11 research outputs found
Interrogating Capabilities of IoT Devices
This research is supported by the UK Research Councils’ Digital Economy IT as a Utility Network+ (EP/K003569/1) and the dot.rural Digital Economy Hub (EP/G066051/1).Postprin
Recommended from our members
Global burden of 288 causes of death and life expectancy decomposition in 204 countries and territories and 811 subnational locations, 1990–2021: a systematic analysis for the Global Burden of Disease Study 2021
BACKGROUND Regular, detailed reporting on population health by underlying cause of death is fundamental for public health decision making. Cause-specific estimates of mortality and the subsequent effects on life expectancy worldwide are valuable metrics to gauge progress in reducing mortality rates. These estimates are particularly important following large-scale mortality spikes, such as the COVID-19 pandemic. When systematically analysed, mortality rates and life expectancy allow comparisons of the consequences of causes of death globally and over time, providing a nuanced understanding of the effect of these causes on global populations. METHODS The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021 cause-of-death analysis estimated mortality and years of life lost (YLLs) from 288 causes of death by age-sex-location-year in 204 countries and territories and 811 subnational locations for each year from 1990 until 2021. The analysis used 56 604 data sources, including data from vital registration and verbal autopsy as well as surveys, censuses, surveillance systems, and cancer registries, among others. As with previous GBD rounds, cause-specific death rates for most causes were estimated using the Cause of Death Ensemble model-a modelling tool developed for GBD to assess the out-of-sample predictive validity of different statistical models and covariate permutations and combine those results to produce cause-specific mortality estimates-with alternative strategies adapted to model causes with insufficient data, substantial changes in reporting over the study period, or unusual epidemiology. YLLs were computed as the product of the number of deaths for each cause-age-sex-location-year and the standard life expectancy at each age. As part of the modelling process, uncertainty intervals (UIs) were generated using the 2·5th and 97·5th percentiles from a 1000-draw distribution for each metric. We decomposed life expectancy by cause of death, location, and year to show cause-specific effects on life expectancy from 1990 to 2021. We also used the coefficient of variation and the fraction of population affected by 90% of deaths to highlight concentrations of mortality. Findings are reported in counts and age-standardised rates. Methodological improvements for cause-of-death estimates in GBD 2021 include the expansion of under-5-years age group to include four new age groups, enhanced methods to account for stochastic variation of sparse data, and the inclusion of COVID-19 and other pandemic-related mortality-which includes excess mortality associated with the pandemic, excluding COVID-19, lower respiratory infections, measles, malaria, and pertussis. For this analysis, 199 new country-years of vital registration cause-of-death data, 5 country-years of surveillance data, 21 country-years of verbal autopsy data, and 94 country-years of other data types were added to those used in previous GBD rounds. FINDINGS The leading causes of age-standardised deaths globally were the same in 2019 as they were in 1990; in descending order, these were, ischaemic heart disease, stroke, chronic obstructive pulmonary disease, and lower respiratory infections. In 2021, however, COVID-19 replaced stroke as the second-leading age-standardised cause of death, with 94·0 deaths (95% UI 89·2-100·0) per 100 000 population. The COVID-19 pandemic shifted the rankings of the leading five causes, lowering stroke to the third-leading and chronic obstructive pulmonary disease to the fourth-leading position. In 2021, the highest age-standardised death rates from COVID-19 occurred in sub-Saharan Africa (271·0 deaths [250·1-290·7] per 100 000 population) and Latin America and the Caribbean (195·4 deaths [182·1-211·4] per 100 000 population). The lowest age-standardised death rates from COVID-19 were in the high-income super-region (48·1 deaths [47·4-48·8] per 100 000 population) and southeast Asia, east Asia, and Oceania (23·2 deaths [16·3-37·2] per 100 000 population). Globally, life expectancy steadily improved between 1990 and 2019 for 18 of the 22 investigated causes. Decomposition of global and regional life expectancy showed the positive effect that reductions in deaths from enteric infections, lower respiratory infections, stroke, and neonatal deaths, among others have contributed to improved survival over the study period. However, a net reduction of 1·6 years occurred in global life expectancy between 2019 and 2021, primarily due to increased death rates from COVID-19 and other pandemic-related mortality. Life expectancy was highly variable between super-regions over the study period, with southeast Asia, east Asia, and Oceania gaining 8·3 years (6·7-9·9) overall, while having the smallest reduction in life expectancy due to COVID-19 (0·4 years). The largest reduction in life expectancy due to COVID-19 occurred in Latin America and the Caribbean (3·6 years). Additionally, 53 of the 288 causes of death were highly concentrated in locations with less than 50% of the global population as of 2021, and these causes of death became progressively more concentrated since 1990, when only 44 causes showed this pattern. The concentration phenomenon is discussed heuristically with respect to enteric and lower respiratory infections, malaria, HIV/AIDS, neonatal disorders, tuberculosis, and measles. INTERPRETATION Long-standing gains in life expectancy and reductions in many of the leading causes of death have been disrupted by the COVID-19 pandemic, the adverse effects of which were spread unevenly among populations. Despite the pandemic, there has been continued progress in combatting several notable causes of death, leading