Contents of this issue

Differential EBV protein-specific antibody response between responders and non-responders to EBVSTs immunotherapy

Epstein-Barr virus (EBV) is associated with a diverse range of lymphomas. EBV-specific T-cell (EBVST) immunotherapies have shown promise in safety and clinical effectiveness in treating EBV-associated lymphomas, but not all patients respond to treatment. To identify the set of EBV-directed antibody responses associated with clinical response in patients with EBV-associated lymphomas, we comprehensively characterized the immune response to the complete EBV proteome using a custom protein microarray in 56 EBV-associated lymphoma patients who were treated with EBVST infusions enrolled in Phase I clinical trials. Significant differences in antibody profiles between responders and non-responders emerged at 3 months post-EBVST infusion. Twenty-five IgG antibodies were present at significantly higher levels in non-responders compared to responders at 3 months post-EBVST infusion, and 10 of these IgG antibody associations remained after adjustment for sex, age, and cancer diagnosis type. Random forest prediction analysis further confirmed that these 10 antibodies were important for predicting clinical response. Differential IgG antibody responses were directed against LMP2A (four fragments), BGRF1/BDRF1 (two fragments), LMP1, BKRF2, BKRF4, and BALF5. Paired analyses using blood samples collected at both pre-infusion and 3 months post-EBVST infusion indicated an increase in the mean antibody level for six other anti-EBV antibodies (IgG: BGLF2, LF1, BGLF3; IgA: BGLF3, BALF2, BBLF2/3) in non-responders. Overall, our results indicate that EBV-directed antibodies can be biomarkers for predicting the clinical response of individuals with EBV-associated lymphomas treated with EBVST infusions.

The importance of DNA sequence for nucleosome positioning in transcriptional regulation

Nucleosome positioning is a key factor for transcriptional regulation. Nucleosomes regulate the dynamic accessibility of chromatin and interact with the transcription machinery at every stage. Influences to steer nucleosome positioning are diverse, and the according importance of the DNA sequence in contrast to active chromatin remodeling has been the subject of long discussion. In this study, we evaluate the functional role of DNA sequence for all major elements along the process of transcription. We developed a random forest classifier based on local DNA structure that assesses the sequence-intrinsic support for nucleosome positioning. On this basis, we created a simple data resource that we applied genome-wide to the human genome. In our comprehensive analysis, we found a special role of DNA in mediating the competition of nucleosomes with cis-regulatory elements, in enabling steady transcription, for positioning of stable nucleosomes in exons, and for repelling nucleosomes during transcription termination. In contrast, we relate these findings to concurrent processes that generate strongly positioned nucleosomes in vivo that are not mediated by sequence, such as energy-dependent remodeling of chromatin.

Development of machine learning models predicting mortality using routinely collected observational health data from 0-59 months old children admitted to an intensive care unit in Bangladesh: critical role of biochemistry and haematology data

INTRODUCTION

Treatment in the intensive care unit (ICU) generates complex data where machine learning (ML) modelling could be beneficial. Using routine hospital data, we evaluated the ability of multiple ML models to predict inpatient mortality in a paediatric population in a low/middle-income country.

METHOD

We retrospectively analysed hospital record data from 0-59 months old children admitted to the ICU of Dhaka hospital of International Centre for Diarrhoeal Disease Research, Bangladesh. Five commonly used ML models- logistic regression, least absolute shrinkage and selection operator, elastic net, gradient boosting trees (GBT) and random forest (RF), were evaluated using the area under the receiver operating characteristic curve (AUROC). Top predictors were selected using RF mean decrease Gini scores as the feature importance values.

RESULTS

Data from 5669 children was used and was reduced to 3505 patients (10% death, 90% survived) following missing data removal. The mean patient age was 10.8 months (SD=10.5). The top performing models based on the validation performance measured by mean 10-fold cross-validation AUROC on the training data set were RF and GBT. Hyperparameters were selected using cross-validation and then tested in an unseen test set. The models developed used demographic, anthropometric, clinical, biochemistry and haematological data for mortality prediction. We found RF consistently outperformed GBT and predicted the mortality with AUROC of ≥0.87 in the test set when three or more laboratory measurements were included. However, after the inclusion of a fourth laboratory measurement, very minor predictive gains (AUROC 0.87 vs 0.88) resulted. The best predictors were the biochemistry and haematological measurements, with the top predictors being total CO2, potassium, creatinine and total calcium.

CONCLUSIONS

Mortality in children admitted to ICU can be predicted with high accuracy using RF ML models in a real-life data set using multiple laboratory measurements with the most important features primarily coming from patient biochemistry and haematology.

Genomic factors shaping codon usage across the Saccharomycotina subphylum

Codon usage bias, or the unequal use of synonymous codons, is observed across genes, genomes, and between species. The biased use of synonymous codons has been implicated in many cellular functions, such as translation dynamics and transcript stability, but can also be shaped by neutral forces. The Saccharomycotina, the fungal subphylum containing the yeasts Saccharomyces cerevisiae and Candida albicans , has been a model system for studying codon usage. We characterized codon usage across 1,154 strains from 1,051 species to gain insight into the biases, molecular mechanisms, evolution, and genomic features contributing to codon usage patterns across the subphylum. We found evidence of a general preference for A/T-ending codons and correlations between codon usage bias, GC content, and tRNA-ome size. Codon usage bias is also distinct between the 12 orders within the subphylum to such a degree that yeasts can be classified into orders with an accuracy greater than 90% using a machine learning algorithm trained on codon usage. We also characterized the degree to which codon usage bias is impacted by translational selection. Interestingly, the degree of translational selection was influenced by a combination of genome features and assembly metrics that included the number of coding sequences, BUSCO count, and genome length. Our analysis also revealed an extreme bias in codon usage in the Saccharomycodales associated with a lack of predicted arginine tRNAs. The order contains 24 species, and 23 are computationally predicted to lack tRNAs that decode CGN codons, leaving only the AGN codons to encode arginine. Analysis of Saccharomycodales gene expression, tRNA sequences, and codon evolution suggests that extreme avoidance of the CGN codons is associated with a decline in arginine tRNA function. Codon usage bias within the Saccharomycotina is generally consistent with previous investigations in fungi, which show a role for both genomic features and GC bias in shaping codon usage. However, we find cases of extreme codon usage preference and avoidance along yeast lineages, suggesting additional forces may be shaping the evolution of specific codons.

Self-incompatibility phenotypes of SRK mutants can be predicted with high accuracy

Only very limited information is available on why some non-synonymous variants severely alter gene function while others have no effect. To identify the characteristic features of mutations that strongly influence gene function, this study focused on S-locus receptor kinase, SRK, which encodes a highly polymorphic receptor kinase expressed in stigma papillary cells that underlies a female determinant of self-incompatibility in Brassicaceae. A set of 299 Arabidopsis thaliana transformants expressing mutated SRKb from A. lyrata was constructed and analyzed to determine the genotype and self-incompatibility phenotype of each transformant. Almost all the transformants showing the self-incompatibility defect contained mutations in AlSRKb that altered localization to the plasma membrane. The observed mutations occurred in amino acid residues that were highly conserved across S haplotypes and whose predicted locations were in the interior of the protein. These mutations were likely to underlie the self-incompatibility defect as they caused significant changes to amino acid properties. Such findings suggested that mutations causing the self-incompatibility defect were more likely to result from changes to AlSRKb biosynthesis than from loss of function. In addition, this study showed the RandomForest and Extreme Gradient Boosting methods could predict self-incompatibility phenotypes of SRK mutants with high accuracy.

Statistical methods for chemical mixtures: a roadmap for practitioners

Quantitative characterization of the health impacts associated with exposure to chemical mixtures has received considerable attention in current environmental and epidemiological studies. With many existing statistical methods and emerging approaches, it is important for practitioners to understand when each method is best suited for their inferential goals. In this study, we conduct a review and comparison of 11 analytical methods available for use in mixtures research, through extensive simulation studies for continuous and binary outcomes. These methods fall in three different classes: identifying important components of a mixture, identifying interactions and creating a summary score for risk stratification and prediction. We carry out an illustrative data analysis in the PROTECT birth cohort from Puerto Rico. Most importantly we develop an integrated package "CompMix" that provides a platform for mixtures analysis where the practitioner can implement a pipeline for several types of mixtures analysis. Our simulation results suggest that the choice of methods depends on the goal of analysis and there is no clear winner across the board. For selection of important toxicants in the mixture and for identifying interactions, Elastic net by Zou et al. (Enet), Lasso for Hierarchical Interactions by Bien et al (HierNet), Selection of nonlinear interactions by a forward stepwise algorithm by Narisetty et al. (SNIF) have the most stable performance across simulation settings. Additionally, the predictive performance of the Super Learner ensembling method by Van de Laan et al. and HierNet are found to be superior to the rest of the methods. For overall summary or a cumulative measure, we find that using the Super Learner to combine multiple Environmental Risk Scores can lead to improved risk stratification properties. We have developed an R package "CompMix: A comprehensive toolkit for environmental mixtures analysis", allowing users to implement a variety of tasks under different settings and compare the findings. In summary, our study offers guidelines for selecting appropriate statistical methods for addressing specific scientific questions related to mixtures research. We identify critical gaps where new and better methods are needed.

