design through simulation studies under various practical scenarios and showed that the proposed design is robust and performed well.

Bayesian Multivariate Logistic Regression for Superiority and Inferiority Decision-Making under Observable Treatment Heterogeneity

The effects of treatments may differ between persons with different characteristics. Addressing such treatment heterogeneity is crucial to investigate whether patients with specific characteristics are likely to benefit from a new treatment. The current paper presents a novel Bayesian method for superiority decision-making in the context of randomized controlled trials with multivariate binary responses and heterogeneous treatment effects. The framework is based on three elements: a) Bayesian multivariate logistic regression analysis with a Pólya-Gamma expansion; b) a transformation procedure to transfer obtained regression coefficients to a more intuitive multivariate probability scale (i.e., success probabilities and the differences between them); and c) a compatible decision procedure for treatment comparison with prespecified decision error rates. Procedures for a priori sample size estimation under a non-informative prior distribution are included. A numerical evaluation demonstrated that decisions based on a priori sample size estimation resulted in anticipated error rates among the trial population as well as subpopulations. Further, average and conditional treatment effect parameters could be estimated unbiasedly when the sample was large enough. Illustration with the International Stroke Trial dataset revealed a trend toward heterogeneous effects among stroke patients: Something that would have remained undetected when analyses were limited to average treatment effects.

Dopamine and acetylcholine have distinct roles in delay- and effort-based decision-making in humans

In everyday life, we encounter situations that require tradeoffs between potential rewards and associated costs, such as time and (physical) effort. The literature indicates a prominent role for dopamine in discounting of both delay and effort, with mixed findings for delay discounting in humans. Moreover, the reciprocal antagonistic interaction between dopaminergic and cholinergic transmission in the striatum suggests a potential opponent role of acetylcholine in these processes. We found opposing effects of dopamine D2 (haloperidol) and acetylcholine M1 receptor (biperiden) antagonism on specific components of effort-based decision-making in healthy humans: haloperidol decreased, whereas biperiden increased the willingness to exert physical effort. In contrast, delay discounting was reduced under haloperidol, but not affected by biperiden. Together, our data suggest that dopamine, acting at D2 receptors, modulates both effort and delay discounting, while acetylcholine, acting at M1 receptors, appears to exert a more specific influence on effort discounting only.

Simultaneous Inference of Multiple Binary Endpoints in Biomedical Research: Small Sample Properties of Multiple Marginal Models and a Resampling Approach

In biomedical research, the simultaneous inference of multiple binary endpoints may be of interest. In such cases, an appropriate multiplicity adjustment is required that controls the family-wise error rate, which represents the probability of making incorrect test decisions. In this paper, we investigate two approaches that perform single-step p $p$ -value adjustments that also take into account the possible correlation between endpoints. A rather novel and flexible approach known as multiple marginal models is considered, which is based on stacking of the parameter estimates of the marginal models and deriving their joint asymptotic distribution. We also investigate a nonparametric vector-based resampling approach, and we compare both approaches with the Bonferroni method by examining the family-wise error rate and power for different parameter settings, including low proportions and small sample sizes. The results show that the resampling-based approach consistently outperforms the other methods in terms of power, while still controlling the family-wise error rate. The multiple marginal models approach, on the other hand, shows a more conservative behavior. However, it offers more versatility in application, allowing for more complex models or straightforward computation of simultaneous confidence intervals. The practical application of the methods is demonstrated using a toxicological dataset from the National Toxicology Program.

Grouping in working memory guides chunk formation in long-term memory: Evidence from the Hebb effect

The Hebb effect refers to the improvement in immediate memory performance on a repeated list compared to unrepeated lists. That is, participants create a long-term memory representation over repetitions, on which they can draw in working memory tests. These long-term memory representations are likely formed by chunk acquisition: The whole list becomes integrated into a single unified representation. Previous research suggests that the formation of such chunks is rather inflexible and only occurs when at least the beginning of the list repeats across trials. However, recent work has shown that repetition learning strongly depends on participants recognizing the repeated information. Hence, successful chunk formation may depend on the recognizability of the repeated part of a list, and not on its position in the list. Across six experiments, we compared these two alternatives. We tested immediate serial recall of eight-letter lists, some of which partially repeated across trials. We used different partial-repetition structures, such as repeating only the first half of a list, or only every second item. We manipulated the salience of the repeating structure by spatially grouping and coloring the lists according to the repetition structure. We found that chunk formation is more flexible than previously assumed: Participants learned contiguous repeated sequences regardless of their position within the list, as long as they were able to recognize the repeated structure. Even when the repeated sequence occurred at varying positions over repetitions, learning was preserved when the repeated sequence was made salient by the spatial grouping. These findings suggest that chunk formation requires recognition of which items constitute a repeating group, and demonstrate a close link between grouping of information in working memory, and chunk formation in long-term memory.

