Optimization Of Multienzyme Production Biology Essay

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The production of crude cellulosic and hemicellulosic enzyme Endoglucanase, Xylanase, and Mannanase by Aspergillus Terreus K1 in solid state fermentation on Palm kernel expeller (PKE) was optimized by the response surface methodology (RSM). The results shows that four factors had significant effect on the enzyme production (P < 0.05), that is the temperature, moisture, inoculum concentration, and initial pH. Employing PKE as solid substrate, maximum endoglucanase, mannanase, and xylanase was obtained (17.37 U/g, 41.23 U/g, and 53.14 U/g respectively) at 29.3 oC, 68.8% moisture, pH 4.9, and 6% inocula). These activities being close to the predicted (18.59 U/g, 42.40 U/g, and 52.79 U/g respectively). Verification of individual optimization shows that a maximum production of 18.10 U/g endoglucanase, U/g Mannanase and U/g Xylanase is possible when PKE were fermented under optimum condition.

Keywords: Solid state fermentation, Aspergillus terreus, Palm Kernel Expeller, Responce surface methodology

Introduction

Feed resources represent a major component of economic animal production in Asia as the use of certain feed resources (e.g. soybean and cereal) in animal diets creates a competitive conflict with human nutrition (Vasta et al., 2008). Thus, the introduction of agro-industrial by-products as alternative with the aim for a realistic potential level of production and the preservation of animal health is a necessity in sustainable animal production. PKE, the main by-product of the palm oil industry (PKE) shows great potential as Malaysia is the major palm oil exporter (MPOB, 2012). PKE is a good source of energy and protein for ruminants but it is sparingly used in poultry feeds (up to 20% by weight) (Chong et al., 2003; Soltan, 2009) as it contains high level NSPs, mainly mannans, and small amount of cellulose, xylose and other polysaccharides (Jaafar and Javis, 1992). This suggests that at least three main cellulolytic and hemicellulolytic enzymes are needed to improve the nutritive value of PKE. These enzymes are used to digest the internal glycosidic bonds of cellulose, mannan and xylan resulting in a conversion to mannose, glucose and xylose, respectively. Previous study by Ng et al. (2002) and Saen et al. (2011) had showed that commercial enzyme can be used to degrade these fibrous compounds with an increase the monosaccharide content as well as metabolize energy. However, most of these enzyme used in livestock production are imported and are usually design to target specific target (Ibrahim 2008)

Bioprocessing and discoveries of more robust tropical enzyme-producing fungi renders its industrial application more feasible and economical. Filamentous fungi, being excellent decomposers of vegetal material, have been widely used to produce hydrolytic enzymes for industrial application. Those belong to the genus Aspergillus has been used throughout the world for the production of cellulase (Bakir et al., 2001), mannanase (Kurakake and Komaki, 2001; Lin and Chen, 2004; Puchart et al., 2004), and xylanase (Lu et al. 2003).

Solid state fermentation (SSF) has gained a renewed interest in recent years for the production of many enzymes due to lower operation costs and energy requirements, and simpler plant equipment as compared to submerged fermentation (SmF) (Mitchell and Losane, 1992; Pandey et al. 2001). Nevertheless, efficacy of such SSF is dependent of various parameters such as initial pH, temperature, moisture content, and inoculum size (Lu et al. 2003; Baysal et al. 2003). Thus, for development of efficient enzyme production system, some parameters should be optimized due to their impact on the economy and practicability of the process. Response surface methodology (RSM) is a collection of statistical techniques and a useful tools for optimisation of targets metabolites as it eliminates the drawbacks of classical methods which takes into account one parameter at a time (Liu and Wang 2007; Sayyad et al. 2007; Deepak et al. 2008). Second-order models like central composite design are widely used in RSM as they can take on a wide variety of functional forms, and this flexibility allows them to more closely approximate the true response surface. RMS has recently used for the modelling and optimisation of several bioprocess, including fermentation, enzymatic reaction, product recovery, and enzyme immobilisation techniques (Ismail, 2005, Levin et al 2008; Bonugli-Santos et al 2010; Su et al., 2011; Zhang et al 2011).

