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The age of a water particle in a shallow man-made reservoir is defined as the time elapsed since it entered it. Analyzing this diagnostic timescale provides valuable information for optimally sizing and operating such structures. Here, the constituent-oriented age and residence time theory (CART) is used to obtain not only the mean age, but also the water age distribution function at each location. The method is applied to 10 different shallow reservoirs of simple geometry (rectangular), in a steady-state framework. The results show that complex, multimodal water age distributions are found, implying that focusing solely on simple statistics (e.g., mean or median age) fails to reflect the complexity of the actual distribution of water age. The latter relates to the fast or slow pathways that water particles may take for traveling from the inlet to the outlet of the reservoirs.

Age can be evaluated at any time and position to understand transport processes taking place in the aquatic environment, including for reactive tracers. In the framework of the Constituent-oriented Age and Residence time Theory (CART), the age of a constituent or an aggregate of constituents, including the water itself, is usually defined as the time elapsed since leaving the boundary where the age is set or reset to zero. The age is evaluated as the ratio of the age concentration to the concentration, which are the solution of partial differential equations. The boundary conditions for the concentration and age concentration cannot be prescribed independently of each other. Instead, they must be derived from boundary conditions designed beforehand for the age distribution function (the histogram of the ages, the age theory core variable), even when this variable is not calculated explicitly. Consistent boundary conditions are established for insulating, departure and arrival boundaries. Gas exchanges through the water–air interface are also considered. Age fields ensuing from consistent boundary conditions and, occasionally, non-consistent ones are discussed, suggesting that the methodology advocated herein can be utilized by most age calculations, be they used for diagnosing the results of idealised models or realistic ones.

Objective This study investigated the influence of Coronavirus Disease 2019 (COVID-19) on lung function in early convalescence phase. Methods A retrospective study of COVID-19 patients at the Fifth Affiliated Hospital of Sun Yat-sen University were conducted, with serial assessments including lung volumes (TLC), spirometry (FVC, FEV1), lung diffusing capacity for carbon monoxide (DLCO),respiratory muscle strength, 6-min walking distance (6MWD) and high resolution CT being collected at 30 days after discharged. Results Fifty-seven patients completed the serial assessments. There were 40 non-severe cases and 17 severe cases. Thirty-one patients (54.3%) had abnormal CT findings. Abnormalities were detected in the pulmonary function tests in 43 (75.4%) of the patients. Six (10.5%), 5(8.7%), 25(43.8%) 7(12.3%), and 30 (52.6%) patients had FVC, FEV1, FEV1/FVC ratio, TLC, and DLCO values less than 80% of predicted values, respectively. 28 (49.1%) and 13 (22.8%) patients had PImax and PEmax values less than 80% of the corresponding predicted values. Compared with non-severe cases, severe patients showed higher incidence of DLCO impairment (75.6%vs42.5%, p  = 0.019), higher lung total severity score (TSS) and R20, and significantly lower percentage of predicted TLC and 6MWD. No significant correlation between TSS and pulmonary function parameters was found during follow-up visit. Conclusion Impaired diffusing-capacity, lower respiratory muscle strength, and lung imaging abnormalities were detected in more than half of the COVID-19 patients in early convalescence phase. Compared with non-severe cases, severe patients had a higher incidence of DLCO impairment and encountered more TLC decrease and 6MWD decline.

Mathematical modeling is critical to our understanding of how infectious diseases spread at the individual and population levels. This book gives readers the necessary skills to correctly formulate and analyze mathematical models in infectious disease epidemiology, and is the first treatment of the subject to integrate deterministic and stochastic models and methods. Mathematical Tools for Understanding Infectious Disease Dynamicsfully explains how to translate biological assumptions into mathematics to construct useful and consistent models, and how to use the biological interpretation and mathematical reasoning to analyze these models. It shows how to relate models to data through statistical inference, and how to gain important insights into infectious disease dynamics by translating mathematical results back to biology. This comprehensive and accessible book also features numerous detailed exercises throughout; full elaborations to all exercises are provided. Covers the latest research in mathematical modeling of infectious disease epidemiologyIntegrates deterministic and stochastic approachesTeaches skills in model construction, analysis, inference, and interpretationFeatures numerous exercises and their detailed elaborationsMotivated by real-world applications throughout

