Refractories for Ammonia Production in Fertilizer Unit

*Indra Nath Chakraborty*

### **Abstract**

Apart from being used as a fuel, natural gas is used extensively for production of ammonia-based fertilizers. During the process of ammonia production natural gas is steam reformed for the production of Hydrogen and the same is converted into Ammonia, by Haber's process, using nitrogen from air. Refractories are required for reformer lining since they are operated at high temperatures as well as in corrosive gas, primarily Carbon Monoxide and Hydrogen, environment. The refractories selected for reformer, thus, should resist the reformer operating temperature as well as the aforementioned gases. Owing to the presence of steam in the working environment magnesia and lime-based basic refractories cannot be used owing to their hydration tendency and thus, aluminosilicate refractories are the only choice. The effect of H2 and CO on aluminosilicate refractory is the primary focus of this paper. The main concerns are the reduction of siliceous components of the refractory by hydrogen and carbon deposition due to Carbon monoxide decomposition by Boudouard reaction. The effect of these gases on aluminosilicate refractories have been reviewed and based on the outcome suitable refractories have been recommended for ammonia production.

**Keywords:** ammonia, fertilizer, steam reform, natural gas Haber's process, hydrogen, carbon monoxide, steam, Boudouard reaction, aluminosilicate refractories

### **1. Introduction**

Ammonia is one of the major raw materials for fertilizer industries. It is used for the production of nitro compounds, urea, ammonium sulfate, ammonium phosphate etc. The best and cheapest available source of nitrogen, for ammonia production, is air. Whereas hydrogen, required for ammonia production, is produced from various feedstocks but currently it is derived mostly from fossil fuels. Natural gas, Naphtha, Fuel oil, coal is used for the production of hydrogen. Natural gas is the most preferred option since it is the cheapest in terms of relative investment as well as relative specific energy requirements for ammonia production.

Any industrial process involving high temperature requires refractory. Since the production of Ammonia from natural gas, Ammonia-based fertilizer as well as its derivative involves high temperature, their production, thus, also requires refractory. The literal meaning of refractory is "Stubborn." In the context of industrial processes, refractories connote the materials which are not markedly affected by their environment. In other words, refractories retain their original features as well as characteristics in aggressive industrial process conditions. During their usage, refractories are exposed to:

Against this backdrop the selected refractories, for any industrial process, should ensure that they do not undergo chemical degradation and also can withstand abrasive actions and mechanical as well as thermal stresses of the environment. In this context it also is prudent to mention that no refractory is everlasting. The primary objective is to select a refractory, for any industrial process, such that the impact of the aforementioned industrial process conditions is minimal and the refractory life is maximized. Performance of the refractory is one of the major determinants of the economic efficiency of virtually all industrial processes.

In this context it should also be mentioned that all the aforementioned refractory wearing parameters are not important or significant for all the industrial processes and as a consequence all the refractory properties are not relevant for a given process environment. For the requisite or best performance, the refractory wear contributors, for a given industrial process, need to be identified and refractories should be selected such that it can withstand the identified critical wear parameters. The relevant refractory properties, which would counter the critical wear contributors, need to be optimized. Identification of critical wear parameters, hence, is one of the most important steps for maximization of refractory life at the lowest cost. Apart from the refractory properties, operating parameters of the industrial units also play a key role in determining the refractory performance.

In this paper a brief production process of Ammonia, from natural gas, would be presented (**Figure 1**) and the emphasis would be on the refractory selection process for the ammonia production unit. The primary focus of this discussion would be to understand the operating condition at each step of ammonia production. Once the operating conditions are identified, rationale behind recommending refractories for the various units of the ammonia production process would be discussed. Needless to mention, the primary objective of this paper is to correlate the operating conditions with refractory properties, not the impact of process parameters on efficiency of Hydrogen and thus, ammonia production. For improving the process efficiency catalysts are used at all stages of hydrogen production as depicted in **Figure 1**. Catalysts and their impact on the Hydrogen / Ammonia production process is also not part of this paper. It is evident from the flow diagram (**Figure 1**) that at different stages of the process, part of the hydrogen yield is used as reactant. Prior to venturing into the hydrogen production process, basic information on refractory material would be shared and this would set the backdrop of the rationale behind the refractory recommendations for hydrogen/Ammonia production from natural gas.

*Refractories for Ammonia Production in Fertilizer Unit DOI: http://dx.doi.org/10.5772/intechopen.104934*

**Figure 1.** *Process diagram for ammonia production.*

#### **1.1 Refractory**

Chemically, refractories can be classified as Acidic, Basic and Neutral. The chosen refractory should be compatible with the chemical environment of the process equipment. For example, in case the environment of a process equipment, during operation, is chemically basic, a basic refractory needs to be selected for its lining. Based on the shape, refractory can be classified as shaped, which colloquially is known as bricks, and monolithic refractories. Unlike bricks, monolithics do not come with any specific shape. Shaping of the monolithic materials is done during installation as per the contour of the process equipment. A large number of refractory products fall in the monolithics category. Castable, which is akin to concrete with different material bases and cement, constitutes the largest volume of monolithics. Apart from castables there are numerous monolithic materials, which are installed mechanically, that is, their usage enhances the refractory installation rate, and thus, assists in reduction of process equipment downtime.

Monolithic refractories, in general, have the following advantages over shaped ones.

Morphological features of refractories are schematically illustrated in **Figure 2**. All classes of refractories consist of granular material of various sizes, which are termed as aggregates and they are bonded together by very small sized material, which is designated as matrix. Refractories can be conceptualized as the matrix being the continuous phase, where the aggregates are embedded. In majority of the cases, aggregate and matrix chemistry are very different from each other. This makes refractories a heterogeneous material and thus, more complex. Apart from aggregate and matrix, pores are an inherent constituent of the refractories. Refractory porosity and its size distribution can be varied by controlling the proportion of aggregate of different sizes used in the refractory formulation.

**Figure 3** illustrates different kinds of pores present in the refractories. Channel pores are the ones which are open from both the ends, though the connecting path may be tortuous. Open pores are the ones which are open from one end but closed from the other. Closed pore, as the name suggests, is closed from all around, i. e. it is not accessible by any gas or liquid in contact with the refractory.

Pore concentration as well as its size distribution in the refractories virtually governs its mechanical as well as thermal properties. For example, channel pores are the only ones which contribute to gas permeation. As the combined concentration of channel and open pores grows higher, the vulnerability of the refractories to chemical attack increases. Total concentration of all 3 types of pores determine the thermal conductivity as well as the strength. Pore concentration of refractories also determines their abrasion resistance, elasticity etc.

*Refractories for Ammonia Production in Fertilizer Unit DOI: http://dx.doi.org/10.5772/intechopen.104934*

**Figure 3.** *Schematic representation of types of pores in a refractory.*

Selected refractories for any industrial application should conform to the requisite pore structure, its concentration as well as size. For example, for low thermal conductivity, pore concentration of the refractories should be high. On the contrary, for high strength, good resistance to abrasion as well as chemical attack, the refractory porosity should be low. So, not only the chemical compatibility of the refractories with the operating environment ensures the desired performance but its morphological features also play a significant role.

It is, thus, prudent that against the backdrop of the operating conditions of each of the units for Ammonia production, refractory recommendation is made. In the next section, hence, the Ammonia production process as well as the influence of the process parameters on the process efficiency would be discussed in brief. This will set the tone as well as backdrop of refractory selection for the fertilizer industry, particularly for ammonia production units.

### **2. Natural gas conversion into ammonia**

No dearth of information is available in the literature for commercial ammonia production by Haber - Bosch process [1–14]. Among fossil fuels, Natural gas is the most preferred option for Hydrogen generation for Ammonia production. It is well known that the major constituent of Natural Gas is Methane. Apart from Methane, natural gas also contains hydrocarbons of higher molecular weight. Higher hydrocarbons are usually converted into methane by hydrocracking (Eq. 1) prior to further processing.

$$\rm C\_nH\_m + \{(4n-m)/2\}H\_2 = nCH\_4 \tag{1}$$

The hydrocracking reactions are endothermic. The reactions are carried out at (65–140 bar) and (400–800°C), in the presence of hydrogen.

