Hasil untuk "Demography. Population. Vital events"

D. Corsi, M. Neuman, J. Finlay et al.

Paige Ramsden, Sean L. McGee, Matt Nicholl

Restricted by event horizon suppression, tidal disruption events (TDEs) provide a unique window into otherwise hidden supermassive black holes (SMBHs) at the lower end of the mass spectrum, allowing the connection between star formation and SMBH mass to be explored across a broad stellar mass range. We derive stellar masses and specific star formation rates using Prospector fits to UV-MIR broadband spectral energy distributions (SEDs) for 42 TDE hosts, together with a high-mass comparison sample, and combine these with SMBH mass estimates from the literature. We first verify our approach by reproducing the established result that quenched galaxies host more massive SMBHs than star-forming systems at fixed stellar mass, a result widely interpreted as evidence for SMBH growth driving the blue-to-red sequence transition. However, examining the TDE sample in isolation reveals a trend reversal at lower masses, uncovering a surprising population of low-mass ($10^{9.6} \lesssim M_{\rm gal} \lesssim 10^{10.5}$ M$_\odot$), quenched galaxies hosting SMBHs systematically less massive ($M_{\rm BH} \lesssim 10^{6.5}$ M$_\odot$) than those in star-forming galaxies of comparable stellar mass. After ruling out degeneracies in our SED fits, we conclude that this reflects a physical difference in the quenching mechanism between these TDE hosts and the more massive galaxies. This is unlikely to be driven by AGN feedback, and could instead result from environmental processes, which can end star formation and hinder SMBH growth. We also show that the quenched and post-starburst population within the TDE sample is likely under-represented due to selection biases, suggesting the true fraction could be even higher than observed.

Giorgos Tzigkounakis, Jonathan Brown

Background: Despite the rapid development and distribution of COVID-19 vaccines, the pandemic continues to challenge global health systems. With vaccine inequity and hesitancy, especially in low-income populations and specific demographic cohorts, alternative therapeutic strategies to mitigate COVID-19 symptoms and reduce viral clearance time remain vital. Propolis, a natural bee product with immunomodulatory and antiviral properties, has demonstrated efficacy against other viral pathogens, suggesting potential as an adjunctive therapy for COVID-19. Objectives: This study protocol outlines a randomized, triple-blind, placebo-controlled clinical trial to assess the efficacy of a Greek propolis hydroalcoholic extract as an adjunct to standard care in hospitalized COVID-19 patients. The primary objectives are to evaluate the extract’s impact on viral clearance time and hospitalization duration, with secondary objectives examining body temperature, cough severity, quality of life, and safety. Methods: A total of 441 severe acute respiratory syndrome coronavirus 2-positive adult patients will be enrolled and stratified by age and vaccination status. Participants will be randomly assigned to one of three arms: (i) propolis extract, (ii) placebo, or (iii) control (standard care only). Primary outcomes include time to negative reverse transcription polymerase chain reaction tests and hospital discharge. Secondary measures involve cough severity and quality-of-life assessments through Visual Analog Scale and Leicester Cough Questionnaire scores, fever duration and resolution patterns, and safety through adverse events and mortality tracking. Statistical analysis will include Kaplan–Meier survival curves, Cox regression for confounders, and analysis of variance for quality-of-life scores. Conclusion: This study aims to validate the therapeutic potential of propolis as a natural, accessible adjunctive treatment for COVID-19. Findings may provide critical evidence supporting propolis in symptom relief, viral clearance, and healthcare burden reduction in resource-limited settings. Relevance for patients: Participants in the intervention arm may experience improved clinical outcomes, such as faster recovery and symptom alleviation, while all patients will continue to receive standard care in alignment with current clinical protocols.

