Global age-sex-specific fertility, mortality, healthy life expectancy (HALE), and population estimates in 204 countries and territories, 1950-2019 : a comprehensive demographic analysis for the Global Burden of Disease Study 2019

Background: Accurate and up-to-date assessment of demographic metrics is crucial for understanding a wide range of social, economic, and public health issues that affect populations worldwide. The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2019 produced updated and comprehensive demographic assessments of the key indicators of fertility, mortality, migration, and population for 204 countries and territories and selected subnational locations from 1950 to 2019. Methods: 8078 country-years of vital registration and sample registration data, 938 surveys, 349 censuses, and 238 other sources were identified and used to estimate age-specific fertility. Spatiotemporal Gaussian process regression (ST-GPR) was used to generate age-specific fertility rates for 5-year age groups between ages 15 and 49 years. With extensions to age groups 10–14 and 50–54 years, the total fertility rate (TFR) was then aggregated using the estimated age-specific fertility between ages 10 and 54 years. 7417 sources were used for under-5 mortality estimation and 7355 for adult mortality. ST-GPR was used to synthesise data sources after correction for known biases. Adult mortality was measured as the probability of death between ages 15 and 60 years based on vital registration, sample registration, and sibling histories, and was also estimated using ST-GPR. HIV-free life tables were then estimated using estimates of under-5 and adult mortality rates using a relational model life table system created for GBD, which closely tracks observed age-specific mortality rates from complete vital registration when available. Independent estimates of HIV-specific mortality generated by an epidemiological analysis of HIV prevalence surveys and antenatal clinic serosurveillance and other sources were incorporated into the estimates in countries with large epidemics. Annual and single-year age estimates of net migration and population for each country and territory were generated using a Bayesian hierarchical cohort component model that analysed estimated age-specific fertility and mortality rates along with 1250 censuses and 747 population registry years. We classified location-years into seven categories on the basis of the natural rate of increase in population (calculated by subtracting the crude death rate from the crude birth rate) and the net migration rate. We computed healthy life expectancy (HALE) using years lived with disability (YLDs) per capita, life tables, and standard demographic methods. Uncertainty was propagated throughout the demographic estimation process, including fertility, mortality, and population, with 1000 draw-level estimates produced for each metric. Findings: The global TFR decreased from 2·72 (95% uncertainty interval [UI] 2·66–2·79) in 2000 to 2·31 (2·17–2·46) in 2019. Global annual livebirths increased from 134·5 million (131·5–137·8) in 2000 to a peak of 139·6 million (133·0–146·9) in 2016. Global livebirths then declined to 135·3 million (127·2–144·1) in 2019. Of the 204 countries and territories included in this study, in 2019, 102 had a TFR lower than 2·1, which is considered a good approximation of replacement-level fertility. All countries in sub-Saharan Africa had TFRs above replacement level in 2019 and accounted for 27·1% (95% UI 26·4–27·8) of global livebirths. Global life expectancy at birth increased from 67·2 years (95% UI 66·8–67·6) in 2000 to 73·5 years (72·8–74·3) in 2019. The total number of deaths increased from 50·7 million (49·5–51·9) in 2000 to 56·5 million (53·7–59·2) in 2019. Under-5 deaths declined from 9·6 million (9·1–10·3) in 2000 to 5·0 million (4·3–6·0) in 2019. Global population increased by 25·7%, from 6·2 billion (6·0–6·3) in 2000 to 7·7 billion (7·5–8·0) in 2019. In 2019, 34 countries had negative natural rates of increase; in 17 of these, the population declined because immigration was not sufficient to counteract the negative rate of decline. Globally, HALE increased from 58·6 years (56·1–60·8) in 2000 to 63·5 years (60·8–66·1) in 2019. HALE increased in 202 of 204 countries and territories between 2000 and 2019

Global burden of 369 diseases and injuries in 204 countries and territories, 1990–2019: a systematic analysis for the Global Burden of Disease Study 2019

