SifiNet: a robust and accurate method to identify feature gene sets and annotate cells

SifiNet is a robust and accurate computational pipeline for identifying distinct gene sets, extracting and annotating cellular subpopulations, and elucidating intrinsic relationships among these subpopulations. Uniquely, SifiNet bypasses the cell clustering stage, commonly integrated into other cellular annotation pipelines, thereby circumventing potential inaccuracies in clustering that may compromise subsequent analyses. Consequently, SifiNet has demonstrated superior performance in multiple experimental datasets compared with other state-of-the-art methods. SifiNet can analyze both single-cell RNA and ATAC sequencing data, thereby rendering comprehensive multi-omic cellular profiles. It is conveniently available as an open-source R package.

Amyotrophic lateral sclerosis stratification: unveiling patterns with virome, inflammation, and metabolism molecules

Amyotrophic lateral sclerosis (ALS) is an untreatable and clinically heterogeneous condition primarily affecting motor neurons. The ongoing quest for reliable biomarkers that mirror the disease status and progression has led to investigations that extend beyond motor neurons' pathology, encompassing broader systemic factors such as metabolism, immunity, and the microbiome. Our study contributes to this effort by examining the potential role of microbiome-related components, including viral elements, such as torque tenovirus (TTV), and various inflammatory factors, in ALS. In our analysis of serum samples from 100 ALS patients and 34 healthy controls (HC), we evaluated 14 cytokines, TTV DNA load, and 18 free fatty acids (FFA). We found that the evaluated variables are effective in differentiating ALS patients from healthy controls. In addition, our research identifies four unique patient clusters, each characterized by distinct biological profiles. Intriguingly, no correlations were found with site of onset, sex, progression rate, phenotype, or C9ORF72 expansion. A remarkable aspect of our findings is the discovery of a gender-specific relationship between levels of 2-ethylhexanoic acid and patient survival. In addition to contributing to the growing body of evidence suggesting altered peripheral immune responses in ALS, our exploratory research underscores metabolic diversity challenging conventional clinical classifications. If our exploratory findings are validated by further research, they could significantly impact disease understanding and patient care customization. Identifying groups based on biological profiles might aid in clustering patients with varying responses to treatments.

Enhancing torsional sampling using fully adaptive simulated tempering

Enhanced sampling algorithms are indispensable when working with highly disconnected multimodal distributions. An important application of these is the conformational exploration of particular internal degrees of freedom of molecular systems. However, despite the existence of many commonly used enhanced sampling algorithms to explore these internal motions, they often rely on system-dependent parameters, which negatively impact efficiency and reproducibility. Here, we present fully adaptive simulated tempering (FAST), a variation of the irreversible simulated tempering algorithm, which continuously optimizes the number, parameters, and weights of intermediate distributions to achieve maximally fast traversal over a space defined by the change in a predefined thermodynamic control variable such as temperature or an alchemical smoothing parameter. This work builds on a number of previously published methods, such as sequential Monte Carlo, and introduces a novel parameter optimization procedure that can, in principle, be used in any expanded ensemble algorithms. This method is validated by being applied on a number of different molecular systems with high torsional kinetic barriers. We also consider two different soft-core potentials during the interpolation procedure and compare their performance. We conclude that FAST is a highly efficient algorithm, which improves simulation reproducibility and can be successfully used in a variety of settings with the same initial hyperparameters.

The influence of the COVID-19 pandemic on physical activity in Stockholm County - Evidence from time series models of smartphone measured daily steps data spanning over 3 years

BACKGROUND

It has been reported that physical activity levels decreased during the COVID-19 pandemic. Previous studies often relied on self-reported physical activity, which has low accuracy. Studies based on objectively measured physical activity have had short data collection periods, thereby not allowing the consideration of pre-pandemic levels of physical activity or the influence over the different waves of the pandemic.

METHODS

In this study, we utilize smartphone-measured step data from a nonprobability sample in Stockholm County, Sweden, where measures to limit the spread of COVID-19 differed from those in many other countries. The results are based on 522 individuals and 532,739 person-days with step data spanning from 2019 to 2021. Generalized additive models were fitted for each individual, and meta-regression was used to combine the results from individual models.

RESULTS

Daily steps decreased during the first wave but increased during the third wave compared to individual pre-pandemic levels. The decrease in daily steps occurred primarily in young individuals and those with occupations allowing remote work. Individuals of retirement age on the contrary increased their daily steps during the same period.

CONCLUSIONS

This study reveal that the influence of the COVID-19 pandemic was temporary and that younger age and the possibility of working from home were associated with a decreasing trend in physical activity.

Integrative machine learning approaches for predicting disease risk using multi-omics data from the UK Biobank

We train prediction and survival models using multi-omics data for disease risk identification and stratification. Existing work on disease prediction focuses on risk analysis using datasets of individual data types (metabolomic, genomics, demographic), while our study creates an integrated model for disease risk assessment. We compare machine learning models such as Lasso Regression, Multi-Layer Perceptron, XG Boost, and ADA Boost to analyze multi-omics data, incorporating ROC-AUC score comparisons for various diseases and feature combinations. Additionally, we train Cox proportional hazard models for each disease to perform survival analysis. Although the integration of multi-omics data significantly improves risk prediction for 8 diseases, we find that the contribution of metabolomic data is marginal when compared to standard demographic, genetic, and biomarker features. Nonetheless, we see that metabolomics is a useful replacement for the standard biomarker panel when it is not readily available.

Swimming behaviour of Atlantic salmon kelts migrating past a hydropower plant dam: Effects of hydraulics and dam operations

Hydropower plants commonly impede the downstream migration of Atlantic salmon (Salmo salar) kelts. Thus, understanding the effects of hydraulic conditions on kelt behaviour and passage performance at dams is crucial for developing effective mitigation measures. In this study, we investigated the influence of hydraulic conditions on kelt passage performance and swimming behaviour at a Norwegian hydropower plant. We combined biological data from 48 kelts collected via acoustic telemetry with hydraulic data modelled using computational fluid dynamics. We assessed kelt passage performance using metrics such as time-to-pass, total number of detections, and total number of detections per day. Additionally, we analysed swimming depths and speeds in relation to the hydraulic conditions created by different dam operating conditions. We found that the dam operation schedule impacted the kelts' ability to find a route past the dam. Though kelts could have passed the dam throughout the study period via a submerged pipe at the dam (which had seemingly sufficient discharge for the kelts to find), 98 % of the kelts instead waited for a spill gate to open partway through the study period. The swimming depth analysis indicated diel variation, with kelts swimming nearer to the water surface during the night. We found that swimming speed increased with increasing kelt body length, particularly under high turbulence kinetic energy and during the day. Furthermore, kelts swam faster as water velocity increased, but slowed down again as turbulence intensity increased. Our findings reveal the effects of hydraulic conditions and dam operations on the migration behaviour of Atlantic salmon kelts. This provides valuable insights for developing strategies to optimise dam operations and improve fish passage performance, including the need to spill enough water to increase passage success and will contribute to sustainable management of Atlantic salmon populations in regulated rivers.

Investigating the spatial and temporal variation of vape retailer provision in New Zealand: A cross-sectional and nationwide study

Smoking rates have decreased in Aotearoa New Zealand in recent years however, vaping has shown a dramatic upward trend especially among young people; up to 10% of young New Zealanders are now regular vapers. Importantly, the long-term health consequences for their future life are largely unknown. The accessibility of vape retailers is important, particularly in relation to the youths' daily activities and places such as schools where they spend a considerable amount of time and socialise. Despite this, we know little about the spatial patterning of vape retailers and even less of their socio-spatial patterning around schools. This ecological study utilised data from the New Zealand Specialist Vape Retailers register on nationwide vape retailer locations and combined them with whole-population sociodemographic characteristics and primary and secondary school data. We identified the prevalence of vape retailers and their spatial distribution by area-level deprivation, ethnicity and urban-rural classification by using descriptive statistics and (spatial) statistical modelling on the area-, school- and individual students-level (using disaggregated data on students). We found that almost 97% of all vape retailers are located within 1,600m (∼20-min walk) and 29% within 400m (∼5-min walk) of schools. Our research also identified increasing inequities by deprivation and ethnicity both for the overall population and particularly for students in the most deprived areas who experience a disproportionate presence and increase of new vape store retailers that disadvantage schools and students in these areas. This difference was particularly prominent for Pasifika populations in major urban environments.

