Sampling Bias Research Articles

Patient perspectives towards skin of color clinics: results from a survey study.

Skin of color clinics (SOCCs) are dermatology clinics that provide specialized care for patients with melanated skin. Little is known about patient perceptions and attitudes toward these clinics. We sought to perform a survey study to assess the opinions of patients of color previously seen in our dermatology department toward SOCC. A 14-question survey was sent by email to patients of color seen at our institution between January 1, 2015 and December 31, 2021. Out of 264 participants who completed the survey, 176 (66.7%) indicated they would prefer to be seen in a SOCC and 88 (33.3%) preferred to be seen in a general dermatology clinic (GDC). Clinic choice varied significantly by ethnicity, with Black/African American participants being the most likely to prefer SOCC over GDC than other races/ethnicities. Those interested in SOCC were primarily motivated by a search for expertise in the skin of color (SOC) and did not have a preference for the skin color of the SOCC dermatologist. Those interested in GDC were happy with their current care. This study was limited by the single-center design and sample bias. Our results overall provide positive patient feedback for the creation of SOCCs as centers for expertise in SOC. The preference for SOCC varied by race/ethnicity, highlighting that clinic choice is best left to the patient.

Vitamin D Levels and Temporomandibular Disorders: A Bidirectional Two-Sample Mendelian Randomization Analysis.

Growing researches explore vitamin D's role in temporomandibular disorders (TMDs), but the link between vitamin D and TMDs remains debated. To clarify the causal relationship, we conducted a Mendelian randomization (MR) analysis using data from genome-wide association studies (GWAS). The GWAS dataset of vitamin D (GWAS ID: ukb-d-30890_irnt; sample size: 329247) was obtained from the IEU Open GWAS project. And that of TMDs (GWAS ID: finn-b-TEMPORO; sample size: 134280), initiated on August 25th, 2017 and publicly released on December 18th, 2023, was extracted from the FinnGen dataset, whose cases were diagnosed based on the revised International Classification of Diseases, 10th Edition (ICD-10) code K07.6. Both datasets were obtained from the European population. According to three assumptions of MR analysis, a bi-directional MR analysis was performed to measure the causal relationship, with Inverse variance weighted (IVW) as the primary method and MR Egger and Weighted median as supplement. Moreover, diverse sensitivity analyses, including Cochran's Q test, MR Egger intercept, Mendelian randomized polymorphism RESidual Sum and Outlier (MR-PRESSO), and leave-one-out analysis, were used to verify the stability of the findings. The MR analysis supported causal effects of vitamin D levels on TMDs risks within the European population using IVW method [odds ratio = 1.316; 95% confidence interval = 1.086 to 1.595; P = 0.005], supported by MR Egger and Weighted median. While there was no indication that TMDs have a direct impact on vitamin D levels [β: -0.00738, standard error = 0.00665; P = 0.568]. The study revealed that within the European population higher levels of vitamin D led to higher risks of developing temporomandibular disorders, but found no obvious evidence that TMDs are causally associated with vitamin D. The conclusion should be cautiously interpreted, given the selection bias of TMDs patients sample.

Paleobiology and paleoecology of the woolly rhinoceros (Coelodonta antiquitatis) in Northern and Central Europe: New insights from multi-proxy data

The woolly rhinoceros, Coelodonta antiquitatis, was an emblematic component of Pleistocene faunas in Eurasia, which went extinct around ca. 12.5 ky BP. The loss of its tundra-steppe habitat due to climatic changes is considered the main cause for its extinction, whereas human impact was limited. In this study, we investigated the paleobiology and paleoecology of C. antiquitatis during the last glacial interval (Würm/Weichselian; 130 to 11.7 kya). We explored evolutionary trends for diet, physiology, and habitat via dental wear, enamel hypoplasia, body mass, age structure, mortality curves and stable isotopes (carbon and oxygen). Our results confirmed that C. antiquitatis was a large-sized species, with body mass around 2000–2500 kg, and with C3 grazing or mixed-feeding habits. Age structure and mortality curves revealed potential sampling and/or taphonomical biases at a few localities (e.g., Brixham cave, Ofnethöhle, North Sea), and indicated several vulnerability periods (birth, weaning, cow-calf separation/maturity) also retrieved by hypoplasia analyses. We observed some spatio-temporal fluctuations of body mass (1850–2955 kg), dietary preferences (strict to variable grazing) and hypoplasia prevalence (7.41–47.06 %) of C. antiquitatis depending on the locality, but correlation to specific climatic events (stadials-interstadials) is difficult without exact datation. These variations were however limited, highlighting a rather strict climatic niche and suggesting a high vulnerability to climatic and vegetation changes.