Background: In an era of shifting global agendas and expanded emphasis on non-communicable diseases and injuries along with communicable diseases, sound evidence on trends by cause at the national level is essential. The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) provides a systematic scientific assessment of published, publicly available, and contributed data on incidence, prevalence, and mortality for a mutually exclusive and collectively exhaustive list of diseases and injuries. Methods: GBD estimates incidence, prevalence, mortality, years of life lost (YLLs), years lived with disability (YLDs), and disability-adjusted life-years (DALYs) due to 369 diseases and injuries, for two sexes, and for 204 countries and territories. Input data were extracted from censuses, household surveys, civil registration and vital statistics, disease registries, health service use, air pollution monitors, satellite imaging, disease notifications, and other sources. Cause-specific death rates and cause fractions were calculated using the Cause of Death Ensemble model and spatiotemporal Gaussian process regression. Cause-specific deaths were adjusted to match the total all-cause deaths calculated as part of the GBD population, fertility, and mortality estimates. Deaths were multiplied by standard life expectancy at each age to calculate YLLs. A Bayesian meta-regression modelling tool, DisMod-MR 2.1, was used to ensure consistency between incidence, prevalence, remission, excess mortality, and cause-specific mortality for most causes. Prevalence estimates were multiplied by disability weights for mutually exclusive sequelae of diseases and injuries to calculate YLDs. We considered results in the context of the Socio-demographic Index (SDI), a composite indicator of income per capita, years of schooling, and fertility rate in females younger than 25 years. Uncertainty intervals (UIs) were generated for every metric using the 25th and 975th ordered 1000 draw values of the posterior distribution. Findings: Global health has steadily improved over the past 30 years as measured by age-standardised DALY rates. After taking into account population growth and ageing, the absolute number of DALYs has remained stable. Since 2010, the pace of decline in global age-standardised DALY rates has accelerated in age groups younger than 50 years compared with the 1990–2010 time period, with the greatest annualised rate of decline occurring in the 0–9-year age group. Six infectious diseases were among the top ten causes of DALYs in children younger than 10 years in 2019: lower respiratory infections (ranked second), diarrhoeal diseases (third), malaria (fifth), meningitis (sixth), whooping cough (ninth), and sexually transmitted infections (which, in this age group, is fully accounted for by congenital syphilis; ranked tenth). In adolescents aged 10–24 years, three injury causes were among the top causes of DALYs: road injuries (ranked first), self-harm (third), and interpersonal violence (fifth). Five of the causes that were in the top ten for ages 10–24 years were also in the top ten in the 25–49-year age group: road injuries (ranked first), HIV/AIDS (second), low back pain (fourth), headache disorders (fifth), and depressive disorders (sixth). In 2019, ischaemic heart disease and stroke were the top-ranked causes of DALYs in both the 50–74-year and 75-years-and-older age groups. Since 1990, there has been a marked shift towards a greater proportion of burden due to YLDs from non-communicable diseases and injuries. In 2019, there were 11 countries where non-communicable disease and injury YLDs constituted more than half of all disease burden. Decreases in age-standardised DALY rates have accelerated over the past decade in countries at the lower end of the SDI range, while improvements have started to stagnate or even reverse in countries with higher SDI. Interpretation: As disability becomes an increasingly large component of disease burden and a larger component of health expenditure, greater research and developm nt investment is needed to identify new, more effective intervention strategies. With a rapidly ageing global population, the demands on health services to deal with disabling outcomes, which increase with age, will require policy makers to anticipate these changes. The mix of universal and more geographically specific influences on health reinforces the need for regular reporting on population health in detail and by underlying cause to help decision makers to identify success stories of disease control to emulate, as well as opportunities to improve. Funding: Bill & Melinda Gates Foundation. © 2020 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 licens

Mapping local patterns of childhood overweight and wasting in low- and middle-income countries between 2000 and 2017