Y. Yun, Ga Eun Choi, Ji-Ye Lee et al.

Urban forests, as a form of green infrastructure, play a vital role in enhancing urban resilience, environmental health, and quality of life. However, users perceive and utilize these spaces in diverse ways. This study aims to identify latent perception types among urban forest visitors and analyze their behavioral, demographic, and policy-related characteristics in Incheon Metropolitan City (Republic of Korea). Using latent class analysis, four distinct visitor types were identified: multipurpose recreationists, balanced relaxation seekers, casual forest users, and passive forest visitors. Multipurpose recreationists preferred active physical use and sports facilities, while balanced relaxation seekers emphasized emotional well-being and cultural experiences. Casual users engaged lightly with forest settings, and passive forest visitors exhibited minimal recreational interest. Satisfaction with forest elements such as vegetation, facilities, and management conditions varied across visitor types and age groups, especially among older adults. These findings highlight the need for perception-based green infrastructure planning. Policy recommendations include expanding accessible neighborhood green spaces for aging populations, promoting community-oriented events, and offering participatory forest programs for youth engagement. By integrating user segmentation into urban forest planning and governance, this study contributes to more inclusive, adaptive, and sustainable management of urban green infrastructure.

Heyao Zhu, Yimeng Zhao, Zirui Zhang et al.

With the intensiffcation of population aging and increasing burden of chronic diseases, the demand for vital signs monitoring is becoming increasingly urgent. A key challenge facing current non-contact detection technologies using millimeter wave (mmWave) radar is the low efffciency of multi-channel signal fusion in array radar systems based on equal weighting. To address this challenge, this paper proposes a vital sign enhancement detection method for multiple input and multiple output (MIMO) bio-radar, driven by multidimensional physiological characteristics, which overcomes traditional limitations through a two-stage fusion strategy. Stage 1: Enhanced Vital Sign Detection Using Single-Channel Signals Based on Physiological Characteristics. First, a chest wall multi-scattering point model is constructed. For single channel time-distance two-dimensional echo signals, effective range bins are selected based on the respiratory/cardiac physiological frequency band energy ratio, and the signal-to-noise ratio (SNR) of respiration/heart signals is enhanced using phase-aligned maximal ratio combining (MRC). Stage 2: Multi-Channel Fusion Based on Organ Radiation Spatial Distribution Characteristics. The spatial radiation characteristics of cardiopulmonary organs are introduced for the ffrst time as the theoretical foundation for SNR-based channel screening, channel attribute identiffcation, and multi-channel weighted fusion. Then, we propose a template matching method to extract respiratory rate (RR) and heart rate (HR) by adopting physical models of respiration and cardiac activities. The experimental results demonstrate the existence of the spatial distribution characteristics of organ radiation. In addition, we analyzed the impact of distance and state on the algorithm from these two aspects.

Franck Meyer, Kyunghoon Hur, Edward Choi

Despite the remarkable progress of deep-learning methods generating a target vital sign waveform from a source vital sign waveform, most existing models are designed exclusively for a specific source-to-target pair. This requires distinct model architectures, optimization procedures, and pre-processing pipelines, resulting in multiple models that hinder usability in clinical settings. To address this limitation, we propose the Multi-Directional Vital-Sign Converter (MD-ViSCo), a unified framework capable of generating any target waveform such as electrocardiogram (ECG), photoplethysmogram (PPG), or arterial blood pressure (ABP) from any single input waveform with a single model. MD-ViSCo employs a shallow 1-Dimensional U-Net integrated with a Swin Transformer that leverages Adaptive Instance Normalization (AdaIN) to capture distinct waveform styles. To evaluate the efficacy of MD-ViSCo, we conduct multi-directional waveform generation on two publicly available datasets. Our framework surpasses state-of-the-art baselines (NabNet & PPG2ABP) on average across all waveform types, lowering Mean absolute error (MAE) by 8.8% and improving Pearson correlation (PC) by 4.9% over two datasets. In addition, the generated ABP waveforms satisfy the Association for the Advancement of Medical Instrumentation (AAMI) criterion and achieve Grade B on the British Hypertension Society (BHS) standard, outperforming all baselines. By eliminating the need for developing a distinct model for each task, we believe that this work offers a unified framework that can deal with any kind of vital sign waveforms with a single model in healthcare monitoring.

Mubaris Nadeem, Johannes Zenkert, Christian Weber et al.

