Population Counts Research Articles

Abstract 4117645: Ischemic Heart Disease Trends in Canada: A 30-Year Epidemiological Study

Introduction: Ischemic heart disease (IHD) results from coronary artery blockages that impede myocardial blood flow. We aim to examine the temporal trends of IHD in Canada from 1990 to 2019, assessing age-standardized mortality, disability-adjusted life years (DALYs), and other health indicators. Methods: We analyzed data from the 2019 Global Burden of Disease study on Canadian IHD cases over three decades. The study investigated age-standardized deaths, DALYs, years lived with disability (YLDs), years of life lost (YLLs), prevalence, and incidence across both sexes. We performed statistical analyses using Microsoft Excel 16.63.1. Results: Over the period from 1990 to 2019, Canadian men and women experienced notable decreases in IHD incident rates [-45.11%], overall mortality [-60.64%], YLLs [-62.824%], prevalence [-26.778%], YLDs [-48.729%], and DALYs [-50.467%]. Detailed analysis revealed a reduction in death rates by 61.311% in men and 61.119% in women, while YLLs decreased by 63.115% for men and 63.808% for women. Similarly, DALYs showed a decline of 62.67% for men and 63.25% for women. Prevalence rates fell by 25.417% in women and 29.594% in men, whereas YLDs decreased by 51.148% for women and 49.172% for men. The 2019 data confirmed a per 100,000 population count of 48.78 YLDs, 1020.28 YLLs, and 1069.06 DALYs across both sexes. Conclusions: This study illustrates a significant decrease in ischemic heart disease (IHD) metrics in Canada from 1990 to 2019, highlighting notable reductions in mortality, prevalence, and disability rates among both genders. The findings underscore the effectiveness of healthcare improvements and public health initiatives and IHD. Despite these advancements, IHD continues to pose a substantial health challenge, indicating the need for ongoing efforts in medical care, policy-making, and lifestyle modifications to further alleviate the disease's burden in Canada.

Biodiversity Assessment of Trees and Shrubs at Tirunelveli’s Science Centre in Southern India: A Conservation Perspective

A comprehensive taxonomical floristic survey conducted at the District Science Centre in Tirunelveli identified 84 species of trees and shrubs, documenting each plant species with detailed information on medicinal uses, family classification, commercial importance, and population count of individual plants. The survey identified 33 families and 68 genera, with Fabaceae being the most dominant family, followed by Rubiaceae, Arecaceae, and Malvaceae. Additionally, the survey highlighted a list of vulnerable, threatened, and endangered tree species such as Cycas beddomei Dyer., Santalum album L., emphasizing the need for conservation efforts based on their spatial reach and anthropogenic pressures. These species face various threats, including loss of habitat, agriculture, pollution load, and recreational disturbances, and were assessed according to the IUCN Red List criteria. This study provides valuable insights for botanists, conservationists, and policymakers, promoting sustainable management and protection of plant biodiversity in the region.

Efficacy of Jatropha curcas extracts amended with sulphur against Aspergillus flavus and Aspergillus ear rot on maize at harvest

Jatropha curcas (Euphorbiaceae) is often considered a “miracle plant” due to its multiple uses, including its extracts reported insecticidal and fungicidal effects. This study assessed the efficacy of aqueous extract from dried leaves of J. curcas and Sulphur 99.5% WP fungicide in managing Aspergillus flavus growth and Aspergillus ear rot (AER) in maize at harvest across two sites. Treatments included botanical extracts at 300 g/L (T1), 350 g/L (T2), Sulphur at 5 g/L (T3) and 10 g/L (T4), and combinations of botanical extracts with Sulphur at both low and high concentrations (T5 and T6). Additionally, Aflasafe TZ01™ and untreated control were used as checks. At site 1, T2 and T6 significantly reduced AER incidence (10% and 13.33%, respectively) compared to the untreated control (70%). At site 2, most treatments, particularly the combinations of botanicals and sulphur, had lower AER incidences than the control. All treatments significantly reduced AER severity at site 1, where the untreated control exhibited the highest severity (67%). A similar trend was observed at site 2, although Aflasafe showed severity levels comparable to the control. Significant differences in A. flavus population counts were observed among treatments at both sites, with higher dosages of botanical extracts and their combinations with sulphur showing lower counts than the untreated control. The counts, in Colony Forming Units per gram (CFU/g), ranged from 171.5 (T6) to 490.9 (untreated control) and 80.5 (T6) to 554.4 (untreated control) in sites 1 and 2, respectively. These findings indicate that Jatropha curcas, either alone or in combination with sulphur, hold potential as a component of integrated management strategies against Aspergillus flavus infections at the pre-harvest stage.