A double burden of malnutrition occurs when individuals, household members or communities experience both undernutrition and overweight. Here, we show geospatial estimates of overweight and wasting prevalence among children under 5 years of age in 105 low- and middle-income countries (LMICs) from 2000 to 2017 and aggregate these to policy-relevant administrative units. Wasting decreased overall across LMICs between 2000 and 2017, from 8.4% (62.3 (55.1–70.8) million) to 6.4% (58.3 (47.6–70.7) million), but is predicted to remain above the World Health Organization’s Global Nutrition Target of <5% in over half of LMICs by 2025. Prevalence of overweight increased from 5.2% (30 (22.8–38.5) million) in 2000 to 6.0% (55.5 (44.8–67.9) million) children aged under 5 years in 2017. Areas most affected by double burden of malnutrition were located in Indonesia, Thailand, southeastern China, Botswana, Cameroon and central Nigeria. Our estimates provide a new perspective to researchers, policy makers and public health agencies in their efforts to address this global childhood syndemic

Predicting the environmental suitability for onchocerciasis in Africa as an aid to elimination planning

Recent evidence suggests that, in some foci, elimination of onchocerciasis from Africa may be feasible with mass drug administration (MDA) of ivermectin. To achieve continental elimination of transmission, mapping surveys will need to be conducted across all implementation units (IUs) for which endemicity status is currently unknown. Using boosted regression tree models with optimised hyperparameter selection, we estimated environmental suitability for onchocerciasis at the 5 × 5-km resolution across Africa. In order to classify IUs that include locations that are environmentally suitable, we used receiver operating characteristic (ROC) analysis to identify an optimal threshold for suitability concordant with locations where onchocerciasis has been previously detected. This threshold value was then used to classify IUs (more suitable or less suitable) based on the location within the IU with the largest mean prediction. Mean estimates of environmental suitability suggest large areas across West and Central Africa, as well as focal areas of East Africa, are suitable for onchocerciasis transmission, consistent with the presence of current control and elimination of transmission efforts. The ROC analysis identified a mean environmental suitability index of 0.71 as a threshold to classify based on the location with the largest mean prediction within the IU. Of the IUs considered for mapping surveys, 50.2% exceed this threshold for suitability in at least one 5×5-km location. The formidable scale of data collection required to map onchocerciasis endemicity across the African continent presents an opportunity to use spatial data to identify areas likely to be suitable for onchocerciasis transmission. National onchocerciasis elimination programmes may wish to consider prioritising these IUs for mapping surveys as human resources, laboratory capacity, and programmatic schedules may constrain survey implementation, and possibly delaying MDA initiation in areas that would ultimately qualify

Predicting the environmental suitability for onchocerciasis in Africa as an aid to elimination planning

