Human bony labyrinth is an indicator of population history and dispersal from Africa.

The dispersal of modern humans from Africa is now well documented with genetic data that track population history, as well as gene flow between populations. Phenetic skeletal data, such as cranial and pelvic morphologies, also exhibit a dispersal-from-Africa signal, which, however, tends to be blurred by the effects of local adaptation and in vivo phenotypic plasticity, and that is often deteriorated by postmortem damage to skeletal remains. These complexities raise the question of which skeletal structures most effectively track neutral population history. The cavity system of the inner ear (the so-called bony labyrinth) is a good candidate structure for such analyses. It is already fully formed by birth, which minimizes postnatal phenotypic plasticity, and it is generally well preserved in archaeological samples. Here we use morphometric data of the bony labyrinth to show that it is a surprisingly good marker of the global dispersal of modern humans from Africa. Labyrinthine morphology tracks genetic distances and geography in accordance with an isolation-by-distance model with dispersal from Africa. Our data further indicate that the neutral-like pattern of variation is compatible with stabilizing selection on labyrinth morphology. Given the increasingly important role of the petrous bone for ancient DNA recovery from archaeological specimens, we encourage researchers to acquire 3D morphological data of the inner ear structures before any invasive sampling. Such data will constitute an important archive of phenotypic variation in present and past populations, and will permit individual-based genotype-phenotype comparisons

Clinical Characteristics, Racial Inequities, and Outcomes in Patients with Breast Cancer and COVID-19: A COVID-19 and Cancer Consortium (CCC19) Cohort Study

BACKGROUND: Limited information is available for patients with breast cancer (BC) and coronavirus disease 2019 (COVID-19), especially among underrepresented racial/ethnic populations. METHODS: This is a COVID-19 and Cancer Consortium (CCC19) registry-based retrospective cohort study of females with active or history of BC and laboratory-confirmed severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) infection diagnosed between March 2020 and June 2021 in the US. Primary outcome was COVID-19 severity measured on a five-level ordinal scale, including none of the following complications, hospitalization, intensive care unit admission, mechanical ventilation, and all-cause mortality. Multivariable ordinal logistic regression model identified characteristics associated with COVID-19 severity. RESULTS: 1383 female patient records with BC and COVID-19 were included in the analysis, the median age was 61 years, and median follow-up was 90 days. Multivariable analysis revealed higher odds of COVID-19 severity for older age (aOR per decade, 1.48 [95% CI, 1.32-1.67]); Black patients (aOR 1.74; 95 CI 1.24-2.45), Asian Americans and Pacific Islander patients (aOR 3.40; 95 CI 1.70-6.79) and Other (aOR 2.97; 95 CI 1.71-5.17) racial/ethnic groups; worse ECOG performance status (ECOG PS ≥2: aOR, 7.78 [95% CI, 4.83-12.5]); pre-existing cardiovascular (aOR, 2.26 [95% CI, 1.63-3.15])/pulmonary comorbidities (aOR, 1.65 [95% CI, 1.20-2.29]); diabetes mellitus (aOR, 2.25 [95% CI, 1.66-3.04]); and active and progressing cancer (aOR, 12.5 [95% CI, 6.89-22.6]). Hispanic ethnicity, timing, and type of anti-cancer therapy modalities were not significantly associated with worse COVID-19 outcomes. The total all-cause mortality and hospitalization rate for the entire cohort was 9% and 37%, respectively however, it varied according to the BC disease status. CONCLUSIONS: Using one of the largest registries on cancer and COVID-19, we identified patient and BC-related factors associated with worse COVID-19 outcomes. After adjusting for baseline characteristics, underrepresented racial/ethnic patients experienced worse outcomes compared to non-Hispanic White patients. FUNDING: This study was partly supported by National Cancer Institute grant number P30 CA068485 to Tianyi Sun, Sanjay Mishra, Benjamin French, Jeremy L Warner; P30-CA046592 to Christopher R Friese; P30 CA023100 for Rana R McKay; P30-CA054174 for Pankil K Shah and Dimpy P Shah; KL2 TR002646 for Pankil Shah and the American Cancer Society and Hope Foundation for Cancer Research (MRSG-16-152-01-CCE) and P30-CA054174 for Dimpy P Shah. REDCap is developed and supported by Vanderbilt Institute for Clinical and Translational Research grant support (UL1 TR000445 from NCATS/NIH). The funding sources had no role in the writing of the manuscript or the decision to submit it for publication. CLINICAL TRIAL NUMBER: CCC19 registry is registered on ClinicalTrials.gov, NCT04354701

Individual-based modelling of population growth and diffusion in discrete time

Individual-based models (IBMs) of human populations capture spatio-temporal dynamics using rules that govern the birth, behavior, and death of individuals. We explore a stochastic IBM of logistic growth-diffusion with constant time steps and independent, simultaneous actions of birth, death, and movement that approaches the Fisher-Kolmogorov model in the continuum limit. This model is well-suited to parallelization on high-performance computers. We explore its emergent properties with analytical approximations and numerical simulations in parameter ranges relevant to human population dynamics and ecology, and reproduce continuous-time results in the limit of small transition probabilities. Our model prediction indicates that the population density and dispersal speed are affected by fluctuations in the number of individuals. The discrete-time model displays novel properties owing to the binomial character of the fluctuations: in certain regimes of the growth model, a decrease in time step size drives the system away from the continuum limit. These effects are especially important at local population sizes of <50 individuals, which largely correspond to group sizes of hunter-gatherers. As an application scenario, we model the late Pleistocene dispersal of Homo sapiens into the Americas, and discuss the agreement of model-based estimates of first-arrival dates with archaeological dates in dependence of IBM model parameter settings

