extfieldsofstudy: Chemistry Physics

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Isolation, identification, and bioactivity of steroids isolates from Hydrilla verticillata petroleum ether fraction

Hydrilla verticillata contains some active compounds that potential as an antioxidant, antibacterial, anticancer, antimicrobial and antitumor. One of the active compounds in Hydrilla verticillata is steroids. This research aimed to isolate, to identify and to determine the toxicity and antioxidant activity of steroid compounds in petroleum ether (PE) fraction of Hydrilla verticillata. Hydrilla verticillata biomass powder was extracted by maceration using ethanol solvent. The ethanol extract was hydrolyzed with 2 N of hydrochloric acid and then partitioned with petroleum ether solvent. The steroid compounds from petroleum ether fraction were separated with Preparative Thin Layer Chromatography (TLC) and Column Chromatography. The steroid isolates were identified by UV-Vis and FTIR spectrophotometer. The toxicity level and antioxidant assay of steroid isolates were determined by BSLT and DPPH method, respectively. The result of the study showed that extraction through maceration produced 4.54% yield, whereas the product yield of the partition using petroleum ether was 65.41%. The steroid isolates from TLC and Column Chromatography separation has toxicity and antioxidant properties. The LC50 value of steroid TLC isolate was 1.41 and 12.2 ppm. The LC5o value of steroid Column Chromatography isolates (H2, H4 and H10) were 4.32, 8.24 and 10.35 ppm. The EC50 value of H2 Column Chromatography isolate was 23.00 ppm.

Introduction
Hydrilla verticillata are aquatic plants that are often found in Indonesian waters [1]. This plant is found in rivers, reservoirs and lakes [2]. According to Hasanah [3], Lake Ranu Grati Pasuruan is a lake dominated by H. verticillata, but there has not been a maximum utilization due to the lack of exploration from the community.
Utilization of H. verticillata in Developing Countries such as India has been used as fish food and organic fertilizer [4], whereas in Indonesia H. verticillata is still used as an aquarium decoration. The H. verticillata has various antioxidant and antimicrobial activities [5], antitumor [2], antimalarial [6], antiaging, detoxification agent [7], and anti-inflammatory [8]. These diverse activities are due to the presence of secondary metabolites in H. verticillata. The secondary metabolite compounds contained in H. verticillata include alkaloids, flavonoids, saponins, triterpenoids and steroids [9].
Secondary metabolites are known as potential compounds as drugs. Steroids are secondary metabolites that have certain activities, such as the algal extract of Tydemnia expeditions containing  [10]. According to [11], stigmast-5-en-3β-ol steroids and stigmasterol from Dysoxylum alliceum stem bark which can be used as anticancer in MCF-7 breast cancer. The potential of steroid compounds as a drug can be the basis for the importance of isolating steroid compounds from H. verticillata.
H. verticillata in this study were taken from Ranu Grati Pasuruan Lake. The presence of H. verticillata in the lake is very abundant, but its utilization is still not optimal. H. verticillata in this study will be isolated steroid compounds using ethanol maceration, hydrolized than partitioned using petroleum ether, separated using TLC and column chromatography which will then be tested for toxicity to shrimp larvae of Artemia Salina L and tested for antioxidant activity against DPPH. The results of the steroid isolate obtained can be identified by UV-Vis and FTIR.

Sample Preparation.
H. verticillata is taken from the surface of Ranu Grati Lake water. Samples taken as much as 10 Kg, then dried for 7 days. Furthermore, the dry sample was mashed in the Materia Medika Batu and sieved with a size of ± 90 mesh.

2.2.2.
Analysis of Water Content. Porcelain cups are heated in an oven at 100 -105 ºC for 15 minutes to remove the water content. The plates were stored in a desiccator for 10 minutes, then the plates were weighed and the same treatment was repeated until they reached a constant weight. The 5 g of H. verticillata powder was put into the cup and heated at a temperature of 100 -105 ºC ± 15 minutes. The cup was put in a desiccator for 10 minutes then weighed and the same treatment was repeated until it reached a constant weight.

Extraction of Hydrilla verticillata Active Compounds.
Extraction of active components in the sample is done by maceration extraction or immersion of the sample with ethanol solvent. A total of 100 g of H. verticillata fine powder was added with 500 mL ethanol and then shaken using a shaker for 24 hours at a speed of 120 rpm and soaked for 24 hours. The immersion extract is filtered with a Buchner funnel to produce filtrate and residue. The filtrate is collected in an Erlenmeyer and the residue is extracted again with the same solvent and the treatment is repeated 5 times. The five extracts from the extraction are combined into one, then concentrated using a rotary evaporator vacuum. The concentrated ethanol extract obtained was then weighed and the yield calculated.

Hydrolysis and Concentrated ethanol Extract
Partition. Concentrated ethanol extract of H. verticillata was taken as much as 10 g and placed into a beaker, then added 10 mL HCl for hydrolysis. The hydrolysis process is carried out for 1 hour and homogenized with a magnetic stirrer at room temperature, then added saturated sodium bicarbonate to neutral pH. The hydrolysis results are then partitioned with 50 mL of petroleum ether in a separating funnel and allowed to stand until two layers are formed, the organic layer and the water layer. Each layer formed is then separated. The organic layer is taken then concentrated with a vacuum rotary evaporator. The results of the petroleum ether fraction obtained can be weighed and the yield calculated. The results of ethanol extract and petroleum ether fraction H. verticillata were tested phytochemically by using Liebermann Burchard reagent, each sample was put into a test tube, then dissolved in 0.5 mL chloroform and 0.5 mL acetic acid anhydrous. The mixture is then added to 1-2 mL of sulfuric acid in the tube wall, if a bluish-green is formed it indicates the presence of steroid compounds in the extraction and hydrolysis-partition results.

Separation of Steroid Compounds by Preparative Thin Layer
Chromatography. Samples were taken as the result of partition 10 mg and then diluted with 10 mL of petroleum ether. Separation P-TLC used steroids with silica gel plate F254 is activated by heating in an oven at a temperature of 100-105 °C for 30 minutes. Each plate with a size of 10 x 20 cm. petroleum ether fractions which have been reconstituted with the solvent spotted at a distance of 1 cm from the bottom edge of the plate with the capillary tube, then drain and eluted using the eluent n-hexaneethyl acetate (4:1). The stain of separation results then observed under UV light at wavelengths of 254 and 366 nm.

Separation of H. verticillata petroleum ether fraction by Step Gradient Polarity (SGP) Column
Chromatography. The 0.067 g petroleum ether fraction of H. verticillata was initially dissolved in 1 mL of n-hexane: ethyl acetate (95:5) solvent. Several samples of dissolved petroleum ether fraction are then put into a column. Separation of n-hexane fraction compounds using column chromatography with a stationary phase in the form of silica gel 60, column diameter of 1 cm, and column length of 50 cm. The elution process is carried out in a gradient with a comparison of n-hexane: ethyl acetate mobile phase (95: 5, 90:10, 85:15, 80:20, 75:25, and 70:30) with a volume of 100 mL each. The results in the form of eluate from column chromatography will be accommodated into vials every 2 mL [12].
2.2.8. Monitoring of Steroid Isolate Results with ATLC. Even-numbered vials on the results of the separation of column chromatography will be grouped based on the comparison of their mobile phase and monitored by Analytical Thin Layer Chromatography (ATLC). The monitoring process uses a 10x10 cm F254 silica gel plate which has been activated at 110 °C for 30 minutes. The isolates from each vial were then bottled on a silica plate that was marked ± 1 cm from the bottom edge and top edge. The bottling of isolates was carried out with capillary tubes 10 times periodically by drying, then eluted with the mobile phase of n-hexane: ethyl acetate (17:3) [13].
The next process is to analyze the results of the spot on the plate, if a bluish-green colour is formed then it shows the presence of steroids [14]. Stains on plates that have the same Rf value and the same spots, then combined into one as the same fraction, then the results of these isolates can be tested for toxicity to Artemia s and tested its antioxidant activity against DPPH.
2.2.9. Toxicity Test of Steroid Isolates. Toxicity tests on the steroid isolates resulting from TLC and column chromatography began with the hatching of Artemia salina L. eggs. A. salina eggs were hatched in hatching containers filled with seawater which was aerated and irradiated with fluorescent/incandescent lamps. Eggs of Artemia salina L. as much as 2.5 mg were put into a container containing 250 mL of seawater, then aerated for ± 48 hours with a hatching temperature of 25-30 °C. Hatched shrimp larvae are ready to be used as test animals.
The results of steroid isolates in each vial weighing 1 mg were dissolved with n-hexane as much as 10 mL to make a stock solution of 100 ppm. The stock solutions obtained were then pipetted 100, 200, 300, 400 and 500 μL respectively, which were each put into 5 vials and evaporated by the solvent. After the solvent in each vial evaporates 100 mL of dimethyl sulfoxide (DMSO) is added, one drop of 60% bread yeast solution, 2 mL of seawater, is shaken until mixed. The mixed solution was put into a 10 mL volumetric flask and then demarcated with seawater and homogenized. After each solution was marked, the concentration of the solution was 1, 2, 3, 4, and 5 ppm, then each concentration of the solution was transferred into a vial bottle and 10 larvae of Artemia salina shrimp were added for observation. 2.2.10.2 Measurement of Antioxidant Activity in Samples. Each isolate was dissolved in a 95% ethanol sailor with a concentration of 50 ppm and its antioxidant activity was tested using a UV-Vis spectrophotometer at the obtained λmax. Then the best antioxidant activity test results vary in concentrations of 1, 2, 3, 4, and 5 ppm. Isolate each concentration pipetted 3 mL and added 1 mL DPPH 0.2 mM then incubated at 37 for 90 minutes, then absorbance was measured using a UV-Vis spectrophotometer at λmax that was obtained. The absorbance data obtained from each concentration of each sample was calculated as a per cent (%) of its antioxidant activity.

