Distribution, Prevalence, and Causative Agents of Fungal Keratitis: A Systematic Review and Meta-Analysis (1990 to 2020).

Objectives: This study aims to provide an overview of the prevalence, distribution, and causative agents of fungal keratitis. Methods: All the articles with data on the prevalence of fungal keratitis among various patient groups from January 1, 1990 to May 27, 2020 were retrieved through a systematic search in PubMed, Scopus, Web of Science, and Google Scholar. Data were extracted, and the pooled estimated prevalence of fungal keratitis, yeast/mold infection, the spectrum and frequency of various causative agents, and the pooled estimated prevalence of mixed infections were calculated in general and in various countries (wherever possible) using meta-analysis. Results: From 11,235 articles retrieved in the primary search step, 169 met the inclusion criteria. The 169 eligible articles were divided into six groups and analyzed separately. The pooled prevalence of fungal keratitis was variable with values ranging from 0.05% among postkeratoplasty patients to 43.01% among patients with a clinical suspicion of fungal keratitis. There was also a country-dependent variation in the prevalence (Paraguay: 50.1% (95% CI, 35.11, 65.00); Ireland: 1.1% (95% CI, 0.03, 6.04)). Except for postkeratoplasty cases (yeast: 51.80%), in all patient groups, molds were more common than yeasts. Although more than 50 distinct species of fungi have been found to cause fungal keratitis, Fusarium species followed by Aspergillus species were the most common causes of the disease. In general, 9.29% (95% CI, 6.52, 12.38) of fungal keratitis cases were mixed with bacterial agents. Conclusion: The prevalence of fungal keratitis can vary dramatically depending on the patient groups and geographical origin; however, the dominant causative agents are generally similar.

Application of periodic parameters and their effects on the ANN landfill gas modeling.

To reach a practical landfill gas management system and to diminish the negative environmental impacts from landfills, accurate methane (CH4) prediction is essential. In this study, the preprocessing steps including minimizing multicollinearity, removal of outliers, and errors with missing data imputation are applied to enhance the data quality. This study is the first at employing periodic parameters in the two-stage non-linear auto-regressive model with exogenous inputs (NARX) with the aim of providing a convenient and precise approach to predict the daily CH4 collection rate from a municipal landfill in Regina, SK, Canada. Using a stepwise procedure, various volumes of training data were assessed, and concluded that employing the 3-year training data reduced the mean absolute percentage error (MAPE) of the CH4 prediction model by 26.97% at the testing stage. The favorable artificial neural network model performance was obtained using the day of the year (DOY) as a sole input of the time series model with MAPE of 2.12% showing its acceptable ability in CH4 prediction. Using an only DOY-based model is especially remarkable because of its simplicity and high accuracy showing a convenient and effective approach in time landfill gas modeling, particularly for the landfills with no reliable climatic data.

Distribution, antifungal susceptibility pattern and intra-Candida albicans species complex prevalence of Candida africana: A systematic review and meta-analysis.

Candida africana is a pathogenic species within the Candida albicans species complex. Due to the limited knowledge concerning its prevalence and antifungal susceptibility profiles, a comprehensive study is overdue. Accordingly, we performed a search of the electronic databases for literature published in the English language between 1 January 2001 and 21 March 2020. Citations were screened, relevant articles were identified, and data were extracted to determine overall intra-C. albicans complex prevalence, geographical distribution, and antifungal susceptibility profiles for C. africana. From a total of 366 articles, 41 were eligible for inclusion in this study. Our results showed that C. africana has a worldwide distribution. The pooled intra-C. albicans complex prevalence of C. africana was 1.67% (95% CI 0.98-2.49). Prevalence data were available for 11 countries from 4 continents. Iran (3.02%, 95%CI 1.51-4.92) and Honduras (3.03%, 95% CI 0.83-10.39) had the highest values and Malaysia (0%) had the lowest prevalence. Vaginal specimens were the most common source of C. africana (92.81%; 155 out of 167 isolates with available data). However, this species has also been isolated from cases of balanitis, from patients with oral lesions, and from respiratory, urine, and cutaneous samples. Data concerning the susceptibility of C. africana to 16 antifungal drugs were available in the literature. Generally, the minimum inhibitory concentrations of antifungal drugs against this species were low. In conclusion, C. africana demonstrates geographical variation in prevalence and high susceptibility to antifungal drugs. However, due to the relative scarcity of existing data concerning this species, further studies will be required to establish more firm conclusions.

Application of a multi-stage neural network approach for time-series landfill gas modeling with missing data imputation.

To mitigate the greenhouse gas effect, accurate and precise landfill gas prediction models are required for more precise prediction of the amount and recovery time of methane gas from landfills. When the study associates to greenhouse gas emissions problems, time series prediction models are of considerable interests, in which significant past records of gas data are required. This study is the first to specially impute the missing methane (CH4) data for applying in time series artificial neural network (ANN) model in an attempt to predict daily CH4 generation rate from a landfill in Regina, SK, Canada. Pre-processing was conducted on data to evaluate independent and significant meteorological input variables and provide suitable dataset for developing CH4 generation models. A two-stage time series model proposed in this study was performed by missing data imputation at the first stage, followed by a neural network auto-regressive model with exogenous inputs (NARX) at the second stage. The model with 3 layers, 5 climatic variables and 9 neurons in the hidden layer was the optimal structure. This model shows the high performance in CH4 prediction with the average index of agreement of 0.92 and the average mean absolute percentage error (MAPE) of 3.03% during the testing stage. Missing data imputation coupled with NARX method decreased the mean squared error (MSE) of the model by 84% (compared to Multilayer Perceptrons neural network model) in the testing period representing the effectiveness of missing data estimation coupling with time series ANN models in daily CH4 generation prediction.