Topsoil porosity prediction across habitats at large scales using environmental variables

Soil porosity and its reciprocal bulk density are important environmental state variables that enable modelers to represent hydraulic function and carbon storage. Biotic effects and their 'dynamic' influence on such state variables remain largely unknown for larger scales and may result in important, yet poorly quantified environmental feedbacks. Existing representation of hydraulic function is often invariant to environmental change and may be poor in some systems, particularly non-arable soils. Here we assess predictors of total porosity across two comprehensive national topsoil (0-15 cm) data sets, covering the full range of soil organic matter (SOM) and habitats (n = 1385 & n = 2570), using generalized additive mixed models and machine learning. Novel aspects of this work include the testing of metrics on aggregate size and livestock density alongside a range of different particle size distribution metrics. We demonstrate that porosity trends in Great Britain are dominated by biotic metrics, soil carbon and land use. Incorporating these variables into porosity prediction improves performance, paving the way for new dynamic calculation of porosity using surrogate measures with remote sensing, which may help improve prediction in data sparse regions of the world. Moreover, dynamic calculation of porosity could support representation of feedbacks in environmental and Earth System Models. Representing the hydrological feedbacks from changes in structural porosity also requires data and models at appropriate spatial scales to capture conditions leading to near-saturated soil conditions. Classification. Environmental Sciences.

Sedimentary ancient DNA reveals the impact of anthropogenic land use disturbance and ecological shifts on fish community structure in small lowland lake

Freshwater fish biodiversity and abundance are decreasing globally. The drivers of decline are primarily anthropogenic; however, the causative links between disturbances and fish community change are complex and challenging to investigate. We used a suite of sedimentary DNA methods (droplet digital PCR and metabarcoding) and traditional paleolimnological approaches, including pollen and trace metal analysis, ITRAX X-ray fluorescence and hyperspectral core scanning to explore changes in fish abundance and drivers over 1390 years in a small lake. This period captured a disturbance trajectory from pre-human settlement through subsistence living to intensive agriculture. Generalized additive mixed models explored the relationships between catchment inputs, internal drivers, and fish community structure. Fish community composition distinctly shifted around 1350 CE, with the decline of a sensitive Galaxias species concomitant with early land use changes. Total fish abundance significantly declined around 1950 CE related to increases in ruminant bacterial DNA (a proxy for ruminant abundance) and cadmium flux (a proxy for phosphate fertilizers), implicating land use intensification as a key driver. Concurrent shifts in phytoplankton and zooplankton suggested that fish communities were likely impacted by food web dynamics. This study highlights the potential of sedDNA to elucidate the long-term disturbance impacts on biological communities in lakes.

Brain and Blood Transcriptome-Wide Association Studies Identify Five Novel Genes Associated with Alzheimer's Disease

INTRODUCTION

Transcriptome-wide Association Studies (TWAS) extend genome-wide association studies (GWAS) by integrating genetically-regulated gene expression models. We performed the most powerful AD-TWAS to date, using summary statistics from cis -eQTL meta-analyses and the largest clinically-adjudicated Alzheimer's Disease (AD) GWAS.

METHODS

We implemented the OTTERS TWAS pipeline, leveraging cis -eQTL data from cortical brain tissue (MetaBrain; N=2,683) and blood (eQTLGen; N=31,684) to predict gene expression, then applied these models to AD-GWAS data (Cases=21,982; Controls=44,944).

RESULTS

We identified and validated five novel gene associations in cortical brain tissue ( PRKAG1 , C3orf62 , LYSMD4 , ZNF439 , SLC11A2 ) and six genes proximal to known AD-related GWAS loci (Blood: MYBPC3 ; Brain: MTCH2 , CYB561 , MADD , PSMA5 , ANXA11 ). Further, using causal eQTL fine-mapping, we generated sparse models that retained the strength of the AD-TWAS association for MTCH2 , MADD , ZNF439 , CYB561 , and MYBPC3 .

DISCUSSION

Our comprehensive AD-TWAS discovered new gene associations and provided insights into the functional relevance of previously associated variants.

A semiparametric Gaussian mixture model for chest CT-based 3D blood vessel reconstruction

Computed tomography (CT) has been a powerful diagnostic tool since its emergence in the 1970s. Using CT data, 3D structures of human internal organs and tissues, such as blood vessels, can be reconstructed using professional software. This 3D reconstruction is crucial for surgical operations and can serve as a vivid medical teaching example. However, traditional 3D reconstruction heavily relies on manual operations, which are time-consuming, subjective, and require substantial experience. To address this problem, we develop a novel semiparametric Gaussian mixture model tailored for the 3D reconstruction of blood vessels. This model extends the classical Gaussian mixture model by enabling nonparametric variations in the component-wise parameters of interest according to voxel positions. We develop a kernel-based expectation-maximization algorithm for estimating the model parameters, accompanied by a supporting asymptotic theory. Furthermore, we propose a novel regression method for optimal bandwidth selection. Compared to the conventional cross-validation-based (CV) method, the regression method outperforms the CV method in terms of computational and statistical efficiency. In application, this methodology facilitates the fully automated reconstruction of 3D blood vessel structures with remarkable accuracy.

Predictors of chronic kidney disease survival in type 2 diabetes: a 12-year retrospective cohort study utilizing estimated glomerular filtration rate

Predicting the course of kidney disease in individuals with both type 1 and type 2 diabetes mellitus (DM) is a significant clinical and policy challenge. In several regions, DM is now the leading cause of end-stage renal disease. The aim of this study to identify both modifiable and non-modifiable risk factors, along with clinical markers and coexisting conditions, that increase the likelihood of stage 3-5 chronic kidney disease (CKD) development in individuals with type 2 DM in the United Arab Emirates (UAE). This was a single-center retrospective cohort study based on data derived from electronic medical records of UAE patients with DM who were registered at outpatient clinics at Tawam Hospital in Al Ain, UAE, between January 2011 and December 2021. Type 2 DM patients aged ≥ 18 years who had serum HbA1c levels ≥ 6.5% were included in the study. Patients with type 1 DM, who had undergone permanent renal replacement therapy, who had under 1 year of follow-up, or who had missing or incomplete data were excluded from the study. Factors associated with diabetic patients developing stage 3-5 CKD were identified through Cox regression analysis and a fine and gray competing risk model to account for competing events that could potentially hinder the development of CKD. A total of 1003 patients were recruited for the study. The mean age of the study cohort at baseline was 70.6 ± 28.2 years. Several factors were found to increase the risk of developing stage 3-5 CKD: advancing age (HR 1.005, 95% CI 1.002-1.009, p = 0.026), a history of hypertension (HR 1.69, 95% CI 1.032-2.8, p = 0.037), a history of heart disease (HR 1.49, 95% CI 1.16-1.92, p = 0.002), elevated levels of serum creatinine (HR 1.006, 95% CI 1.002-1.010, p = 0.003), decreased levels of estimated glomerular filtration rate (eGFR) (HR 0.943, 95% CI, 0.938-0.947; p < 0.001), and the use of beta-blockers (HR 139, 95% CI 112-173, p = 0.003). Implementing preventative measures, initiating early interventions, and developing personalized care plans tailored to address specific risk factors are imperative for reducing the impact of CKD. Additionally, the unforeseen findings related to eGFR highlight the ongoing need for research to deepen our understanding of the complexities of kidney disease.

Assessing Clinician Utilization of Next-Generation Antibiotics Against Resistant Gram-Negative Infections in U.S. Hospitals : A Retrospective Cohort Study

BACKGROUND

The U.S. antibiotic market failure has threatened future innovation and supply. Understanding when and why clinicians underutilize recently approved gram-negative antibiotics might help prioritize the patient in future antibiotic development and potential market entry rewards.

OBJECTIVE

To determine use patterns of recently U.S. Food and Drug Administration (FDA)-approved gram-negative antibiotics (ceftazidime-avibactam, ceftolozane-tazobactam, meropenem-vaborbactam, plazomicin, eravacycline, imipenem-relebactam-cilastatin, and cefiderocol) and identify factors associated with their preferential use (over traditional generic agents) in patients with gram-negative infections due to pathogens displaying difficult-to-treat resistance (DTR; that is, resistance to all first-line antibiotics).

DESIGN

Retrospective cohort.

SETTING

619 U.S. hospitals.

PARTICIPANTS

Adult inpatients.

MEASUREMENTS

Quarterly percentage change in antibiotic use was calculated using weighted linear regression. Machine learning selected candidate variables, and mixed models identified factors associated with new (vs. traditional) antibiotic use in DTR infections.

RESULTS

Between quarter 1 of 2016 and quarter 2 of 2021, ceftolozane-tazobactam (approved 2014) and ceftazidime-avibactam (2015) predominated new antibiotic usage whereas subsequently approved gram-negative antibiotics saw relatively sluggish uptake. Among gram-negative infection hospitalizations, 0.7% (2551 [2631 episodes] of 362 142) displayed DTR pathogens. Patients were treated exclusively using traditional agents in 1091 of 2631 DTR episodes (41.5%), including "reserve" antibiotics such as polymyxins, aminoglycosides, and tigecycline in 865 of 1091 episodes (79.3%). Patients with bacteremia and chronic diseases had greater adjusted probabilities and those with do-not-resuscitate status, acute liver failure, and Acinetobacter baumannii complex and other nonpseudomonal nonfermenter pathogens had lower adjusted probabilities of receiving newer (vs. traditional) antibiotics for DTR infections, respectively. Availability of susceptibility testing for new antibiotics increased probability of usage.

