**Evaluation of Di**ff**erent Algorithms for Estimating the Growing Stock Volume of** *Pinus massoniana* **Plantations Using Spectral and Spatial Information from a SPOT6 Image**

**Jingjing Zhou 1,2, Zhixiang Zhou 1,2, Qingxia Zhao 3, Zemin Han 1,2, Pengcheng Wang 1,2, Jie Xu 4 and Yuanyong Dian 1,2,\***

Received: 28 March 2020; Accepted: 9 May 2020; Published: 12 May 2020

**Abstract:** Precise growing stock volume (GSV) estimation is essential for monitoring forest carbon dynamics, determining forest productivity, assessing ecosystem forest services, and evaluating forest quality. We evaluated four machine learning methods: classification and regression trees (CART), support vector machines (SVM), artificial neural networks (ANN), and random forests (RF), for their reliability in the estimation of the GSV of *Pinus massoniana* plantations in China's northern subtropical regions, using remote sensing data. For all four methods, models were generated using data derived from a SPOT6 image, namely the spectral vegetation indices (SVIs), texture parameters, or both. In addition, the effects of varying the size of the moving window on estimation precision were investigated. RF almost always yielded the greatest precision independently of the choice of input. ANN had the best performance when SVIs were used alone to estimate GSV. When using texture indices alone with window sizes of 3 × 5 × 5 or 9 × 9, RF achieved the best results. For CART, SVM, and RF, *R*2 decreased as the moving window size increased: the highest *R*2 values were achieved with 3 × 3 or 5 × 5 windows. When using textural parameters together with SVIs as the model input, RF achieved the highest precision, followed by SVM and CART. Models using both SVI and textural parameters as inputs had better estimating precision than those using spectral data alone but did not appreciably outperform those using textural parameters alone.

**Keywords:** machine learning algorithms; forest growing stock volume; SPOT6 imagery; *Pinus massoniana* plantations

#### **1. Introduction**

Forest growing stock volume (GSV) is one of the most important forest characteristics, both economically and environmentally, because it is a key determinant of forest productivity [1]. Precisely estimating forest GSVs on large scales is crucial for monitoring forest carbon dynamics, assessing forest ecosystem services, and evaluating forest quality [2–5]. Traditional ground-based GSV estimation strategies that rely on field measurements of tree height and diameters at breast height (DBH) are time-consuming and labor-intensive [1]. GSV is estimated over large areas almost everywhere in the world, and estimates are often precise. The (additional) benefits of including remote

sensing (RS) data in this process are that spatially explicit information can be produced (i.e., maps) and that the precision of population parameter estimates can be improved.

Several modelling approaches, both parametric and non-parametric, have proven capable of precisely estimating forest variables such as the leaf area index, canopy cover, height, basal area, and stock volume based on RS data [4,6–10]. The most popular parametric regression methods for modelling forest attributes are simple or multiple linear regression models. Commonly used non-parametric models include classification and regression trees (CART), k-nearest neighbor (k-NN), random forest (RF), and support vector machine (SVM). Although many researchers have performed comparative evaluations of different machine learning algorithms (MLAs), no single technique has been revealed as universally superior for predicting forest inventory attributes [8,11,12]. Different machine learning algorithms (MLAs) have different estimating precisions; for example, CART achieves the lowest precision, SVM and ANN tend to achieve moderate performance, and RF has achieved the highest precision and lowest error for the forest variables under consideration [4]. Zhang et al. [12] found that ANN and SVM were similarly effective at estimating sawgrass aboveground biomass (AGB), with correlation coefficients (r values) exceeding 0.9. However, ANN offered the most precise total biomass estimates (r = 0.94). Wang et al. [13] found that the MLR method generally outperformed SVM and RF at predicting site-level AGB. The GSV of *Pinus massoniana* plantations has not been estimated extensively in China's northern subtropical regions. Therefore, there is a need to evaluate and compare the performance of different MLAs in this specific task.

