Context-aware machine-learning-based error detection

The rapid growth of Internet-enabled devices in recent years has resulted in immense amounts of data. However, the data generated from this also carries an inherent risk, namely the presence of erroneous data. Such errors in the data lead to degradation of the underlying application. Eliminating errors from data is therefore a widely studied area of research. With the breakthrough, of machine learning, a new opportunity has been created to develop error detection systems. However, such systems require large amounts of labeled data to achieve the desired power. Since labeling data is an extremely repetitive and time-consuming task, such datasets are rarely available in practice. 'Active learning' as a subfield of machine learning manages to deal with this limitation. Using a small amount of labeled data, this involves training models for error detection that achieve promising results. In our work, we address how to improve the development of such models using information about the context in which the data was generated. We present three different pipeline approaches for developing error detection models, each based on a two-stage architecture: The first stage gathers context knowledge using a tool called RTClean, and the subsequent stage uses this knowledge to support error detection driven by active learning. Evaluation results showed that this two-stage structure could increase accuracy by 10-40% in optimal scenarios. However, certain cases showed significant performance deficits, indicating potential inefficiencies. A key finding was that RTClean needs to be improved to ensure the success of pipelines built upon it. In conclusion, the utilization of RTClean to enhance active learning-based error detection offers promising avenues for minimizing human intervention in the process.Der rasante Zuwachs an internetfähigen Geräten in den letzten Jahren führte zu immensen Datenmengen. Jedoch bergen die daraus generierten Daten auch ein inhärentes Risiko, nämlich die Präsenz von fehlerhaften Daten. Solche Fehler in den Daten führen zu einer Beeinträchtigung der zugrunde liegenden Anwendung der Daten. Fehler aus Daten zu eliminieren, ist daher ein weit untersuchtes Forschungsfeld. Mit dem Durchbruch, des maschinellen Lernen wurde eine neue Möglichkeit geschaffen, Fehlererkennungssysteme zu entwickeln. Solche Systeme benötigen jedoch große gelabelte Datenmengen, um die gewünschte Mächtigkeit zu erreichen. Da das Labeln von Daten eine äußerst repetitive und zeitaufwendige Arbeit ist, sind in der Praxis nur selten solche Datensätze verfügbar. 'Aktives Lernen' als Teilgebiet des maschinellen Lernens schafft es, mit dieser Einschränkung umzugehen. Mithilfe von wenigen gelabelten Daten werden dabei Modelle zur Fehlererkennung trainiert, die vielversprechende Ergebnisse erreichen. In unserer Arbeit beschäftigen wir uns damit, die Entwicklung solcher Modelle mithilfe von Informationen über den Kontext in dem die Daten generiert wurden, zu verbessern. Wir präsentieren drei verschiedene Pipeline-Ansätze zur Entwicklung von Fehlererkennungsmodellen, die jeweils auf einer zweistufigen Architektur basieren: Die erste Stufe sammelt Kontextwissen mittels eines Tools namens RTClean, und die nachfolgende Stufe nutzt dieses Wissen, um die durch aktives Lernen gesteuerte Fehlererkennung zu unterstützen. Die Evaluierungsergebnisse zeigten, dass diese zweistufige Struktur in optimalen Szenarien die Genauigkeit um 10-40% steigern konnte. Bestimmte Fälle weisen jedoch erhebliche Leistungsdefizite auf, was auf mögliche Ineffizienzen hinweist. Eine wichtige Erkenntnis ist, dass RTClean verbessert werden muss, um den Erfolg der darauf aufbauenden Pipelines zu gewährleisten. Zusammenfassend lässt sich sagen, dass die Verwendung von RTClean zur Verbesserung der auf aktivem Lernen basierenden Fehlererkennung vielversprechende Wege zur Minimierung menschlicher Eingriffe in den Prozess bietet

Contrôle actif par Simulation aux Grandes Echelles d'un écoulement de canal turbulent

Dans cette étude, nous analysons en terme de réduction de traînée moyenne et d'efficacité énergétique l'influence de la position du plan de détection et du nombre de Reynolds sur une stratégie de contrôle par opposition. Il s'avère que pour un nombre de Reynolds fixé, la position du plan de détection associée au maximum de réduction de traînée semble correspondre aux régions de production de la turbulence. Nos résultats confirment que la réduction de traînée diminue avec l'augmentation du nombre de Reynolds et que l'efficacité énergétique est maximale pour une position du plan de détection différente de celle correspondant au maximum de réduction de traînée