The objective of the present study was to apply central composite design (CCD) based Response Surface Methodology (RSM) to optimize the fermentation parameters for hemicellulase and cellulase production by A. terreus K1 in SSF using PKE as sole carbon source.

Materials and methods

Sample

PKE were collected from two commercial kernel oil extraction factories in Malaysia; Klang and Kuantan. The fresh sample were divided into two equal portions, where one portion was immediately packed and stored at 4oC for the isolation of fungi. The other portion were grounded to uniform size of 2.5mm and stored at 4oC to be used in SSF later.

Isolation and Preparation of spore suspension

For isolation of effective fungal strains, serial dilution technique was used, where 0.1mL diluent was pipetted onto potato dextrose agar plates, spread with a glass spreader and incubated at 30oC for 5 - 7 days for observation. Each colony that was formed were transferred to a fresh PDA plates, sub-cultured and maintained on PDA slant at 4oC with periodic (30-days) sub-culturing.

Spore suspensions were prepared by adding Tween-80 (0.1%) to five-day-old cultures grown on PDA slant at 30oC and gently brushing the mycelium with a sterile wire loop. Spores were counted by using haemocytometer and the concentration of the spore suspension was adjusted to a final spore count of 1.0 x 107 spores/mL.

Screening and Identification of potential isolates

To screen for the best enzyme producer, each isolates were grown in SSF at 30oC for 7-days, using PKE as sole carbon source. The enzyme activity (Endoglucanase, Mannanase, and Xylanase) of each isolates were accessed. The best enzyme producer was identified by the analysis of the genomic ITS-region using standard methodology of White et al. (1990).

Optimization of Enzyme Production.

Response surface methodology was used to optimize the Solid state fermentation process for enhanced hemicellulose and cellulose production. The Design-Expert® Software (Version 8.0) was used for the statistical design of experiments and for data analysis. A central composite design with four factors and five levels, with six replicated centre points, was employed. The range and center point values of the four independent variables was presented in Table 1. The full experimental design with respect to the real value of the independent variables and attained values for the response (Cellulase, Mannanase and Xylanase activity), is presented in Table 2. The experiment was carried out in duplicate and the mean enzyme activity was taken as response Y.

Data from the CCD (Table 2) were analysed by the least squares method to fit the second-order polynomial model, according to the following equation:

Y = Bo + B1X1 + B2X2 + B3X3 + B4X4 + B11X12 + B22X22 + B33X32 + B44X42 + B12X1X2 + B13X1X3 + B14X1X4 + B23X2X3 + B24X2X4 + B34X3X4

where Y is the measured response, B0 is the intercept term, B1, B2, B3,B4 are linear coefficient, B11, B22, B33, B14 are quadratic coefficient, B12, B13, B23, B24 are interaction coefficient and X1, X2 X3,X4 are coded independent variables.

The statistical analysis of the model was performed in the form of Analysis of Variance (ANOVA) generated by Design-Expert software.

Solid state fermentation

Growth medium composed grounded PKE (2.5 mm), with varying pH and moisture content according to the experimental design. All growth medium were sterilized by autoclaving prior to treatment. SSF were carried out in 500 mL Erlenmeyer flask containing 30g of growth medium, inoculated with different concentration of inocula and were incubated for 5 days at different temperature, according to the experimental design.

Enzyme extraction and Enzyme assays

Enzyme were extracted by shaking PKE in 50mM citrate buffer (pH 5)(1:10) at 4oC for 24hr, centrifuged at 10,000 rpm for 10 min, and filtered through whatman No 1 filter paper. The filtrate used for the analysis of endoglucanase, xylanase and mannanase.