Oral Cancer Incidence Among Adult Males With Current or Former Use of Cigarettes or Smokeless Tobacco: Population-Based Study.

Tobacco use has been identified as a risk factor for oral cancer worldwide. However, relative oral cancer incidence among adults who smoke cigarettes, use smokeless tobacco products (ST), have transitioned from cigarettes to ST, quit cigarettes and/or ST ("quitters"), or never used tobacco has not been well studied. We aim to present population-based oral cancer incidence rates for adults who smoke cigarettes, use ST, are former smokers who now use ST, or quit. We estimated cross-sectional incidence rates and incidence rate ratios (IRRs) using data from statewide cancer registries (Colorado, Florida, North Carolina, and Texas) and population counts derived from national surveys using combined data from 2014-2017. A random-effect meta-analysis approach was used to summarize estimates among these groups, based on multiple imputation-based IRR estimates by state and age group while considering potential heterogeneity. A total of 19,536 oral cancer cases were identified among adult males 35 years and older in the study geographies and period. The oral cancer incidence rate among adults who smoke was significantly higher than the ST group (2.6 times higher, 95% CI 2.0-3.3, P<.001), 3.6 (95% CI 3.2-4.1, P<.001) times higher than the never users, and 2.4 (95% CI 1.8-3.1, P<.001) times higher compared to former smokers who now use ST. The IRR among the ST group relative to never users was 1.4 (95% CI 1.1-1.9, P=.02). The IRR between former smokers who now use ST and those who quit was 1.4 (95% CI 1.0-2.1, P=.08). Findings from this population-based study with a large number of oral cancer cases support significantly high oral cancer incidence among adults who smoke and a lower risk of oral cancer incidence among never users, quitters, users of ST, and former smokers who now use ST compared to cigarettes. Future studies with detailed control of tobacco history and other relevant confounders are needed to confirm these findings.

LarvaeCountAI: a robust convolutional neural network-based tool for accurately counting the larvae of Culex annulirostris mosquitoes.

Accurate counting of mosquito larval populations is essential for maintaining optimal conditions and population control within rearing facilities, assessing disease transmission risks, and implementing effective vector control measures. While existing methods for counting mosquito larvae have faced challenges such as the impact on larval mortality rate, multiple parameters adjustment and limitations in availability and affordability, recent advancements in artificial intelligence, particularly in AI-driven visual analysis, hold promise for addressing these issues. Here, we introduce LarvaeCountAI, an open-source convolutional neural network (CNN)-based tool designed to automatically count Culex annulirostris mosquito larvae from videos captured in laboratory environments. LarvaeCountAI does not require videos to be recorded using an advanced setup; it can count larvae with high accuracy from videos captured using a simple setup mainly consisting of a camera and commonly used plastic trays. Using the videos enables LarvaeCountAI to capitalise on the continuous movement of larvae, enhancing the likelihood of accurately counting a greater number of larvae. LarvaeCountAI adopts a non-invasive approach, where larvae are simply placed in trays and imaged, minimising any potential impact on larval mortality. This approach addresses the limitations associated with previous methods involving mechanical machines, which often increase the risk of larval mortality as larvae pass through multiple sections for counting purposes. The performance of LarvaeCountAI was tested using 10 video samples. Validation results demonstrated the excellent performance of LarvaeCountAI, with an accuracy ranging from 96.25% to 99.13% across 10 test videos and an average accuracy of 97.88%. LarvaeCountAI represents a remarkable advancement in mosquito surveillance technology, offering a robust and efficient solution for monitoring larval populations. LarvaeCountAI can contribute to developing effective strategies for reducing disease transmission and safeguarding public health by providing timely and accurate data on mosquito larvae abundance.