Contrôle optimal par Simulation aux Grandes Echelles d’un écoulement de canal turbulent

Une stratégie de contrôle optimal est mise en œuvre pour réduire par soufflage/aspiration aux parois la traînée d’un écoulement de canal plan tridimensionnel. Ici, contrairement à l’étude de Bewley et al. (2001),  une Simulation aux Grandes Echelles est utilisée comme modèle réduit des équations de Navier-Stokes. Pour Ret=100, le contrôle parvient à relaminariser complètement l’écoulement alors que pour Ret=180, une réduction de traînée de 55% est obtenue mais sans relaminarisation

Combinatorial effects on gene expression at the Lbx1/Fgf8 locus resolve split-hand/foot malformation type 3

Split-Hand/Foot Malformation type 3 (SHFM3) is a congenital limb malformation associated with tandem duplications at the LBX1/FGF8 locus. Yet, the disease patho-mechanism remains unsolved. Here we investigate the functional consequences of SHFM3-associated rearrangements on chromatin conformation and gene expression in vivo in transgenic mice. We show that the Lbx1/Fgf8 locus consists of two separate, but interacting, regulatory domains. Re-engineering of a SHFM3-associated duplication and a newly reported inversion in mice results in restructuring of the chromatin architecture. This leads to ectopic activation of the Lbx1 and Btrc genes in the apical ectodermal ridge (AER) in an Fgf8-like pattern induced by AER-specific enhancers of Fgf8. We provide evidence that the SHFM3 phenotype is the result of a combinatorial effect on gene misexpression in the developing limb. Our results reveal insights into the molecular mechanism underlying SHFM3 and provide conceptual framework for how genomic rearrangements can cause gene misexpression and disease.This study was supported by grants from the Deutsche Forschungsgemeinschaft (MU 880/16-1, MU 880/20-1) to S.M. We thank the transgenic unit, sequencing core and animal facility of Max Planck Institute for Molecular Genetics for technical assistance, Ute Fischer for technical support and Norbert Brieske for help with whole mount in situ hybridizations and image processing

Efficient generation of osteoclasts from human induced pluripotent stem cells and functional investigations of lethal CLCN7‐related osteopetrosis

Human induced pluripotent stem cells (hiPSCs) hold great potential for modeling human diseases and the development of innovative therapeutic approaches. Here, we report on a novel, simplified differentiation method for forming functional osteoclasts from hiPSCs. The three-step protocol starts with embryoid body formation, followed by hematopoietic specification, and finally osteoclast differentiation. We observed continuous production of monocyte-like cells over a period of up to 9 weeks, generating sufficient material for several osteoclast differentiations. The analysis of stage-specific gene and surface marker expression proved mesodermal priming, the presence of monocyte-like cells, and of terminally differentiated multinucleated osteoclasts, able to form resorption pits and trenches on bone and dentine in vitro. In comparison to peripheral blood mononuclear cell (PBMC)-derived osteoclasts hiPSC-derived osteoclasts were larger and contained a higher number of nuclei. Detailed functional studies on the resorption behavior of hiPSC-osteoclasts indicated a trend towards forming more trenches than pits and an increase in pseudoresorption. We used hiPSCs from an autosomal recessive osteopetrosis (ARO) patient (BIHi002-A, ARO hiPSCs) with compound heterozygous missense mutations p.(G292E) and p.(R403Q) in CLCN7, coding for the Cl-/H+-exchanger ClC-7, for functional investigations. The patient's leading clinical feature was a brain malformation due to defective neuronal migration. Mutant ClC-7 displayed residual expression and retained lysosomal co-localization with OSTM1, the gene coding for the osteopetrosis-associated transmembrane protein 1, but only ClC-7 harboring the mutation p.(R403Q) gave strongly reduced ion currents. An increased autophagic flux in spite of unchanged lysosomal pH was evident in undifferentiated ARO hiPSCs. ARO hiPSC-derived osteoclasts showed an increased size compared to hiPSCs of healthy donors. They were not able to resorb bone, underlining a loss-of-function effect of the mutations. In summary, we developed a highly reproducible, straightforward hiPSC-osteoclast differentiation protocol. We demonstrated that osteoclasts differentiated from ARO hiPSCs can be used as a disease model for ARO and potentially also other osteoclast-related diseases. (c) 2021 The Authors. Journal of Bone and Mineral Research published by Wiley Periodicals LLC on behalf of American Society for Bone and Mineral Research (ASBMR)