Endoglucanase (carboxymethylcellulase, endo-1,4-b-D-glucanase; EC 3.2.1.4) were determined according to the method of Grajek (1987), whereas xylanase activity was estimated  by method of Bailey et al. (1992). Concentration of free carboxymethyl glucose and xylose units which reacted with dinitrosalicylic acid reagent was estimated using the DNS method (Miller 1959). Cellulase and Xylanase activity was expressed in international units (IU) where one IU is the amount of enzyme required to release 1µ mole glucose (xylose in case of xylanase assay) equivalent in 1ml of enzyme solution in one min.

β-Mannanase assay was determine based on manufacture protocol (Megazyme, Ireland) with slight modification. About 0.2 ml of the previously prepared PKE filtrate was added to 0.2 ml of the substrate (Azo-Carob Galactomannan) solution and stirred for 5 s on a vortex stirrer and incubated at 40°C for 10 min. After that, 1 ml of ethanol (~95%) was added to the mixture and was stirred continuously for another 10 s on the vortex stirrer. The mixture was allowed to equilibrate to room temperature for 10 min and then centrifuged at 3,000 rpm for 10 min. The supernatant solution was poured directly from the centrifuge tube into a cuvette and the absorbance was measured using spectrophotometer at 590 nm. Different concentrations of pure endo-1,4-β-mannanase (Megazyme, Ireland) was used for the preparation of standard curve following the same procedure as previously described.

Enzyme activity assay were carried out in triplicate, where the average enzyme activity obtained was used as the response.

Table 1. Coded values of variables used in central composite design

Independent variables

Level

-2

-1

0

1

2

Temperature (oC)

25

30

34

37

42

Moisture (%)

40

50

60

70

80

pH

3.0

4.5

6.0

7.5

9.0

Inoculum (%)

3

6

9

12

15

Results and Discussion

Lignocellulosic-degrading enzyme represents enzymes that are important in the degradation process of biomass cell wall. Abilities of different fungi for production these enzymes have been studied, and it clearly shows high variation in production level, depending on the type of carbon source used and microorganism used (ref). In this study, easily available agricultural residues like PKE were used as the carbon source. Fungi were isolated from raw PKE obtain from commercial kernel oil extraction factories by using potato dextrose agar. Ten fungi were isolated in which Aspergillus terreus K1 was selected as the best lignocellulosic-degrading enzyme producer.

In order to obtain optimum production of lignocellulosic enzymes by Aspergillus terreus K1, four parameters which affect the production of enzyme by were statistically optimized by using RSM. These are temperature (X1), moisture (X2), medium pH (X3) and inoculum concentration (X4). The maximum and minimum levels of these parameters for trials in CCD were shown in Table 1. To make the regression model accurate, center point was replicated six times. A total of 30 experiments were performed according to the experimental design given in Table 2, together with the experimental results and predicted activities for each enzyme as estimated from the model equations. This approach was chosen in order to preserve the significance of the interaction effect, which would have been lost if the classic methods of varying the level of one parameter at a time, while fixing the other variable at constant is choosen.

Optimization of Endoglucanase production

The ANOVA summary for endoglucanase production is shown in Table 3. Model validity was estimated as a function of its "coefficients of determination" (R2), which can provides a measure of variability in the observed response values that can be explained by the experimental factors and their interactions. In this experiment, a R2 value of 0.9739 indicates that the models were appropriate and could be used for quantitative prediction of endoglucanase production. In addition, the model F-value implies that the model is significant (P<0.01), and the "Lack of fit test" of 1.76 implies that it is non-significant relative to pure error.

Analysis of the P values is used to check the significance of each coefficients. This analysis is required to understand the pattern of the mutual interactions between the best variables. The smaller the magnitude of the P, the more significant is the corresponding coefficient. In general, statistical analysis showed significant effect on individual factor and their interaction, with exception to interaction between temperature and pH, moisture and inoculum, as well as pH and inoculum (P<0.05). The contour plot of these interactions shows relatively broad plateau region (Figure 1), meaning to say that the endoglucanase activity changes a little when these factors varies.

