Comparing Methods to Impute Missing Daily Ground-Level PM10 Concentrations between 2010-2017 in South Africa.
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ABSTRACT:
SUBMITTER: Arowosegbe OO
PROVIDER: S-EPMC8037804 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Particulate matter less than or equal to 10 μm in aerodynamic diameter (PM10 µg/m3) is a priority air pollutant and one of the most widely monitored ambient air pollutants in South Africa. This study analyzed PM10 from monitoring 44 sites across four provinces of South Africa (Gauteng, Mpumalanga, Western Cape and KwaZulu-Natal) and aimed to present spatial and temporal variation in the PM10 concentration across the provinces. In addition, potential influencing factors of PM10 variations around the three site categories (Residential, Industrial and Traffic) were explored. The spatial trend in daily PM10 concentration variation shows PM10 concentration can be 5.7 times higher than the revised 2021 World Health Organization annual PM10 air quality guideline of 15 µg/m3 in Gauteng province during the winter season. Temporally, the highest weekly PM10 concentrations of 51.4 µg/m3, 46.8 µg/m3, 29.1 µg/m3 and 25.1 µg/m3 at Gauteng, Mpumalanga, KwaZulu-Natal and Western Cape Province were recorded during the weekdays. The study results suggest a decrease in the change of annual PM10 levels at sites in Gauteng and Mpumalanga Provinces. An increased change in annual PM10 levels was reported at most sites in Western Cape and KwaZulu-Natal.
| S-EPMC8706960 | biostudies-literature
Project description:Urbanization and industrialization have spurred air pollution, making it a global problem. An understanding of the spatiotemporal characteristics of PM2.5 and PM10 concentrations (particulate matter with an aerodynamic diameter of less than 2.5 ?m and 10 ?m, respectively) is necessary to mitigate air pollution. We compared the characteristics of PM2.5 and PM10 concentrations and their trends of China, India, and the U.S. from 2014 to 2017. Particulate matter levels were lowest in the U.S., while China showed higher concentrations, and India showed the highest. Interestingly, significant declines in PM2.5 and PM10 concentrations were found in some of the most polluted regions in China as well as the U.S. No comparable decline was observed in India. A strong seasonal trend was observed in China and India, with the highest values occurring in winter and the lowest in summer. The opposite trend was noted for the U.S. PM2.5 was highly correlated with PM10 for both China and India, but the correlation was poor for the U.S. With regard to reducing particulate matter pollutant concentrations, developing countries can learn from the experiences of developed nations and benefit by establishing and implementing joint regional air pollution control programs.
| S-EPMC6068888 | biostudies-other
Project description:BackgroundGenotyping accounts for a substantial part of the cost of genomic selection (GS). Using both dense and sparse SNP chips, together with imputation of missing genotypes, can reduce these costs. The aim of this study was to identify the set of candidates that are most important for dense genotyping, when they are used to impute the genotypes of sparsely genotyped animals. In a real pig pedigree, the 2500 most recently born pigs of the last generation, i.e. the target animals, were used for sparse genotyping. Their missing genotypes were imputed using either Beagle or LDMIP from T densely genotyped candidates chosen from the whole pedigree. A new optimization method was derived to identify the best animals for dense genotyping, which minimized the conditional genetic variance of the target animals, using either the pedigree-based relationship matrix (MCA), or a genotypic relationship matrix based on sparse marker genotypes (MCG). These, and five other methods for selecting the T animals were compared, using T?=?100 or 200 animals, SNP genotypes were obtained assuming Ne =100 or 200, and MAF thresholds set to D?=?0.01, 0.05 or 0.10. The performances of the methods were compared using the following criteria: call rate of true genotypes, accuracy of genotype prediction, and accuracy of genomic evaluations using the imputed genotypes.ResultsFor all criteria, MCA and MCG performed better than other selection methods, significantly so for all methods other than selection of sires with the largest numbers of offspring. Methods that choose animals that have the closest average relationship or contribution to the target population gave the lowest accuracy of imputation, in some cases worse than random selection, and should be avoided in practice.ConclusionMinimization of the conditional variance of the genotypes in target animals provided an effective optimization procedure for prioritizing animals for genotyping or sequencing.
| S-EPMC4283150 | biostudies-literature
Project description:Summary:Genome-wide association studies have become common over the last ten years, with a shift towards targeting rare variants, especially in pedigree-data. Despite lower costs, sequencing for rare variants still remains expensive. To have a relatively large sample with acceptable cost, imputation approaches may be used, such as GIGI for pedigree data. GIGI is an imputation method that handles large pedigrees and is particularly good for rare variant imputation. GIGI requires a subset of individuals in a pedigree to be fully sequenced, while other individuals are sequenced only at relevant markers. The imputation will infer the missing genotypes at untyped markers. Running GIGI on large pedigrees for large numbers of markers can be very time consuming. We present GIGI-Quick as a method to efficiently split GIGI's input, run GIGI in parallel and efficiently merge the output to reduce the runtime with the number of cores. This allows obtaining imputation results faster, and therefore all subsequent association analyses. Availability and and implementation:GIGI-Quick is open source and publicly available via: https://cse-git.qcri.org/Imputation/GIGI-Quick. Contact:msaad@hbku.edu.qa. Supplementary information:Supplementary data are available at Bioinformatics online.