T Regulatory Cell Subsets Do Not Restore for One Year After Acute COVID-19.

COVID-19, caused by SARS-CoV-2, triggers a complex immune response, with T regulatory cells (Tregs) playing a crucial role in maintaining immune homeostasis and preventing excessive inflammation. The current study investigates the function of T regulatory cells during COVID-19 infection and the subsequent recovery period, emphasizing their impact on immune regulation and inflammation control. We conducted a comprehensive analysis of Treg subpopulations in peripheral blood samples from COVID-19 patients at different stages: acute infection, early convalescence, and long-term recovery. Flow cytometry was employed to quantify Tregs including "naïve", central memory (CM), effector memory (EM), and terminally differentiated CD45RA+ effector cells (TEMRA). Additionally, the functional state of the Tregs was assessed by the expression of purinergic signaling molecules (CD39, CD73). Cytokine profiles were assessed through multiplex analysis. Our findings indicate a significant decrease in the number of Tregs during the acute phase of COVID-19, which correlates with heightened inflammatory markers and increased disease severity. Specifically, we found a decrease in the relative numbers of "naïve" and an increase in EM Tregs, as well as a decrease in the absolute numbers of "naïve" and CM Tregs. During the early convalescent period, the absolute counts of all Treg populations tended to increase, accompanied by a reduction in pro-inflammatory cytokines. Despite this, one year after recovery, the decreased subpopulations of regulatory T cells had not yet reached the levels observed in healthy donors. Finally, we observed the re-establishment of CD39 expression in all Treg subsets; however, there was no change in CD73 expression among Tregs. Understanding these immunological changes across different T regulatory subsets and adenosine signaling pathways offers important insights into the disease's pathogenesis and provides a broader view of immune system dynamics during recovery.

Population displacement after earthquakes: benchmarking predictions based on housing damage

In the aftermath of an earthquake, the number of residents whose housing was destroyed is often used to approximate the number of people displaced (i.e., rendered homeless) after the event. While this metric can provide rapid situational awareness regarding potential long-term housing needs, more recent research highlights the importance of additional factors beyond housing damage within the scope of household displacement and return (e.g., utility disruption, tenure, place attachment). This study benchmarks population displacement estimates using this simplified conventional approach (i.e., only considering housing destruction) through three scenario models for recent earthquakes in Haiti, Japan, and Nepal. These model predictions are compared with officially reported values and alternate mobile location data-based estimates from the literature. The results highlight the promise of scenario models to realistically estimate population displacement and potential long-term housing needs after earthquakes, but also highlight a large range of uncertainty in the predicted values. Furthermore, purely basing displacement estimates on housing damage offers no view on how the displaced population counts vary with time as compared to more comprehensive models that include other factors influencing population return or alternative approaches, such as using mobile location data.