Global burden and strength of evidence for 88 risk factors in 204 countries and 811 subnational locations, 1990–2021: a systematic analysis for the Global Burden of Disease Study 2021

Background: Understanding the health consequences associated with exposure to risk factors is necessary to inform public health policy and practice. To systematically quantify the contributions of risk factor exposures to specific health outcomes, the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021 aims to provide comprehensive estimates of exposure levels, relative health risks, and attributable burden of disease for 88 risk factors in 204 countries and territories and 811 subnational locations, from 1990 to 2021. Methods: The GBD 2021 risk factor analysis used data from 54 561 total distinct sources to produce epidemiological estimates for 88 risk factors and their associated health outcomes for a total of 631 risk–outcome pairs. Pairs were included on the basis of data-driven determination of a risk–outcome association. Age-sex-location-year-specific estimates were generated at global, regional, and national levels. Our approach followed the comparative risk assessment framework predicated on a causal web of hierarchically organised, potentially combinative, modifiable risks. Relative risks (RRs) of a given outcome occurring as a function of risk factor exposure were estimated separately for each risk–outcome pair, and summary exposure values (SEVs), representing risk-weighted exposure prevalence, and theoretical minimum risk exposure levels (TMRELs) were estimated for each risk factor. These estimates were used to calculate the population attributable fraction (PAF; ie, the proportional change in health risk that would occur if exposure to a risk factor were reduced to the TMREL). The product of PAFs and disease burden associated with a given outcome, measured in disability-adjusted life-years (DALYs), yielded measures of attributable burden (ie, the proportion of total disease burden attributable to a particular risk factor or combination of risk factors). Adjustments for mediation were applied to account for relationships involving risk factors that act indirectly on outcomes via intermediate risks. Attributable burden estimates were stratified by Socio-demographic Index (SDI) quintile and presented as counts, age-standardised rates, and rankings. To complement estimates of RR and attributable burden, newly developed burden of proof risk function (BPRF) methods were applied to yield supplementary, conservative interpretations of risk–outcome associations based on the consistency of underlying evidence, accounting for unexplained heterogeneity between input data from different studies. Estimates reported represent the mean value across 500 draws from the estimate's distribution, with 95% uncertainty intervals (UIs) calculated as the 2·5th and 97·5th percentile values across the draws. Findings: Among the specific risk factors analysed for this study, particulate matter air pollution was the leading contributor to the global disease burden in 2021, contributing 8·0% (95% UI 6·7–9·4) of total DALYs, followed by high systolic blood pressure (SBP; 7·8% [6·4–9·2]), smoking (5·7% [4·7–6·8]), low birthweight and short gestation (5·6% [4·8–6·3]), and high fasting plasma glucose (FPG; 5·4% [4·8–6·0]). For younger demographics (ie, those aged 0–4 years and 5–14 years), risks such as low birthweight and short gestation and unsafe water, sanitation, and handwashing (WaSH) were among the leading risk factors, while for older age groups, metabolic risks such as high SBP, high body-mass index (BMI), high FPG, and high LDL cholesterol had a greater impact. From 2000 to 2021, there was an observable shift in global health challenges, marked by a decline in the number of all-age DALYs broadly attributable to behavioural risks (decrease of 20·7% [13·9–27·7]) and environmental and occupational risks (decrease of 22·0% [15·5–28·8]), coupled with a 49·4% (42·3–56·9) increase in DALYs attributable to metabolic risks, all reflecting ageing populations and changing lifestyles on a global scale. Age-standardised global DALY rates attributable to high BMI and high FPG rose considerably (15·7% [9·9–21·7] for high BMI and 7·9% [3·3–12·9] for high FPG) over this period, with exposure to these risks increasing annually at rates of 1·8% (1·6–1·9) for high BMI and 1·3% (1·1–1·5) for high FPG. By contrast, the global risk-attributable burden and exposure to many other risk factors declined, notably for risks such as child growth failure and unsafe water source, with age-standardised attributable DALYs decreasing by 71·5% (64·4–78·8) for child growth failure and 66·3% (60·2–72·0) for unsafe water source. We separated risk factors into three groups according to trajectory over time: those with a decreasing attributable burden, due largely to declining risk exposure (eg, diet high in trans-fat and household air pollution) but also to proportionally smaller child and youth populations (eg, child and maternal malnutrition); those for which the burden increased moderately in spite of declining risk exposure, due largely to population ageing (eg, smoking); and those for which the burden increased considerably due to both increasing risk exposure and population ageing (eg, ambient particulate matter air pollution, high BMI, high FPG, and high SBP). Interpretation: Substantial progress has been made in reducing the global disease burden attributable to a range of risk factors, particularly those related to maternal and child health, WaSH, and household air pollution. Maintaining efforts to minimise the impact of these risk factors, especially in low SDI locations, is necessary to sustain progress. Successes in moderating the smoking-related burden by reducing risk exposure highlight the need to advance policies that reduce exposure to other leading risk factors such as ambient particulate matter air pollution and high SBP. Troubling increases in high FPG, high BMI, and other risk factors related to obesity and metabolic syndrome indicate an urgent need to identify and implement interventions

