Wetland Mapping in Great Lakes Using Sentinel-1/2 Time-Series Imagery and DEM Data in Google Earth Engine

The Great Lakes (GL) wetlands support a variety of rare and endangered animal and plant species. Thus, wetlands in this region should be mapped and monitored using advanced and reliable techniques. In this study, a wetland map of the GL was produced using Sentinel-1/2 datasets within the Google Earth Engine (GEE) cloud computing platform. To this end, an object-based supervised machine learning (ML) classification workflow is proposed. The proposed method contains two main classification steps. In the first step, several non-wetland classes (e.g., Barren, Cropland, and Open Water), which are more distinguishable using radar and optical Remote Sensing (RS) observations, were identified and masked using a trained Random Forest (RF) model. In the second step, wetland classes, including Fen, Bog, Swamp, and Marsh, along with two non-wetland classes of Forest and Grassland/Shrubland were identified. Using the proposed method, the GL were classified with an overall accuracy of 93.6% and a Kappa coefficient of 0.90. Additionally, the results showed that the proposed method was able to classify the wetland classes with an overall accuracy of 87% and a Kappa coefficient of 0.91. Non-wetland classes were also identified more accurately than wetlands (overall accuracy = 96.62% and Kappa coefficient = 0.95)

Wetland Mapping in Great Lakes Using Sentinel-1/2 Time-Series Imagery and DEM Data in Google Earth Engine

The Great Lakes (GL) wetlands support a variety of rare and endangered animal and plant species. Thus, wetlands in this region should be mapped and monitored using advanced and reliable techniques. In this study, a wetland map of the GL was produced using Sentinel-1/2 datasets within the Google Earth Engine (GEE) cloud computing platform. To this end, an object-based supervised machine learning (ML) classification workflow is proposed. The proposed method contains two main classification steps. In the first step, several non-wetland classes (e.g., Barren, Cropland, and Open Water), which are more distinguishable using radar and optical Remote Sensing (RS) observations, were identified and masked using a trained Random Forest (RF) model. In the second step, wetland classes, including Fen, Bog, Swamp, and Marsh, along with two non-wetland classes of Forest and Grassland/Shrubland were identified. Using the proposed method, the GL were classified with an overall accuracy of 93.6% and a Kappa coefficient of 0.90. Additionally, the results showed that the proposed method was able to classify the wetland classes with an overall accuracy of 87% and a Kappa coefficient of 0.91. Non-wetland classes were also identified more accurately than wetlands (overall accuracy = 96.62% and Kappa coefficient = 0.95)

Distribution of Total Volatile Organic Compounds at taxi drivers in Tehran

Air pollution is currently the most serious environmental health threat worldwide. Volatile Organic Compounds (VOC) are considered as the main effective factors in causing air pollution. Vehicles are among the major sources which emit these compounds, so it seems that automobiles’ microenvironment is one of the places where people are exposed to high concentration of VOC. Evaluating the exposure amount of Total Volatile Organic Compounds (TVOC) can indeed be used as an indicator to estimate the amount of exposure to every individual VOC. This study was conducted on the concentration of TVOC inside Tehran taxies for a period of one year. For this purpose, a real time instrument equipped with photo-ionization detector (PID) was used. Consequently, the highest and the lowest measured TVOC in taxies equaled 3.33 ppm and 0.72 ppm, respectively. In addition, the arithmetic mean of TVOC concentration was 1.77±0.53 ppm inside the examined taxies. In this study, the parameters like measurement time, climate and vehicle conditions were found to have significant effect on the amount of exposure to TVOC

The Effect of Mineral Trioxide Aggregate Mixed with Chlorhexidine as Direct Pulp Capping Agent in Dogs Teeth: A Histologic Study

