Unsupervised discovery of ancestry-informative markers and genetic admixture proportions in biobank-scale datasets.

Admixture estimation plays a crucial role in ancestry inference and genome-wide association studies (GWASs). Computer programs such as ADMIXTURE and STRUCTURE are commonly employed to estimate the admixture proportions of sample individuals. However, these programs can be overwhelmed by the computational burdens imposed by the 105 to 106 samples and millions of markers commonly found in modern biobanks. An attractive strategy is to run these programs on a set of ancestry-informative SNP markers (AIMs) that exhibit substantially different frequencies across populations. Unfortunately, existing methods for identifying AIMs require knowing ancestry labels for a subset of the sample. This supervised learning approach creates a chicken and the egg scenario. In this paper, we present an unsupervised, scalable framework that seamlessly carries out AIM selection and likelihood-based estimation of admixture proportions. Our simulated and real data examples show that this approach is scalable to modern biobank datasets. OpenADMIXTURE, our Julia implementation of the method, is open source and available for free.

Ranking mobility and impact inequality in early academic careers.

How difficult is it for an early career academic to climb the ranks of their discipline? We tackle this question with a comprehensive bibliometric analysis of 57 disciplines, examining the publications of more than 5 million authors whose careers started between 1986 and 2008. We calibrate a simple random walk model over historical data of ranking mobility, which we use to 1) identify which strata of academic impact rankings are the most/least mobile and 2) study the temporal evolution of mobility. By focusing our analysis on cohorts of authors starting their careers in the same year, we find that ranking mobility is remarkably low for the top- and bottom-ranked authors and that this excess of stability persists throughout the entire period of our analysis. We further observe that mobility of impact rankings has increased over time, and that such rise has been accompanied by a decline of impact inequality, which is consistent with the negative correlation that we observe between such two quantities. These findings provide clarity on the opportunities of new scholars entering the academic community, with implications for academic policymaking.

NSRGRN: a network structure refinement method for gene regulatory network inference.

The elucidation of gene regulatory networks (GRNs) is one of the central challenges of systems biology, which is crucial for understanding pathogenesis and curing diseases. Various computational methods have been developed for GRN inference, but identifying redundant regulation remains a fundamental problem. Although considering topological properties and edge importance measures simultaneously can identify and reduce redundant regulations, how to address their respective weaknesses whilst leveraging their strengths is a critical problem faced by researchers. Here, we propose a network structure refinement method for GRN (NSRGRN) that effectively combines the topological properties and edge importance measures during GRN inference. NSRGRN has two major parts. The first part constructs a preliminary ranking list of gene regulations to avoid starting the GRN inference from a directed complete graph. The second part develops a novel network structure refinement (NSR) algorithm to refine the network structure from local and global topology perspectives. Specifically, the Conditional Mutual Information with Directionality and network motifs are applied to optimise the local topology, and the lower and upper networks are used to balance the bilateral relationship between the local topology's optimisation and the global topology's maintenance. NSRGRN is compared with six state-of-the-art methods on three datasets (26 networks in total), and it shows the best all-round performance. Furthermore, when acting as a post-processing step, the NSR algorithm can improve the results of other methods in most datasets.

Fast, accurate ranking of engineered proteins by target-binding propensity using structure modeling.

Deep-learning-based methods for protein structure prediction have achieved unprecedented accuracy, yet their utility in the engineering of protein-based binders remains constrained due to a gap between the ability to predict the structures of candidate proteins and the ability toprioritize proteins by their potential to bind to a target. To bridge this gap, we introduce Automated Pairwise Peptide-Receptor Analysis for Screening Engineered proteins (APPRAISE), a method for predicting the target-binding propensity of engineered proteins. After generating structural models of engineered proteins competing for binding to a target using an established structure prediction tool such as AlphaFold-Multimer or ESMFold, APPRAISE performs a rapid (under 1 CPU second per model) scoring analysis that takes into account biophysical and geometrical constraints. As proof-of-concept cases, we demonstrate that APPRAISE can accurately classify receptor-dependent vs. receptor-independent adeno-associated viral vectors and diverse classes of engineered proteins such as miniproteins targeting the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spike, nanobodies targeting a G-protein-coupled receptor, and peptides that specifically bind to transferrin receptor or programmed death-ligand 1 (PD-L1). APPRAISE is accessible through a web-based notebook interface using Google Colaboratory (https://tiny.cc/APPRAISE). With its accuracy, interpretability, and generalizability, APPRAISE promises to expand the utility of protein structure prediction and accelerate protein engineering for biomedical applications.