Global age-sex-specific mortality, life expectancy, and population estimates in 204 countries and territories and 811 subnational locations, 1950–2021, and the impact of the COVID-19 pandemic: a comprehensive demographic analysis for the Global Burden of Disease Study 2021

Background: Estimates of demographic metrics are crucial to assess levels and trends of population health outcomes. The profound impact of the COVID-19 pandemic on populations worldwide has underscored the need for timely estimates to understand this unprecedented event within the context of long-term population health trends. The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021 provides new demographic estimates for 204 countries and territories and 811 additional subnational locations from 1950 to 2021, with a particular emphasis on changes in mortality and life expectancy that occurred during the 2020–21 COVID-19 pandemic period. Methods: 22 223 data sources from vital registration, sample registration, surveys, censuses, and other sources were used to estimate mortality, with a subset of these sources used exclusively to estimate excess mortality due to the COVID-19 pandemic. 2026 data sources were used for population estimation. Additional sources were used to estimate migration; the effects of the HIV epidemic; and demographic discontinuities due to conflicts, famines, natural disasters, and pandemics, which are used as inputs for estimating mortality and population. Spatiotemporal Gaussian process regression (ST-GPR) was used to generate under-5 mortality rates, which synthesised 30 763 location-years of vital registration and sample registration data, 1365 surveys and censuses, and 80 other sources. ST-GPR was also used to estimate adult mortality (between ages 15 and 59 years) based on information from 31 642 location-years of vital registration and sample registration data, 355 surveys and censuses, and 24 other sources. Estimates of child and adult mortality rates were then used to generate life tables with a relational model life table system. For countries with large HIV epidemics, life tables were adjusted using independent estimates of HIV-specific mortality generated via an epidemiological analysis of HIV prevalence surveys, antenatal clinic serosurveillance, and other data sources. Excess mortality due to the COVID-19 pandemic in 2020 and 2021 was determined by subtracting observed all-cause mortality (adjusted for late registration and mortality anomalies) from the mortality expected in the absence of the pandemic. Expected mortality was calculated based on historical trends using an ensemble of models. In location-years where all-cause mortality data were unavailable, we estimated excess mortality rates using a regression model with covariates pertaining to the pandemic. Population size was computed using a Bayesian hierarchical cohort component model. Life expectancy was calculated using age-specific mortality rates and standard demographic methods. Uncertainty intervals (UIs) were calculated for every metric using the 25th and 975th ordered values from a 1000-draw posterior distribution. Findings: Global all-cause mortality followed two distinct patterns over the study period: age-standardised mortality rates declined between 1950 and 2019 (a 62·8% [95% UI 60·5–65·1] decline), and increased during the COVID-19 pandemic period (2020–21; 5·1% [0·9–9·6] increase). In contrast with the overall reverse in mortality trends during the pandemic period, child mortality continued to decline, with 4·66 million (3·98–5·50) global deaths in children younger than 5 years in 2021 compared with 5·21 million (4·50–6·01) in 2019. An estimated 131 million (126–137) people died globally from all causes in 2020 and 2021 combined, of which 15·9 million (14·7–17·2) were due to the COVID-19 pandemic (measured by excess mortality, which includes deaths directly due to SARS-CoV-2 infection and those indirectly due to other social, economic, or behavioural changes associated with the pandemic). Excess mortality rates exceeded 150 deaths per 100 000 population during at least one year of the pandemic in 80 countries and territories, whereas 20 nations had a negative excess mortality rate in 2020 or 2021, indicating that all-cause mortality in these countries was lower during the pandemic than expected based on historical trends. Between 1950 and 2021, global life expectancy at birth increased by 22·7 years (20·8–24·8), from 49·0 years (46·7–51·3) to 71·7 years (70·9–72·5). Global life expectancy at birth declined by 1·6 years (1·0–2·2) between 2019 and 2021, reversing historical trends. An increase in life expectancy was only observed in 32 (15·7%) of 204 countries and territories between 2019 and 2021. The global population reached 7·89 billion (7·67–8·13) people in 2021, by which time 56 of 204 countries and territories had peaked and subsequently populations have declined. The largest proportion of population growth between 2020 and 2021 was in sub-Saharan Africa (39·5% [28·4–52·7]) and south Asia (26·3% [9·0–44·7]). From 2000 to 2021, the ratio of the population aged 65 years and older to the population aged younger than 15 years increased in 188 (92·2%) of 204 nations. Interpretation: Global adult mortality rates markedly increased during the COVID-19 pandemic in 2020 and 2021, reversing past decreasing trends, while child mortality rates continued to decline, albeit more slowly than in earlier years. Although COVID-19 had a substantial impact on many demographic indicators during the first 2 years of the pandemic, overall global health progress over the 72 years evaluated has been profound, with considerable improvements in mortality and life expectancy. Additionally, we observed a deceleration of global population growth since 2017, despite steady or increasing growth in lower-income countries, combined with a continued global shift of population age structures towards older ages. These demographic changes will likely present future challenges to health systems, economies, and societies. The comprehensive demographic estimates reported here will enable researchers, policy makers, health practitioners, and other key stakeholders to better understand and address the profound changes that have occurred in the global health landscape following the first 2 years of the COVID-19 pandemic, and longer-term trends beyond the pandemic

