Effective population size (Ne(t)) is a fundamental parameter in population genetics and phylodynamics that quantifies genetic diversity and reveals demographic history. Coalescent-based methods enable the inference of Ne(t) trajectories through time from phylogenies reconstructed from molecular sequence data. Understanding the ecological and environmental drivers of population dynamics requires linking Ne(t) to external covariates. Existing approaches typically impose log-linear relationships between covariates and Ne(t), which may fail to capture complex biological processes and can introduce bias when the true relationship is nonlinear. We present a flexible Bayesian framework that integrates covariates into coalescent models with piecewise-constant Ne(t) through a Gaussian process (GP) prior. The GP, a distribution over functions, naturally accommodates nonlinear covariate effects without restrictive parametric assumptions. This formulation improves estimation of covariate-Ne(t) relationships, mitigates bias under nonlinear associations, and yields interpretable uncertainty quantification that varies across the covariate space. To balance global covariate-driven patterns with local temporal dynamics, we couple the GP prior with a Gaussian Markov random field that enforces smoothness in Ne(t) trajectories. Through simulation studies and three empirical applications - yellow fever virus dynamics in Brazil (2016-2018), late-Quaternary musk ox demography, and HIV-1 CRF02-AG evolution in Cameroon - we demonstrate that our method both confirms linear relationships where appropriate and reveals nonlinear covariate effects that would otherwise be missed or mischaracterized. This framework advances phylodynamic inference by enabling more accurate and biologically realistic modeling of how environmental and epidemiological factors shape population size through time.
Background: The rise of mobile technology and health apps has increased the use of person-generated health data (PGHD). PGHD holds significant potential for clinical decision-making but remains challenging to manage. Objective: This study aimed to enhance the clinical utilization of wearable health data by developing the Validation and Inspection Tool for Armband-Based Lifelog Data (VITAL), a pipeline for data integration, visualization, and quality management, and evaluating its usability. Methods: The study followed a structured process of requirement gathering, tool implementation, and usability evaluation. Requirements were identified through input from four clinicians. Wearable health data from Samsung, Apple, Fitbit, and Xiaomi devices were integrated into a standardized dataframe at 10-minute intervals, focusing on biometrics, activity, and sleep. Features of VITAL support data integration, visualization, and quality management. Usability evaluation involved seven clinicians performing tasks, completing the Unified Theory of Acceptance and Use of Technology (UTAUT) survey, and participating in interviews to identify usability issues. Results: VITAL successfully integrated wearable data, thus enabling all participants to complete tasks with minimal errors without prior participant training. UTAUT survey results were positive, with average scores of 4.2 for performance expectancy, 3.96 for effort expectancy, and 4.14 for intention to use, indicating high user satisfaction and intent to adopt the tool. Conclusions: By enhancing wearable data integration, visualization, and quality management, the VITAL prototype shows significant potential for clinical application. Positive feedback highlights its promise, while emphasizing the need for further studies to confirm its real-world effectiveness.
In the late 1990s while exploring methods for estimating population age structure using the post-capture longevity of fruit flies sampled from the wild (referred to as residual demography) I discovered an identity in which the fraction of individuals x days old in a stationary population equals the fraction that die x days later. I co-authored a paper containing this identity in 2004 as part of a larger publication with my biodemography colleagues where we extended the concept for practical application. In 2009 demographer James Vaupel published a proof of this identity and referred to it using the eponym Carey’s Equality. The Vaupel paper was then followed six years later (2015) by a surprise—the identity had been published in French 30 years earlier in the gray literature by demographer Nicolas Brouard. Remarkably the identity had never been cited in either the searchable (journal) literature or in any of the mainstream demography texts, treatises, encyclopedias or reference books. Here I tell the story of how I discovered this identity, why it is important, implications for human demography, and lessons learned along the way.