The spectral information in optical remote sensing images generally provides insufficient information to precisely assess the state of vegetation in dense forests with complex structures. Therefore, texture parameters derived from remote sensing images are commonly analyzed to assess the spatial distribution of image tone variance and thereby acquire spatial information on vegetation cover, especially in complex and dense forests [14]. Several recent studies have concluded that textural parameters from high-resolution satellite images are more useful than spectral information for estimating forest variables because the interpretation of spectral data is complicated by saturation when studying multi-layer forests with full canopy cover [6,12,14–19]. However, it is not yet clear whether combining spectral and texture-based features can enable more precise estimation of forest variables than analyses based on textural information alone. Chrysafis et al. [5] argued that combining spatial and textural information yielded at most marginal improvements in precision over texture-only models when estimating GSV in a Mediterranean forest. However, others have reported that forest variables such as the leaf area index (LAI) can be estimated more precisely by combining spectral and textural features than by relying solely on one type or the other [6,15,17]. Texture is a complex parameter and obtaining precise texture information over the entire forest area is challenging. This is because texture values are highly sensitive to texture measures, landscape type, physiological growth period, associated parameters (i.e., moving window size, direction), and the type of remote sensing image [5,20]. It is still worthwhile to explore whether a large or small moving window size should be used in various forest types, and with an inversion algorithm.

*Pinus massoniana* is a major coniferous tree that is widely distributed in the subtropical forests of South China and exhibits relatively high tolerance to acid rain, drought, and phosphorus deficiency [21,22]. It is central to forest ecosystems and is also an economically important source of timber and wood pulp [23]. Few efforts have been made to evaluate the capability of modern remote sensing techniques for mapping *Pinus massonian* GSV in the subtropical area of China [24,25]. A growing stock volume map of *Pinus massoniana* trees in in the subtropical area of China has never been generated and published. The specific objectives of this study were therefore to: (1) examine the ability of four MLAs (CART, SVM, ANN, and RF) to predict the GSV of *Pinus massoniana* plantations, (2) compare the effectiveness of spectral vegetation indices (SVIs) and texture data derived from SPOT6 images for forest GSV quantification, and (3) assess the influence of texture index window size on the precision of forest GSV estimation.

#### **2. Materials and Methods**

#### *2.1. Study Area*

This study focused on an approximately 7600 ha region of *Pinus massoniana* plantations near Taizi mountain in Jingshan County of China's Hubei Province (30◦48 –31◦02 N and 112◦48 –113◦03 E) (Figure 1). Topographically, the area is characterized by a hilly landscape with moderate slope and its elevation is between 40.3 and 467.4 m above sea level. It has a subtropical monsoon climate with a mean temperature of 16.4 ◦C. The average annual precipitation is 1094.6 mm, of which over 53% falls between April and August. The dominant soil type at the site is yellow-brown soil. Yellow-brown soil is a transitional soil between yellow, red, and brown soils, which are common in mixed subtropical evergreen broad-leaved and deciduous broad-leaved forests widely. The landscape of the study area is characterized by pure forests, mixed forests, shrub land, water, cultivations, and forest openings. The percentage of forest cover is 85%. The main tree species in the area are *Pinus massoniana* Lamb. and *Quercus acutissima* Carr. Less abundant species include *Cunninghamia lanceolata (Lamb.)* Hook., *Ilex chinensis* Sims, *Platycarya strobilacea* Sieb.et Zucc., *Liquidambar formosana* Hance, *Dalbergia hupeana* Hance, *Pistacia chinensis* Bunge., and *Celtis sinensis* Pers. [26].

**Figure 1.** A portion of the study area and the location of the sample plots over a SPOT6 Multispectral Image (MSI) image acquired on 6 August 2015.

#### *2.2. Data*

#### 2.2.1. Field Data and Stand Volume Estimation

The field sample plots were established in August 2015, August 2018, and November 2019. In total, 68 square plots of the size 20 × 20 m each were established in pure *Pinus massoniana* plantations by applying the method of combining stratified sampling and random sampling. Considering the standards for division of *Pinus massoniana* plantation age, there were 17 plots of young forest, 11 of middle-age forest, 15 of near-mature forest, 11 of mature forest, and 14 of over-mature forest. Sample plots in every group of forest ages were distributed randomly in terms of geography. In each plot, trees with DBH over 5 cm were tallied. DBH, tree height (H), and crown diameter (CD) of the individual tree in the plot were all measured. Trees with a DBH below 5 cm were excluded. The LAI-2200 instrument (LI-COR Inc., Li-Cor, Lincoln, NE, USA, 2010) was used to indirectly measure the leaf area index (LAI) of each plot. Measurements were taken either under overcast conditions, or alternatively within two hours after sunrise or before sunset. At each site, two above-canopy and nine low-canopy readings were taken with an opaque, 180◦, view-restricting cap placed over the sensor in order to mask out the operator [6]. After finding the total number of all trees in a plot, the stand density (Density) was estimated in trees per hectare (trees ha−1). The survey also provided information about aspect, slope, and slope position. A differential global positioning unit (Trimble GeoXH6000 GPS units) was used to locate the center and the four corners of each plot, allowing the plots to be geo-referenced against satellite data. The summarized characteristics of the 68 plots are documented in Table 1.