Global age-sex-specific mortality, life expectancy, and population estimates in 204 countries and territories and 811 subnational locations, 1950–2021, and the impact of the COVID-19 pandemic: a comprehensive demographic analysis for the Global Burden of Disease Study 2021

BACKGROUND: Estimates of demographic metrics are crucial to assess levels and trends of population health outcomes. The profound impact of the COVID-19 pandemic on populations worldwide has underscored the need for timely estimates to understand this unprecedented event within the context of long-term population health trends. The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021 provides new demographic estimates for 204 countries and territories and 811 additional subnational locations from 1950 to 2021, with a particular emphasis on changes in mortality and life expectancy that occurred during the 2020–21 COVID-19 pandemic period. METHODS: 22 223 data sources from vital registration, sample registration, surveys, censuses, and other sources were used to estimate mortality, with a subset of these sources used exclusively to estimate excess mortality due to the COVID-19 pandemic. 2026 data sources were used for population estimation. Additional sources were used to estimate migration; the effects of the HIV epidemic; and demographic discontinuities due to conflicts, famines, natural disasters, and pandemics, which are used as inputs for estimating mortality and population. Spatiotemporal Gaussian process regression (ST-GPR) was used to generate under-5 mortality rates, which synthesised 30 763 location-years of vital registration and sample registration data, 1365 surveys and censuses, and 80 other sources. ST-GPR was also used to estimate adult mortality (between ages 15 and 59 years) based on information from 31 642 location-years of vital registration and sample registration data, 355 surveys and censuses, and 24 other sources. Estimates of child and adult mortality rates were then used to generate life tables with a relational model life table system. For countries with large HIV epidemics, life tables were adjusted using independent estimates of HIV-specific mortality generated via an epidemiological analysis of HIV prevalence surveys, antenatal clinic serosurveillance, and other data sources. Excess mortality due to the COVID-19 pandemic in 2020 and 2021 was determined by subtracting observed all-cause mortality (adjusted for late registration and mortality anomalies) from the mortality expected in the absence of the pandemic. Expected mortality was calculated based on historical trends using an ensemble of models. In location-years where all-cause mortality data were unavailable, we estimated excess mortality rates using a regression model with covariates pertaining to the pandemic. Population size was computed using a Bayesian hierarchical cohort component model. Life expectancy was calculated using age-specific mortality rates and standard demographic methods. Uncertainty intervals (UIs) were calculated for every metric using the 25th and 975th ordered values from a 1000-draw posterior distribution. FINDINGS: Global all-cause mortality followed two distinct patterns over the study period: age-standardised mortality rates declined between 1950 and 2019 (a 62·8% [95% UI 60·5–65·1] decline), and increased during the COVID-19 pandemic period (2020–21; 5·1% [0·9–9·6] increase). In contrast with the overall reverse in mortality trends during the pandemic period, child mortality continued to decline, with 4·66 million (3·98–5·50) global deaths in children younger than 5 years in 2021 compared with 5·21 million (4·50–6·01) in 2019. An estimated 131 million (126–137) people died globally from all causes in 2020 and 2021 combined, of which 15·9 million (14·7–17·2) were due to the COVID-19 pandemic (measured by excess mortality, which includes deaths directly due to SARS-CoV-2 infection and those indirectly due to other social, economic, or behavioural changes associated with the pandemic). Excess mortality rates exceeded 150 deaths per 100 000 population during at least one year of the pandemic in 80 countries and territories, whereas 20 nations had a negative excess mortality rate in 2020 or 2021, indicating that all-cause mortality in these countries was lower during the pandemic than expected based on historical trends. Between 1950 and 2021, global life expectancy at birth increased by 22·7 years (20·8–24·8), from 49·0 years (46·7–51·3) to 71·7 years (70·9–72·5). Global life expectancy at birth declined by 1·6 years (1·0–2·2) between 2019 and 2021, reversing historical trends. An increase in life expectancy was only observed in 32 (15·7%) of 204 countries and territories between 2019 and 2021. The global population reached 7·89 billion (7·67–8·13) people in 2021, by which time 56 of 204 countries and territories had peaked and subsequently populations have declined. The largest proportion of population growth between 2020 and 2021 was in sub-Saharan Africa (39·5% [28·4–52·7]) and south Asia (26·3% [9·0–44·7]). From 2000 to 2021, the ratio of the population aged 65 years and older to the population aged younger than 15 years increased in 188 (92·2%) of 204 nations. INTERPRETATION: Global adult mortality rates markedly increased during the COVID-19 pandemic in 2020 and 2021, reversing past decreasing trends, while child mortality rates continued to decline, albeit more slowly than in earlier years. Although COVID-19 had a substantial impact on many demographic indicators during the first 2 years of the pandemic, overall global health progress over the 72 years evaluated has been profound, with considerable improvements in mortality and life expectancy. Additionally, we observed a deceleration of global population growth since 2017, despite steady or increasing growth in lower-income countries, combined with a continued global shift of population age structures towards older ages. These demographic changes will likely present future challenges to health systems, economies, and societies. The comprehensive demographic estimates reported here will enable researchers, policy makers, health practitioners, and other key stakeholders to better understand and address the profound changes that have occurred in the global health landscape following the first 2 years of the COVID-19 pandemic, and longer-term trends beyond the pandemic. FUNDING: Bill & Melinda Gates Foundation

Global, regional, and national progress towards Sustainable Development Goal 3.2 for neonatal and child health: all-cause and cause-specific mortality findings from the Global Burden of Disease Study 2019