LIMITATION

Residual confounding.

CONCLUSION

Despite FDA approval of 7 next-generation gram-negative antibiotics between 2014 and 2019, clinicians still frequently treat resistant gram-negative infections with older, generic antibiotics with suboptimal safety-efficacy profiles. Future antibiotics with innovative mechanisms targeting untapped pathogen niches, widely available susceptibility testing, and evidence demonstrating improved outcomes in resistant infections might enhance utilization.

PRIMARY FUNDING SOURCE

U.S. Food and Drug Administration; NIH Intramural Research Program.

Haplotype function score improves biological interpretation and cross-ancestry polygenic prediction of human complex traits

We propose a new framework for human genetic association studies: at each locus, a deep learning model (in this study, Sei) is used to calculate the functional genomic activity score for two haplotypes per individual. This score, defined as the Haplotype Function Score (HFS), replaces the original genotype in association studies. Applying the HFS framework to 14 complex traits in the UK Biobank, we identified 3619 independent HFS-trait associations with a significance of p < 5 × 10-8. Fine-mapping revealed 2699 causal associations, corresponding to a median increase of 63 causal findings per trait compared with single-nucleotide polymorphism (SNP)-based analysis. HFS-based enrichment analysis uncovered 727 pathway-trait associations and 153 tissue-trait associations with strong biological interpretability, including 'circadian pathway-chronotype' and 'arachidonic acid-intelligence'. Lastly, we applied least absolute shrinkage and selection operator (LASSO) regression to integrate HFS prediction score with SNP-based polygenic risk scores, which showed an improvement of 16.1-39.8% in cross-ancestry polygenic prediction. We concluded that HFS is a promising strategy for understanding the genetic basis of human complex traits.

Phenotypic and genomic characterisation of performance of tropically adapted chickens raised in smallholder farm conditions in Ethiopia

Background

In sub-Saharan Africa, 80% of poultry production is on smallholder village farms, where chickens are typically reared outdoors in free-ranging conditions. There is limited knowledge on chickens' phenotypic characteristics and genetics under these conditions.

Objective

The present is a large-scale study set out to phenotypically characterise the performance of tropically adapted commercial chickens in typical smallholder farm conditions, and to examine the genetic profile of chicken phenotypes associated with growth, meat production, immunity, and survival.

Methods

A total of 2,573 T451A dual-purpose Sasso chickens kept outdoors in emulated free-ranging conditions at the poultry facility of the International Livestock Research Institute in Addis Ababa, Ethiopia, were included in the study. The chickens were raised in five equally sized batches and were individually monitored and phenotyped from the age of 56 days for 8 weeks. Individual chicken data collected included weekly body weight, growth rate, body and breast meat weight at slaughter, Newcastle Disease Virus (NDV) titres and intestinal Immunoglobulin A (IgA) levels recorded at the beginning and the end of the period of study, and survival rate during the same period. Genotyping by sequencing was performed on all chickens using a low-coverage and imputation approach. Chicken phenotypes and genotypes were combined in genomic association analyses.

Results

We discovered that the chickens were phenotypically diverse, with extensive variance levels observed in all traits. Batch number and sex of the chicken significantly affected the studied phenotypes. Following quality assurance, genotypes consisted of 2.9 million Single Nucleotide Polymorphism markers that were used in the genomic analyses. Results revealed a largely polygenic mode of genetic control of all phenotypic traits. Nevertheless, 15 distinct markers were identified that were significantly associated with growth, carcass traits, NDV titres, IgA levels, and chicken survival. These markers were located in regions harbouring relevant annotated genes.

Conclusion

Results suggest that performance of chickens raised under smallholder farm conditions is amenable to genetic improvement and may inform selective breeding programmes for enhanced chicken productivity in sub-Saharan Africa.

Prediction of Deoxynivalenol Contamination in Wheat via Infrared Attenuated Total Reflection Spectroscopy and Multivariate Data Analysis

The climate crisis further exacerbates the challenges for food production. For instance, the increasingly unpredictable growth of fungal species in the field can lead to an unprecedented high prevalence of several mycotoxins, including the most important toxic secondary metabolite produced by Fusarium spp., i.e., deoxynivalenol (DON). The presence of DON in crops may cause health problems in the population and livestock. Hence, there is a demand for advanced strategies facilitating the detection of DON contamination in cereal-based products. To address this need, we introduce infrared attenuated total reflection (IR-ATR) spectroscopy combined with advanced data modeling routines and optimized sample preparation protocols. In this study, we address the limited exploration of wheat commodities to date via IR-ATR spectroscopy. The focus of this study was optimizing the extraction protocol for wheat by testing various solvents aligned with a greener and more sustainable analytical approach. The employed chemometric method, i.e., sparse partial least-squares discriminant analysis, not only facilitated establishing robust classification models capable of discriminating between high vs low DON-contaminated samples adhering to the EU regulatory limit of 1250 μg/kg but also provided valuable insights into the relevant parameters shaping these models.

Do trees respond to pollution? A network study of the impact of pollution on spruce growth from Europe

Tree rings have been reliably used as an environmental proxy over the past decades for environmental reconstructions, simulations and forecasting. In our study, we investigated whether tree-ring chronologies are impacted by pollution. We chose sites in the Krušné hory and the Krkonoše Mountains in the Czech Republic which have a known history of pollution. We sampled Norway spruce (Picea abies [L.] Karst) in both ranges and compared their chronologies. We found no significant difference in the overall radial growth in the chronologies from both regions. However, we observed an increased heterogeneity in the growth of trees from the 1970s till the 1990s. Coherently, a severe reduction in tree growth from the late 1970s and a recovery towards the early 1990s was evident. We collected and analysed soil samples for pH and exchangeable element concentrations. All seven sampling sites' soils were strongly acidic (pHCaCl2 = 3.3 ± 0.4). The average soil base saturation at Krušné hory was higher than at Krkonoše (39% versus 12%), likely due to more intensive liming. Further, we compared these chronologies to other sites in Europe. Analysing 89 sites, we found that most (9 out of 14) of the sites with significantly reduced radial tree growth were located within the former 'Black Triangle', an area which was subjected to heavy industrialisation and pollution from the 1960s to the 1990s. Atmospheric sulphur deposition was found to negatively affect radial tree-growth, while limited quantities of oxidised nitrogen appeared to have a positive effect. Our results are consistent with previous research, indicating that atmospheric SO2 pollution and subsequent acid fog and rime have led to a reduction in annual radial tree growth across the Black Triangle.

Trends in the adoption of diverting loop ileostomy for acute complicated diverticulitis in the United States

BACKGROUND

Acute complicated diverticulitis poses a substantial burden to individual patients and the health care system. A significant proportion of the cases necessitate emergency operations. The choice between Hartmann's procedure and primary anastomosis with diverting loop ileostomy remains controversial.

METHODS

Using American College of Surgeons National Surgical Quality Improvement Program patient user file data from 2012 to 2020, patients undergoing Hartmann's procedure and primary anastomosis with diverting loop ileostomy for nonelective sigmoidectomy for complicated diverticulitis were identified. Major adverse events, 30-day mortality, perioperative complications, operative duration, reoperation, and 30-day readmissions were assessed.

RESULTS

Of 16,921 cases, 6.3% underwent primary anastomosis with diverting loop ileostomy, showing a rising trend from 5.3% in 2012 to 8.4% in 2020. Primary anastomosis with diverting loop ileostomy patients, compared to Hartmann's procedure, had similar demographics and fewer severe comorbidities. Primary anastomosis with diverting loop ileostomy exhibited lower rates of major adverse events (24.6% vs 29.3%, P = .001). After risk adjustment, primary anastomosis with diverting loop ileostomy had similar risks of major adverse events and 30-day mortality compared to Hartmann's procedure. While having lower odds of respiratory (adjusted odds ratio 0.61, 95% confidence interval 0.45-0.83) and infectious (adjusted odds ratio 0.78, 95% confidence interval 0.66-0.93) complications, primary anastomosis with diverting loop ileostomy was associated with a 36-minute increment in operative duration and increased odds of 30-day readmission (adjusted odds ratio 1.30, 95% confidence interval 1.07-1.57) compared to Hartmann's procedure.