The integration of vital signs in healthcare has witnessed a steady rise, promising health professionals to assist in their daily tasks to improve patient treatment. In life-threatening situations, like rescue operations, crucial decisions need to be made in the shortest possible amount of time to ensure that excellent treatment is provided during life-saving measurements. The integration of vital signs in the treatment holds the potential to improve time utilization for rescuers in such critical situations. They furthermore serve to support health professionals during the treatment with useful information and suggestions. To achieve such a goal, the KIRETT project serves to provide treatment recommendations and situation detection, combined on a wrist-worn wearable for rescue operations.This paper aims to present the significant role of vital signs in the improvement of decision-making during rescue operations and show their impact on health professionals and patients in need.

You Zhou, Lijiang Chen, Guangxia Cui et al.

Ovarian tumor, as a common gynecological disease, can rapidly deteriorate into serious health crises when undetected early, thus posing significant threats to the health of women. Deep neural networks have the potential to identify ovarian tumors, thereby reducing mortality rates, but limited public datasets hinder its progress. To address this gap, we introduce a vital ovarian tumor pathological recognition dataset called \textbf{ViTaL} that contains \textbf{V}isual, \textbf{T}abular and \textbf{L}inguistic modality data of 496 patients across six pathological categories. The ViTaL dataset comprises three subsets corresponding to different patient data modalities: visual data from 2216 two-dimensional ultrasound images, tabular data from medical examinations of 496 patients, and linguistic data from ultrasound reports of 496 patients. It is insufficient to merely distinguish between benign and malignant ovarian tumors in clinical practice. To enable multi-pathology classification of ovarian tumor, we propose a ViTaL-Net based on the Triplet Hierarchical Offset Attention Mechanism (THOAM) to minimize the loss incurred during feature fusion of multi-modal data. This mechanism could effectively enhance the relevance and complementarity between information from different modalities. ViTaL-Net serves as a benchmark for the task of multi-pathology, multi-modality classification of ovarian tumors. In our comprehensive experiments, the proposed method exhibited satisfactory performance, achieving accuracies exceeding 90\% on the two most common pathological types of ovarian tumor and an overall performance of 85\%. Our dataset and code are available at https://github.com/GGbond-study/vitalnet.

T. Kessler, Lynette Rayman

Background: College students are assumed to be generally healthy, thus, elevated blood pressure can be easily missed in this population. However, recent research on college students has demonstrated increasing rates of elevated blood pressure. Situations that increase risk of elevated BP include higher levels of stress related to college education as well as other common stress producing events in life. Additionally, college students may engage in behaviors that increase risk such as eating poor diets, drinking alcohol, and not exercising regularly. The purpose of this study was to assess the prevalence of elevated blood pressure and risk factors in undergraduate college students and develop a campus wide educational initiative. Methods: Undergraduate students at a faith-based, Midwestern university (n = 138) participated in a cross-sectional study. Demographic data, standardized BP measurements, risk factors, and perceived stress levels were collected via a Google form and in-person assessments. Results: Fifty-two percent of college students had an elevated systolic blood pressure, and 30% had elevated diastolic blood pressure. Male students had significantly higher systolic (X2 = 101.343, p = .005) and diastolic blood pressure readings (X2 = 144.44, p < .001) compared to female students. There was no association between year in school and stress levels (X2 = 315.83, p = .102). Stress and systolic blood pressure were not correlated (r = .121, p = .180) nor were stress and diastolic blood pressure (r = .075, p = .408). Following the educational initiative, 96% of students (n = 91) were able to accurately define elevated blood pressure, risk factors for hypertension, and strategies to lower blood pressure. Conclusions: It is vital that blood pressure assessments become a priority for college students. These assessments must be followed by interventions aimed at reducing blood pressure levels, stress, and risk factors related to hypertension to prevent the long-term effects of cardiovascular disease. Healthcare providers on college campuses, including and perhaps most effectively students in health-related fields, should be involved in working with this population to increase awareness and screening efforts.

