In Mozambique, the prevalence of malaria in children under 5 years of age is among the highest in the world, but limited data exist on determinants of care-seeking behaviour for malaria. This study aimed at determining the trends and factors associated with care-seeking behaviour for fever among children under 5 years of age and to assess the treatment practices for malaria.Secondary data analysis of two cross-sectional studies. Descriptive statistics were used to summarize socio-economic and demographic characteristics of participants, using data from the 2011 Demographic and Health Survey and 2015 Indicators of Immunization, Malaria and HIV/AIDS Survey. Complex sampling logistic regression model was used to identify factors associated with care-seeking behaviour, with estimated adjusted odds ratio and respective 95% confidence intervals, only for 2015 IMASIDA data.A total of 10,452 and 5168 children under 5 years of age were enrolled in the 2011 DHS and 2015 IMASIDA, respectively. Care-seeking for fever in public and private sectors remained stable during this period . The main place where care was sought in both surveys was public hospitals . Prescription of anti-malarial drugs increased from 42.9% (385/897) in 2011 to 53.8% (524/974) in 2015. Artemether–lumefantrine was the most used anti-malarial drug for febrile children in both surveys and its use increased from 59.0% (219/373) in 2011 to 89.3% (457/512) in 2015. Data from 2015 elucidated that care-seeking was more common in children whose mothers had a secondary level of education (AOR = 2.27 [95% CI 1.15–4.49]) and among those in poorer quintile (AOR = 1.46 [95% CI 0.83–1.90]). Mothers with higher education level (AOR = 0.16 [95% CI 0.34–0.78]) were less likely to seek out care. People from Manica (AOR = 2.49 [1.03–6.01]), Sofala ([AOR = 2.91 [1.03–8.24]), Inhambane (AOR = 3.95 [1.25–12.45]), Gaza (AOR = 3.25 [1.22–8.65]) and Maputo Province (AOR = 2.65 [1.10–6.41]) were more likely to seek care than people from Maputo City.Data from this study showed that care-seeking in Mozambique remained suboptimal. Interventions to raise the awareness for early care-seeking during episodes of fever should be urgently reinforced and intensified. Mozambique is ranked among the countries with high burden of malaria  and in 2Early diagnosis and prompt and correct treatment are essential for a favourable malaria outcome, reducing its morbidity and mortality , 5. ThusThere is limited data on malaria care-seeking in Mozambique and there are no published reports describing factors associated with malaria care-seeking behaviour in Mozambique. There is an urgent need to determine the patterns and factors associated with care-seeking behaviour for malaria, as such knowledge is crucial for designing strategies aiming to improve malaria diagnosis and treatment –12. In tThis is a quantitative, observational study that analysed two national, cross-sectional studies in which data were collected in two periods of time: 2011 and 2015. A secondary data analysis using the 2011 DHS data and the 2015 IMASIDA data was conducted, to describe socio-economic, demographic characteristics and treatment practices. The 2015 IMASIDA data was also used to identify factors associated with care-seeking behaviour. It was decided not to use the 2011 DHS data to identify the predictors of the care-seeking, because the data was collected long time ago and so that the situation may have changed and so it does not make much sense to identify these factors.Both surveys used nationally representative samples. The 2011 DHS included 13,964 households distributed over 611 census enumeration areas (EAs) while 2015 IMASIDA included 7169 households distributed over 307 EAs. The response rate was 98.9% and 98% in 2011 DHS and 2015 IMASIDA, respectively. Methods for both surveys have been previously described ) than in urban areas (20.3 [95% CI 20.3–25.8]), but this difference was not observed in 2011 . Missing data comprised less than 1%.Care was sought for 62.6% , of the children with a history of fever in the 2 weeks prior to the survey in 2011 and 63.7% , in 2015. Table In both surveys, care-seeking was higher in urban areas as compared to rural areas. In 2011, care-seeking was 75.0% [95% CI 69.8–79.7] in urban areas as compared to 58.2% [95% CI 53.8–62.5] in rural areas. Similarly, in 2015 care-seeking was 74.9% [95% CI 68.4–80.4] in urban and 60.9% [95% CI 55.6–65.9] in rural areas.In 2011, Cabo Delgado province reported the lowest care-seeking (33.5%), while in 2015, Zambézia province had the lowest care-seeking (58.6%) reported. When comparing by region, the provinces situated in the southern region of the country presented the highest care-seeking behaviour, excluding Maputo City  in 2011 to 53.8% (524/974) in 2015 , but women with higher education (beyond high school) were less likely to seek care (AOR = 0.16 [95% CI 0.34–0.78]). People from the poorer quintile were more likely to seek care than people from the poorest (AOR = 1.46 [95% CI 0.83–1.90]). People from rural areas were less likely to seek care than people from urban areas (AOR = 0.58 [0.34–1.00]). In relation to provinces, people from Manica (AOR = 2.49 [1.03–6.01]), Sofala ([AOR = 2.91 [1.03–8.24]), Inhambane (AOR = 3.95 [1.25–12.45]), Gaza (AOR = 3.25 [1.22–8.65]) and Maputo Province (AOR = 2.65 [1.10–6.41]) were more likely to seek care than people from Maputo City. Missing data comprised less than 2%.This is the first study describing factors associated with care-seeking behaviour for fever in Mozambique. In this study the pattern of care-seeking for fever and its predictors among children under 5 years of age were investigated. The study data showed that despite the fact that the percentage of children with fever doubled from 13.7% in 2011 DHS to 27.2% in 2015 IMASIDA, care-seeking remained stable at 63% in this period. This figure is lower than the 70% defined by Mozambique’s National Malaria Control Program . This fiThe percentage of children for whom care was not sought, found in both surveys, was similar to what was reported in a study conducted in Senegal, in which 37% of children with fever did not receive any treatment or medical advice . HoweverThis results clearly demonstrate important differences in care-seeking for fever by geographic region. Of note, Zambézia which is the province with highest malaria prevalence in the country and the second most populous province in the country, had one of the lowest reported care-seeking behaviours for fever. This low care-seeking in Zambézia has previously been found in a study on care-seeking behaviour for any disease or wound in any age . The repHigher care-seeking rates in the southern region of Mozambique might partially be related to the fact that literacy and access to medical services in the southern region is also higher . This suIn Mozambique care for fever was mostly sought at public hospitals. This finding is different from Zambia where most of the caretakers sought treatment of fever for their children from CHW, friends, relatives, traditional healers or spiritualists , and froData from 2015 IMASIDA showed that mother’s education was positively associated with care-seeking behaviour, as has been shown in other studies , 23, 33.Care-seeking behaviour for fever was also associated with place of residence. Caretakers from rural areas were less likely to seek care for febrile children than those from urban areas. Similar findings have been reported in previous studies conducted in other sub Saharan Africa countries , 19, 34.Wealth of the caregiver was also associated with care-seeking behaviour for fever, a finding similar to other settings , 24. CarDespite the better access and conditions found in Maputo City, care-seeking was higher in other provinces like Manica, Sofala, Inhambane, Gaza and Maputo province than in Maputo City, which might be not only associated with the self-medication mentioned before but also to the fact that the interventions to raise awareness for early care-seeking are implemented by the CHW and Maputo City is the only province in Mozambique without CHW Program . Care-seThis study had two important limitations. First, the data related to fever and treatment practices by caretakers was self-reported. It is possible that some participants have had difficulties in recalling all relevant details or may have been influenced by social-desirability bias. However, given that only episodes of fever in the 2 weeks prior to the surveys were considered, this may have contributed to minimize the recall bias. Secondly, although both surveys used nationally and regionally representative samples, their sample sizes were different.This study showed that care-seeking for fever in children under 5 in Mozambique remained sub optimal from 2011 to 2015. This low care-seeking placed febrile children with malaria infection at serious risks of progression to severe malaria disease and death due to lack of or delayed treatment. This study highlights that interventions to raise awareness for early care-seeking during episodes of fever, along with interventions to increase community awareness about malaria treatment particularly in Zambézia Province, should be urgently reinforced and intensified in order to save lives and control the epidemic.