| S-EPMC5925782 | biostudies-literature
Project description:MotivationMissingness in label-free mass spectrometry is inherent to the technology. A computational approach to recover missing values in metabolomics and proteomics datasets is important. Most existing methods are designed under a particular assumption, either missing at random or under the detection limit. If the missing pattern deviates from the assumption, it may lead to biased results. Hence, we investigate the missing patterns in free mass spectrometry data and develop an omnibus approach GMSimpute, to allow effective imputation accommodating different missing patterns.ResultsThree proteomics datasets and one metabolomics dataset indicate missing values could be a mixture of abundance-dependent and abundance-independent missingness. We assess the performance of GMSimpute using simulated data (with a wide range of 80 missing patterns) and metabolomics data from the Cancer Genome Atlas breast cancer and clear cell renal cell carcinoma studies. Using Pearson correlation and normalized root mean square errors between the true and imputed abundance, we compare its performance to K-nearest neighbors' type approaches, Random Forest, GSimp, a model-based method implemented in DanteR and minimum values. The results indicate GMSimpute provides higher accuracy in imputation and exhibits stable performance across different missing patterns. In addition, GMSimpute is able to identify the features in downstream differential expression analysis with high accuracy when applied to the Cancer Genome Atlas datasets.Availability and implementationGMSimpute is on CRAN: https://cran.r-project.org/web/packages/GMSimpute/index.html.Supplementary informationSupplementary data are available at Bioinformatics online.
| S-EPMC6956786 | biostudies-literature
Project description:BackgroundMultiple studies have reported a high burden of hypertension in sub-Saharan Africa, but none have examined early stage hypertension. We examined contemporary prevalence of diagnosed, treated, and controlled stage I (130-139/80-89 mm Hg) and II (≥140/90 mm Hg) hypertension in the general population of sub-Saharan Africa.MethodsWe analyzed World Health Organization STEPwise Approach to Noncommunicable Disease Risk Factor Surveillance surveys from 17 sub-Saharan Africa countries including 85 371 respondents representing 85 million individuals from 2010 to 2017. We extracted demographic variables, blood pressure, self-reported hypertension diagnosis/awareness, and treatment status to estimate prevalence of stage I and II hypertension and treatment by country. We examined diagnosis and treatment trends by national sociodemographic index, a marker of development.ResultsStage I hypertension prevalence (regardless of diagnosis/treatment) was >25% in 13 of 17 countries, highest in Sudan (35.3% [95% CI, 33.7%-37.0%]), and lowest in Eritrea (20.2% [18.8%-21.6%]). Combined stages I and II hypertension prevalence was >50% in 13 countries; <20% were diagnosed in every country. Treatment among those diagnosed ranged from 26% to 63%, and control (<140/90 mm Hg) from 4% to 17%. In 8 of 9 countries reporting on behavioral interventions (eg, salt reduction, weight loss, exercise, and smoking cessation), <60% of diagnosed individuals received counseling. Rates of diagnosis, but not treatment, were positively associated with sociodemographic index (P=0.008), although there was substantial variation between countries even at similar levels of development.ConclusionsHypertension is common in sub-Saharan Africa but rates of diagnosis, treatment, and control markedly low. There is a large population with early stage hypertension that may benefit from behavioral counseling to prevent progression. Our analyses suggest that success in population hypertension care may be achieved independently of socioeconomic development, highlighting a need for policymakers to identify best practices in those countries that outperform similar or more developed countries.
| S-EPMC9771997 | biostudies-literature
Project description:Equine encephalosis virus (EEV) is a neglected virus endemic to South Africa and is considered to generally result in mild disease in equines. Specimens were analyzed from live horses that presented with undefined neurological, febrile, or respiratory signs, or sudden and unexpected death. Between 2010 and 2017, 111 of 1523 (7.3%) horse samples tested positive for EEV using a nested real-time reverse transcriptase polymerase chain reaction (rRT-PCR). Clinical signs were reported in 106 (7.2%) EEV positive and 1360 negative horses and included pyrexia (77/106, 72.6%), icterus (20/106, 18.9%) and dyspnea (12/106, 11.3%). Neurological signs were inversely associated with EEV infection (OR < 1, p < 0.05) relative to EEV negative cases despite a high percentage of animals presenting with neurological abnormalities (51/106, 48.1%). Seventeen of the EEV positive horses also had coinfections with either West Nile (5/106, 4.7%), Middelburg (4/106, 3.8%) or African Horse sickness virus (8/106, 7.6%). To investigate a possible genetic link between EEV strains causing the observed clinical signs in horses, the full genomes of six isolates were compared to the reference strains. Based on the outer capsid protein (VP2), serotype 1 and 4 were identified as the predominant serotypes with widespread reassortment between the seven different serotypes.