The subsequent step for hydrogen generation is Sulfur removal from natural gas. Sulfur bearing compounds in natural gas need to be removed since it deactivates the catalysts used in the Ammonia production process. Sulfur, which is present in the natural gas as Thiol – Sulfur (RSH), is removed by catalytic hydrogenation and in the process hydrogen sulfide is generated (Eq. 2).

$$\text{CH}\_2 + \text{RSH} \boxplus \text{RH} + \text{H}\_2\text{S} \text{ (gas)}\tag{2}$$

Hydrodesulfurization reaction occurs at 300 to 400°C and 30 to 130 bar absolute pressure. H2S generated by this process is passed through beds of zinc oxide yielding zinc sulfide (Eq. 3).

$$\text{Zn} + \text{ZnO} \oplus \text{ZnS} + \text{H}\_2\text{O} \tag{3}$$

Desulphurized methane is treated with high-temperature steam (700–1000°C) at 3–25 bar pressure, in the presence of a Nickel catalyst in Primary Reformer. The following reactions (Eqs. 4 and 5) occur during interaction of Methane with steam.

$$\text{CH}\_4 + \text{H}\_2\text{O} \rightleftharpoons \text{CO} + \text{3H}\_2\text{ }\Delta\text{H} = +206.1 \text{ kJ/mol} \tag{4}$$

$$\text{CH}\_4 + 2\text{H}\_2\text{O} \rightleftharpoons \text{CO}\_2 + 4\text{H}\_2\text{ }\Delta\text{H} = +165.0 \text{ kJ/mol} \tag{5}$$

All the reforming reactions are endothermic. The primary objective of the steam reforming process is to maximize Hydrogen yield. CO and CO2 are the byproducts of the steam reforming process. Apart from the steam reforming reactions, Dry Reforming and Water Gas Shift (WGS) reactions (Eqs. 6 and 7) also occur by virtue of interactions between the steam reforming reaction products, viz. CO and CO2, steam and Methane.

$$\text{CH}\_4 + \text{CO}\_2 \rightleftharpoons 2\text{CO} + 2\text{H}\_2 \qquad \Delta\text{H} = 247.3 \text{ kJ/mol} \tag{6}$$

$$\text{CO} + \text{H}\_2\text{O} \rightleftharpoons \text{CO}\_2 + \text{H}\_2 \qquad \Delta\text{H} = -41.15 \text{ kJ/mol} \tag{7}$$

WGS is called so since by virtue of this reaction the ratio of Water Gas constituents, viz. CO, and H2, are altered. In this specific case, the reaction is carried out in such a way that the reaction proceeds in favor of Hydrogen generation. The yield of Primary reformers typically contains 60% Hydrogen. All the reactions in Primary Reformer are reversible in nature. Owing to this reason pressure, temperature, and ratio of the reactants determine the extent of Hydrogen generation. **Figures 4** and **5** illustrate the impact of Steam - Methane ratio, temperature, and pressure on the reforming process. The observations are in the expected line of the reversible reactions and thus, the conversion of natural gas into CO and H2 increases with:

**Figure 4.** *Effect of pressure on the steam reforming process in primary reformer for the operating temperature of 800°C [6].*

*Refractories for Ammonia Production in Fertilizer Unit DOI: http://dx.doi.org/10.5772/intechopen.104934*

#### **Figure 5.**

*Effect of temperature on the steam reforming process in primary reformer for operating pressure of 30 Bar [6].*

In this context it is prudent to mention that Nickel catalyst, which is used for steam reforming reaction, also activates Boudouard reaction (Eq. 8) [6]. Since this reaction is reversible in nature, higher pressure favors the reaction to proceed to the right, that is, higher pressure favors CO decomposition.

$$\text{2CO} \rightleftharpoons \text{C} + \text{CO}\_2\tag{8}$$

By virtue of this reaction Carbon deposition tendency is fairly severe. This reaction has a significant impact on the life of refractories as well as its performance, which would be discussed in the refractory selection section.

The yield of theprimary reformer, that is, Hydrogen (H2), unreacted CH4, unreacted water (steam), CO, CO2, Nitrogen, Argon, etc. are fed in the secondary reformer, via Transfer Line, for further processing to increase the Hydrogen yield. Air, apart from the yield of the Primary reformer, is a feed for the secondary reformer. The primary objective of the Secondary reformer is to complete Steam Reforming as well as WGS reactions to produce further quantities of Hydrogen and adjust the hydrogen and nitrogen ratio for Ammonia production.

Apart from the continuation of steam reforming, dry reforming, and WGS reactions (Reactions 4–7), the following reactions also take place in Secondary Reformers.

$$\text{2CH}\_4 + \text{O}\_2 = \text{2CO} + 4\text{H}\_2 \tag{9}$$

$$\text{2CH}\_4 + \text{3O}\_2 = \text{2CO} + 4\text{H}\_2\text{O} \tag{10}$$

$$\text{CH}\_4 + 2\text{O}\_2 = \text{CO}\_2 + 2\text{H}\_2\text{O} \tag{11}$$

In addition to these reactions combustion of Hydrogen as well as Carbon Monoxide also occurs. The exothermic reactions raise the temperature of gases in the secondary reformer to �1000°C and the conversion to hydrogen achieved is of the order of 99% and the rest is a mix of CO, CO2, CH4, and water in chemical equilibrium.

**Figure 6.** *Effect of temperature on water gas shift (WGS) reaction equilibrium [9].*

The yield of the secondary reformer is further treated in the WGS reactor to increase the Hydrogen concentration (Eq. 7). Owing to the exothermic nature of the WGS reaction, lower temperature favors the formation of Hydrogen (**Figure 6**). The yield of secondary reformers is processed in 2 stages, viz. High-temperature WGS reactor at 300–450°C and Low - temperature WGS reactor at 200–250°C.

All oxygen-containing substances, including water, are poisons for the ammonia synthesis catalyst and thus, need to be removed. The gas mix from the WGS reactor, hence, is further processed, for removal of CO and CO2 by Pressure Swing Adsorption (PSA) or Cryogenic Distillation (CD). After the bulk removal of CO by WGS reaction and CO2 removal by PSA or CD, the typical synthesis gas still contains 0.2–0.5 vol % CO and 0.005–0.2 vol % CO2.

Methanation (Eqs. 12 and 13) is the simplest method to reduce the concentrations of the carbon oxides well below 10 ppm and is widely used in ammonia production units. There are two main purposes for methanation, viz. to purify synthesis gas, i. e. remove traces of carbon oxides, and to manufacture methane. CO and CO2 methanation is carried out in the temperature range of 200–600°C and 350–450°C, respectively.

$$\text{CO} + \text{3H}\_2 \oplus \text{CH}\_4 + \text{H}\_2\text{O} \tag{12}$$

$$\text{CO}\_2 + 4\text{H}\_2 \oplus \text{CH}\_4 + 2\text{H}\_2\text{O} \tag{13}$$

The hydrogen - nitrogen mix, obtained after methanation, is used for Ammonia production by the Haber - Bosch process.

### **3. Refractories of ammonia production**

It is apparent from the previous section that the environment of all the units for ammonia production contains steam, since it is one of the reactants used in ammonia production. MgO or CaO based, that is, basic, refractories, thus, would be unsuitable since they are prone to hydration. The hydration of both CaO and MgO yields hydroxides. The formation of these oxides is accompanied by large volume expansion. As a result, the soundness of the basic refractories in the ammonia production unit would be destroyed and thus, are unsuitable.

*Refractories for Ammonia Production in Fertilizer Unit DOI: http://dx.doi.org/10.5772/intechopen.104934*

Against this backdrop, it can be concluded that chemically only aluminosilicate refractories would be suitable. Aluminosilicate refractories, as the name suggests, are based on Alumina (Al2O3) and Silica (SiO2). One compound, viz. Mullite (3Al2O3.2SiO2), which contains 72% Al2O3, is the only thermally stable binary phase in the aluminosilicate system. With the increase of alumina content of aluminosilicate refractories, their refractoriness, i. e. temperature withstanding capability, increases. Apart from alumina and silica, aluminosilicate refractories also contain certain impurities, which are inherently present in the natural aluminosilicate raw materials used for their production. Primary impurities in aluminosilicate refractories are iron oxide, titanium dioxide, alkali, and alkaline earth oxides and these minor constituents play a significant role in deciding the ultimate refractory performance in any given environment and hence, the impact of these impurities also should be evaluated.

#### **3.1 Impact of hydrogen on aluminosilicate refractories**

The effect of Hydrogen gas on fused silica and a wide range of aluminosilicate refractories has been studied in depth [15–20]. The primary effect is reduction of silica by hydrogen gas as per Eq. 14 [16].

$$\text{SiO}\_2\text{ (s)} + \text{H}\_2\text{ (g)} \overset{\text{Higher Temperature}}{\underset{\text{Lower Temperature}}{\longleftrightarrow}} \text{SiO}\text{ (g)} + \text{H}\_2\text{O}\text{ (g)}\tag{14}$$

The gaseous products generated by virtue of reaction 14 are carried off by the process stream. Downstream, when the temperature is conducive for solidification of SiO, it condenses and gets deposited as SiO2 and Si mix causing heat-exchanger fouling and product contamination. **Figure 7** illustrates the effect of time as well as temperature on the reduction of fused silica by Hydrogen gas. As expected, with the increase in duration of fused silica and hydrogen interaction the loss of silica increases. The effect is similar when the interaction temperature increases.

The effect of time and temperature in the hydrogen environment on aluminosilicate refractories of different silica concentration follow the similar trend. **Figure 8**

**Figure 7.** *Effect of time and temperature on fused silica reduction [15].*

#### **Figure 8.**

*Effect of temperature on reduction of refractories with different silica content after reduction for 33 hrs in 100% H2 atmosphere [16].*

#### **Figure 9.**

*Effect of time on reduction of refractories with different silica content for reduction by hydrogen at 2600°F in 100% H2 atmosphere [17].*

illustrates that for a given level of silica in aluminosilicate refractories, increase of temperature causes higher loss of silica. It is also seen that for a given temperature, silica loss increases with the increase of silica concentration in aluminosilicate refractories.

**Figure 9** illustrates the effect of time on the silica loss of aluminosilicate refractories with different silica content. As contemplated, the increase of refractory hydrogen interaction duration causes greater silica loss. It also is in the expected line that for a given refractory - hydrogen interaction duration, silica loss increases with its silica concentration. But it is observed that upto 10% silica concentration in the refractory, the silica loss by SiO2 reduction is marginal.

**Figure 10** reports the impact of pressure as well as silica concentration of refractories in the hydrogen environment at 2400°F. Loss of silica, for all levels of pressure, increases with increase of silica concentration. It is fairly evident from the results that for a given silica concentration, the silica loss decreases with the increase of hydrogen gas pressure. In other words, higher operating pressure would protect the siliceous part of aluminosilicate refractories from the Hydrogen present in reformers. The operating pressure of primary as well as secondary reformers is >30 bar and thus, the silica loss from the aluminosilicate refractories is expected to be low.