Running out of empty space: environmental lidar and the crowded ancient landscape of Campeche, Mexico

As airborne lidar surveys reveal a growing sample of urbanised tropical landscapes, questions linger about the sampling bias of such research leading to inflated estimates of urban extent and population magnitude. ‘Found’ datasets from remote sensing conducted for non-archaeological purposes and thus not subject to archaeological site bias, provide an opportunity to address these concerns through pseudorandom sampling. Here, the authors present their analysis of an environmental lidar dataset from Campeche, Mexico, which reveals previously unrecorded urbanism and dense regional-scale settlement. Both characteristics, the authors argue, are therefore demonstrably ubiquitous across the central Maya Lowlands.

SpaGRA: Graph augmentation facilitates domain identification for spatially resolved transcriptomics

Recent advances in spatially resolved transcriptomics (SRT) have provided new opportunities for characterizing spatial structures of various tissues. Graph-based geometric deep learning have gained widespread adoption for spatial domain identification tasks. Currently, most methods define adjacency relation between cells or spots by their spatial distance in SRT data, which overlooks key biological interactions like gene expression similarities, and leads to inaccuracies in spatial domain identification. To tackle this challenge, we propose a novel method, SpaGRA (https://github.com/sunxue-yy/SpaGRA), for automatic multi-relationship construction based on graph augmentation. SpaGRA uses spatial distance as prior knowledge and dynamically adjusts edge weights with multi-head graph attention networks (GATs). This helps SpaGRA to uncover diverse node relationships and enhance message passing in geometric contrastive learning. Additionally, SpaGRA uses these multi-view relationships to construct negative samples, addressing sampling bias posed by random selection. Experimental results show that SpaGRA demonstrates superior domain identification performance on multiple datasets generated from different protocols. Using SpaGRA, we analyzed the functional regions in the mouse hypothalamus, identified key genes related to heart development in mouse embryos, and observed cancer-associated fibroblasts enveloping cancer cells in the latest Visium HD data. Overall, SpaGRA can effectively characterize spatial structures across diverse SRT datasets.

Prevalence and predictors of internet use among Ghanaian older adults: evidence from the Ghana 2021 Population and Housing Census

Internet use among older adults has several beneficial impacts on their health and general wellbeing. However, there are limited studies on internet use among older adults in low- and middle-income countries, including Ghana. Also, studies on internet use among older adults have used surveys rather than census data, which minimises the potential for sampling bias and improves representativeness. Therefore, this study used census data to examine the prevalence and predictors of internet use among Ghanaian older adults. This study utilised the Ghana 2021 Population and Housing Census. The data were analysed using Pearson’s chi-square test and binary logistic regression. Overall, 52.9% of older adults used the internet 3 months preceding the census. Older adults mainly use mobile phones to access the internet. The significant predictors of internet use were age, sex, educational status, marital status, religious affiliation, place of residence, sight and hearing impairment, household wealth status, and ecological zone. Internet use among Ghanaian older adults was relatively high. Policymakers should consider these intrapersonal (age, sex, educational status, and sight and hearing impairments), interpersonal (marital status and household wealth), institutional (religious affiliation), and community (place of residence and ecological zone) level factors when developing interventions to promote internet use among older adults.

A brief review of non-avian dinosaur biogeography: state-of-the-art and prospectus.