Cleanup of soils contaminated with toxic metals is a difficult task due to the method inefficiency and the destructive nature of clean-up techniques on soil ecosystems.This study was performed to improve the removal efficiency of an acetic acid washing solution for the removal of lead (Pb) from soil. Acetic acid was used in combination with different concentrations of potassium chloride. In order to maximize the removal of Pb from the leachate, different electrode combinations were applied to the washing solutions.Acetic acid/potassium chloride washing solutions and electrochemical reduction were applied to lead-contaminated soil obtained from an impact berm of a major military shooting range in Ibadan, southwestern Nigeria. The soil was subjected to 5% acetic acid/5% potassium chloride (KCL) and 5% acetic acid/10% KCL solutions in an ex-situ batch experiment. The leachate was electrochemically reduced using 12 volt direct current with a current of 7 amps and 2.5 amps, with aluminum (Al)-Al, iron (Fe)-Fe, Al-Fe and Fe-Al electrodes.The 5% acetic acid/5% KCL proved more efficient for Pb removal in soil with values ranging from 74.9% to 86.9% for 3% soil pulp densities with one washing time of 6 hours. Removal efficiency of Pb from the contaminated soil significantly decreased as the soil pulp density increased. The Al-Al and Al-Fe bipolar electrode combinations showed better removal efficiency of Pb from the leachates with values of 93.7% and 95.6% for 7 amps and 94.5% and 97.3% for 2.5 amps, respectively.The combined 5% acetic acid and 5% potassium chloride washing solution enhances the removal efficiency of Pb in soil and poses less risk to the soil ecosystem and the environment in general. This is due to its persistence and toxicity. Lead is known to cause health problems such as cognitive impairments, behavioral disorders, stroke and even death.Effective soil washing produces a complex leachate solution that contains metal ions, and in order to remove the metal ions from the leachate, the leachate requires treatment. Secondary treatment like nanofiltration and electrochemical treatment to remove leached metal ions from leachates have been used.21This study was performed to improve the removal efficiency of an acetic acid washing solution for the removal of lead from soil. Acetic acid was used in combination with different concentrations of potassium chloride. In order to maximize the removal of lead from the leachate, different electrode combinations were applied on the washing solutions.AASAtomic absorption spectrophotometerAlAluminumCuCopperEDTAEthylenediaminetetraacetic acidFeIronKCLPotassium chlorideNaClSodium chloridePbLeadZnZinc2. It consisted of a front slope that was directly impacted by bullet pellets from shooting activities and back slope. The front slope of the berm was segmented into seven sampling areas with a length of about 14 meters each. At each sampling area, three contaminated top soil samples (0–15 cm in depth) were randomly (about 10 meters apart on a triangular pattern) collected and mixed together to form a single bulk sample. A total of seven contaminated top soil samples (25 kg each) were collected in plastic bags and transported to the laboratory where they were air-dried and crushed and sieved through a 4 mm mesh sieve. A 20 kg portion each of these seven sieved contaminated top soil samples were thoroughly mixed together to obtained a composite sample. Furthermore, a portion (20 kg) of this composited soil was set aside while the remainder was fractionated to 2.00, 0.85, 0.425, 0.250, 0.180 and 0.150 mm particle sizes aggregate using corresponding mesh size sieves. In addition, the total number of bullet pellets found within each of the seven top soil samples were sorted and quantified before sample pretreatment. The corresponding numbers of bullet pellets in the seven soil samples were 358, 590, 375, 295, 311, 132 and 56, respectively, and were between 1.45 to 10.7 grams . The berm soil properties  were previously reported using standardized methods.−1 sodium acetate which had been adjusted to pH 5.0 with acetic acid. The reducible fraction was extracted with 0.04 mol L−1 hydroxylamine -hydrogen chloride in 25% (v/v) acetic acid. The oxidizable fraction was determined by treatment of the sample with 30% hydrogen peroxide adjusted to pH 2.0 with nitric acid, while the residual fraction was digested with a 2:1 mixture of concentrated hydrofluoric  and perchloric acids .Figure 1), composited soil, 2.00, 0.85, 0.425, 0.250, 0.180 and 0.150 mm fractions (Table 1) using an atomic absorption spectrophotometer (AAS) Buck Scientific, model 200A after digesting 2.0 g of soil with 20 mL aqua-regia for 3 hours in a hot water bath and final extract volume made to 25 mL using a standard volumetric flask.Lead contaminated soil was obtained from an impact berm of a major military shooting range in Ibadan, southwestern Nigeria. A systematic sampling procedure was adopted for the impact berm. The impact berm measured about 100 m in length and 18 m in width, with an estimated area of 1800 m samples  kg each Table 2). The bottles were agitated using an end-to-end (Edmund Buhler SM 25) mechanical shaker for 2 hours, after which the mixtures were filtered using Whatman  filter paper. The soil residue in the filter paper was then washed with deionized water sequentially twice and both supernatants from the initial leaching and washing were combined together and stored for analysis. The agitation process was repeated for 4, 6, 8 and 10 hours intervals on other sample bottles with the same weight/volume of soil and solution and with the same volume of 100 mL 5% KCL and 400 mL 5% acetic acid washing solution. The entire procedure was repeated using a different washing solution of 100 mL 10% KCl and 400 mL 5% acetic acid following the same weight/volume and interval agitating for 2 to 10 hrs. The supernatants were analyzed directly for Pb using AAS  and the percentage removal efficiency of Pb calculated using A washing procedure was applied to the composited soil, 0.850 and 0.150 mm fractions, in order to investigate which washing solutions are most effective and to determine the effect of different concentrations of potassium chloride on the effectiveness of acetic acid for leaching Pb from the contaminated soil. In the batch washing experiment, 15, 30, 45, 60, 75 and 90 grams of the soil samples  was weighted into six separate plastic bottles (in duplicate). Into each of these bottles was added 100 mL 5% KCL and 400 mL 5% acetic acid to give a total volume of 500 mL of washing solution. This weight/volume represents 3, 6, 9, 12, 15 and 18% soil pulp density  = 18%) and the batch tests were coded S3, S6, S9, S12, S15, and S18 respectively  was calculated using the equation proposed by Wuana et al.1 and CS are the concentrations of Pb in the supernatant (mg/L) and soil sample (mg/kg) determined using AAS, respectively. In addition, V1 is the volume of supernatant (L) and MS is the weight of the soil (kg) used for the washing experiment. CS: Composite-24499 mg/kg, 0.85 mm-15610 mg/kg and 0.150 mm-13245 mg/kg. V1: 0.5L. MS: 0.015, 0.030, 0.045, 0.060, 0.075 and 0.090 kg.Where, CTo evaluate the efficiency of the electrochemical reduction of Pb ions in the washing solutions, the reduction was carried out in the combined washing solutions using aluminum (Al)-Al, iron (Fe)-Fe, Al-Fe and Fe-Al bipolar electrodes. During the experiment, 100 ml of the combined leached solution was placed in a 250 mL glass beaker. A pair of electrodes, 14 cm long and 2 cm in diameter, were placed 5 cm apart inside the solution. The set up was subjected to electro-analytical measurement using the bipolar electrodes with a 12 volt direct current power source with current intensities of 7 amps and 2.5 amps for 5, 10, 15, 20, 25 and 30 minutes. Before and after the electrochemical reduction process, 10 ml of the electrolyte was directly analyzed for Pb by AAS.Removal efficiency of Pb was calculated by the equation:0 is the initial Pb concentration (mg/L) before electrochemical reduction and Ct is the Pb concentration (mg/L) after the experiment at a given time interval.Where CFigure 1A and B). In the analysis of fractionated particle sizes of the composited soil, it was observed that Pb levels varied significantly, with the composite (24499 mg/kg) and 2.00 mm particle size (23138 mg/kg) having the highest levels, while the other five particle sizes  also had relatively high, but similar levels. Levels of Cu and Zn were about the same for the various soil particle sizes with ranges of 529–1054 mg/kg for Cu and 96–183 mg/kg for Zn. From the speciation study (Table 3), Pb and Cu levels were more predominant in the carbonate and organic fraction, which is similar to an earlier reported study carried out within the entire range.According to previous studies, the berm soil is reducing in nature (pH 6.78±0.26) and contains high percentage sand (63.3±9.2%) and organic matter (3.03±2.09%) content.Figure 2). However, the removal efficiency of Pb for 5% acetic acid and 10% KCL was lower than for 5% acetic acid and 5% KCL washing solution. The removal efficiencies in this