*Refractories for Ammonia Production in Fertilizer Unit DOI: http://dx.doi.org/10.5772/intechopen.104934*

**Figure 10.** *Effect of hydrogen pressure on reduction of refractories with different silica content. These data are for 2400°F and hydrogen flow of 4.6 liters/minute [17].*

#### **3.2 Impact of carbon monoxide on aluminosilicate refractories**

The effect of Carbon Monoxide (CO) on the refractories is attributed to its decomposition as per the reverse Boudouard/Bell reaction (Eq. 8) [21–28]. In the context of refractories, it can be stated that CO gas diffuses into open as well as channel pores. By virtue of the reverse Boudouard reaction, carbon is deposited in the pores. Deposited carbon grows and generates stress within the refractories. When the stress exceeds the strength of the refractories, cracks form and the refractories get damaged. In the worst possible scenario, the refractory is destroyed. **Figure 11** illustrates the stability of CO as a function of temperature. With the increase in temperature, the stability of CO increases. It is apparent from the figure that destruction of refractories, by deposition of carbon, will not occur when the temperature exceeds 1100°C and at <500°C, though CO decomposition is thermodynamically possible, the reaction kinetics is slow and hence, CO decomposition rate is low. Refractories, thus, are vulnerable to destruction by CO in the 500–700°C range.

Metallic iron is a known catalyst for CO decomposition [27, 28]. The CO decomposition process proceeds by reduction of iron oxides by CO, to metallic iron. The iron

**Figure 11.** *CO - CO2 equilibrium as per FACTSage software [21].*

subsequently is carburized into Fe - carbide, which decomposes into Fe according to Eq. 15.

$$\mathbf{Fe3C} = \mathbf{3Fe} + \mathbf{C} \tag{15}$$

The Fe, formed via Iron Carbide decomposition, has high surface activity and hence, enhances the CO decomposition rate. The catalytic activity is believed to proceed via the formation of Iron Carbide (Fe3C).

### **3.3 Impact of simultaneous presence of hydrogen and CO on aluminosilicate refractories**

Apart from reverse Boudouard reaction, reverse water gas reaction also contributes to carbon deposition in refractories as per Eq. 16. This reaction, however, can proceed only below 680°C [29]. Beyond this temperature Carbon monoxide and Hydrogen are stable phases and hence, carbon deposition within the refractory is not expected. This prediction, however, is based on the assumption of standard states, i. e. the activity of the reactants and the products are 1. In real situations the reactant as well as product activities would be less than 1, which means there would be certain deviations in the temperature predicted above.

$$\text{CO} + \text{H}\_2 \rightleftharpoons \text{C} + \text{H}\_2\text{O} \tag{16}$$

Simultaneous presence of H2 and CO, which is the case for the reformers, enhances the CO decomposition rate when the temperature exceeds 577°C [23]. The presence of H2, in the CO environment, not only alters the carbon deposition rate but also determines the temperature at which the maximum carbon deposition occurs. For example, for 0.8% and 19.9% H2, maximum Carbon deposition occurs at 530 and 630°C, respectively. At lower temperature, however, the effect of hydrogen is marginal [28]. It, thus, is a precondition that refractories for the CO environment should be low in their iron oxide content.

#### **3.4 Impact of steam on aluminosilicate refractories**

Steam, in general, appears to be inter towards the oxide refractory materials. Steam, however, interacts with silica as per the Eq. 17, which is reversible in nature. It is evident from this equation that SiO2 in the presence of steam is converted into Si(OH)4.

$$\text{SiO}\_{2(s)} + 2\text{H}\_2\text{O}\_{(g)} \rightleftharpoons \text{Si(OH)}\_{4(g)}\tag{17}$$

**Figure 12** illustrates the effect of temperature as well as time, on the weight loss of cristobalite, at 0.84-atmosphere steam pressure. The magnitude of silica loss, via Si (OH)4 formation, increases with the increase of temperature up to 1350°C. Beyond 1350°C, however, the rate, as well as the magnitude of silica loss, reduces. This observation has been attributed to the reversal of Eq. 17. The opinion related to the temperature impact on Si(OH)4 formation in aluminosilicate refractories, however, appears to be different from that for pure silica. It is also believed that at <980°C steam reacts with siliceous components of aluminosilicate refractories and yields Si (OH)4. At >1000°C, however, the same reactants primarily yield SiO19.

*Refractories for Ammonia Production in Fertilizer Unit DOI: http://dx.doi.org/10.5772/intechopen.104934*

**Figure 12.** *Effect of steam temperature on the loss of silica by 0.84-atmosphere pressure [17].*

**Figure 13.**

*Effect of steam pressure on the volatilization of - 325 mesh 24 gm Cristabolite in 2 hours at 900°C. the steam flow rate was maintained at 2.73 cm/second [17].*

**Figure 13** illustrates the impact of steam pressure on cristobalite weight loss by virtue of Eq. 17. It is obvious from the figure that with the increase of pressure, the silica loss increases. The silica loss is related to pressure by Eq. 18 [17].

$$\text{Weight Loss, } \%= 2.02 \ge 10^{-3} \text{ P}^{1.34} \tag{18}$$

The index of pressure in Eq. 18 is >1 but <2. Indices of 1 and 2 indicate the reaction is controlled by transportation through the boundary layer and reaction at the interface, respectively. The mechanism of Si(OH)4 formation, by virtue of Silica - steam interaction (Eq. 17), thus, is not very clearly defined.

**Figure 14** illustrates the impact of steam, on the weight loss of silica in aluminosilicate refractories, in the hydrogen environment. As has been observed earlier, the weight loss increases with the increase in silica content of the refractories. When steam is present in the environment, along with hydrogen, the rate of silica reduction is reduced significantly. Additionally, silica loss shows only marginal dependence on the silica content of the refractory, when steam is present in the hydrogen

**Figure 14.** *Effect of steam on reduction of refractories with different silica content in a hydrogen atmosphere at 2400°F [18].*

environment. In short, the impact of hydrogen on silica reduction is reduced significantly when steam and hydrogen are present simultaneously. This implies that the presence of steam in the working environment, which will be the case in both the reformers, would protect the aluminosilicate refractories better by reducing the reductant effect of hydrogen.

### **3.5 Impact of simultaneous presence of hydrogen and steam on aluminosilicate refractories**

**Figure 15** illustrates the impact of the simultaneous presence of Hydrogen and steam on silica loss of various aluminosilicate refractories. It is evident from the figure that the silica loss in the hydrogen atmosphere comes to a rest, for the aluminosilicate refractories, in the presence of steam. In other words, the reduction of silica by hydrogen ceases when Hydrogen and steam are present simultaneously. The corollary of the same is the adverse impact of steam, on aluminosilicate refractories, is annulled by the presence of hydrogen.

**Figure 15.** *Impact of steam on the reduction of aluminosilicate refractories at 2500°F by hydrogen [18].*

*Refractories for Ammonia Production in Fertilizer Unit DOI: http://dx.doi.org/10.5772/intechopen.104934*

**Figure 16.**

*Effect of silica loss on strength of refractories. The reduction was carried out in the hydrogen atmosphere [16].*

**Figure 17.** *Impact of 25 mm steam on strength reduction of 85% alumina brick in a hydrogen atmosphere at 2500°F [16].*

#### **3.6 Impact of gaseous environment on the refractory characteristics**

Reduction of the siliceous component, by hydrogen, increases the refractory porosity and increased porosity adversely affects the refractory strength [15] **Figure 16** illustrates the impact of silica loss on the strength of 52% alumina bricks. As expected, the increase in silica loss leads to a greater loss of strength. A loss of 10% silica causes approximately 50% strength reduction.

As has been seen in the earlier section, the reduction of silica by hydrogen is inhibited by the presence of steam. The reduction of refractory strength, hence, is lesser in the presence of steam, compared to when Hydrogen is present by itself (**Figure 17**). It is evident from the illustration that the reduction of strength for an aluminosilicate brick is lower by approximately 20% when the hydrogen environment contains steam. In fact, during ammonia production steam is always present in the operating environment of all the units. The impact of hydrogen, as a reductant thus, is expected to be lower in all the units of the ammonia production process.

**Figure 18** illustrates the impact of alumina as well as iron content of the refractories in the CO environment. It is evident that CO by itself gets decomposed into C and CO2 as per the reverse Boudouard (Bell) reaction (Eq. 8). This is reflected by

**Figure 18.** *Impact of 1 Bar carbon monoxide atmosphere on strength for refractories with different alumina and iron content [21].*

**Figure 19.** *Effect of CO - steam on the strength of WTA based Castable at 32 bar pressure [26].*

the reduction of the strength of Fe - free 45 as well as 90% alumina containing refractories with an increase in interaction time with CO. It also is obvious from **Figure 15** that Fe acts as a catalyst for the decomposition of CO. In the presence as well as the absence of iron in the refractories, the strength reduction rate of 50% alumina refractory is faster, compared to the one containing 90% alumina [22]. 50% Alumina refractory is destroyed approximately 5 times faster than 90% alumina products. Lower alumina refractories, thus, are more vulnerable to disintegration due to CO decomposition.