The volumes of each individual growing trees were predicted by applying the allometric model (Equation (1)) based on DBH and H. These models yielded the following expression [27]:

$$\text{V} = 0.00006228789 \times \text{DBH}^{1.849839} \times \text{H}^{0.9843411} \tag{1}$$

where, V is the individual tree volume, DBH is the tree diameter at a height of 1.3 m, and H is the total tree height. The volumes estimated from all individual trees within a sample plot were summed to obtain the GSV of the sample plot, which was then up-scaled to per hectare. The GSV of the plot was measured in cubic meter per hectare (m3 ha−1). The field data revealed that the plot-level GSVs varied widely, ranging from 10.20 to 319.82 m3 ha−1, with a mean of 140.84 m3 ha−1. The inconsistency between the time of sample plot data collected in 2018 and 2019 and remote sensing (RS) image was an important factor which contributed to low precision of forest parameter estimation. We used the growth model of *Pinus massoniana* tree for conversing volume values of 2018 and 2019 to V values of 2015. The formula was as follows [28]:

$$\mathbf{V} = 0.000025058 \times \mathbf{A}^{2.5983243} \text{ (R} = 0.996) \tag{2}$$

where, V is volume of a single tree and A is the stand age.

**Table 1.** The summarized characteristics of the 68 plots surveyed in August 2015, August 2018, and November 2019.

**Table 1.** *Cont.*

#### 2.2.2. SPOT 6 Image and Processing

This study was based on a single SPOT 6 panchromatic-multispectral image that was acquired on 6 August 2015 under clear sky conditions. The panchromatic image had a spatial resolution of 1.5 m while the resolution of the multispectral image was 6 m. The panchromatic image was orthorectified using ground control points and digital elevation model (DEM) data. Then, the corrected panchromatic image was used to rectify the multispectral data. The raw digital number values for the multispectral data were converted to spectral radiance values and then into top of atmosphere (TOA) reflectance. Atmospheric correction was performed using the Fast Line-of-sight Atmospheric Analysis of Spectral Hypercubes (FLAASH) approach [29]. All image processing was performed using the ENVI 5.1 software package.

#### 2.2.3. Spectral Vegetation Indices and Texture Parameters

The average surface reflectance values from the multispectral data were used to compute eight vegetation indices (VIs) (Table 2) that are widely used in forest studies: the Atmospherically Resistant Vegetation Index (ARVI), Difference Vegetation Index (DVI), Enhanced Vegetation Index (EVI), Modified Soil Adjusted Vegetation Index (MSAVI), Normalized Difference Vegetation Index (NDVI), Non-Linear Vegetation Index (NLI), Soil Adjusted Vegetation Index (SAVI), and Simple Ratio (SR). Eight texture parameters—the Mean (MEAN), Homogeneity (HOM), Contrast (CON), Dissimilarity (DIS), Entropy (ENT), Variance (VAR), Angular Second Moment (ASM), and Correlation (COR)—were calculated (see Table 3) based on the SPOT6 panchromatic band at the maximum spatial resolution of 1.5 m using the GLCM method [30].

**Table 2.** Spectral Vegetation index and its calculation formula.

Notes: B, R, and NIR represent SPOT6 reflectance in the blue, red, and near-infrared wavelengths, respectively. Parameters L and γ represent the SAVI term (set to 0.5) and the ARVI term (set to 1), respectively. The coefficients used in the EVI algorithm are C1 = 6.0, C2 = 7.5, C3 = 1, and G = 2.5 [17].

**Table 3.** Formula of texture measurements used in this study [30].

pixels that are summed. μ*i*, μ*j*, σ2 *i* , and σ2 *j* are the means and standard deviations of *Pi* and *Pj*. *P(i, j)* is the normalized cooccurrence matrix.