Recent evidence suggests that, in some foci, elimination of onchocerciasis from Africa may be feasible with mass drug administration (MDA) of ivermectin. To achieve continental elimination of transmission, mapping surveys will need to be conducted across all implementation units (IUs) for which endemicity status is currently unknown. Using boosted regression tree models with optimised hyperparameter selection, we estimated environmental suitability for onchocerciasis at the 5 × 5-km resolution across Africa. In order to classify IUs that include locations that are environmentally suitable, we used receiver operating characteristic (ROC) analysis to identify an optimal threshold for suitability concordant with locations where onchocerciasis has been previously detected. This threshold value was then used to classify IUs (more suitable or less suitable) based on the location within the IU with the largest mean prediction. Mean estimates of environmental suitability suggest large areas across West and Central Africa, as well as focal areas of East Africa, are suitable for onchocerciasis transmission, consistent with the presence of current control and elimination of transmission efforts. The ROC analysis identified a mean environmental suitability index of 071 as a threshold to classify based on the location with the largest mean prediction within the IU. Of the IUs considered for mapping surveys, 502% exceed this threshold for suitability in at least one 5 × 5-km location. The formidable scale of data collection required to map onchocerciasis endemicity across the African continent presents an opportunity to use spatial data to identify areas likely to be suitable for onchocerciasis transmission. National onchocerciasis elimination programmes may wish to consider prioritising these IUs for mapping surveys as human resources, laboratory capacity, and programmatic schedules may constrain survey implementation, and possibly delaying MDA initiation in areas that would ultimately qualify.SUPPORTING INFORMATION : FIGURE S1. Data coverage by year. Here we visualise the volume of data used in the analysis by country and year. Larger circles indicate more data inputs. ‘NA’ indicates records for which no year was reported (eg, ‘pre-2000’). https://doi.org/10.1371/journal.pntd.0008824.s001FIGURE S2. Illustration of covariate values for year 2000. Maps were produced using ArcGIS Desktop 10.6. https://doi.org/10.1371/journal.pntd.0008824.s002FIGURE S3. Environmental suitability of onchocerciasis including locations that have received MDA for which no pre-intervention data are available. This plot shows suitability predictions from green (low = 0%) to pink (high = 100%), representing those areas where environmental conditions are most similar to prior pathogen detections. Countries in grey with hatch marks were excluded from the analysis based on a review of national endemicity status. Areas in grey only represent locations masked due to sparse population. Maps were produced using ArcGIS Desktop 10.6 and shapefiles to visualize administrative units are available at https://espen.afro.who.int/tools-resources/cartography-database. https://doi.org/10.1371/journal.pntd.0008824.s003FIGURE S4. Environmental suitability prediction uncertainty including locations that have received MDA for which no pre-intervention data are available. This plot shows uncertainty associated with environmental suitability predictions colored from blue to red (least to most uncertain). Countries in grey with hatch marks were excluded from the analysis based on a review of national endemicity status. Areas in grey only represent locations masked due to sparse population. Maps were produced using ArcGIS Desktop 10.6 and shapefiles to visualize administrative units are available at https://espen.afro.who.int/tools-resources/cartography-database. https://doi.org/10.1371/journal.pntd.0008824.s004FIGURE S5. Environmental suitability of onchocerciasis excluding morbidity data. This plot shows suitability predictions from green (low = 0%) to pink (high = 100%), representing those areas where environmental conditions are most similar to prior pathogen detections. Countries in grey with hatch marks were excluded from the analysis based on a review of national endemicity status. Areas in grey only represent locations masked due to sparse population. Maps were produced using ArcGIS Desktop 10.6 and shapefiles to visualize administrative units are available at https://espen.afro.who.int/tools-resources/cartography-database. https://doi.org/10.1371/journal.pntd.0008824.s005FIGURE S6. Environmental suitability prediction uncertainty excluding morbidity data. This plot shows uncertainty associated with environmental suitability predictions colored from blue to red (least to most uncertain). Countries in grey with hatch marks were excluded from the analysis based on a review of national endemicity status. Areas in grey only represent locations masked due to sparse population. https://doi.org/10.1371/journal.pntd.0008824.s006FIGURE S7. Covariate Effect Curves for all onchocerciasis occurrences (measures of infection prevalence and disability). On the right set of axes we show the frequency density of the occurrences taking covariate values over 20 bins of the horizontal axis. The left set of axes shows the effect of each on the model, where the mean effect is plotted on the black line and its uncertainty is represented by the upper and lower confidence interval bounds plotted in dark grey. The figures show the fit per covariate relative to the data that correspond to specific values of the covariate. https://doi.org/10.1371/journal.pntd.0008824.s007FIGURE S8. Covariate Effect Curves for all onchocerciasis occurrences (measures of infection prevalence and disability). On the right set of axes we show the frequency density of the occurrences taking covariate values over 20 bins of the horizontal axis. The left set of axes shows the effect of each on the model, where the mean effect is plotted on the black line and its uncertainty is represented by the upper and lower confidence interval bounds plotted in dark grey. https://doi.org/10.1371/journal.pntd.0008824.s008FIGURE S9. ROC analysis for threshold. Results of the area under the receiver operating characteristic (ROC) curve analysis are presented below, with false positive rate (FPR) on the x-axis and true positive rate (TPR) on the y-axis. The red dot on the curve represents the location on the curve that corresponds to a threshold that most closely agreed with the input data. For each of the 100 BRT models, we estimated the optimal threshold that maximised agreement between occurrence inputs (considered true positives) and the mean model predictions as 0·71. https://doi.org/10.1371/journal.pntd.0008824.s009TABLE S1. Guidelines for Accurate and Transparent Health Estimates Reporting (GATHER) checklist. https://doi.org/10.1371/journal.pntd.0008824.s010TABLE S2. Total number of occurrence data classified as point and polygon inputs by diagnostic. We present the total number of occurrence points extracted from the input data sources by diagnostic type. ‘Other diagnostics’ include: DEC Patch test; Knott’s Method (Mazotti Test); 2 types of LAMP; blood smears; and urine tests. https://doi.org/10.1371/journal.pntd.0008824.s011TABLE S3. Total number of occurrence data classified as point and polygon inputs by location. https://doi.org/10.1371/journal.pntd.0008824.s012TABLE S4. Covariate information. https://doi.org/10.1371/journal.pntd.0008824.s013TEXT S1. Details outlining construction of occurrence dataset. https://doi.org/10.1371/journal.pntd.0008824.s014TEXT S2. Covariate rationale. https://doi.org/10.1371/journal.pntd.0008824.s015TEXT S3. Boosted regression tree methodology additional details. https://doi.org/10.1371/journal.pntd.0008824.s016APPENDIX S1. Country-level maps and data results. Maps were produced using ArcGIS Desktop 10.6 and shapefiles to visualize administrative units are available at https://espen.afro.who.int/tools-resources/cartography-database. https://doi.org/10.1371/journal.pntd.0008824.s017This work was primarily supported by a grant from the Bill & Melinda Gates Foundation OPP1132415 (SIH). Financial support from the Neglected Tropical Disease Modelling Consortium (https://www.ntdmodelling.org/), which is funded by the Bill & Melinda Gates Foundation (grants No. OPP1184344 and OPP1186851), and joint centre funding (grant No. MR/R015600/1) by the UK Medical Research Council (MRC) and the UK Department for International Development (DFID) under the MRC/DFID Concordat agreement which is also part of the EDCTP2 programme supported by the European Union (MGB).The Neglected Tropical Disease Modelling Consortium which is funded by the Bill & Melinda Gates Foundation, the UK Medical Research Council (MRC) and the UK Department for International Development (DFID) under the MRC/DFID Concordat agreement which is also part of the EDCTP2 programme supported by the European Union (MGB).http://www.plosNTDS.orgam2022Medical Microbiolog