Dinosaurs potentially originated in the mid-palaeolatitudes of Gondwana 245-235 million years ago (Ma) and may have been restricted to cooler, humid areas by low-latitude arid zones until climatic amelioration made northern dispersals feasible ca 215 Ma. However, this scenario is challenged by new Carnian Laurasian fossils and evidence that even the earliest dinosaurs had adaptations for arid conditions. After becoming globally distributed in the Early-Middle Jurassic (200-160 Ma), dinosaurs experienced vicariance driven by Pangaean fragmentation. Regional extinctions and trans-oceanic dispersals also played a role, and the formation of ephemeral land connections meant that older vicariance patterns were repeatedly overprinted by younger ones, creating a reticulate biogeographic history. Palaeoclimates shaped dispersal barriers and corridors, including filters that had differential effects on different types of dinosaurs. Dinosaurian biogeographic research faces many challenges, not the least of which is the patchiness of the fossil record. However, new fossils, extensive databasing and improved analytical methods help distinguish signal from noise and generate fresh perspectives. In the future, developing techniques for quantifying and ameliorating sampling biases and modelling the dispersal capacities of dinosaurs are likely to be two of the key components in our modern research programme.

Addressing multiple facets of bias and uncertainty in continental scale biodiversity databases

The availability of biodiversity databases is expanding at unprecedented rates. Nevertheless, species occurrence data can be intrinsically biased and contain uncertainties that impact the accuracy and reliability of biodiversity estimates. In this study, we developed a reproducible framework to assess three dimensions of bias—taxonomic, spatial, and temporal—as well as temporal uncertainty associated with data collections. We utilized the vegetation plot data located in Europe, from sPlotOpen, an open-access database, as a case study. The metrics proposed for estimating bias include completeness of the species richness for taxonomic bias, Nearest Neighbor Index for spatial bias, and Pielou’s index for temporal bias. Additionally, we introduced a new method based on a negative exponential curve to model the temporal decay in biodiversity data, aiming to quantify temporal uncertainty. Finally, we assessed the sampling bias considering the influence of various spatial variables (i.e, road density, human population count, Natura 2000 network and topographic roughness). We discovered that the facets of bias and the temporal uncertainty varied throughout Europe, as did the different roles played by spatial variables in determining biases. sPlotOpen showed a clustered distribution of the vegetation plots, and an uneven distribution in sampling completeness, year of sampling and temporal uncertainty. The facets of bias were significantly explained mainly by the presence of Natura 2000 network and marginally by the human population count. These results suggest that employing an efficient procedure to examine biases and uncertainties in data collections can enhance data quality and provide more reliable biodiversity estimates.

Assessing and correcting neighborhood socioeconomic spatial sampling biases in citizen science mosquito data collection

Climatic, ecological, and socioeconomic factors are facilitating the spread of mosquito-borne diseases, heightening the importance of vector surveillance and control. Citizen science is proving to be an effective tool to track mosquito populations, but methods are needed to detect and account for small scale sampling biases in citizen science surveillance. In this article we combine two types of traditional mosquito surveillance records with data from the Mosquito Alert citizen science system to explore the ways in which the socioeconomic characteristics of urban neighborhoods result in sampling biases in citizen scientists’ mosquito reports, while also shaping the spatial distribution of mosquito populations themselves. We use Barcelona, Spain, as an example, and focus on Aedes albopictus, an invasive vector species of concern worldwide. Our results suggest citizen scientists’ sampling effort is focused more in Barcelona’s lower and middle income census tracts than in its higher income ones, whereas Ae. albopictus populations are concentrated in the city’s upper-middle income tracts. High resolution estimates of the spatial distribution of Ae. albopictus risk can be improved by controlling for citizen scientists’ sampling effort, making it possible to provide better insights for efficiently targeting control efforts. Our methodology can be replicated in other cities faced with vector mosquitoes to improve public health responses to mosquito-borne diseases, which impose massive burdens on communities worldwide.