Literature reports enhancement of CO disintegration in the presence of steam [26]. **Figure 19** illustrates that the strength of White Tabular Alumina (WTA) based castable reduces in CO - Steam atmosphere. On the other hand, it is observed that the presence of Nitrogen, in the CO environment, enhances the strength of WTA-based castable, that is, the trend is reversed when Nitrogen is replaced by steam. Nitrogen is an inert gas and by itself, it does not react with aluminosilicate refractories. But when it is present together with CO gas, it alters the interaction process with the refractories and enhances its strength. In fact, Nitrogen is present in the operating environment in secondary reformers and downstream. The adverse effect of steam on castable strength, in the CO environment of the ammonia production unit, is nullified to some extent owing to the simultaneous presence of nitrogen.


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HbE is the second commonest abnormal hemoglobin after sickle cell hemoglobin (HbS). HbE is common in South-East Asia, where its prevalence can reach 30–40%

The thalassemia syndromes are inherited disorders characterized by absence or markedly decreased accumulation of one of the globin subunits of hemoglobin. Individuals with thalassemia disease are not able to make enough hemoglobin,

There is two primary types of thalassemia disease: alpha (α) thalassemias and beta (β) thalassemia disease. In the α-thalassemias, there is absent or decreased production of α-globin subunits, whereas, in the β-thalassemias, there is absent or reduced production of β-globin subunits. Thalassemias affecting the production of delta (δ)- or gamma (γ)-globin subunits are also been described but are rare and not

The α-thalassemia syndromes are usually caused by the deletion of one or more α-globin genes and are sub classified according to the number of α-globin genes that

Glu-Lys and GAG-AAG). Besides being a structural variant, the E variant also causes the production of an abnormal mRNA with less b-globin being synthesized. It is synthesized at a slightly reduced rate and has a homozygous phenotype similar

life.

over 21% [26, 28, 30, 31].

could also shed light on this hypothesis [32].

*Human Blood Group Systems and Haemoglobinopathies*

in some parts of Thailand, Cambodia and in Laos [35].

to heterozygous β thalassemia [34].

*2.2.2 Thalassemia syndromes*

which causes severe anemia [24].

clinically significant disorder

are deleted or mutated [24].

There is two primary types of α-thalassemia:

*<IP_ADDRESS> α-thalassemia*

**12**

The β-thalassemias are characterized by a quantitative deficiency of β-globin chains, can be sub classified into those in which there is a total absence of normal bglobin subunit synthesis or accumulation. The βthalassemias are divided into two main varieties (β0-thalassemia, there is no β-chain production and β<sup>+</sup> thalassemia, there is a partial deficiency of β-chain production) [24]. The molecular basis of the β-thalassemias is very heterogeneous, with over 200 different mutations having been described [38]. In general, the mutations causing β-thalassemia are point mutations affecting a single nucleotide, or a small number of nucleotides, in the bglobin gene. The frequency of carriers of β-thalassemia variants is from 5 to 20% in some areas, although not as high as the frequency of α-thalassemia variants [39].


**Application of Continuous Glucose Monitoring for Assessment of Individual Carbohydrate Requirement during Ultramarathon Race**

**Kengo Ishihara 1,2,\*, Natsuki Uchiyama 1, Shino Kizaki 1, Emi Mori 1,3, Tsutomu Nonaka 4 and Hiroshi Oneda 5**

Received: 6 March 2020; Accepted: 14 April 2020; Published: 17 April 2020

**Abstract:** Background: The current study intended to evaluate the feasibility of the application of continuous glucose monitoring to guarantee optimal intake of carbohydrate to maintain blood glucose levels during a 160-km ultramarathon race. Methods: Seven ultramarathon runners (four male and three female) took part in the study. The glucose profile was monitored continuously throughout the race, which was divided into 11 segments by timing gates. Running speed in each segment was standardized to the average of the top five finishers for each gender. Food and drink intake during the race were recorded and carbohydrate and energy intake were calculated. Results: Observed glucose levels ranged between 61.9–252.0 mg/dL. Average glucose concentration differed from the start to the end of the race (104 ± 15.0 to 164 ± 30.5 SD mg/dL). The total amount of carbohydrate intake during the race ranged from 0.27 to 1.14 g/kg/h. Glucose concentration positively correlated with running speeds in segments (*P* < 0.005). Energy and carbohydrate intake positively correlated with overall running speed (*P* < 0.01). Conclusion: The present study demonstrates that continuous glucose monitoring could be practical to guarantee optimal carbohydrate intake for each ultramarathon runner.

**Keywords:** sports nutrition; continuous glucose monitoring; carbohydrate; trail running; Freestyle Libre

## **1. Introduction**

For the first time in human history, in 2019, Eliud Kipchoge ran the marathon distance in under two hours. Recent advances in the area of sports science significantly contributed to his success. In terms of exercise nutrition, it has been recommended to consume 90 g/h of carbohydrates for endurance exercise [1,2]. This amount has been suggested based on the maximum oxidation of carbohydrate as an energy substrate [3,4] and it is noted that the rate-limiting step to oxidizing this amount of carbohydrate is the gastrointestinal absorption process [1].

A longer distance marathon is known as an ultramarathon, and the popularity of these events has increased in recent years [5]. The total energy expenditure of a 160 km ultramarathon reaches about 13,000 kcal [6]. Thus, nutritional strategies have to be considered for ultramarathon runners wanting to improve their race results, but also for those focusing primarily on finishing the event.

GI distress, which is frequently experienced by runners during all types of endurance exercise, makes the current carbohydrate intake recommendation difficult to achieve [7–10]. Several observation studies have shown that carbohydrate intake during ultramarathon races is lower than the current

recommendation for carbohydrate intake. In addition to these statements and recommendations, the optimal nutritional strategies for ultramarathons have been proposed based on a baseline metabolic model [11]. It has been reported that only one study [12] achieved the carbohydrate amount suggested in the current recommendation, while others achieved less than the 60 g/h lower level of the recommendation. The lowest observed average was 31 g/h in slower runners [13].

A recently published position statement of the International Society of Sports Nutrition recommended the consumption of 150–400 kcal/h (carbohydrate, 30–50 g/h) [9]. Recent practical recommendations for ultramarathon events offered advice to consume tolerable carbohydrate intake quantities during exercise, which corresponded to 0.8–1.0 g/kg/h of carbohydrate [14]. These values were provided by comparing the race diet between fast and slow runners [13] or by comparing the carbohydrate intake of finishers and non-finishers [12].

Optimal nutrition results in a decreased risk of energy depletion, better performance [10], the prevention of acute cognitive decline, and improved athlete safety on ultramarathon courses with technical terrain or those requiring navigation [9]. However, it may prove difficult for the runner to execute the precise nutrition plan [11] and the carbohydrate requirement for ultramarathon racing varies greatly depending on the individual [9].

The aim of this study was to evaluate the feasibility of continuous glucose monitoring to improve the carbohydrate intake of ultrarunners using a continuous glucose monitoring system [15,16].

## **2. Materials and Methods**

#### *2.1. Study Design*

This observational study was designed to determine the minimum carbohydrate requirement to maintain blood glucose level and race speed during ultramarathons. All procedures were approved by the Ryukoku University Human Research Ethics Review Board (No. 2016-08-02). All research procedures complied with the code of ethics of the World Medical Association (Declaration of Helsinki). Written informed consent was obtained from all the participants before the commencement of the study.

#### *2.2. Study Population*

Seven runners (4 male and 3 female) without injuries volunteered to participate in the study. All the runners had completed 2 to 3 races certified by the International Trail Running Association and the sum of finisher's points exceeded 12 in the last 3 years, demonstrating their experience in running Ultramarathons. Participant characteristics are presented in Table 1.

**Table 1.** Clinical characteristics of male and female subjects.

Values are means ± SD (male, *n* = 4; female, *n* = 3).

#### *2.3. Race Course*

The present study was conducted during the 2019 Ultra trail Mt. Fuji (https://www.ultratrailmtfuji. com/), held during the last week of April, around Mt. Fuji in Japan (ambient temperature range: 2.3–19.9 ◦C). The distance of the course covered 165 km and the total elevation was 7942 m. The course included trails, rocks, paths, grasslands, and pavements. The course was divided into 11 segments by 10 timing gates where each runner's passing time was recorded electronically. Distances between each

timing gate were 15 ± 5.4 SD km and varied from 7 to 28 km. Running time and speed between each timing gate were obtained from the official race web site. Running time between each timing gate was 1:58 ± 0:48 and 2:18 ± 0:52 h:m for the top 5 male and female finishers, respectively. All the runners had to run with backpacks to carry necessities, including food, and they could replenish food and fluid at each timing gate.

## *2.4. Running Speed Data Collection and Standardization*

Running speed between each timing gate and overall running speed were obtained from the official race web site. The standard running speed of male and female participants (designated as 100%) for each segment were calculated by averaging the top five male and female finishers, respectively. The running speed of subjects in each segment was standardized using the following formula. The standardized running speed exceeds 100% only when running at a pace comparable to the top 1 and 2 places in each gender:

$$\begin{aligned} \text{\%Running speed} &= \text{(The subject's running speed)} / \text{(Average of top 5 finishers'}\\ &\text{running speed in each gender)} \times 100, \end{aligned} \tag{1}$$

#### *2.5. Glucose Data Collection and Standardization*

Blood glucose profile was monitored by a minimally invasive method known as flash glucose monitoring (FGM). Its details have been reported elsewhere [15,17,18]. Briefly, the FGM system (FreeStyle Libre; Abbott Diabetes Care, Alameda, CA) mechanically reads and continuously measures glucose concentration in the interstitial fluid collected from cells immediately below the skin and produces the corresponding ambulatory glucose profile. Subjects were asked to attach the device more than 1 day before the race. The FGM sensor was applied at the back of the upper arm and glucose concentrations were obtained every 15 min [17].