By applying multiple regression analysis on the experimental data, a second order polynomial equation was found to explain the endoglucanase production regardless of the significance of coefficients (Table 4). The results predicated by the model equation showed that a combination of adjusting the fermentation condition to 30.4oC, 60.5 % moisture, pH 5.3 and 7.5% inoculum would favour maximum endoglucanase yield of 18.10 U/g, which is close to the experimental endoglucanase activity of 20.18 U/g. The larger coefficients for temperature (X1) than the coefficients for other factor indicate that temperature has more significant effect on endoglucanase production.

Table 3: Analysis of variance table (ANOVA) for Endoglucanase production

Source

Sum of

Squares

df

Mean

Square

F-

Value

Prob > F

Model

284.92

14

20.35

39.97

< 0.0001

A-Temperature

77.58

1

77.58

152.36

< 0.0001

B-Moisture

6.17

1

6.17

12.12

0.0033

C-pH

5.84

1

5.84

11.48

0.0041

D-Inoculum

12.42

1

12.42

24.39

0.0002

AB

4.96

1

4.96

9.75

0.0070

AC

0.51

1

0.51

1.01

0.3309

AD

24.38

1

24.38

47.89

< 0.0001

BC

9.78

1

9.78

19.21

0.0005

BD

0.26

1

0.26

0.50

0.4894

CD

0.30

1

0.30

0.59

0.4546

Residual

7.64

15

0.51

Lack of Fit

5.95

10

0.59

1.76

0.2768

Pure Error

1.69

5

0.34

Cor Total

292.55

29

Std. Dev.

0.71

R2

0.9739

C.V. %

5.54

Adjusted R2

0.9495

Predicted R2

0.8556

Table 4: Predictive second order polynomial equation describing the relationship between enzyme activities of various enzymes

Endoglucanase

16.88 - 2.03 X1 - 0.51 X2 - 0.49 X3 - 0.72 X4 - 1.81 X12 - 1.17 X22 - 0.48 X32 - 1.8 X42 - 0.67 X1X2 + 0.22 X1X3 + 1.49 X1X4 - 0.78 X2X3 + 0.13X2X4 + 0.14 X3X4

Mannanase

36.62 - 3.39 X1 + 1.13 X2 + 1.11 X3 - 0.75 X4 - 4.73 X12 - 5.22 X22 - 5.36 X32 + 2.80 X42 + 2.58 X1X2 - 0.28 X1X3 + 1.79 X1X4 - 4.38 X2X3 - 2.22 X2X4 - 1.74 X3X4

Xylanase

49.00 - 4.40 X1 + 2.79 X2 - 6.18 X3 - 4.76 X4 - 3.17 X12 - 0.70 X22 - 1.20 X32 - 3.99 X42 + 10.74 X1X2 + 4.53 X1X3 + 6.68 X1X4 - 4.09 X2X3 + 0.58 X2X4 - 3.24 X3X4

where X1, X2, X3 and X4 are coded values of temperature, moisture, pH and inoculum respectively.

a)

b)

c)

Figure 1: Contour plot showing the effect of a)Temperature and pH ; b) Moisture and Inoculum; c) pH and Inoculum on the production of endoglucanase.

Optimisation of Mannanase production

The R2 value of 0.9884 indicated that 98.84% of the total variability in the response could be explained by the second order polynomial equation (Table 4). The model F-value of 85.18 indicates that the model were significant and there was only a 0.01% chance that a "model F-value" this large could occur due to noise (P<0.01). Moreover, the "Lack of fit test" of 1.52 implies that it is non-significant relative to pure error. The coefficient of variation (CV) is the ratio of the standard error of estimate to the mean value of the observed response, and as a general rule a model can be considered reasonably reproducible if the CV is not greater than 10%. Here, the low values of CV (5.84%) indicated great reliabilities of the experiments performed. All these results (Table 5) showed a good agreement between the experimental and predicted values and implied that the mathematical models were suitable for the simulation of mannanase production in the present study.