Urban wastewater contains a functional human antibody repertoire of mucosal origin

Wastewater-based surveillance of human disease offers timely insights to public health, helping to mitigate infectious disease outbreaks and decrease downstream morbidity and mortality. These systems rely on nucleic acid amplification tests for monitoring disease trends, while antibody-based seroprevalence surveys gauge community immunity. However, serological surveys are resource-intensive and subject to potentially long lead times and sampling bias. We identified and characterized a human antibody repertoire, predominantly secretory IgA, isolated from a central wastewater treatment plant and building-scale wastewater collection points. These antibodies partition to the solids fraction and retain immunoaffinity for SARS-CoV-2 and Influenza A virus antigens. This stable pool could enable real-time tracking for correlates of vaccination, infection, and immunity, aiding in establishing population-level thresholds for immune protection and assessing the efficacy of future vaccine campaigns.

Are Otolaryngologists Seeing More Cough? Longitudinal Trends and Patterns.

Otolaryngologists play an increasing role in managing cough, but little data exists examining the demographics of this patient population and the referral patterns that influence their access to care. This study sought to elucidate these factors using a longitudinal, nationwide database to minimize sampling bias and identify trends representative of the national population. Nationally representative survey. National Ambulatory Medical Care Survey (NAMCS). Visits with a diagnosis and chief complaint of cough between 2005 and 2019 in NAMCS were examined. Univariable and multivariable analyses were performed to compare patient demographics between visits to surgical specialists, medical specialists, and primary care physicians. Otolaryngologists made up more than 84% of surgical specialist visits. There was a 0.52% [0.20%-0.84%] increase per year in the proportion of visits attributed to surgical specialists. Based on a sensitivity analysis of the multivariable model, Hispanic patients (adjusted odds ratio, aOR: 0.88 [0.78-0.99] vs White) and patients living outside of metropolitan areas (aOR: 0.77 [0.61-0.99] vs living within) were less likely to see surgical specialists than primary care doctors for their cough. Patients who were referred (aOR: 1.47 [1.28-1.72] vs not referred) and with chronic cough (aOR: 1.47 [1.23-1.75] vs acute/subacute) were more likely to see a surgical specialist. Otolaryngologists are increasingly called upon to evaluate and consider treatment for cough. Identifying patient groups with limited access underscores the need for enhanced education about otolaryngologists' roles and integrated care approaches to improve access to specialized cough treatment. Level 4.

RTG-GNN: A novel rock topology-guided approach for permeability prediction using graph neural networks

Digital core permeability is a crucial factor in rock engineering, reservoir simulation, and underground applications, serving as the foundation for evaluating fluid flow underground. However, predicting digital core permeability using artificial intelligence presents challenges such as high model calculation complexity, strong resource dependence, and low efficiency. To overcome these challenges, this paper presents a novel hybrid enhanced rock topology-guided graph neural network (RTG-GNN) to accurately characterize the pore structure of digital cores and predict permeability. This model takes advantage of the physical attributes of pores and throats, utilizing them as features for nodes and edges. It improves information exchange through a gating mechanism and incorporates an attention mechanism for weighted aggregation of neighboring data, culminating in accurate permeability predictions. Furthermore, a scheme for constructing a comprehensive permeability distribution dataset (CPDD) is proposed for digital cores. This scheme effectively mitigates challenges like inadequate training data, subjective sampling bias, and data drift. Multiple experimental results demonstrate that RTG-GNN surpasses most methods in various performance indicators. Additionally, the RTG-GNN model boasts a lightweight design, occupying only 17% to 44% of the size of mainstream models and requiring just 15% to 38% of GPU resources for training. Remarkably, while maintaining high accuracy and low error rates, the computational load is reduced by a factor ranging from 19.06 to 636.41, the training speed is enhanced by 12.12 to 86.47 times, and the inference speed is increased by 6.76 to 16.14 times. The experiment further confirms that both single core and mixed rock datasets, acquired through the CPDD scheme, exhibit excellent generalization and reliability.

Educator's blueprint: Key considerations for using social media in survey-based medical education research.