Interpretable deep learning methods for multiview learning.

BACKGROUND: Technological advances have enabled the generation of unique and complementary types of data or views (e.g. genomics, proteomics, metabolomics) and opened up a new era in multiview learning research with the potential to lead to new biomedical discoveries. RESULTS: We propose iDeepViewLearn (Interpretable Deep Learning Method for Multiview Learning) to learn nonlinear relationships in data from multiple views while achieving feature selection. iDeepViewLearn combines deep learning flexibility with the statistical benefits of data and knowledge-driven feature selection, giving interpretable results. Deep neural networks are used to learn view-independent low-dimensional embedding through an optimization problem that minimizes the difference between observed and reconstructed data, while imposing a regularization penalty on the reconstructed data. The normalized Laplacian of a graph is used to model bilateral relationships between variables in each view, therefore, encouraging selection of related variables. iDeepViewLearn is tested on simulated and three real-world data for classification, clustering, and reconstruction tasks. For the classification tasks, iDeepViewLearn had competitive classification results with state-of-the-art methods in various settings. For the clustering task, we detected molecular clusters that differed in their 10-year survival rates for breast cancer. For the reconstruction task, we were able to reconstruct handwritten images using a few pixels while achieving competitive classification accuracy. The results of our real data application and simulations with small to moderate sample sizes suggest that iDeepViewLearn may be a useful method for small-sample-size problems compared to other deep learning methods for multiview learning. CONCLUSION: iDeepViewLearn is an innovative deep learning model capable of capturing nonlinear relationships between data from multiple views while achieving feature selection. It is fully open source and is freely available at https://github.com/lasandrall/iDeepViewLearn .

Moving Beyond Mortality: Development and Application of a Desirability of Outcome Ranking (DOOR) Endpoint for Hospital-Acquired Bacterial Pneumonia and Ventilator-Associated Bacterial Pneumonia.

BACKGROUND: Hospital-acquired and ventilator-associated bacterial pneumonia (HABP/VABP) are frequently caused by multidrug-resistant organisms. Patient-centered endpoints in clinical trials are needed to develop new antibiotics for HABP/VABP. Desirability of outcome ranking (DOOR) is a paradigm for the design, analysis, and interpretation of clinical trials based on a patient-centered, benefit-risk evaluation. METHODS: A multidisciplinary committee created an infectious diseases DOOR endpoint customized for HABP/VABP, incorporating infectious complications, serious adverse events, and mortality. We applied this to 2 previously completed, large randomized controlled trials for HABP/VABP. ZEPHyR compared vancomycin to linezolid and VITAL compared linezolid to tedizolid. For each trial, we evaluated the DOOR distribution and probability, including DOOR component and partial credit analyses. We also applied DOOR in subgroup analyses. RESULTS: In both trials, the HABP/VABP DOOR demonstrated similar overall clinical outcomes between treatment groups. In ZEPHyR, the probability that a participant treated with linezolid would have a more desirable outcome than a participant treated with vancomycin was 50.2% (95% confidence interval [CI], 45.1%--55.3%). In VITAL, the probability that a participant treated with tedizolid would have a more desirable outcome than a participant treated with linezolid was 48.7% (95% CI, 44.8%-52.6%). The DOOR component analysis revealed that participants treated with tedizolid had a less desirable outcome than those treated with linezolid when considering clinical response alone. However, participants with decreased renal function had improved overall outcomes with tedizolid. CONCLUSIONS: The HABP/VABP DOOR provided more granular information about clinical outcomes than is typically presented in clinical trials. HABP/VABP trials would benefit from prospectively using DOOR.

Growth ranking of hybrid aspen genotypes and its linkage to leaf gas exchange.