Identification of Steroid Compounds.
Identification of isolated compounds using UV-Vis, FT-IR. Steroid isolates from column chromatography were identified by UV-Vis at a wavelength of 200-800 nm. Steroid isolates were also identified using FT-IR by mixing the evaporated steroid isolates with 0.2 g KBr pellets.

Results and discussion
3.1. Sample preparation Hydrilla verticillata in this study obtained from Ranu Grati Lake. H. verticillata is dried to reduce the water content contained in it so that it does not interfere with the extraction process. H. verticillata dried obtained as much as 1.1 Kg and then mashed in Materia Medika Batu with a size of 90 mesh. Refinement of the sample is intended to enlarge the surface area of the particles to facilitate contact of the solvent at the time of extraction [16]. Dry samples of H. verticillata were obtained in the form of 950 g of powder.

Analysis of water content
The results of measurement of H. verticillata water content in this study were 6.53 %. These results indicate that the water content of H. verticillata meets the Ministry of Health's Republic of Indonesia standards [17] with a required maximum limit of 10%. These results indicate that the water content in this study did not interfere with the extraction process because the results obtained did not exceed the specified maximum limit.

Maceration extraction
The extraction process in this study was carried out as many as 4 replications to produce sediment and filtrate. Repetition is stopped when the maceration filtrate is clear green which indicates that the compound has been extracted. The maceration filtrate from several replications was concentrated with a vacuum rotary evaporator so that a concentrated ethanol extract of 4.54 % was obtained.

Hydrolysis and partition
Concentrated ethanol extract is then hydrolyzed to break the glycone and aglycone bonds in secondary metabolites which are still in the form of glycosides. Hydrolysis is carried out by the addition of HCl 2 N as a catalyst, as for the alleged glycoside termination reaction. The hydrolysates obtained are then partitioned (liquid-liquid extraction) using petroleum ether solvents which are nonpolar to take steroid compounds which are also nonpolar. The partition process produces two liquid phases, namely the organic phase which contains steroid compounds and the aqueous phase which contains the salt resulting from the hydrolysis reaction. The organic phase was taken on each partition result which was carried out 3 times and obtained a concentrated petroleum ether fraction of 65.41%.  Table 1.

Separation by thin layer chromatography
The steroid compounds in H. verticillata petroleum ether fraction was separated using Preparative TLC with n-hexane: ethyl acetate (3.75:1.25) eluent. Identification of compounds separated on TLC plates using a stain that looked and value of Rf. The results were shown in Figure 1. From Figure 1, there are two stains with green or bluish-green colour that alleged as steroid compounds.

Separation by Column Chromatography
Separation of compounds by column chromatography the elucidation process begins with a variation of the nonpolar eluent, n-hexane: ethyl acetate (95: 5, 90:10, 85:15, 80:20, 75:25, and 70:30). It is assumed that nonpolar steroid compounds will be eluted and separated first. Nonpolar samples will pass through the stationary phase while those that are polar will be retained in the stationary phase. The silanol hydroxyl group contained in silica will form hydrogen bonds with polar compounds in the sample. The eluate from the separation is collected every 2 mL/min into the vial.

Monitoring Column Chromatography Results with ATLC
The eluate results of column chromatography separations contained in the vials are then monitored by Analytical TLC for further grouping. Vial grouping is based on the similarity of the chemical content profile of the stains formed at TLC and the same Rf value. The compounds contained in the sample have different speeds when passing through the column so that the migrated compounds are first shown with a high Rf value. The results of grouping isolates in this study obtained 12 combined isolates. The monitoring results showed the presence of isolated compounds with a single stain and mixture, a single stain formed in TLC showed a good level of separation of a compound. Six single isolates obtained in this study were thought to be 3 steroid isolates and 3 triterpenoid isolates as in Table 2 below.  Table 2, isolate H2, H4 and H10 which is a suspected steroid compound will be continued with a toxicity test against Artemia salina shrimp larvae. In addition, there is also the petroleum ether fraction whose toxicity is tested to determine the potential of the sample before and after it is isolated by column chromatography.

Toxicity Test of Steroid Isolates
Toxicity tests in this study were carried out on TLC and Column Chromatography isolates that were suspected as steroid compounds resulting from column chromatography. Test results in the form of mortality data were calculated using MINITAB to obtain LC50 values that indicate the level of sample toxicity. The results of LC50 TLC and Column Chromatography isolates can be seen in Table 3. Based on these results indicate that TLC and Column Chromatography isolates have a high level of toxicity. The toxic activity of each sample in this study was due to the presence of secondary metabolites found in the fraction of petroleum ether and isolate B. Secondary metabolite compounds such as steroids that have -OH groups can bind to integral proteins in cell membranes. According to Budaraga et al. [18] in his research on Cinnamomum burmanni stated that the -OH group in flavonoid compounds can bind to the integral protein in the larval cell membrane. This can inhibit the active transport of Na + / K + ions so that it will cause integral proteins in the cell membrane to swell because Na + / K + ions accumulate in the cell and rupture. This is what can cause shrimp larvae to die. LC50 values of the two test samples in this study each <30 ppm, these results indicate the potential as an antitumor and anticancer.

Antioxidant Activity Test against DPPH
The antioxidant activity test in this study was carried out on isolate which was suspected as a steroid compound from column chromatography. The test results in the form of data% of antioxidant activity were calculated using GraphPad PRISM to obtain an EC50 value indicating the level of antioxidant activity of the sample. The results of EC50 in steroid isolate can be seen in Figure 2 and Table 4.  The results of the calculation of per cent (%) of antioxidant activity in Table 4.2 show that the isolate steroid Hydrilla has antioxidant activity. The value of per cent (%) of antioxidants increases with the increase in the concentration used. The highest value of steroid isolates was at a concentration of 5 ppm. This shows that the more concentration used, the more DPPH radicals are stabilized by antioxidant compounds found in the sample. Data obtained from the calculation of the value of per cent (%) of antioxidant activity is used to calculate EC50 which is the main parameter in the measurement of antioxidant activity. EC50 results from all Hydrilla verticillata steroid isolates were obtained at 23.00 ppm. Antioxidants are very strong if the EC50 value is less than 50 [19].

Identification of steroid compounds
The identification with UV-Vis obtained the maximum wavelength in the petroleum ether fraction 203, 206.9 and 275 nm. The maximum wavelength obtained in steroid isolates was 275 nm. These results were further identified by FTIR and showed the presence of dimethyl geminal groups as a typical steroid group in both samples.