| S-EPMC8001977 | biostudies-literature
Project description:BackgroundSingle-cell RNA-sequencing (scRNA-seq) is becoming indispensable in the study of cell-specific transcriptomes. However, in scRNA-seq techniques, only a small fraction of the genes are captured due to "dropout" events. These dropout events require intensive treatment when analyzing scRNA-seq data. For example, imputation tools have been proposed to estimate dropout events and de-noise data. The performance of these imputation tools are often evaluated, or fine-tuned, using various clustering criteria based on ground-truth cell subgroup labels. This limits their effectiveness in the cases where we lack cell subgroup knowledge. We consider an alternative strategy which requires the imputation to follow a "self-consistency" principle; that is, the imputation process is to refine its results until there is no internal inconsistency or dropouts from the data.ResultsWe propose the use of "self-consistency" as a main criteria in performing imputation. To demonstrate this principle we devised I-Impute, a "self-consistent" method, to impute scRNA-seq data. I-Impute optimizes continuous similarities and dropout probabilities, in iterative refinements until a self-consistent imputation is reached. On the in silico data sets, I-Impute exhibited the highest Pearson correlations for different dropout rates consistently compared with the state-of-art methods SAVER and scImpute. Furthermore, we collected three wetlab datasets, mouse bladder cells dataset, embryonic stem cells dataset, and aortic leukocyte cells dataset, to evaluate the tools. I-Impute exhibited feasible cell subpopulation discovery efficacy on all the three datasets. It achieves the highest clustering accuracy compared with SAVER and scImpute.ConclusionsA strategy based on "self-consistency", captured through our method, I-Impute, gave imputation results better than the state-of-the-art tools. Source code of I-Impute can be accessed at https://github.com/xikanfeng2/I-Impute .
| S-EPMC7677776 | biostudies-literature
Project description:Air pollution both indoors and outdoors is a major cause of various diseases and premature deaths. Negative health effects are more frequently observed in a number of European countries characterized by significant pollution. In Poland, especially in Upper Silesia, the most serious problem is the high concentration of particulate matter (PM) and PM10-bound benzo[a]pyrene (B[a]P). The main source of these two pollutants is so-called "low emissions" associated with the burning of solid fuels mainly in domestic boilers and liquid fuels in road traffic. This study examined the variability in the PM and PM10-bound B[a]P concentrations and their relationships with meteorological parameters, i.e., atmospheric pressure, air temperature and wind speed, in 2018-2021 at 11 monitoring stations. In many Silesian cities, the average annual concentrations of PM10, PM2.5 and B[a]P were much higher than those recorded in other European countries. At each station, the average daily PM10 concentrations were exceeded on 12 to 126 days a year. Taking into account the WHO recommendation for PM2.5, the highest recorded average daily concentration exceeded the permissible level by almost 40 times. The same relationships were observed in all measurement years: PM10 concentrations were negatively correlated with air temperature (R = -0.386) and wind speed (R = -0.614). The highest concentrations were observed in the temperature range from -15 °C to -5 °C, when the wind speed did not exceed 0.5 m·s-1. The calculated lifetime cancer risk (LCR) associated with the exposure to B[a]P in the Silesian Voivodeship suggested 30-429 cases per 1 million people in the heating season depending on the scenario used for the calculations (IRIS, EPA or WHO).
| S-EPMC9819630 | biostudies-literature
Project description:BackgroundClostridioides difficile is a leading cause of healthcare-associated diarrhoea in middle and high-income countries. Up to 2018, there has been no systematic, annual surveillance for C. difficile infections (CDI) in France.AimsTo provide an updated overview of the epidemiology of CDI in France between 2010 and 2017 based on five different data sources.MethodsThis is a descriptive study of retrospective surveillance and alerts data. Incidence of CDI cases was estimated through the CDI incidence survey (2016) and data from the French National Uniform Hospital Discharge Database (PMSI; 2010-16). Testing frequency for CDI was estimated through the CDI incidence survey and point prevalence studies on healthcare-associated infections (HAI; 2012 and 2017). The national early warning response system for HAI (HAI-EWRS, 2012-17) and National Reference Laboratory data (2012-17) were used to follow the number of severe CDI cases and/or outbreaks.ResultsIn 2016, CDI incidence in acute care was 3.6 cases per 10,000 patient days (PD). There was a statistically significant increase in CDI incidence between 2010 and 2016 (+?14% annually) and testing frequency was 47.4 per 10,000 PD. The number of CDI HAI-EWRS notifications decreased between 2015 and 2017 with only a few large outbreaks reported.ConclusionThe CDI incidence estimate increased from 2010, but remained below the European average of 7 per 10,000 PD in 2014; there were fewer severe cases or clusters reported in France. The consistency between PMSI and laboratory-based estimated CDI incidence could allow for more routine monitoring of CDI incidence.
| S-EPMC6724465 | biostudies-literature