In this paper, we present a set of recommendations for using social media as a tool for participant recruitment in survey-based medical education research. Drawing from a limited but growing body of literature, we discuss the opportunities and challenges inherent to social media recruitment. This article builds on the authors' previous educator's blueprints about survey design and administration. We highlight the advantages of social media, including its wide reach, cost-effectiveness, and capability to access diverse and geographically dispersed populations, which can significantly enhance the representativeness of research samples. However, we also caution against potential pitfalls, such as ethical concerns, sampling bias, and the fluid nature of social media platforms. Our recommendations are informed by both empirical evidence and best practices, aiming to provide researchers with practical advice for effectively leveraging social media in survey-based medical education research. We emphasize the importance of selecting suitable platforms and engaging with targeted demographics thoughtfully. By sharing our insights, we hope to assist fellow medical education researchers in navigating the complexities of social media recruitment, thereby enriching the quality and impact of survey-based research in this field.

Self-censorship in using social media in Bangladesh: Does regime structure matter?

influences social media users' self-censorship by examining how repression, legal frameworks, surveillance, and media control affect online expression and free speech. Method: This study on self-censorship in Bangladesh used interviews, content analysis, focus groups, and surveys to understand users' online behavior under a hybrid regime. The results showed that laws such as the Digital Security Act increased self-regulation. Results: The study revealed that regime structure significantly impacts self-censorship on social media in Bangladesh. Democratic environments reduce self-censorship, whereas restrictive regimes increase it. Higher educational levels are associated with greater self-censorship, likely due to heightened awareness of the consequences. Gender and government actions had a minimal impact. An interaction effect demonstrates that a "climate of fear" combined with critical content intensifies self-censorship, emphasizing the role of regime type and freedom of expression in shaping online behavior. Limitations: The study on self-censorship in Bangladesh has limitations, including sample bias, potential inaccuracies due to self-reporting, overlooking regional variations, and cultural factors, which affect the generalizability of findings across different contexts. Contributions: This study provides valuable insights into how the regime structure influences self-censorship on social media in Bangladesh. By highlighting the correlation between authoritarian tendencies and increased self-censorship, this study elucidates the impact of laws such as the Digital Security Act on online behavior. The findings contribute to understanding the broader effects of political regimes on digital expression, offering a framework for examining self-censorship in varying political contexts, and informing future research on digital rights and freedom of expression.

Evaluation of sampling bias in growth and skin health assessment methods of New Zealand-farmed king salmon (Oncorhynchus tshawytscha)

Sampling bias was investigated in New Zealand farmed king salmon (Oncorhynchus tshawytscha) at the end of a feed trial. Two sampling techniques - a non-lethal manual assessment (MA) and a lethal hook and line (HL), were evaluated for the representativity of their samples, with regard to two performance indicators, weight and skin health. Both sampling techniques assessed (MA n = 60, HL n = 4) were biased when compared to the analysis of all the fish in each pen. However, MA samples were biased towards fish with better skin health and higher weight (+ 0.20 kg), while HL selected fish with poorer skin health than the pen population average and lower Fulton's Condition Factor (− 0.25) than MA samples. Our results emphasised the significance of understanding sampling bias so research interpretation is correct.

Upgrading of Grade Group 1 Prostate Cancer at Prostatectomy: Germline Risk Factors in a Prospective Cohort.

Localized prostate tumors show significant spatial heterogeneity, with regions of high-grade disease adjacent to lower-grade disease. Consequently, prostate cancer biopsies are prone to sampling bias, potentially leading to underestimation of tumor grade. To study the clinical, epidemiologic and molecular hallmarks of this phenomenon, we conducted a prospective study of grade upgrading: differences in detected prostate cancer grade between biopsy and surgery. We established a prospective, multi-institutional cohort of men with Grade Group 1 (GG1) prostate cancer on biopsy who underwent radical prostatectomy. Upgrading was defined as detection of GG2+ in the resected tumor. Germline DNA from 192 subjects was subjected to whole-genome sequencing to quantify ancestry, pathogenic variants in DNA damage response genes and polygenic risk. Of 285 men, 67% upgraded at surgery. PSA density and percent of cancer in pre-prostatectomy positive biopsy cores were significantly associated with upgrading. No assessed genetic risk factor was predictive of upgrading, including polygenic risk scores for prostate cancer diagnosis. In a cohort of low-grade prostate cancer patients, a majority upgraded at radical prostatectomy. PSA density and percent of cancer in pre-prostatectomy positive biopsy cores portended the presence of higher-grade disease, while germline genetics was not informative in this setting. Patients with low-risk prostate cancer, but elevated PSA density or percent cancer in positive biopsy cores, may benefit from repeat biopsy, additional imaging or other approaches to complement active surveillance. Further risk stratification of patients with low-risk prostate cancer may provide useful context for active surveillance decision-making.