CONCLUSION

Primary anastomosis with diverting loop ileostomy displayed comparable odds of major adverse events compared to Hartmann's procedure in acute complicated diverticulitis while mitigating infectious and respiratory complication risks. However, primary anastomosis with diverting loop ileostomy was associated with longer operative times and greater odds of 30-day readmission. Evolving guidelines and increasing primary anastomosis with diverting loop ileostomy use suggest a shift favoring primary anastomosis, especially in complicated diverticulitis. Future investigation of disparities in surgical approaches and patient outcomes is warranted to optimize acute diverticulitis care pathways.

SARS-CoV-2 booster vaccine dose significantly extends humoral immune response half-life beyond the primary series

SARS-CoV-2 lipid nanoparticle mRNA vaccines continue to be administered as the predominant prophylactic measure to reduce COVID-19 disease pathogenesis. Quantifying the kinetics of the secondary immune response from subsequent doses beyond the primary series and understanding how dose-dependent immune waning kinetics vary as a function of age, sex, and various comorbidities remains an important question. We study anti-spike IgG waning kinetics in 152 individuals who received an mRNA-based primary series (first two doses) and a subset of 137 individuals who then received an mRNA-based booster dose. We find the booster dose elicits a 71-84% increase in the median Anti-S half life over that of the primary series. We find the Anti-S half life for both primary series and booster doses decreases with age. However, we stress that although chronological age continues to be a good proxy for vaccine-induced humoral waning, immunosenescence is likely not the mechanism, rather, more likely the mechanism is related to the presence of noncommunicable diseases, which also accumulate with age, that affect immune regulation. We are able to independently reproduce recent observations that those with pre-existing asthma exhibit a stronger primary series humoral response to vaccination than compared to those that do not, and further, we find this result is sustained for the booster dose. Finally, via a single-variate Kruskal-Wallis test we find no difference between male and female humoral decay kinetics, however, a multivariate approach utilizing  Least Absolute Shrinkage and Selection Operator (LASSO) regression for feature selection reveals a statistically significant (p < 1 × 10 - 3 ), albeit small, bias in favour of longer-lasting humoral immunity amongst males.

Association of Interhospital Transfer With Outcomes of Extracorporeal Membrane Oxygenation: A Contemporary Analysis

BACKGROUND

Extracorporeal membrane oxygenation (ECMO) has emerged as a life-sustaining measure for individuals with end-stage cardiopulmonary derangements. An estimated one-third of patients must be transferred to a specialized center to receive this intervention. Therefore, the present study sought to characterize the impact of interhospital transfer (IHT) status on outcomes following ECMO.

METHODS

The 2016-2020 National Inpatient Sample was queried to identify all adult (≥18 years) hospitalizations for ECMO. Patients were stratified based on transfer status from another acute care hospital. Multivariable regression models were developed to assess the association between transfer status and outcomes of interest. Patient and operative factors associated with IHT were identified using regression.

RESULTS

Of an estimated 61,180 hospitalizations entailing ECMO, 21,410 (35.0%) were transfers. Annual transfer volume doubled over the study period, from 2915 to 5945 (nptrend < .001). The predicted morality risk of non-transfers decreased between 2016 and 2020 but remained similar in transferred patients. Following adjustment, transfer was associated with increased odds of in-hospital mortality, complications, duration of stay, and hospitalization costs. Patients experiencing transfer were less likely to be of black race and private insurance status.

CONCLUSION

Despite increasing transfer volume and utilization of ECMO, IHT was associated with significant mortality and hospital complication risks. Further work to reduce adverse outcomes, resource burden, and socioeconomic differences within IHT may improve accessibility to this life-saving modality.

A framework for longitudinal latent factor modelling of treatment response in clinical trials with applications to Psoriatic Arthritis and Rheumatoid Arthritis

OBJECTIVE

Clinical trials involve the collection of a wealth of data, comprising multiple diverse measurements performed at baseline and follow-up visits over the course of a trial. The most common primary analysis is restricted to a single, potentially composite endpoint at one time point. While such an analytical focus promotes simple and replicable conclusions, it does not necessarily fully capture the multi-faceted effects of a drug in a complex disease setting. Therefore, to complement existing approaches, we set out here to design a longitudinal multivariate analytical framework that accepts as input an entire clinical trial database, comprising all measurements, patients, and time points across multiple trials.

METHODS

Our framework composes probabilistic principal component analysis with a longitudinal linear mixed effects model, thereby enabling clinical interpretation of multivariate results, while handling data missing at random, and incorporating covariates and covariance structure in a computationally efficient and principled way.

RESULTS

We illustrate our approach by applying it to four phase III clinical trials of secukinumab in Psoriatic Arthritis (PsA) and Rheumatoid Arthritis (RA). We identify three clinically plausible latent factors that collectively explain 74.5% of empirical variation in the longitudinal patient database. We estimate longitudinal trajectories of these factors, thereby enabling joint characterisation of disease progression and drug effect. We perform benchmarking experiments demonstrating our method's competitive performance at estimating average treatment effects compared to existing statistical and machine learning methods, and showing that our modular approach leads to relatively computationally efficient model fitting.

CONCLUSION

Our multivariate longitudinal framework has the potential to illuminate the properties of existing composite endpoint methods, and to enable the development of novel clinical endpoints that provide enhanced and complementary perspectives on treatment response.

The karyometric signature is altered in fallopian tubes with serous tubal intraepithelial carcinoma

OBJECTIVE

Recent evidence suggests that the fimbriated end of the fallopian tube harbors the precursor cells for many high-grade ovarian cancers, opening the door for development of better screening methods that directly assess the fallopian tube in women at risk for malignancy. Previously we have shown that the karyometric signature is abnormal in the fallopian tube epithelium in women at hereditary risk of ovarian cancer. In this study, we sought to determine whether the karyometric signature in serous tubal intraepithelial carcinoma (STIC) is significantly different from normal, and whether an abnormal karyometric signature can be detected in histologically normal tubal epithelial cells adjacent to STIC lesions.

METHODS

The karyometric signature was measured in epithelial cells from the proximal and fimbriated portion of the fallopian tube in fallopian tube specimens removed from women at: 1) average risk for ovarian cancer undergoing surgery for benign gynecologic indications (n = 37), 2) hereditary risk of ovarian cancer (germline BRCA alterations) undergoing risk-reducing surgery (n = 44), and 3) diagnosed with fimbrial STICs (n = 17).

RESULTS

The karyometric signature in tubes with fimbrial STICs differed from that of tubes with benign histology. The degree of karyometric alteration increased with increasing proximity to fimbrial STICs, ranging from moderate in the proximal portion of the tube, to greatest in both normal appearing fimbrial cells near STICs as well as in fimbrial STIC lesions.

CONCLUSION

These data demonstrate an abnormal karyometric signature in STICs that may extend beyond the STIC, potentially providing an opportunity for early detection of fallopian tube neoplasia.

Contamination bias in the estimation of child maltreatment causal effects on adolescent internalizing and externalizing behavior problems

BACKGROUND

When unaddressed, contamination in child maltreatment research, in which some proportion of children recruited for a nonmaltreated comparison group are exposed to maltreatment, downwardly biases the significance and magnitude of effect size estimates. This study extends previous contamination research by investigating how a dual-measurement strategy of detecting and controlling contamination impacts causal effect size estimates of child behavior problems.

METHODS

This study included 634 children from the LONGSCAN study with 63 cases of confirmed child maltreatment after age 8 and 571 cases without confirmed child maltreatment. Confirmed child maltreatment and internalizing and externalizing behaviors were recorded every 2 years between ages 4 and 16. Contamination in the nonmaltreated comparison group was identified and controlled by either a prospective self-report assessment at ages 12, 14, and 16 or by a one-time retrospective self-report assessment at age 18. Synthetic control methods were used to establish causal effects and quantify the impact of contamination when it was not controlled, when it was controlled for by prospective self-reports, and when it was controlled for by retrospective self-reports.

RESULTS

Rates of contamination ranged from 62% to 67%. Without controlling for contamination, causal effect size estimates for internalizing behaviors were not statistically significant. Causal effects only became statistically significant after controlling contamination identified from either prospective or retrospective reports and effect sizes increased by between 17% and 54%. Controlling contamination had a smaller impact on effect size increases for externalizing behaviors but did produce a statistically significant overall effect, relative to the model ignoring contamination, when prospective methods were used.

CONCLUSIONS

The presence of contamination in a nonmaltreated comparison group can underestimate the magnitude and statistical significance of causal effect size estimates, especially when investigating internalizing behavior problems. Addressing contamination can facilitate the replication of results across studies.