Based on the statistical analysis, only the interaction between temperature and pH had no significant effect (P<0.05) on mannanase production. The three dimensional response surface and contour plot (Figure 2) were used to represent the interaction between temperature and pH, in which it shows circular contour plot of response surfaces indicating that the effect of temperature on mannanase production is not dependent on initial pH, and vice versa. By solving the inverse matrix, the optimal values for mannanase production of four variables in uncoded units were 31.2oC, 60.8 % moisture, pH 6.4 and 6.00% inoculum. Under the optimum condition, the predicted maximum mannanase production was 42.03 U/g, which is close to the actual mannanase activity of 52.29 U/g.

Figure 2: Three dimensional Response surface plot showing the effect of temperature and pH, and their interaction effect on production of mannanase.

Table 5: Analysis of variance table (ANOVA) for Mannanase production

Source

Sum of

Squares

df

Mean

Square

F-

Value

Prob > F

Model

2967.30

14

211.95

85.18

< 0.0001

A-Temperature

197.75

1

197.75

79.48

< 0.0001

B-Moisture

27.29

1

27.29

10.97

0.0051

C-pH

26.32

1

26.32

10.58

0.0058

D-Inoculum

11.88

1

11.88

4.77

0.0464

AB

62.40

1

62.40

25.08

0.0002

AC

0.76

1

0.76

0.30

0.5899

AD

29.91

1

29.91

12.02

0.0038

BC

322.98

1

322.98

129.81

< 0.0001

BD

68.08

1

68.08

27.36

0.0001

CD

41.76

1

41.76

16.79

0.0011

Residual

34.83

14

2.49

Lack of Fit

13.67

9

1.52

0.36

0.9139

Pure Error

21.17

5

4.23

Cor Total

2967.30

14

211.95

85.18

< 0.0001

Std. Dev.

1.58

R2

0.9884

C.V. %

5.84

Adjusted R2

0.9768

Predicted R2

0.9558

Optimisation of Xylanase production

The statistical significance of the fitted model was evaluated which is essential for determining patterns of interaction between experimental variables (Table 6). As observed, the computed model's F-value of 44.58 with a probability value P< 0.01 indicated that the selected quadratic regression model fitted well to the experimental data. The lack of fit F-value (3.83) implied that the lack of fit was insignificant and hence the model provided a good fit to the data.

Table 2 shows the yields of xylanase produced by Aspergillus terreus K1. As can be seen, highest xylanase (67.67 U/g) was produced when it was grown at 30 oC, initial moisture of 70%, pH 4.5 and inoculum size of 6% (Run 11) whereas, the minimum xylanase activity (22.53 U/g) was produced when the fermentation process was conducted at incubation temperature, moisture, pH and inoculum of 25oC, 60%, pH 6 and 9%, respectively (Run 8). By applying multiple regression analysis to the test results, the second-order polynomial equation representing xylanase production was obtained (Table 4). Using the design Expert software, the optimal condition for xylanase production were predicted to be at 29.3oC, 69.6 % moisture, pH 4.6 and 7.7% inoculum, with yield of 69.04 U/g Xylanase, which is close to the actual mannanase activity of 63.73 U/g.

By analysing the effect of temperature (fixing the other variable at zero level), it can be seen that the optimum temperature for xylanase production would have been at 36.5oC (Figure 5). In order to bring the incubation temperature to near ambient without affecting the enzyme production, we can modified the other variable, in this case, slight change in pH (adjusting pH 4.5 to pH 4.6) can greatly reduce the incubation temperature (from 36.5oC to 31.9oC).