Conclusion
The steroid isolates from TLC and Column Chromatography separation has toxicity and antioxidant properties. Based on toxicity test, LC50 value of steroid TLC isolate was 1.41 and 12.2 ppm, whereas the LC50 value of Column Chromatography isolates (H2, H4 ad H10) was 4.32, 8.24 and 10.35 ppm. EC50 value of H2 Column Chromatography isolate was 23.00 ppm.


provenance: AgriculturalAndFoodSciences,2019-2020-Agricultural And Food Sciences-2020.jsonl.gz.gz

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SIMULATING CROP YIELD, SOIL NITROGEN, AND ORGANIC CARBON IN NO-TILLAGE CROP SEQUENCES IN A SUBTROPICAL CLIMATE IN BRAZIL

Brazil stands out worldwide for its high grain production in areas of direct sowing. The objective of this study was to simulate and assess the relationship of soil organic carbon content and nitrogen, crop yield, and biomass of two crop sequences under the no-tillage system in a subtropical region of São Paulo, Brazil, using CSM-CROPGRO-Soybean and CSM-CERES-Maize models. The modeling was carried out considering the meteorological conditions of Jaboticabal, SP, Brazil. The treatments consisted of combining two summer crops (maize and soybean) with maize cultivation as a winter crop. The average biomass and productivity for corn were 15594 kg ha −1 and 5996 kg ha −1 , respectively, and for soybeans they were 5905 kg ha −1 and 3441 kg ha respectively. For soil organic carbon and nitrogen, a small variation was observed between years, and in addition there was a decline in their levels after a year with low biomass production. In our study, the RMSE and MAPE values between the observed and simulated productivity by the model were 2.21 kg ha −1 and 44.24%, respectively. The analysis of main components for the cultivation of corn explained 83.9% of the variability, and for the cultivation of soy, 93.5%. Among the tested models, the CROPGRO was the one with the best accuracy.

INTRODUCTION
The no-tillage system (NTS) has been presented as an alternative to mitigate the emission of greenhouse gases (GHG) arising from agricultural practices (Lal, 2015;Bayer et al., 2016;Paustian et al., 2016;De Araújo Santos et al., 2019). It is considered a low carbon agriculture system, resulting in increases in soil carbon stocks after some years of its implementation (Lal, 2015;De Araújo Santos et al., 2019;Silva et al., 2019). Besides, the NTS is also characterized by improving the physical, chemical, and biological structure of the soil (Raphael et al., 2016;Rosolem et al., 2016;Calonego et al., 2017).
However, some studies have reported that in the NTS, the increase in organic matter will only be effective when a species that is efficient in biological nitrogen fixation process is incorporated into the crop rotation . It is estimated that for every 10 units of carbon sequestered in the soil, there is a need to immobilize one unit of nitrogen (Six et al., 2002).
Aiming at the feasibility of employing decision support systems in different regions and climates, computational models seem to be a valuable alternative to estimate the amount of carbon and organic nitrogen in the soil, depending on climatic conditions and cultural and soil management practices, in a given period (Weber et al., 2016). The decision support system for agrotechnology transfer (DSSAT) is widely used to simulate crop yield, development, and income. The residual component of soil organic matter (SOM) of the CENTURY model was incorporated into the DSSAT, allowing for simulations and for conducting long-term sustainability analyses (Liu et al., 2017).
The objective of this study was to simulate and assess the relationship of soil organic carbon content and nitrogen, crop yield and crop biomass of two sequencing crops (soybeans and maize) under the NTS in a subtropical region of São Paulo, Brazil, using the CSM-CROPGRO-Soybean and CSM-CERES-Maize models.

Location and characterization of the experimental area
This experiment was carried out in the municipality of Jaboticabal, SP, at the coordinates 21°15′22′′ S and 48°18′58′′ O at 550 m altitude, during July and August 2016 for the 2015/2016 agricultural year. The climate of the region, according to the classification of Thornthwaite (1948), is of the B1rB'4a', humid mesothermal, with little water deficiency, presenting an average annual temperature of 22.2°C with the average of the hottest month being over 22ºC and the average coldest month being above 18ºC. There is an average annual precipitation of 1.425 mm, with higher volumes from October to March (Rolim & Aparecido, 2016).
The soil was classified as eutrophic Red Latosol, clay texture (Santos et al., 2013). Since 2001, the area has been under the NTS and the results presented in this study correspond to the years 2003 to 2016. Before the implementation of the system, it was used for the production of soybeans and corn in a conventional soil tillage system for 25 years.
The treatments consisted of a combination of two sequences of summer crops with one winter crops. For the summer, there was either monoculture of maize (Zea mays L.) (MM) or soybean (Glycine max L.) (SS), and maize was the winter crop. For more information on the crop treatments and experimental design, consult Marcelo et al., (2009), De Araújo Santos et al., (2019 and Xavier et al., (2020).

Modeling procedures and input variables
The CROPGRO (Jones et al., 2001), CERES (Jones & Kiniry, 1986) and CENTURY (Parton et al., 1994) models were applied to simulate crop yield, crop biomass, and soil organic carbon and nitrogen from 2004 to 2016 for the cultivation of maize and soybeans. Site-specific input variables, such as soil texture (sand, silt, and clay contents), soil density, and SOC and SNT contents, and average annual productivity for soybeans and corn were extracted from published works (Marcelo, 2007;Marcelo et al., 2009;Marcelo, 2011;Martins et al., 2012) with results for the years 2003,2005,2006,2007,2009, and 2010 from the experimental area described in 2.1. The data from meteorological elements for Jaboticabal, SP, Brazil were used in the DSSAT program. The daily inputs were maximum temperature, minimum temperature, wind speed, relative humidity, precipitation, and global solar radiation. These variables were obtained from the Meteorological Station of the Faculdade de Ciências Agrárias e Veterinárias da Universidade Estadual Paulista "Júlio de Mesquita Filho" (FCAV-UNESP). The FAO 56 method was used to estimate crop evapotranspiration (ETc).

Data analysis and model evaluation metrics
The data were initially analyzed using descriptive statistics (mean, standard error of the mean, standard deviation, minimum, maximum, coefficient of variation, asymmetry, and kurtosis) of the simulated data. Subsequently, the percentage deviation (%) (Equation 1) of the estimated values from the observed values of productivity were established (kg ha −1 ): The performances of the models were evaluated using linear regression analysis, in which the independent variables were those of the observed data and those dependent on the results extracted from the DSSAT. The adjusted coefficient of determination (R 2 adj.) (Equation 2) was calculated according to Cornell & Berger (1987) and the square root of the mean error (RMSE) (Equation 3) and absolute percentage of the error (MAPE) (Equation 4) (Willmott, 1981): Where: N is the number of points in the data sample, K is the number of independent regressors, that is, the number of variables in the model, excluding the constant. Where: N is the number of points in the data sample; Yobsi is the observed value of Y, and Yesti is the estimated value of Y.
Principal component analysis is an exploratory multivariate technique that condenses the information contained in a set of original variables into a set of smaller dimensions, composed of new latent variables, preserving a relevant amount of the original information. The new variables are the eigenvectors (main components) generated by linear combinations of the original variables, constructed with the eigenvalues of the covariance matrix.
The main components whose eigenvalues were higher than the unit were considered according to the criterion established by Kaiser (1958). The coefficients of the linear functions, which define the main components, were used to interpret their meaning, using the sign and the relative size of the coefficients as an indication of the weight to be assigned to each variable. Only coefficients with high values were considered for interpretation, usually those greater than or equal to 0.50 in absolute value. These analyses were processed using R (R Development Core Team, 2017).