washing solution ranged from 16.0–69.3%. The highest values of 64.1%, 69.3 and 68.9% were recorded for 3% soil pulp density and 10 hour duration interval for the composite, 0.85 mm and 0.150 mm fractions, respectively. The 2, 4, 6 and 8 hours washing durations for the 3–18% soil pulp densities all recorded values below 60% (Table 2). It was also observed that increasing soil pulp density, irrespective of washing duration, corresponded to a decrease in the percentage removal efficiency of Pb. Meanwhile, for each soil pulp density, the percentage removal of Pb increased with duration of washing time .The results of the electrochemical reduction of Pb from the combined washing solutions using different electrode combinations are illustrated in Table 4). This test showed no significant difference in percentage removal efficiency of Pb for the composite, 0.85 mm and 0.150 mm soil fractions across the different pulp densities and washing times obtained by the 5% acetic acid and 5% KCL solution. However, a significant difference was observed in percentage removal of Pb within the composite, 0.85 mm and 0.150 mm fractions for 2 to 6 hours extraction with the 5% acetic acid and 10% KCL washing solution. This indicates that the 5% acetic acid and 5% KCL washing solution is likely more efficient for Pb removal. In comparison, the statistical analysis (p=0.05) indicated that there was a significant difference between the percentage removal efficiency of Pb obtained by the two washing solutions when applying similar washing durations of 2, 4, 6, 8 and 10 hours for the different soil fractions. Increasing soil pulp density leads to decrease in Pb removal efficiency for both washing solutions. This may be attributed to possible oversaturation of the washing solution with the soil and the limited amount of potassium chloride available for exchange with Pb within the soil pores system.The presence of corroded bullet fragments resulting from abrasion and weathering could account for the high lead concentrations in the soil fractions. Bioavailable fractions of Pb in the soils could also pose a significant environmental threat considering the high concentration of lead.Generally, the removal of Pb from the contaminated soils showed higher percentage removal efficiency with 5% acetic acid and 5% KCL compared with the 5% acetic acid and 10% KCL solution. This may be because the extractability of Pb from contaminated soil is greatly influenced by the pH and concentration of the washing fluid. At lower pH , the protons (hydron) added from the acetic acid can react with soil surface sites  and enhance desorption of Pb cations, which are transferred into the washing solutions.15The percentage removal of Pb in the contaminated soil for both washing solutions was observed to be lower than the 97% reported by Guemiza et al. when using 0.125 M sulfuric acid and 4 M NaCL with a pulp density of 10% at 1 hour washing time, although this value was obtained after three successive acid leachings.32In this study, Al-Al and Al-Fe electrode combinations proved efficient for electrochemical removal of Pb from leachate. Increasing the duration of the electrochemical reduction process may possibly further increase the removal efficiency of Pb according to the observed trend. Results obtained compared closely with the 94% Pb removal from acid and saline leachates using mild steel electrodes, 57–76% using −2.0 V potential sources,33In conclusion, the lead concentration in soils at the impact berm related positively with the number of corroded bullet fragments in the soil. The concentration of lead was very high in the composited and 2.00 mm soil fraction, which suggests the presence of small weathered fragmented bullet fragments in the soil. The 5% acetic acid and 5% KCL washing solution was the most efficient solution for Pb removal when it was applied with a 3% soil pulp density and 6 hours washing duration. Longer contact times between the soil and washing solution are desirable. In addition, increasing the number of washing steps will further improve removal efficiency. The Al-Al and Al-Fe bipolar electrode combinations showed the best removal efficiency of Pb from the leachates with values well over 90% at current intensities of 7 and 2.5 A. Although acetic acid is known to be less efficient at Pb removal from contaminated soil, the introduction of potassium chloride enhanced the removal efficiency of Pb, and this washing solution may be an alternative to strong acid and chelating agents. The combined 5% acetic acid and 5% potassium chloride washing solution should pose less risk to the soil ecosystem and the environment in general. Further studies are required to determine the effectiveness of much lower strength acetic acid in combination with other salts for lead removal in contaminated soil.


Lima’s topography and aging vehicular fleet results in severe air pollution with limited amounts of monitors to effectively quantify PM2.5 levels for epidemiologic studies. We developed an advanced machine learning model to estimate daily PM2.5 concentrations at a 1 km2 spatial resolution in Lima, Peru from 2010 to 2016. We combined aerosol optical depth (AOD), meteorological fields from the European Centre for Medium-Range Weather Forecasts (ECMWF), parameters from the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem), and land use variables to fit a random forest model against ground measurements from 16 monitoring stations. Overall cross-validation R2  for the random forest model was 0.70 (5.97 μg/m3). Mean PM2.5 for ground measurements was 24.7 μg/m3 while mean estimated PM2.5 was 24.9 μg/m3 in the cross-validation dataset. The mean difference between ground and predicted measurements was −0.09 μg/m3 (Std.Dev. = 5.97 μg/m3), with 94.5% of observations falling within 2 standard deviations of the difference indicating good agreement between ground measurements and predicted estimates. Surface downwards solar radiation, temperature, relative humidity, and AOD were the most important predictors, while percent urbanization, albedo, and cloud fraction were the least important predictors. Comparison of monthly mean measurements between ground and predicted PM2.5 shows good precision and accuracy from our model. Furthermore, mean annual maps of PM2.5 show consistent lower concentrations in the coast and higher concentrations in the mountains, resulting from prevailing coastal winds blown from the Pacific Ocean in the west. Our model allows for construction of long-term historical daily PM2.5 measurements at 1 km2 spatial resolution to support future epidemiological studies.It is well recognized that exposure to fine particulate matter (PM PM2.5and Asia –6. Howevand Asia ,8. Curre3, 24-h mean) [2.5 monitors in Lima, 77% of the days between 2014 and 2015 exceeded the WHO’s 24-h standards [2.5, and potentially to curtail Lima’s air pollution effects via new policies to improve air quality standards.Lima, Peru is the third-most populous and the second-most polluted major city in the Americas . Lima’s -h mean) . A studytandards . Moreovetandards . Yet, th2.5 concentrations [2.5 concentrations with broad spatial coverage, expanding the ground monitoring networks into the rural areas where ground measurements are lacking [2.5 in southern California, China, and Pittsburgh, Pennsylvania [2.5 [2.5 in the United States, Middle East, and China [Many of the studies investigating air pollution in Lima have been cross-sectional in design, with childhood asthma as a popular health outcome ,20. To dtrations . Satellitrations . AOD can lacking . Most co lacking . These psylvania –27. A Munia [2.5 . MAIAC And China –30.2.5 in the Northeast U.S. with and without AOD, with results indicating that the AOD model (R2 = 0.79) has higher predicting power compared to the non-AOD model (R2 = 0.48) [2.5 concentrations over the Yangtze River Delta of China using MAIAC AOD and ground measurements from 2013 and 2014 with results showing good fit between ground measurements and prediction estimates  R2 = 0.81 for 2013 and 0.73 for 2014) [2.5 concentrations in Beijing at 1 km2 spatial resolution with high accuracy  [2.5 and satellite MAIAC AOD, derived from statistical models including generalized linear regression and generalized additive modeling, is greatly improved when land use and meteorological parameters are included; nonetheless, results such as these suggests that MAIAC AOD by itself is a strong predictor of PM2.5 concentrations [Implementation of remote sensing techniques have proven successful in China and the United States ,23. Usinto 0.86) . The stutrations .2.5 in Lima, Peru. This presents an opportunity to implement satellite remote sensing techniques in building a model to estimate ground-level PM2.5 in a region with critically high levels of air pollution and limited number of epidemiological studies to assess its impact on health risks. In this analysis, we build a PM2.5 exposure model to estimate daily PM2.5 concentrations at 1 km2 spatial resolution in Lima for years 2010 to 2016. This exposure model is derived from satellite MAIAC AOD, simulation data from chemical transport models (CTMs), meteorological