E. Mertens, Junior Ocira, D. Sagastume et al.

Objective To forecast the annual burden of type 2 diabetes and related socio-demographic disparities in Belgium until 2030. Methods This study utilized a discrete-event transition microsimulation model. A synthetic population was created using 2018 national register data of the Belgian population aged 0–80 years, along with the national representative prevalence of diabetes risk factors obtained from the latest (2018) Belgian Health Interview and Examination Surveys using Multiple Imputation by Chained Equations (MICE) as inputs to the Simulation of Synthetic Complex Data (simPop) model. Mortality information was obtained from the Belgian vital statistics and used to calculate annual death probabilities. From 2018 to 2030, synthetic individuals transitioned annually from health to death, with or without developing type 2 diabetes, as predicted by the Finnish Diabetes Risk Score, and risk factors were updated via strata-specific transition probabilities. Results A total of 6722 [95% UI 3421, 11,583] new cases of type 2 diabetes per 100,000 inhabitants are expected between 2018 and 2030 in Belgium, representing a 32.8% and 19.3% increase in T2D prevalence rate and DALYs rate, respectively. While T2D burden remained highest for lower-education subgroups across all three Belgian regions, the highest increases in incidence and prevalence rates by 2030 are observed for women in general, and particularly among Flemish women reporting higher-education levels with a 114.5% and 44.6% increase in prevalence and DALYs rates, respectively. Existing age- and education-related inequalities will remain apparent in 2030 across all three regions. Conclusions The projected increase in the burden of T2D in Belgium highlights the urgent need for primary and secondary preventive strategies. While emphasis should be placed on the lower-education groups, it is also crucial to reinforce strategies for people of higher socioeconomic status as the burden of T2D is expected to increase significantly in this population segment.

Muhammad Zeeshan, Richard O'Shaughnessy

Gravitational waves (GW), emanating from binary black holes (BBH), encode vital information about their source. GW signals enable us to deduce key properties of the BBH population across the universe, including mass, spin, and eccentricity distribution. While the masses and spins of binary components are already recognized for their insights into formation, eccentricity stands out as a distinct and quantifiable indicator of formation and evolution. Yet, despite its significance, eccentricity is notably absent from most parameter estimation (PE) analyses associated with GW signals. To evaluate the precision with which the eccentricity distribution can be deduced, we generated two synthetic populations of eccentric binary black holes (EBBH) characterized by non-spinning, non-precessing dynamics and mass ranges between $10 M_\odot$ and $50 M_\odot$. This was achieved using an eccentric power law model, encompassing $100$ events with eccentricity distributions set at $σ_ε= 0.05$ and $σ_ε= 0.15$. This synthetic EBBH ensemble was contrasted against a circular binary black holes (CBBH) collection to discern how parameter inferences would vary without eccentricity. Employing Markov Chain Monte Carlo (MCMC) techniques, we constrained vital model parameters, including the event rate ($\mathcal{R}$), mass distribution, minimum mass ($m_{min}$), maximum mass ($m_{max}$), and the eccentricity distribution ($σ_ε$). Our analysis demonstrates that eccentric population inference can identify the signatures of even modest eccentricities, given sufficiently many events. Conversely, our study shows that an analysis neglecting eccentricity may draw biased conclusions about population parameters for populations with the optimistic values of eccentricity distribution used in our research.

Callum Witten, William McClymont, Nicolas Laporte et al.

While JWST has observed galaxies assembling as early as $z\sim14$, evidence of galaxies with significant old stellar populations in the Epoch of Reionisation (EoR) -- the descendants of these earliest galaxies -- are few and far between. Bursty star-formation histories (SFHs) have been invoked to explain the detectability of the earliest UV-bright galaxies, but also to interpret galaxies showing Balmer breaks without nebular emission lines. We present the first spectroscopic evidence of a $z\sim7.9$ galaxy, A2744-YD4, which shows a Balmer break and emission lines, indicating the presence of both a mature and young stellar population. The spectrum of A2744-YD4 shows peculiar emission line ratios suggesting a relatively low ionisation parameter and high gas-phase metallicity. A median stack of galaxies with similar emission line ratios reveals a clear Balmer break in their stacked spectrum. This suggests that a mature stellar population ($\sim 80$ Myr old) has produced a chemically enriched, disrupted interstellar medium. Based on SED-fitting and comparison to simulations, we conclude that the observed young stellar population is in fact the result of a rejuvenation event following a lull in star formation lasting $\sim 20$ Myr, making A2744-YD4 and our stack the first spectroscopic confirmation of galaxies that have rejuvenated following a mini-quenched phase. These rejuvenating galaxies appear to be in an exceptional evolutionary moment where they can be identified. Our analysis shows that a young stellar population of just $\sim 30 \%$ of the total stellar mass would erase the Balmer break. Hence, 'outshining' through bursty SFHs of galaxies in the early Universe is likely plaguing attempts to measure their stellar ages and masses accurately.