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

Evaluieren der LIDAR und Photogrammetrie Höhendaten für Feature-basierende Wald-Analyse

1\. Introduction 1.1. Geospatial applications in forestry 1.2. Motivation 1.3. Statement of the question 1.4. Structure of the research 2\. Terms and basics 2.1. Single tree in forest mensuration 2.1.1. Tree height measurement 2.1.2. Single tree parameters in forestry 2.2. Remote sensing in forestry 2.3. Optical imagery 2.3.1. Illumination condition in spectral images 2.3.2. Tree measurement in aerial images 2.4. Airborne laser scanning 2.4.1. Ranging systems 2.4.2. Noise 2.5. LIDAR-based and image-based elevation data 3\. Image Processing 3.1. Pixel-based vs. object-oriented image analysis 3.2. Scale- space 3.3. Segmentation 3.3.1. Edge-based and region-based segmentation 3.3.2. Watershed transformation 3.3.3. Object-oriented segmentation of single trees 4\. Feature Extraction 4.1. Features in single tree extraction 4.2. Template- matching 4.3. Local maxima 4.4. Top hat slices 4.5. Surface analysis 5\. Methodology 5.1. Eye-finger concept 5.2. Data preparation 5.2.1. Noise and filters in processing the LIDAR data 5.2.2. Selective or Non-smoothing filter 5.3. Boundary delineation 5.4. Morphological Analysis 5.5. Marble-Rolling segmentation 5.6. Region growing 5.6.1. Region growing of non-tree areas 5.6.2. Segmentation refinement of the stand boundary 5.6.3. Region growing of single trees 5.7. Feature-based classification 5.8. Segment geometry 5.9. Segment roughness 5.10. Qualitative and quantitative evaluation 6\. Experimental Results 6.1. Study area Accuracy assessment of the DSM datasets 6.2. Stand-wise roughness analysis Single tree roughness analysis 6.3. Reference data for single tree detection 6.3.1. Traditional methods in forestry 6.3.2. Terrestrial laser scanning and photogrammetry of single trees 6.4. Stand-wise qualitative evaluation of the CHM 6.4.1. Analysis of height variations 6.4.2. Form analysis 6.5. Quantitative evaluation of single tree delineation 7\. Conclusion and outlooks 7.1. Height-based forest mensuration 7.2. From stand-wise analysis to single tree extraction 7.3. Outlook 8\. References 9\. Appendices 10\. Abbreviations 11\. CVFor qualitative and quantitative detection of forest resources, there is a demand to extract the stand parameters in forestry not only at stand level but also for single trees; high resolution digital surface models may be suitable for this aim. The dependence of most of stand features on tree height, demonstrates the importance of this element in the characterisation of forest stands from an environmental perspective and for the purpose of the timber industry. Airborne laser point clouds and photogrammetric stereo images are the two main data acquisition sources to generate the digital surface models for detailed and large areas of forests. The main aim of this research is to study these two surface models for feature extraction approaches based on height data. In the represented research neither the spectral information of the images nor the intensity values of the LIDAR data are used. For this purpose a novel concept, called eye-finger, is developed to simulate and analyze the surface models by touching the top levels of the tree crown with closed eyes. To translate the feeling of the human sense of touch into the language of machine vision, the geometric and morphological features are defined and evaluated on both laser-based and image-based canopy height models. Because of the high degree of noise in the laser data, a filter should be implemented before the comparison and the segmentation steps. The developed non-smoothing filter in this work removes the problematic pixels, while the roughness and the form of the trees remain unchanged. The work focuses on European mixed-forests, consisting of coniferous and deciduous trees. The roughness parameters used in the production industry are implemented to extract the surface characteristics of mixed-forests. The average-based parameters like Ra show the dependency of these surface evaluators on the age and tree type of the stand. The roughness parameters related to the standard deviation of the surface, measure the finer variations on the surface. An advanced roughness evaluator called Rfstd is developed in this research which captures minor height variations on the canopy and is independent from the mean height of the stand. At the single tree level an object-oriented strategy is mapped out. To extract