Spatial Modeling of Sociodemographic Risk for COVID-19 Mortality

In early 2020, the Coronavirus Disease 19 (COVID-19) rapidly spread across the United States (US), exhibiting significant geographic variability. While several studies have examined the predictive relationships of differing factors on COVID-19 deaths, few have looked at spatiotemporal variation at refined geographic scales. The objective of this analysis is to examine this spatiotemporal variation in COVID-19 deaths with respect to association with socioeconomic, health, demographic, and political factors. We use multivariate regression applied to Health and Human Services (HHS) regions as well as nationwide county-level geographically weighted random forest (GWRF) models. Analyses were performed on data from three separate time frames which correspond to the spread of distinct viral variants in the US: pandemic onset until May 2021, May 2021 through November 2021, and December 2021 until April 2022. Multivariate regression results for all regions across three time windows suggest that existing measures of social vulnerability for disaster preparedness (SVI) are predictive of a higher degree of mortality from COVID-19. In comparison, GWRF models provide a more robust evaluation of feature importance and prediction, exposing the value of local features for prediction, such as obesity, which is obscured by coarse-grained analysis. Overall, GWRF results indicate that this more nuanced modeling strategy is useful for determining the spatial variation in the importance of sociodemographic risk factors for predicting COVID-19 mortality.

Use of machine learning to identify characteristics associated with severe hypoglycemia in older adults with type 1 diabetes: a post-hoc analysis of a case-control study

INTRODUCTION

Severe hypoglycemia (SH) in older adults (OAs) with type 1 diabetes is associated with profound morbidity and mortality, yet its etiology can be complex and multifactorial. Enhanced tools to identify OAs who are at high risk for SH are needed. This study used machine learning to identify characteristics that distinguish those with and without recent SH, selecting from a range of demographic and clinical, behavioral and lifestyle, and neurocognitive characteristics, along with continuous glucose monitoring (CGM) measures.

RESEARCH DESIGN AND METHODS

Data from a case-control study involving OAs recruited from the T1D Exchange Clinical Network were analyzed. The random forest machine learning algorithm was used to elucidate the characteristics associated with case versus control status and their relative importance. Models with successively rich characteristic sets were examined to systematically incorporate each domain of possible risk characteristics.

RESULTS

Data from 191 OAs with type 1 diabetes (47.1% female, 92.1% non-Hispanic white) were analyzed. Across models, hypoglycemia unawareness was the top characteristic associated with SH history. For the model with the richest input data, the most important characteristics, in descending order, were hypoglycemia unawareness, hypoglycemia fear, coefficient of variation from CGM, % time blood glucose below 70 mg/dL, and trail making test B score.

CONCLUSIONS

Machine learning may augment risk stratification for OAs by identifying key characteristics associated with SH. Prospective studies are needed to identify the predictive performance of these risk characteristics.

Gaining Biological Insights through Supervised Data Visualization

Dimensionality reduction-based data visualization is pivotal in comprehending complex biological data. The most common methods, such as PHATE, t-SNE, and UMAP, are unsupervised and therefore reflect the dominant structure in the data, which may be independent of expert-provided labels. Here we introduce a supervised data visualization method called RF-PHATE, which integrates expert knowledge for further exploration of the data. RF-PHATE leverages random forests to capture intricate featurelabel relationships. Extracting information from the forest, RF-PHATE generates low-dimensional visualizations that highlight relevant data relationships while disregarding extraneous features. This approach scales to large datasets and applies to classification and regression. We illustrate RF-PHATE's prowess through three case studies. In a multiple sclerosis study using longitudinal clinical and imaging data, RF-PHATE unveils a sub-group of patients with non-benign relapsingremitting Multiple Sclerosis, demonstrating its aptitude for time-series data. In the context of Raman spectral data, RF-PHATE effectively showcases the impact of antioxidants on diesel exhaust-exposed lung cells, highlighting its proficiency in noisy environments. Furthermore, RF-PHATE aligns established geometric structures with COVID-19 patient outcomes, enriching interpretability in a hierarchical manner. RF-PHATE bridges expert insights and visualizations, promising knowledge generation. Its adaptability, scalability, and noise tolerance underscore its potential for widespread adoption.

A ubiquitous GC content signature underlies multimodal mRNA regulation by DDX3X

The road from transcription to protein synthesis is paved with many obstacles, allowing for several modes of post-transcriptional regulation of gene expression. A fundamental player in mRNA biology is DDX3X, an RNA binding protein that canonically regulates mRNA translation. By monitoring dynamics of mRNA abundance and translation following DDX3X depletion, we observe stabilization of translationally suppressed mRNAs. We use interpretable statistical learning models to uncover GC content in the coding sequence as the major feature underlying RNA stabilization. This result corroborates GC content-related mRNA regulation detectable in other studies, including hundreds of ENCODE datasets and recent work focusing on mRNA dynamics in the cell cycle. We provide further evidence for mRNA stabilization by detailed analysis of RNA-seq profiles in hundreds of samples, including a Ddx3x conditional knockout mouse model exhibiting cell cycle and neurogenesis defects. Our study identifies a ubiquitous feature underlying mRNA regulation and highlights the importance of quantifying multiple steps of the gene expression cascade, where RNA abundance and protein production are often uncoupled.

Patient and physician assessment in difficult-to-treat rheumatoid arthritis: patterns of subjective perception at early stages of b/tsDMARD treatment

OBJECTIVES

To analyse the trajectories of Disease Activity Score 28 (DAS28), patient global assessment (PGA) and physician global assessment (PhGA) and to assess their predictive capabilities on difficult-to-treat rheumatoid arthritis (D2TRA) classification.

METHODS

Longitudinal study of patients with rheumatoid arthritis (RA) from 2020 to 2022. Based on the D2TRA EULAR (European Alliance of Associations for Rheumatology) definition, patients were classified as D2TRA according to biological or targeted synthetic disease-modifying antirheumatic drugs (b/tsDMARDs) failure due to inefficacy (D2TRA-inefficacy) or other reasons (D2TRA-other). Patients who did not fulfil the D2TRA criteria were classified as NoD2TRA. DAS28, PGA and PhGA scores collected every 6 months during the first 24 months of b/tsDMARD treatment were used to identify different trajectories using latent class mixed models (LCMM).

RESULTS

The study population comprised 255 patients with RA, of whom 167 were NoD2TRA, 58 D2TRA-inefficacy and 30 D2TRA-other. LCMM stratified patients into two different trajectories for DAS28 and PhGA and three for PGA according to the most stable model. The most notable variation occurred during the first 6 months of treatment, thereafter remaining stable during the follow-up period. Most D2TRA-inefficacy patients fitted the trajectory, showing higher values of the studied parameters. NoD2TRA followed the trajectory with lower values, and D2TRA-other were distributed more homogeneously across all trajectories.

CONCLUSIONS

The assessment of disease activity, together with patients' and physicians' perceptions, form a key element in the correct discrimination of patients who are going to develop D2TRA-inefficacy. However, identifying those patients who will be D2TRA-other remains challenging, whether by subjective or objective parameters.

Determinants of Gastrointestinal Group B Streptococcus Carriage in Adults

Background

Streptococcus agalactiae (Group B Streptococcus, GBS) is a commensal Gram-positive bacterium found in the human gastrointestinal and urogenital tracts. Much of what is known about GBS relates to the diseases it causes in pregnant people and neonates. However, GBS is a common cause of disease in the general population with 90% of GBS mortality occurring in non-pregnant people. There are limited data about the predisposing factors for GBS and the reservoirs in the body. To gain an understanding of the determinants of gastrointestinal GBS carriage, we used stool samples and associated metadata to determine the prevalence and abundance of GBS in the gut microbiome of adults and find risk factors for GBS status.

Methods

We used 754 stool samples collected from adults in Wisconsin from 2016-2017 to test for the prevalence and abundance of GBS using a Taqman probe-based qPCR assay targeting two GBS-specific genes: cfp and sip. We compared the microbiome compositions of the stool samples by GBS status using 16S rRNA analysis. We compared associations with GBS status and 557 survey variables collected during sample acquisition (demographics, diet, overall health, and reproductive health) using univariate and multivariate analyses.