Matheus Hansen, Fabio A. C. C. Chalub

This work introduces the concept of Variable Size Game Theory (VSGT), in which the number of players in a game is a strategic decision made by the players themselves. We start by discussing the main examples in game theory: dominance, coexistence, and coordination. We show that the same set of pay-offs can result in coordination-like or coexistence-like games depending on the strategic decision of each player type. We also solve an inverse problem to find a $d$-player game that reproduces the same fixation pattern of the VSGT. In the sequel, we consider a game involving prosocial and antisocial players, i.e., individuals who tend to play with large groups and small groups, respectively. In this game, a certain task should be performed, that will benefit one of the participants at the expense of the other players. We show that individuals able to gather large groups to perform the task may prevail, even if this task is costly, providing a possible scenario for the evolution of eusociality. The next example shows that different strategies regarding game size may lead to spontaneous separation of different types, a possible scenario for speciation without physical separation (sympatric speciation). In the last example, we generalize to three types of populations from the previous analysis and study compartmental epidemic models: in particular, we recast the SIRS model into the VSGT framework: Susceptibles play 2-player games, while Infectious and Removed play a 1-player game. The SIRS epidemic model is then obtained as the replicator equation of the VSGT. We finish with possible applications of VSGT to be addressed in the future.

Mikolaj Czerkawski, Christos Ilioudis, Carmine Clemente et al.

The treatment of interfering motion contributions remains one of the key challenges in the domain of radar-based vital sign monitoring. Removal of the interference to extract the vital sign contributions is demanding due to overlapping Doppler bands, the complex structure of the interference motions and significant variations in the power levels of their contributions. A novel approach to the removal of interference through the use of a probabilistic deep learning model is presented. Results show that a convolutional encoder-decoder neural network with a variational objective is capable of learning a meaningful representation space of vital sign Doppler-time distribution facilitating their extraction from a mixture signal. The approach is tested on semi-experimental data containing real vital sign signatures and simulated returns from interfering body motions. The application of the proposed network enhances the extraction of the micro-Doppler frequency corresponding to the respiration rate is demonstrated.

S. Leonard, Brielle Formanowski, C. Phibbs et al.

Chronic hypertension accounts for a substantial fraction of obstetric and neonatal morbidity, particularly for Black and Native Hawaiian or Other Pacific Islander people. OBJECTIVE: To evaluate whether there are individual- and population-level associations between chronic hypertension and pregnancy complications, and to assess differences across seven racial–ethnic groups. METHODS: This population-based study used linked vital statistics and hospitalization discharge data from all live and stillbirths in California (2008–2018), Michigan (2008–2020), Oregon (2008–2020), Pennsylvania (2008–2014), and South Carolina (2008–2020). We used multivariable log-binomial regression models to estimate risk ratios (RRs) and population attributable risk (PAR) percentages with 95% CIs for associations between chronic hypertension and several obstetric and neonatal outcomes, selected based on prior evidence and pathologic pathways. We adjusted models for demographic factors (race and ethnicity, payment method, educational attainment), age, body mass index, obstetric history, delivery year, and state, and conducted analyses stratified across seven racial–ethnic groups. RESULTS: The study included 7,955,713 pregnancies, of which 168,972 (2.1%) were complicated by chronic hypertension. Chronic hypertension was associated with several adverse obstetric and neonatal outcomes, with the largest adjusted PAR percentages observed for preeclampsia with severe features or eclampsia (22.4; 95% CI 22.2–22.6), acute renal failure (13.6; 95% CI 12.6–14.6), and pulmonary edema (10.7; 95% CI 8.9–12.6). Estimated RRs overall were similar across racial–ethnic groups, but PAR percentages varied. The adjusted PAR percentages (95% CI) for severe maternal morbidity—a widely used composite of acute severe events—for people who were American Indian or Alaska Native, Asian, Black, Latino, Native Hawaiian or Other Pacific Islander, White, and Multiracial or Other were 5.0 (1.1–8.8), 3.7 (3.0–4.3), 9.0 (8.2–9.8), 3.9 (3.6–4.3), 11.6 (6.4–16.5), 3.2 (2.9–3.5), and 5.5 (4.2–6.9), respectively. CONCLUSION: Chronic hypertension accounts for a substantial fraction of obstetric and neonatal morbidity and contributes to higher complication rates, particularly for people who are Black or Native Hawaiian or Other Pacific Islander.