RESULTS AND DISCUSSION
The mean of maize biomass for all simulated years was 15594 ± 597 kg ha −1 , while for soybean it was 5905 ± 164 kg ha −1 ( Table 1). The crop yield was 5996 ± 275 and 3441 ± 121 kg ha −1 for maize and soybean, respectively (Table 1). According to the Companhia Nacional de Abastecimento-CONAB (2010), the forecast of maize yield for the 2010 harvest was 3906 kg ha −1 in Brazil, with a surplus of 2000 kg ha −1 compared to the DSSAT simulation. The soybean yield forecast from CONAB in 2010 was 2629 Engenharia Agrícola, Jaboticabal, v.40, n.4, p.536-544, jul./aug. 2020 kg ha −1 , with a value lower than the means found for the simulated experiment in the DSSAT. The values of soil organic carbon and nitrogen were the same for both crop sequences (Table 1). The values of the coefficient of variation of the organic carbon of the soil were low, thus demonstrating that the variation of this variable during the 13-year chronosequence was quite limited.
The use of maize in crop sequences is a practice highly recommended in the literature since maize has a high potential for biomass production when compared to soybeans (De Araújo . Furthermore, the physiological processes of maize cause the crop to produce a biomass rich in carbon (Taiz & Zeiger, 2010), resulting in a slower breakdown and hence increasing the soil carbon content (Martins et al., 2012).
Another reason for the possible relationship of the highest levels of soil organic carbon in areas with maize cultivation in the sequence of crops is that their roots are the main pathways for the release of plant exudates into the soil, which end up interfering with microbial activity that causes them to release exudates that enrich the soil with organic carbon (Austin et al., 2017;Faucon et al., 2017). TABLE 1. Descriptive statistics of crop biomass (kg ha −1 ) and crop yield (kg ha −1 ) for maize in the sequence maize-maize and soybean in the sequence soy-maize and soil surface total organic carbon at maturity (OCTAM, kg ha −1 ), soil organic carbon at maturity (OCAM, kg ha −1 ), soil surface total nitrogen at maturity (ONTAM, kg ha −1 ), and soil nitrogen at maturity (ONAM, kg ha −1 ). The results of the temporal variation of crop yield and weight showed similar behaviors for both soybean ( Figure 1A) and maize ( Figure 1B). Olibone et al., (2010) and Caires et al., (2015) observed that crop weight production indicates, in most cases, good yield values.
The levels of organic carbon decreased slightly after 2010 (Figure 1). This fall in SOC and SON levels occurred just after the years when crop yields were low. This indicates that the production of crop weight did not exceed that of previous years when compared to years with a higher crop biomass/yield. In this way, the microorganisms may have consumed the organic matter present in the area, causing the decrease. Such behavior was observed for the two sequences studied. It is worth mentioning that although there was a decrease in the levels of total organic carbon, this variation was very low (Table 1).
The levels of organic nitrogen, as well as those of carbon, also decreased as time went on, and this was already expected since as the levels of organic matter in the soil decreased, there was also a decrease in nitrogen (Batjes, 1996).
Engenharia Agrícola, Jaboticabal, v.40, n.4, p.536-544, jul./aug. 2020 FIGURE 1. Temporal variation of crop biomass (kg ha −1 ) and crop yield (kg ha −1 ) for maize in the sequence maize-maize and soybean in the sequence soy-maize and soil surface total organic carbon at maturity (OCTAM, kg ha −1 ), soil organic carbon at maturity (OCAM, kg ha −1 ), soil surface total nitrogen at maturity (ONTAM, kg ha −1 ), and soil nitrogen at maturity (ONAM, kg ha −1 ).   The model adjustment (R²adj. 0.91, p < 0.05) was significant only for soybean productivity (Figure 2). The adjusted coefficient of determination indicated an optimal fit of the model. With R²adj. 0.91, this means that 91% of the variation in soybean yield was explained by the model. Yang et al., (2014) found an R 2 of 0.96 between simulated and observed data for soybean yield in an area without irrigation, while in irrigated areas, Dogan et al., (2007) found values of R 2 of 0.94 and 0.88 for 2003 and 2004, respectively. Thus, it can be seen that the CROPGRO model can satisfactorily simulate soybean productivity under different management conditions in Jaboticabal, São Paulo, Brazil.
Still, for the performance of the model, it is worth noting that both RMSE and MAPE are good metrics to use for calibrating the model because the RMSE has the same unit of measurement as the simulated variables and the MAPE is given as a percentage (Yang et al., 2014). In our study, the RMSE and MAPE values between the observed and simulated productivity by the model were 2.21 kg ha −1 and 44.24%, respectively. Coelho et al., (2018), simulating sugarcane productivity for the municipality of Jaboticabal, found RMSE values for the cultivars studied ranged between 1.91 and 2.58 Mg ha −1 . For the cultivation of maize, the two factors together explained 83.9% of the total variation of the original data. The first process (PC 1) represents 49.7% of the variability and the second process (PC 2) 34.2%. The process contained in PC 1 is the most important for this study, as it is derived from the highest eigenvalue and has the highest percentage of explanation (49.7%), with the variables that most contribute to this being represented by weight (−0, 83), yield (−0.86), soil total organic carbon at maturity (OCTAM, kg ha −1 ), and soil organic carbon at maturity (OCAM, kg ha −1 ), (−0.86) (Figure 3). FIGURE 3. Biplot chart showing the biomass (kg ha −1 ), yield (kg ha −1 ) soil surface total organic carbon at maturity (OCTAM, kg ha −1 ), soil organic carbon at maturity (OCAM, kg ha −1 ), soil surface total nitrogen at maturity (ONTAM, kg ha −1 ), and soil nitrogen at maturity (ONAM, kg ha −1 ) for maize.
According to the signs of the factorial loads, PC 1 is negative and strongly correlated with yield, followed by OCTAM, OCAM, and biomass (Table 3). We can understand the relationship between nitrogen, productivity, and biomass in CP1 as being related to conservationist practices, such as the direct seeding system (NTS), which disturbs the soil less and applies a greater contribution of organic waste (Li et al., 2017), and this accumulation of organic matter in the soil directly influences the productivity of the crop. For CP 2, the factorial loads were also negative for ONTAM (−0.95) and ONAM (−0.95) ( Table 3). CP 2 is only being influenced by soil nitrogen. Nitrogen is the nutrient required in the greatest quantity by plants (Obour et al., 2017), but nitrogen absorption has very complex dynamics in the soil (Silva et al., 2005), which may justify the process of the second main component containing only nitrogen.
In the main component analysis for soybean cultivation, both factors explained 93.5% of the total variation in the original data. CP 1 represents 61.2%, and PC 2 32.3% (Figure 4). The process contained in CP 1, namely the variables that contribute the most to this, are represented by OCTAM (0.93), OCAM (0.96), ONTAM (0.95), and ONAM (0.95). The factorial loads are all negative (Table 4). PC 1 is represented by C and N. They are the main components of SOM and their stocks will vary depending on the rates of addition, particularly by waste. In agricultural systems, the stocks of organic C and N are also influenced by the management system adopted (Souza et al., 2009). PC 2 is represented by biomass (−0.96) and productivity (−0.98), and is also represented by negative factor loads. Productivity is influenced by the amount of biomass. These variables are favored by the vegetation cover formed due to the use of the no-till system (Muraishi et al., 2005).

CONCLUSIONS
Among the tested models, CROPGRO had the best accuracy. Therefore, DSSAT can be used to simulate soybean yield under the NTS for the climatic conditions of Jaboticabal/SP. The levels of carbon and organic nitrogen in the soil showed little variation over the years, but there was a small decrease after years with low biomass production. The use of multivariate techniques is a useful tool to verify the relationship of organic carbon and nitrogen in the soil with crop productivity when using simulated data.

ACKNOWLEDGMENTS
This study was financed in part by the Coordenação de Aperfeicoamento de Pessoal de Nível Superior (CAPES, Brazil) under the funding code 001 and the Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) (Processo 142384/2017-8).


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A Universal Approach to Molecular Identification of Rumen Fluke Species Across Hosts, Continents, and Sample Types

Rumen fluke are parasitic trematodes that affect domestic and wild ruminants across a wide range of countries and habitats. There are 6 major genera of rumen fluke and over 70 recognized species. Accurate species identification is important to investigate the epidemiology, pathophysiology and economic impact of rumen fluke species but paramphistomes are morphologically plastic, which has resulted in numerous instances of misclassification. Here, we present a universal approach to molecular identification of rumen fluke species, including different life-cycle stages (eggs, juvenile and mature fluke) and sample preservation methods (fresh, ethanol- or formalin-fixed, and paraffin wax-embedded). Among 387 specimens from 173 animals belonging to 10 host species and originating from 14 countries on 5 continents, 10 rumen fluke species were identified based on ITS-2 intergenic spacer sequencing, including members of the genera Calicophoron, Cotylophoron, Fischeroedius, Gastrothylax, Orthocoelium, and Paramphistomum. Pairwise comparison of ITS-2 sequences from this study and GenBank showed >98.5% homology for 80% of intra-species comparisons and <98.5% homology for 97% of inter-species comparisons, suggesting that some sequence data may have been entered into public repositories with incorrect species attribution based on morphological analysis. We propose that ITS-2 sequencing could be used as a universal tool for rumen fluke identification across host and parasite species from diverse technical and geographical origins and form the basis of an international reference database for accurate species identification.