The transcriptome landscape of developing barley seeds

Cereal grains are an important source of food and feed. To provide comprehensive spatiotemporal information about biological processes in developing seeds of cultivated barley (Hordeum vulgare L. subsp. vulgare), we performed a transcriptomic study of the embryo, endosperm, and seed maternal tissues collected from grains 4-32 days after pollination. Weighted gene co-expression network and motif enrichment analyses identified specific groups of genes and transcription factors (TFs) potentially regulating barley seed tissue development. We defined a set of tissue-specific marker genes and families of TFs for functional studies of the pathways controlling barley grain development. Assessing selected groups of chromatin regulators revealed that epigenetic processes are highly dynamic and likely play a major role during barley endosperm development. The repressive H3K27me3 modification is globally reduced in endosperm tissues and at specific genes related to development and storage compounds. Altogether, this atlas uncovers the complexity of developmentally regulated gene expression in developing barley grains.

Fast and flexible joint fine-mapping of multiple traits via the Sum of Single Effects model

We introduce mvSuSiE, a multi-trait fine-mapping method for identifying putative causal variants from genetic association data (individual-level or summary data). mvSuSiE learns patterns of shared genetic effects from data, and exploits these patterns to improve power to identify causal SNPs. Comparisons on simulated data show that mvSuSiE is competitive in speed, power and precision with existing multi-trait methods, and uniformly improves on single-trait fine-mapping (SuSiE) in each trait separately. We applied mvSuSiE to jointly fine-map 16 blood cell traits using data from the UK Biobank. By jointly analyzing the traits and modeling heterogeneous effect sharing patterns, we discovered a much larger number of causal SNPs (>3,000) compared with single-trait fine-mapping, and with narrower credible sets. mvSuSiE also more comprehensively characterized the ways in which the genetic variants affect one or more blood cell traits; 68% of causal SNPs showed significant effects in more than one blood cell type.

Predicting nutrition and environmental factors associated with female reproductive disorders using a knowledge graph and random forests

OBJECTIVE

Female reproductive disorders (FRDs) are common health conditions that may present with significant symptoms. Diet and environment are potential areas for FRD interventions. We utilized a knowledge graph (KG) method to predict factors associated with common FRDs (for example, endometriosis, ovarian cyst, and uterine fibroids).

MATERIALS AND METHODS

We harmonized survey data from the Personalized Environment and Genes Study (PEGS) on internal and external environmental exposures and health conditions with biomedical ontology content. We merged the harmonized data and ontologies with supplemental nutrient and agricultural chemical data to create a KG. We analyzed the KG by embedding edges and applying a random forest for edge prediction to identify variables potentially associated with FRDs. We also conducted logistic regression analysis for comparison.

RESULTS

Across 9765 PEGS respondents, the KG analysis resulted in 8535 significant or suggestive predicted links between FRDs and chemicals, phenotypes, and diseases. Amongst these links, 32 were exact matches when compared with the logistic regression results, including comorbidities, medications, foods, and occupational exposures.

DISCUSSION

Mechanistic underpinnings of predicted links documented in the literature may support some of our findings. Our KG methods are useful for predicting possible associations in large, survey-based datasets with added information on directionality and magnitude of effect from logistic regression. These results should not be construed as causal but can support hypothesis generation.

CONCLUSION

This investigation enabled the generation of hypotheses on a variety of potential links between FRDs and exposures. Future investigations should prospectively evaluate the variables hypothesized to impact FRDs.

Improved data quality and statistical power of trial-level event-related potentials with Bayesian random-shift Gaussian processes

Studies of cognitive processes via electroencephalogram (EEG) recordings often analyze group-level event-related potentials (ERPs) averaged over multiple subjects and trials. This averaging procedure can obscure scientifically relevant variability across subjects and trials, but has been necessary due to the difficulties posed by inference of trial-level ERPs. We introduce the Bayesian Random Phase-Amplitude Gaussian Process (RPAGP) model, for inference of trial-level amplitude, latency, and ERP waveforms. We apply RPAGP to data from a study of ERP responses to emotionally arousing images. The model estimates of trial-specific signals are shown to greatly improve statistical power in detecting significant differences in experimental conditions compared to existing methods. Our results suggest that replacing the observed data with the de-noised RPAGP predictions can potentially improve the sensitivity and accuracy of many of the existing ERP analysis pipelines.

First evidence for human occupation of a lava tube in Arabia: The archaeology of Umm Jirsan Cave and its surroundings, northern Saudi Arabia

Recent advances in interdisciplinary archaeological research in Arabia have focused on the evolution and historical development of regional human populations as well as the diverse patterns of cultural change, migration, and adaptations to environmental fluctuations. Obtaining a comprehensive understanding of cultural developments such as the emergence and lifeways of Neolithic groups has been hindered by the limited preservation of stratified archaeological assemblages and organic remains, a common challenge in arid environments. Underground settings like caves and lava tubes, which are prevalent in Arabia but which have seen limited scientific exploration, offer promising opportunities for addressing these issues. Here, we report on an archaeological excavation and a related survey at and around Umm Jirsan lava tube in the Harrat Khaybar, north-western Saudi Arabia. Our results reveal repeated phases of human occupation of the site ranging from at least the Neolithic through to the Chalcolithic/Bronze Age. Pastoralist use of the lava tube and surrounding landscape is attested in rock art and faunal records, suggesting that Umm Jirsan was situated along a pastoral route linking key oases. Isotopic data indicates that herbivores primarily grazed on wild grasses and shrubs rather than being provided with fodder, while humans had a diet consistently high in protein but with increasing consumption of C3 plants through-time, perhaps related to the emergence of oasis agriculture. While underground and naturally sheltered localities are globally prominent in archaeology and Quaternary science, our work represents the first such combined records for Saudi Arabia and highlight the potential for interdisciplinary studies in caves and lava tubes.

Activation of fluoride anion as nucleophile in water with data-guided surfactant selection

A principal component surfactant_map was developed for 91 commonly accessible surfactants for use in surfactant-enabled organic reactions in water, an important approach for sustainable chemical processes. This map was built using 22 experimental and theoretical descriptors relevant to the physicochemical nature of these surfactant-enabled reactions, and advanced principal component analysis algorithms. It is comprised of all classes of surfactants, i.e. cationic, anionic, zwitterionic and neutral surfactants, including designer surfactants. The value of this surfactant_map was demonstrated in activating simple inorganic fluoride salts as effective nucleophiles in water, with the right surfactant. This led to the rapid development (screening 13-15 surfactants) of two fluorination reactions for β-bromosulfides and sulfonyl chlorides in water. The latter was demonstrated in generating a sulfonyl fluoride with sufficient purity for direct use in labelling of chymotrypsin, under physiological conditions.

Investigation of the shared biological mechanisms and common biomarker APTAF1 of sleep deprivation and mild cognitive impairment using integrated bioinformatics analysis

Introduction: The relationship between sleep loss and cognitive impairment has long been widely recognized, but there is still a lack of complete understanding of the underlying mechanisms and potential biomarkers. The purpose of this study is to further explore the shared biological mechanisms and common biomarkers between sleep loss and cognitive impairment. Methods: The mitochondria-related genes and gene expression data were downloaded from the MitoCarta3.0 and Gene Expression Omnibus (GEO) databases. We identified the differentially expressed mitochondrial-related genes by combing the differentially expressed genes (DEGs) in sleep deprivation (SD) and mild cognitive impairment (MCI) datasets with mitochondria-related gene lists. Shared DEGs were then further analyzed for enrichment analysis. Next, the common biomarker was identified using two machine learning techniques and further validated using two independent GEO datasets. Then GSEA and GSVA were conducted to analyze the functional categories and pathways enriched for the common biomarker. Finally, immune infiltration analysis was used to investigate the correlation of immune cell infiltration with the common biomarker in SD and MCI. Results: A total of 32 mitochondrial-related differentially expressed genes were identified in SD and MCI. GO analysis indicated that these genes were significantly enriched for mitochondrial transport, and KEGG analysis showed they were mainly involved in pathways of neurodegenerative diseases. In addition, ATPAF1, which was significantly down-regulated in both SD and MCI, was identified through machine learning algorithms as the common biomarker with favorable diagnostic performance. GSEA and GSVA revealed that ATPAF1 was mainly involved in metabolic pathways, such as oxidative phosphorylation, acetylcholine metabolic process, valine, leucine and isoleucine degradation. Immune infiltration analysis showed that the expression of ATPAF1 was correlated with changes in immune cells, especially those key immune cell types associated with SD and MCI. Discussion: This study firstly revealed that mitochondrial dysfunction may be the common pathogenesis of sleep loss and mild cognitive impairment and identified ATPAF1 as a possible biomarker and therapeutic target involved in SD and MCI.