Evolving epidemiological patterns of thyroid cancer and estimates of overdiagnosis in 2013–17 in 63 countries worldwide: a population-based study

The incidence of thyroid cancer has been increasing in many countries, mainly due to overdiagnosis. Given these rapid changes, we aim to assess the specific features of the thyroid cancer diagnosis epidemic and provide estimates of overdiagnosis across countries spanning five continents and identify areas in which coping strategies are needed. Two types of data were retrieved from the International Agency for Research on Cancer (IARC) Global Cancer Observatory database. The long-term annual incidence of thyroid cancer by sex and 5-year age group for all ages from 1980 to 2017 was obtained from continuous population-based registries available in the Cancer Incidence in Five Continents (CI5) plus, with 97 registries from 43 countries selected. Sex-age-specific thyroid cancer cases, overall and by subtype, along with corresponding population counts were retrieved from all registries included in the latest volume of CI5 (CI5-XII), with 385 registries in 63 countries. Annual mortality data from 1980 to 2022 were obtained from WHO, with population counts supplemented by UN population estimates. We estimated age-standardised rates (ASRs) of thyroid cancer incidence and mortality for all ages by sex using direct age standardisation, with the world population as a reference. Long-term annual trends of ASRs were compared between incidence and mortality since 1980. Subtype distribution was calculated for thyroid cancer incidence during 2013-17. We estimated the numbers of thyroid cancer cases and overdiagnosed cases and extrapolated to the whole country using a previously developed and validated method. Thyroid cancer incidence rates rose during 1980-2017 for most countries, with the highest rates seen in South Korea, Cyprus, Ecuador, China, and Türkiye for females and males. An upward trend was seen until the early 2010s, followed by a downward trend in South Korea, the USA, Canada, and Israel and some Western European countries, such as France, Italy, Austria, and Ireland. The difference between the highest and lowest incidence rates ranged from less than 10·0 per 100 000 females in the early 1980s to 101·4 per 100 000 females in 2012. For males, the difference between the highest and lowest incidence rates ranged from 2·7 per 100 000 to 23·5 per 100 000 over the study period. Mortality rates were substantially lower, with a difference between the highest and lowest rates across countries of around 1·0-2·0 per 100 000 for both sexes throughout the study period. During 2013-17, papillary thyroid cancer contributed to the large variation in ASRs of thyroid cancer incidence. The mortality rates of thyroid cancer increased with age for all countries, whereas the observed age-specific incidence rates showed an inverted U-shape in most countries. The magnitude of overdiagnosis varied across countries, ranging from no overdiagnosis (in Uganda, Zimbabwe, and Trinidad and Tobago) to more than 85·0% of thyroid cancer cases being overdiagnosed in females (in Cyprus, China, South Korea, and Türkiye). Overall, 1 736 133 (75·6%) of 2 297 057 cases were attributable to overdiagnosis, including 1 368 181 females and 367 952 males. Although the incidence of thyroid cancer has reached a plateau or decrease in some high-income countries, the magnitude of overdiagnosis is still very large and the expansion of overdiagnosis of thyroid cancer to the transitioning countries has been rapid, which makes it a global public health challenge that needs to be addressed. National Natural Science Foundation of China, Guangdong Basic and Applied Basic Research Foundation, Young Talents Program of Sun Yat-sen University Cancer Center, Italian Association for Cancer Research, and the Italian Ministry of Health (Ricerca Corrente).

A Dataset of Visible Light and Thermal Infrared Images for Health Monitoring of Caged Laying Hens in Large-Scale Farming.

Considering animal welfare, the free-range laying hen farming model is increasingly gaining attention. However, in some countries, large-scale farming still relies on the cage-rearing model, making the focus on the welfare of caged laying hens equally important. To evaluate the health status of caged laying hens, a dataset comprising visible light and thermal infrared images was established for analyses, including morphological, thermographic, comb, and behavioral assessments, enabling a comprehensive evaluation of the hens' health, behavior, and population counts. To address the issue of insufficient data samples in the health detection process for individual and group hens, a dataset named BClayinghens was constructed containing 61,133 images of visible light and thermal infrared images. The BClayinghens dataset was completed using three types of devices: smartphones, visible light cameras, and infrared thermal cameras. All thermal infrared images correspond to visible light images and have achieved positional alignment through coordinate correction. Additionally, the visible light images were annotated with chicken head labels, obtaining 63,693 chicken head labels, which can be directly used for training deep learning models for chicken head object detection and combined with corresponding thermal infrared data to analyze the temperature of the chicken heads. To enable the constructed deep-learning object detection and recognition models to adapt to different breeding environments, various data enhancement methods such as rotation, shearing, color enhancement, and noise addition were used for image processing. The BClayinghens dataset is important for applying visible light images and corresponding thermal infrared images in the health detection, behavioral analysis, and counting of caged laying hens under large-scale farming.