The glucose concentration of each runner during the race was standardized by subtracting the resting fasting glucose concentration of the runner and was expressed as an increase from resting fasting glucose level (Δglucose). The average, highest, lowest, and the difference between the highest and lowest levels of Δglucose in each segment were used as representative values in each segment (Figure 1).

**Figure 1.** Schematic presentation of the standardization of glucose levels during the race. The overall race course was divided into 11 segments (arrows) by 10 timing gates. The altitude profile of the race course (filled area) and the change of glucose level (solid line) of the first 12 h of the race is shown as a representative result. ΔGlucose level was obtained by subtracting the resting fasting glucose concentration of each runner (dashed line). \*, highest value of Δglucose in each segment; †, lowest value of Δglucose in each segment; dotted line, average value of Δglucose in each segment. Running speed (%) was calculated by dividing each runner's running speed by the average running speed of top 5 finishers.

#### *2.6. Diet Supply Data Collection*

Runners were asked to record their entire food and drink intake throughout the race. They reported the timing and volume of consumed food products and fluids based on pictures taken throughout the race. Food products and fluids consumed more than 60 min before the race start were not included in the calculation of nutritional intake. The energy and carbohydrate intake during the race were calculated based on the nutrition information provided by manufacturers. If data was not available, intakes were calculated based on the standard tables of food composition in Japan 2015 - (7th revised edition) [19]. The energy and carbohydrate intake were expressed relative to kg of pre-race body weight, per hour of running time. All foods were categorized with reference to previous research [20] as: sports drinks (isotonic and hypertonic formulas), gels, cola, other fluids (all other drinks consumed), sweets, fruits, bars, noodles, bread, rice products and other solids (all other products consumed).

#### *2.7. Statistics*

The data reported in the text, tables, and figures are presented as means and standard deviations, unless otherwise specified. Data were processed and analyzed in GraphPad Prism for Mac (version 8.3.1, GraphPad Inc., San Diego, CA, USA). Pearson's correlation coefficients were used to investigate the associations between running speed, glucose level, and carbohydrate intake. One-way ANOVA followed by Tukey's post-hoc test were used to compare the differences between each runner's blood glucose level. Results were considered significant when *P* < 0.05.

#### **3. Results**

## *3.1. General Results*

The running speed of the participants ranged from 3.90 to 7.22 km/h with a standardized running speed ranging from 49.0% to 90.1%.

#### *3.2. Relationship between Glucose Level and Running Speed*

All participants were within the expected normoglycemic range during exercise (72–252 mg/dL) with the exception of one participant who exhibited a lowest value of 61.9 mg/dL as shown in Table 2. Carbohydrate mainly supplied total energy intake during the race (77.6 ± 8.58SD% of total energy intake).

**Table 2.** The total energy and nutrient intake, and glucose concentration during the ultramarathon.

FS and MS, female and male standard running speed, which correspond to the average of the top 5 finishers in each sex. F, female; M, male.

Each runner consumed carbohydrates from liquids, gels, fruits, sweets or solids as shown in Table 3. Six of 7 runners consumed more than 55% of their carbohydrates from liquids and gels (55.3% to 74.8%) except for one runner (28.4%, subject 3). Carbohydrate intake from solids ranged from 21.1% to 42.8% in the six runners and 63.8% in the other runner, who showed the highest fat intake among 7 runners (subject 3).

**Table 3.** Carbohydrates consumed per product type (g/kg/h).

Subject numbers are identical to Table 2. The subtotal of each category is shown in bold.

The average, highest, lowest, and the difference between the highest and lowest levels of Δglucose in 11 segments were subjected to correlation analysis between running speed and blood glucose level. Figure 2 shows the relationship between glucose level and running speeds in each segment. The lowest (r2 = 0.2397, *P* = 0.0028; r2 = 0.1397, *P* = 0.0501 for male and female, respectively) and average (r2 = 0.1650, *P* = 0.0155; r2 = 0.0531, *P* = 0.2381 for male and female, respectively) levels of Δglucose had a significant positive correlation with running speed, but not for the highest levels of Δglucose (r2 = 0.0005, *P* = 0.8952; r2 = 0.0125, *P* = 0.5704 for male and female, respectively) in male runners. Similar but not significant tendencies were observed in female runners. Interestingly, a significant inverse correlation (r2 = 0.1198, *P* = 0.0417; r2 = 0.0107, *P* = 0.6011 for male and female, respectively) was observed between running speed and the difference between highest and lowest (D) in male runners.

**Figure 2.** Scatter plots showing relationships between glucose level and running speed. The lowest (**A**), average (**B**), highest (**C**), and difference between highest and lowest (**D**) value of Δglucose levels were calculated as described in Figure 1. Each plot indicates one segment.

#### *3.3. Relationship between Energy and Carbohydrate Intake and Running Speed*

Energy intake exhibited a significant positive correlation with running speed (r2 = 0.8142, *P* = 0.0054). Energy intake ranged from 1.41 to 5.40 kcal/kg/h, which is the equivalent of 86.2 to 226.7 kcal/h. A significant correlation was also found between carbohydrate intake and running speed (r2 = 0.7955, *P* = 0.0070). Carbohydrate intake ranged from 0.27 to 1.14 g/h/kg (1.1 to 4.6 kcal/h/kg), which is the equivalent of 16.3 to 52.9 g/h. The energy intake from carbohydrates contributed 63% to 87% of the total energy consumed during the race. No significant correlations were observed between running speed and energy intake from protein and fat (Figure 3).

**Figure 3.** Scatter plots showing relationships between nutrient intake and running speed. The intake of energy (**A**), carbohydrate (**B**), protein (**C**), and fat (**D**) were calculated based on consumed food products and fluids. Each plot indicates one runner.

#### *3.4. Relationship between the Amount of Carbohydrate Intake and Maintenance of Glucose Level during Race*

Carbohydrate intake of the seven participants varied within the range of 0.27 to 1.14 g/kg/h and the carbohydrate intake of four subjects (0.27, 0.28, 0.34, and 0.64 g/kg/h) were less than the recently published practical recommendations for ultramarathons. The lowest Δglucose levels of the four subjects were 55.5%, 27.2%, 54.3%, and 66.9% compared to that of the subject who consumed 0.85 g/kg/h, respectively (*P* < 0.05). Likewise, the average level of Δglucose of the four subjects were 48.2%, 68.6%, and 73.6% compared to that of the subject who consumed 0.85 g/kg/h, respectively (*P* < 0.05). Runners who consumed 1.04 or 0.28 g/kg/h of carbohydrate showed higher values in the highest Δ glucose levels and the difference between the highest and lowest blood glucose among seven runners, which seemed to be their specific characteristics (*P* < 0.05, Figure 4).

**Figure 4.** Relationships between carbohydrate intake and the lowest (**A**), average (**B**), highest (**C**), and the difference between the highest and lowest (**D**) value of Δglucose levels. Carbohydrate intake of each runner is expressed in the X-axis. Data are expressed using box-and-whisker plots to indicate the minimum, first quartile, median, third quartile, and maximum. Bar height indicates the average of the dots. Values without common superscript are significantly different, *P* < 0.05

#### **4. Discussion**

The aim of this study was to evaluate the feasibility of continuous glucose monitoring to improve the carbohydrate intake [9,13] of ultrarunners using a continuous glucose monitoring system. Overall carbohydrate intake in three of seven subjects were far below the recommended carbohydrate intake (30–50 g/h or 0.8 g/kg/h). A significant positive relationship was observed between higher carbohydrate intake and faster running speed as was expected from the results of previous studies [12,13]. The present study demonstrates that the avoidance of relatively low blood glucose concentrations, achieved through the intake of sufficient carbohydrates, impaired running speed during the ultramarathon. Conversely, there was no association between the highest blood glucose concentrations obtained with running speed, indicating that control of glucose homeostasis, rather than the rapid availability of carbohydrates, is the key determinant of performance. Runners consuming less than 0.8 g/kg/h of carbohydrates tended to have a reduced running speed associated with a result of low blood glucose.

Carbohydrate intake of 30–60 g/h is an established recommendation for endurance sports, with even higher amounts (i.e., up to 90 g/h and a glucose:fructose ratio of 2:1) being advocated for exercise bouts lasting more than 3 h [1,2]. However, there is a disparity between this recommendation and actual intakes in ultramarathon runners. Observation studies have demonstrated that actual carbohydrate intake during ultramarathons is less than 60 g/h in most runners [6,13,21], including slower runners consuming 37 g/h [14], with very few runners taking more than 60g of carbohydrates [22,23]. There are numerous barriers to achieve consumption of 90 g/h of a multiple-transportable carbohydrate blend. First, the absolute exercise intensity of an ultramarathon is not as high as some other endurance activities

because of its extremely long duration (6, 13, 24, 48, 72 h, 6 or 10 days) [24]. Secondly, the rate-limiting step for oxidizing 90 g of carbohydrate per hour is intestinal absorption which may be affected by undertaking exercise of this intensity and duration due to changes in splanchnic blood flow. In addition, ultramarathon runners lose appetite as a result of heat, endotoxin, or vertical shaking of their digestive system during rough terrain races [24–26]. Thirdly, a practical limitation is that ultramarathon runners have to carry their food and fluid in their backpacks during long hours of racing, resulting in an increase in exercise intensity due to the additional weight being carried [14]. Fourthly, runners may have physical difficulties in consuming foods when they are keeping balance with both hands when running down steep mountains or climbing steep slopes.