the position of the single trees, a novel method, called marble-rolling is developed. The seed-objects, as the result of this process are employed for the supervised region-growing algorithm. Simultaneously, the shape characteristics of the growing segments are evaluated and optimized with morphological functions. For the characterisation of the single trees, both geometric features and morphological feature are implemented. The geometric feature provided better results to distinguish the coniferous tree-segments from the deciduous ones. The results of the segmentation are compared with a reference dataset. The test area, with mainly coniferous trees, is defined with a combination of terrestrial laser scanning and close-range photogrammetry. The evaluation of the topological relationships of the reference and target dataset provides high completeness and correctness results for the single tree extraction based on the airborne laser data.Zur qualitativen und quantitativen Erfassung der Waldressourcen gibt es den Bedarf, Parameter für Waldgebiete nicht nur auf der Ebene von Beständen, sondern auch für Einzelbäume zu erheben; hochauflösende Oberflächenmodelle können dafür zweckmäßig sein. Die Abhängigkeit der meisten Kenngrößen für Waldbestände von Baumhöhen zeigt, dass dieses Element der Charakterisierung von Waldbeständen sowohl für Umweltbelange als auch für die Holzindustrie sehr wichtig ist. Airborne Laser-Scanning und photogrammetrische Stereobilder sind die wichtigsten Datenerfassungsquellen, um detaillierte digitale Oberflächenmodelle für große Waldflächen zu generieren. Das Hauptziel dieser Forschungsarbeit ist die Untersuchung der beiden Oberflächenmodelle zur Merkmalsextraktion aus den Höhendaten. In der dargestellten Arbeit werden weder die Spektralkanäle der Luftbilder noch die Intensitätswerte der LIDAR- Daten verwendet. Zu diesem Zweck wird das neuartige Konzept „Eye-Finger“ entwickelt, um die Oberflächenmodelle durch Berühren der oberen Baumkronebenen mit geschlossenen Augen zu simulieren und zu analysieren. Um das „Gefühl des menschlichen Tastsinns“ in die Sprache der Bildverarbeitung zu übersetzen, werden die geometrischen und morphologischen Merkmale definiert und dadurch sowohl das laserbasierte wie auch das bildbasierte Höhenmodell bewertet. Um den Einfluss des Rauschens bei der Auswertung von der Laserdaten zu minimieren, muss ein Filter vor dem Vergleich und Segmentierung implementiert werden. Der entwickelte „Non-Smoothing“ Filter entfernt die problematischen Pixel, während die Rauheit und die Form des Baumes unverändert bleibt. Diese Arbeit konzentriert sich auf europäische Mischwälder, die aus Laub- und Nadelbäumen bestehen. Um die Oberflächeneigenschaften der Mischwälder zu extrahieren werden in der industriellen Fertigungstechnik entwickelte Rauheitsparameter implementiert. Die Parameter, basierend auf der durchschnittlichen Höhe des Bestandes wie Ra , zeigen die Abhängigkeit dieser Oberflächen-Evaluatoren vom Alter und Baumtyp des Bestandes. Die Rauheitsparameter, bezogen auf die Standardabweichung der Oberflächen, messen die feineren Variationen auf der Oberfläche. Ein weiterer Rauheitsparameter, genannt Rfstd, wurde in dieser Forschungsarbeit entwickelt, um die kleinen Höhenunterschiede auf den Oberflächen unabhängig von der mittleren Höhe des Bestandes zu ermitteln. Für die Einzelbaumebene konnte eine objektorientierte Strategie entworfen werden. Um die Position der einzelnen Bäume zu extrahieren, wurde das neue Verfahren „Marble-Rolling“ entwickelt. Die „Seed- Objects“, als das Ergebnis dieses Prozesses, werden für das gesteuerte „Region-Growing“ der Baumsegmente verwendet. Gleichzeitig wurden die Formeigenschaften der wachsenden Segmente ausgewertet und mit morphologischen Funktionen optimiert. Zur Charakterisierung der Form und Oberfläche einzelner Bäume werden sowohl geometrische wie auch morphologische Merkmale verwendet. Die geometrischen Merkmale erwiesen sich als besser geeignet, um Nadelbaumsegmente von Laubbaumsegmenten zu unterscheiden. Die Ergebnisse der Segmentierung wurden mit einem Referenzdatensatz verglichen, der aus einer Kombination von terrestrischem Laserscanning und Nahbereich-Photogrammetrie aufgenommen wurde. In hauptsächlich mit Nadelbäumen bestandenem Testbereich konnte bei der Evaluierung der topologischen Beziehungen zwischen Referenz- und Zieldatensatz eine hohe Vollständigkeit und Korrektheit der Einzelbaumextraktion mit den von Flugzeug gewonnen Laserdaten festgestellt werden