People who survive tuberculosis face clinical and societal consequences after recovery, including increased risks of recurrent tuberculosis, premature death, reduced lung function, and ongoing stigma. To describe the size of this issue, we aimed to estimate the number of individuals who developed first-episode tuberculosis between 1980 and 2019, the number who survived to 2020, and the number who have been treated within the past 5 years or 2 years. In this modelling study, we estimated the number of people who survived treated tuberculosis using country-level WHO data on tuberculosis case notifications, excluding those who died during treatment. We estimated the number of individuals surviving untreated tuberculosis using the difference between WHO country-level incidence estimates and notifications, applying published age-stratified and HIV-stratified case fatality ratios. To estimate survival with time, post-tuberculosis life tables were developed for each country-year by use of UN World Population Prospects 2019 mortality rates and published post-tuberculosis mortality hazard ratios. Between 1980 and 2019, we estimate that 363 million people (95% uncertainty interval [UI] 287 million–438 million) developed tuberculosis, of whom 172 million (169 million–174 million) were treated. Individuals who developed tuberculosis between 1980 and 2019 had lived 3480 million life-years (95% UI 3040 million–3920 million) after tuberculosis by 2020, with survivors younger than 15 years at the time of tuberculosis development contributing 12% (95% UI 7–17) of these life-years. We estimate that 155 million tuberculosis survivors (95% UI 138 million–171 million) were alive in 2020, the largest proportion (47% [37–57]) of whom were in the WHO South-East Asia region. Of the tuberculosis survivors who were alive in 2020, we estimate that 18% (95% UI 16–20) were treated in the past 5 years and 8% (7–9) were treated in the past 2 years. The number of tuberculosis survivors alive in 2020 is more than ten times the estimated annual tuberculosis incidence. Interventions to alleviate respiratory morbidity, screen for and prevent recurrent tuberculosis, and reduce stigma should be immediately prioritised for recently treated tuberculosis survivors. UK Medical Research Council, the UK Department for International Development, the National Institute for Health Research, and the European and Developing Countries Clinical Trials Partnership.

This book provides a solution to the ecological inference problem, which has plagued users of statistical methods for over seventy-five years: How can researchers reliably infer individual-level behavior from aggregate (ecological) data? In political science, this question arises when individual-level surveys are unavailable (for instance, local or comparative electoral politics), unreliable (racial politics), insufficient (political geography), or infeasible (political history). This ecological inference problem also confronts researchers in numerous areas of major significance in public policy, and other academic disciplines, ranging from epidemiology and marketing to sociology and quantitative history. Although many have attempted to make such cross-level inferences, scholars agree that all existing methods yield very inaccurate conclusions about the world. In this volume, Gary King lays out a unique--and reliable--solution to this venerable problem. King begins with a qualitative overview, readable even by those without a statistical background. He then unifies the apparently diverse findings in the methodological literature, so that only one aggregation problem remains to be solved. He then presents his solution, as well as empirical evaluations of the solution that include over 16,000 comparisons of his estimates from real aggregate data to the known individual-level answer. The method works in practice. King's solution to the ecological inference problem will enable empirical researchers to investigate substantive questions that have heretofore proved unanswerable, and move forward fields of inquiry in which progress has been stifled by this problem.

Literature Review. For optimal Distal Radius Fracture (DRF) rehabilitation and fracture prevention, it is important to understand the epidemiology and factors predictive of injury, chronic pain, chronic disability, and subsequent fracture. To summarize the literature reporting on DRF epidemiology, risk factors, and prognostic factors. Literature synthesis. Although incidence varies globally, DRFs are common across the lifespan and appear to be on the rise. Risk of DRF is determined by personal factors (age, sex/gender, lifestyle, health condition) and environmental factors (population density, climate). For example, age and sex influence risk such that DRF is most common in boys/young men and older women. The most common causes of DRF in the pediatric and young adult age groups include playing/sporting activities and motor vehicle accidents. In contrast, the most common mechanism of injury in older adults is a low-energy trauma because of a fall from a standing height. Poorer health outcomes are associated with older age, being female, poor bone healing (or having an associated fracture of the ulnar styloid), having a compensated injury, and a lower socioeconomic status. Risk stratification according to predictors of chronic pain and disability enable therapists to identify those patients who will benefit from advocacy for more comprehensive assessment, targeted interventions, and tailored educational strategies. The unique opportunity for secondary prevention of osteoporotic fracture after DRF has yet to be realized by treating therapists in the orthopedic community. V.