Results

We found 137/754 (18%) of participants had detectable GBS in their stool samples with a median abundance of 104 copies per nanogram of starting DNA. There was no difference in GBS status or abundance based on gender. Beta-diversity, Bray-Curtis and Unweighted UniFrac, was significantly different based on carrier status of the participant. Prior to p-value correction, 59/557 (10.6%) survey variables were significantly associated with GBS carrier status and 11/547 (2.0%) variables were significantly associated with abundance (p-value<0.05). After p-value correction, 2/547 (0.4%) variables were associated with GBS abundance: an increased abundance of GBS was associated with a decreased frequency since last dental checkup (p<0.001) and last dental cleaning (p<0.001). Increased GBS abundance was significantly associated with increased frequency of iron consumption (p=0.007) after p-value correction in multivariate models.

Conclusions

GBS is found in stool samples from adults in Wisconsin at similar frequencies as pregnant individuals screened with rectovaginal swabs. We did not find associations between risk factors historically associated with GBS in pregnant people, suggesting that risk factors for GBS carriage in pregnancy may differ from those in the general population. We found that frequency of iron consumption and dental hygiene are risk factors for GBS carriage in Wisconsin adults. Given that these variables were not assayed in previous GBS surveys, it is possible they also influence carriage in pregnant people. Taken together, this work serves as a foundation for future work in developing approaches to decrease GBS abundance in carriers.

Identification of commensal gut microbiota signatures as predictors of clinical severity and disease progression in multiple sclerosis

Background

Multiple sclerosis (MS) is a chronic autoimmune disease of the central nervous system and a leading cause of neurological disability in young adults. Clinical presentation and disease course are highly heterogeneous. Typically, disease progression occurs over time and is characterized by the gradual accumulation of disability. The risk of developing MS is driven by complex interactions between genetic and environmental factors, including the gut microbiome. How the commensal gut microbiota impacts disease severity and progression over time remains unknown.

Methods

In a longitudinal study, disability status and associated clinical features in 60 MS patients were tracked over 4.2 ± 0.97 years, and the baseline fecal gut microbiome was characterized via 16S amplicon sequencing. Progressor status, defined as patients with an increase in Expanded Disability Status Scale (EDSS), were correlated with features of the gut microbiome to determine candidate microbiota associated with risk of MS disease progression.

Results

We found no overt differences in microbial community diversity and overall structure between MS patients exhibiting disease progression and non-progressors. However, a total of 45 bacterial species were associated with worsening disease, including a marked depletion in Akkermansia , Lachnospiraceae, and Oscillospiraceae , with an expansion of Alloprevotella , Prevotella-9 , and Rhodospirillales . Analysis of the metabolic potential of the inferred metagenome from taxa associated with progression revealed a significant enrichment in oxidative stress-inducing aerobic respiration at the expense of microbial vitamin K 2 production (linked to Akkermansia ), and a depletion in SCFA metabolism (linked to Lachnospiraceae and Oscillospiraceae ). Further, statistical modeling demonstrated that microbiota composition and clinical features were sufficient to robustly predict disease progression. Additionally, we found that constipation, a frequent gastrointestinal comorbidity among MS patients, exhibited a divergent microbial signature compared with progressor status.

Conclusions

These results demonstrate the utility of the gut microbiome for predicting disease progression in MS. Further, analysis of the inferred metagenome revealed that oxidative stress, vitamin K 2 and SCFAs are associated with progression.

Abstract Figure

Prostate-derived circulating microRNAs add prognostic value to prostate cancer risk calculators

Prostate cancer is the second leading cause of malignancy-related deaths among American men. Active surveillance is a safe option for many men with less aggressive disease, yet definitively determining low-risk cancer is challenging with biopsy alone. Herein, we sought to identify prostate-derived microRNAs in patient sera and serum extracellular vesicles, and determine if those microRNAs improve upon the current clinical risk calculators for prostate cancer prognosis before and after biopsy. Prostate-derived intracellular and extracellular vesicle-contained microRNAs were identified by small RNA sequencing of prostate cancer patient explants and primary cells. Abundant microRNAs were included in a custom microRNA PCR panel that was queried in whole serum and serum extracellular vesicles from a diverse cohort of men diagnosed with prostate cancer. The levels of these circulating microRNAs significantly differed between indolent and aggressive disease and improved the area under the curve for pretreatment nomograms of prostate cancer disease risk. The microRNAs within the extracellular vesicles had improved prognostic value compared to the microRNAs in the whole serum. In summary, quantifying microRNAs circulating in extracellular vesicles is a clinically feasible assay that may provide additional information for assessing prostate cancer risk stratification.

The impact of agricultural management on soil aggregation and carbon storage is regulated by climatic thresholds across a 3000 km European gradient

Organic carbon and aggregate stability are key features of soil quality and are important to consider when evaluating the potential of agricultural soils as carbon sinks. However, we lack a comprehensive understanding of how soil organic carbon (SOC) and aggregate stability respond to agricultural management across wide environmental gradients. Here, we assessed the impact of climatic factors, soil properties and agricultural management (including land use, crop cover, crop diversity, organic fertilization, and management intensity) on SOC and the mean weight diameter of soil aggregates, commonly used as an indicator for soil aggregate stability, across a 3000 km European gradient. Soil aggregate stability (-56%) and SOC stocks (-35%) in the topsoil (20 cm) were lower in croplands compared with neighboring grassland sites (uncropped sites with perennial vegetation and little or no external inputs). Land use and aridity were strong drivers of soil aggregation explaining 33% and 20% of the variation, respectively. SOC stocks were best explained by calcium content (20% of explained variation) followed by aridity (15%) and mean annual temperature (10%). We also found a threshold-like pattern for SOC stocks and aggregate stability in response to aridity, with lower values at sites with higher aridity. The impact of crop management on aggregate stability and SOC stocks appeared to be regulated by these thresholds, with more pronounced positive effects of crop diversity and more severe negative effects of crop management intensity in nondryland compared with dryland regions. We link the higher sensitivity of SOC stocks and aggregate stability in nondryland regions to a higher climatic potential for aggregate-mediated SOC stabilization. The presented findings are relevant for improving predictions of management effects on soil structure and C storage and highlight the need for site-specific agri-environmental policies to improve soil quality and C sequestration.

Direct and indirect consequences of PAB1 deletion in the regulation of translation initiation, translation termination, and mRNA decay

Cytoplasmic poly(A)-binding protein (PABPC; Pab1 in yeast) is thought to be involved in multiple steps of post-transcriptional control, including translation initiation, translation termination, and mRNA decay. To understand these roles of PABPC in more detail for endogenous mRNAs, and to distinguish its direct effects from indirect effects, we have employed RNA-Seq and Ribo-Seq to analyze changes in the abundance and translation of the yeast transcriptome, as well as mass spectrometry to assess the abundance of the components of the yeast proteome, in cells lacking the PAB1 gene. We observed drastic changes in the transcriptome and proteome, as well as defects in translation initiation and termination, in pab1Δ cells. Defects in translation initiation and the stabilization of specific classes of mRNAs in pab1Δ cells appear to be partly indirect consequences of reduced levels of specific initiation factors, decapping activators, and components of the deadenylation complex in addition to the general loss of Pab1's direct role in these processes. Cells devoid of Pab1 also manifested a nonsense codon readthrough phenotype indicative of a defect in translation termination, but this defect may be a direct effect of the loss of Pab1 as it could not be attributed to significant reductions in the levels of release factors.

Discordant calls across genotype discovery approaches elucidate variants with systematic errors

Large-scale high-throughput sequencing data sets have been transformative for informing clinical variant interpretation and for use as reference panels for statistical and population genetic efforts. Although such resources are often treated as ground truth, we find that in widely used reference data sets such as the Genome Aggregation Database (gnomAD), some variants pass gold-standard filters, yet are systematically different in their genotype calls across genotype discovery approaches. The inclusion of such discordant sites in study designs involving multiple genotype discovery strategies could bias results and lead to false-positive hits in association studies owing to technological artifacts rather than a true relationship to the phenotype. Here, we describe this phenomenon of discordant genotype calls across genotype discovery approaches, characterize the error mode of wrong calls, provide a list of discordant sites identified in gnomAD that should be treated with caution in analyses, and present a metric and machine learning classifier trained on gnomAD data to identify likely discordant variants in other data sets. We find that different genotype discovery approaches have different sets of variants at which this problem occurs, but there are characteristic variant features that can be used to predict discordant behavior. Discordant sites are largely shared across ancestry groups, although different populations are powered for the discovery of different variants. We find that the most common error mode is that of a variant being heterozygous for one approach and homozygous for the other, with heterozygous in the genomes and homozygous reference in the exomes making up the majority of miscalls.