#### 2.2.4. Optimum Window Selection

Image texture is a function of the image's spatial resolution. Texture parameters derived with the GLCM method are highly sensitive to the moving window size [20]. Small window sizes are known to exaggerate differences within the window but retain a high spatial resolution, whereas larger windows may cause inefficient extraction of texture information due to over-smoothing of textural variations [39]. There is no consensus as to what moving window size is optimal, so it is necessary to test a range of window sizes to determine which provides the best speed and precision when estimating GSV. Therefore, seven window sizes were used to estimate the stand volume in this work: 3 × 3, 5 × 5, 7 × 7, 9 × 9, 11 × 11, 13 × 13, and 15 × 15.

#### *2.3. Machine Learning Algorithms (MLAs)*

#### 2.3.1. Classification and Regression Tree (CART)

The CART algorithm is a basic machine learning method proposed by Breiman et al. [40]. The regression tree algorithm was used to build models because the response variable in this study was the forest GSV. The CART algorithm was implemented with the "rpart" in the R software package (version 3.4.3; R Core Team, Viennna, Austria, 2017). Feature selection and pruning are the two core issues in decision tree algorithms. The Gini index and post-pruning were used as the binary split criterion for the selection of split features and to modify the tree, respectively.

#### 2.3.2. Support Vector Machine (SVM)

Support vector machines (SVM) use a nonlinear kernel function to project input data onto a high-dimensional feature space, where complex non-linear patterns can be simply represented [12,41]. In the new hyperspace, the SVM aims to identify an optimal hyperplane that fits the data and minimizes the training error and the complexity of the model [42]. The Gaussian radial basis kernel function of the form was used in this study [43,44]. The best regularization and bandwidth parameter were determined using the training data. The SVM model was implemented with "e1071" packages in the R software package.

#### 2.3.3. Artificial Neural Network (ANN)

A multi-layer neural network with a back-propagation algorithm, which may be the most popular type, was used in this study [42,45]. In this context, the neurons in the input layer were the predicted variables selected by the Boruta algorithm, while the number of neurons in the hidden layer, which carried weights representing the linkages between the predictors and GSV data, was determined using both the training and validation data. The output layer was a single neuron that represented the output values of the forest GSV. We examined the number of neurons ranging from 1 to 10 in the hidden layer to build different models. The ANN algorithm was implemented with the "neuralnet" package in the R software package.

#### 2.3.4. Random Forest (RF)

Ntree (i.e., to the number of variables) and Mtry (i.e., to the number of variables to randomly sample as candidates at each split) parameters are highly sensitive to prediction robustness of RF algorithms [12]. Wang et al. [46], Breiman [47], and Zhao et al. [4] have all indicated that the default values are often a good choice, and that the influence of user-defined parameters on the sensitivity of prediction is very small. Thus, the Ntree value was set to 500 and the Mtry value was set to one-third of the predictive variables. The RF algorithms were implemented with the "randomForest" package in the R software package.

#### *2.4. Model Testing and Comparison*

The field data was randomly divided into training (70%) and testing (30%) data. The testing data (*n* = 20) was used to validate the CART, SVM, ANN, and RF models. A random selection of training samples was repeated 100 times to reduce variability. Three common statistical parameters, namely the coefficient of determination (*R*2), root mean square error (RMSE), and relative RMSE (rRMSE) were calculated to evaluate the models' performances.

$$\text{RMSE} = \sqrt{\frac{\sum\_{i=1}^{n} \left(\hat{y}\_i - y\_i\right)^2}{n}} \tag{3}$$

$$\text{rRMSE} = \text{RMSE} / \overline{y} \tag{4}$$

where *y*ˆ*i* was the predicted forest GSV and *yi* was the forest GSV observed in the field.

#### **3. Results**

#### *3.1. E*ff*ects of Moving Window Size on the Precision of GSV*

The effect of varying the moving window size on the precision of GSV estimation depended on the algorithm used for variable selection. The *R*2 decreased with increasing moving window size when CART, SVM, and RF were used to estimate the GSV. In other words, 3 × 3 and 5 × 5 window yielded the highest *R*2 and lowest RMSE and rRMSE when using these methods (Table 4). For the ANN method, the *R*2 initially decreased with the moving window size and then increased, the highest *R*2 values (0.83) was achieved with this model when using a window size of 15 × 15 in this case (Table 4).

CART = classification and regression tree, SVM = support vector machine, ANN = artificial neural network (ANN), RF = random forest, *R*2 = the coefficient of determination, RSME = root mean square error, and rRMSE = the relative RMSE.