Background Sustainable Development Goal 3.2 has targeted elimination of preventable child mortality, reduction of neonatal death to less than 12 per 1000 livebirths, and reduction of death of children younger than 5 years to less than 25 per 1000 livebirths, for each country by 2030. To understand current rates, recent trends, and potential trajectories of child mortality for the next decade, we present the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2019 findings for all-cause mortality and cause-specific mortality in children younger than 5 years of age, with multiple scenarios for child mortality in 2030 that include the consideration of potential effects of COVID-19, and a novel framework for quantifying optimal child survival. Methods We completed all-cause mortality and cause-specific mortality analyses from 204 countries and territories for detailed age groups separately, with aggregated mortality probabilities per 1000 livebirths computed for neonatal mortality rate (NMR) and under-5 mortality rate (USMR). Scenarios for 2030 represent different potential trajectories, notably including potential effects of the COVID-19 pandemic and the potential impact of improvements preferentially targeting neonatal survival. Optimal child survival metrics were developed by age, sex, and cause of death across all GBD location-years. The first metric is a global optimum and is based on the lowest observed mortality, and the second is a survival potential frontier that is based on stochastic frontier analysis of observed mortality and Healthcare Access and Quality Index. Findings Global U5MR decreased from 71.2 deaths per 1000 livebirths (95% uncertainty interval WI] 68.3-74-0) in 2000 to 37.1 (33.2-41.7) in 2019 while global NMR correspondingly declined more slowly from 28.0 deaths per 1000 live births (26.8-29-5) in 2000 to 17.9 (16.3-19-8) in 2019. In 2019,136 (67%) of 204 countries had a USMR at or below the SDG 3.2 threshold and 133 (65%) had an NMR at or below the SDG 3.2 threshold, and the reference scenario suggests that by 2030,154 (75%) of all countries could meet the U5MR targets, and 139 (68%) could meet the NMR targets. Deaths of children younger than 5 years totalled 9.65 million (95% UI 9.05-10.30) in 2000 and 5.05 million (4.27-6.02) in 2019, with the neonatal fraction of these deaths increasing from 39% (3.76 million 95% UI 3.53-4.021) in 2000 to 48% (2.42 million; 2.06-2.86) in 2019. NMR and U5MR were generally higher in males than in females, although there was no statistically significant difference at the global level. Neonatal disorders remained the leading cause of death in children younger than 5 years in 2019, followed by lower respiratory infections, diarrhoeal diseases, congenital birth defects, and malaria. The global optimum analysis suggests NMR could be reduced to as low as 0.80 (95% UI 0.71-0.86) deaths per 1000 livebirths and U5MR to 1.44 (95% UI 1-27-1.58) deaths per 1000 livebirths, and in 2019, there were as many as 1.87 million (95% UI 1-35-2.58; 37% 95% UI 32-43]) of 5.05 million more deaths of children younger than 5 years than the survival potential frontier. Interpretation Global child mortality declined by almost half between 2000 and 2019, but progress remains slower in neonates and 65 (32%) of 204 countries, mostly in sub-Saharan Africa and south Asia, are not on track to meet either SDG 3.2 target by 2030. Focused improvements in perinatal and newborn care, continued and expanded delivery of essential interventions such as vaccination and infection prevention, an enhanced focus on equity, continued focus on poverty reduction and education, and investment in strengthening health systems across the development spectrum have the potential to substantially improve USMR. Given the widespread effects of COVID-19, considerable effort will be required to maintain and accelerate progress. Copyright (C) 2021 The Author(s). Published by Elsevier Ltd

Entwurf und Implementierung von sicheren Smart-Contracts für mobile Objektverfolgungssysteme