For these reasons, discrepancies easily occur between the recommended amount and the actual amount of carbohydrate intake. However, the optimal amount of carbohydrate varies greatly depending on the individual [9]. Therefore, the application of a continuous glucose monitoring system could be a practical and fast method to estimate optimal carbohydrate intake for each runner.

Given the duration typical of ultramarathons (6 to 48 h), it is not feasible to meet carbohydrate consumption in its entirety during a race. Energy deficiency is common in ultramarathons [8–10,12,13,20,21,27]. Several studies using a doubly labeled water technique or respiratory gas analysis have estimated that energy expenditure during ultramarathons is about 13000 kcal [6,28,29]. The amount of carbohydrates consumed during a 160 km ultramarathon can be speculated from indirect calorimetry. The respiratory exchange ratio was 0.91 during the first 64.5km of the 160km race [29] and was 0.85 immediately after the 330km race [30]. Therefore, carbohydrate oxidation likely provided 50.0%–68.3% of energy expenditure, which is equal to 6500–9100 kcal (1625–2275 g) in the 160 km race.

Gluconeogenesis and hepatic glycogenolysis play an important role to maintain blood glucose levels during prolonged exercise in a fasted or carbohydrate deficient status. Previous studies have reported rates of gluconeogenesis and hepatic glycogenolysis as 0.07 g/kg/h and 0.03 g/kg/h, respectively, in a resting state in low carbohydrate-fed subjects [31]. The sum of these two values (0.1 g/kg/h), endogenous glucose production, would be the minimum amount of carbohydrate required to maintain blood glucose during a resting state. The endogenous glucose production significantly increases to 0.36 g/kg/h during exercise at 55% of peak power output [31] or to 0.48 g/kg/h during exercise at the lactate threshold level in fasted, well trained subjects [32]. Consistently with these findings, three subjects in the present study with a carbohydrate intake of less than 0.48 g/kg/h could not maintain their blood glucose concentrations during the ultramarathon race.

The main limitation of this study is the small number of participants. The present study supports the effectiveness of a recently published position statement of the International Society of Sports Nutrition [10] and practical recommendation for ultramarathon participants to prevent hypoglycemia during exercise. Relationships among carbohydrate intake, the lowest Δglucose, and running speed are relevant in male runners rather than female runners. These observations coincide with the previously reported gender-specific differences in fuel utilization during exercise. Women showed higher lipid oxidation caused by higher plasma adiponectin [33], higher muscle triglyceride utilization [34], low plasma glucose [35], and higher fasting hepatic glucose uptake [36] compared to men. However, more subjects are required to conclude that the observed differences between male and female runners were derived from gender-specific factors.

Hydration and GI distress are negligible factors affecting running speed. Hydration is a factor causing GI distress [37], but these factors could not be standardized in the study. Dehydration issues were not observed, which may be associated a steady rain during the race. These two factors should be quantitatively assessed and statistically analyzed as a factor affecting running speed in larger numbers of participants.

The insufficient standardization of food intake before and during the race is another limitation. The following factors should be appropriately controlled in future research: pre-race meals within 48 h of the start of the race, caffeine intake, gastrointestinal distress, and objective recording of food and drink intake by action cameras as reported [20].

Another limitation of this study is a slower rise and generally lower glucose peak values in the FGM system used in the present study as compared with the blood sampling, and this may underestimate the effect of carbohydrate ingestion on glucose response [18]. Nevertheless, the non-invasive and fast understanding of fluctuations of glucose level according to the specific characteristics of each athlete would be useful to plan and modify a personal nutrient strategy during an ultramarathon race.

The other limitation of the present study was large fluctuations in running speed in the ultramarathon. The running speeds in 11 segments varied in a range of 5.5 to 14.3 km/h and 4.8 to 11.8 km/h even in top five male and female runners, respectively. We speculated that these fluctuations in running speed were mainly associated with two factors: terrain [26] and physiological changes such as muscle fatigue and energy deficiency. Therefore, the running speeds of the subjects were standardized using the top 5 finishers to explore the relationship between blood glucose levels and running speed. The precise and objective power meters for running, which are already applicable in cycling studies [38], or accurate physical workload calculation based on GPS monitoring, would enable more accurate analysis between running performance and blood glucose.

#### **5. Conclusions**

In conclusion, the present study demonstrates that continuous glucose monitoring could be practical to guarantee optimal carbohydrate intake for each ultramarathon runner. Decreases in blood glucose during ultramarathons may be attributed to many factors, including sub-optimal carbohydrate intake. Additionally, individual characteristics such as the sex, age, or energy intake of each runner may have had a greater influence on blood glucose fluctuations across the race; thus, utilizing a continuous glucose monitor may help inform better race nutrition strategies.

**Author Contributions:** Conceptualization, K.I.; methodology, K.I., E.M. and T.N.; software, K.I., N.U. and S.K.; validation, K.I. and T.N.; formal analysis, K.I.; investigation, K.I., N.U., S.K. and T.N.; resources, K.I. and H.O.; data curation, K.I., N.U., S.K. and T.N; writing—original draft preparation, K.I.; writing—review and editing, K.I. and T.N.; visualization, K.I. and T.N.; supervision, K.I.; project administration, K.I.; funding acquisition, K.I. and H.O. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was funded by RYUKOKU University and Nagatasangyo.co. (Hyogo, Japan).

**Acknowledgments:** We express our gratitude and deep appreciation to Gethin H. Evans, PhD in Manchester Metropolitan University for supporting academic writing. We thank Masumi Nakao, Yuko Koshiba, Shin Uehira and Yasuhito Okumura for recruiting participants. We also thank all the participants for their cooperation in the investigation.

**Conflicts of Interest:** The author declare that his study has been financed by RYUKOKU university and Nagatasangyo.co. (Hyogo, Japan). They did not participate in the experimental design, data collection, data analysis, interpretation of the data, writing of the manuscript, or in the decision to publish the results.

#### **References**

© 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).


**3. Results**

112 Biodiversity Conservation and Utilization in a Diverse World

The amplification reaction used for amplification of the D-loop fragment was also used (with little modifications in temperature cycling) in the other experiments according to the

The amplification reaction was carried out in a 25 μl reaction mixture consisting of 1.25 unit Taq polymerase (DyNAzyme), 1X enzyme buffer (1X is 10 mM Tris-HCl, pH 8.8 at 25 0C, 1.5 mM MgCl2, 50 mM KCl and 0.1% Triton X-100) supplied by the manufacture, 1 μM of each forward and reverse primer, 0.2 mM dNTPs and 100 ng of DNA. The reaction mixture was overlaid with sterile mineral oil and was run in an MJ research PTC-100 Thermocycler. The temperature cycling was as follows: 30 cycles of 45 seconds at 94°C; 1 minute at 58°C and 1 minute at 71°C, followed by a final extension at 71°C for 5 minutes. All PCR amplifications included a negative control reaction which lacked template DNA. No product was seen in any negative control. Small quantities of the reaction products (5 μl each) were used for electrophoresis with an appropriate size marker on 1.5% agarose in 1X-Tris acetate buffer

After electrophoresis the gels were stained with ethidium bromide and were examined with UV lamp at a wave length 312 nm to verify amplification of the chosen specific fragment. The PCR products were purified using QIAquick PCR purification kit (Qiagen, Inc.) and the resulting purified products were used in the subsequent sequencing reactions. Sequencing was performed on an Applied Biosystems 310 genetic analyzer (Applied Biosystem) using Big Dye terminator cycle sequencing ready reaction mixture according to manufacturer's

Pairwise sequence alignments were carried out using NCBI-BLASTN 2.2.5 version & PSI BLAST. Multiple sequence alignments were done using the MUSCLE 3.6 software and CLUSTALW (1.82). Analysis, manipulation, conservation plots, positional entropy plot and conserved region analysis was done using the BIOEDIT package. Variable sites were

Phylogenetic model selection was done using the FINDMODEL server available from the HCV LANL database at (http://hcv.lanl.gov/ /content/hcv-db/findmodel/). A Bayesian phylogenetic tree was constructed by Markov chain Monte Carlo (MCMC) method as implemented in the MR BAYES 3.1 package using the Hasegawa-Kishino-Yano plus Gamma model HKY+G substitution model with an invariant four category gamma distribution among sites. A 50% consensus tree was generated and the analysis was repeated two times. Maximum parsimony tree was conducted using MEGA version 4, with 1000 bootstraps for

extracted from the multiple sequence alignment using the MEGA 3.1 package **[12]**.

**2.3. The amplification reaction**

conditions of each experiment.

instructions (Applied Biosystems).

**2.5. Phylogenetic analysis**

reliability.

**2.4. Sequence analysis and multiple sequence alignment**

(TAE).

Our experience in the field of molecular identification or DNA barcoding through a series of published research papers are represented in this section Results with some illustrated figures and tables are represented here but the complete information could be obtained through obtaining the complete published papers from the publication section.

Shows the Positional entropy plot of the D-loop for the buffalo, and cow sequences The Bayesian phylogenetic trees of cow and buffalo sequences were constructed using MRBAYES software (Figure 2) and Maximum parsimony tree using the Kimura twoparameter model and the closest neighbor interchange method of the MEGA 3.1 software package (Figure 3). Table 1. Shows the Substitution events detected in complete D-loop sequences from multiple sequence alignments between cows and buffaloes.