Coexistence, resource partitioning, and fisheries management: A tale of two mesopredators in equatorial waters

Rock hind (Epinephelus adscensionis) and spotted moray (Gymnothorax moringa) are ubiquitous mesopredators that co-occur in the nearshore waters of Ascension Island in the South Atlantic Ocean, where they have significant cultural and subsistence value, but management of their non-commercial take is limited. This isolated volcanic system is home to high biomass and low species diversity, which poses two key questions: How can two mesopredators that perform similar ecological roles coexist? And if these two species are so ecologically similar, can they be managed using the same approach? Here, we combined acoustic telemetry, stomach content analysis, and stable isotope analysis to (i) explore space use and diet choices within and between these two species and (ii) to assess appropriate species-specific management options. Although rock hind had high residency and small calculated home ranges (0.0001-0.3114 km2), spotted moray exhibited shorter periods of residency (<3 months) before exiting the array. Vertical space use differed significantly across the 20-month tracking period, with individual differences in vertical space observed for both species. A hierarchical generalized additive model using 12-h averaged depth data identified that rock hind occurred lower in the water column than spotted moray, with both species occupying moderately deeper depths at night versus day (+1.6% relative depth). Spotted moray depth was also significantly predicted by lunar illumination. Aggregating samples by species and tissue type, Bayesian ecological niche modeling identified a 53.14%-54.15% and 78.02%-97.08% probability of niche overlap from fin clip and white muscle, respectively, whereas limited stomach content data indicated a preference for piscivorous prey. Variability in niche breadth between years suggests these species may exploit a range of prey items over time. These findings indicate that although these two species perform a similar ecological role by feeding on prey occupying the same trophic levels, subtle differences in movement behaviors between them suggest a one-rule-fits-all management approach is not likely the most effective option.

Heterogeneous macroeconomic factors' effects on stocks across sizes, styles, and sectors in the South Korean market

Knowledge of the key macroeconomic variables that influence stock volatility across capital sizes, styles, and sectors can provide clues for investment strategies and policy decisions. We use the GARCH-MIDAS model with feature selection to analyze Korean Benchmark Indices from 2009 to 2022. This study maximizes memory retention through an optimal fractional differentiated price series and uses an adaptive lasso penalty for feature selection. The housing price-sales index and realized volatility were consistently influential across most indices and sectors. The GARCH-MIDAS model, paired with these variables, significantly improves long-term stock volatility forecasts. This study underscores the need to monitor housing prices in South Korea because of their substantial effects on long-term stock volatility.

Tissue-specific atlas of trans-models for gene regulation elucidates complex regulation patterns

BACKGROUND

Deciphering gene regulation is essential for understanding the underlying mechanisms of healthy and disease states. While the regulatory networks formed by transcription factors (TFs) and their target genes has been mostly studied with relation to cis effects such as in TF binding sites, we focused on trans effects of TFs on the expression of their transcribed genes and their potential mechanisms.

RESULTS

We provide a comprehensive tissue-specific atlas, spanning 49 tissues of TF variations affecting gene expression through computational models considering two potential mechanisms, including combinatorial regulation by the expression of the TFs, and by genetic variants within the TF. We demonstrate that similarity between tissues based on our discovered genes corresponds to other types of tissue similarity. The genes affected by complex TF regulation, and their modelled TFs, were highly enriched for pharmacogenomic functions, while the TFs themselves were also enriched in several cancer and metabolic pathways. Additionally, genes that appear in multiple clusters are enriched for regulation of immune system while tissue clusters include cluster-specific genes that are enriched for biological functions and diseases previously associated with the tissues forming the cluster. Finally, our atlas exposes multilevel regulation across multiple tissues, where TFs regulate other TFs through the two tested mechanisms.

CONCLUSIONS

Our tissue-specific atlas provides hierarchical tissue-specific trans genetic regulations that can be further studied for association with human phenotypes.

Inference of genomic landscapes using ordered Hidden Markov Models with emission densities (oHMMed)

BACKGROUND

Genomes are inherently inhomogeneous, with features such as base composition, recombination, gene density, and gene expression varying along chromosomes. Evolutionary, biological, and biomedical analyses aim to quantify this variation, account for it during inference procedures, and ultimately determine the causal processes behind it. Since sequential observations along chromosomes are not independent, it is unsurprising that autocorrelation patterns have been observed e.g., in human base composition. In this article, we develop a class of Hidden Markov Models (HMMs) called oHMMed (ordered HMM with emission densities, the corresponding R package of the same name is available on CRAN): They identify the number of comparably homogeneous regions within autocorrelated observed sequences. These are modelled as discrete hidden states; the observed data points are realisations of continuous probability distributions with state-specific means that enable ordering of these distributions. The observed sequence is labelled according to the hidden states, permitting only neighbouring states that are also neighbours within the ordering of their associated distributions. The parameters that characterise these state-specific distributions are inferred.

RESULTS

We apply our oHMMed algorithms to the proportion of G and C bases (modelled as a mixture of normal distributions) and the number of genes (modelled as a mixture of poisson-gamma distributions) in windows along the human, mouse, and fruit fly genomes. This results in a partitioning of the genomes into regions by statistically distinguishable averages of these features, and in a characterisation of their continuous patterns of variation. In regard to the genomic G and C proportion, this latter result distinguishes oHMMed from segmentation algorithms based in isochore or compositional domain theory. We further use oHMMed to conduct a detailed analysis of variation of chromatin accessibility (ATAC-seq) and epigenetic markers H3K27ac and H3K27me3 (modelled as a mixture of poisson-gamma distributions) along the human chromosome 1 and their correlations.

CONCLUSIONS

Our algorithms provide a biologically assumption free approach to characterising genomic landscapes shaped by continuous, autocorrelated patterns of variation. Despite this, the resulting genome segmentation enables extraction of compositionally distinct regions for further downstream analyses.

Partial-linear single-index transformation models with censored data

In studies with time-to-event outcomes, multiple, inter-correlated, and time-varying covariates are commonly observed. It is of great interest to model their joint effects by allowing a flexible functional form and to delineate their relative contributions to survival risk. A class of semiparametric transformation (ST) models offers flexible specifications of the intensity function and can be a general framework to accommodate nonlinear covariate effects. In this paper, we propose a partial-linear single-index (PLSI) transformation model that reduces the dimensionality of multiple covariates into a single index and provides interpretable estimates of the covariate effects. We develop an iterative algorithm using the regression spline technique to model the nonparametric single-index function for possibly nonlinear joint effects, followed by nonparametric maximum likelihood estimation. We also propose a nonparametric testing procedure to formally examine the linearity of covariate effects. We conduct Monte Carlo simulation studies to compare the PLSI transformation model with the standard ST model and apply it to NYU Langone Health de-identified electronic health record data on COVID-19 hospitalized patients' mortality and a Veteran's Administration lung cancer trial.

Improved multi-ancestry fine-mapping identifies cis -regulatory variants underlying molecular traits and disease risk

Multi-ancestry statistical fine-mapping of cis -molecular quantitative trait loci ( cis -molQTL) aims to improve the precision of distinguishing causal cis -molQTLs from tagging variants. However, existing approaches fail to reflect shared genetic architectures. To solve this limitation, we present the Sum of Shared Single Effects (SuShiE) model, which leverages LD heterogeneity to improve fine-mapping precision, infer cross-ancestry effect size correlations, and estimate ancestry-specific expression prediction weights. We apply SuShiE to mRNA expression measured in PBMCs (n=956) and LCLs (n=814) together with plasma protein levels (n=854) from individuals of diverse ancestries in the TOPMed MESA and GENOA studies. We find SuShiE fine-maps cis -molQTLs for 16 % more genes compared with baselines while prioritizing fewer variants with greater functional enrichment. SuShiE infers highly consistent cis -molQTL architectures across ancestries on average; however, we also find evidence of heterogeneity at genes with predicted loss-of-function intolerance, suggesting that environmental interactions may partially explain differences in cis -molQTL effect sizes across ancestries. Lastly, we leverage estimated cis -molQTL effect-sizes to perform individual-level TWAS and PWAS on six white blood cell-related traits in AOU Biobank individuals (n=86k), and identify 44 more genes compared with baselines, further highlighting its benefits in identifying genes relevant for complex disease risk. Overall, SuShiE provides new insights into the cis -genetic architecture of molecular traits.

Are teachers meeting students' needs in untracked science classrooms? Evidence based on a causal inferential approach

Tracking has been criticized for relegating disadvantaged students to lower track courses in which students encounter a greater lack of instructional support. While an end to tracks through detracking is a possible solution, there are concerns that detracking will create more heterogeneous classrooms, making it harder for teachers to provide adequate support to their students. Using the 2015 PISA dataset, this study conducts a causal inferential analysis to understand the differences in student perceptions of teaching in tracked and untracked environments. The results provide evidence that students' needs, with respect to adaptation of instruction and provision of individualized feedback and support, are being met to the same extent on average in tracked and untracked science classes, suggesting that teachers may not necessarily have a harder time meeting the needs of students in untracked classes.