Older patients with acute decompensated heart failure (ADHF) have persistently poor outcomes including frequent rehospitalization despite guidelines-based therapy. We hypothesized that such patients have multiple, severe impairments in physical function, cognition, and mood that are not addressed by current care pathways. We prospectively examined frailty, physical function, cognition, mood, and quality of life in 27 consecutive older patients with ADHF at 3 medical centers and compared these with 197 participants in 3 age-matched cohorts: stable heart failure (HF) with preserved ejection fraction (n = 80), stable HF with reduced ejection fraction (n = 56), and healthy older adults (n = 61). Based on Fried criteria, frailty was present in 56% of patients with ADHF versus 0 for the age-matched chronic HF and health cohorts. Patients with ADHF had markedly reduced Short Physical Performance Battery score (5.3 ± 2.8) and 6-minute walk distance (178 ± 102 m) (p <0.001 vs other cohorts), with severe deficits in all domains of physical function: balance, mobility, strength, and endurance. In the patients with ADHF, cognitive impairment (78%) and depression (30%) were common, and quality of life was poor. In conclusion, older patients with ADHF are frequently frail with severe and widespread impairments in physical function, cognition, mood, and quality of life that may contribute to their persistently poor outcomes, are frequently unrecognized, are not addressed in current ADHF care paradigms, and are potentially modifiable with targeted interventions.

Large old trees (LOTs) are important ecological assets that contribute significantly to biodiversity, ecosystem functioning and local culture. This study analyzed the abundance, species composition, spatial and altitudinal distribution patterns, and conservation needs of LOTs in Baisha County, tropical southern China. We conducted a comprehensive field survey of 301 LOTs and recorded their biological characteristics, geographical locations, and environmental conditions. Species importance values were calculated, and the spatial distribution was analyzed using GIS techniques. Redundancy analysis (RDA) examined the relationships between LOT diversity and environmental factors. The results indicated a complex and diverse stock dominated by species from the Moraceae family, particularly of the genus Ficus. The structural analysis displayed a skewed age distribution, with a higher frequency of younger trees and a decline in older classes. Spatial analysis showed that LOTs are concentrated in the northwestern and central areas and are favored by microclimatic conditions, soil types, and historical land-use practices. The abundance and species richness of LOTs were greater at intermediate elevations. Redundancy analysis highlighted the intricate relationships between LOT diversity, abundance, and socioeconomic factors. This study provided crucial insights into the ecology and conservation requirements for LOTs in Baisha. The findings underscored the importance of targeted conservation efforts, particularly for older trees and mid-elevation habitats. We recommended integrating ecological research, long-term monitoring, traditional ecological knowledge, and community involvement in formulating conservation strategies to preserve these ecologically and culturally significant trees for future generations.

COPD has been perceived as being a disease of older men. However, >7 million women are estimated to live with COPD in the USA alone. Despite a growing body of literature suggesting an increasing burden of COPD in women, the evidence is limited. To assess and synthesize the available evidence among population-based epidemiologic studies and calculate the global prevalence of COPD in men and women. A systematic review and meta-analysis reporting gender-specific prevalence of COPD was undertaken. Gender-specific prevalence estimates were abstracted from relevant studies. Associated patient characteristics as well as custom variables pertaining to the diagnostic method and other important epidemiologic covariates were also collected. A Bayesian random-effects meta-analysis was performed investigating gender-specific prevalence of COPD stratified by age, geography, calendar time, study setting, diagnostic method, and disease severity. Among 194 eligible studies, summary prevalence was 9.23% (95% credible interval [CrI]: 8.16%-10.36%) in men and 6.16% (95% CrI: 5.41%-6.95%) in women. Gender prevalences varied widely by the World Health Organization Global Burden of Disease subregions, with the highest female prevalence found in North America (8.07% vs 7.30%) and in participants in urban settings (13.03% vs 8.34%). Meta-regression indicated that age ≥40 and bronchodilator testing contributed most significantly to heterogeneity of prevalence estimates across studies. We conducted the largest ever systematic review and meta-analysis of global prevalence of COPD and the first large gender-specific review. These results will increase awareness of COPD as a critical woman's health issue.