Comparative single-cell analyses identify shared and divergent features of human and mouse kidney development

Mammalian kidneys maintain fluid homeostasis through the cellular activity of nephrons and the conjoined collecting system. Each epithelial network originates from distinct progenitor cell populations that reciprocally interact during development. To extend our understanding of human and mouse kidney development, we profiled chromatin organization (ATAC-seq) and gene expression (RNA-seq) in developing human and mouse kidneys. Data were analyzed at a species level and then integrated into a common, cross-species multimodal data set. Comparative analysis of cell types and developmental trajectories identified conserved and divergent features of chromatin organization and linked gene activity, revealing species- and cell-type specific regulatory programs. Identification of human-specific enhancer regions linked through GWAS studies to kidney disease highlights the potential of developmental modeling to provide clinical insight.

Acute coronary syndrome screening in patients presenting with arteriosclerosis in health check-ups: a case-control study

OBJECTIVES

This research aimed to develop a simple and effective acute coronary syndrome (ACS) screening model in order to intervene early and focus on prevention in patients presenting with arteriosclerosis.

DESIGN

A case-control study.

SETTING

The study used a cross-sectional survey to collect data from 2243 patients who completed anonymous electronic medical record (EMR) data and coronary angiography was gathered at a hospital in Shandong Province between December 2013 and April 2016.

PARTICIPANTS

Adults 18 years old and above diagnosed as ACS or non-ACS according to the records in hospital EMR database, and with completed basic information (age and sex).

PREDICTORS

54 laboratory biomarkers and demographic factors (age and sex).

STATISTICAL ANALYSIS

A dataset without missing data of all patients' laboratory indicators and demographic factors was divided into training set and validation set after being balanced. After the training set balanced, area under the curve of random forest (AUCRF) and least absolute shrinkage and selection operator (LASSO) regression were used for feature extraction. Then two set random forest models were established with the different feature sets, and the process of comparison and analysis was made to evaluate models for the optimal model including sensitivity, accuracy and AUC receiver operating characteristic curves with the internal validation set.

MAIN OUTCOME MEASURES

To establish an ACS screening model.

RESULTS

An RF model with 31 features selected by LASSO with an AUC of 0.616 (95% CI 0.650 to 0.772), a sensitivity of 0.832 and an accuracy of 0.714 in the validation set. The other RF model with 27 features selected by AUCRF with an AUC of 0.621 (95% CI 0.664 to 0.785), a sensitivity of 0.849 and an accuracy of 0.728 in the validation set.

CONCLUSIONS

The established ACS screening model with 27 clinical features provides a better performance for practical solution in predicting ACS.

Predicting surgical department occupancy and patient length of stay in a paediatric hospital setting using machine learning: a pilot study

Objective

Early and accurate prediction of hospital surgical-unit occupancy is critical for improving scheduling, staffing and resource planning. Previous studies on occupancy prediction have focused primarily on adult healthcare settings, we sought to develop occupancy prediction models specifically tailored to the needs and characteristics of paediatric surgical settings.

Materials and methods

We conducted a single-centre retrospective cohort study at a surgical unit in a tertiary-care paediatric hospital in Boston, Massachusetts, USA. We developed a hierarchical modelling framework for predicting next-day census using multiple types of data—from bottom-up patient-specific orders and procedures to top-down temporal variables and departmental admission statistics.

Results

The model predicted upcoming admissions and discharges with a median error of 17%–21% (2–3 patients per day), and next-day census with a median error of 7% (n=3). The primary factors driving these predictions included day of week and scheduled surgeries, as well as procedure duration, procedure type and days since admission. We found that paediatric surgical procedure duration was highly predictive of postoperative length of stay.

Discussion

Our hierarchical modelling framework provides an overview of the factors driving capacity issues in the paediatric surgical unit, highlighting the importance of both top-down temporal features (eg, day of week) as well as bottom-up electronic health records (EHR)derived features (eg, orders for patient) for predicting next-day census. In the practice, this framework can be implemented stepwise, from top to bottom, making it easier to adopt.

Conclusion

Modelling frameworks combining top-down and bottom-up features can provide accurate predictions of next-day census in a paediatric surgical setting.

Secular trends in physical fitness of Slovenian boys and girls aged 7 to 15 years from 1989 to 2019: a population-based study

Using the population-based data we aim to estimate the general population trends of multiple components of physical fitness of children, identify critical structural changes in these trends, and evaluate the potential changes in differences in the test scores between the children. During the entire study period, 1989-2019, median body mass index and triceps skinfold increased in both genders and all age groups. Muscular fitness, in general, showed negative trends, with some exceptions: during the post-2010 period, children were mostly experiencing the improvement of isometric strength of the upper body. The neuromuscular components of physical fitness showed positive trends, especially in girls. Cardiorespiratory fitness has been declining in all age groups until the last decade, with the largest decreases occurring before 2000. In the last decade, the trends reversed. The flexibility indicator revealed the largest differences between boys and girls, with boys mainly experiencing negative and girls mostly positive trends. The variability of the test scores mostly increased during the study period. This increasing variance suggests that-despite generally favourable trends in the last decade-children in Slovenia have been facing increasing inequalities in their development, which can potentially lead to future inequalities in health.

Identifying clot composition using intravascular diffuse reflectance spectroscopy in a porcine model of endovascular thrombectomy

BACKGROUND

Endovascular thrombectomy has revolutionized the management of acute ischemic stroke and proven superior to stand-alone intravenous thrombolysis for large vessel occlusions. However, failed or delayed revascularization may occur as a result of a mismatch between removal technique and clot composition. Determination of clot composition before thrombectomy provides the possibility to adapt the technique to improve clot removal efficacy. We evaluated the application of diffuse reflectance spectroscopy (DRS) for intravascular determination of clot composition in vivo.

METHODS

Three clot types, enriched in red blood cells or fibrin or with a mixed content, were prepared from porcine blood and injected into the external carotids of a domestic pig. A guidewire-like DRS probe was used to investigate the optical spectra of clots, blood and vessel wall. Measurement positions were confirmed with angiography. Spectra were analyzed by fitting an optical model to derive physiological parameters. To evaluate the method's accuracy, photon scattering and blood and methemoglobin contents were included in a decision tree model and a random forest classification.

RESULTS

DRS could differentiate between the three different clot types, blood and vessel wall in vivo (p<0.0001). The sensitivity and specificity for detection was 73.8% and 98.8% for red blood cell clots, 80.6% and 97.8% for fibrin clots, and 100% and 100% for mixed clots, respectively.

CONCLUSION

Intravascular DRS applied via a custom guidewire can be used for reliable determination of clot composition in vivo. This novel approach has the potential to increase efficacy of thrombectomy procedures in ischemic stroke.

A framework to score the effects of structural variants in health and disease

While technological advances improved the identification of structural variants (SVs) in the human genome, their interpretation remains challenging. Several methods utilize individual mechanistic principles like the deletion of coding sequence or 3D genome architecture disruptions. However, a comprehensive tool using the broad spectrum of available annotations is missing. Here, we describe CADD-SV, a method to retrieve and integrate a wide set of annotations to predict the effects of SVs. Previously, supervised learning approaches were limited due to a small number and biased set of annotated pathogenic or benign SVs. We overcome this problem by using a surrogate training-objective, the Combined Annotation Dependent Depletion (CADD) of functional variants. We use human and chimpanzee derived SVs as proxy-neutral and contrast them with matched simulated variants as proxy-deleterious, an approach that has proven powerful for short sequence variants. Our tool computes summary statistics over diverse variant annotations and uses random forest models to prioritize deleterious structural variants. The resulting CADD-SV scores correlate with known pathogenic and rare population variants. We further show that we can prioritize somatic cancer variants as well as noncoding variants known to affect gene expression. We provide a website and offline-scoring tool for easy application of CADD-SV.