The first published information on rumen fluke emerged in 1790, describing adult fluke within the rumen of red deer in Europe (14). Multiple changes were subsequently made to the nomenclature until Fischoeder created the genus Paramphistomum in 1903. There is a lack of full agreement or consistency around nomenclature in the literature, with the names rumen fluke and stomach fluke or paramphistome and amphistome largely used interchangeably (15). They mainly belong to six different families: Paramphistomidae, Gastrodiscidae, Gastrothylacidae, Olveriidae, Balanorchiidae, and Stephanopharyngidae; all in the Superfamily Paramphistomoidea, Stiles and Goldberger, 1910 (16); under which, more than 70 species have been described (17).
Paramphistomes have a worldwide distribution, with different species endemic in specific areas and host species. In some countries, multiple species of rumen fluke are present, sometimes existing as co-infections in a single host animal, e.g., in India (18) and Japan (19). Calicophoron daubneyi (formerly Paramphistomum daubneyi) is the dominant species in Europe, e.g., in Spain (2), Italy (20), the United Kingdom and Ireland (4, 21,22). Some species are found across multiple host species in geographically distant areas, e.g., Paramphistomum leydeni has been found in goats in China (6), cattle in the Netherlands (23) and Argentina (3), sheep in Ireland (24), and reindeer in Finland (25).
Adult rumen fluke appear to pose little or no significant concerns for animal health and productivity (26). Heavy infestations of immature rumen fluke feeding on the intestinal lining, however, can cause disease, known as larval paramphistomosis (27). In India, mortality rates due to immature paramphistomes may reach 80-90% in domestic ruminants (28), which might be associated with differences in rumen fluke species or burden, or the nutritional, health, immune, and genetic status of the host. There is growing concern that rumen fluke prevalence is increasing significantly in Europe (29,30). For example, in Ireland, prevalence of paramphistomes, based on detection of rumen fluke eggs in feces, increased from 3% in 2009 to around 28% in 2013 in cattle (21). Concurrently, Toolan et al. (2015) reported a rise in prevalence of rumen fluke eggs detected at local Veterinary Investigation Centers between 2010 and 2013, from 12.4 to 22% in sheep. Increased rumen fluke prevalence may lead to an increase in cases of acute larval paramphistomosis (31). Indeed, there is evidence of the disease in cattle in Ireland (32), calves in England (33); and even mortality in adult sheep in Scotland (34).
Diagnosis of rumen fluke infection can be based on inspection of the reticulo-rumen for presence of adult flukes at slaughter (7,22), on detection of rumen fluke eggs in feces (4, 21), or on detection of immature paramphistomes in excreta or at post mortem (35). Rumen fluke eggs are very similar morphologically to eggs from the liver fluke, Fasciola hepatica, which could lead to misdiagnosis of infection status and treatment response (4). In addition, rumen fluke are a "plastic" group morphologically, with variability in size and pigmentation. For example, in our previous work, rumen fluke from sheep appeared smaller than those from cattle, even though they proved to belong to the same species. Conversely, the finding of C. daubneyi and P. leydeni in cattle in the Netherlands was a surprise, both because P. leydeni is relatively unknown, but also because the two species could not be differentiated morphologically (23). Accurate species identification is important for understanding of the rumen fluke life-cycle and control options. Until the mid-2010s, it was assumed that the predominant rumen fluke in the British Isles belonged to the species, Paramphistomum cervi, which has an aquatic planorbid snail as intermediate host (27). Using molecular identification, Gordon et al. (2013) identified that rumen fluke infecting sheep in Scotland were, in fact, C. daubneyi, a finding since confirmed by others for sheep and cattle across the whole of the UK and Ireland (21,22). C. daubneyi does not have an aquatic snail as intermediate host, rather, it uses the same mud snail, Galba truncatula, as the liver fluke (36,37). This represents a radical change in our understanding of rumen fluke epidemiology, which may include ecological competition for snail hosts and encystment habitats between rumen fluke and liver fluke. It may also be important in understanding the emergence of rumen fluke, as this may be linked to changes in climatic conditions that favor survival of intermediate mud snail host species (38) or cercarial life stages on pasture (39).
Species identification and taxonomy of rumen fluke were initially based on a system introduced by Näsmark (40), with identification by morphological and histological methods. More recently, this approach has been refined through light microscopy (1,25,41) and detailed scanning electron microscopy (25,42,43). These methods are, however, laborious, technically demanding, low throughput and time-consuming, and have resulted in several instances of misidentification and generation of synonyms (44,45). Those problems can be overcome with molecular methods such as the polymerase chain reaction (PCR), to amplify specific regions of rumen fluke DNA, followed by sequencing of the amplified region (17). Commonly used DNA markers include nuclear ribosomal genes 28S and 18S (46) and their related spacers, and mitochondrial DNA (mtDNA) c oxidase subunit I (Cox1) genes (21,47). The most frequently used and versatile genetic marker for phylogenetic analysis has been the internal transcribed spacer (ITS-2) region of ribosomal DNA (rDNA), which has been used successfully in Europe (20,48,49), South America (3), and Asia (17,18,50).
As laboratory methodology and reagents for DNA extraction and amplification from various sample types become more sophisticated, and awareness of the importance and emergence of rumen fluke grows, there is an opportunity to conduct both prospective and retrospective studies of rumen fluke epidemiology with accurate species identification using archived and freshly collected material. The aim of our study was to establish a universal approach to rumen fluke identification across fluke species, host species, continents and specimen types. In addition to identification from adult and juvenile rumen fluke obtained at post mortem and identification using DNA extracted from rumen fluke eggs, the use of DNA from ethanol-, formalinand paraffin wax-preserved specimens was examined to allow for use of historical archived specimen collections.

Parasite Material
Samples were collected by farmers, veterinarians and scientists across the world (see Acknowledgments for list of those who submitted) for diagnostic or research purposes. These were made available for this study to ascertain the suitability of our methodology across countries, host species, parasite species and sample types, and to generate preliminary data on the geographic and host-species distribution of paramphistome species. A total of 387 specimens were processed, including at least 2 or 3 individual specimens extracted from 173 host animals native to: Australia (2), Belgium (2), Cuba (6), Dominican Republic (1), India (1), Ireland (46), Italy (3), the Netherlands (17), New Caledonia (51), Slovakia (2), St. Kitts (7), Tanzania (2), the UK (33), and the USA (1).
Adult fluke (n = 344) were obtained from the reticulorumen and juveniles (n = 20) from the intestines of naturally infected ruminants and camelids at slaughter or post-mortem. The animals examined comprised of: alpaca (2), bison (1), cattle (112), goat (3), llama (2), red deer (2), reindeer (1), rusa deer (13), sheep (36), and water buffalo (1). In addition, a limited number of formalin-fixed (n = 4, i.e., two adult fluke each from two cows in Northern Ireland) and wax-embedded (n = 3 individual flukes, collected from one cow in England) samples were included. Those samples had originally been prepared for histopathological examination, and were used to provide proof of concept for the applicability of the proposed approach to archived sample material. Rumen fluke eggs (n = 23) were either collected at the Moredun Research Institute (n = 6 from two individual fecal samples and n = 6 from one pooled fecal sample) using published methods (52), or received extracted and in ethanol (n = 11 from four different host animals).

Genomic DNA Extraction
DNA was extracted from individual adult flukes or pools of immatures of ∼25 mg. Samples stored in formalin were washed three times in 1X phosphate buffered saline (PBS) prior to DNA extraction. For eggs stored in ethanol, 200 µl aliquots were left to sediment for 3 min, before siphoning off the excess ethanol and rinsing three times in 1X PBS to ensure all eggs were free of sediment, and repeating sedimentation. DNA extraction was performed using Qiagen DNEasy Blood and Tissue kit (QIAGEN, Germany) as specified by the manufacturer. The lysis step involving Proteinase K was performed overnight for ethanolfixed as well as formalin-fixed samples. For paraffin wax-fixed samples, sections of host and parasite tissue were cut from wax blocks provided by APHA, Lasswade. Initial attempts to extract DNA from wax blocks using the Qiagen kit were not successful. Therefore, DNA from paraffin wax-fixed samples was extracted using Ambion RecoverAll Total Nucleic Acid Isolation Kit (Life Technologies, USA) as per the manufacturer's protocol.

Polymerase Chain Reaction
The ITS-2 rDNA, plus partial flanking 5.8S and 28S regions were amplified using the generic trematode primers, ITS-2Trem (Table 1) (19,20). PCR was conducted using a reaction volume of 25 µl containing 10x Buffer (Invitrogen, USA), 12.5 pmol of each primer (Eurofins, Germany), 0.2 mM of each dNTP (Invitrogen, USA), 2 mM MgCl 2 (Invitrogen, USA), 2.5U platinum Taq polymerase (Invitrogen, USA) and 1 µl of template DNA. The PCR was performed in a GeneAMP PCR system 2720 thermal cycler (Applied Biosystems, USA) under the following For rumen fluke DNA that appeared to be damaged as a result of prior processing, e.g., formalin fixation, (see Results), primers were designed to amplify a smaller ITS-2 fragment within the larger ITS-2 region of <200 bp (ITS-2Short, Table 1). The reagent volumes were as previously described, but with a total reaction volume of 50 µl. PCR conditions were: 95 • C for 10 min; 40 cycles of 95 • C for 30 s; 58 • C for 30 s; 72 • C for 1 min; and 72 • C 10 min. 20 µl of PCR product was run on a 2% agarose gel, the gel was inspected on an ultraviolet transilluminator (Thermo Scientific, USA), bands of the correct amplicon length were excised, and DNA extracted using a QIAquick Gel Extraction Kit (QIAGEN, Germany), according to the manufacturer's instructions.
Where species identity based on ITS-2 sequence data was ambiguous, e.g., because a perfect match was not found in online reference databases, the large subunit ribosomal DNA 28S was also examined. Amplification of the 900 bp 28S rRNA fragment was achieved by use of Dig28S primers ( Table 1) (46). Master Mix and PCR parameters were as described above for amplification of ITS-2, but with annealing temperature 56 • C.
The specificity of primers for rumen fluke species was tested against a panel of control trematode and gastrointestinal nematode DNA extracts originating from bovine or ovine hosts (except for the free-living planarian, Arthurdendyus triangulatus; and the fish eye fluke, Diplostomum spathaceum) ( Table 2). Apart from A. triangulatus (0.25 g section used) and Fasciola gigantica (extracted from a crude lysate), all samples were prepared from single adult worms using the method described above. A positive control of 10 ng/µl C. daubneyi DNA and a negative control of nuclease-free (NF) H 2 O were included in each PCR assay.