Factors affecting intraperitoneal pressure (IPP) and its prognostic value in predicting leak risk and gastrointestinal symptoms in adult peritoneal dialysis patients: a systematic review and meta-analysis.

Intraperitoneal pressure measurementoffers therapeutic and prognostic benefits in predicting leak risks and gastrointestinal symptoms in Peritoneal Dialysis (PD) patients. This systematic review aims to evaluate the prognostic utility of intraperitoneal pressure measurements and different estimated intraperitoneal pressure equations in predicting the risk of non-infectious complications in PD patients. Databases including MEDLINE, EMBASE and Cochrane were searched up to July 2023. Randomised and non-randomised trials were included, focusing on End-Stage Kidney Disease (ESKD) patients undergoing PD. Primary outcomes were variables associated with intraperitoneal pressure, while secondary outcomes included PD-related non-infectious complications and gastrointestinal symptoms. Data synthesis combined meta-analysis with narrative synthesis.This review has been registered on PROSPERO (CRD42023475138). Out of 1828 identified studies, 12 were included for systematic review and 10 for meta-analysis. Body Mass Index (BMI) and Body Surface Area (BSA) showed a consistent positive correlation with intraperitoneal pressure (BMI: r = 0.49, 95% CI 0.35-0.61, I2 = 67.39%, p = 0.003; BSA: r = 0.2, 95% CI 0.08-0.31, I2 = 14.10%, p = 0.324). Conversely, the association between intraperitoneal pressure and age, intraperitoneal volume, and Charlson Comorbidity Index were less consistent. Subgroup analysis demonstrated an association between higher intraperitoneal pressure in patients with increased BMI and BSA. However, the relationship between intraperitoneal pressure and non-infectious mechanical complications remained inconclusive. This review underscores a significant association between intraperitoneal pressure and anthropometric measures (BMI and BSA). The majority of the studies identified included a small sample and considerable bias. However, the association between intraperitoneal pressure and clinically relevant outcomes was not clear. While increasing body mass index and body surface areas are associated with increasing intraperitoneal pressure, the clinical relevance of measuring intraperitoneal pressure in an adult population remains unclear, particularly given the absence of an association with clinically relevant non-infectious outcomes.

Bidirectional consistency with temporal-aware for semi-supervised time series classification

Semi-supervised learning (SSL) has achieved significant success due to its capacity to alleviate annotation dependencies. Most existing SSL methods utilize pseudo-labeling to propagate useful supervised information for training unlabeled data. However, these methods ignore learning temporal representations, making it challenging to obtain a well-separable feature space for modeling explicit class boundaries. In this work, we propose a semi-supervised Time Series classification framework via Bidirectional Consistency with Temporal-aware (TS-BCT), which regularizes the feature space distribution by learning temporal representations through pseudo-label-guided contrastive learning. Specifically, TS-BCT utilizes time-specific augmentation to transform the entire raw time series into two distinct views, avoiding sampling bias. The pseudo-labels for each view, generated through confidence estimation in the feature space, are then employed to propagate class-related information into unlabeled samples. Subsequently, we introduce a temporal-aware contrastive learning module that learns discriminative temporal-invariant representations. Finally, we design a bidirectional consistency strategy by incorporating pseudo-labels from two distinct views into temporal-aware contrastive learning to construct a class-related contrastive pattern. This strategy enables the model to learn well-separated feature spaces, making class boundaries more discriminative. Extensive experimental results on real-world datasets demonstrate the effectiveness of TS-BCT compared to baselines.