The Dynamic Response of Municipal Budgets to Revenue Shocks

We study the fiscal and tax response to intergovernmental grants, exploiting quasi-experimental variation within Germany's fiscal equalization scheme triggered by census revisions of population counts. Municipal budgets do not adjust instantly. Instead, spending and investments adapt within five years to revenue gains, while the adjustment to losses is more rapid. Yet the long-run response is symmetric. The tax response is particularly slow, stretching over more than a decade. Well-known empirical anomalies such as the so-called flypaper effect may thus reflect a short-run phenomenon, while long-run fiscal behavior appears more consistent with standard theories of fiscal federalism. (JEL E62, H71, H72, H77, R51)

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.

Individual-Level Exposure to Residential Redlining in 1940 and Mortality Risk

Historic redlining, the practice by the Home Owners' Loan Corporation (HOLC) of systematically denying credit to borrowers in neighborhoods that were inhabited by primarily African American individuals, has been associated with poor community outcomes, but the association with individual risk of death is not clear. To examine if exposure to residential redlining practices by HOLC in 1940 is associated with increased risk of death later in life. The study linked individuals who resided within HOLC-graded neighborhoods (defined as Census Enumeration Districts) in 1940 with administrative death records data. The study estimated hazard ratios as well as age-specific life expectancy gaps (at age 55, 65, and 75 years) for HOLC grading exposure. This was done using methods that adapted standard parametric survival analysis to data with limited mortality coverage windows and incomplete observations of survivors. The analysis sample consisted of 961 719 individual-level observations across 13 912 enumeration districts within 30 of the largest US cities (based on 1940 population counts) across 23 states. Data were analyzed between December 1, 2023, and September 4, 2024. The exposure was HOLC grade based on historic HOLC maps, with A representing "best" or creditworthy areas; B, "still desirable"; C, "definitely declining"; and D, "hazardous" areas not worthy of credit (ie, redlined), and the main outcome was age at death from the Social Security Numident file. The 961 719-person individual sample had a mean (SD) age of 19.26 (9.26) years in 1940 and a mean (SD) age at death of 76.83 (9.22) years. In a model adjusted for sex (52.48% female; 47.52% male), race and ethnicity (7.36% African American; 92.64% White), and latent place effects, a 1-unit lower HOLC grade was associated with an 8% (hazard ratio, 1.08 [95% CI, 1.07-1.09]) increased risk of death. At age 65 years, these hazard differentials translated into an estimated life expectancy gap of -0.49 (95% CI, -0.56 to -0.43) years for each 1-unit decrease of the HOLC grade. This study found that individuals who resided within redlined neighborhoods in 1940 had lower life expectancy later in life than individuals who resided within other HOLC-graded areas.

Geospatial Data Aggregation Methods for Novel Geographies: Validating Congressional District Life Expectancy Estimates.