**Figure 1.** Positional Entropy Plot of the D loop for the Buffalo, and cow sequences. The X axis is the nucleotide position, while the Y axis is the entropy (lack of information) at that position. The central region is mainly conserved, and the beginning and end regions are highly variable (shaded areas).

Biological Identifications Through DNA Barcodes 115

**Figure 3.** Maximum parsimony tree constructed using the Kimura two-parameter model and the closest neighbor interchange method of the MEGA 3.1 software package. The numbers show the percentage of

bootstrap confidence.

**Figure 2.** Bayesian phylogenetic tree of the cow and buffalo sequences using the MRBAYES software.

**Figure 2.** Bayesian phylogenetic tree of the cow and buffalo sequences using the MRBAYES software.

**Figure 3.** Maximum parsimony tree constructed using the Kimura two-parameter model and the closest neighbor interchange method of the MEGA 3.1 software package. The numbers show the percentage of bootstrap confidence.

116 Biodiversity Conservation and Utilization in a Diverse World

Biological Identifications Through DNA Barcodes 117

**Figure 5.** Phylogenetic tree constructed between the Egyptian and GenBank database chicken samples.

Sample DQ629875 was used as out-group for its high diversity.

**Table 1.** Substitution events detected in complete D-loop sequences from multiple sequence alignment between Cows (35 animals) and buffaloes (53 animals).

Shows the PCR amplification of chicken mitochondrial D loop fragments while the phylogenetic tree constructed between the Egyptian and GenBank database chicken samples is represented in Figure 5. The Polymorphic sites and their positions are shown in Table 2.

**Figure 4.** PCR amplification of chicken mitochondrial D loop fragment. The PCR reactions were run on 1% agarose gel, stained with ethidium bromide and examined with UV. Samples from 1 to 4 for Dandarawi breed and samples from 5 to 8 for Fayoumi breed. The *Hae* III digest of Ф X174 DNA was used as ladder (1353, 1078, 872, and 603 base pairs).

Total number of indels (insertions/

between Cows (35 animals) and buffaloes (53 animals).

used as ladder (1353, 1078, 872, and 603 base pairs).

Animals Cows (35) Buffaloes (53) Cows &

Substitutions and genetic distances Value S E Value S E Value S E

Genetic distance between the two groups 0.156 0.016 Overall (all animals) distance 0.06 0.006

**Table 1.** Substitution events detected in complete D-loop sequences from multiple sequence alignment

Shows the PCR amplification of chicken mitochondrial D loop fragments while the phylogenetic tree constructed between the Egyptian and GenBank database chicken samples is represented in Figure 5. The Polymorphic sites and their positions are shown in Table 2.

**Figure 4.** PCR amplification of chicken mitochondrial D loop fragment. The PCR reactions were run on 1% agarose gel, stained with ethidium bromide and examined with UV. Samples from 1 to 4 for Dandarawi breed and samples from 5 to 8 for Fayoumi breed. The *Hae* III digest of Ф X174 DNA was

Total numbers of transitions 44 20 100 Total numbers of transversions 4 1 61

deletions) 1 7 15

Total number of substitutions 49 28 176 R ratio (transversions/transitions) 0.09 0.05 0.61

Genetic distance within group 0.023 0.003 0.007 0.002

buffaloes (88)

**Figure 5.** Phylogenetic tree constructed between the Egyptian and GenBank database chicken samples. Sample DQ629875 was used as out-group for its high diversity.

Biological Identifications Through DNA Barcodes 119

**Figure 7.** Agarose gel representing the PCR-amplified product of ITS. M: 50 bp DNA size marker, lane

5'CAATCTAATAAGTTTCAGACTTTCGGGGCTTTGGGTATAATTTATGCTATATTGTCTATTGGG ATTTTAGGGTTTATTGTATGAGCTCATCATATATTTACAGTAGGAATAGATGTTGATTCTCGAG CTTATTTTACCGCTGCTACGATAATTATTGCGGTACCTACAGGTATTAAGGTTTTTTCTTGATT ATCTACAATGTATGGGTCAGTAGTAAAGTGAGGAGTAGTTGTTTTGTGAAATTTTGGTTTTATT

**Figure 8.** Sequence of the mitochondrial gene fragment coding for cytochrome oxidase subunit I(COI)

5'GNCACCTGATTTAGATCACGTCATAATGGTTTGTTTTGGTTACTCTTGTGACCAAGAGAATT TATACCATTGCATGGCGATACCGACGAGGGCTGCAGCAAAGGTAAATACGTATGATACGGTT TATATACTCGAAACAAATGTAGCACAGATACAGTAAGTGTCCGTGCTGAAATTTTCATTCAA AAACACAATGCTCACAAAATTTCACAACTCACATCAATTTCCACAAATTACTATGTTTTTCAT CGATTTAAGGACTAAGTGATCCCCCATATTGAGTCTTGGTTTTTTTCTTCTCGATAGCACAACT TACTTCCAAAGGAAGTGAAAAGGTTTGTCGGAATGGTTACCGACTTACTGTCGCATACGCCTT CCCCGTAACCAAAAGGGACCGGTTAAACACCCACCAATTCGAAGCGGTTGTCGCCCAAAAA GGGACCGGTTTGGAAGACCGTTATCCACCGTTAAAACCTAAATTACAGGTTTGTTTTTTTCAT

TTTTTATTCAGGTTAGGTGGTTTAACCGGGGTGGTTTTGTCAAA3'

**Figure 9.** Sequence of the internal transcribed spacers (ITS) region in the predacious mite*, Amblyseius*

1: ITS fragment and lane 2: blank.

in the predacious mite, *Amblyseius swirskii.*

TGGTCCTTTTTCGAAAAAACACAN3

*swirskii.* N: unknown base.

**Table 2.** The Polymorphic sites and their positions. The nucleotide positions were given with respect to the Egyptian nucleotide numbers in GenBank database. The left column shows the breeds with their accession numbers.

The following Figures: show polyacrylamide gel representing the PCR-amplified fragment of CO1 and its sequence (Figures 6 & 8) while the PCR-amplified fragment of ITS region and its sequence were presented in Figures 7 & 9.

**Figure 6.** 10% polyacrylamide gel representing the PCR-amplified product of CO1. M: 50 bp DNA size marker, lane 1: CO1 fragment and lane 2: blank (PCR cocktail without DNA).

its sequence were presented in Figures 7 & 9.

accession numbers.

 The Polymorphic sites and their positions. The nucleotide positions were given with respect to the Egyptian nucleotide numbers in GenBank database. The left column shows the breeds with their

The following Figures: show polyacrylamide gel representing the PCR-amplified fragment of CO1 and its sequence (Figures 6 & 8) while the PCR-amplified fragment of ITS region and

**Figure 6.** 10% polyacrylamide gel representing the PCR-amplified product of CO1. M: 50 bp DNA size

marker, lane 1: CO1 fragment and lane 2: blank (PCR cocktail without DNA).

**Figure 7.** Agarose gel representing the PCR-amplified product of ITS. M: 50 bp DNA size marker, lane 1: ITS fragment and lane 2: blank.

5'CAATCTAATAAGTTTCAGACTTTCGGGGCTTTGGGTATAATTTATGCTATATTGTCTATTGGG ATTTTAGGGTTTATTGTATGAGCTCATCATATATTTACAGTAGGAATAGATGTTGATTCTCGAG CTTATTTTACCGCTGCTACGATAATTATTGCGGTACCTACAGGTATTAAGGTTTTTTCTTGATT ATCTACAATGTATGGGTCAGTAGTAAAGTGAGGAGTAGTTGTTTTGTGAAATTTTGGTTTTATT TTTTTATTCAGGTTAGGTGGTTTAACCGGGGTGGTTTTGTCAAA3'

**Figure 8.** Sequence of the mitochondrial gene fragment coding for cytochrome oxidase subunit I(COI) in the predacious mite, *Amblyseius swirskii.*

5'GNCACCTGATTTAGATCACGTCATAATGGTTTGTTTTGGTTACTCTTGTGACCAAGAGAATT TATACCATTGCATGGCGATACCGACGAGGGCTGCAGCAAAGGTAAATACGTATGATACGGTT TATATACTCGAAACAAATGTAGCACAGATACAGTAAGTGTCCGTGCTGAAATTTTCATTCAA AAACACAATGCTCACAAAATTTCACAACTCACATCAATTTCCACAAATTACTATGTTTTTCAT CGATTTAAGGACTAAGTGATCCCCCATATTGAGTCTTGGTTTTTTTCTTCTCGATAGCACAACT TACTTCCAAAGGAAGTGAAAAGGTTTGTCGGAATGGTTACCGACTTACTGTCGCATACGCCTT CCCCGTAACCAAAAGGGACCGGTTAAACACCCACCAATTCGAAGCGGTTGTCGCCCAAAAA GGGACCGGTTTGGAAGACCGTTATCCACCGTTAAAACCTAAATTACAGGTTTGTTTTTTTCAT TGGTCCTTTTTCGAAAAAACACAN3

**Figure 9.** Sequence of the internal transcribed spacers (ITS) region in the predacious mite*, Amblyseius swirskii.* N: unknown base.

Sequencing of a fragment of ITS region (ITS1, ITS2, 5.8S) indicated almost complete identity of the Egyptian samples with *Neoseiulus swirskii,* accession number EU 310505 (= *A. swirskii*). Regardless the locality within Egypt, taxa were less identical when compared to the related species *N. Cucumeris (*Oud*.) N. andersoni* (Chant), and *N. fallacis* (Gar-man).