fields from a forecast model, and land use parameters. The resulting daily estimates of PM2.5 may be used in epidemiologic studies to assess its impact on both cardiovascular and respiratory health outcomes, and potentially support policies that will mitigate air pollution in Lima, Peru.To date, remote sensing techniques have not been utilized in air pollution research in Lima, Peru due to insufficient ground monitoring data to correlate and validate model results. However, in recent years, the Servicio Nacional de Meteorología e Hidrología del Perú (SENAMHI) stations from the Ministry of Environment have begun collecting daily concentrations of PM2.2.1.Lima is the capital city of Peru and has over 10 million inhabitants. The city is nestled at 154 m above sea level in the valleys of the Chillón, Rímac, and Lurín rivers, overlooking the Pacific Ocean in the west and the Andes Mountains lying ~3000 m above sea level in the east. The study region spans from ~80 km north to south and 40 km east to west, which includes the city of Lima and the seaport of Callao, together known as the Lima Metropolitan Area.2 pixels was developed to cover the study region, and a 10 km buffer was added to ensure accuracy of any other parameters that need to be interpolated from coarser resolutions down to the modeling grid cells. The added buffer also allowed for better estimation of PM2.5 concentrations near the outer boundaries of the study area. With the 10 km buffer, the total number of pixels increased to 5959 during the model development and training period. In 2.5 level at each monitor. The JHU network is part of the Genetic Asthma Susceptibility to Indoor Pollution in Peru, GASP study [A grid of 2865 1 kmSP study .2.2.2.5 and PM10 concentrations in Lima, Peru. These 10 monitoring stations are Thermo Beta 5014i monitors utilizing; the beta ray attenuation method and are calibrated three times a year  [10 starting in 2010 and PM2.5 from 2014 to 2016, and its ten sites contributed 6389 daily observations from 2014 to 2016. Additionally, data from 15 mobile air quality monitors located in Pampas de San Juan de Miraflores were provide by Johns Hopkins University (JHU stations) [2 grids that contained more than 1 JHU station were averaged, which reduced the number of stations from 15 to 6. The JHU sites provided 2081 daily observations from six grid cells to the model fitting dataset. There are ten SENAMHI stations that measure PMer 2014) . SENAMHItations) . These m2.3.2 spatial resolution retrieved using the MAIAC (Multiangle Implementation of Atmospheric Correction) algorithm was obtained from the MAIAC science team at NASA’s Goddard Space Flight Center. The MAIAC algorithm accomplishes atmospheric correction by first gridding the data to a fixed 1 km2 grid and accumulating of up to 16 days of measurements [Satellite aerosol optical depth (AOD) at 1 kmurements . Using aurements .2 box of MAIAC AOD centered at the Arica site to assess validity and accuracy from 2010 to 2015. AERONET is a ground-based remote sensing network that provides global observations of AOD [2 of 0.82 [AOD measurements from Arica , the neas of AOD . AERONETs of AOD ,38. The  of 0.82 . Daily d of 0.82  for 2010 of 0.82 .2.4.2 spatial resolution [2.5, and planetary boundary layer height (PBL). There parameters were interpolated to the 1 km2 modeling grid using an inverse distance weighting method.SENAMHI produces Weather Research and Forecast model coupled with Chemistry (WRF-Chem) simulations for air quality forecasts in Lima at 5 kmsolution . WRF-Chesolution . CTMs sisolution . SENAMHIsolution . In briesolution . WRF-Che2.5.2 spatial resolution [2 modeling grid using inverse distance weighting. Subsequently, a daily average was calculated for each variable. As part of the cross-validation process, a correlation analysis was performed on temperature, wind, and pressure between WRF-Chem and ECMWF. Furthermore, temperature and dew point from ECMWF was used to calculate relative humidity [Data at 6-h increments for 28 parameters including dew point, temperature, wind, and pressure were downloaded for January 2010 through December 2016 from the European Centre for Medium-Range Weather Forecasts (ECMWF) archive  at 12.5-solution , and inthumidity . In addi2.6.™ yearly population data for 2010 through 2016 was used [2 grids to match the MAIAC AOD grid cells, and a percent urbanization was calculated by dividing the area classified as urban in each 1 km2 grid cell by the total area of that cell. Normalized difference vegetation index (NDVI) data at 500 m spatial resolution (MYD13A1 Version 6) was downloaded from the LAADS DAAC for years 2010 to 2016 [Elevation data from the Advanced Spaceborne Thermal Emission and Reflection Radiometer Global Digital Elevation Map (ASTER GDEM) was downloaded from EARTHDATA for Lima, Peru . Census was used . Land uswas used . The 30- to 2016 . Since N2.7.mtry) of the tree and the number of trees or subset of samples to be averaged (ntree). Comparison of results with different settings of mtry and ntree was conducted to achieve the best prediction accuracy. The 16 variables used in the random forest model training includes predicted MAIAC AOD from the gap-filling method, NDVI, percent urbanization, road category 3 distance, elevation, population density, interpolated WRF-Chem simulated PM2.5, temperature, surface pressure, albedo, cloud fraction, PBL, and wind V and U components, and interpolated relative humidity and surface solar radiation downwards from ECMWF, with mtry, and ntree set at 6, and 1000, respectively.A random forest (RF) model was used to fit 16 predictors to 8470 ground measurements. The RF model’s advantages include its accuracy in learning and classifying features, its ability to include a large number of input variables, and its output of variable importance. Random forest is a supervised machine learning model that works by averaging a set of decision trees that calculates the best predictions based on a subset of predictors . The RF 2.5 and is better suited for a moderate to small sample size datasets.A 10-fold cross-validation (CV) process was carried out on the RF model to validate the prediction results. The model fitting dataset, consisting of 8470 ground observations, were randomly divided into 10 segments with each segment containing 10% of the data. Nine of the segments were used as a training dataset set to fit the model and the remaining segment is used as a testing dataset to make predictions. This process was repeated 10 times, each time dividing the dataset at different intervals to ensure that the segments are not repeated. After the 10th repetition, the total number of predictions based on the testing dataset was combined into one dataset and is equal to the original number of ground observations. This CV technique is commonly used in similar studies estimating PM3.3.1.2.5 started on 2 March 2010 and ended on 31 December 2016. In total, 2232 daily predictions were made between 2010 and 2016. In Daily predictions of PM2.5 for all JHU monitors from November 2011 to March 2013 is 18.9 (4.7) μg/m3 with mean individual monitors ranging from 16.8 (4.0) (JHU Station 11) to 19.9 (5.8) μg/m3 (JHU Station 9). The homogeneity of JHU measurements may be due to the spatial location of these monitors being clustered within 2–3 km in the south region of the study domain. In general, ground JHU measurements peak to 29 μg/m3 around April of 2012 and gradually decrease to 12.5 μg/m3 in September of 2012 before increasing to a high of 30.5 μg/m3 in March of 2013. It is unclear if PM2.5 levels peak at this point or continue to increase as data beyond this period is unavailable for JHU monitors. All JHU monitors share this temporal trend; nonetheless, this similarity may again be due to the clustered location of the JHU monitors.2.5 for all SENAMHI monitors from April 2014 to December 2016 is 26.7 (11.6) μg/m3 with mean individual monitors ranging from 15.2 (5.3) μg/m3 (Station CDM) to 38.3 (12.2) μg/m3 (Station ATE). SENAMHI PM2.5 tend to peak at 52.1 μg/m3 between July and August of 2014 (winter) and gradually decrease to 11.8 μg/m3 around November and December (summer), before increasing again to a peak of 319.2 μg/m3 from March to April of 2015). Temporal trends ails/ indicate PM2.5 decreases from May of 2015 to a low of 13.3 μg/m3 in February of 2016 before increasing to a peak of 63.6 μg/m3 in June of 2016. Although most monitors within the SENAMHI network share this temporal trend, there is spatial variation coinciding with the location of the monitors. The three monitors closest to the shore  all have the lowest mean PM2.5 measurements , while the three monitors with the highest measurements  are located further inland closer to the Andes Mountains;. The differences in trends between JHU and SENAMHI networks may lee a result of the JHU monitors being located in the southern part of Lima, where trends in temperature, winds, and other predictors of PM2.5 may be different compared to the SENAMHI stations. Furthermore, SENAMHI stations are distributed across a larger area oi the study domain and may have the potential to detect more spatial variability compared to JHU monitors. Although there is variability in the range of PM2.5 levels; between the two monitoring networks both networks suggests that PM2.5 levels are highest during the Summer; although JHU and SENAMHI stations share peaks in common during the months of March through May, ground measurements are only available for JHU sites from November of 2011 to March of 2013 and from April of 2014 to December of 2015, with no spatial or temporal similarities to the SENAMHI network. Therefore, a continuous and fair comparison of the two networks is not possible.SENAMHI measurements show a slightly different temporal pattern. Mean (Std. Dev.) PM3.2.2 and root mean square error (RMSE) was 0.60 (6.85 μg/m3)); however, the RF model was found to outperform the traditional LME model. The RF R2 (RMSE) was 0.70 (5.95 μg/m3), and the CV R2 (RMSE) was 0.70 (5.97 μg/m3), indicating that the model is stable and that there is good fit between the predictors and the ground measurements. 