Sequence and Phylogenetic Analysis
Purified PCR products were analyzed on a NanoDrop 1000 Spectrophotometer (Thermo Scientific, USA) to determine quantity and purity of DNA, before sending to Eurofins MWG Operon (Germany) at a concentration of 5 ng/µl for direct nucleotide sequencing. From each infected animal, at least two individual flukes were sequenced. The chromatogram files were aligned using Lasergene 10 core suite software SeqMan Pro (DNASTAR, USA) to assess the quality of the sequences and, where necessary, assemble the full fragment length. The final sequences were compared to reference sequences in GenBank using BLASTn at the European Bioinformatics Institute website (http://www.ebi.ac.uk/). For phylogenetic analysis, sequences were aligned by MUSLE using Alignment Explorer in MEGA6 (53). For each assembled sequence, a nucleotide query was conducted using NCBI BLAST and the closest matching sequences downloaded in full. Duplicate reference sequences, sequences originating from "unidentified paramphistome" or unverified with regards to source in GenBank, and sequences with <98% percentage identity or <98% query cover compared to any of the sequences generated in the current study were manually removed. The remaining reference sequences, together with any control sequences previously generated in-house using relevant primers, were added to the MEGA6 alignment. All sequences were manually cropped to equal lengths, i.e., 364 and 831 nt for ITS-2 and 28S, respectively. A Neighbor-Joining phylogenetic tree was constructed in Tree Explorer to assess the level of heterogeneity. The evolutionary distances were estimated using the Maximum Composite Likelihood method in MEGA, which accounts for multiple hits, heterogeneity of substitutions patterns between nucleotides, and inequality of nucleotide frequencies (53). Support for the tree topology was assessed by the bootstrap method with 1,000 pseudoreplicates. For the 10 species identified in the current study, the Pairwise Distance function in MEGA6 was utilized to calculate % homology for pairs that had been assigned the same species names (intra-specific) or different species names (inter-specific). All available sequences available on GenBank (155) were used (trimmed to 299 bp segment to allow for inclusion of sequences that were shorter than those generated in the current study), together with unique sequences generated in this study, i.e., one for the majority of species, and two sequences for three species showing intraspecies heterogeneity; Supplementary Table 1).
DNA extracts from paraffin wax-embedded samples and those stored in formalin for over a year (extracted from C. daubneyi, UK) did not yield sufficient quality PCR product for sequencing when using the generic ITS-2Trem primers. In these instances, the ITS-2Short primers designed to amplify a smaller ITS-2 section were successful in producing 171-209 bp sized fragments, omitting the first ∼200 bp of the larger ITS-2 fragment. This section had a high enough degree of divergence in the trematodes examined in this study to discriminate between species, the lowest nucleotide difference being 3 base differences (between C. microbothrium and C. calicophorum).
Most sequences derived from our collection showed 99.6-100% identity to the species matched most closely in GenBank (Figure 1). By contrast, GenBank-derived sequences clustered in multiple clades for O. streptocoelium, P. cervi, C. cotylophorum, and F. elongatus (two clades each) and G. crumenifer (three clades) with bootstrap support of 90-99% for nodes separating clades (Figure 1). Based on pair-wise comparison, intraspecific homology was >98.5% for 80% of sequence pairs, compared to <98.5% for 97% of interspecific comparisons, with a small number of intra-specific comparisons falling in the distribution observed for inter-specific comparisons, or vice versa (Figure 2).
For one species, C. cotylophorum, which was uniquely associated with rumen fluke from St. Kitts in our study, very few ITS2 sequences were available for comparison. The ITS2 sequences (n = 16) from St. Kitts were 100% identical to each other and to C. cotylophorum from Bos indicus (zebu) in Tripura, India [JX678257 (46) but distinct from the second sequence in GenBank reported to originate from C. cotylophorum, also isolated from Indian zebu -KC503917. To obtain greater certainty about the identity of specimens from St. Kitts, 28S RNA gene sequencing was also used. Identification as C. cotylophorum was supported by 100% identity to an 803 bp segment of the 28S rRNA gene [JX678278, (46)], which originates from the same study as the closest ITS-2 match (Supplementary Figure 3).
With the exception of C. cotylophorum, every rumen fluke species that was identified more than once was found in more than one host species, often including livestock and wildlife (e.g., C. calicophorum, F. elongatus, O. streptocoelium, and P. leydeni) or livestock and camelids (C. daubneyi) ( Table 3). Multiple FIGURE 2 | Distribution of pair-wise sequence identity between all available ITS-2 GenBank (n = 155) sequences and unique in-house generated sequences (n = 13) for the 10 paramphistome species found in this study. Green bars showing nucleotide differences between species and blue within species. Sequences were aligned using MEGA6 and trimmed to the same length of 299 bp before determining the number of base differences per site between sequences using the Pairwise Distance computation function. Pairs showing <90% homogeneity were omitted, but can be seen in Supplementary Figure 2 and Supplementary Table 2. rumen fluke species per host species were identified in livestock (cattle, sheep, goats) and Rusa deer, whereas only a single rumen fluke species was identified in most camelids and wildlife species, possibly reflecting the number of samples included in the study. The three most common rumen fluke species were identified in more than one country (Table 3), as was P. leydeni, whereby the countries could be geographically clustered (e.g., for C. daubneyi and P. leydeni in Europe), or distant (e.g., for C. microbothrioides in the USA and Dominican Republic, or C. microbothrium in Cuba and Tanzania). Per country, one or more rumen fluke species could be identified, although this was largely an artifact of the study design, which was opportunistic and aimed at maximizing species diversity to allow rigorous assessment of the typing methodology across rumen fluke species from different host species and geographic origins.

DISCUSSION
Accurate identification of rumen fluke species is important for understanding of disease epidemiology and control options.
Examples include studies of spread of infectious agents through animal movements (51), origins and biosecurity risks posed by non-indigenous species of parasitological importance (7) and recognition of relevant intermediate host snail species (4). Pathology and severity of paramphistomosis may also be affected by the rumen fluke species (21), although relatively little is known about this, potentially due in part to difficulties with species identification.
Historically, morphological identification has been used, which has led to some taxonomic confusion and errors. For example, Willmott (54) morphologically identified two rumen fluke species, Paramphistomum hiberniae and Paramphistomum scotiae, found in Irish and Scottish cattle, respectively, slaughtered at a Glasgow abattoir. Willmott suggested that P. hiberniae was present in the Netherlands, too, and suggested samples from France, marked as P. cervi, were likely to be P. microbothrium (since reclassified as C. microbothrium). Subsequently, Odening (44) reviewed this histologically, identifying P. leydeni, P. scotiae and P. hiberniae as later synonyms of P. cervi. Since then, and as exemplified  in this study, C. daubneyi has been the only paramphistome species to be identified within Great Britain. It is unclear whether both species previously co-existed or whether one of them was previously misidentified (4). The distinction is of major relevance for rumen fluke control, because P. cervi uses aquatic planorbid snails as intermediate hosts whereas C. daubneyi uses mud snails. Incorrect classification of rumen fluke species could result in misdirected and hence ineffective control efforts.