Interactions of residential waste composition and collection truck compartment design on GIS route optimization.

Waste collection is an important functional element in a modern waste management system; and may account for up to half of the total expenditure on waste management in industrialized nations. Most optimization of waste collection studies include truck route distance and fuel consumption considerations without explicitly considering the inter-relationships of the model parameters. This study however delineates the complex inter-relationships of waste composition, collection frequency, collection type, and truck compartment configurations in a small waste collection zone in Austin, Texas. A total of 48 different scenarios are modelled and investigated. Truck travel distances are found sensitive to collection frequency, truck capacity, volume ratio of truck compartment, and waste density. The results showed that the increase in waste density and waste collection frequency helped to save up to 18.2% in travel distances and 41.9% in travel time. Waste composition is significant in travel distance, regardless of truck design. Increasing truck capacity by 25% helped to save 4.1-24.4% of truck travel distances. Optimal volume ratio of truck compartments was 50:50 (50% volume for garbage and 50% volume for recyclables); a finding that is different than what is currently reported in the literature; pointing to the site-specific nature of studies of this type. The use of dual compartment trucks helps to reduce travel distances by up to 23.0% and travel time by up to 14.3%. It appears that the minimization of operation time within the collection area is key to an efficient system.

Effects of groundwater metal contaminant spatial distribution on overlaying kriged maps.

Groundwater is a major source of drinking water for many Canadians, and contamination by heavy metals poses a significant risk to people and the environment. In this study, three water quality indices are studied in the vicinity of an unlined landfill in a semiarid climate. The study investigates indices using geostatistical analysis and ordinary kriging. This study employs a novel coupling technique in order to compare the index-based maps to a groundwater quality map from overlapping heavy metal kriged maps. A total of 11 heavy metals were evaluated in preliminary analysis, but only four (Mn, As, Fe, and U) had higher concentrations than allowable limits in some or all of the monitoring wells at the site. Results from mean-based classification of indices suggest the aquifer in proximity to the landfill has been impacted by metal contaminants. Kriged maps show that the spatial variations of Mn and U are similar, while results of Fe and As are also similar. However, the two sets of maps have distinctly different patterns. Maps for indices show an elevated plateau extending from the unlined landfill to the southeast corner, implying that the landfill may have negatively impacted groundwater quality. A groundwater quality map is developed by overlaying the heavy metal maps. The resulting map shows that the north and west parts of the study have lower groundwater pollution with respect to metal contaminants. The groundwater quality map may be more applicable for practitioners who need comprehensive water quality measurement.

Assessment of waste characteristics and their impact on GIS vehicle collection route optimization using ANN waste forecasts.

Combining an artificial neural network (ANN) waste prediction model with a geographic information system (GIS) waste collection route optimization, the paper shows how the compositional features of waste materials affect the optimized truck route time, distance, and air emissions. Using data from Austin, Texas, USA, a nonlinear autoregressive ANN model is used to predict the waste generation rate of the recycling and garbage streams for the year 2023 in four sub-areas of the city. This ANN model resulted in mean absolute percentage errors ranging from 10.92% to 16.51%. Modified compositions of the recycling and garbage streams are then used as inputs, along with the year 2023 generation rates, to create 6 modified and 3 non-modified scenarios that reflect possible future changes in waste composition. These waste stream scenarios are then used as input parameters to determine optimal waste collection routes with minimal travel distance in each of the four sub-areas using the GIS vehicle routing problem network analysis tool. Results of these 36 scenarios yield changes in travel distance of up to 19.9%, when compared to the non-modified composition. Further, dual compartment trucks were compared to single compartment trucks and found to save between 10.3 and 16.0% in travel distance and slightly reduce emissions but had a 15.7-19.8% increase in collection time. Results suggest temporal changes in waste composition and characteristics are important in GIS route optimization studies.

Evaluation of the bias and precision of regression techniques and machine learning approaches in total dissolved solids modeling of an urban aquifer.

TDS is modeled for an aquifer near an unlined landfill in Canada. Canadian Drinking Water Guidelines and other indices are used to evaluate TDS concentrations in 27 monitoring wells surrounding the landfill. This study aims to predict TDS concentrations using three different modeling approaches: dual-step multiple linear regression (MLR), hybrid principal component regression (PCR), and backpropagation neural networks (BPNN). An analysis of the bias and precision of each models follows, using performance evaluation metrics and statistical indices. TDS is one of the most important parameters in assessing suitability of water for irrigation, and for overall groundwater quality assessment. Good agreement was observed between the MLR1 model and field data, although multicollinearity issues exist. Percentage errors of hybrid PCR were comparable to the dual-step MLR method. Percentage error for hybrid PCR was found to be inversely proportional to TDS concentrations, which was not observed for dual-step MLR. Larger errors were obtained from the BPNN models, and higher percentage errors were observed in monitoring wells with lower TDS concentrations. All models in this study adequately describe the data in testing stage (R2 > 0.86). Generally, the dual-step MLR and hybrid PCR models fared better (R2avg = 0.981 and 0.974, respectively), while BPNN models performed worse (R2avg = 0.904). For this dataset, both regression and machine learning models are more suited to predict mid-range data compared to extreme values. Advanced regression methods (hybrid PCR and dual-step MLR) are more advantageous compared to BPNN.