2.5 concentrations. The slope and intercept from the RF model CV is 1.05 and —1.04 μg/m3, respectively, indicating a good fit . Results from our CV indicates the our model slightly overestimate lower PM2.5 measurements and underestimates higher PM2.5 measurements. Furthermore, in 2.5 measurements are plotted against the mean of each pair. The mean difference between observations in the CV dataset was −0.09 μg/m3 with a standard deviation of 5.97 μg/m3. The Bland–Aleman plot indicates that there is good agreement between the ground and predicted measurements, with 94.5% of the observations falling within 2 standard deviations of the mean differences. 2.5.As linear mixed effects model (LME) was original conducted  R2.5 concentrations across our study region in μg/m3 in 2.5 concentrations start at 14.6 μg/m3 along the coastline and gradually increases up to 48.5 μg/m3 against the Andes Mountains on the east. Monitors with lower mean PM2.5 measurements are also those that are located closer to the coastline, and are at a lower elevation. Temporally, PM2.5 levels are highest during 2010 and dip during 2011 to 2014, before increasing back up in2015 through 2016. Although ground measurements are not available for 2010, the increase in predicted mean annual PM2.5 from 2015 t0 2016, can be observed in the monthly mean measurements from the SENAMHI monitors  before decreasing during the months of November to March . Although this monthly trend is different from those observed in the JHU ground measurements, they are consistent with monthly mean SENAMHI ground measurements. This may be due to a smaller number of ground measurements for JHU compared to SENAMHI in the model fitting dataset. Furthermore, JHU monitors produced weekly measurements, which had to be interpolated to daily estimates for model fitting; therefore, monthly trends may not be meaningful for JHU measurements.monitors , which s4.2.5 have been limited in South America due to lack of ground monitoring data. Previous studies have estimated historical ambient PM2.5 concentrations globally from a combination of satellite remote sensing data and chemical transport models; however, these studies were conducted at coarse resolution  and were evaluated by ground PM2.5 measurements from the literature. Furthermore, results from these studies do not provide daily measurements to aid in epidemiological health studies [2.5 monitors in South America prior to February of 2016; yet, Chile is the only country with known spatiotemporal and forecast models of PM2.5 [2.5 model was constructed using three winter months of hourly PM2.5 measurements from 11 monitors and incorporated CTMs; however, their model did not incorporate satellite remote sensing techniques to enhance prediction capabilities, and could only forecast PM2.5 levels in the proceeding 48-h period [2.5 in Peru is constructed through kriging techniques using ArcGIS for the province of Cusco [2.5 at “subjectively chosen hot spots” using standalone laser photometers to augment ground measurements [2.5 health studies, it does not provide daily historical measurements for epidemiologic studies that investigate population health effects due to acute exposure to PM2.5, especially outside of Cusco, like Lima, where pollution levels are much higher.Until recently, studies modeling the concentration of PM studies . Brazil,of PM2.5 ,55. The h period . The onlof Cusco . The Cusurements . Althoug2.5 model is the first advanced model in Peru to incorporate both satellite remote sensing data and CTM outputs to provide daily ground measurements at 1-km2 resolution in Lima, the most populated and polluted region of Peru, to aid in epidemiologic studies. A major strength of this study is the ability to estimate PM2.5 in Lima at a high resolution through the implementation of MAIAC gap-filled AOD. Our finer-scale model is able to capture local spatiotemporal trends and, compared to coarser resolution products, are better suited for use in epidemiological health studies that require daily measurements of exposure at fine-resolution. Additionally, predictions from our model correspond well at each ground monitor station  meters above ground, while the mean height for SENAMHI monitors is 213.4 (Std. Dev. = 90.9) meters, indicating that SENAMHI monitors have a wider range of elevation height compared to JHU monitors. Additionally, JHU monitors also have a more homogeneous level of PM2.5 since their daily values were interpolated from weekly measurements and comprised of 25% of the total ground measurements, which may add to the explanation of why elevation had relatively lower importance in the RF model. To counter the effects of elevation in the model, distance from shoreline was added to the model as a predictor. Although distance from coast should have explained much of the variation in PM2.5 as the annual maps suggests, this variable did not improve the “out of bag” R2 in the RF model and also did not change the resulting predictions maps and was subsequently discarded from the final model. A possible reason for why distance from coast did not improve model performance may be due to the cluster of JHU monitors all residing close to the coast. Because of their proximity to each other, as well as to the coast, the JHU monitors do not exhibit enough spatial variability both in terms of PM2.5 levels to impact model performance. Furthermore, Lima’s distinct topography and geographic location also lends to the spatial distribution of PM2.5 concentrations. As discussed previously, much of Lima’s production of PM2.5 stems from an aging vehicular fleet located mostly in the densely populated urban areas in and around the metropolitan cities. Additionally, PM2.5 is also being produced in rural areas from biomass burning as fuel. The spatial pattern of PM2.5 seen in the annual prediction maps may be a result of persistent and prevailing coastal winds from the south and southwest pushing pollutants from the coastal cities and trapping them against the Andes Mountains in the east and northeast [Nonetheless, our study uses an emerging ensemble classifier—the random forest model—to generate our estimates which comes with limitations and uncertainties. Currently, annual predictions from the RF model show that concentrations of PMortheast . This phortheast . Nonethe2.5 in the entire study domain for 2014 to 2016 and conversely borrowing prediction capabilities of SENAMHI measurements to estimate PM2.5 in the entire study domain from 2011 to 2013). Nonetheless, JHU measurements served the purpose of increasing our sample size and helped make our model more stable and robust. When JHU measurements were not included in our RF model (the CV R2 was 0.67 (RMSE = 6.68 μg/m3)), they were subsequently kept in the model fitting dataset to enhance not only sample size, but also to provide additional spatial and temporal quality to the ground measurements. Finally, before utilizing and applying the model-derived dataset in epidemiological studies, future research will focus on evaluating model forecasting capacity on a daily basis. Furthermore, the SENAMHI ground monitors have longer periods of PM10 measurements. Future study will also explore converting PM10 measurements to PM2.5 to maximize ground observations in the model fitting process [2.5 and PM10 concentrations at each of the 10 SENAMHI stations with Pearson correlation coefficients ranging from 0.49 to 0.72, and that the annual PM2.5/PM10 for the stations range from 0.21 to 0.44, indicating that PM2.5 concentrations represent 21% to 44% of the total PM10 in Lima [Consequently, a limitation of this study is the lack of monitors located at higher altitudes to validate our results. All monitors are located centrally in the urbanized metropolitan area of Lima, with no monitors in the far corners of the North, East, and South in our study domain. Furthermore, all JHU monitors are clustered within a few kilometers of each other in the mid-southern region of Lima, covering six of the 2970 grid cells in the study domain, which may affect their predictive capabilities on the rest of the study domain leading to the lack of spatial variability from