In recent years, cryptocurrencies implemented on top of Blockchains became very popular, with Bitcoin as the most prominent example. However, novel Blockchain-based platforms such as Ethereum also support distributed applications beyond cryptocurrencies through so-called smart contracts. Technically, smart contracts are programs, whose code and execution state is stored in the Blockchain, inherently featuring the ability to transfer (electronic) money during their execution. In this Bachelor thesis, we investigate how smart contracts can be used to implement a distributed crowdsensing application for tracking mobile objects by a crowd of privately owned mobile devices. Such a system could be used, for instance, to nd lost or stolen objects, such as keys, vehicles (cars, bicycles, . . . ), or pets tagged with short-range radio transmitters implemented using readily available Bluetooth or RFID technology. These objects can then be detected by smartphones of private users in the vicinity of the object, effectively implementing a huge sensor network covering many parts of the world without any upfront investments by a central entity. Although highly attractive, implementing a crowdsensing application on top of a Blockchain platform such as Ethereum comes with several challenges. First of all, users need incentives to participate in searching for mobile objects. A natural incentive is a monetary reward that participants automatically receive through the smart contract when reporting sightings (timestamped positions) of wanted objects. However, this directly brings up the problem of malicious participants (attackers) who try to get the reward without actually executing the work of searching for the object by simply reporting fake positions. Therefore, one major goal of this Bachelor thesis is to counter such attacks by proposing effective counter-measures, and implementing and evaluating them for the Ethereum platform. In detail, we propose a basic reputation-based approach for detecting fake positions which judges each sighting made by a mobile devices according to the reputation of that device, implemented by a smart contract. Furthermore, advanced attacks are identified compromising the basic reputation-based approach and effective counter-measures to these advanced attacks are proposed. Identified advanced attacks include reputation farming, where the attacker tries to aggregate reputation first before launching the attack, and the so-called copycat attack, where the attacker simply copies already submitted valid sightings form honest participants, making his fake positions indistinguishable from valid positions. Our evaluations analyses the monetary cost of executing smart contracts with and without our security mechanisms. The results show that the overhead included by our reputation-based approach is at maximum 45% of the cost of a smart contract without implemented security mechanisms.In den letzten Jahren wurden Kryptowährungen, die auf Blockchains basieren, sehr populär, mit Bitcoin als prominentestem Beispiel. Neuartige Blockchain-basierte Plattformen, wie Ethereum, unterstützen jedoch auch