#### *3.2. Performance of CART, SVM, ANN, and RF*

To evaluate the performance of CART, SVM, ANN, and RF for predicting the forest GSV when using only textural parameters, four machine learning algorithms were compared in terms of their precision (*R*2) and RMSE (Figure 2). When the window size was set as 3 × 3, 5 × 5, and 9 × 9, RF demonstrated the best regression performance in terms of model precision and RMSE. The ANN achieved the highest precision when the window size was 11 × 11, 13 × 13, or 15 × 15 (Figure 2).

**Figure 2.** The coefficient of determination (R2) and root mean square error (RMSE) the forest GSV estimated by different regression models (classification and regression tree, CART; support vector machine, SVM; artificial neural network, ANN; random forest, RF) with different textural information calculated with different window sizes; (**a**) 3 × 3 window size, (**b**) 5 × 5 window size, (**c**) 7 × 7 window size, (**d**) 9 × 9 window size, (**e**) 11 × 11 window size, (**f**) 13 × 13 window size, and (**g**) 15 × 15 window size.

On the other hand, a comparative analysis based on texture parameters and textural parameters together with SVIs demonstrated that RF was the best method for estimating GSV. The *R*2 values for models generated using this method ranged from 0.82 to 0.86 (Figure 3). For models using only SVI data as inputs, ANN yielded the highest precision followed by SVM and RF. When using textural parameters together with SVIs as inputs, SVM achieved the second highest precision (Figure 3).

**Figure 3.** Scatter plots of predicted versus observed growing stock volume (GSV) using classification and regression tree (CART), support vector machine (SVM), artificial neural network (ANN), and random forest (RF) models for (**a**) spectral vegetation indices (SVIs), (**b**) texture features, and (**c**) SVIs plus texture features. *R*2 = the coefficient of determination, RSME = root mean square error, and rRMSE = the relative RMSE.

#### *3.3. Performance of Spectral, Texture, and Fusion of Spectral and Texture Information*

Regression models using SVIs as inputs achieved worse estimating precision than those using textural parameters independent of the choice of regression method. The *R*2 value for the model based on texture data alone (the *R*2 values were 0.76, 0.75, 0.66, and 0.82 for CART, SVM, ANN, and RF, respectively) was substantially higher than that for the model based on SVIs alone (Figure 3). Likewise, the RMSE and rRMSE values for the model based on textural parameters alone were considerably lower than those for the model based on SVIs alone. The RMSE values were 36.61 m3/ha, 37.96 m3/ha, 45.44 m3/ha, and 32.36m3/ha, and the rRMSE values was 29.29%, 30.37%, 31.45%, and 25.89% for CART, SVM, ANN, and RF, respectively. Fusing spatial and textural information did not greatly increase the estimating precision relative to that achieved using textural parameters alone, although minor improvements were observed with the SVM and RF methods (Figure 3). The *R*2 values achieved with the CART methods when using textural parameters alone as inputs were almost

equal to those achieved using textural parameters together with SVIs (Figure 3). Figure 4 indicates the importance of explanatory variables when the RF algorithm is used. The NDVI and MEAN calculated with 3 × 3 and 5 × 5 window sizes, were of higher importance than the other variables.

**Figure 4.** The importance of explanatory variables: (**a**) spectral vegetation indices (SVIs), (**b**) texture features, (**c**) SVIs plus texture features when the Random Forest (RF) algorithm was used. VImp = the variable's importance values, NDVI = Normalized Difference Vegetation Index, ARVI = Atmospherically Resistant Vegetation Index, DVI = Difference Vegetation Index, EVI = Enhanced Vegetation Index, SAVI = Soil Adjusted Vegetation Index, MSAVI= Modified Soil Adjusted Vegetation Index, NLI = Non-linear Vegetation Index, SR = Simple Ratio. Texture (3 × 3 + 5 × 5) was the value of textural parameters when window size was set as 3 × 3 or 5 × 5. MEAN 3 × 3, VAR 3 × 3, DIS 3 × 3, ASM 3 × 3, CON 3 × 3, COR 3 × 3, ENT 3 × 3, and HOM 3 × 3 were Mean, Variance, Dissimilarity, Angular Second Moment, Contrast, Correlation, Entropy, and Homogeneity values when the moving window size was set as 3 × 3. MEAN 5 × 5, VAR 5 × 5, DIS 5 × 5, ASM 5 × 5, CON 5 × 5, COR 5 × 5, ENT 5 × 5, and HOM 5 × 5 were Mean, Variance, Dissimilarity, Angular Second Moment, Contrast, Correlation, Entropy, and Homogeneity values when the moving window size was set as 5 × 5.