Uncertainty directed factorial clinical trials

The development and evaluation of novel treatment combinations is a key component of modern clinical research. The primary goals of factorial clinical trials of treatment combinations range from the estimation of intervention-specific effects, or the discovery of potential synergies, to the identification of combinations with the highest response probabilities. Most factorial studies use balanced or block randomization, with an equal number of patients assigned to each treatment combination, irrespective of the specific goals of the trial. Here, we introduce a class of Bayesian response-adaptive designs for factorial clinical trials with binary outcomes. The study design was developed using Bayesian decision-theoretic arguments and adapts the randomization probabilities to treatment combinations during the enrollment period based on the available data. Our approach enables the investigator to specify a utility function representative of the aims of the trial, and the Bayesian response-adaptive randomization algorithm aims to maximize this utility function. We considered several utility functions and factorial designs tailored to them. Then, we conducted a comparative simulation study to illustrate relevant differences of key operating characteristics across the resulting designs. We also investigated the asymptotic behavior of the proposed adaptive designs. We also used data summaries from three recent factorial trials in perioperative care, smoking cessation, and infectious disease prevention to define realistic simulation scenarios and illustrate advantages of the introduced trial designs compared to other study designs.

A Bayesian latent-subgroup platform design for dose optimization

The US Food and Drug Administration launched Project Optimus to reform the dose optimization and dose selection paradigm in oncology drug development, calling for the paradigm shift from finding the maximum tolerated dose to the identification of optimal biological dose (OBD). Motivated by a real-world drug development program, we propose a master-protocol-based platform trial design to simultaneously identify OBDs of a new drug, combined with standards of care or other novel agents, in multiple indications. We propose a Bayesian latent subgroup model to accommodate the treatment heterogeneity across indications, and employ Bayesian hierarchical models to borrow information within subgroups. At each interim analysis, we update the subgroup membership and dose-toxicity and -efficacy estimates, as well as the estimate of the utility for risk-benefit tradeoff, based on the observed data across treatment arms to inform the arm-specific decision of dose escalation and de-escalation and identify the OBD for each arm of a combination partner and an indication. The simulation study shows that the proposed design has desirable operating characteristics, providing a highly flexible and efficient way for dose optimization. The design has great potential to shorten the drug development timeline, save costs by reducing overlapping infrastructure, and speed up regulatory approval.

Positive affect modulates memory by regulating the influence of reward prediction errors

How our decisions impact our memories is not well understood. Reward prediction errors (RPEs), the difference between expected and obtained reward, help us learn to make optimal decisions-providing a signal that may influence subsequent memory. To measure this influence and how it might go awry in mood disorders, we recruited a large cohort of human participants to perform a decision-making task in which perceptually memorable stimuli were associated with probabilistic rewards, followed by a recognition test for those stimuli. Computational modeling revealed that positive RPEs enhanced both the accuracy of memory and the temporal efficiency of memory search, beyond the contribution of perceptual information. Critically, positive affect upregulated the beneficial effect of RPEs on memory. These findings demonstrate how affect selectively regulates the impact of RPEs on memory, providing a computational mechanism for biased memory in mood disorders.

Unsupervised Bayesian classification for models with scalar and functional covariates

We consider unsupervised classification by means of a latent multinomial variable which categorizes a scalar response into one of the L components of a mixture model which incorporates scalar and functional covariates. This process can be thought as a hierarchical model with the first level modelling a scalar response according to a mixture of parametric distributions and the second level modelling the mixture probabilities by means of a generalized linear model with functional and scalar covariates. The traditional approach of treating functional covariates as vectors not only suffers from the curse of dimensionality, since functional covariates can be measured at very small intervals leading to a highly parametrized model, but also does not take into account the nature of the data. We use basis expansions to reduce the dimensionality and a Bayesian approach for estimating the parameters while providing predictions of the latent classification vector. The method is motivated by two data examples that are not easily handled by existing methods. The first example concerns identifying placebo responders on a clinical trial (normal mixture model) and the other predicting illness for milking cows (zero-inflated mixture of the Poisson model).