north to south in the study domain. Additionally, JHU ground measurements were collected from late 2011 to early 2013, while the SENAMHI measurements were collected from mid-2014 through 2016, which impact model predictive abilities across the years (i.e., borrowing prediction capabilities of JHU measurements to estimate PM process . Silva e in Lima .5.2.5 exposure model is the first of its kind in both Lima and South America, incorporating satellite remote sensing data, meteorological fields from chemical transport models, and land use parameters to estimate daily PM2.5 measurements at 1-km resolution, with greater spatial and temporal coverage than previous studies conducted in Peru. Predicted daily PM2.5 levels by our model allow for construction of consistent long-term historical measurements that bridges the data gaps created by sparse data quality from both the SENAMHI and JHU monitor networks, and would provide strong data support for epidemiologic studies that focus on both cardiovascular and respiratory outcomes in Lima. Our future research will focus on converting PM10 to PM2.5 from the SENAMHI monitors to maximize ground observations across years prior to 2014, and improve model stability and precision, and further improve on the accuracy of our predictions for use in urgently needed epidemiologic studies to assess the impact of air pollution in Lima, Peru.Our satellite-driven PMSupplemental


Fusarium oxysporum f. sp. niveum (FON), occurs worldwide and is responsible for substantial yield losses in watermelon-producing areas of the southeastern United States. Management of this disease largely relies on the use of integrated pest management . Knowledge about race structure and genetic diversity of FON in the southeastern US is limited. To determine genetic diversity of the pathogen, FON isolates were collected from symptomatic watermelon plants in commercial fields in Georgia and Florida, USA, and identified based on morphological characteristics and PCR analysis using FON-specific primers. Discriminant analysis of principal components (DAPC) of 99 isolates genotyped with 15 simple sequence repeat (SSR) markers grouped the isolates in eight distinct clusters with two prominent clusters (clusters 1 and 8). Cluster 1 consisted of a total of 14 isolates, out of which 85.7% of the isolates were collected in Florida. However, most of the isolates (92.4%) in cluster 8 were collected in Georgia. Both DAPC and pairwise population differentiation analysis (ФPT) revealed that the genetic groups were closely associated with geographical locations of pathogen collection. Three races of FON  were identified in the phenotypic analysis; with race 3 identified for the first time in Georgia. Overall, 5.1%, 38.9% and 55.9% of the isolates were identified as race 0, race 2 and race 3, respectively. The majority of the isolates in cluster 1 and cluster 8 belonged to either race 2 (35.6%) or race 3 (45.8%). Additionally, no relationship between genetic cluster assignment and races of the isolates was observed. The information obtained on genotypic and phenotypic diversity of FON in the southeastern US will help in development of effective disease management programs to combat Fusarium wilt.Fusarium wilt of watermelon, caused by  Citrullus lanatus) is an economically important crop belonging to the Cucurbitaceae family, with a farm gate value of $124 million in GA [Fusarium oxysporum f. sp. niveum (FON), which is host specific to watermelon. FON was first identified by E. F. Smith in South Carolina and Georgia [Watermelon  to examine the genetic and phenotypic (race) diversity of FON isolates collected from watermelon in the southeastern US, and ii) to determine if FON isolates are genetically structured by race or geographic location.FON isolates were collected from different watermelon-producing counties in GA and FL in 2012 and 2013 . IsolateF. oxysporum [2, 0.8 μl of 2.5 mM dNTP, 0.5 μl of 1 μM M13 tagged forward primer, 2 μl of 1 μM reverse primer, 1.8 μl of 1 μM of M13 primer (M13-TGTAAAACGACGGCCAGT) fluorescently labeled with IRD Dye 700 CW fluorophore , 0.04 μl of GoTaq Flexi DNA polymerase, 0.86 μl of PCR grade water  and 1 μl of FON DNA (2.5 ng/μl). The amplification was performed in a T100 thermal cycler  using a protocol as described by Li et al. [Ninety-nine single spore FON isolates were genotyped using 15 published SSR markers for xysporum , 25  that each MLG could have occurred from an outcrossing event [sex value indicates that the repeated MLG likely arose from clonal reproduction. The P values for ρsex were estimated by 1000 random permutations of the data.Clones are genetically identical individuals that arise due to asexual reproduction. However, based on the sample size, the number of markers used, the number of alleles for each marker, and the allele frequencies, the same MLG may arise by recombination or outcrossing, and not necessarily be indicative of a clone. To confirm that the repeated genotypes are clones and not arising from an outcrossing event, we used MLGsim v2.0  to calcung event . A signih) for each SSR locus. Analysis of molecular variance (AMOVA) was used to calculate genetic differentiation (ФPT) among populations. To estimate the major patterns of variation within and among populations from different geographical locations, a principal coordinate analysis (PCoA) was conducted with GenAIEx. AMOVA was also used to calculate genetic variation within and among different FON races. To determine if two individuals taken at random have unique genotypes, genotypic diversity (Ĝ) was estimated in the R package adegenet v 1.4–1 [To determine the genetic diversity within and genetic structure among FON populations, GenAIEx v.6.5  was used v 1.4–1 , 31. In  v 1.4–1 , 31. DAPFifty-nine single spore FON isolates were selected for race typing. The fifty-nine single spore isolates selected for race typing were representative of each field and county location in GA and FL. The set of differential cultivars used to determine FON races under greenhouse conditions included: Sugar Baby (no resistance to any race), Charleston Gray (resistant to race 0), Calhoun Gray (resistant to race 1), and PI-296341-FR (resistant to race 2) [6 spores/ml by adding sterile distilled water (SDW).To prepare FON inoculum, isolates were grown on PDA at 25°C for 7 days. Five mycelial plugs (5 mm in diameter) from the edge of a growing colony were transferred aseptically to a 250-ml flask containing 200 ml quarter strength potato dextrose broth (PDB). The liquid culture was incubated for 14 days at 23±1°C on an orbital shaker  at 150 rpm. Colonized liquid PDB was filtered through three layers of sterile cheesecloth and the concentration of spore suspension (>95% microconidia) was adjusted to 1×106 spores/ml) near the base of each watermelon seedling. Ten plants/isolate/cultivar in separate pots were inoculated and the same number of plants treated with SDW served as a negative control. After inoculation, seedlings were maintained at 28°C day and 20°C evening with 70–80% relative humidity in the greenhouse. The percentage of seedlings showing the characteristic symptoms of Fusarium wilt such as yellowing, stunting, or wilting were evaluated weekly and disease incidence was recorded after 4 weeks. Additionally, plants were also rated for disease severity at the end of 4 weeks using a scale from 0 to 9 with a score of 0 representing asymptomatic plants, 3 for plants with cotyledon lesions, 5 for plants showing slight wilting and stunting, 7 for plants with severe wilting and stunting and 9 for dead plants. Plants rated as 0 were classified as resistant, 1 or 3 as intermediate resistant and 5, 7 or 9 as susceptible [For greenhouse evaluation, watermelon seedlings of the four differentials were grown in pots (9-cm diameter) containing a mixture of sand:peat:vermiculite . Seedlings were inoculated at the first true-leaf stage by pipetting 5 ml of conidial suspension  and one (MB5) did not amplify for the majority of the isolates. Hence, FON population genetic analyses were based on 9 polymorphic SSR loci. The number of alleles for each of the 9 loci varied from 3 to 16. For the 99 FON isolates, 14 MLG were identified. It was observed that around 75% of the genotypes were repeated and hence the genotypic diversity for the 99 isolates was very low (Ĝ = 0.423). Additionally, for each population, the genotypic diversity was very low (Ĝ <0.423) except for FL population (Ĝ = 0.600) . Rarefie2) were found across the populations from the southeastern US  indicating that these repeated MLG’s did not likely arise from recombination, and were the result of clonal reproduction (11) and MLG 14 was shared among two populations (14).Six repeated MLG’s (gstern US . The ρseoduction . MoreovePT) was performed on the four populations. Significant differences were observed between the FL population and GA1, GA2, GA3 populations (P≤0.002) (Pairwise population differentiation (ФP≤0.002) , indicatP≤0.002) . For theP≤0.002) . The GA PT) was performed for the three FON races detected . Significant partitioning of the genetic variation by race was not observed (Partitioning of the