BACKGROUND: Afforestation of non-forestland is a new measure by the European Union to enhance climate mitigation and biodiversity. Hybrid aspen (Populus tremula L. × P. tremuloides Michx.) is among the suitable tree species for afforestation to produce woody biomass. However, the best performing genotypic material for intensive biomass production and its physiological adaptation capacity is still unclear. We compared 22 hybrid aspen genotypes growth and leaf physiological characteristics (stomatal conductance, net photosynthesis, intrinsic water-use efficiency) according to their geographical north- or southward transfer (European P. tremula parent from 51° to 60° N and North American P. tremuloides parent from 45° to 54° N) to hemiboreal Estonia (58° N) in a completely randomized design progeny trial. We tested whether the growth ranking of genotypes of different geographical origin has changed from young (3-year-old) to mid-rotation age (13-year-old). The gas exchange parameters were measured in excised shoots in 2021 summer, which was characterised with warmer (+ 4 °C) and drier (17% precipitation from normal) June and July than the long-term average. RESULTS: We found that the northward transfer of hybrid aspen genotypes resulted in a significant gain in growth (two-fold greater diameter at breast height) in comparison with the southward transfer. The early selection of genotypes was generally in good accordance with the middle-aged genotype ranking, while some of the northward transferred genotypes showed improved growth at the middle-age period in comparison with their ranking during the early phase. The genotypes of southward transfer demonstrated higher stomatal conductance, which resulted in higher net photosynthesis, and lower intrinsic water-use efficiency (iWUE) compared with northward transfer genotypes. However, higher photosynthesis did not translate into higher growth rate. The higher physiological activity of southern transferred genotypes was likely related to a better water supply of smaller and consequently more shaded trees under drought. Leaf nitrogen concentration did not have any significant relation with tree growth. CONCLUSIONS: We conclude that the final selection of hybrid aspen genotypes for commercial use should be done in 10-15 years after planting. Physiological traits acquired during periods of droughty conditions may not fully capture the growth potential. Nonetheless, we advocate for a broader integration of physiological measurements alongside traditional traits (such as height and diameter) in genotype field testing to facilitate the selection of climate-adapted planting material for resilient forests.

The dissection of a despotic society: exploration, dominance and hormonal traits.

Naked mole-rats (Heterocephalus glaber) live in large colonies with one breeding female (queen), one to three breeding males (BMs) and the remainder are non-reproductive subordinates. The animals have a linear dominance rank with the breeders at the top of the hierarchy. We investigated how dominance rank in naked mole-rats differs with exploration (the propensity to explore a novel environment) and related endocrine markers. Exploration behaviour, faecal progestagen metabolite (fPM), faecal glucocorticoid metabolite (fGCM), faecal androgen metabolite (fAM) and plasma prolactin concentrations were quantified in breeding, high-, middle- and low-ranked females and males from five naked mole-rat colonies. There were no significant differences between the dominance rank and exploration behaviour. Interestingly, the queens and high-ranking females had higher fGCM and fAM concentrations compared with middle- and low-ranked females. The queens had significantly higher fPM concentrations than all other ranked females, since they are responsible for procreation. In the males, the BMs had higher fGCM concentrations compared with high- and low-ranked males. In addition, BMs and middle-ranking males had overall higher prolactin levels than all other ranked males, which could be linked to cooperative care. Overall, the results suggest that physiological reproductive suppression is linked to high dominance rank.

idenMD-NRF: a ranking framework for miRNA-disease association identification.

Identifying miRNA-disease associations is an important task for revealing pathogenic mechanism of complicated diseases. Different computational methods have been proposed. Although these methods obtained encouraging performance for detecting missing associations between known miRNAs and diseases, how to accurately predict associated diseases for new miRNAs is still a difficult task. In this regard, a ranking framework named idenMD-NRF is proposed for miRNA-disease association identification. idenMD-NRF treats the miRNA-disease association identification as an information retrieval task. Given a novel query miRNA, idenMD-NRF employs Learning to Rank algorithm to rank associated diseases based on high-level association features and various predictors. The experimental results on two independent test datasets indicate that idenMD-NRF is superior to other compared predictors. A user-friendly web server of idenMD-NRF predictor is freely available at http://bliulab.net/idenMD-NRF/.

A universal approach for integrating super large-scale single-cell transcriptomes by exploring gene rankings.