Discordance Between Very Low-Density Lipoprotein Cholesterol and Low-Density Lipoprotein Cholesterol Increases Cardiovascular Disease Risk in a Geographically Defined Cohort

BACKGROUND

Clinical risk scores are used to identify those at high risk of atherosclerotic cardiovascular disease (ASCVD). Despite preventative efforts, residual risk remains for many individuals. Very low-density lipoprotein cholesterol (VLDL-C) and lipid discordance could be contributors to the residual risk of ASCVD.

METHODS AND RESULTS

Cardiovascular disease-free residents, aged ≥40 years, living in Olmsted County, Minnesota, were identified through the Rochester Epidemiology Project. Low-density lipoprotein cholesterol (LDL-C) and VLDL-C were estimated from clinically ordered lipid panels using the Sampson equation. Participants were categorized into concordant and discordant lipid pairings based on clinical cut points. Rates of incident ASCVD, including percutaneous coronary intervention, coronary artery bypass grafting, stroke, or myocardial infarction, were calculated during follow-up. The association of LDL-C and VLDL-C with ASCVD was assessed using Cox proportional hazards regression. Interaction between LDL-C and VLDL-C was assessed. The study population (n=39 098) was primarily White race (94%) and female sex (57%), with a mean age of 54 years. VLDL-C (per 10-mg/dL increase) was significantly associated with an increased risk of incident ASCVD (hazard ratio, 1.07 [95% CI, 1.05-1.09]; P<0.001]) after adjustment for traditional risk factors. The interaction between LDL-C and VLDL-C was not statistically significant (P=0.11). Discordant individuals with high VLDL-C and low LDL-C experienced the highest rate of incident ASCVD events, 16.9 per 1000 person-years, during follow-up.

CONCLUSIONS

VLDL-C and lipid discordance are associated with a greater risk of ASCVD and can be estimated from clinically ordered lipid panels to improve ASCVD risk assessment.

Relationship between perceived physical literacy and obesity-related outcomes in adolescents: the EHDLA study

Purpose

The aim of the present study was to examine the relationship between perceived physical literacy and obesity-related outcomes among adolescents from Spain.

Methods

This is a secondary cross-sectional analysis including a total sample of 845 Spanish adolescents (55.3% girls) aged 12-17 years from the Valle de Ricote (Region of Murcia) from the Eating Healthy and Daily Life Activities (EHDLA) project. Physical literacy was evaluated using the Spanish Perceived Physical Literacy Instrument for adolescents (S-PPLI). Body mass index was computed by taking the participants' body weight in kilograms and dividing it by the square of their height in meters, and body mass index (z score) and overweight/obesity and obesity were computed by the World Health Organization age- and sex-specific thresholds. Waist circumference was measured using a constant tension tape. Moreover, the waist-to-height ratio was calculated, and therefore, abdominal obesity was determined. Skinfold measurements were taken at the triceps and medial calf using calibrated steel calipers.

Results

In general, the overall trend was downward (i.e., the higher the PPLI score the lower the obesity-related indicators), with the approximate significance of smooth terms being statistically significant for all models examined (p < 0.001). Adolescents with lower perceived physical literacy (PPL) showed the highest estimated marginal means of body mass index, body mass index z score, waist circumference, waist-to-height ratio, and skinfold (triceps and calf) and predictive probabilities of having excess weight, obesity, and abdominal obesity, while their counterparts with high PPL had the lowest. In addition, significant differences were observed for all the obesity-related indications between adolescents with low PPL and those with medium PPL (p-adjusted < 0.05 for all indicators), as well as with those with high PPL (p-adjusted < 0.05 for all indicators). Moreover, these significant differences were also shown for most indicators between adolescents with medium PPL and those with high PPL (except for obesity).

Conclusion

Physical literacy could play a crucial role in maintaining more desirable obesity-related outcomes in adolescents. Adolescents with high perceived physical literacy showed lower obesity-related indicators (i.e., body mass index, body mass index z score, waist circumference, waist-to-height ratio, skinfolds), as well as a lower probability of having excess weight, obesity, and abdominal obesity.

A Hierarchical Bayesian approach to small area estimation of health insurance coverage in Ethiopian administrative zones for better policies and programs

Sample surveys are extensively used to provide reliable direct estimates for large areas or domains with enough sample sizes at national and regional levels. However, zones are unplanned domains by the Demographic and Health Survey (DHS) program and need more sample sizes to produce direct survey estimates with adequate precision. Conducting surveys in small areas (like zones) is too expensive and time-consuming, making it unfeasible for developing countries like Ethiopia. Therefore, this study aims to use the Hierarchical Bayes (HB) Small Area Estimation (SAE) model to estimate the Community-Based Health Insurance (CBHI) coverage at the zone levels in Ethiopia. To achieve this, we combined the 2019 Ethiopia Mini-Demographic and Health Survey (EMDHS) data with the 2007 population census data. SAE has addressed the challenge of producing reliable parameter estimates for small or even zero sample sizes across Ethiopian zones by utilizing auxiliary information from the population census. The results show that model-based estimates generated by the SAE approach are more accurate than direct survey estimates of CBHI. A map of CBHI scheme coverage was also used to visualize the spatial variation in the distribution of CBHI scheme coverage. From the CBHI scheme coverage map, we noticed notable variations in CBHI scheme coverage across Ethiopian zones. Additionally, this research identified areas with high and low CBHI scheme coverage to improve decision-making and increase coverage in Ethiopia. One of the novelties of this paper is estimating the non-sampled zones; therefore, the policymakers will give equal attention similar to the sampled zones.

Interfacility transfer of the critically ill: Transfer status does not influence survival

PURPOSE

To estimate differences in case-mix adjusted hospital mortality between adult ICU patients who are transferred during their ICU-stay and those who are not.

METHODS

19,260 visits to 12 ICUs in Nova Scotia (NS), Canada April 2018-September 2023 were analyzed. Data were obtained from the NS Provincial ICU database. Generalized additive models (GAMs) were used to estimate differences in case-mix adjusted hospital mortality between patients who underwent transfer and those who did not.

RESULTS

1040/19,260 (5%) ICU visits involved interfacility-transfer. No difference in hospital mortality was identified between transferred and non-transferred patients by GAM (OR, 0.99, 95% CI, 0.82 to 1.19; p = 0.91). No mortality difference was observed between patients undergoing a single transfer versus multiple (OR, 0.87; 95% CI, 0.45 to -1.69; p = 0.68). A GAM including the categories no transfer, one transfer, and multiple transfers identified a difference in hospital mortality for patients that underwent multiple transfers compared to non-transferred patients (OR, 0.68; 95% CI, 0.46 to 1.00, p = 0.05), but no difference was identified in a post-hoc matched cohort sensitivity analysis (OR, 0.68; 95% CI, 0.46 to 1.01, p = 0.05).

CONCLUSION

The transfer of critically ill patients between ICUs in Nova Scotia did not impact case-mix adjusted hospital mortality.

Federated Fuzzy Clustering for Decentralized Incomplete Longitudinal Behavioral Data

The use of medical data for machine learning, including unsupervised methods such as clustering, is often restricted by privacy regulations such as the Health Insurance Portability and Accountability Act (HIPAA). Medical data is sensitive and highly regulated and anonymization is often insufficient to protect a patient's identity. Traditional clustering algorithms are also unsuitable for longitudinal behavioral health trials, which often have missing data and observe individual behaviors over varying time periods. In this work, we develop a new decentralized federated multiple imputation-based fuzzy clustering algorithm for complex longitudinal behavioral trial data collected from multisite randomized controlled trials over different time periods. Federated learning (FL) preserves privacy by aggregating model parameters instead of data. Unlike previous FL methods, this proposed algorithm requires only two rounds of communication and handles clients with varying numbers of time points for incomplete longitudinal data. The model is evaluated on both empirical longitudinal dietary health data and simulated clusters with different numbers of clients, effect sizes, correlations, and sample sizes. The proposed algorithm converges rapidly and achieves desirable performance on multiple clustering metrics. This new method allows for targeted treatments for various patient groups while preserving their data privacy and enables the potential for broader applications in the Internet of Medical Things.

Validation of the internal structure of the Brazilian Dental Vulnerability Scale

OBJECTIVE

This study aimed to evaluate evidence of validity of internal structure of the Brazilian Dental Vulnerability Scale (EVO-BR) when applied in Brazil.

METHODS

This is a psychometric study that seeks to validate a scale elaborated by evidence of internal structure. Data collection was conducted in 18 basic health units that implement the Brazilian Healthcare Planning (PAS) methodology, across the five regions of Brazil. The initial version of the EVO-BR contained 41 items that measured dental vulnerability and was applied to users of the Brazilian Unified Health System (SUS) aged 18 years or older who were in basic health units for consultation with higher education professionals. To evaluate the evidence, the following statistical analyses were performed: exploratory factor analysis, confirmatory factor analysis, and network analysis.