SNPs in Sheep: Characterization of Lithuanian Sheep Populations

In Lithuania, there are two recognised native sheep breeds: old native Lithuanian Coarsewooled and Lithuanian Blackface. In addition, in 2005, primitive Heidschnucke-type Skudde sheep were imported to Lithuania and were argued to possibly represent a lost Lithuanian sheep type. The aim of the study was to investigate the genetic variation in the two Lithuanian native sheep breeds, compare them with the imported Skudde sheep and establish the historical patterns of admixture and the genetic relatedness of Lithuanian sheep to British, Central European and Nordic sheep breeds included in the SheepHapMap study. In total, 72 individuals, representing two Lithuanian native and imported Skudde sheep breeds, were genotyped using a Neogen 12K Illumina Infinium chip. The population analysis was carried out by model-based clustering, principal component analysis and neighbour net analysis, and showed similar patterns for the Lithuanian sheep populations. Lithuanian Coarsewooled and Skudde in Lithuania have unique divergence and possibly some shared ancestry, while the Lithuanian Blackface conforms to a modern synthetic breed. The study clearly showed that the Coarsewooled and the Skudde breeds are distinct from each other. Historical data strongly suggest that the Coarsewooled breed represents a local breed, while the Skudde origin is less directly linked to the geographical area of modern-day Lithuania. Within the modern-day Lithuanian context, the Lithuanian Coarsewooled sheep is very important historical sheep type for conservation.

Predictors of district nursing care utilisation for community-living people in the Netherlands: an exploratory study using claims data

OBJECTIVE

To explore predictors of district nursing care utilisation for community-living (older) people in the Netherlands using claims data. To cope with growing demands in district nursing care, knowledge about the current utilisation of district nursing care is important.

SETTING

District nursing care as a part of primary care.

PARTICIPANTS

In this nationwide study, claims data were used from the Dutch risk adjustment system and national information system of health insurers. Samples were drawn of 5500 pairs of community-living people using district nursing care (cases) and people not using district nursing care (controls) for two groups: all ages and aged 75+ years (total N=22 000).

OUTCOME MEASURES

The outcome was district nursing care utilisation and the 114 potential predictors included predisposing factors (eg, age), enabling factors (eg, socioeconomic status) and need factors (various healthcare costs). The random forest algorithm was used to predict district nursing care utilisation. The performance of the models and importance of predictors were calculated.

RESULTS

For the population of people aged 75+ years, most important predictors were older age, and high costs for general practitioner consultations, aid devices, pharmaceutical care, ambulance transportation and occupational therapy. For the total population, older age, and high costs for pharmaceutical care and aid devices were the most important predictors.

CONCLUSIONS

People in need of district nursing care are older, visit the general practitioner more often, and use more and/or expensive medications and aid devices. Therefore, close collaboration between the district nurse, general practitioner and the community pharmacist is important. Additional analyses including data regarding health status are recommended. Further research is needed to provide an evidence base for district nursing care to optimise the care for those with high care needs, and guide practice and policymakers' decision-making.

Development of a lupus nephritis suboptimal response prediction tool using renal histopathological and clinical laboratory variables at the time of diagnosis

Objective

Lupus nephritis (LN) is an immune complex-mediated glomerular and tubulointerstitial disease in patients with SLE. Prediction of outcomes at the onset of LN diagnosis can guide decisions regarding intensity of monitoring and therapy for treatment success. Currently, no machine learning model of outcomes exists. Several outcomes modelling works have used univariate or linear modelling but were limited by the disease heterogeneity. We hypothesised that a combination of renal pathology results and routine clinical laboratory data could be used to develop and to cross-validate a clinically meaningful machine learning early decision support tool that predicts LN outcomes at approximately 1 year.

Methods

To address this hypothesis, patients with LN from a prospective longitudinal registry at the Medical University of South Carolina enrolled between 2003 and 2017 were identified if they had renal biopsies with International Society of Nephrology/Renal Pathology Society pathological classification. Clinical laboratory values at the time of diagnosis and outcome variables at approximately 1 year were recorded. Machine learning models were developed and cross-validated to predict suboptimal response.

Results

Five machine learning models predicted suboptimal response status in 10 times cross-validation with receiver operating characteristics area under the curve values >0.78. The most predictive variables were interstitial inflammation, interstitial fibrosis, activity score and chronicity score from renal pathology and urine protein-to-creatinine ratio, white blood cell count and haemoglobin from the clinical laboratories. A web-based tool was created for clinicians to enter these baseline clinical laboratory and histopathology variables to produce a probability score of suboptimal response.

Conclusion

Given the heterogeneity of disease presentation in LN, it is important that risk prediction models incorporate several data elements. This report provides for the first time a clinical proof-of-concept tool that uses the five most predictive models and simplifies understanding of them through a web-based application.

An annotation database for chemicals of emerging concern in exposome research

BACKGROUND

Chemicals of Emerging Concern (CECs) include a very wide group of chemicals that are suspected to be responsible for adverse effects on health, but for which very limited information is available. Chromatographic techniques coupled with high-resolution mass spectrometry (HRMS) can be used for non-targeted screening and detection of CECs, by using comprehensive annotation databases. Establishing a database focused on the annotation of CECs in human samples will provide new insight into the distribution and extent of exposures to a wide range of CECs in humans.

OBJECTIVES

This study describes an approach for the aggregation and curation of an annotation database (CECscreen) for the identification of CECs in human biological samples.

METHODS

The approach consists of three main parts. First, CECs compound lists from various sources were aggregated and duplications and inorganic compounds were removed. Subsequently, the list was curated by standardization of structures to create "MS-ready" and "QSAR-ready" SMILES, as well as calculation of exact masses (monoisotopic and adducts) and molecular formulas. The second step included the simulation of Phase I metabolites. The third and final step included the calculation of QSAR predictions related to physicochemical properties, environmental fate, toxicity and Absorption, Distribution, Metabolism, Excretion (ADME) processes and the retrieval of information from the US EPA CompTox Chemicals Dashboard.

RESULTS

All CECscreen database and property files are publicly available (DOI: https://doi.org/10.5281/zenodo.3956586). In total, 145,284 entries were aggregated from various CECs data sources. After elimination of duplicates and curation, the pipeline produced 70,397 unique "MS-ready" structures and 66,071 unique QSAR-ready structures, corresponding with 69,526 CAS numbers. Simulation of Phase I metabolites resulted in 306,279 unique metabolites. QSAR predictions could be performed for 64,684 of the QSAR-ready structures, whereas information was retrieved from the CompTox Chemicals Dashboard for 59,739 CAS numbers out of 69,526 inquiries. CECscreen is incorporated in the in silico fragmentation approach MetFrag.

DISCUSSION

The CECscreen database can be used to prioritize annotation of CECs measured in non-targeted HRMS, facilitating the large-scale detection of CECs in human samples for exposome research. Large-scale detection of CECs can be further improved by integrating the present database with resources that contain CECs (metabolites) and meta-data measurements, further expansion towards in silico and experimental (e.g., MassBank) generation of MS/MS spectra, and development of bioinformatics approaches capable of using correlation patterns in the measured chemical features.

Exploring Multiple Strategic Problem Solving Behaviors in Educational Psychology Research by Using Mixture Cognitive Diagnosis Model

A mixture cognitive diagnosis model (CDM), which is called mixture multiple strategy-Deterministic, Inputs, Noisy "and" Gate (MMS-DINA) model, is proposed to investigate individual differences in the selection of response categories in multiple-strategy items. The MMS-DINA model system is an effective psychometric and statistical approach consisting of multiple strategies for practical skills diagnostic testing, which not only allows for multiple strategies of problem solving, but also allows for different strategies to be associated with different levels of difficulty. A Markov chain Monte Carlo (MCMC) algorithm for parameter estimation is given to estimate model, and four simulation studies are presented to evaluate the performance of the MCMC algorithm. Based on the available MCMC outputs, two Bayesian model selection criteria are computed for guiding the choice of the single strategy DINA model and multiple strategy DINA models. An analysis of fraction subtraction data is provided as an illustration example.

Spatio-Thermal Variability and Behaviour as Bio-Thermal Indicators of Heat Stress in Dairy Cows in a Compost Barn: A Case Study

Simple Summary

The thermal distribution inside a compost-bedded pack barn and the behavioural aspects plays an important role in terms of welfare and sustainability for dairy cows. Through a spatial variability assessment of thermal conditions in a compost barn, we found different regions with comfortable or stressful conditions based on air and bed temperature, as well as wind speed. Regarding the behaviour of cows with different number of lactations, we observed a higher probability of water intake in primiparous cows and increased walking behaviour in multiparous cows during the hottest periods. We suggest that special attention must be given to environmental control in a compost barn, mainly during hot seasons, to avoid different hot spots inside the facility. Additionally, with unbalanced environmental resources, the hierarchy of multiparous over primiparous cows might predominate the alleviation of the herd’s thermal stress.