The diversity of social complexity in termites

Sociality underpins major evolutionary transitions and significantly influences the structure and function of complex ecosystems. Social insects, seen as the pinnacle of sociality, have traits like obligate sterility that are considered 'master traits', used as single phenotypic measures of this complexity. However, evidence is mounting that completely aligning both phenotypic and evolutionary social complexity, and having obligate sterility central to both, is erroneous. We hypothesize that obligate and functional sterility are insufficient in explaining the diversity of phenotypic social complexity in social insects. To test this, we explore the relative importance of these sterility traits in an understudied but diverse taxon: the termites. We compile the largest termite social complexity dataset to date, using specimen and literature data. We find that although functional and obligate sterility explain a significant proportion of variance, neither trait is an adequate singular proxy for the phenotypic social complexity of termites. Further, we show both traits have only a weak association with the other social complexity traits within termites. These findings have ramifications for our general comprehension of the frameworks of phenotypic and evolutionary social complexity, and their relationship with sterility.

Error-based Implicit Learning in Language: The Effect of Sentence Context and Constraint in a Repetition Paradigm

Prediction errors drive implicit learning in language, but the specific mechanisms underlying these effects remain debated. This issue was addressed in an EEG study manipulating the context of a repeated unpredictable word (repetition of the complete sentence or repetition of the word in a new sentence context) and sentence constraint. For the manipulation of sentence constraint, unexpected words were presented either in high-constraint (eliciting a precise prediction) or low-constraint sentences (not eliciting any specific prediction). Repetition-induced reduction of N400 amplitudes and of power in the alpha/beta frequency band was larger for words repeated with their sentence context as compared with words repeated in a new low-constraint context, suggesting that implicit learning happens not only at the level of individual items but additionally improves sentence-based predictions. These processing benefits for repeated sentences did not differ between constraint conditions, suggesting that sentence-based prediction update might be proportional to the amount of unpredicted semantic information, rather than to the precision of the prediction that was violated. In addition, the consequences of high-constraint prediction violations, as reflected in a frontal positivity and increased theta band power, were reduced with repetition. Overall, our findings suggest a powerful and specific adaptation mechanism that allows the language system to quickly adapt its predictions when unexpected semantic information is processed, irrespective of sentence constraint, and to reduce potential costs of strong predictions that were violated.

Identifying Differential Methylation in Cancer Epigenetics via a Bayesian Functional Regression Model

DNA methylation plays an essential role in regulating gene activity, modulating disease risk, and determining treatment response. We can obtain insight into methylation patterns at a single-nucleotide level via next-generation sequencing technologies. However, complex features inherent in the data obtained via these technologies pose challenges beyond the typical big data problems. Identifying differentially methylated cytosines (dmc) or regions is one such challenge. We have developed DMCFB, an efficient dmc identification method based on Bayesian functional regression, to tackle these challenges. Using simulations, we establish that DMCFB outperforms current methods and results in better smoothing and efficient imputation. We analyzed a dataset of patients with acute promyelocytic leukemia and control samples. With DMCFB, we discovered many new dmcs and, more importantly, exhibited enhanced consistency of differential methylation within islands and their adjacent shores. Additionally, we detected differential methylation at more of the binding sites of the fused gene involved in this cancer.

Sleep in the wild: the importance of individual effects and environmental conditions on sleep behaviour in wild boar

Sleep serves vital physiological functions, yet how sleep in wild animals is influenced by environmental conditions is poorly understood. Here we use high-resolution biologgers to investigate sleep in wild animals over ecologically relevant time scales and quantify variability between individuals under changing conditions. We developed a robust classification for accelerometer data and measured multiple dimensions of sleep in the wild boar (Sus scrofa) over an annual cycle. In support of the hypothesis that environmental conditions determine thermoregulatory challenges, which regulate sleep, we show that sleep quantity, efficiency and quality are reduced on warmer days, sleep is less fragmented in longer and more humid days, while greater snow cover and rainfall promote sleep quality. Importantly, this longest and most detailed analysis of sleep in wild animals to date reveals large inter- and intra-individual variation. Specifically, short-sleepers sleep up to 46% less than long-sleepers but do not compensate for their short sleep through greater plasticity or quality, suggesting they may pay higher costs of sleep deprivation. Given the major role of sleep in health, our results suggest that global warming and the associated increase in extreme climatic events are likely to negatively impact sleep, and consequently health, in wildlife, particularly in nocturnal animals.