Clinical features, risk factors, and outcome of cerebral venous thrombosis in Tehran, Iran

Introduction: Despite increasing the use of magnetic resonance imaging (MRI), cerebral venous sinus thrombosis (CVST) has remained an under-diagnosed condition. In this study, characteristics and frequency of various risk factors of CVST patients in a tertiary referral hospital were closely assessed. Methods: Patients with an unequivocal diagnosis of CVST confirmed by MRI and magnetic resonance venography during 6 years of the study were included. All data from the onset of symptoms regarding clinical signs and symptoms, hospital admission, seasonal distribution, medical and drug history, thrombophilic profile, D-dimer, neuroimaging, cerebrospinal fluid findings, mortality, and outcome were collected and closely analyzed. Result: A total of 53 patients with female to male ratio of 3.07 and mean age of 33.7 years were included in the study. Headache and papilledema were the most frequent clinical features (44 and 36 patients, respectively). An underlying disease (diagnosed previously or after admission) was the most common identified risk factor for CVST in both females and males (21 patients). A total of 15 women used the oral contraceptive pill (OCP) where 12 of them had simultaneously other predisposing factors. Overall, 19 patients (36%) had more than one contributing factor. D-dimer had a sensitivity of 71.4% in CVST patients. The mortality of patients in this study was 3.7% (n = 2). Focal neurologic deficit and multicranial nerve palsy were associated with poor outcome which defined as death, recurrence, and massive intracranial hemorrhage due to anticoagulation (P = 0.050 and 0.004, respectively). Conclusion: Unlike most of the CVST studies in which OCP was the main factor; in this study, an underlying disease was the most identified cause. Considering the high probability of multiple risk factors in CVST that was shown by this study, appropriate work up should be noted to uncover them