Advancement in single-cell RNA sequencing leads to exponential accumulation of single-cell expression data. However, there is still lack of tools that could integrate these unlimited accumulations of single-cell expression data. Here, we presented a universal approach iSEEEK for integrating super large-scale single-cell expression via exploring expression rankings of top-expressing genes. We developed iSEEEK with 11.9 million single cells. We demonstrated the efficiency of iSEEEK with canonical single-cell downstream tasks on five heterogenous datasets encompassing human and mouse samples. iSEEEK achieved good clustering performance benchmarked against well-annotated cell labels. In addition, iSEEEK could transfer its knowledge learned from large-scale expression data on new dataset that was not involved in its development. iSEEEK enables identification of gene-gene interaction networks that are characteristic of specific cell types. Our study presents a simple and yet effective method to integrate super large-scale single-cell transcriptomes and would facilitate translational single-cell research from bench to bedside.

HyMM: hybrid method for disease-gene prediction by integrating multiscale module structure.

MOTIVATION: Identifying disease-related genes is an important issue in computational biology. Module structure widely exists in biomolecule networks, and complex diseases are usually thought to be caused by perturbations of local neighborhoods in the networks, which can provide useful insights for the study of disease-related genes. However, the mining and effective utilization of the module structure is still challenging in such issues as a disease gene prediction. RESULTS: We propose a hybrid disease-gene prediction method integrating multiscale module structure (HyMM), which can utilize multiscale information from local to global structure to more effectively predict disease-related genes. HyMM extracts module partitions from local to global scales by multiscale modularity optimization with exponential sampling, and estimates the disease relatedness of genes in partitions by the abundance of disease-related genes within modules. Then, a probabilistic model for integration of gene rankings is designed in order to integrate multiple predictions derived from multiscale module partitions and network propagation, and a parameter estimation strategy based on functional information is proposed to further enhance HyMM's predictive power. By a series of experiments, we reveal the importance of module partitions at different scales, and verify the stable and good performance of HyMM compared with eight other state-of-the-arts and its further performance improvement derived from the parameter estimation. CONCLUSIONS: The results confirm that HyMM is an effective framework for integrating multiscale module structure to enhance the ability to predict disease-related genes, which may provide useful insights for the study of the multiscale module structure and its application in such issues as a disease-gene prediction.

A comparison of morphokinetic models and morphological selection for prioritizing euploid embryos: a multicentre cohort study.

STUDY QUESTION: Are morphokinetic models better at prioritizing a euploid embryo for transfer over morphological selection by an embryologist? SUMMARY ANSWER: Morphokinetic algorithms lead to an improved prioritization of euploid embryos when compared to embryologist selection. WHAT IS KNOWN ALREADY: PREFER (predicting euploidy for embryos in reproductive medicine) is a previously published morphokinetic model associated with live birth and miscarriage. The second model uses live birth as the target outcome (LB model). STUDY DESIGN, SIZE, DURATION: Data for this cohort study were obtained from 1958 biopsied blastocysts at nine IVF clinics across the UK from January 2021 to December 2022. PARTICIPANTS/MATERIALS, SETTING, METHODS: The ability of the PREFER and LB models to prioritize a euploid embryo was compared against arbitrary selection and the prediction of four embryologists using the timelapse video, blinded to the morphokinetic time stamp. The comparisons were made using calculated percentages and normalized discounted cumulative gain (NDCG), whereby an NDCG score of 1 would equate to all euploid embryos being ranked first. In arbitrary selection, the ploidy status was randomly assigned within each cycle and the NDGC calculated, and this was then repeated 100 times and the mean obtained. MAIN RESULTS AND THE ROLE OF CHANCE: Arbitrary embryo selection would rank a euploid embryo first 37% of the time, embryologist selection 39%, and the LB and PREFER ploidy morphokinetic models 46% and 47% of the time, respectively. The AUC for LB and PREFER model was 0.62 and 0.63, respectively. Morphological selection did not significantly improve the performance of both morphokinetic models when used in combination. There was a significant difference between the NDGC metric of the PREFER model versus embryologist selection at 0.96 and 0.87, respectively (t = 14.1, P < 0.001). Similarly, there was a significant difference between the LB model and embryologist selection with an NDGC metric of 0.95 and 0.87, respectively (t = 12.0, P < 0.001). All four embryologists ranked embryos similarly, with an intraclass coefficient of 0.91 (95% CI 0.82-0.95, P < 0.001). LIMITATIONS, REASONS FOR CAUTION: Aside from the retrospective study design, limitations include allowing the embryologist to watch the time lapse video, potentially providing more information than a truly static morphological assessment. Furthermore, the embryologists at the participating centres were familiar with the significant variables in time lapse, which could bias the results. WIDER IMPLICATIONS OF THE FINDINGS: The present study shows that the use of morphokinetic models, namely PREFER and LB, translates into improved euploid embryo selection. STUDY FUNDING/COMPETING INTEREST(S): This study received no specific grant funding from any funding agency in the public, commercial or not-for-profit sectors. Dr Alison Campbell is minor share holder of Care Fertility. All other authors have no conflicts of interest to declare. Time lapse is a technology for which patients are charged extra at participating centres. TRIAL REGISTRATION NUMBER: N/A.