Due to rapid urbanization over the past 20 years, many newly developed areas have lagged in socio-economic maturity, creating an imbalance with older cities and leading to the rise of "ghost cities." However, due to the complexity of socio-economic factors, no global studies have measured this phenomenon. We propose a unified framework based on urban vitality theory and multi-source data, validated by various data sources. We derived 8841 natural cities globally with an area over 5 square kiloxmeters and divided each into new urban areas (developed after 2005) and old urban areas (developed before 2005). Urban vitality was gauged using the density of road networks, points of interest (POIs), and population density with 1 km resolution across morphological, functional, and social dimensions. By comparing urban vitality in new and old urban areas, we quantify the ghost cities index (GCI) globally using the theory of urban vitality for the first time. The results reveal that the vitality of new urban areas is 7.69% that of old ones. The top 5% (442) of cities were designated as ghost cities, a finding mirrored by news media and other research. This study sheds light on strategies for sustainable global urbanization, crucial for the United Nations' Sustainable Development Goals.
Xinyue Alice Zhang, Angelo Ricarte, Dominic W. Pesce
et al.
The Event Horizon Telescope has produced resolved images of the supermassive black holes Sgr A* and M87*, which present the largest shadows on the sky. In the next decade, technological improvements and extensions to the array will enable access to a greater number of sources, unlocking studies of a larger population of supermassive black holes through direct imaging. In this paper, we identify 12 of the most promising sources beyond Sgr A* and M87* based on their angular size and millimeter flux density. For each of these sources, we make theoretical predictions for their observable properties by ray tracing general relativistic magnetohydrodynamic models appropriately scaled to each target's mass, distance, and flux density. We predict that these sources would have somewhat higher Eddington ratios than M87*, which may result in larger optical and Faraday depths than previous EHT targets. Despite this, we find that visibility amplitude size constraints can plausibly recover masses within a factor of 2, although the unknown jet contribution remains a significant uncertainty. We find that the linearly polarized structure evolves substantially with Eddington ratio, with greater evolution at larger inclinations, complicating potential spin inferences for inclined sources. We discuss the importance of 345 GHz observations, milli-Jansky baseline sensitivity, and independent inclination constraints for future observations with upgrades to the Event Horizon Telescope (EHT) through ground updates with the next-generation EHT (ngEHT) program and extensions to space through the Black Hole Explorer (BHEX).
Cardiovascular disease (CVD) is the leading cause of death and premature mortality worldwide, with occupational environments significantly influencing CVD risk, underscoring the need for effective cardiac monitoring and early warning systems. Existing methods of monitoring vital signs require subjects to remain stationary, which is impractical for daily monitoring as individuals are often in motion. To address this limitation, we propose MERIT, a multimodality-based wearable system designed for precise ECG waveform monitoring without movement restrictions. Daily activities, involving frequent arm movements, can significantly affect sensor data and complicate the reconstruction of accurate ECG signals. To mitigate motion impact and enhance ECG signal reconstruction, we introduce a deep independent component analysis (Deep-ICA) module and a multimodal fusion module. We conducted experiments with 15 subjects. Our results, compared with commercial wearable devices and existing methods, demonstrate that MERIT accurately reconstructs ECG waveforms during various office activities, offering a reliable solution for fine-grained cardiac monitoring in dynamic environments.
Abstract Purpose ‘Ireland Lights Up’ (ILU) is a community-based walking programme which runs in over 1000 Gaelic Athletic Clubs (GAA) in Ireland every January. It consists of clubs turning on their floodlights on a designated evening to facilitate group walking events around the perimeter of the playing fields. Formative research has indicated that ILU can potentially increase physical activity (PA) and social connectedness. As part of a broader study, this research aims to capture the impact of ILU in diverse community settings across Ireland by measuring its current reach in participating clubs and identifying the most viable implementation strategies to improve potential reach to underserved and disadvantaged populations. This phase of the research will provide a comprehensive overview of the demographics and behaviours of both participants and non-participants in ILU, exploring the factors influencing reach. Methods The broader study will employ a hybrid type-two effectiveness-implementation design utilising the RE-AIM and PRISM frameworks. This phase of the study will employ a mixed methodology, collecting qualitative data through observations in 10 - 12 implementing clubs and one-to-one semi-structured interviews with club leaders and participants. Quantitative data will be obtained through questionnaires distributed to all ILU-participating clubs, complemented by attendance records and manual club counts. Non-participating GAA clubs will also be interviewed to enhance understanding of reach. Results The study will document the percentage of individuals in the community engaged in ILU, comparing participant characteristics to non-participants and the target population, particularly emphasising socially disadvantaged groups. The study will also report participant and stakeholder perceptions and expectations regarding reach. Conclusions The overarching aim of the study is to develop a structured framework for involving marginalised, isolated, and vulnerable populations in physical activity within the context of sports club settings. To effectively implement strategies, a complete understanding of the demographics and behaviours of those involved is vital. The aim of this phase of the research is to discern and identify implementation strategies that advance both reach and participation. Funding Source Funded by SETU PhD Scholarship Programme.
