- A Dirichlet Model of Alignment Cost in Mixed-Membership Unsupervised Clustering JOURNAL OF COMPUTATIONAL AND GRAPHICAL STATISTICS 2022
Deconvoluting complex correlates of COVID-19 severity with a multi-omic pandemic tracking strategy.
2022; 13 (1): 5107
The SARS-CoV-2 pandemic has differentially impacted populations across race and ethnicity. A multi-omic approach represents a powerful tool to examine risk across multi-ancestry genomes. We leverage a pandemic tracking strategy in which we sequence viral and host genomes and transcriptomes from nasopharyngeal swabs of 1049 individuals (736 SARS-CoV-2 positive and 313 SARS-CoV-2 negative) and integrate them with digital phenotypes from electronic health records from a diverse catchment area in Northern California. Genome-wide association disaggregated by admixture mapping reveals novel COVID-19-severity-associated regions containing previously reported markers of neurologic, pulmonary and viral disease susceptibility. Phylodynamic tracking of consensus viral genomes reveals no association with disease severity or inferred ancestry. Summary data from multiomic investigation reveals metagenomic and HLA associations with severe COVID-19. The wealth of data available from residual nasopharyngeal swabs in combination with clinical data abstracted automatically at scale highlights a powerful strategy for pandemic tracking, and reveals distinct epidemiologic, genetic, and biological associations for those at the highest risk.
View details for DOI 10.1038/s41467-022-32397-8
View details for PubMedID 36042219
Extracting hierarchical features of cultural variation using network-based clustering.
Evolutionary human sciences
High-dimensional datasets on cultural characters contribute to uncovering insights about factors that influence cultural evolution. Because cultural variation in part reflects descent processes with a hierarchical structure - including the descent of populations and vertical transmission of cultural traits - methods designed for hierarchically structured data have potential to find applications in the analysis of cultural variation. We adapt a network-based hierarchical clustering method for use in analysing cultural variation. Given a set of entities, the method constructs a similarity network, hierarchically depicting community structure among them. We illustrate the approach using four datasets: pronunciation variation in the US mid-Atlantic region, folklore variation in worldwide cultures, phonemic variation across worldwide languages and temporal variation in first names in the US. In these examples, the method provides insights into processes that affect cultural variation, uncovering geographic and other influences on observed patterns and cultural characters that make important contributions to them.
View details for DOI 10.1017/ehs.2022.15
View details for PubMedID 36276878
View details for PubMedCentralID PMC9583705
Effects of cultural transmission of surnaming decisions on the sex ratio at birth.
Theoretical population biology
The patriarchal tradition of surnaming a child after its father in Han Chinese families may contribute to their preference for sons, a major cause of the abnormally high SRB (sex ratio at birth) in China. This high SRB can subsequently contribute to the marriage squeeze on males of marriageable age. Encouraging matrilineal surnaming has been proposed as a strategy that could potentially reduce son preference and help to adjust the imbalance in SRB. Here, we model factors that are likely to influence surnaming decisions, including cultural transmission of parents' surnaming decisions, the cultural value of a daughter, reward given to matrilineal surnaming, and awareness of current imbalance in SRB. Mathematical and computational analyses suggest that offering a significant reward and raising public awareness of the problems inherent in an excess of marriage-age males may overcome the son preference and reduce the male-biased SRB.
View details for DOI 10.1016/j.tpb.2021.07.001
View details for PubMedID 34358559
Diffusion Histology Imaging Combining Diffusion Basis Spectrum Imaging (DBSI) and Machine Learning Improves Detection and Classification of Glioblastoma Pathology.
Clinical cancer research : an official journal of the American Association for Cancer Research
PURPOSE: Glioblastoma (GBM) is one of the deadliest cancers with no cure. While conventional MRI has been widely adopted for examining GBM clinically, accurate neuroimaging assessment of tumor histopathology for improved diagnosis, surgical planning, and treatment evaluation, remains an unmet need in the clinical management of GBMs.EXPERIMENTAL DESIGN: We employ a novel Diffusion Histology Imaging (DHI) approach, combining diffusion basis spectrum imaging (DBSI) and machine learning, to detect, differentiate, and quantify areas of high cellularity, tumor necrosis, and tumor infiltration in GBM.RESULTS: Gd-enhanced T1W or hyper-intense FLAIR failed to reflect the morphological complexity underlying tumor in GBM patients. Contrary to the conventional wisdom that apparent diffusion coefficient (ADC) negatively correlates with increased tumor cellularity, we demonstrate disagreement between ADC and histologically confirmed tumor cellularity in glioblastoma specimens, whereas DBSI-derived restricted isotropic diffusion fraction positively correlated with tumor cellularity in the same specimens. By incorporating DBSI metrics as classifiers for a supervised machine learning algorithm, we accurately predicted high tumor cellularity, tumor necrosis, and tumor infiltration with 87.5%, 89.0% and 93.4% accuracy, respectively.CONCLUSIONS: Our results suggest that DHI could serve as a favorable alternative to current neuroimaging techniques for guiding biopsy or surgery as well as monitoring therapeutic response in the treatment of glioblastoma.
View details for DOI 10.1158/1078-0432.CCR-20-0736
View details for PubMedID 32694155