Use of artificial intelligence embryo selection based on static images to predict first-trimester pregnancy loss.

RESEARCH QUESTION: Can an artificial intelligence embryo selection assistant predict the incidence of first-trimester spontaneous abortion using static images of IVF embryos? DESIGN: In a blind, retrospective study, a cohort of 172 blastocysts from IVF cases with single embryo transfer and a positive biochemical pregnancy test was ranked retrospectively by the artificial intelligence morphometric algorithm ERICA. Making use of static embryo images from a light microscope, each blastocyst was assigned to one of four possible groups (optimal, good, fair or poor), and linear regression was used to correlate the results with the presence or absence of a normal fetal heart beat as an indicator of ongoing pregnancy or spontaneous abortion, respectively. Additional analyses included modelling for recipient age and chromosomal status established by preimplantation genetic testing for aneuploidy (PGT-A). RESULTS: Embryos classified as optimal/good had a lower incidence of spontaneous abortion (16.1%) compared with embryos classified as fair/poor (25%; OR = 0.46, P = 0.005). The incidence of spontaneous abortion in chromosomally normal embryos (determined by PGT-A) was 13.3% for optimal/good embryos and 20.0% for fair/poor embryos, although the difference was not significant (P = 0.531). There was a significant association between embryo rank and recipient age (P = 0.018), in that the incidence of spontaneous abortion was unexpectedly lower in older recipients (21.3% for age ≤35 years, 17.9% for age 36-38 years, 16.4% for age ≥39 years; OR = 0.354, P = 0.0181). Overall, these results support correlation between risk of spontaneous abortion and embryo rank as determined by artificial intelligence; classification accuracy was calculated to be 67.4%. CONCLUSIONS: This preliminary study suggests that artificial intelligence (ERICA), which was designed as a ranking system to assist with embryo transfer decisions and ploidy prediction, may also be useful to provide information for couples on the risk of spontaneous abortion. Future work will include a larger sample size and karyotyping of miscarried pregnancy tissue.

Desirability of outcome ranking for obstetrical trials: illustration and application to the ARRIVE trial.

BACKGROUND: In randomized trials, 1 primary outcome is typically chosen to evaluate the consequences of an intervention, whereas other important outcomes are relegated to secondary outcomes. This issue is amplified for many obstetrical trials in which an intervention may have consequences for both the pregnant person and the child. In contrast, desirability of outcome ranking, a paradigm shift for the design and analysis of clinical trials based on patient-centric evaluation, allows multiple outcomes-including from >1 individual-to be considered concurrently. OBJECTIVE: This study aimed to describe desirability of outcome ranking methodology tailored to obstetrical trials and to apply the methodology to maternal-perinatal paired (dyadic) outcomes in which both individuals may be affected by an intervention but may experience discordant outcomes (eg, an obstetrical intervention may improve perinatal but worsen maternal outcomes). STUDY DESIGN: This secondary analysis applies the desirability of outcome ranking methodology to data from the Eunice Kennedy Shriver National Institute of Child Health and Human Development Maternal-Fetal Medicine Units Network ARRIVE trial. The original analysis found no substantial difference in the primary (perinatal composite) outcome, but a decreased risk of the secondary outcome of cesarean delivery with elective induction at 39 weeks. In the present desirability-of-outcome-ranking analysis, dyadic outcomes ranging from spontaneous vaginal delivery without severe neonatal complication (most desirable) to cesarean delivery with perinatal death (least desirable) were classified into 8 categories ranked by overall desirability by experienced investigators. Distributions of the desirability of outcome ranking were compared by estimating the probability of having a more desirable dyadic outcome with elective induction at 39 weeks of gestation than with expectant management. To account for various perspectives on these outcomes, a complementary analysis, called the partial credit strategy, was used to grade outcomes on a 100-point scale and estimate the difference in overall treatment scores between groups using a t test. RESULTS: All 6096 participants from the trial were included. The probability of a better dyadic outcome for a randomly selected patient who was randomized to elective induction was 53% (95% confidence interval, 51-54), implying that elective induction led to a better overall outcome for the dyad when taking multiple outcomes into account concurrently. Furthermore, the desirability-of-outcome-ranking probability of averting cesarean delivery with elective induction was 52% (95% confidence interval, 51-53), which was not at the expense of an operative vaginal delivery or a poorer outcome for the perinate (ie, survival with a severe neonatal complication or perinatal death). Randomization to elective induction was also advantageous in most of the partial credit score scenarios. CONCLUSION: Desirability-of-outcome-ranking methodology is a useful tool for obstetrical trials because it provides a concurrent view of the effect of an intervention on multiple dyadic outcomes, potentially allowing for better translation of data for decision-making and person-centered care.