BACKGROUND: Two seminal studies in the 1980s, by Preston and Coale (1982) and Arthur and Vaupel (1984), generalized the Lotka equations developing the variable-r methods. OBJECTIVE: Time changes in sex ratios (males:females) are studied from the perspective of the variable-r method to estimate the contributions of fertility, mortality, and net-migration. METHODS: The time change in sex ratios can be calculated as a comparison of the growth rates of the sexes. The difference is then decomposed into population composition, and the age-specific components of fertility, mortality, and net migration. Thirteen countries with long historical demographic series are used to illustrate the time trends in sex ratios. RESULTS: Most countries are moving towards a greater number of males per females. The greatest changes were observed in Norway and Sweden, with males catching up with females at older ages due to survival. Meanwhile, the sex differential in net-migration flows explains the decline in sex ratios in Spain and Australia and the increase in the United States. CONCLUSIONS: Our results shed light on the debate on female–male population imbalances and how fertility, mortality, and net migration contribute to these disparities. CONTRIBUTION: Vaupel and Canudas-Romo (2003) wrote a paper in honor of Vaupel’s mentor Nathan Keyfitz on his 90th anniversary. The paper focused on a common topic of interest of all three researchers, namely mathematical demography. In honor of the legacy of the work of Jim Vaupel, we revive that idea with a collection of mathematical derivations that are simple, yet powerful in their demographic interpretation.
Traditional network interdiction problems focus on removing vertices or edges from a network so as to disconnect or lengthen paths in the network; network diversion problems seek to remove vertices or edges to reroute flow through a designated critical vertex or edge. We introduce the all-pairs vitality maximization problem (VIMAX), in which vertex deletion attempts to maximize the amount of flow passing through a critical vertex, measured as the all-pairs vitality of the vertex. The assumption in this problem is that in a network for which the structure is known but the physical locations of vertices may not be known (e.g. a social network), locating a person or asset of interest might require the ability to detect a sufficient amount of flow (e.g., communications or financial transactions) passing through the corresponding vertex in the network. We formulate VIMAX as a mixed integer program, and show that it is NP-Hard. We compare the performance of the MIP and a simulated annealing heuristic on both real and simulated data sets and highlight the potential increase in vitality of key vertices that can be attained by subset removal. We also present graph theoretic results that can be used to narrow the set of vertices to consider for removal.
Understanding the relationship between micromechanics and macroscopic plastic deformation is vital for elucidating the deformation mechanism of amorphous solids, such as granular materials. In this study, we directly measure T1 events, which are topological rearrangements of particles, and the associated microscopic stresses in dense packings of photoelastic disks under pure shear. We observe a remarkable similarity between the evolution of the total number of T1 events and the global stress-strain curve. The competition between the birth and death of T1 events establishes a dynamic equilibrium after the yield strain, contributing to the overall plastic behavior. Despite the erratic stress fluctuations of individual T1 events, the local stresses decrease on average once T1 events occur, indicating their soft characteristics. Furthermore, we demonstrate that the microscopic stress fluctuations exhibit a long-range anisotropic spatial correlation similar to the Eschelby character, but with a distinct scaling of $r^{-1.5}$. Interestingly, we also find a striking similarity in the correlation functions of T1 and non-T1 regions. These findings establish a significant connection between macroscopic mechanical behavior and the elementary deformation of T1 events, shedding light on the fundamental understanding of granular materials as amorphous solids with contact-scale plastic deformation.