Cost and outcomes of Hospital-based Usual cAre versus Tele-monitor self-directed Rehabilitation (HUATR) in patients with total knee arthroplasty: A randomized, controlled, non-inferiority trial

OBJECTIVE

To evaluate the clinical efficacy and cost-effectiveness of telemonitored self-directed rehabilitation (TR) compared with hospital-based rehabilitation (HBR) for patients with total knee arthroplasty (TKA).

DESIGN

In this randomized, non-inferiority clinical trial, 114 patients with primary TKA who were able to walk independently preoperatively were randomized to receive HBR (n = 58) or TR (n = 56). HBR comprised at least five physical therapy sessions over 10 weeks. TR comprised a therapist-led onboarding session, followed by a 10-week unsupervised home-based exercise program, with asynchronous monitoring of rehabilitation outcomes using a telemonitoring system. The primary outcome was fast-paced gait speed at 12 weeks, with a non-inferiority margin of 0.10 m/s. For economic analysis, quality-adjusted-life-years (QALY) was the primary economic outcome (non-inferiority margin, 0.027 points).

RESULTS

In Bayesian analyses, TR had >95% posterior probability of being non-inferior to HBR in gait speed (week-12 adjusted TR-HBR difference, 0.02 m/s; 95%CrI, -0.05 to 0.10 m/s; week-24 difference, 0.01 m/s; 95%CrI, -0.07 to 0.10 m/s) and QALY (0.006 points; 95%CrI, -0.006 to 0.018 points). When evaluated from a societal perspective, TR was associated with lower mean intervention cost (adjusted TR-HBR difference, -S$227; 95%CrI, -112 to -330) after 24 weeks, with 82% probability of being cost-effective compared with HBR at a willingness to pay of S$0/unit of effect for the QALYs.

CONCLUSIONS

In patients with uncomplicated TKAs and relatively good preoperative physical function, home-based, self-directed TR was non-inferior to and more cost-effective than HBR over a 24-week follow-up period. TR should be considered for this patient subgroup.

Retrospective analysis of veno-occlusive disease/sinusoidal obstruction syndrome in paediatric patients undergoing hematopoietic cell transplantation -a multicentre study

Background

Sinusoidal obstruction syndrome is a potentially fatal complication following hematopoietic cell transplantation, high-intensity chemotherapies and increasingly seen with calicheamicin based leukemia therapies. Paediatric specific European Society for Blood and Marrow Transplantation (pEBMT) diagnostic criteria have demonstrated benefit in single center studies compared to historic criteria. Yet, the extent to which they have been universally implemented remains unclear.

Methods

We conducted a retrospective multi-centre study to examine the potential impact of the Baltimore, modified Seattle and pEBMT criteria on the incidence, severity, and outcomes of sinusoidal obstruction syndrome among paediatric hematopoietic cell transplantation patients.

Findings

The incidence of sinusoidal obstruction syndrome in this cohort (n = 488) was higher by pEBMT (21.5%) vs historic modified Seattle (15.6%) and Baltimore (7.0%) criteria (p < 0.001). Application of pEBMT criteria identified 44 patients who were not previously diagnosed with sinusoidal obstruction syndrome. Overall, 70.5% of all patients diagnosed with sinusoidal obstruction syndrome ultimately developed very severe disease and almost half of diagnosed patients required critical care support. Overall survival was significantly lower in patients who were diagnosed with sinusoidal obstruction syndrome vs those who were not.

Interpretation

Taken together, pEBMT criteria may be a sensitive method for prompter diagnosis of patients who subsequently develop severe/very severe sinusoidal obstruction syndrome. To our knowledge, this is the first multi-centre study in the United States (US) to demonstrate that pEBMT guidelines are associated with earlier detection of sinusoidal obstruction syndrome. Since early initiation of definitive treatment for sinusoidal obstruction syndrome has been associated with improved survival in paediatric patients and implementation of pEBMT criteria appears feasible in the US, universal adoption should facilitate prompter diagnosis and lead to improved outcomes of children with sinusoidal obstruction syndrome.