Biological Identifications Through DNA Barcodes 121

nucleotide Base position

Thymine position 172

Guanine position 158 Cytosine position 267 Adenine position 293

Thymine positions 32 and 267 Guanine position 293 Cytosine position 72

Guanine positions 193, 266 and 273

Thymine positions 26, 36, 186 & 190 Cytosine positions 253, 294 and 295.

Guanine positions 172, 231 and 300

Adenine 27 and 174

Adenine position 190 Thymine position 271 Cytosine position 349 insertion of Adenine position 164-165 deletion of Adenine position 259

Guanine position 299 Thymine positions 260, 261 and 317 Cytosine positions 225 and 259 deletion of Adenine deletion at position 273

**Table 4.** Nucleotide variation in a specific 12S rRNA gene fragment of four studied Bovidae species. Nucleotide positions correspond to Egyptian buffaloes GenBank accession numbers (FJ828575-FJ828583).

Considering multiple alignment results between homologous 16S rRNA sequences obtained from GenBank database with the reference sequence, it was shown that, the entire 16S rRNA fragment (422 bp. in size) contains more than 57 variable sites (from base no. 21 to base no. 323) inside the two conserved regions. The bases outside this variable region are completely conserved in the four species (Figure 10 and Table 5). From these variable sites, 25 specific nucleotides were chosen (which gave clear significant results in both types of alignment comparisons (two and multiple alignment sequences programs) as a reference for identification of unknown species (from base no. 21 to base no. 308). It was also shown that the size of the amplified fragments were less by one nucleotide (421 bp) in case of goat and

Detection of specific variable sites between Egyptian buffalo 16S rRNA gene fragment and the other studied three species is shown to be a good marker for identification of the four studied species. The detected variable sites can be classified as represented in both Fig. 10 and Table 5.

Guanine at positions 110, 132 and 196

Cytosine positions 71, 269, 271 and 348.

No. of

6 + 2

6 + 1

SNPs Specificity Representing

8all buffaloes (haplotypes 1 & 2)

3 buffalo haplotype 1 only

4 buffalo haplotype 2 only

12 cattle (*Bos taurus*)

indels sheep (*Ovis aries)*

deletion goat (*Capra hircus)*

two nucleotides (420 bp) in case of both cattle and sheep.

According to the molecular analysis (Table 3), samples are grouped into three groups.

**Table 3.** The variable sites (a = Adenine, c = Cytosine, g = Guanine, t = Thymine, - = deletion and n = Not detected) detected in a fragment of nuclear ITS region of six samples of *A. swirskii* collected from citrus and grapes in the Nile delta of Egypt.

The results of 12S rRNA showed that, two haplotypes of 12S rRNA sequences were identified from the multiple alignment results between the nine tested Egyptian buffalo sequences and other examples of homologous buffalo sequences selected from GenBank database. Two buffalo haplotypes were revealed, of which haplotype 1 which include Egyptian buffaloes and haplotype 2 which include Chinese swamp buffalo; breed: Haikou (accession AY702618), Mediterranean (accession AY488491) and *Bubalus bubalis* (accession AF231028). The detected SNPs can be classified as shown in table 4.

Eleven SNPs were detected which can be used to discriminate between subfamily Bovinae, represented by buffalo and cattle and the subfamily Caprinae represented by sheep and goat.

Nucleotide number (Accession No. EU924213)

Sample Accession

Sample 3 EU924213

Sample 1 EU924212

Sample 5 EU924216

goat.

Number

citrus and grapes in the Nile delta of Egypt.

Sequencing of a fragment of ITS region (ITS1, ITS2, 5.8S) indicated almost complete identity of the Egyptian samples with *Neoseiulus swirskii,* accession number EU 310505 (= *A. swirskii*). Regardless the locality within Egypt, taxa were less identical when compared to the related

According to the molecular analysis (Table 3), samples are grouped into three groups.

2 3 3 3 3 5 8 8 1 4 5

Sample 4 EU924214 c c c a c c a t a c c a g t c a t a c c - a g - a g

Sample 2 EU924215 a a t a t c a t a - c a g t c a t a c c - a g - a g

*N. swirskii* EU310505 a a t - t g t - - - t - t t t t - t - t t t t t a t

Sample 6 EU924217 n n n - t g t - - - t a g a t a - c - t t a t - g t

The results of 12S rRNA showed that, two haplotypes of 12S rRNA sequences were identified from the multiple alignment results between the nine tested Egyptian buffalo sequences and other examples of homologous buffalo sequences selected from GenBank database. Two buffalo haplotypes were revealed, of which haplotype 1 which include Egyptian buffaloes and haplotype 2 which include Chinese swamp buffalo; breed: Haikou (accession AY702618), Mediterranean (accession AY488491) and *Bubalus bubalis* (accession

Eleven SNPs were detected which can be used to discriminate between subfamily Bovinae, represented by buffalo and cattle and the subfamily Caprinae represented by sheep and

**Table 3.** The variable sites (a = Adenine, c = Cytosine, g = Guanine, t = Thymine, - = deletion and n = Not detected) detected in a fragment of nuclear ITS region of six samples of *A. swirskii* collected from

1 4 6 1 4 7

Group ITS1 5.8S rRNA ITS2

1 4 8 1 4 9 1 6 3 1 8 2 2 1 6 2 2 3 2 4 9

c c c a c c a t a c c a g t c a t a c c - a g - a g

a a t - t g t - - - t - t t t t - t - t t t t t a -

n a t - t g t - - - t a g a t t - c c t t a t g g t

2 6 2 2 7 1 2 7 4 2 8 7 3 0 0 3 0 4

3 1 4 3 1 5

3 2 3 3 3 2

species *N. Cucumeris (*Oud*.) N. andersoni* (Chant), and *N. fallacis* (Gar-man).

Phylogenetic

Group 1

Group 3

Group 2

AF231028). The detected SNPs can be classified as shown in table 4.

**Table 4.** Nucleotide variation in a specific 12S rRNA gene fragment of four studied Bovidae species. Nucleotide positions correspond to Egyptian buffaloes GenBank accession numbers (FJ828575-FJ828583).

Considering multiple alignment results between homologous 16S rRNA sequences obtained from GenBank database with the reference sequence, it was shown that, the entire 16S rRNA fragment (422 bp. in size) contains more than 57 variable sites (from base no. 21 to base no. 323) inside the two conserved regions. The bases outside this variable region are completely conserved in the four species (Figure 10 and Table 5). From these variable sites, 25 specific nucleotides were chosen (which gave clear significant results in both types of alignment comparisons (two and multiple alignment sequences programs) as a reference for identification of unknown species (from base no. 21 to base no. 308). It was also shown that the size of the amplified fragments were less by one nucleotide (421 bp) in case of goat and two nucleotides (420 bp) in case of both cattle and sheep.

Detection of specific variable sites between Egyptian buffalo 16S rRNA gene fragment and the other studied three species is shown to be a good marker for identification of the four studied species. The detected variable sites can be classified as represented in both Fig. 10 and Table 5.

Biological Identifications Through DNA Barcodes 123

Guanine (36, 189, and 297)

Thymine (190 and 221)

Guanine (252 and 254) Thymine (227) Cytosine (50).

Thymine (102, 129 and 249)

Cytosine (298 and 308)

Adenine (298) Cytosine (102, 129 and 249) Thymine (308)

Guanine (119, 171, and 251) Adenine (122) Thymine (167) Cytosine (295 and 301)

Thymine (29) Cytosine (21 and 182)

Specificity Representing nucleotides Base position

Three Cattle (*Bos taurus*) Guanine (55)

**Table 5.** Nucleotide variation in a specific 16S rRNA gene fragment of four studied Bovidae species. Nucleotide Positions correspond to Egyptian buffaloes GenBank accession numbers (FJ748599–FJ748607)

The ability of molecular trees to encompass both short and long periods of time is based on the observation that different genes evolve at different rates. The DNA specifying ribosomal RNA (rRNA) changes relatively slowly, so comparisons of DNA sequences in these genes are useful for investigating relationships between taxa that diverged hundreds of millions of years ago. Studies of the genes for rRNA have shown, for example, that fungi are more closely related to animals than to green plants—something that certainly could not have

In contrast, the DNA in mitochondria (mtDNA) evolves relatively rapidly and can be used

The methodology used in DNA barcoding has been straightforward. Sequences of the barcoding region are obtained from various individuals. The resulting sequence data are then used to construct a phylogenetic tree using a distance-based 'neighbour-joining' method.  The term 'DNA barcode' seems to imply that each species is characterized by a unique sequence,

**4.1. DNA barcoding, genome evolution & phylogenetic trees**

No. of Variable sites (SNPs)

Five

Five

**4. Discussion**

Six River buffaloes

Seven Sheep (*Ovis aries*)

Four Goat (*Capra hircus*)

Group one (river buffaloes and cattle, Subfamily Bovinae)

Group two (sheep and goat, Subfamily Caprinae).

been deduced from morphological comparisons alone.

to investigate more recent evolutionary events.

**Figure 10.** Multiple sequence alignments result showing the total variable sites between river buffalo (BBU), Cattle (Bost, Bosi and JBC), Sheep (Ovis) and goat (Capra) in the specific 16S rRNA fragment. Sequences of the 16S rRNA fragment of Egyptian buffaloes (FJ748599–FJ748607). Differing nucleotides are noted (T, A, G and C)

**Table 5.** Nucleotide variation in a specific 16S rRNA gene fragment of four studied Bovidae species. Nucleotide Positions correspond to Egyptian buffaloes GenBank accession numbers (FJ748599–FJ748607)