Geographic bias effects on interpretations of secular trends of Hf isotope times series in zircons

The analysis of εHf(t) time series data from zircon reveals notable discrepancies based on sample type (igneous versus detrital), statistical weighting methodology, and geographic sampling bias. These differences warrant caution when interpreting data in the context of tectonic settings and the history of supercontinents. In terms of tectonic setting, accretionary orogens dominate in both sedimentary basins that host detrital zircons and in igneous zircon sources. Because of differences between the various time series, emphasis in this study is on peaks, valleys and secular trends and not on absolute εHf(t) values. Specifically, the igneous time series for εHf(t) in zircons shows more peaks and valleys than corresponding detrital time series for both weighted and unweighted data (weighting corrects for disproportionate geographic sampling). Also, sample-based (each sample considered separately) and site-based (samples grouped by geographic location and age) results align closely to the igneous time series, whereas the site-based detrital series displays more negative εHf(t) values. Regardless of the type of time series, the failure to compensate for disproportionate geographic sampling increases the prospects of producing an unrepresentative time-series. Nine zircon age peaks (both detrital and igneous) have corresponding εHf(t) peaks (3200, 2700, 2500, 2150, 1500, 1100, 750 Ma) and two have corresponding age valleys (1800–2000, 550 Ma). With exception of a geographically widespread 1500 Ma peak, most of the εHf(t) peaks and valleys are controlled by specific geographic regions and are likely not be global in extent.Two distinct periods (200–0 and 1800–1600 Ma) display εHf(t) signatures that rise steadily for 100–200 Myr, coinciding with the final stages of supercontinent assembly and the transition to the retreat of exterior orogens. An εHf(t) peak at 750 Ma and a high at 1400–1100 Ma partly overlap with supercontinent breakup and valleys at 550 Ma and 900 Ma with supercontinent assembly. A large εHf(t) valley at 2000–1800 Ma corresponds with the onset of craton collisions that led to the final assembly of Columbia at 1800–1600 Ma. The steep rise in εHf(t) in the last 200 Myr in both igneous and detrital zircons is controlled by sites in Circum-Pacific orogens in North and South America and Southwest Asia, and it parallels the breakup of Pangea. The general increase in zircon εHf(t) in the last 500 Myr in both detrital and igneous data reflects an increase in the proportion of isotopically juvenile components in accretionary orogens.

Genomic analysis of Strongyloides stercoralis and Strongyloides fuelleborni in Bangladesh.

About 600 million people are estimated to be infected with Strongyloides stercoralis, the species that causes most of the human strongyloidiasis cases. S. stercoralis can also infect non-human primates (NHPs), dogs and cats, rendering these animals putative sources for zoonotic human S. stercoralis infection. S. fuelleborni is normally found in old world NHPs but occasionally also infects humans, mainly in Africa. Dogs in southeast Asia carry at least two types of Strongyloides, only one of which appears to be shared with humans ("dog only" and "human and dog" types). For S. stercoralis with molecular taxonomic information, there is a strong sampling bias towards southeast and east Asia and Australia. In order to extend the geographic range of sampling, we collected human and dog derived Strongyloides spp. and hookworms from two locations in Bangladesh and subjected them to molecular taxonomic and genomic analysis based on nuclear and mitochondrial sequences. All hookworms found were Necator americanus. Contrary to earlier studies in Asia, we noticed a rather high incidence of S. fuelleborni in humans. Also in this study, we found the two types of S. stercoralis and no indication for genetic isolation from the southeast Asian populations. However, we found one genomically "dog only" type S. stercoralis in a human sample and we found two worms in a dog sample that had a nuclear genome of the "dog only" but a mitochondrial genome of the "human and dog" type. S. fuelleborni may play a more prominent role as a human parasite in certain places in Asia than previously thought. The introgression of a mitochondria haplotype into the "dog only" population suggests that rare interbreeding between the two S. stercoralis types does occur and that exchange of genetic properties, for example a drug resistance, between the two types is conceivable.