Calicophoron calicophorum
While histological examination appears to be more reliable than morphological identification (41), both are still flawed in that variations within species can be noted due to a number of factors including host species, paramphistome age, fixation status and plane of section during slide preparation (17). To resolve these issues, molecular characterization is increasingly being used, especially ITS-2 sequencing. To amplify the ITS-2 region of the rRNA gene of paramphistomes, a range of different primers has been used for adult trematode specimens (3,20,49,55), larvae (55), and eggs (19). Most studies focused on a limited number of countries, host species or sample types. Here, we demonstrate the utility of a universal set of ITS-2 primers across adult rumen fluke, juvenile rumen fluke, and fluke eggs from multiple hosts and locations in detection and identification of 10 rumen fluke species belonging to multiple genera. A suitable level of interspecific variation required for species identification existed within the ITS-2 region, although amplification of the full ITS-2 region was not always feasible where DNA was fragmented as a result of degradation through storage or fixation. Others have reported similar difficulties in the amplification of this region in formalin-preserved samples (3). The ITS-2 primers designed here to amplify a shorter region appear to allow identification through a smaller segment of the ITS-2. Snail samples were also tested but often yielded a mixture of ITS-2 amplicons from different trematode species (data not shown) and we don't recommend generic ITS-2 PCR without sequencing as a diagnostic method for this specimen type.
Whereas ITS-2 amplification and sequencing are relatively straightforward and routine, interpretation of sequence data to species level was challenging for several species due to inconsistencies in comparator data available in GenBank, particularly for G. crumenifer, which was distributed across three clades, with levels of intra-specific homology that were clear outliers relative to the distribution of pair-wise homologies within species (Figure 2). GenBank is a public data repository and is known to contain data errors (56). Only GenBank entries that matched closely with C. cotylophorum were classed as "unverified" but considering the difficulties in morphological identification of rumen fluke species, it is quite possible that genetic sequence data has been attributed without verification or incorrectly to other rumen fluke species too. To overcome this issue, development of an international, curated consensus reference database would be desirable. A Trematode.net database has been added to the veteran Nematode.net, as part of the collective Helminth.net, but as of yet, no paramphistome species are listed (57). We propose that ITS2 sequencing be used for definitive identification of paramphistomes. Where ITS2 comparator data is limited or lacking, as for C. cotylophorum in the current study, additional DNA sequencing could be used. For example, following a debate as to whether P. cervi and P. leydeni were distinct species, their complete mitochondrial genomes were compared and revealed a genetic divergence of 3.1% in the ITS-2 region, compared with a 14.7% variation in mtDNA (6). The higher divergence and resolving power of mtDNA also makes the Cox1 region preferable for examining genetic diversity within species (7,21). Lack of published reference data made this method unsuitable for the heterogeneity of rumen fluke species considered in the current study.
Identification of G. crumenifer was particularly problematic. Only one specimen in our collection, originating from a water buffalo in India, belonged to this species, and it showed 100% genetic identity with one of the reported G. crumenifer sequences in GenBank. Identity with other reported G. crumenifer sequences, however, was low. Intraspecific variation has also been reported for the Cox1 region of G. crumenifer (16). It has been suggested that a high level of heterogeneity between isolates from different countries may suggest a higher degree of virulence (50) but in the absence of a gold standard, questions about species identity remain. The 28S rRNA region is being used increasingly for paramphistome species differentiation, due to its higher resolving power and greater nucleotide variation resolving previous issues with groupings of paramphistomes (46), but misclassification of original specimens could still affect interpretation of 28S data.
In conclusion, a total of 387 paramphistome specimens from 173 individual animals belonging to 10 host species and originating from 17 countries across multiple continents were successfully identified as 10 different paramphistome species through molecular methods. This demonstrates the utility of ITS2 sequencing as a generic approach for identification of paramphistomes, regardless of origin or phenotype. Moreover, the method is suitable for a variety of specimen types, including those with limited DNA template quality. Additional targets such as 28S (16) and Cox1 (51,58) can be used to complement the available ITS-2 data, particularly to confirm identification of apparently new species. The lack of consistent clustering of rumen fluke species in the ITS2-phylogeny, combined with the fact that some supposed intra-specific ITS2 homologies fall far outside their normal distribution and well within the distribution of inter-specific homologies, raises concerns about the accuracy of attribution of sequence data to species in GenBank. Establishment of an international consensus reference database would be of great value to support accurate identification of rumen fluke species and studies of their distribution, emergence, virulence, and control.

DATA AVAILABILITY STATEMENT
The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

AUTHOR CONTRIBUTIONS
PS and RZ conceived of the study. PS coordinated national and international collaborations and sample collection. GM developed the laboratory methodology and performed all analyses. GM and RZ drafted the manuscript. All authors reviewed the final manuscript.


extfieldsofstudy: Physics Chemistry

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The effect of starch-hydrocolloid interaction on starch digestibility, pasting and physicochemical properties: A review

In complex food matrix, blending starch with non-starch hydrocolloids such as gum arabic, xanthan gum, guar gum, carrageenan, locust bean gum, and pectin may impact starch digestibility, pasting and physicochemical properties. The starch-hydrocolloids blending have specific application in the food product as an emulsifier, gelling agent thickener, stabilizer and inhibiting formation of ice and sugar crystal. As a thickener, hydrocolloid can increase the viscosity of food products and inhibit the accessibility of α-amylase to the starch granules. Effect of hydrocolloid on starch digestibility, pasting and physicochemical properties depends on starch source, type and concentration of hydrocolloid, and method of preparation starch-hydrocolloid mixture. This review summarized several studies about the effect of starch-hydrocolloid interaction on the starch digestibility, pasting and physicochemical properties. Furthermore, the mechanism of starch-hydrocolloid interaction and their applications in food product are also discussed. The pupose of this review is to provide information of several applications and recent utilization trend of starch-hydrocolloid blend in food systems.

Introduction
Starch is a source of carbohydrate that has semi-crystalline structure which consist of two major types of α-glucan on molecular level, the linear amylose and the branched amylopectin [1]. Starch granules are insoluble in cold water and relatively dense, vary in shape, size (1 to 100 µm). Starch can be extracted from many commodities such as tubers (potato, cassava, sweet potato, etc), cereals (rice, sorghum, wheat), seeds (lotus seed, jackfruit seed, avocado seed) and fruits (banana, breadfruit, etc) [2,3]. Structure and chemical composition of starch are dependent on its origin. Application of starch in a food product has to consider the presence of other components in food system such as protein, lipid, mineral, dietary fiber, phenolic compounds, non-starch polysaccharides, etc. These components can interact with  [4]. Several factors such as the type of starch and NSC, composition and concentration of starch-NSC mixture, processing condition determine the effect of NSC on starch properties [3,5].
Hydrocolloids, the term used in this review is non-starch polysaccharides which is a heterogeneous group of long chain polymers characterized by a formation of viscous dispersions and/or gels when dispersed in water [6]. Hydrocolloids include xanthan gum, carrageenan, guar gum, gum arabic, pectin, locust bean gum, gellan, konjac-glucomannan, etc. Blending with hydrocolloids is one of the methods to modify the starch properties and the rheological properties of food properties [7]. The properties of starch-hydrocolloids mixture are dependent on the type of starch and hydrocolloids, concentration of starch and hydrocolloids, level and type of interaction, and preparation condition [8].
This review focuses on the effect of starch blending with hydrocolloids on digestibility, pasting and physicochemical properties of starch. In the food industry, the starch-hydrocolloids mixture has specific application as thickener, emulsifiers, gelling agent or stabilizer such as stabilization of emulsion and foams [7] . Some studies have reported the application of starch-hydrocolloid blending in food products such as oat starch-xanthan gum as a thickening agent [9], rice flour-xanthan gum as a thickening agent in gluten-free bread [10], wheat flour-arabic gum as a anti staling agent [11], chickpea flour-pectin as strengthening agent in pasta making [12].
The interaction between starch and hydrocolloids has a nutritional impact on food products. According to [13], starch-hydrocolloids interaction can alter food texture, structure, and viscosity, thereby changing the accessibility of enzymes to starch granules. This review purposes to summarize a number of studies on the interaction effect of starch and hydrocolloids such as carrageenan, xanthan gum, pectin, guar gum, gum arabic, locust bean gum, gellan, konjac-glucomannan on the digestibility, pasting and physicochemical properties of starch. The summary of studies on the effect of starchhydrocolloid blending on starch digestibility, pasting and physicochemical properties is presented in table 1. This summary was based on the type of starch and hydrocolloid mixture and how the effect of its mixture on starch digestibility, pasting and physicochemical properties.  The presence of hydrocolloids significantly reduced starch hydrolysis even over a short reaction time and the effect on the blood glucose level after ingestion was dependent on the type of hydrocolloid. Higher hydrocolloids concentration led to a higher increase in the peak and final viscosities. Xanthan and guar gum were more effective in increasing starch paste viscosity than pectin and konjac-glucomannan [16] Corn starch Pectin Pectin decreased starch digestibility and induced reduction in rapidly digestion starch [17] International