Bayesian mixture modelling with ranked set samples.

We consider the Bayesian estimation of the parameters of a finite mixture model from independent order statistics arising from imperfect ranked set sampling designs. As a cost-effective method, ranked set sampling enables us to incorporate easily attainable characteristics, as ranking information, into data collection and Bayesian estimation. To handle the special structure of the ranked set samples, we develop a Bayesian estimation approach exploiting the Expectation-Maximization (EM) algorithm in estimating the ranking parameters and Metropolis within Gibbs Sampling to estimate the parameters of the underlying mixture model. Our findings show that the proposed RSS-based Bayesian estimation method outperforms the commonly used Bayesian counterpart using simple random sampling. The developed method is finally applied to estimate the bone disorder status of women aged 50 and older.

Associations Between Program Doximity Reputation Rank and Well-Being in General Surgery Residents.

INTRODUCTION: Burnout and mistreatment are prevalent among surgical residents with considerable program-level variation. Applicants consider "program reputation," among other factors, when ranking programs. Although highly subjective, the only available measure of program reputation is from a physician survey by Doximity. It is unknown how program reputation is associated with resident well-being and mistreatment. METHODS: Resident burnout and personal accomplishment were assessed via the 2019 post-American Board of Surgery In-Training Examination survey. Additional outcomes included mistreatment, thoughts of attrition, and suicidality. Residents were stratified into quartiles based on their program's Doximity reputation rank. Multivariable logistic regression models examined the relationship between each outcome with Doximity rank quartile. RESULTS: 6956 residents (85.6% response rate) completed the survey. Higher-ranked programs had significantly higher burnout rates (top-quartile 41.3% versus bottom-quartile 33.2%; odds ratio [OR] 1.35, 95% confidence interval [CI] 1.04-1.76). There was no significant difference in personal accomplishment by program rank (OR 1.26, 95% CI 0.86-1.85). There also was no significant association between program rank and sexual harassment (OR 0.90, 95% CI 0.70-1.17), gender discrimination (OR 1.14, 95% CI 0.86-1.52), racial discrimination (OR 1.18, 95% CI 0.91-1.54), or bullying (OR 1.03, 95% CI 0.76-1.40). Suicidality (P = 0.97) and thoughts of attrition (P = 0.80) were also not associated with program rank. CONCLUSIONS: Surgical residents at higher-ranked programs report higher rates of burnout but have similar rates of mistreatment and personal accomplishment. Higher-ranked programs should be particularly vigilant to trainee burnout, and all programs should employ targeted interventions to improve resident well-being. This study highlights the need for greater transparency in reporting objective program-level quality measures pertaining to resident well-being.

Harm from Residential Indoor Air Contaminants.

This study presents a health-centered approach to quantify and compare the chronic harm caused by indoor air contaminants using disability-adjusted life-year (DALY). The aim is to understand the chronic harm caused by airborne contaminants in dwellings and identify the most harmful. Epidemiological and toxicological evidence of population morbidity and mortality is used to determine harm intensities, a metric of chronic harm per unit of contaminant concentration. Uncertainty is evaluated in the concentrations of 45 indoor air contaminants commonly found in dwellings. Chronic harm is estimated from the harm intensities and the concentrations. The most harmful contaminants in dwellings are PM2.5, PM10-2.5, NO2, formaldehyde, radon, and O3, accounting for over 99% of total median harm of 2200 DALYs/105 person/year. The chronic harm caused by all airborne contaminants in dwellings accounts for 7% of the total global burden from all diseases.