to improved global life expectancy over the study period. Each of the seven GBD super-regions showed an overall improvement from 1990 and 2021, obscuring the negative effect in the years of the pandemic. Additionally, our findings regarding regional variation in causes of death driving increases in life expectancy hold clear policy utility. Analyses of shifting mortality trends reveal that several causes, once widespread globally, are now increasingly concentrated geographically. These changes in mortality concentration, alongside further investigation of changing risks, interventions, and relevant policy, present an important opportunity to deepen our understanding of mortality-reduction strategies. Examining patterns in mortality concentration might reveal areas where successful public health interventions have been implemented. Translating these successes to locations where certain causes of death remain entrenched can inform policies that work to improve life expectancy for people everywhere. FUNDING Bill & Melinda Gates Foundation
Třetí rozměr ve vizualizaci hluku - Návrh nových metod pro vizualizace spojitých jevů
3D kartografická vizualizace souvislého časově závislého jevu není snadný úkol. Zaměření tohoto výzkumu je motivováno snahou o vizualizaci právě takového jevu. Na základě současného stavu poznání jsme implementovali nové vizualizační metody pro vizualizaci spojitých časově závislých jevů. Všechny vizualizace jsou vytovřeny pro aplikaci v oblasti hluku generovaného silniční dopravou ve městě. Všechny vizualizace využívají třetí rozměr mapové scény. První dvě metody se zaměřují na změny hluku ve vertikálním rozměru (tj. ve výšce). Třetí metoda vychází z principu space–time cube (tzv. časoprostorové krychle), a proto využívá časovou proměnnou jako třetí rozměr. Pro demonstrační účely byly všechny metody implementovány v online aplikaci. Dále bylo provedeno uživatelské testování těchto aplikací. Tento článek tedy popisuje návrh, implementaci a uživatelské hodnocení nově navržených metod pro vizualizaci třetího rozměru.3D cartographic visualization of a continuous time-dependent phenomenon is not an easy task. The focus of this research is motivated by the struggle to visualize such a phenomenon. Based on the current state of the art, we implemented new visualization methods to visualize continuous time-dependent phenomena. All visualizations are based on the use case of road-traffic-generated noise in outdoor urban areas. These visualizations utilize the third dimension of the map scene. The first two methods focus on the variations of the noise in the vertical dimension (i.e. height). The third method is based on the idea of space–time cube and therefore utilizes the time variable as the third dimension. For demonstration purposes, all methods were implemented in an online application. Furthermore, user testing of those applications was conducted. This paper thus describes design, implementation and user evaluation of newly proposed methods for third dimension visualization
The Third Dimension in Noise Visualization–a Design of New Methods for Continuous Phenomenon Visualization
3D cartographic visualization of a continuous time-dependent phenomenon is not an easy task. The focus of this research is motivated by the struggle to visualize such a phenomenon. Based on the current state of the art, we implemented new visualization methods to visualize continuous time-dependent phenomena. All visualizations are based on the use case of road-traffic-generated noise in outdoor urban areas. These visualizations utilize the third dimension of the map scene. The first two methods focus on the variations of the noise in the vertical dimension (i.e. height). The third method is based on the idea of space–time cube and therefore utilizes the time variable as the third dimension. For demonstration purposes, all methods were implemented in an online application. Furthermore, user testing of those applications was conducted. This paper thus describes design, implementation and user evaluation of newly proposed methods for third dimension visualization.Urban Data Scienc
Chronic Hepatitis C Virus Infection Modulates the Transcriptional Profiles of CD4+ T Cells
Background. Chronic hepatitis C (CHC) is associated with altered cell-mediated immune response. Objective. The aim of the study was to characterize functional alterations in CD4+ T cell subsets and myeloid-derived suppressor cells (MDSCs) during chronic hepatitis C virus (HCV) infection. Methodology. The expression levels of the lineage-defining transcriptional factors (TFs) T-bet, Gata3, Rorγt, and Foxp3 in circulating CD4+ T cells and percentages of MDSCs in peripheral blood were evaluated in 33 patients with CHC, 31 persons, who had spontaneously cleared the HCV infection, and 30 healthy subjects. Analysis. The CD4+ T cells TFs T-bet (T-box expressed in T cells), Foxp3 (Forkhead box P3 transcription factor), Gata3 (Gata-binding protein 3), and Rorγt (retinoic-acid-related orphan receptor gamma) and activation of CD8+ T cells, as well as percentages of MDSCs, were measured by multicolor flow cytometry after intracellular and surface staining of peripheral blood mononuclear cells with fluorescent monoclonal antibodies. Result. The patients with CHC had significantly lower percentages of CD4+ T cells expressing Rorγt and Gata3 and higher percentages of Foxp3-expressing CD4+ T cells than healthy controls and persons who spontaneously cleared HCV infection. The ratios of T-bet+/Gata3+ and Foxp3+/Rorγt+ CD4+ T cells were the highest in the patients with CHC. In the patients with CHC, the percentages of Gata3+ and Rorγt+ CD4+ T cells and the percentages of T-bet+ CD4+ T cells and CD38+/HLA-DR+ CD8+ T cells demonstrated significant positive correlations. In addition, the percentage of CD38+/HLA-DR+ CD8+ T cells correlated negatively with the percentage of MDSCs. Conclusion. Chronic HCV infection is associated with downregulation of TFs Gata3 and Rorγt polarizing CD4+ T cells into Th2 and Th17 phenotypes together with upregulation of Foxp3 responsible for induction of regulatory T cells suppressing immune response