Fan Wu, Youlan Zheng, N. Zhao et al.

Background Evidence regarding clinical features and outcomes of individuals with non-obstructive chronic bronchitis (NOCB) remains scarce, especially in never-smokers. We aimed to investigate the clinical features and 1-year outcomes of individuals with NOCB in the Chinese population. Methods We obtained data on participants in the Early Chronic Obstructive Pulmonary Disease Study who had normal spirometry (post-bronchodilator forced expiratory volume in 1 s/forced vital capacity ≥0.70). NOCB was defined as chronic cough and sputum production for at least 3 months for two consecutive years or more at baseline in participants with normal spirometry. We assessed the differences in demographics, risk factors, lung function, impulse oscillometry, CT imaging and frequency of acute respiratory events between participants with and without NOCB. Results NOCB was present in 13.1% (149/1140) of participants with normal spirometry at baseline. Compared with participants without NOCB, those with NOCB had a higher proportion of men and participants with smoke exposure, occupational exposure, family history of respiratory diseases and worse respiratory symptoms (all p<0.05), but there was no significant difference in lung function. Never-smokers with NOCB had higher rates of emphysema than those without NOCB, but airway resistance was similar. Ever-smokers with NOCB had greater airway resistance than those without NOCB, but emphysema rates were similar. During 1-year follow-up, participants with NOCB had a significantly increased risk of acute respiratory events compared with participants who did not have NOCB, after adjustment for confounders (risk ratio 2.10, 95% CI 1.32 to 3.33; p=0.002). These results were robust in never-smokers and ever-smokers. Conclusions Never-smokers and ever-smokers with NOCB had more chronic obstructive pulmonary disease-related risk factors, evidence of airway disease and greater risk of acute respiratory events than those without NOCB. Our findings support expanding the criteria defining pre-COPD to include NOCB.

D. Lotakis, Jack P. Vernamonti, Peter Ehrlich et al.

INTRODUCTION Recurrent febrile episodes represent a diagnostic challenge in the pediatric traumatic brain injury (TBI) population as they may indicate presence of infection versus sterile neuro-storming. Procalcitonin (PCT) is a promising biomarker used in pediatric sepsis; however, data are limited regarding use in TBI. We hypothesized PCT helps discern neuro-storming from sepsis in children with TBI. MATERIALS AND METHODS A single-institution retrospective review (2014-2021) identified pediatric patients (aged 0-18 y) with moderate-to-severe TBI and intensive care unit admission > 2 d. Patients with multiple febrile events who underwent infectious evaluation including cultures and PCT drawn within 48 h of fever were included. Demographics, vital signs, infectious biomarkers including PCT, and culture data were captured. Univariate and multivariate analyses were performed to determine variables associated with culture positive status. RESULTS One hundred and fifty six patients were admitted to the intensive care unit with moderate-to-severe TBI during the study period. Eighty five patients (54%) experienced recurrent febrile episodes. Twenty four (28%) met inclusion criteria, undergoing 32 total infectious workups. Twenty one workups were culture-positive (66%) in a total of 18 patients. Median PCT levels were not statistically different between culture-positive and culture-negative workups (P = 0.94). In multivariate modeling, neither PCT [odds ratio 0.89 (confidence interval: 0.75-1.05)] nor temperature [odds ratio 7.34 (confidence interval: 0.95-57.16)] correlated with positive bacterial cultures. CONCLUSIONS In this small pilot analysis, recurrent febrile episodes were common and PCT did not correlate with sepsis or neuro-storming in pediatric TBI patients. Prospective protocols are needed to better understand the utility of PCT and identify predictors of bacterial infection to improve early diagnosis of sepsis in this population.