Introduction: The aim of the present investigation was to compare the efficacy of mineral trioxide aggregate (MTA) and 0.2% chlorhexidine (CHX) mixture to pure MTA, as a pulp capping material. Methods and Materials: The pulp of 24 lateral incisors and canines from four dogs were exposed and capped either with MTA or MTA+0.2% CHX. After 2 months the animals were sacrificed and the teeth were prepared for histological evaluation in terms of calcified bridge formation, the degree of inflammation and presence of necrosis. The Fisher’s exact test was used for data analysis. Results: The results showed that formation of complete calcified bridge in MTA specimens was significantly more than MTA+CHX (P<0.05). No significant difference was found in the degree of inflammation and necrosis between MTA and MTA+CHX groups (P>0.05). Conclusion: Mixing MTA with CHX as pulp capping agent had a significant negative impact on formation of calcified bridge on directly capped dog’s teeth.Keywords: Chlorhexidine; Mineral Trioxide Aggregate; Pulp Capping; Vital Pulp Therap

The Frequency of Medically Compromised Patients in Endodontic Offices in Iran

Introduction: As the result of epidemiological transition and aging of Iranian population, the frequencies of systemic diseases among patients in of need endodontic treatment has increased, especially within developed cities. However, there have been no concise reports of systemic diseases in Iranian patients. Based on this need, the present investigation was conducted to assess the frequency of systemic disease among patients referred to endodontic private practice in three main cities in Iran. Materials and Methods: In a retrospective study, the frequency of systematic diseases were abstracted from the health records of patients who were referred to three private practices limited to endodontics in Kerman, Mashhad, and Tehran between 1994 to 2011. Results: Overall, 15,413 records of patients were assessed. The patterns of systematic diseases among endodontic patients in these three cities were different. The overall frequency of systemic disease in Kerman was significantly higher than two other cities (Kerman: 55.03%, Mashhad: 24.32%, Tehran: 22.16%; P<0.001). The most commonly occurring diseases were cardiac disease, hypertension, allergy and neurological disorders. Conclusion: Since the number of endodontic patients with systematic diseases is considerably significant and varied, special training and educations for treatment of medically compromised patient should be considered at both post- and undergraduate training

ELULC-10, a 10 m European land use and land cover map using Sentinel and landsat data in Google Earth Engine

Land Use/Land Cover (LULC) maps can be effectively produced by cost-effective and frequent satellite observations. Powerful cloud computing platforms are emerging as a growing trend in the high utilization of freely accessible remotely sensed data for LULC mapping over large-scale regions using big geodata. This study proposes a workflow to generate a 10 m LULC map of Europe with nine classes, ELULC-10, using European Sentinel-1/-2 and Landsat-8 images, as well as the LUCAS reference samples. More than 200 K and 300 K of in situ surveys and images, respectively, were employed as inputs in the Google Earth Engine (GEE) cloud computing platform to perform classification by an object-based segmentation algorithm and an Artificial Neural Network (ANN). A novel ANN-based data preparation was also presented to remove noisy reference samples from the LUCAS dataset. Additionally, the map was improved using several rule-based post-processing steps. The overall accuracy and kappa coefficient of 2021 ELULC-10 were 95.38% and 0.94, respectively. A detailed report of the classification accuracies was also provided, demonstrating an accurate classification of different classes, such as Woodland and Cropland. Furthermore, rule-based post processing improved LULC class identifications when compared with current studies. The workflow could also supply seasonal, yearly, and change maps considering the proposed integration of complex machine learning algorithms and large satellite and survey data.Peer ReviewedPostprint (published version

The Association of Human Leucocyte Antigen (HLA) Class I and II Genes with Cutaneous and Visceral Leishmaniasis in Iranian Patients: A Preliminary Case-Control Study