Funding

None.

Cardiometabolic Disease Staging and Major Adverse Cardiovascular Event Prediction in 2 Prospective Cohorts

BACKGROUND

Cardiometabolic risk prediction models that incorporate metabolic syndrome traits to predict cardiovascular outcomes may help identify high-risk populations early in the progression of cardiometabolic disease.

OBJECTIVES

The purpose of this study was to examine whether a modified cardiometabolic disease staging (CMDS) system, a validated diabetes prediction model, predicts major adverse cardiovascular events (MACE).

METHODS

We developed a predictive model using data accessible in clinical practice [fasting glucose, blood pressure, body mass index, cholesterol, triglycerides, smoking status, diabetes status, hypertension medication use] from the REGARDS (REasons for Geographic And Racial Differences in Stroke) study to predict MACE [cardiovascular death, nonfatal myocardial infarction, and/or nonfatal stroke]. Predictive performance was assessed using receiver operating characteristic curves, mean squared errors, misclassification, and area under the curve (AUC) statistics.

RESULTS

Among 20,234 REGARDS participants with no history of stroke or myocardial infarction (mean age 64 ± 9.3 years, 58% female, 41% non-Hispanic Black, and 18% diabetes), 2,695 developed incident MACE (13.3%) during a median 10-year follow-up. The CMDS development model in REGARDS for MACE had an AUC of 0.721. Our CMDS model performed similarly to both the ACC/AHA 10-year risk estimate (AUC 0.721 vs 0.716) and the Framingham risk score (AUC 0.673).

CONCLUSIONS

The CMDS predicted the onset of MACE with good predictive ability and performed similarly or better than 2 commonly known cardiovascular disease prediction risk tools. These data underscore the importance of insulin resistance as a cardiovascular disease risk factor and that CMDS can be used to identify individuals at high risk for progression to cardiovascular disease.

Modeling Misspecification as a Parameter in Bayesian Structural Equation Models

Accounting for model misspecification in Bayesian structural equation models is an active area of research. We present a uniquely Bayesian approach to misspecification that models the degree of misspecification as a parameter-a parameter akin to the correlation root mean squared residual. The misspecification parameter can be interpreted on its own terms as a measure of absolute model fit and allows for comparing different models fit to the same data. By estimating the degree of misspecification simultaneously with structural parameters, the uncertainty about structural parameters reflects the degree of model misspecification. This results in a model that produces more reliable inference than extant Bayesian structural equation modeling. In addition, the approach estimates the residual covariance matrix that can be the basis for diagnosing misspecifications and updating a hypothesized model. These features are confirmed using simulation studies. Demonstrations with a variety of real-world examples show additional properties of the approach.

Analyzing population-level trials as N-of-1 trials: An application to gait

Studying individual causal effects of health interventions is important whenever intervention effects are heterogeneous between study participants. Conducting N-of-1 trials, which are single-person randomized controlled trials, is the gold standard for their analysis. As an alternative method, we propose to re-analyze existing population-level studies as N-of-1 trials, and use gait as a use case for illustration. Gait data were collected from 16 young and healthy participants under fatigued and non-fatigued, as well as under single-task (only walking) and dual-task (walking while performing a cognitive task) conditions. As a reference to the N-of-1 trials approach, we first computed standard population-level ANOVA models to evaluate differences in gait parameters (stride length and stride time) across conditions. Then, we estimated the effect of the interventions on gait parameters on the individual level through Bayesian repeated-measures models, viewing each participant as their own trial, and compared the results. The results illustrated that while few overall population-level effects were visible, individual-level analyses revealed differences between participants. Baseline values of the gait parameters varied largely among all participants, and the effects of fatigue and cognitive task were also heterogeneous, with some individuals showing effects in opposite directions. These differences between population-level and individual-level analyses were more pronounced for the fatigue intervention compared to the cognitive task intervention. Following our empirical analysis, we discuss re-analyzing population studies through the lens of N-of-1 trials more generally and highlight important considerations and requirements. Our work encourages future studies to investigate individual effects using population-level data.