Place is a critical determinant of health. Recent novel analyses have explored health outcome estimation for small geographies, such as census tracts, as well as health outcome aggregation to geopolitical geographies with accountable political representatives, such as congressional districts. In one such application, combining these approaches, researchers aggregated census tract estimates of life expectancy to the congressional district level to derive local estimates, but such an approach has not been validated. Here, we compared two sources and approaches to calculating life expectancy data for Pennsylvania congressional districts. We used 2010-2015 census tract life expectancy estimates from the US Small-area Life Expectancy Estimates Project (LEEP) and dasymetric methods to compute population-weighted life expectancy aggregated to the congressional district level. Using georeferenced Vital Statistics data, we aggregated age-specific census tract death and population counts to congressional districts and used abridged life tables to estimate life expectancy. To validate the dasymetric aggregated estimates we compared absolute differences, assessed the correlation, and created Bland-Altman plots to visualize the agreement between the two measures. We found strong agreement between congressional district estimates of life expectancy at birth derived using the dasymetric LEEP model-based approach and Vital Statistics direct estimates approach, though life expectancy at older ages (75 and older) showed weak correlations. This validation contributes to our understanding of geospatial aggregation methods for novel geographies including congressional districts. Health outcome data aggregated to the congressional district geography can support congressional policy making aimed at improving population health outcomes.

Impact of Comorbid Pathology and Severity of Clinical Course on Anti-infective Protection in Patients with COVID-19

Objective — to establish and evaluate the impact of comorbid pathology and the severity of the clinical course on the activity of anti-infective protection in patients with coronavirus disease 2019 (COVID-19), based on the analysis of the absolute and relative counts of major immune cell populations in peripheral blood. Materials and methods. A cross-sectional prospective study was conducted involving 204 patients with COVID-19-associated pneumonia of mild, moderate and severe stages. The cohort included 106 (51.97 %) women and 98 (48.03 %) men, with an average age of (55.93 ± 8.75) years. Anti-infective ­protection was studied using comprehensive clinical blood analysis, including the count of major immune-competent cells. Patients were categorised into groups based on comorbi­dity, taking into account the predominant concurrent pathology at the time of examination (endocrine, cardiovascular or other). Results and discussion. More severe COVID-19 cases were characterised by leukopenia, relative and absolute granulocytopenia, neutropenia, a slight hyporegenerative nuclear shift to the right and a reduction in the number of segmented neutrophils by 24.63—34.72 % (p ≤ 0.047—0.009) and eosinophilic granulocytes by 52.94—70.59 % (p ≤ 0.004—0.001). This was accompanied by relative and absolute agranulocytosis due to lymphocytosis (by 20.10—63.33 %; p ≤ 0.05—0.003) and monocytosis (by 40.0—74.12 %; p ≤ 0.046—0.049), indicating an active inflammatory infectious process of viral etiology against a background of decreased resistance and increased activity of the lymphocyte-macrophage link with the beginning of the formation of specific immune protection (cellular and humoral response to coronavirus intervention). Mild cases of COVID-19 showed a higher absolute count of peripheral blood granulocytes (by 48.25 %; p = 0.018), neutrophils, particularly segmented neutrophils and eosinophilic granulocytes, compared to moderate and severe cases (by 32.36—53.18 % (p ≤ 0.049—0.01) and 3.4 times (p ≤ 0.005—0.002)) and the lowest content of monocytes (by 35.41—42.57 %; p ≤ 0.049—0.046). In cases with concurrent endocrine and cardiological pathologies against a background of a more severe clinical course of COVID-19, immunoinflammatory changes intensified due to absolute and relative leukopenia, neutrophilic granulocytopenia, neutropenia, with the presence of relative lymphocytosis and monocytosis, and normal ESR indicators, confirming the inflammatory process of viral etiology, which, in the absence of treatment, may tend to form post-COVID and/or long-term effects of COVID-19 with multisystem involvement. Conclusions. The study found an enhancement of the immunoinflammatory response and an increase in the depletion of cellular factors of non-specific anti-infective protection with the progression of the clinical manifestations of COVID-19, particularly against the backdrop of comorbid endocrine and/or cardiological pathology.

Excess Death Rates by State During the COVID-19 Pandemic: United States, 2020‒2023.