Background: Leishmaniasis is currently considered a re-emerging or emerging infection based on the geographic region. The outcome of leishmaniasis vastly depends on Leishmania-host interaction. This preliminary study aimed to show the association of human leukocyte antigen (HLA) class I and II genes with healed and non-healed cutaneous leishmaniasis (CL), and symptomatic and asymptomatic visceral leishmaniasis (VL) compared with control groups in Iran. Methods: Ninety-five people, including 31 patients versus 64 individuals in the control group, were enrolled. Among them, 20 patients had confirmed CL based on amastigote observation, 10 had improved CL and 10 non-healed CL. Eleven patients were suffering from confirmed VL based on direct agglutination test (Five asymptomatic and six symptomatic VL cases). Besides, they were residents in an endemic area of VL in the northwest of Iran. To select a control group, it was ensured that they had no history of leishmaniasis. Peripheral blood samples were collected from each patient. After DNA extraction, HLA typing was conducted using polymerase chain reaction - sequence-specific priming (PCR-SSP). Subsequently, data were statistically analyzed by SPSS.   Results: There was a statistical relationship between the presence of HLA-A26 and CL, healed CL and the existence of the B38 allele, C1 allele and symptomatic VL, as well as B1.4 allele and asymptomatic VL (P˂0.05). Conclusion: This primary finding indicates that several HLA genes have a potential role in the susceptibility of Iranian people to CL and VL

Wet deposition of hydrocarbons in the city of Tehran-Iran

Air pollution in the city of Tehran has been a major problem for the past three decades. The direct effects of hydrocarbon contaminants in the air are particularly important such as their carcinogenic, mutagenic, and teratogenic effects which can be transported to other environments via dry and wet deposition. In the present study, rainwater samples were collected and analyzed for 16 polycyclic aromatic hydrocarbons (PAHs), benzene, toluene, ethyl benzene, and xylene (BTEX) as well as fuel fingerprints in two ranges of gasoline (C5–C11) and diesel fuel (C12–C20) using a gas chromatograph equipped with a flame ionization detector (GC/FID). Mean concentrations of ∑16 PAHs varied between 372 and 527 µg/L and for BTEX was between 87 and 188 µg/L with maximum of 36 µg/L for toluene. Both gasoline range hydrocarbons (GRH) and diesel range hydrocarbons (DRH) were also present in the collected rainwater at concentrations of 190 and 950 µg/L, respectively. Hydrocarbon transports from air to soil were determined in this wet deposition. Average hydrocarbon transportation for ∑PAHs, BTEX, GRH, and DRH was 2,747, 627, 1,152, and 5,733 µg/m2, respectively

Time Series of Satellite Imagery Improve Deep Learning Estimates of Neighborhood-Level Poverty in Africa

To combat poor health and living conditions, policymakers in Africa require temporally and geographically granular data measuring economic well-being. Machine learning (ML) offers a promising alternative to expensive and time-consuming survey measurements by training models to predict economic conditions from freely available satellite imagery. However, previous efforts have failed to utilize the temporal information available in earth observation (EO) data, which may capture developments important to standards of living. In this work, we develop an EO-ML method for inferring neighborhood-level material-asset wealth using multi-temporal imagery and recurrent convolutional neural networks.1 Our model outperforms state-of-the-art models in several aspects of generalization, explaining 72% of the variance in wealth across held-out countries and 75% held-out time spans. Using our geographically and temporally aware models, we created spatiotemporal material-asset data maps covering the entire continent of Africa from 1990 to 2019, making our data product the largest dataset of its kind. We showcase these results by analyzing which neighborhoods are likely to escape poverty by the year 2030, which is the deadline for when the Sustainable Development Goals (SDG) are evaluated

Occupational Exposure to Carbon Monoxide of Taxi Drivers in Tehran, Iran

Occupational exposure to carbon monoxide (CO) of taxi drivers has seldom been evaluated in Iran. Accordingly, in-vehicle CO levels were evaluated during 6 months inside the taxis between May 2009 and October 2010. The CO concentrations of 36 personal samples were collected using a direct reading instrument equipped with electrochemical sensor. The arithmetic mean of the personal monitoring CO levels was 19.84 ± 4.24 ppm per day, with a range of 13.29-33.46 ppm. The observed concentrations of CO fell well lower than occupational standards. Exposures to CO during traffic flow in the evening were considerably higher than those measured in the morning. The weekdays, months and atmospheric environment had a significant effect on exposure to CO (p< 0.0001). The average CO level was 19.84 ± 4.24 ppm, which was higher than the outdoor CO levels (3.21 ppm). In conclusion, the penetration of outdoor CO pollution and engine combustion/exhaust infiltration constituted the main sources of the taxis drivers' personal exposure to CO