genetic variation (Фobserved . Moreoveobserved .DAPC supported K = 8 clusters. None of the clusters in DAPC overlapped . ClusterFifty-nine FON isolates from the eight genetic clusters identified above (K = 8)  were selWith respect to geographical location, GA isolates accounted for 14.3% (2/14) of cluster 1, out of which 7.1% (1/14) each were identified as race 0 and race 3, respectively. In contrast, FL isolates from the same cluster accounted for 85.7% (12/14), and 42.8% (6/14) of the isolates were identified as race 2 and race 3, respectively. Cluster 8 includes 66.1% (39/59) of the race identified isolates, out of which 61% (36/59) were collected in GA, where 2.6% (1/39), 38.5% (15/39) and 51.3% (20/39) identified as race 0, 2 and 3, respectively. Florida isolates in the same cluster accounted for only 7.7% (3/39) of the race identified isolates, all of which belonged to race 3 only. Other clusters  included only one isolate each either identified as race 0, race 2 or race 3 . For insThe population genetic structure of FON in the southeastern US was investigated based on nine published SSR markers , 25. In Fusarium oxysporum pathosystems. For example, in Fusarium oxysporum f. sp. cubense , fungal populations from different geographical locations were clonal. Ten clonal lineages were identified, and among them, the two largest lineages had pantropical distribution [Fusarium oxysporum f. sp. albedinis  isolates collected from different geographical locations in Morocco were found to be less diverse and clonal [Among the FON populations collected throughout the southeastern US, we observed clones that occurred at a high frequency and were widespread at the regional level. Overall, six clones were distributed across the four populations obtained from GA and FL. Additionally, most of the isolates were associated with only few MLG’s (11 and 14). One clone was distributed across all four populations (GA1-GA3 and FL), whereas another clone was only distributed across two populations (GA3 and FL). Among the four FON populations, clones represented 87 of the 99 FON isolates . Similarribution . Similard clonal .Phytophthora infestans [Sclerotinia sclerotiorum [Stagonosporopsis citrulli [The diversity among GA populations was comparatively lower ; whereas, the FL population had a higher diversity than the GA populations  . The reanfestans  and Sclerotiorum  and Stagcitrulli .PT statistics and analysis of variance of principal components. Significant differences were observed between GA and FL populations  . Prior tFONSIX6 is present in race 0 and race 1 but absent in race 2. Further they speculated that race 0 can be a less aggressive form of race 1. In Fusarium oxysporum f. sp. lycopersici, SIX genes (avirulence genes) are located in chromosome 14 (supernumerary chromosome or pathogenicity chromosome) and are responsible for virulence/aggressiveness in different races of the pathogen. The researchers claim that evolution of new races can be attributed to the horizontal transfer of this supernumerary chromosome. Although, FONSIX6 was determined in FON race 0 and race 1, its location in the chromosome is still undetermined. If FONSIX6 is located on a supernumerary chromosome, FON race 1 could have acquired this avirulence gene during by horizontal gene transfer, but this needs to be confirmed. Effector genes specific in FON race 2 and race 3, if any, are yet to be discovered. Further, it will be interesting to investigate the effector repertoire and their chromosome locations to shed some light in the evolution of FON race 2 and race 3.The phenotypic study revealed that few isolates were race 0. In addition, none of the isolates were identified as race 1. It is plausible that race 0 and 1 may be an ancestral race and that races 2 and 3 may be of more recent origin . AdditioComparable results were observed in a previous study by Bruton et al. , where aIn our study, a strong correlation of FON genotypes with geographical locations as compared to pathological races was observed. Previous studies had similar observations where FON races did not correlate with the genetic diversity. Kim et al.  studied Fusarium oxysporum pathosystems, similar observations were made. Castano et al. [Fusarium oxysporum f. sp. dianthi in southern Spain using random amplified polymorphic DNA (RAPD)-PCR, DNA sequence analysis of the TEF1-α gene, and race-specific molecular markers. High genetic homogeneity within the races in the investigated population was observed, which was consistent with earlier reported studies. Additionally, Alves-Santos et al. [F. oxysporum f. sp. phaseoli strains in Spain using intergenic spacer (IGS) region polymorphism of ribosomal DNA, electrophoretic karyotype patterns, vegetative compatibility and pathogenicity analyses. No correlation between pathogenicity and VCG, IGS restriction fragment length polymorphism, or electrophoretic karyotype was observed. Contrastingly, in a study of Fusarium oxysporum f. sp. vasinfectum (FOV) , random amplified polymorphic DNA (RAPD) markers clustered the 46 FOV isolates into three groups based on geographical locations and pathological reactions. Three distinct virulence groups were identified that corresponded with three races 3, 4 and A [In other o et al.  studied s et al.  studied  4 and A .In summary, this study provides new insights into the current status of genetic and phenotypic diversity of FON in the southeastern US. A new race of FON, race 3, was identified for the first time in GA and the population genetic analyses based on SSR markers was useful in describing the genetic diversity of FON isolates. The population genetic analyses divided the FON isolates from GA and FL into eight distinct clusters with two prominent clusters (cluster 1 mostly from FL and cluster 8 mostly from GA). Determining races and genetic diversity of FON prevalent in watermelon production in this region facilitates development of more effective disease management programs.S1 File(CSV)Click here for additional data file.S2 File(XLSX)Click here for additional data file.


Purpose: Varian Halcyon is a novel machine with dual-layer leaves, single flattening filter free (FFF) energy and an enclosed bore. The purpose of this study was to compare the differences in dosimetry and plan parameters of intensity-modulated radiation therapy (IMRT) plans between the Halcyon and Trilogy accelerators.Methods and Materials: A total of 30 IMRT plans from cervical carcinoma patients were retrospectively analyzed on the Trilogy and Eclipse v13.5 treatment planning systems (TPSs). For each patient, a new plan based on Halcyon was created with the same planning parameters and optimization constraints using the Eclipse Version 15.1 TPS. To compare plan qualities, dosimetry parameters regarding planning target volume (PTV), organs at risk (OARs), monitor unit (MU) efficiency, segment size and treatment time were evaluated. Evaluation of the helical diode array system was performed with gamma-index analysis.Results: The dose distribution of the target volume of the Halcyon and Trilogy plans showed no significant difference (p > 0.05). The mean doses of rectum and both femoral heads for Halcyon plans were significantly reduced compared to those for Trilogy plans (p < 0.05). Compared to Trilogy, Halcyon increased the number of MUs from 1542.9±248.3 MU to 2514.9±328.2 MU (p = 0.00) and decreased the delivery time from 11.28±1.36 min to 3.26±0.26 min (p = 0.00). The average segment areas of Halcyon plans for proximal and distal multileaf collimators (MLCs) were 42.1 ± 31.2 cm2 and 28.4 ± 23.7 cm2, respectively, and that of Trilogy plans was 27.3 ± 16.9 cm2. The mean gamma index (3 mm/3%) results for the Halcyon and Trilogy plans were 99.41±0.26 and 99.76±0.32 (p > 0.05), respectively.Conclusions: All Halcyon treatment plans were recognized as clinically acceptable and had statistically better OAR sparing with higher delivery efficiency. The Halcyon system exhibited fast treatment delivery of IMRT with good dosimetric agreement using ArcCHECK. In recent years, with the continuous development of precision radiotherapy, intensity-modulated radiotherapy (IMRT) and volumetric-modulated radiotherapy (VMAT) technologies have been widely used For accurate IMRT delivery, it has been suggested that simple adjustments to multileaf collimator (MLC) segmentation could allow the photon fluence of the beam to be entirely modulated without any need for a flattening filter (FF). Studies have pointed out that flattening filter free (FFF) beams have a significant impact on the therapeutic efficiency, comfort, imaging quality, dose calculation, radiation protection, etc. The MLC is a critical component to provide an efficient means of shaping the treatment field The novel Halcyon  accelerator uses FFF mode beams (800 MU/min) and is configured with an alternative design for a dual-layer MLC (5 cm/s). The FFF beam and low transmission of MLC may decrease the OAR dose. However, the patient setup of every fraction on Halcyon is verified entirely through megavoltage (MV) imaging, so daily imaging is imposed by the treatment planning system (TPS), and the imaging dose is automatically