Table 6: Analysis of variance table (ANOVA) for Xylanase production

Source

Sum of

Squares

df

Mean

Square

F-

Value

Prob > F

Model

3054.87

14

218.20

44.85

< 0.0001

A-Temprature

166.20

1

166.20

34.16

< 0.0001

B-Moisture

142.38

1

142.38

29.26

0.0002

C-pH

698.24

1

698.24

143.51

< 0.0001

D-Inoculum

439.57

1

439.57

90.35

< 0.0001

AB

875.38

1

875.38

179.92

< 0.0001

AC

156.13

1

156.13

32.09

0.0001

AD

367.19

1

367.19

75.47

< 0.0001

BC

205.56

1

205.56

42.25

< 0.0001

BD

3.80

1

3.80

0.78

0.3943

CD

120.07

1

120.07

24.68

0.0003

Residual

58.38

12

4.87

Lack of Fit

26.83

7

3.83

0.61

0.7354

Pure Error

31.55

5

6.31

Cor Total

3113.25

26

Std. Dev.

2.21

R2

0.9812

C.V. %

5.03

Adjusted R2

0.9594

Predicted R2

0.8944

Optimization of multienzyme production

The co-production of endoglucanase, mannanase and xylanase can be found in many enzyme production systems when growing microorganism in agrobio-waste. Nevertheless, the efficient production of this enzyme mixture is dependent on various factors. As shows by current finding in this paper, it is noted that each individual enzyme typically being produced under different set of condition. In order to find the best environment for the coproduction of these enzyme (with emphasize of mannanse production), optimization were done by using Design-Expert software with the activity of all three enzyme were used as response. It is predicted that by incubating of PKE at 30.5 oC, 62.7% moisture, pH 5.8, and 6% of A. terreus K1 spore (1.0 x 10-7 spores/ml), a maximum endoglucanase, mannanase, and xylanase can be obtained (17.37 U/g, 41.23 U/g, and 53.14 U/g respectively). Verification of this predicted condition were conducted as triplicate and the enzyme activity obtain (18.59 ± 1.39 U/g, 42.40 ± 4.89 U/g, and 52.79 ± 4.80 U/g respectively) is close to the predicted value.

It is said that enzyme production are subjected to induction or catabolic repression. Since PKE constitute mainly mannan polymer, it is expected that mannanase will be the major enzyme produced according to the study by Lee (2007). However, results of this study shows otherwise. The higher xylanase activity could be due to the induction by both xylan and cellulose present in PKE itself (Biely et al., 1985; Royer and Nakas 1989; Tuohy et al., 1992) or throught the induction by end-product of mannanase action (Sachslehner et al. 1997). Another point of view is that the production of mannanse is growth-dependent and is an induced enzyme (Feng et al. 2003). When proper induceer is present, mannanase will be produced, but once depleted or when the cell are in stationary phase, the production will cease immediately.

Temperature represents one of the main factors affecting the growth of fungi. Fungi have been shown to be able to tolerate wide range of temperature, typically from 30-40oC, with some are able to survive in extreme temperature (Vandamme et al., 2007; Chellapandi and Jani, 2009; Sohail et al., 2009; Facchini et al., 2010; Tao et al., 2010 ; Wang et al ., 2006; Lin and Chen, 2004; Kurakake and Komaki, 2001). Despite the wide temperature tolerance, the optimum temperature predicted to be 30oC in this study, a temperature which is close to the natural habitat this fungi were isolated. Similar to the production of endoglucanase, the production of mannanase are much more affected by changes in temperature, where temperature above 32oC mark a decrease in mannanase productivity. It is proposed that the mRNA involve in the synthesis mannanase are only stable within a certain temperature range, whereby decrease in temperature will gradually stabalize and prolong the production of this enzyme, but below the range will cease mannanase production due to decrease in biochemical process (Feng et al. 2003). This is not the case for xylanase production,

Unlike the mannanase and endoglucanase production, xylanase production seems to be more significantly affected by changes in pH (as shown by the larger value of coeeficient estimination, X4). Based on the time course of enzyme production study by Lee (2007), it is observed that the production of mannanse occur during growth of fungal, whereas the optimum production of endocglucanase and xylanase occurs later. During the growth of fungal, pH will decrease initially and then slightly increase with incubation time due to the accumulation of soluble reducing sugar (Kurakake and Komaki, 2001). Thus, too low initial pH will affect the production of mannanase, but too high of initial pH will lead to too alkaline environment which might cease the subsequent endoglucanase or xylanase production.