Abstract

The spatial variability and behavioural aspects of compost-bedded pack barns remain unknown in subtropical regions. In this study, we investigated whether spatial variability occurs in the thermal environment of a compost barn and how the behaviour of dairy cows with different numbers of lactations differs in this system. The spatial sampling design comprised a grid of 108 locations inside the facility. At each location, we measured air temperature, relative humidity, wind speed, and bed temperature at 9:00, 12:00, and 15:00. We performed 24-h behavioural observations. Regarding spatial variability, the north face showed high air temperature values, and the distribution of relative humidity varied from the north to the south face. Kriging maps revealed a high bedding temperature trend, indicating heterogeneous ventilation management. Primiparous cows visited the water trough during the hottest hours of the day, whereas multiparous cows displayed a higher probability of walking during these periods. In conclusion, we observed a heterogeneous management of ventilation through the spatial distribution of the thermal environment inside the compost-bedded pack barn, with multiparous cows exhibiting dominance over primiparous cows seeking environmental resources.

Estimating epidemiologic dynamics from cross-sectional viral load distributions

Estimating an epidemic's trajectory is crucial for developing public health responses to infectious diseases, but incidence data used for such estimation are confounded by variable testing practices. We show instead that the population distribution of viral loads observed under random or symptom-based surveillance, in the form of cycle threshold (Ct) values, changes during an epidemic and that Ct values from even limited numbers of random samples can provide improved estimates of an epidemic's trajectory. Combining multiple such samples and the fraction positive improves the precision and robustness of such estimation. We apply our methods to Ct values from surveillance conducted during the SARS-CoV-2 pandemic in a variety of settings and demonstrate new approaches for real-time estimates of epidemic trajectories for outbreak management and response.

The evolution of a tropical biodiversity hotspot

The tropics are the source of most biodiversity yet inadequate sampling obscures answers to fundamental questions about how this diversity evolves. We leveraged samples assembled over decades of fieldwork to study diversification of the largest tropical bird radiation, the suboscine passerines. Our phylogeny, estimated using data from 2389 genomic regions in 1940 individuals of 1283 species, reveals that peak suboscine species diversity in the Neotropics is not associated with high recent speciation rates but rather with the gradual accumulation of species over time. Paradoxically, the highest speciation rates are in lineages from regions with low species diversity, which are generally cold, dry, unstable environments. Our results reveal a model in which species are forming faster in environmental extremes but have accumulated in moderate environments to form tropical biodiversity hotspots.

Retest Reliability of Integrated Speed-Accuracy Measures

Cognitive tasks borrowed from experimental psychology are often used to assess individual differences. A cardinal issue of this transition from experimental to correlational designs is reduced retest reliability of some well-established cognitive effects as well as speed-accuracy trade-off. The present study aimed to address these issues by examining the retest reliability of various methods for speed-accuracy integration and by comparing between two types of task modeling: difference scores and residual scores. Results from three studies on executive functions show that (a) integrated speed-accuracy scoring is generally more reliable as compared with nonintegrated methods: mean response time and accuracy; and (b) task modeling, especially residual scores, reduced reliability. We thus recommend integrating speed and accuracy, at least for measuring executive functions.

The Role of Disease Severity and Demographics in the Clinical Course of COVID-19 Patients Treated with Convalescent Plasma

Treatment of patients with COVID-19 using convalescent plasma from recently recovered patients has been shown to be safe, but the time course of change in clinical status following plasma transfusion in relation to baseline disease severity has not yet been described. We analyzed short, descriptive daily reports of patient status in 7,180 hospitalized recipients of COVID-19 convalescent plasma in the Mayo Clinic Expanded Access Program. We assessed, from the day following transfusion, whether the patient was categorized by his or her physician as better, worse or unchanged compared to the day before, and whether, on the reporting day, the patient received mechanical ventilation, was in the ICU, had died or had been discharged. Most patients improved following transfusion, but clinical improvement was most notable in mild to moderately ill patients. Patients classified as severely ill upon enrollment improved, but not as rapidly, while patients classified as critically ill/end-stage and patients on ventilators showed worsening of disease status even after treatment with convalescent plasma. Patients age 80 and over showed little or no clinical improvement following transfusion. Clinical status at enrollment and age appear to be the primary factors in determining the therapeutic effectiveness of COVID-19 convalescent plasma among hospitalized patients.

Women's health in The BMJ: a data science history

OBJECTIVE

To determine how the representation of women's health has changed in clinical studies over the course of 70 years.

DESIGN

Observational study of 71 866 research articles published between 1948 and 2018 in The BMJ.

MAIN OUTCOME MEASURES

The incidence of women-specific health topics over time. General linear, additive and segmented regression models were used to estimate trends.

RESULTS

Over 70 years, the overall odds that a word in a BMJ research article was 'woman' or 'women' increased by an annual factor of 1.023, but this rate of increase varied by clinical specialty with some showing little or no change. The odds that an article was about some aspect of women-specific health increased much more slowly, by an annual factor of 1.004. The incidence of articles about particular areas of women-specific medicine such as pregnancy did not show a general increase, but rather fluctuated over time. The incidence of articles making any mention of women, gender or sex declined between 1948 and 2005, after which it rose steeply so that by 2018 few papers made no mention of them at all.

CONCLUSIONS

Over time women have become ever more prominent in BMJ research articles. However, the importance of women-specific health topics has waxed and waned as researchers responded ephemerally to medical advances, public health programmes, and sociolegal changes. The appointment of a woman editor-inchief in 2005 may have had a dramatic effect on whether women were mentioned in research articles.

Bayesian modeling of COVID-19 cases with a correction to account for under-reported cases

The novel of COVID-19 disease started in late 2019 making the worldwide governments came across a high number of critical and death cases, beyond constant fear of the collapse in their health systems. Since the beginning of the pandemic, researchers and authorities are mainly concerned with carrying out quantitative studies (modeling and predictions) overcoming the scarcity of tests that lead us to under-reporting cases. To address these issues, we introduce a Bayesian approach to the SIR model with correction for under-reporting in the analysis of COVID-19 cases in Brazil. The proposed model was enforced to obtain estimates of important quantities such as the reproductive rate and the average infection period, along with the more likely date when the pandemic peak may occur. Several under-reporting scenarios were considered in the simulation study, showing how impacting is the lack of information in the modeling.

Relationship between peripapillary vessel density and visual field in glaucoma: a broken-stick model

BACKGROUND/AIMS

To determine the tipping point at which peripapillary vessel density (pVD), measured using optical coherence tomography angiography (OCT-A), is associated with detectable visual field (VF) loss in cases of open-angle glaucoma (OAG).

METHODS

Peripapillary retinal nerve fibre layer (pRNFL) thickness and pVD were measured using OCT and OCT-A in 166 healthy participants, those with suspected glaucoma and patients with early-stage OAG. All participants were Koreans. A broken-stick model was used to determine the tipping points below which pRNFL thickness (pRNFLT) or pVD reduction was associated with significant VF loss. The slopes were computed above and below the tipping points for the relationship between pVD, pRNFLT and VF sensitivity (VFS).

RESULTS

Tipping points were detected for pRNFLT globally and in the inferior and superior quadrants, at which there were significant differences between the slopes above and below the tipping points. The slopes above the tipping points were approximately zero. However, no tipping point was found for pVD in the global area or regional sectors.

CONCLUSION

The relationship between pVD and VFS does not show a tipping point. This suggests that the correlation between pVD-VFS may be linear and stronger than that of pRNFLT-VFS, even at the early stage of glaucoma.

High temperatures drive offspring mortality in a cooperatively breeding bird

An improved understanding of life-history responses to current environmental variability is required to predict species-specific responses to anthopogenic climate change. Previous research has suggested that cooperation in social groups may buffer individuals against some of the negative effects of unpredictable climates. We use a 15-year dataset on a cooperative breeding arid zone bird, the southern pied babbler Turdoides bicolor, to test (i) whether environmental conditions and group size correlate with survival of young during three development stages (egg, nestling, fledgling) and (ii) whether group size mitigates the impacts of adverse environmental conditions on survival of young. Exposure to high mean daily maximum temperatures (mean T_max) during early development was associated with reduced survival probabilities of young in all three development stages. No young survived when mean T_max > 38°C, across all group sizes. Low survival of young at high temperatures has broad implications for recruitment and population persistence in avian communities given the rapid pace of advancing climate change. Impacts of high temperatures on survival of young were not moderated by group size, suggesting that the availability of more helpers in a group is unlikely to buffer against compromised offspring survival as average and maximum temperatures increase with rapid anthropogenic climate change.