verteilte Anwendungen jenseits von Kryptowährungen durch so genannte Smart Contracts. Technisch gesehen handelt es sich bei Smart Contracts um Programme, deren Code und Ausführungszustand in der Blockchain gespeichert wird und die inhärent die Fähigkeit besitzen, während ihrer Ausführung (elektronisches) Geld zu transferieren. In dieser Bachelorarbeit wird untersucht, wie Smart Contracts dazu verwendet werden können, eine verteilte Crowdsensing-Anwendung zur Verfolgung mobiler Objekte durch eine Menge privater mobiler Geräte zu implementieren. Ein solches System könnte z.B. dazu verwendet werden, verlorene oder gestohlene Gegenstände wie Schlüssel, Fahrzeuge (Autos, Fahrräder, ...) oder Haustiere zu finden, die mithilfe von leicht verfügbaren Bluetooth- oder RFID-Technologie implementierten Funksendern ausgestattet sind. Diese Gegenstände können dann von Smartphones privater Nutzer in der Nähe des Objekts erkannt werden, wodurch ein riesiges Sensornetzwerk entsteht, das viele Teile der Welt abdeckt und ohne Vorabinvestitionen durch eine zentrale Entität aufgesetzt werden kann. Obwohl die Implementierung einer Crowdsensing-Anwendung auf einer Blockchain-Plattform wie Ethereum attraktiv ist, bringt sie auch einige Herausforderungen mit sich. Zunächst benötigen die Benutzer Anreize, sich an der Suche nach mobilen Objekten zu beteiligen. Ein natürlicher Anreiz ist eine monetäre Belohnung, die die Teilnehmer durch den Smart Contract automatisch erhalten, wenn sie Sichtungen (Positionen mit einem Zeitstempel) gesuchter Objekte melden. Dies wirft jedoch direkt das Problem böswilliger Teilnehmer (Angreifer) auf, die versuchen, die Belohnung zu erhalten, ohne den Aufwand der Suche nach dem Objekt tatsächlich nachzugehen, indem sie einfach falsche Positionen melden. Ein Ziel dieser Bachelorarbeit ist es daher, solchen Angriffen durch den Entwurf wirksamer Gegenmaßnahmen zu begegnen und diese für die Ethereum-Plattform zu implementieren und auszuwerten. Im Einzelnen schlagen wir einen grundlegenden, durch einen Smart Contract implementierten reputationsbasierten Ansatz für die Erkennung von gefälschten Positionen vor, der jede Sichtung durch ein mobiles Gerät nach der Reputation dieses Geräts beurteilt. Darüber hinaus werden fortgeschrittene Angriffe identifiziert, die den grundlegenden reputationsbasierten Ansatz gefährden, sowie wirksame Gegenmaßnahmen gegen diese fortgeschrittenen Angriffe vorgeschlagen. Zu den identifizierten fortgeschrittenen Angriffen gehören so genannte Reputation-Farming-Angriffe, bei denen der Angreifer zuerst versucht, Reputation zu aggregieren, bevor er den Angriff startet. Des Weiteren wird der so genannte Copy-Cat-Angriff identifiziert und behandelt, bei dem der Angreifer bereits eingereichte gültige Sichtungen von ehrlichen Teilnehmern kopiert, so dass seine gefälschten Positionen nicht von gültigen Positionen unterschieden werden können. Unsere Bewertung analysiert die monetären Kosten der Ausführung von Smart Contracts mit und ohne unsere Sicherheitsmechanismen. Die Ergebnisse zeigen, dass die von unserem reputationsbasierten Ansatz verursachten Mehrkosten maximal 45% betragen