On finding natural antibiotics based on TCM formulae.

CONTEXT: Novel kinds of antibiotics are needed to combat the emergence of antibacterial resistance. Natural products (NPs) have shown potential as antibiotic candidates. Current experimental methods are not yet capable of exploring the massive, redundant, and noise-involved chemical space of NPs. In silico approaches are needed to select NPs as antibiotic candidates. OBJECTIVE: This study screens out NPs with antibacterial efficacy guided by both TCM and modern medicine and constructed a dataset aiming to serve the new antibiotic design. METHOD: A knowledge-based network is proposed in this study involving NPs, herbs, the concepts of TCM, and the treatment protocols (or etiologies) of infectious in modern medicine. Using this network, the NPs candidates are screened out and compose the dataset. Feature selection of machine learning approaches is conducted to evaluate the constructed dataset and statistically validate the im- portance of all NPs candidates for different antibiotics by a classification task. RESULTS: The extensive experiments prove the constructed dataset reaches a convincing classification performance with a 0.9421 weighted accuracy, 0.9324 recall, and 0.9409 precision. The further visu- alizations of sample importance prove the comprehensive evaluation for model interpretation based on medical value considerations.

Discovering clinical drug-drug interactions with known pharmacokinetics mechanisms using spontaneous reporting systems and electronic health records.

OBJECTIVE: Although the mechanisms behind pharmacokinetic (PK) drug-drug interactions (DDIs) are well-documented, bridging the gap between this knowledge and clinical evidence of DDIs, especially for serious adverse drug reactions (SADRs), remains challenging. While leveraging the FDA Adverse Event Reporting System (FAERS) database along with disproportionality analysis tends to detect a vast number of DDI signals, this abundance complicates further investigation, such as validation through clinical trials. Our study proposed a framework to efficiently prioritize these signals and assessed their reliability using multi-source Electronic Health Records (EHR) to identify top candidates for further investigation. METHODS: We analyzed FAERS data spanning from January 2004 to March 2023, employing four established disproportionality methods: Proportional Reporting Ratio (PRR), Reporting Odds Ratio (ROR), Multi-item Gamma Poisson Shrinker (MGPS), and Bayesian Confidence Propagating Neural Network (BCPNN). Building upon these models, we developed four ranking models to prioritize DDI-SADR signals and cross-referenced signals with DrugBank. To validate the top-ranked signals, we employed longitudinal EHRs from Vanderbilt University Medical Center and the All of Us research program. The performance of each model was assessed by counting how many of the top-ranked signals were confirmed by EHRs and calculating the average ranking of these confirmed signals. RESULTS: Out of 189 DDI-SADR signals identified by all four disproportionality methods, only two were documented in the DrugBank database. By prioritizing the top 20 signals as determined by each of the four disproportionality methods and our four ranking models, 58 unique DDI-SADR signals were selected for EHR validations. Of these, five signals were confirmed. The ranking model, which integrated the MGPS and BCPNN, demonstrated superior performance by assigning the highest priority to those five EHR-confirmed signals. CONCLUSION: The fusion of disproportionality analysis with ranking models, validated through multi-source EHRs, presents a groundbreaking approach to pharmacovigilance. Our study's confirmation of five significant DDI-SADRs, previously unrecorded in the DrugBank database, highlights the essential role of advanced data analysis techniques in identifying ADRs.

Heterogeneous Thresholds, Social Ranking, and the Emergence of Vague Categories.

Threshold models in which an individual's response to a particular state of the world depends on whether an associated measured value exceeds a given threshold are common in a variety of social learning and collective decision-making scenarios in both natural and artificial systems. If thresholds are heterogeneous across a population of agents, then graded population level responses can emerge in a context in which individual responses are discrete and limited. In this article, I propose a threshold-based model for social learning of shared quality categories. This is then combined with the voting model of fuzzy categories to allow individuals to learn membership functions from their peers, which can then be used for decision-making, including ranking a set of available options. I use agent-based simulation experiments to investigate variants of this model and compare them to an individual learning benchmark when applied to the ranking problem. These results show that a threshold-based approach combined with category-based voting across a social network provides an effective social mechanism for ranking that exploits emergent vagueness.