Influence of Temperature and Precipitation on the Effectiveness of Water, Sanitation, and Handwashing Interventions against Childhood Diarrheal Disease in Rural Bangladesh: A Reanalysis of the WASH Benefits Bangladesh Trial

BACKGROUND

Diarrheal disease is a leading cause of childhood morbidity and mortality globally. Household water, sanitation, and handwashing (WASH) interventions can reduce exposure to diarrhea-causing pathogens, but meteorological factors may impact their effectiveness. Information about effect heterogeneity under different weather conditions is critical to refining these targeted interventions.

OBJECTIVES

We aimed to determine whether temperature and precipitation modified the effect of low-cost, point-of-use WASH interventions on child diarrhea.

METHODS

We analyzed data from a trial in rural Bangladesh that compared child diarrhea prevalence between clusters (N = 720 ) that were randomized to different WASH interventions between 2012 and 2016 (NCT01590095). We matched temperature and precipitation measurements to diarrhea outcomes (N = 12,440 measurements, 6,921 children) by geographic coordinates and date. We estimated prevalence ratios (PRs) using generative additive models and targeted maximum likelihood estimation to assess the effectiveness of each WASH intervention under different weather conditions.

RESULTS

Generally, WASH interventions most effectively prevented diarrhea during monsoon season, particularly following weeks with heavy rain or high temperatures. The PR for diarrhea in the WASH interventions group compared with the control group was 0.49 (95% CI: 0.35, 0.68) after 1 d of heavy rainfall, with a less-protective effect [PR = 0.87 (95% CI: 0.60, 1.25)] when there were no days with heavy rainfall. Similarly, the PR for diarrhea in the WASH intervention group compared with the control group was 0.60 (95% CI: 0.48, 0.75) following above-median temperatures vs. 0.91 (95% CI: 0.61, 1.35) following below-median temperatures. The influence of precipitation and temperature varied by intervention type; for precipitation, the largest differences in effectiveness were for the sanitation and combined WASH interventions.

DISCUSSION

WASH intervention effectiveness was strongly influenced by precipitation and temperature, and nearly all protective effects were observed during the rainy season. Future implementation of these interventions should consider local environmental conditions to maximize effectiveness, including targeted efforts to maintain latrines and promote community adoption ahead of monsoon seasons. https://doi.org/10.1289/EHP13807.

An easily implemented single-visit survey method for intermittently available and imperfectly detectable wildlife applied to the Florida east coast diamondback terrapin (Malaclemys terrapin tequesta)

Single-visit surveys of plots are often used for estimating the abundance of species of conservation concern. Less-than-perfect availability and detection of individuals can bias estimates if not properly accounted for. We developed field methods and a Bayesian model that accounts for availability and detection bias during single-visit visual plot surveys. We used simulated data to test the accuracy of the method under a realistic range of generating parameters and applied the method to Florida's east coast diamondback terrapin in the Indian River Lagoon system, where they were formerly common but have declined in recent decades. Simulations demonstrated that the method produces unbiased abundance estimates under a wide range of conditions that can be expected to occur in such surveys. Using terrapins as an example we show how to include covariates and random effects to improve estimates and learn about species-habitat relationships. Our method requires only counting individuals during short replicate surveys rather than keeping track of individual identity and is simple to implement in a variety of point count settings when individuals may be temporarily unavailable for observation. We provide examples in R and JAGS for implementing the model and to simulate and evaluate data to validate the application of the method under other study conditions.

Age and associated outcomes among patients receiving venovenous extracorporeal membrane oxygenation for acute respiratory failure: analysis of the Extracorporeal Life Support Organization registry

PURPOSE

Venovenous extracorporeal membrane oxygenation (VV-ECMO) can be used to support patients with refractory acute respiratory failure, though guidance on patient selection is lacking. While age is commonly utilized as a factor in establishing the potential VV-ECMO candidacy of these patients, little is known regarding its association with outcome. We studied the association between increasing patient age and outcomes among patients with acute respiratory failure receiving VV-ECMO.

METHODS

In this registry-based cohort study, we used individual patient data from 144 centres. We included adult patients (≥ 18 years of age) receiving VV-ECMO from 2017 to 2022. The primary outcome was hospital mortality. Secondary outcomes included a composite of complications following initiation of VV-ECMO. We conducted Bayesian analyses to estimate the association between chronological age and outcomes.