Impact of starch-hydrocolloid interaction on digestibility
The presence of other components influences the rate and extent of starch digestibility in a food matrix. Inhibitory effect during starch hydrolysis might be caused by some molecules naturally occurring in food system [27]. Hydrocolloids have the potential effect to increase the viscosity of food products which altered the accessibility of starch granules to the α-amylase [13]. Blending starch with hydrocolloids is expected to alter the rate and extend of starch digestion. Some previous studies have reported that hydrocolloids decreased starch hydrolysis in gels as reported in the studies on the addition of agar, konjac-glucomannan (KG), and xanthan gum to the rice starch [15] and guar gum, xanthan gum, KG, and pectin to high-amylose corn starch [28], xanthan gum, guar gum, KG, and pectin to gelatinized potato starch [16]. The other studies also found that hydrocolloids reduced the blood glucose response of waxy maize and potato starch [29] and some type of hydrocolloids inhibited starch digestibility and alter the pattern of starch digestion [30]. Furthermore, Sasaki and Kohyama [28] have reported that the higher viscosities and retarding enzyme diffusion were not the only factors as underlying mechanism of starch digestibility reduction in the presence of hydrocolloids. The other factors may play a role as well.
Konjac-glucomannan, agar and xanthan gum decreased starch hydrolysis rate which was inversely to the concentration of hydrocolloid. Each of hydrocolloid has the optimum concentration to reduce the starch hydrolysis. At the same concentration, xanthan gum and konjac-glucomannan had the higher suppressing effect on starch hydrolysis than agar [15]. The presence of hydrocolloids alter the blood glucose level after ingestion which were dependent on the hydrocolloid type. The presence of xanthan gum decreased the blood glucose levels at 30 min, whereas pectin and guar gum decreased the level at 60 min. Xanthan gum had the most suppressive effect on starch digestibility among other hydrocolloids such as konjac-glucomannan, pectin and guar gum [16,28]. Furthermore, Tester and Sommerville [25] have reported that κ-carrageenan have a higher effect than xanthan gum, pectin, gum arabic and guar gum on the restriction of normal maize starch hydrolysis.
The addition of guar gum inhibited in vitro starch digestion, decreased rapidly digestible starch and increased resistant starch, especially when 0.30% guar gum was added to 30% lotus seed starch [18]. Bai et al [1] found that the decrease of starch digestibility in the presence of pectin might be due to physical rather than chemical factors. They have explained that two possible factors were (1) enzyme bound with pectin, inhibiting the access of the enzyme to inner starch granule and (2) the presence of pectin increased viscosity of starch suspension which caused slower diffusion rate of enzyme. The mechanism of reducing starch digestibility by blending with hydrocolloids might be dependent on type of hydrocolloids. The properties of hydrocolloids solution are influenced by its molecular size, concentration, distribution, and charge [28].

Effect of starch-hydrocolloid interaction on pasting properties
Gelatinization occurs when the starch granules are heated up to pasting temperature in excess water, which causes granules to swell as hydrogen bond in amorphous region disrupted and water is absorbed [31]. Some studies have reported that starch-hydrocolloid interaction can alter pasting properties of starch [20,21,31]. Different type and concentration of hydrocolloids often have different effects on the pasting properties of starch, such as the addition of guar gum increased peak, final, breakdown and setback viscosities of potato, yam, yam bean and sweet potato starches [32], gum arabic decreased peak viscosity of wheat starch [33], fenugreek gum increased peak viscosity time, setback and final viscosities and decreased breakdown of corn starch [34], xanthan increased peak and final viscosities, breakdown and setback of rice starch [35], guar gum decreased final and setback viscosities of lotus seed starch [18], pectin decreased breakdown and setback of corn starch [17], ß-glucans increased peak, final,  [36]. Whereas hydrocolloid had little effect on pasting properties [19]. Effect of hydrocolloid on pasting properties of starch is dependent on several factors such as: type of starch or hydrocolloid, ratio of starch and hydrocolloid, preparation methods of starchhydrocolloid blending, and measurement conditions [31].
Pasting properties of starch with the presence of hydrocolloid often vary with the starch and hydrocolloid type and its concentration. Pectin at lower concentration (0.5 and 1.0%) decreased peak viscosity, conversely at higher concentration (above 2%) increased peak viscosity of corn starch [17]. Final and setback viscosities of lotus seed starch decreased by the addition of 0.03% and 0.15% guar gum, conservely increases by the addition of 0.30%, 0.60%, and 90% guar gum [18]. Addition of arabic gum decreased peak viscosity, breakdown and setback value of all type of tapioca starch but had no effect on setback of cationic tapioca starch [37].  have reported that breakdown decreased and peak viscosity time, setback and final viscosity increased with increasing concentration of hydrocolloid. Furthermore, Weber et al [38] have reported that addition of guar gum decreased peak, final, breakdown and setback viscosities on corn starch, conversely addition xanthan increased peak, final, breakdown and final viscosity of corn starch.
Several mechanisms were proposed to explain the effect of hydrocolloid on pasting properties of starch. The pasting properties of starch-hydrocolloid mixture are influenced by: (1) the network structure of molecules in the continuous phase, (2) the characteristics of the hydrocolloid in the continues phase, (3) granule swelling in the starch-hydrocolloid system, (4) interactions between swollen starch granule and granule fragments, interaction between the continues phase and dispersion phase, any electrostatic interaction between starch granule and hydrocolloid molecules [24,39]. In addition, hydrocolloid structural characteristics such as molecular weight, the degree of branching, molecular flexibility, and presence and type of an anionic charge may be some factors that influence of hydrocolloids to interact with a starch polymer [31,40]. The alteration of pasting properties of starch by adding gum that due to an association of the swollen starch with the gum or with the fractions of soluble amylose and low molecular weight amylopectin in starch paste, and competition of the starch with the gum for the free water in the system [38].

Effect of starch-hydrocolloid interaction on functional properties
As well as pasting properties, effect of starch-hydrocolloid interaction on functional properties of starch depends on several factors, such as type of starch and hydrocolloid, ratio of starch and hydrocolloid mixture, methods of preparation, etc. Some studies found that the presence of hydrocolloid increase granule swelling [39,41]. The other studies have opposite results that the presence of hydrocolloid molecules caused strengthen granules and restrict granule to swell [24,32,37]. Swelling power (SP) and solubility of starch-hydrocolloid mixture was influenced by type and concentration of hydrocolloid. SP of lotus seed starch decreased by the presence of 0.03% and 0.15% guar gum, conversely by the presence of 0.30%, 0.60% and 0.90% guar gum [18]. SP and solubility of tapioca starch, anionic tapioca starch and cationic tapioca starch decreased by addition gum arabic, whereas the extend of the decrease in SP and solubility by gum arabic addition depends on type of an anionic charge of tapioca starch [37]. The presence of guar gum and locust bean gum slightly decreased of SP for tuber starches and the presence of guar gum also decreased solubility of tuber starches except yam starch [32]. Yellow mustard mucilage and buckwheat and pea starches interacted by hydrogen bond to restricted starch granule swelling and solubility [42].
Decrease in SP might be caused by several factors. Tapioca starch were completely wrapped by xanthan gum layer, which delayed granule swelling, limitation of increase in viscosity and inhibit starch granule to gelatinize [39]. A similar mechanism for gum arabic and starch mixture was also reported by cationic tapioca starch were wrapped tightly by a guar gum and prevent amylose leach out. Therefore, Chen et al [40] have speculated that decreased in SP of starch-galactomannan blending was caused by interaction between galactomannan with inner molecules on the surface of the starch granules and galactomannan coated on the surface of the starch granules. Freeze-thaw stability is an important functional property of starch that was used to evaluate the stability of starch gel during freezing and thawing. Freeze-thaw stability was measured by water that separates from starch pastes or gels (syneresis) [43]. Some previous studies found that the presence of hydrocolloid increased the stability of starch gel or reduced starch syneresis [8,21,33,44] and another study found the opposite result [37]. Muadklay and Charoenrein [43] found that different types of hydrocolloid have a different effect on syneresis of starch gels; 0.25% and 0.50% of xanthan gum was more effective in reducing syneresis than konjac-glucomannan and locust bean gum.

Conclusions
Effect of hydrocolloid on starch digestibility, pasting and physicochemical properties depends on starch source, type and concentration of hydrocolloid, and method of preparation starch-hydrocolloid mixture. Generally, addition of hydrocolloid decreases starch digestibility, alters pasting and physicochemical properties. Hydrocolloid structural characteristics such as molecular weight, the degree of branching, molecular flexibility, and presence of an anionic charge, type of an anionic charge influence the interaction of hydrocolloids with starch polymer.