The neuroplasticity of division of labor: worker polymorphism, compound eye structure and brain organization in the leafcutter ant Atta cephalotes

Our understanding of how sensory structure design is coupled with neural processing capacity to adaptively support division of labor is limited. Workers of the remarkably polymorphic fungus-growing ant Atta cephalotes are behaviorally specialized by size: the smallest workers (minims) tend fungi in dark subterranean chambers while larger workers perform tasks outside the nest. Strong differences in worksite light conditions are predicted to influence sensory and processing requirements for vision. Analyzing confocal scans of worker eyes and brains, we found that eye structure and visual neuropils appear to have been selected to maximize task performance according to light availability. Minim eyes had few ommatidia, large interommatidial angles and eye parameter values, suggesting selection for visual sensitivity over acuity. Large workers had larger eyes with disproportionally more and larger ommatidia, and smaller interommatidial angles and eye parameter values, indicating peripheral sensory adaptation to ambient rainforest light. Optic lobes and mushroom body collars were disproportionately small in minims. Within the optic lobe, lamina and lobula relative volumes increased with worker size whereas medulla volume decreased. Visual system phenotypes thus correspond to task specializations in dark or light environments and illustrate a functional neuroplasticity underpinning division of labor in this socially complex agricultural ant.

Long-term natural history of visual acuity in eyes with choroideremia: a systematic review and meta-analysis of data from 1004 individual eyes

BACKGROUND/AIMS

Best-corrected visual acuity (BCVA) is the most common primary endpoint in treatment trials for choroideremia (CHM) but the long-term natural history of BCVA is unclear.

METHODS

We searched in seven databases to identify studies that reported BCVA of untreated eyes with CHM. We sought individual-level data and performed segmented regression between BCVA and age. For eyes followed longitudinally, we introduced a horizontal translation factor to each dataset to account for different ages at onset of a rapid BCVA decline.

RESULTS

We included 1004 eyes from 23 studies. BCVA of the right and left eyes was moderately correlated (r=0.60). BCVA as a function of age followed a 2-phase decline (slow followed by rapid decline), with an estimated transition age of 39.1 years (95% CI 33.5 to 44.7). After the introduction of horizontal translation factors to longitudinal datasets, BCVA followed a 2-phase decline until it reached 0 letters (r²=0.90). The BCVA decline rate was 0.33 letters/year (95% CI -0.38 to 1.05) before 39 years, and 1.23 letters/year (95% CI 0.55 to 1.92) after 39 years (p=0.004).

CONCLUSION

BCVA in eyes with CHM follows a 2-phase linear decline with a transition age of approximately 39 years. Future trials enrolling young patients may not be able to use BCVA as a primary or sole endpoint, but rather, may need to employ additional disease biomarkers that change before age 39. BCVA may still have utility as a primary endpoint for patients older than 39 years who have measurable BCVA decline rates.

Global ecosystem thresholds driven by aridity

Aridity, which is increasing worldwide because of climate change, affects the structure and functioning of dryland ecosystems. Whether aridification leads to gradual (versus abrupt) and systemic (versus specific) ecosystem changes is largely unknown. We investigated how 20 structural and functional ecosystem attributes respond to aridity in global drylands. Aridification led to systemic and abrupt changes in multiple ecosystem attributes. These changes occurred sequentially in three phases characterized by abrupt decays in plant productivity, soil fertility, and plant cover and richness at aridity values of 0.54, 0.7, and 0.8, respectively. More than 20% of the terrestrial surface will cross one or several of these thresholds by 2100, which calls for immediate actions to minimize the negative impacts of aridification on essential ecosystem services for the more than 2 billion people living in drylands.

High-Throughput Field Phenotyping Traits of Grain Yield Formation and Nitrogen Use Efficiency: Optimizing the Selection of Vegetation Indices and Growth Stages

High-throughput, non-invasive phenotyping is promising for evaluating crop nitrogen (N) use efficiency (NUE) and grain yield (GY) formation under field conditions, but its application for genotypes differing in morphology and phenology is still rarely addressed. This study therefore evaluates the spectral estimation of various dry matter (DM) and N traits, related to GY and grain N uptake (Nup) in high-yielding winter wheat breeding lines. From 2015 to 2017, hyperspectral canopy measurements were acquired on 26 measurement dates during vegetative and reproductive growth, and 48 vegetation indices from the visible (VIS), red edge (RE) and near-infrared (NIR) spectrum were tested in linear regression for assessing the influence of measurement stage and index selection. For most traits including GY and grain Nup, measurements at milk ripeness were the most reliable. Coefficients of determination (R²) were generally higher for traits related to maturity than for those related to anthesis canopy status. For GY (R² = 0.26-0.51 in the three years, p < 0.001), and most DM traits, indices related to the water absorption band at 970 nm provided better relationships than the NIR/VIS indices, including the normalized difference vegetation index (NDVI), and the VIS indices. In addition, most indices including RE bands, notably NIR/RE combinations, ranked above the NIR/VIS group. Due to index saturation, the index differentiation was most apparent in the highest-yielding year. For grain Nup and total Nup, the RE/VIS index MSR_705_445 and the simple ratio R780_R740 ranked highest, followed by other RE indices. Among the vegetative organs, R² values were mostly highest and lowest for leaf and spike traits, respectively. For each trait, index and partial least squares regression (PLSR) models were validated across years at milk ripeness, confirming the suitability of optimized index selection. PLSR improved the prediction errors of some traits but not consistently the R² values. The results suggest the use of sensor-based phenotyping as a useful support tool for screening of yield potential and NUE and for identifying contributing plant traits-which, due to their expensive and cumbersome destructive determination are otherwise not readily available. Water band and RE indices should be preferred over NIR/VIS indices for DM traits and N-related traits, respectively, and milk ripeness is suggested as the most reliable stage.

Twenty-four-month outcomes of inflammatory choroidal neovascularisation treated with intravitreal anti-vascular endothelial growth factors: a comparison between two treatment regimens

Background and aim

There is still no established treatment regimen for eyes with inflammatory choroidal neovascularisation (iCNV) treated with intravitreal anti-vascular endothelial growth factor (VEGF) injections. This study compared the 24-month outcomes of two treatment regimens of anti-VEGF injections in eyes with iCNV.

Methods

Eyes with iCNV treated with anti-VEGF injections were divided into two groups: eyes treated with a loading phase of 3 monthly injections and then re-treated as needed (LOADING group) and eyes treated as needed from the beginning (PRN group). Visual acuity (VA), number of injections and iCNV recurrences at 24 months were compared between the groups.

Results

Eighty-two eyes were included, 42 in the LOADING and 40 in the PRN group. Baseline VA (mean(SD)) was 57.3 (15.8) letters in the LOADING vs 60.7 (15.6) letters in the PRN group (p=0.32). The VA (mean (95% CI)) increased at 3 months (+14.8 (10.6 to 18.9) and +11.2 (6.4 to 16) letters in the LOADING and PRN group, respectively) and remained significantly higher than baseline over the entire follow-up in both groups (all p<0.001). At 24 months, there was no difference in VA between the LOADING and PRN group (72.3 (14.0) vs 74.7 (11.3) letters, p=0.36) but the LOADING group received significantly more injections (median (Q1–Q3)) than the PRN (4.5 (3–7) vs 2.5 (2–3.2), p<0.0001). The iCNV recurrences were similar in both groups.

Conclusions

iCNV responded well to anti-VEGF with significant and sustained VA improvement. The loading phase did not confer any advantage in terms of outcomes. PRN regimen from the beginning was as effective as more intensive treatment.

Machine Learning Approaches to Develop Pedotransfer Functions for Tropical Sri Lankan Soils

Poor data availability on soil hydraulic properties in tropical regions hampers many studies, including crop and environmental modeling. The high cost and effort of measurement and the increasing demand for such data have driven researchers to search for alternative approaches. Pedotransfer functions (PTFs) are predictive functions used to estimate soil properties by easily measurable soil parameters. PTFs are popular in temperate regions, but few attempts have been made to develop PTFs in tropical regions. Regression approaches are widely used to develop PTFs worldwide, and recently a few attempts were made using machine learning methods. PTFs for tropical Sri Lankan soils have already been developed using classical multiple linear regression approaches. However, no attempts were made to use machine learning approaches. This study aimed to determine the applicability of machine learning algorithms in developing PTFs for tropical Sri Lankan soils. We tested three machine learning algorithms (artificial neural networks (ANN), k-nearest neighbor (KNN), and random forest (RF)) with three different input combination (sand, silt, and clay (SSC) percentages; SSC and bulk density (BD); SSC, BD, and organic carbon (OC)) to estimate volumetric water content (VWC) at −10 kPa, −33 kPa (representing field capacity (FC); however, most studies in Sri Lanka use −33 kPa as the FC) and −1500 kPa (representing the permanent wilting point (PWP)) of Sri Lankan soils. This analysis used the open-source data mining software in the Waikato Environment for Knowledge Analysis. Using a wrapper approach and best-first search method, we selected the most appropriate inputs to develop PTFs using different machine learning algorithms and input levels. We developed PTFs to estimate FC and PWP and compared them with the previously reported PTFs for tropical Sri Lankan soils. We found that RF was the best algorithm to develop PTFs for tropical Sri Lankan soils. We tried to further the development of PTFs by adding volumetric water content at −10 kPa as an input variable because it is quite an easily measurable parameter compared to the other targeted VWCs. With the addition of VWC at −10 kPa, all machine learning algorithms boosted the performance. However, RF was the best. We studied the functionality of finetuned PTFs and found that they can estimate the available water content of Sri Lankan soils as well as measurements-based calculations. We identified RF as a robust alternative to linear regression methods in developing PTFs to estimate field capacity and the permanent wilting point of tropical Sri Lankan soils. With those findings, we recommended that PTFs be developed using the RF algorithm in the related software to make up for the data gaps present in tropical regions.