RESULTS

We included 27,811 patients receiving VV-ECMO. Of these, 11,533 (41.5%) died in hospital. For the analysis conducted using weakly informed priors, and as compared to the reference category of age 18-29, the age brackets of 30-39 (odds ratio [OR] 1.17, 95% credible interval [CrI] 1.06-1.31), 40-49 (OR 1.65, 95% CrI 1.49-1.82), 50-59 (OR 2.39, 95% CrI 2.16-2.61), 60-69 (OR 3.29, 95% CrI 2.97-3.67), 70-79 (OR 4.57, 95% CrI 3.90-5.37), and ≥ 80 (OR 8.08, 95% CrI 4.85-13.74) were independently associated with increasing hospital mortality. Similar results were found between increasing age and post-ECMO complications.

CONCLUSIONS

Among patients receiving VV-ECMO for acute respiratory failure, increasing age is significantly associated with poorer outcomes, and this association emerges as early as 30 years of age.

Alterations in zonal distribution and plasma membrane localization of hepatocyte bile acid transporters in patients with NAFLD

BACKGROUND

NAFLD is highly prevalent with limited treatment options. Bile acids (BAs) increase in the systemic circulation and liver during NAFLD progression. Changes in plasma membrane localization and zonal distribution of BA transporters can influence transport function and BA homeostasis. However, a thorough characterization of how NAFLD influences these factors is currently lacking. This study aimed to evaluate the impact of NAFLD and the accompanying histologic features on the functional capacity of key hepatocyte BA transporters across zonal regions in human liver biopsies.

METHODS

A novel machine learning image classification approach was used to quantify relative zonal abundance and plasma membrane localization of BA transporters (bile salt export pump [BSEP], sodium-taurocholate cotransporting polypeptide, organic anion transporting polypeptide [OATP] 1B1 and OATP1B3) in non-diseased (n = 10), NAFL (n = 9), and NASH (n = 11) liver biopsies. Based on these data, membrane-localized zonal abundance (MZA) measures were developed to estimate transporter functional capacity.

RESULTS

NAFLD diagnosis and histologic scoring were associated with changes in transporter membrane localization and zonation. Increased periportal BSEPMZA (mean proportional difference compared to non-diseased liver of 0.090) and decreased pericentral BSEPMZA (-0.065) were observed with NASH and also in biopsies with higher histologic scores. Compared to Non-diseased Liver, periportal OATP1B3MZA was increased in NAFL (0.041) and NASH (0.047). Grade 2 steatosis (mean proportional difference of 0.043 when compared to grade 0) and grade 1 lobular inflammation (0.043) were associated with increased periportal OATP1B3MZA.

CONCLUSIONS

These findings provide novel mechanistic insight into specific transporter alterations that impact BA homeostasis in NAFLD. Changes in BSEPMZA likely contribute to altered BA disposition and pericentral microcholestasis previously reported in some patients with NAFLD. BSEPMZA assessment could inform future development and optimization of NASH-related pharmacotherapies.

Network meta analysis to predict the efficacy of an approved treatment in a new indication

Drug repurposing refers to the process of discovering new therapeutic uses for existing medicines. Compared to traditional drug discovery, drug repurposing is attractive for its speed, cost, and reduced risk of failure. However, existing approaches for drug repurposing involve complex, computationally-intensive analytical methods that are not widely used in practice. Instead, repurposing decisions are often based on subjective judgments from limited empirical evidence. In this article, we develop a novel Bayesian network meta-analysis (NMA) framework that can predict the efficacy of an approved treatment in a new indication and thereby identify candidate treatments for repurposing. We obtain predictions using two main steps: first, we use standard NMA modeling to estimate average relative effects from a network comprised of treatments studied in both indications in addition to one treatment studied in only one indication. Then, we model the correlation between relative effects using various strategies that differ in how they model treatments across indications and within the same drug class. We evaluate the predictive performance of each model using a simulation study and find that the model minimizing root mean squared error of the posterior median for the candidate treatment depends on the amount of available data, the level of correlation between indications, and whether treatment effects differ, on average, by drug class. We conclude by discussing an illustrative example in psoriasis and psoriatic arthritis and find that the candidate treatment has a high probability of success in a future trial.