Objectives. To estimate state-level excess death rates during 2020 to 2023 and examine differences by region and partisan orientation. Methods. We modeled death and population counts from the Centers for Disease Control and Prevention to estimate excess death rates for the United States, 9 census divisions, and 50 states. We compared excess death rates for states with different partisan orientations, measured by the party of the seated governor and the level of partisan representation in state legislatures. Results. The United States experienced 1 277 697 excess deaths between March 2020 and July 2023. Almost 90% of these deaths were attributed to COVID-19, and 51.5% occurred after vaccines were available. The highest excess death rates first occurred in the Northeast and then shifted to the South and Mountain states. Between weeks ending June 20, 2020, through March 19, 2022, excess death rates were higher in states with Republican governors and greater Republican representation in state legislatures. Conclusions. Excess death rates during the COVID-19 pandemic varied considerably across the US states and were associated with partisan representation in state government, although the influence of confounding variables cannot be excluded. (Am J Public Health. 2024;114(9):882-891. https://doi.org/10.2105/AJPH.2024.307731).

High-resolution population maps derived from Sentinel-1 and Sentinel-2

Detailed population maps play an important role in diverse fields ranging from humanitarian action to urban planning. Generating such maps in a timely and scalable manner presents a challenge, especially in data-scarce regions. To address it we have developed Popcorn, a population mapping method whose only inputs are free, globally available satellite images from Sentinel-1 and Sentinel-2; and a small number of aggregate population counts over coarse census districts for calibration. Despite the minimal data requirements our approach surpasses the mapping accuracy of existing schemes, including several that rely on building footprints derived from high-resolution imagery. E.g., we were able to produce population maps for Rwanda with 100m GSD based on less than 400 regional census counts. In Kigali, those maps reach an R2 score of 66% w.r.t. a ground truth reference map, with an average error of only ±10 inhabitants/ha. Conveniently, Popcorn retrieves explicit maps of built-up areas and local building occupancy rates, making the mapping process interpretable and offering additional insights, for instance about the distribution of built-up, but unpopulated areas, e.g., industrial warehouses. With our work we aim to democratize access to up-to-date and high-resolution population maps, recognizing that some regions faced with particularly strong population dynamics may lack the resources for costly micro-census campaigns. Project page: https://popcorn-population.github.io/.

Small-area population forecasting in a segregated city using density-functional fluctuation theory

Policy decisions concerning housing, transportation, and resource allocation would all benefit from accurate small-area population forecasts. However, despite the success of regional-scale migration models, developing neighborhood-scale forecasts remains a challenge due to the complex nature of residential choice. Here, we introduce an innovative approach to this challenge by extending density-functional fluctuation theory (DFFT), a proven approach for modeling group spatial behavior in biological systems, to predict small-area population shifts over time. The DFFT method uses observed fluctuations in small-area populations to disentangle and extract effective social and spatial drivers of segregation, and then uses this information to forecast intra-regional migration. To demonstrate the efficacy of our approach in a controlled setting, we consider a simulated city constructed from a Schelling-type model. Our findings indicate that even without direct access to the underlying agent preferences, DFFT accurately predicts how broader demographic changes at the city scale percolate to small-area populations. In particular, our results demonstrate the ability of DFFT to incorporate the impacts of segregation into small-area population forecasting using interactions inferred solely from steady-state population count data.

A unified approach to long-term population monitoring of grizzly bears in the Greater Yellowstone Ecosystem