'S'-shaped curve: modelling trends in smoking prevalence, uptake and cessation in Great Britain from 1973 to 2016

BACKGROUND

It is believed that declines in smoking prevalence naturally slow over time as the smoking population 'hardens' and that progress has come primarily from reducing uptake rather than increasing cessation. To address these issues, we undertook the first formal attempt to model the trajectory of smoking prevalence and indices of uptake and cessation in Great Britain from 1973 to 2016.

METHODS

Using data from the General Lifestyle Survey between 1973 and 2008, the Integrated Household Survey between 2009 and 2014 and the Annual Population Survey between 2015 and 2016, this study modelled year-on-year changes in smoking prevalence, ever-smoking in 18-24-year-olds as an index of uptake, and quit ratios as an index of cessation.

RESULTS

For all three outcomes, changes over time were best fitted by what may be broadly characterised as 'S'-shaped curves: segmented functions characterised by initial rapid progress, a slowing or reversal, then renewed progress. Smoking prevalence in Great Britain showed a decelerating decline over time between 1973 and 2000, but then, after the introduction of the National 'Smoking Kills' tobacco control plan, the decline accelerated again and has remained nearly linear at -0.67 percentage points per year. Ever-smoking showed a decelerating decline which eventually ceased and began increasing around 1994 but then declined again after 2000. Quit ratios rose rapidly then slowed and then accelerated around 2000 and again more recently in 2013.

CONCLUSION

Long-term trends in smoking prevalence, uptake and cessation have followed a broadly 'S'-shaped trend suggesting that they are responsive to major tobacco control initiatives. The decline in prevalence has resulted both from reductions in uptake and increases in cessation.

Hydraulic fracturing and active coarsening position the lumen of the mouse blastocyst

During mouse pre-implantation development, the formation of the blastocoel, a fluid-filled lumen, breaks the radial symmetry of the blastocyst. The factors that control the formation and positioning of this basolateral lumen remain obscure. We found that accumulation of pressurized fluid fractures cell-cell contacts into hundreds of micrometer-size lumens. These microlumens eventually discharge their volumes into a single dominant lumen, which we model as a process akin to Ostwald ripening, underlying the coarsening of foams. Using chimeric mutant embryos, we tuned the hydraulic fracturing of cell-cell contacts and steered the coarsening of microlumens, allowing us to successfully manipulate the final position of the lumen. We conclude that hydraulic fracturing of cell-cell contacts followed by contractility-directed coarsening of microlumens sets the first axis of symmetry of the mouse embryo.

Short-Term Electricity Price Forecasting with a Composite Fundamental-Econometric Hybrid Methodology

Various power exchanges are nowadays being affected by a plethora of factors that, as a whole, cause considerable instabilities in the system. As a result, traders and practitioners must constantly adapt their strategies and look for support for their decision-making when operating in the market. In many cases, this calls for suitable electricity price forecasting models that can account for relevant aspects for electricity price forecasting. Consequently, fundamental-econometric hybrid approaches have been developed by many authors in the literature, although these have rarely been applied in short-term contexts, where other considerations and issues must be addressed. Therefore, this work aims to develop a robust hybrid methodology that is capable of making the most of the advantages fundamental and the hybrid model in a synergistic manner, while also providing insight as to how well these models perform across the year. Several methods have been utilised in this work in order to modify the hybridisation approach and the input datasets for enhanced predictive accuracy. The performance of this proposal has been analysed in the real case study of the Iberian power exchange and has outperformed other well-recognised and traditional methods.

Prevalence of injecting drug use in Estonia 2010-2015: a capture-recapture study

Background

It has been observed in an earlier study that the number of people who inject drugs (PWID) in Estonia is declining. We provide nationwide estimates of the number of PWID in Estonia for years 2010–2015 and compare different modelling strategies to minimise over-coverage-induced bias in capture-recapture estimates.

Methods

We obtained data from the Estonian Causes of Death Registry (DR) for opioid-related deaths, the Estonian Health Insurance Fund (HIF) for opioid-related overdose and drug dependence treatment episodes, and the Estonian Police and Border Guard Board (PB) drug-related misdemeanours. Datasets were linked by identifier based on sex, date of birth, and initials; a capture-recapture method was used to estimate the number of PWID aged 15 or more, each year from 2010 to 2015. Log-linear regression maximum likelihood (ML) and Bayesian methods were used; over-coverage of police data was accounted for.

Results

The annual population size estimates of the number of PWID (aged 15 and over) varied from 6000 to 17,300 (ML estimates not accounting for over-coverage of PB) to 1500–2300 (Bayesian estimates accounting for over-coverage). Bayesian estimates indicated a slight decrease in the number of PWID, and the median estimates were > 2000 in years 2010–2012 and < 1800 in years 2013–2015.

Conclusions

Over-coverage of a registry can have a great impact on the estimates of the size of the target population. Bayesian estimates accounting for this over-coverage may provide better estimates of the target population size.

Electronic supplementary material

The online version of this article (10.1186/s12954-019-0289-3) contains supplementary material, which is available to authorized users.

Moderating Effect of Cortical Thickness on BOLD Signal Variability Age-Related Changes

The time course of neuroanatomical structural and functional measures across the lifespan is commonly reported in association with aging. Blood oxygen-level dependent signal variability, estimated using the standard deviation of the signal, or "BOLDSD," is an emerging metric of variability in neural processing, and has been shown to be positively correlated with cognitive flexibility. Generally, BOLDSD is reported to decrease with aging, and is thought to reflect age-related cognitive decline. Additionally, it is well established that normative aging is associated with structural changes in brain regions, and that these predict functional decline in various cognitive domains. Nevertheless, the interaction between alterations in cortical morphology and BOLDSD changes has not been modeled quantitatively. The objective of the current study was to investigate the influence of cortical morphology metrics [i.e., cortical thickness (CT), gray matter (GM) volume, and cortical area (CA)] on age-related BOLDSD changes by treating these cortical morphology metrics as possible physiological confounds using linear mixed models. We studied these metrics in 28 healthy older subjects scanned twice at approximately 2.5 years interval. Results show that BOLDSD is confounded by cortical morphology metrics. Respectively, changes in CT but not GM volume nor CA, show a significant interaction with BOLDSD alterations. Our study highlights that CT changes should be considered when evaluating BOLDSD alternations in the lifespan.

Ecological and life history traits are associated with Ross River virus infection among sylvatic mammals in Australia

Background

Ross River virus (RRV) is Australia’s most important arbovirus given its annual burden of disease and the relatively large number of Australians at risk for infection. This mosquito-borne arbovirus is also a zoonosis, making its epidemiology and infection ecology complex and cryptic. Our grasp of enzootic, epizootic, and zoonotic RRV transmission dynamics is imprecise largely due to a poor understanding of the role of wild mammalian hosts in the RRV system.

Methods

The current study applied a piecewise structural equation model (PSEM) toward an interspecific comparison of sylvatic Australian mammals to characterize the ecological and life history profile of species with a history of RRV infection relative to those species with no such history among all wild mammalian species surveyed for RRV infection. The effects of species traits were assessed through multiple causal pathways within the PSEM framework.

Results

Sylvatic mammalian species with a history of RRV infection tended to express dietary specialization and smaller population density. These species were also characterized by a longer gestation length.

Conclusions

This study provides the first interspecific comparison of wild mammals for RRV infection and identifies some potential targets for future wildlife surveys into the infection ecology of this important arbovirus. An applied RRV macroecology may prove invaluable to the epidemiological modeling of RRV epidemics across diverse sylvatic landscapes, as well as to the development of human and animal health surveillance systems.

Electronic supplementary material

The online version of this article (10.1186/s12898-019-0220-5) contains supplementary material, which is available to authorized users.