Pathogenicity and molecular‐phylogenetic analysis revealed a distinct position of the banana finger‐tip rot pathogen among the Burkholderia cenocepacia genomovars

Banana (Musa spp.) is one of the most widely cultivated subtropical fruits around the globe. Banana cultivation has been extensively increased in southeastern Iran over the last two decades. Recently, banana fruits possessing rotten and blackened fingertip symptoms were observed in Sistan‐Baluchestan, Iran. Isolation and characterization of the causal agent showed that the pathogen belongs to the multifaceted bacterial species Burkholderia cenocepacia. Pathogenicity tests and host range assays showed that the strains were pathogenic on banana, as well as carrot, onion and potato. All the strains were resistant to 50 mg L−1 rifampicin and 200 mg L−1 copper sulphate. Phylogenetic analysis of 16S rRNA and recA gene sequences showed that the strains belong to two different genomovars of B. cenocepacia (III‐A and III‐B), which also include environmental and cystic fibrosis associated strains of the species. The results obtained from recA phylogeny were confirmed using multilocus sequence analysis (MLSA), although MLSA showed that the banana strains were clustered as a novel phylogroup among the members of both genomovars. Banana‐pathogenic B. cenocepacia strains isolated in Iran were different from the strains isolated in Taiwan, as the ‘B. cepacia epidemic strain marker’ reported in the Taiwanese strains was absent from Iranian strains. To the authors’ knowledge, this is the first MLSA‐based study on the banana‐pathogenic strains of B. cenocepacia. However, further in‐depth molecular studies are needed to decipher the relationships between the banana fingertip rot pathogen and the clinical strains of B. cenocepacia

Deep venous thrombosis and pulmonary thromboembolism among COVID-19 patients

Objective: Venous thrombosis arises from the formation of clots within the venous wall, precipitating an inflammatory cascade. This study aimed to obtain the statistics of confirmed cases of deep venous thrombosis (DVT) through Doppler ultrasound and pulmonary embolism (PE) via pulmonary computed tomography (CT) angiography within the cohort of COVID-19 patients. Methods: This cross-sectional study was conducted on 265 COVID-19 patients hospitalized at Afzalipour Hospital in Kerman, Iran, during 2020-2021. The patients' records were examined for Doppler ultrasound of the lower extremities and pulmonary CT angiography. Following the establishment of Doppler ultrasound frequencies, an assessment of DVT frequency was conducted among patients who had undergone Doppler ultrasound, correlating with PE assessments via clinical judgment and pulmonary CT angiography. Results: The study revealed a thrombosis prevalence of approximately 6.8%, with around 61.1% of thrombosis cases identified in men. The most prevalent underlying conditions within this cohort were diabetes mellitus and hypertension, accounting for approximately 22.2% of the cases. The outcomes of the regression analysis demonstrated a significant association between thrombosis and C-reactive protein (CRP) ( p= 0.02). Conclusion: In conclusion, venous thromboembolism, encompassing conditions like DVT and PTE, emerges as a heightened occurrence among COVID-19 patients, and this prevalence is notably linked to elevated CRP levels. Acquiring an understanding of the associated risk factors and pertinent symptoms equips physicians with the tools to diagnose individuals at risk, ultimately mitigating avoidable fatalities and curbing treatment expenditures through the effective management and assessment of these risk element