Konstantin Leyde, Stephen R. Green, Alexandre Toubiana
et al.
We apply neural posterior estimation for fast-and-accurate hierarchical Bayesian inference of gravitational wave populations. We use a normalizing flow to estimate directly the population hyper-parameters from a collection of individual source observations. This approach provides complete freedom in event representation, automatic inclusion of selection effects, and (in contrast to likelihood estimation) without the need for stochastic samplers to obtain posterior samples. Since the number of events may be unknown when the network is trained, we split into sub-population analyses that we later recombine; this allows for fast sequential analyses as additional events are observed. We demonstrate our method on a toy problem of dark siren cosmology, and show that inference takes just a few minutes and scales to $\sim 600$ events before performance degrades. We argue that neural posterior estimation therefore represents a promising avenue for population inference with large numbers of events.
WeappreciateDr.Hu’s thoughtful commentsonour recentlypublished systematic review that critically assessed the Alzheimer’s disease (AD) drug trial landscape across the past two decades. Dr. Hu raises some concerns related to targeting diversity in trial participants for recruitment and poses several hypothetical questions regarding (1) the basis of inclusion of a diverse sample if biological correlates do not vary, (2) the intention behind inclusionwhile cautioning aimless overenrollment of diverse populations, and (3) the value of assessing social determinants of health (SDOH) if there is no influence on biological outcomes. We agree the problem is complex and value the space for conversation. However,wehave some conjectures on several vital points in response. In the United States, despite race–and ethnicity to a lesser extent– being a social construction, both are integral parts of our identity (individual to population) since they are externally imposed/reinforced via formal structures (census, laws, policies) and informally through group/culture dynamics. Daily, both constructs innervate our social interactions, may impact access to health-care services, play a latent role in where we live, and how we perform and participate in society. As a result, it is essential to consider race and ethnicity in trials, beyond demographic characterization. A strand of scientific argument supports the notion that it is counterproductive to include more diverse participants in drug trials if the biological mechanisms have slight variance. However, if non-Hispanic, White, middle-class, and relatively healthy participants are continually enrolled in drug trials, then we only know how those drugs work within that population. We see this reasoning as defeatist and in violation of the Belmont Report’s core principles, which originate from a deep history of unethical practices toward ethnic and racially minoritized research participants. As a result, there is a scientific, ethical, and social mandate to move toward representation. To clarify, our study did not recommend baseless overenrollment of racially and ethnically diverse populations into clinical trials. Clinical trials will not be able to tell us how drugs work in all groups or how SDOH may affect treatment response. Randomization creates statistical independence between the treatment assignment and potential outcomes (that is, it establishes exchangeability between groups). Thus, these trials give us population-level inferences. Given this design limitation, we want the trial participants to look like the underlying population that wewish tomake inferences about, because it gives us better information about how thedrugwillwork in the target population. The overreliance on molecular correlates of disease has severely skewed the landscape of biomedical science as a field and extends into AD and related dementias. One can argue that the lack of progress in AD in disease-modifying drugs is due to an overt, siloed reliance on biological processes to the exclusion of environmental and social processes. In theUnited States, it is established that contributions ofmedical care explain 10% to 20% of an individual’s health outcomes, while SDOH constitutes the remaining 80% to 90%.1 Dr. Hu mentioned that appropriate measures of SDOH are underdeveloped in biomedicine, thus making consideration of such measures in trial recruitment problematic. One can argue that this is a result of a narrow scientific focus that prioritized biological mechanisms. Disciplines of sociology and anthropology, including theirmedical, biological, or environmental subspecialties, have validated measures that have existed for decades and can and should be adopted in biomedicine. Finally, from our global viewpoint, these issues seem to vary across regions and continents. For example, the events in Europe during World War II have led to strict legislation on the collection of race/ethnicity data in several European countries–for example, in France, publicly funded census/statistics bureaus donot collect data on race/ethnicity, generally only collecting nationality data.2 Topics specific to race do not seem to be as deeply ingrained in European organizations, policy, and identity as they are in the United States. Discussions in medical and health research thoughtfully revolve around aspects such as country of origin, religion, ethnicity, socioeconomic status, and/or language proficiency, and race per se is rarely discussed in themedia or other contexts in Europe.