B. L. Queiroz

One of the United Nations’ sustainable development goals is the establishment of high-quality, valid, and reliable civil registry and vital statistics systems necessary for the design, evaluation, and implementation of social, economic and health programs, especially in the context of changes in the pattern of mortality that many countries have been experiencing. Although several methods developed in demography 1,2 allow for following the evolution of the quality of the records and to indirectly estimate the levels of mortality in different localities, the quality of the system of vital statistics allows tracking the development of health conditions in a population, evaluating the public health policies, and assisting the health managers 3,4,5. Recently, the quality of mortality data in Brazil has improved substantially. Estimates of the degree of coverage of death registries in Brazil increased from about 80% in the 1980s to about 95% in 2010 6,7,8. However, regional variability in the quality of the degree of coverage of the death registry and the quality of information on the causes of death in the country is still high 6,7,9. Since 2010, the South and Southeast regions show a complete coverage of adult mortality records. The states of the Northeast and North, even with the trends of improvements in recent years, have locations with low coverage 6,10. The average coverage of mortality in the North Region rose from 65% to 76% from 1980 to 2010, whereas coverage in the South Region increased from 95% to 98% in the same period 10. In the case of smaller areas, such as mesoregions, the impacts of data quality can be even greater. The estimated degree of coverage for the São Paulo metropolitan area is 100%, high quality of data, with the probability of adult death of 0.2364, for individuals aged from 15 to 60 years. In the case of the South Amazonian mesoregion, the estimated degree of coverage is 68%. The observed data indicate a probability of adult death of 0.1102; however after applying the correction it is estimated at 0.1621 10. An exercise with data from the 2010 Demographic Census indicates, for infant mortality, coverage of the events registration of practically 100% in the São Paulo metropolitan area compared with about 50% in the mesoregion of the South Amazon. In summary, directly analyzing the data, without applying correction methods, can lead to erroneous results directly affecting health policies. An important conclusion, however, is that the different estimation methods applied to limited data may present quite different results, complexifying the definition of health strategies. A study comparing different estimates of mortality for Brazil and its regions shows the great discrepancy of these estimates and highlights the importance of continuous investment in the quality of records and in the replicability of the methods used 11. 1 Departamento de Demografia, Universidade Federal de Minas Gerais, Belo Horizonte, Brasil.

Yingqi Wang, Zhongqin Wang, J. Andrew Zhang et al.

Contact-free vital sign monitoring, which uses wireless signals for recognizing human vital signs (i.e, breath and heartbeat), is an attractive solution to health and security. However, the subject's body movement and the change in actual environments can result in inaccurate frequency estimation of heartbeat and respiratory. In this paper, we propose a robust mmWave radar and camera fusion system for monitoring vital signs, which can perform consistently well in dynamic scenarios, e.g., when some people move around the subject to be tracked, or a subject waves his/her arms and marches on the spot. Three major processing modules are developed in the system, to enable robust sensing. Firstly, we utilize a camera to assist a mmWave radar to accurately localize the subjects of interest. Secondly, we exploit the calculated subject position to form transmitting and receiving beamformers, which can improve the reflected power from the targets and weaken the impact of dynamic interference. Thirdly, we propose a weighted multi-channel Variational Mode Decomposition (WMC-VMD) algorithm to separate the weak vital sign signals from the dynamic ones due to subject's body movement. Experimental results show that, the 90${^{th}}$ percentile errors in respiration rate (RR) and heartbeat rate (HR) are less than 0.5 RPM (respirations per minute) and 6 BPM (beats per minute), respectively.

Yingxue Su, Brett Geiger, Ilya Timofeyev et al.

In this paper, we develop a computational approach for computing most likely trajectories describing rare events that correspond to the emergence of non-dominant genotypes. This work is based on the large deviations approach for discrete Markov chains describing the genetic evolution of large bacterial populations. We demonstrate that a gradient descent algorithm developed in this paper results in the fast and accurate computation of most-likely trajectories for a large number of bacterial genotypes. We supplement our analysis with extensive numerical simulations demonstrating the computational advantage of the designed gradient descent algorithm over other, more simplified, approaches.