RESULTS

A total of 1,753 users participated in the study. To adjust the sample, we considered the factorability obtained from Kaiser-Meyer-Olkin (KMO) test = 0.65, Bartlett sphericity test = 8019.7, and a matrix determinant of 0.008. The initial parallel analysis indicated a four-dimensional model and had the items adjusted according to factor loading (ranging from 0.38 to 0.99), common factors (0.13 to 0.89), and Pratt's measure, until the model presented congruence in the statistical and interpretative principles simultaneously. The final model contained 15 items, maintaining the four dimensions indicated by the parallel analysis, and held an explained variance of 68.56%.

CONCLUSIONS

The EVO-BR is a validated scale to measure dental vulnerability and, thus, can contribute to the organization of access to the oral health team in primary health care (PHC) by stratifying the population, as recommended in the Brazilian Healthcare Planning.

An ensemble penalized regression method for multi-ancestry polygenic risk prediction

Great efforts are being made to develop advanced polygenic risk scores (PRS) to improve the prediction of complex traits and diseases. However, most existing PRS are primarily trained on European ancestry populations, limiting their transferability to non-European populations. In this article, we propose a novel method for generating multi-ancestry Polygenic Risk scOres based on enSemble of PEnalized Regression models (PROSPER). PROSPER integrates genome-wide association studies (GWAS) summary statistics from diverse populations to develop ancestry-specific PRS with improved predictive power for minority populations. The method uses a combination ofL 1 (lasso) andL 2 (ridge) penalty functions, a parsimonious specification of the penalty parameters across populations, and an ensemble step to combine PRS generated across different penalty parameters. We evaluate the performance of PROSPER and other existing methods on large-scale simulated and real datasets, including those from 23andMe Inc., the Global Lipids Genetics Consortium, and All of Us. Results show that PROSPER can substantially improve multi-ancestry polygenic prediction compared to alternative methods across a wide variety of genetic architectures. In real data analyses, for example, PROSPER increased out-of-sample prediction R2 for continuous traits by an average of 70% compared to a state-of-the-art Bayesian method (PRS-CSx) in the African ancestry population. Further, PROSPER is computationally highly scalable for the analysis of large SNP contents and many diverse populations.

Simultaneous variable selection and estimation for survival data via the Gaussian seamless- L 0 $$ {L}_0 $$ penalty

We propose a new simultaneous variable selection and estimation procedure with the Gaussian seamless-L 0 $$ {L}_0 $$ (GSELO) penalty for Cox proportional hazard model and additive hazards model. The GSELO procedure shows good potential to improve the existing variable selection methods by taking strength from both best subset selection (BSS) and regularization. In addition, we develop an iterative algorithm to implement the proposed procedure in a computationally efficient way. Theoretically, we establish the convergence properties of the algorithm and asymptotic theoretical properties of the proposed procedure. Since parameter tuning is crucial to the performance of the GSELO procedure, we also propose an extended Bayesian information criteria (EBIC) parameter selector for the GSELO procedure. Simulated and real data studies have demonstrated the prediction performance and effectiveness of the proposed method over several state-of-the-art methods.

Protective personal equipment on coastal environments: Identifying key drivers at a global scale

The contamination of coastal ecosystems by personal protective equipment (PPE) emerged as a significant concern immediately following the declaration of the COVID-19 pandemic by the World Health Organization (WHO). Hence, numerous studies have assessed PPE occurrence on beaches worldwide. However, no predictors on PPE contamination was so far pointed out. The present study investigated social and landscape drivers affecting the PPE density in coastal environments worldwide using a meta-analysis approach. Spatial variables such as urban modification levels, coastal vegetation coverage, population density (HPD), distance from rivers (DNR), and poverty degree (GGRDI) were derived from global satellite data. These variables, along with the time elapsed after WHO declared the pandemic, were included in generalized additive models as potential predictors of PPE density. HPD consistently emerged as the most influential predictor of PPE density (p < 0.00001), exhibiting a positive effect. Despite the presence of complex non-linear relationships, our findings indicate higher PPE density in areas with intermediate GGRDI levels, indicative of emerging economies. Additionally, elevated PPE density was observed in areas located further away from rivers (p < 0.001), and after the initial months of the pandemic. Despite the uncertainties associated with the varied sampling methods employed by the studies comprising our database, this study offers a solid baseline for tackling the global problem of PPE contamination on beachesguiding monitoring assessments in future pandemics.

Generalized functional linear model with a point process predictor

Point process data have become increasingly popular these days. For example, many of the data captured in electronic health records (EHR) are in the format of point process data. It is of great interest to study the association between a point process predictor and a scalar response using generalized functional linear regression models. Various generalized functional linear regression models have been developed under different settings in the past decades. However, existing methods can only deal with functional or longitudinal predictors, not point process predictors. In this article, we propose a novel generalized functional linear regression model for a point process predictor. Our proposed model is based on the joint modeling framework, where we adopt a log-Gaussian Cox process model for the point process predictor and a generalized linear regression model for the outcome. We also develop a new algorithm for fast model estimation based on the Gaussian variational approximation method. We conduct extensive simulation studies to evaluate the performance of our proposed method and compare it to competing methods. The performance of our proposed method is further demonstrated on an EHR dataset of patients admitted into the intensive care units of the Beth Israel Deaconess Medical Center between 2001 and 2008.

Order selection for heterogeneous semiparametric hidden Markov models

Hidden Markov models (HMMs), which can characterize dynamic heterogeneity, are valuable tools for analyzing longitudinal data. The order of HMMs (ie, the number of hidden states) is typically assumed to be known or predetermined by some model selection criterion in conventional analysis. As prior information about the order frequently lacks, pairwise comparisons under criterion-based methods become computationally expensive with the model space growing. A few studies have conducted order selection and parameter estimation simultaneously, but they only considered homogeneous parametric instances. This study proposes a Bayesian double penalization (BDP) procedure for simultaneous order selection and parameter estimation of heterogeneous semiparametric HMMs. To overcome the difficulties in updating the order, we create a brand-new Markov chain Monte Carlo algorithm coupled with an effective adjust-bound reversible jump strategy. Simulation results reveal that the proposed BDP procedure performs well in estimation and works noticeably better than the conventional criterion-based approaches. Application of the suggested method to the Alzheimer's Disease Neuroimaging Initiative research further supports its usefulness.

Cohort-based smoothing methods for age-specific contact rates

The use of social contact rates is widespread in infectious disease modeling since it has been shown that they are key driving forces of important epidemiological parameters. Quantification of contact patterns is crucial to parameterize dynamic transmission models and to provide insights on the (basic) reproduction number. Information on social interactions can be obtained from population-based contact surveys, such as the European Commission project POLYMOD. Estimation of age-specific contact rates from these studies is often done using a piecewise constant approach or bivariate smoothing techniques. For the latter, typically, smoothness is introduced in the dimensions of the respondent's and contact's age (i.e., the rows and columns of the social contact matrix). We propose a smoothing constrained approach-taking into account the reciprocal nature of contacts-introducing smoothness over the diagonal (including all subdiagonals) of the social contact matrix. This modeling approach is justified assuming that when people age their contact behavior changes smoothly. We call this smoothing from a cohort perspective. Two approaches that allow for smoothing over social contact matrix diagonals are proposed, namely (i) reordering of the diagonal components of the contact matrix and (ii) reordering of the penalty matrix ensuring smoothness over the contact matrix diagonals. Parameter estimation is done in the likelihood framework by using constrained penalized iterative reweighted least squares. A simulation study underlines the benefits of cohort-based smoothing. Finally, the proposed methods are illustrated on the Belgian POLYMOD data of 2006. Code to reproduce the results of the article can be downloaded on this GitHub repository https://github.com/oswaldogressani/Cohort_smoothing.

Quantifying the production of plant pollen at the farm scale

Plant pollen is rich in protein, sterols and lipids, providing crucial nutrition for many pollinators. However, we know very little about the quantity, quality and timing of pollen availability in real landscapes, limiting our ability to improve food supply for pollinators. We quantify the floral longevity and pollen production of a whole plant community for the first time, enabling us to calculate daily pollen availability. We combine these data with floral abundance and nectar measures from UK farmland to quantify pollen and nectar production at the landscape scale throughout the year. Pollen and nectar production were significantly correlated at the floral unit, and landscape level. The species providing the highest quantity of pollen on farmland were Salix spp. (38%), Filipendula ulmaria (14%), Rubus fruticosus (10%) and Taraxacum officinale (9%). Hedgerows were the most pollen-rich habitats, but permanent pasture provided the majority of pollen at the landscape scale, because of its large area. Pollen and nectar were closely associated in their phenology, with both peaking in late April, before declining steeply in June and remaining low throughout the year. Our data provide a starting point for including pollen in floral resource assessments and ensuring the nutritional requirements of pollinators are met in farmland landscapes.