Colleen Webber, Abe Hafid, Anastasia Gayowsky
et al.
Objectives
The objective of this study was to describe the receipt of potentially non-beneficial interventions in the last 100 days of life of cancer patients and to examine variations in these interventions according to patient characteristics and cancer site.
Approach
We conducted a population-based retrospective cohort study of all adults age 18+ who died of cancer in Ontario, Canada between January 1, 2013 and December 31, 2017 using linked administrative health data held at ICES. Potentially non-beneficial interventions were captured via hospital discharge records and included chemotherapy, major surgery, intensive care unit admission, cardiopulmonary resuscitation, defibrillation, dialysis, percutaneous coronary intervention, mechanical ventilation, feeding tube placement, blood transfusion and bronchoscopy. We used bivariate analyses and multivariable Poisson regression to examine associations between the receipt of interventions and decedent age, sex, rurality, area-level income, and cancer site.
Results
Among the 125,755 decedents, the most common intervention was blood transfusion (18.1%) and major surgery (12.8%); 23.8% received no interventions, while 14% of decedents received 3+ interventions. Lower intervention rates were observed in older patients (adjusted rate ratio (RR) 0.46, 95% confidence interval (CI) 0.44-0.49 for age 95+ vs. 19-44), females (RR 0.93, 95% CI 0.92-0.94), and individuals living in higher income areas (RR 0.96, 95% CI 0.95-0.98 for highest vs. lowest income quintile). Higher intervention rates were observed in rural patients (RR 1.13, 95% CI 1.11-1.14). Patients with pancreatic cancer had the highest intervention rate (RR 1.13, 95% CI 1.10-1.16), while breast cancer patients had the lowest intervention rate (RR 0.86, 95% CI 0.84-0.89) compared to colorectal cancer patients.
Conclusion
Potentially non-beneficial interventions were common in the last 100 days of life of patients with cancer. Variations in interventions across patient demographics and cancer site may reflect differences in healthcare access, end-of-life care preferences, patients’ prognostic awareness, and disease factors.
We present an automatic approach to discover analytic population models for gravitational-wave (GW) events from data. As more gravitational-wave (GW) events are detected, flexible models such as Gaussian Mixture Models have become more important in fitting the distribution of GW properties due to their expressivity. However, flexible models come with many parameters that lack physical motivation, making interpreting the implication of these models challenging. In this work, we demonstrate symbolic regression can complement flexible models by distilling the posterior predictive distribution of such flexible models into interpretable analytic expressions. We recover common GW population models such as a power-law-plus-Gaussian, and find a new empirical population model which combines accuracy and simplicity. This demonstrates a strategy to automatically discover interpretable population models in the ever-growing GW catalog, which can potentially be applied to other astrophysical phenomena.
Social media is an essential gateway of information and communication for people worldwide. The amount of time spent and reliance of people on social media makes it a vital resource for detecting events happening in real life. Thousands of significant events are posted by users every hour in the form of multimedia. Some individuals and groups target the audience to promote their agenda among these users. Their cause can threaten other groups and individuals who do not share the same views or have specific differences. Any group with a definitive cause cannot survive without the support which acts as a catalyst for their agenda. A phenomenon occurs where people are fed information that motivates them to act on their behalf and carry out their agenda. One is benefit results in the loss of the others by putting their lives, assets, physical and emotional health in danger. This paper introduces a concept of dangerous events to approach this problem and their three main types based on their characteristics: action, scenarios, and sentiment-based dangerous events.