Long-term wildlife research and monitoring programs strive to maintain consistent data collections and analytical methods. Incorporating new techniques is important but can render data sets incongruent and limit their potential to discern trends in demographic parameters. Integrated population models (IPMs) can address these limitations by combining data sources that may span different periods into a unified statistical framework while providing a holistic view of population dynamics. We developed an IPM in a Bayesian framework for grizzly bears (Ursus arctos) in the Greater Yellowstone Ecosystem. We coupled demographic data with multiple, independent population count data to link annual changes in abundance with vital rates over 4 decades (1983–2023). Abundance increased threefold from an estimated 270 individuals in 1984 to 1030 individuals in 2023. Parameter estimates indicated survival of bears ≥2 years of age was high, contributing to robust population growth during the 1980s (λ = 1.023 [50 % interquartile range = 0.993–1.082]) and 1990s (λ = 1.064 [1.023–1.103]). A slowing of population growth started around 2000 (2000s: λ = 1.030 [0.989–1.068]) and continued into the 2010s (λ = 1.021 [0.985–1.057]), due primarily to reductions in survival of bears <2 years of age. These findings corroborate previous research that identified density-dependent effects as a likely cause. The IPM framework provided greater certainty and understanding regarding the dynamic demographic characteristics of the population and serves as a powerful monitoring tool for this long-lived species. Implementation of the IPM allows timely dissemination of demographic data to help inform adaptive management strategies and policy decisions necessary for the continued management and conservation of this population. This robust and flexible monitoring system allows scientists to investigate the effects of a changing ecosystem on population dynamics, incorporate new data sources and statistical models, and respond to changes in monitoring needs for the population. We highlight the efficacy of the IPM in estimating and tracking demographic parameters for a long-lived species, while accommodating shifts in monitoring techniques and data collections typical of long-term wildlife conservation programs worldwide.

Tensor Decomposition for Spatiotemporal Mobility Pattern Learning with Mobile Phone Data

Detecting urban mobility patterns is crucial for policymakers in urban and transport planning. Mobile phone data have been increasingly deployed to measure the spatiotemporal variations in human mobility. This work applied non-negative Tucker decomposition (NTD) to mobile phone-based origin–destination (O-D) matrices to explore mobility patterns’ latent spatial and temporal relationships in the province of Liège, Belgium. Four [Formula: see text] traffic tensors have been built for one regular weekday, one regular weekend day, one holiday weekday, and one holiday weekend day, respectively. The proposed method inferred spatial clusters and temporal patterns while interpreting the correlation between spatial clusters and temporal patterns through geographical visualization. As a result, we found the similarity of O-D and destination–origin (D-O) patterns and the symmetry for the trips of the temporal patterns with evening peak and morning peaks on the weekday. Moreover, we investigated the attraction of different spatial clusters with two temporal patterns on a regular weekday and validated the reconstructed demand using population counts and commuting matrices. Finally, the differences in spatial and temporal interactions have been addressed in detail.

The Ensiling melon biomass with different levels of inclusion of ground corn

The aim of this study was to evaluate silages produced with different mixtures of melon biomass and different levels of ground corn. The experiment was conducted under a completely randomized design in factorial scheme (3 × 4), with four replications. The first factor consieted of three mixtures of plant (branch and leaf) and fruit (melon; scraps) on natural matter (NM) as follows: 0, 100 and 1000 g kg-1 fruit. The second factor consisted of the use of different amounts of ground corn (0, 50, 100 and 200 g kg-1). Experimental silos with capacity for 5 kg and density of 500 kg m-3 were used. After 90 days, the silos were opened and the evaluations were carried out. The data were subjected to analysis of variance and the means were analyzed through the Tukey’s test (fermentative losses, chemical composition, aerobic stability), Scott-Knott test (microbiology and organic acids) and regression, and were compared with a significance level of p&lt;0.05. The best dry matter results were obtained in silages with 0, 100 and 1000 g kg-1 fruit combined with 200 g kg-1 ground corn, which averaged 289.4, 290.4 and 264.1 g kg-1, respectively. Quadratic behavior was observed for effluent losses in silages with 100 g kg-1 fruit presenting a minimum value of 46.8 e and a maximum 56.2 kg t-1 NM. Regarding the mold population count, the highest values were obtained in silages with 100 g kg-1 fruit and 50 g kg-1 ground corn. Silages with 100 and 1000 g kg-1 fruit in melon biomass and 200 g kg-1 ground corn presented superior quality indicators, characterizing these biomass mixtures as the most suitable for silage making.