A diagnostic phase III/IV seamless design to investigate the diagnostic accuracy and clinical effectiveness using the example of HEDOS and HEDOS II

The development process of medical devices can be streamlined by combining different study phases. Here, for a diagnostic medical device, we present the combination of confirmation of diagnostic accuracy (phase III) and evaluation of clinical effectiveness regarding patient-relevant endpoints (phase IV) using a seamless design. This approach is used in the Thyroid HEmorrhage DetectOr Study (HEDOS & HEDOS II) investigating a post-operative hemorrhage detector named ISAR-M THYRO® in patients after thyroid surgery. Data from the phase III trial are reused as external controls in the control group of the phase IV trial. An unblinded interim analysis is planned between the two study stages which includes a recalculation of the sample size for the phase IV part after completion of the first stage of the seamless design. The study concept presented here is the first seamless design proposed in the field of diagnostic studies. Hence, the aim of this work is to emphasize the statistical methodology as well as feasibility of the proposed design in relation to the planning and implementation of the seamless design. Seamless designs can accelerate the overall trial duration and increase its efficiency in terms of sample size and recruitment. However, careful planning addressing numerous methodological and procedural challenges is necessary for successful implementation as well as agreement with regulatory bodies.

Modeling basal body temperature data using horseshoe process regression

Biomedical data often exhibit jumps or abrupt changes. For example, women's basal body temperature may jump at ovulation, menstruation, implantation, and miscarriage. These sudden changes make these data challenging to model: many methods will oversmooth the sharp changes or overfit in response to measurement error. We develop horseshoe process regression (HPR) to address this problem. We define a horseshoe process as a stochastic process in which each increment is horseshoe-distributed. We use the horseshoe process as a nonparametric Bayesian prior for modeling a potentially nonlinear association between an outcome and its continuous predictor, which we implement via Stan and in the R package HPR. We provide guidance and extensions to advance HPR's use in applied practice: we introduce a Bayesian imputation scheme to allow for interpolation at unobserved values of the predictor within the HPR; include additional covariates via a partial linear model framework; and allow for monotonicity constraints. We find that HPR performs well when fitting functions that have sharp changes. We apply HPR to model women's basal body temperatures over the course of the menstrual cycle.

Host and viral determinants of airborne transmission of SARS-CoV-2 in the Syrian hamster

It remains poorly understood how SARS-CoV-2 infection influences the physiological host factors important for aerosol transmission. We assessed breathing pattern, exhaled droplets, and infectious virus after infection with Alpha and Delta variants of concern (VOC) in the Syrian hamster. Both VOCs displayed a confined window of detectable airborne virus (24-48 hr), shorter than compared to oropharyngeal swabs. The loss of airborne shedding was linked to airway constriction resulting in a decrease of fine aerosols (1-10 µm) produced, which are suspected to be the major driver of airborne transmission. Male sex was associated with increased viral replication and virus shedding in the air. Next, we compared the transmission efficiency of both variants and found no significant differences. Transmission efficiency varied mostly among donors, 0-100% (including a superspreading event), and aerosol transmission over multiple chain links was representative of natural heterogeneity of exposure dose and downstream viral kinetics. Co-infection with VOCs only occurred when both viruses were shed by the same donor during an increased exposure timeframe (24-48 hr). This highlights that assessment of host and virus factors resulting in a differential exhaled particle profile is critical for understanding airborne transmission.

Plumage and eggshell colouration covary with the level of sex-specific parental contributions to nest building in birds

Interspecific variation in sex-specific contributions to prenatal parental care, including avian nest building, is becoming increasingly better understood as we amass more information on more species. We examined whether sex-specific nest building contributions covary with the colouration of parents and their eggs in 521 species of Western Palearctic birds. Having colourful plumage and laying colourful eggs are costly because of the deposition of pigments in feathers and eggs and/or forming costly nanostructural substrates in feathers, and so it might be expected that those costs covary with the costs of nest building at the level of individuals and/or across species to produce of a suite of codivergent traits. Using a phylogenetically informed approach, we tested the hypothesis that species in which females alone invest energy building nests exhibit less sexual plumage dichromatism. However, we found comparative support for the opposite of this prediction. We then tested that species in which females alone build nests lay more colourful, and costlier, eggs because the dual costs of building nests and laying colourful eggs can only be borne by higher quality individuals. As expected, we found that species in which females build nests alone or together with males are more likely to lay colourfully pigmented eggs relative to species in which only males build nests. Finally, stochastic character mapping provided evidence of the repeated evolution of female-only nest building. Interspecific sex differences in plumage colouration therefore covary in a complex manner with female pre- (nest building) and post-copulatory (egg production) investment in reproduction.