included in the plan optimization, which may increase the OAR dose. In addition, use of an FFF beam and high leaf speed with fast gantry rotation (15 sec/rotation) may lead to high delivery efficiency. Studies of the clinical applicability of Halcyon are therefore of interest. This study analyzes different plan qualities and delivery efficiencies for the Halcyon and Trilogy accelerators.A total of 30 cervical carcinoma patients who received IMRT using Trilogy  were randomly enrolled in this study. The clinical target volume (CTV) of each patient was contoured to include the primary tumor area, uterus, and pelvic and para-aortic lymph nodes. The corresponding planning target volume (PTV) was generated by symmetrically expanding 0.5 cm from the CTV. The mean ± standard deviation (SD) of the PTV volumes was 838.8±152.3 cc.The Trilogy features a 6 MV FF mode. The beam-shaping collimation consists of upper and lower jaws with a static jaw technique followed by an MLC system. The Trilogy was equipped with a Millennium MLC with a 14.5 cm limit on overtravel of the leaves. The Millennium MLC has 120 leaves, with a maximum leaf speed of 2.5 cm/sec. The central 80 leaves and the outer 40 leaves have projection leaf widths of 0.5 cm and 1.0 cm, respectively, at the isocenter. The leaf has an 8.0 cm radius of curvature with 6.5 cm height. The Halcyon  accelerator is capable of delivering an FFF 6 MV beam with a maximum dose rate 800 MU/min. The device is configured with a dual-layer MLC (SX1 mode). The proximal bank pairs have 58 leaves, and the distal bank pairs have 56 leaves with a standard width of 1 cm at the isocenter. The maximum moving speed of leaves on both layers is 5 cm/sec relative to the isocenter. The leaf transmission was reduced by stacked and staggered leaves. Each leaf can reach across the entire 28 cm field and has a 23.4 cm radius of curvature with 7.7 cm height.40) should not exceed 35%. The restriction for the femoral head was V40<5%. To reduce the dosage for the normal tissue as much as possible, we adjusted the dose-volume optimization constraints to meet the target dose coverage and spare more OARs.Each patient was delivered a daily fraction of 1.8 Gy up to the total prescription dose of 50.4 Gy in 28 fractions. All plans were normalized to cover 95% of the PTV by the prescription dose. The volume of the bladder and rectum receiving 40 Gy . The optimization and dose calculation in Eclipse used the Photon Optimizer  and anisotropic analytical algorithm  with a grid resolution of 2.5×2.5×2.5 mmThe dose-volume histograms (DVHs) of the target and OARs and the RTplan files were exported from the two versions of the planning system. Using an in-house-developed MATLAB 2010b software package, the dosimetric information and segment area were analyzed from the DVH and RTplan files.2%) and 98% (D98%) of the target volume, respectively. Additionally, the homogeneity index (HI), conformity index (CI) and gradient index (GI) of the target were calculated. They were as follows:,  and , where VPres is the volume of the 50% prescription dose line. For OARs, the mean dose (Dmean) and the volumes of the bladder, rectum and femoral heads receiving more than 20, 30 and 40 Gy  were reported. The volume of normal tissue receiving >5 Gy was recorded for the body outline excluding the PTV.For the dosimetric evaluation, the following indices were calculated: the maximum and minimum doses, represented by the doses received by 2%  and the segment areas from the Trilogy and Halcyon were compared. Patient-specific quality assurance was performed on an ArcCHECK helical dosimetry diode array, and the delivery accuracy was evaluated via gamma-index analysis  with a 10% lower dose exclusion threshold.The statistical significance of any difference between the Trilogy plan values and Halcyon optimization wFas assessed by the Wilcoxon signed-rank test, where a p-value <0.05 was considered statistically significant.2%, D98%, HI, CI and GI between plans generated using Halcyon and Trilogy (p > 0.05). The D2 and D98 in the Halcyon IMRT plans were slightly larger than those of the Trilogy IMRT program, which were 54.86 ± 0.16 vs 54.74± 0.42 (p = 0.19) and 50.35 ± 0.42 vs 50.25 ± 0.59 (p = 0.57), respectively. CI and GI were slightly lower for Halcyon plans than Trilogy plans, but neither result was statistically significant (p > 0.05).The mean DVHs of the target for 30 patients are shown in Fig. 30 of the bladder and rectum and, V20 of both femoral heads were significantly lower in Halcyon plans than in Trilogy plans (p <0.05). The mean dose of OARs for both femoral heads and rectum was significantly lower in Halcyon plans (p <0.05). The mean dose for the rectum, left femoral head and right femoral head was reduced by 2.65 Gy, 3.27 Gy, and 2.65 Gy, respectively. The Dmean and V5 of the normal tissue for Halcyon were less than those for the Trilogy plans. However, these differences were not statistically significant (p > 0.05).Average DVHs for the bladder, rectum and femoral heads are shown in Fig. p = 0.00), whereas the delivery time was decreased from 11.28±1.36 min to 3.26±0.26 min (p = 0.00). The average area of the proximal segment of Halcyon was 42.1 ± 31.2 cm2, and that of the distal segment was 28.4 ± 23.7 cm2, whereas the average segment area of Trilogy was 27.3 ± 16.9 cm2. The area ratio between proximal and distal segments was 1.74 ± 0.43, ranging from 0.42 to 8.48, and the percentage of proximal segment area that was greater than the distal segment was 0.046%.Fig. p > 0.05), respectively, whereas with the 2%/2 mm criteria, the average passing rates were 94.96±1.22 and 95.14±1.89 for the Halcyon and Trilogy plans (p > 0.05), respectively.Table For patients with cervical carcinoma, IMRT plays a key role in both definitive and adjuvant treatment and has been demonstrated to reduce radiation-induced complications compared to 3D conformal radiotherapy (3D-CRT) 2%, D98%, CI, HI and GI between the Halcyon and Trilogy platforms. Despite the superposition of the dose of MV-CBCT, Halcyon plans outperform the Trilogy plans in reducing normal organ doses. This difference may be attributed to the low transmission two-layer MLC and the FFF beam.The Halcyon accelerator uses daily MV imaging for patient setup, which would introduce additional normal tissue doses. In this study, MV-CBCT images are acquired via a continuous gantry rotation from 260˚ to 100˚, with a total of 10 MUs for the high-quality mode. For MV-CBCT, Li et al. Compared to 3D-CRT, IMRT can achieve better conformal dose distributions to concave tumor shape and spare nearby normal tissues using field fluence optimization. The beam intensity in the field can be modulated as needed using MLC 5 for the normal tissue was 16.7% Leaf transmission is an important factor that influences IMRT delivery. The transmission ratio of the Varian millennium120 MLC was measured to be 1.4% and 1.5% in the studies conducted by Li et al. Treatment time reductions have a potential clinical impact in terms of reducing the risk of intrafraction motion and enhancing patient comfort during treatment. Treatment times depend on the time required to irradiate the MUs on one hand and on the time for gantry and MLC movement on the other hand. Recently, the Agility MLC  with 160 leaves with very fast motion instead of MLCi has been shown to reduce 6.5 min/42.5% for IMRT with 9 beams 2, which means that it is not suitable for too-large tumors in one isocenter plan. In addition, when looking at deep-seated tumors, the use of the higher-energy photon beam of Trilogy may be advantageous since Halcyon offers only a 6-MV photon beam.Small irregularly shaped fields are common in IMRT plans, of which the dosimetry has been one of the largest challenges, i.e., smaller fields require detailed knowledge of output factors and careful dosimetry Furthermore, the corresponding segment ratio of the upper and lower leaves was analyzed. More than 99.95% of the area ratio is greater than 1, indicating that the segment area of the lower leaves is less than the upper leaves when the plan is optimized. Field area ratios can be up to 8.48. There is also a small amount of the upper segment area that is smaller than the lower area in Halcyon IMRT plan in cervical cancer. This shows that fine QA is expected in the upper and lower layers of MLC dose and mechanical parts. It has been widely reported that the MLC leaf root mean square (RMS) error was closely linked to maximum leaf speeds Utilizing FFF mode, fast leaf velocity delivery and larger segment areas yielded the greatest improvement in treatment time. FFF mode can reduce the OAR dose and increase dose rate. The new MLC has lower leaf transmission and a higher motion speed, which will have better OAR sparing and reduction in delivery time. A main drawback of the current study is that it is not possible to distinguish the exact contribution of each of these factors.In conclusion, the Varian Halcyon system may produce fixed-field IMRT plans. Overall, it was as comparably sparing as the original clinically acceptable plans from Trilogy in the pelvic region, with high efficiency and good dosimetric agreement.