Conclusion

In this present study, multienzyme (cellulose, mannanase and xylanase) production by the fungal Aspergillus terreus K1 by using an agrobio-waste product was successfully optimized using the central composite design. The application of cheap crude enzyme mixture as animal feed additive may, in some cases to be more efficient than the use of pure enzyme in term of production cost and other components present in the crude enzyme may stabilize the enzymes. Furthermore, the applicability of the statistical approach were proven to be a powerful approach as it can predict the overall co-production of enzyme with minimal number of experimental points. Therefore, this fungus isolated could be an attractive alternative source of cellulosic and hemicellulosic enzyme producer, which have gained an interest in recent years for the efficient utilization of PKE as animal feed.

Table 2. Central composite design matrix with experimental and predicted values of endoxylanase production by Aspergillus terreus

Trial Number

Variable

Cellulase

Xylanase

Mannanase

X1

X2

X3

X4

Experimental

Predicted

Experimental

Predicted

Experimental

Predicted

1

-2

0

0

0

10.83

11.38

50.57

52.40

17.79

18.57

2

-1

-1

-1

-1

17.21

16.62

48.53

49.47

33.92

36.16

3

-1

1

-1

-1

11.18

11.55

55.24

55.35

23.74

24.09

4

-1

-1

1

-1

15.89

15.93

39.55

39.12

22.12

21.52

5

-1

1

1

-1

13.32

13.42

46.33

56.53

29.41

28.22

6

-1

-1

-1

1

10.45

11.07

48.65

49.64

30.30

30.31

7

-1

1

-1

1

9.89

10.51

41.36

41.45

47.68

48.43

8

-1

-1

1

1

12.83

13.70

18.93

27.53

22.98

24.49

9

-1

1

1

1

14.12

14.16

49.46

47.89

31.62

30.12

10

0

0

0

-2

9.08

8.33

27.00

25.57

45.76

46.29

11

0

0

-2

0

18.03

17.34

67.67

69.41

28.04

33.61

12

0

-2

0

0

6.55

5.60

46.03

45.14

11.61

10.95

13

0

0

0

0

16.82

15.88

33.71

33.96

37.77

37.34

14

0

0

0

0

17.08

16.62

50.59

49.47

38.02

36.16

15

0

0

0

0

10.72

10.89

46.40

45.58

10.24

9.21

16

0

0

0

0

7.74

7.61

32.39

33.92

25.93

25.38

17

0

0

0

0

11.81

11.51

24.37

23.93

24.99

24.99

18

0

0

0

0

16.72

16.62

48.32

49.47

38.23

36.16

19

0

2

0

0

15.92

16.62

51.01

49.47

33.79

36.16

20

0

0

2

0

11.62

12.00

14.06

5.79

34.67

33.86

21

0

0

0

2

10.64

10.75

51.85

49.71

16.83

18.16

22

1

-1

-1

-1

16.92

16.62

45.82

49.47

37.47

36.16

23

1

1

-1

-1

12.21

12.59

49.96

49.20

26.94

26.50

24

1

-1

1

-1

15.88

16.62

53.00

49.47

35.11

36.16

25

1

1

1

-1

15.84

15.65

56.91

55.98

12.68

12.56

26

1

-1

-1

1

9.99

10.07

40.15

40.50

29.84

29.81

27

1

1

-1

1

11.34

10.78

20.30

22.03

18.12

17.78

28

1

-1

1

1

8.651

9.15

29.22

30.08

19.26

18.93

29

1

1

1

1

13.34

13.10

42.81

43.60

12.49

12.43

30

2

0

0

0

13.71

13.78

33.79

33.39

15.47

16.86

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