Head injury patterns in helmeted and non-helmeted cyclists admitted to a London Major Trauma Centre with serious head injury

Background: Cycle use across London and the UK has increased considerably over the last 10 years. With this there has been an increased interest in cycle safety and injury prevention. Head injuries are an important cause of mortality and morbidity in cyclists. This study aimed to ascertain the frequency of different head injury types in cyclists and whether wearing a bicycle helmet affords protection against specific types of head injury.Methods: A retrospective observational study of all cyclists older than 16 years admitted to a London Major Trauma Centre between 1st January 2011 and 31st December 2015 was completed. A cohort of patients who had serious head injury was identified (n = 129). Of these, data on helmet use was available for 97. Comparison was made between type of injury frequency in helmeted and non-helmeted cyclists within this group of patients who suffered serious head injury.Results: Helmet use was shown to be protective against intracranial injury in general (OR 0.2, CI 0.07–0.55, p = 0.002). A protective effect against subdural haematoma was demonstrated (OR 0.14, CI 0.03–0.72, p = 0.02). Wearing a helmet was also protective against skull fractures (OR 0.12, CI 0.04–0.39, p&lt;0.0001) but not any other specific extracranial injuries. This suggests that bicycle helmets are protective against those injuries caused by direct impact to the head. Further research is required to clarify their role against injuries caused by shearing forces.Conclusions: In a largely urban environment, the use of cycle helmets appears to be protective for certain types of serious intra and extracranial head injuries. This may help to inform future helmet design.</p

Exploring Late Pleistocene hominin dispersals, coexistence and extinction with agent-based multi-factor models

© 2022Toward the end of the Pleistocene, archaic humans in Eurasia such as the Neanderthals and Denisovans were completely replaced by anatomically modern humans dispersing from Africa. The causes underlying the replacement and extinction processes remain controversial, especially regarding the relative importance of random events, and anthropogenic and environmental factors. Here, we use the most comprehensive agent-based modeling framework to date for exploring Late Pleistocene human population dynamics under realistic time-evolving environmental conditions. Model simulations suggest multiple out-of-Africa dispersals. Most of these resulted in only partial replacement of Eurasians and long-term coexistence of spatially structured archaic and modern populations in Eurasia. Moreover, a comparison of empirical and model data suggests that the best-documented extinction process – that of the Neanderthals – did not have a single overarching cause, but spatially and temporally diverse causes and mechanisms, such as environmental fluctuations, and asymmetry in resource exploitation efficiency and reproductive rates. When viewed in isolation, various population properties have central importance for replacements, but their true importance can only be understood in comparison and with interactions with other properties.11Nsciescopu

Exploring Late Pleistocene hominin dispersals, coexistence and extinction with agent-based multi-factor models

Toward the end of the Pleistocene, archaic humans in Eurasia such as the Neanderthals and Denisovans were completely replaced by anatomically modern humans dispersing from Africa. The causes underlying the replacement and extinction processes remain controversial, especially regarding the relative importance of random events, and anthropogenic and environmental factors. Here, we use the most comprehensive agent-based modeling framework to date for exploring Late Pleistocene human population dynamics under realistic time-evolving environmental conditions. Model simulations suggest multiple out-of-Africa dispersals. Most of these resulted in only partial replacement of Eurasians and long-term coexistence of spatially structured archaic and modern populations in Eurasia. Moreover, a comparison of empirical and model data suggests that the best-documented extinction process – that of the Neanderthals – did not have a single overarching cause, but spatially and temporally diverse causes and mechanisms, such as environmental fluctuations, and asymmetry in resource exploitation efficiency and reproductive rates. When viewed in isolation, various population properties have central importance for replacements, but their true importance can only be understood in comparison and with interactions with other properties

Drivers of Late Pleistocene human survival and dispersal: an agent-based modeling and machine learning approach

Understanding Late Pleistocene human dispersals from Africa requires understanding a multifaceted problem with factors varying in space and time, such as climate, ecology, human behavior, and population dynamics. To understand how these factors interact to affect human survival and dispersal, we have developed a realistic agent-based model that includes geographic features, climate change, and time-varying vegetation and food resources. To enhance computational efficiency, we further apply machine learning algorithms. Our approach is new in that it is designed to systematically evaluate a large-scale agent-based model, and identify its key parameters and sensitivities. Results show that parameter interactions are the major source in generating variability in human dispersal and survival/extinction scenarios. In realistic scenarios with geographical features and time-evolving climatic conditions, random fluctuations become a major source of variability in arrival times and success. Furthermore, parameter settings as different as 92% of maximum possible difference, and occupying more than 30% of parameter space can result in similar dispersal scenarios. This suggests that historical contingency (similar causes – different effects) and equifinality (different causes – similar effects) are primary constituents of human dispersal scenarios. While paleoanthropology, archaeology and paleogenetics now provide insights into patterns of human dispersals at an unprecedented level of detail, elucidating the causes underlying these patterns remains a major challenge

Injury type in cyclists admitted with head injury.

<p>Injury type in cyclists admitted with head injury.</p