#### **4. Discussion**

The GSV of *Pinus massoniana* plantations was estimated using four machine learning algorithms (CART, SVM, ANN, and RF) together with spectral and textural information from a SPOT6 image. The effect of varying the window size on the precision of GSV estimation was investigated, and the usefulness of textural parameters from SPOT6 images for forest GSV estimation was assessed.

Image texture is a complex aspect of visual perception [48]. The optimal moving window size for extracting textural parameters from the SPOT6 image generally depends on the choice of regression method. In this study, the CART, RF, and SVM methods performed best with 3 × 3 or 5 × 5 moving windows, while 15 × 15 windows were best for ANN. These results are consistent with those of previous studies [6,49], in which small windows (3 × 3 or 5 × 5) were found to be best when using the multiple linear regression method to estimate forest variables. Small window sizes increase sensitivity to interpixel differences in the proportions of tree crown and shadow and may better detect fine-scale variation in pixel brightness, whereas larger windows may extract texture information inefficiently due to over-smoothing of textural variation [50–52]. Some researchers have suggested that the window size should match that of the sample plots to increase precision [7,53,54]. Moreover, Chryasfis et al. [5] concluded that no single window size could adequately characterize the texture conditions in different phenological scenes when using the bagging least absolute shrinkage and selection operator (LASSO) algorithm. One of the reasons ANN performed well at 15 × 15 windows, while CART, RF, and SVM

performed better at 3 × 3 or 5 × 5 windows, may be related to the autocorrelation of texture parameters from different window sizes.

MLAs have fewer assumptions, higher methodological accuracy, and high non-linear adaptation, and can efficiently model the complex non-linear relationships between forest biophysical parameters and RS data [55–59]. Four machine learning regression algorithms, CART, SVM, ANN, and RF were explored in this study. The RF method yielded more precise GSV estimates than the other tested methods (CART, SVM, and ANN) when texture parameters alone, or a fusion of spectral and texture information, were used. This is consistent with the results of Zhao et al. [4], who found that RF achieved the greatest precision when estimating forest variables in black locust plantations on the Loess Plateau. Similarly, Fallah et al. [60] concluded that SVR and RF outperformed k-NN in volume/ha estimation. These results may be due to the fact that RF is more sensitive to overfitting during model training, and can handle high data dimensionality [47,61,62]. The algorithm of the random forest method naturally includes the interactions of variables which are often ignored in other models because of their complexity [63]. However, Zhang et al. [12] found that ANN and SVM were the best methods for estimating sawgrass marsh AGB, with both methods achieving correlation coefficients (r) above 0.9, while ANN was the best method for total biomass estimation (r = 0.94). ANN achieved the best estimating precision independently of the SVIs of input data in this study. This result may be explained by the fact that ANN has advantages in complex pattern learning and generalizing in noisy environments. For CART, it was observed that for larger values of observed GSV, the predicted values of GSV were systematically lower (i.e., always below the 1:1 line), although high *R*2 was obtained when texture parameters or a combination of texture parameters and SVIs were used. This may be contributed to by over-fitting. Another reason for underestimation may be due to optical saturation under the conditions of high biomass in dense forests [64]. Overall, CART, SVM, ANN, and RF each have their own advantages. The selection of MLAs may be related to the type of RS information applied. For example, in this study, ANN could be used when applying SVIs alone for GSV estimation, and RF was a good choice when texture parameters or a fusion of textural and spectral information were used. The main drawback of these MLAs was that they did not reveal the functional relationships between the target and predictor variables. MLAs are often referred to as "black box" approaches [7,12,42,65].

Models using textural parameters as inputs achieved greater precision than spectral models, confirming that high-resolution textural information is more useful than spectral data for forest parameter estimation, especially in dense and complex forests characterized by a mosaic of dense and sparse vegetation cells [6,7,66]. Our results indicate that combining spectral and textural parameters yields, at best, marginal improvements in estimating precision compared to using textural parameters alone when estimating forest GSV. This is consistent with the conclusions of Chrysafis et al. [5], based on their work on Mediterranean forests. Textural information was indispensable in the estimation of forest parameters, especially when a high-resolution image was adopted. However, there were many uncertainties that needed to be considered, such as window size. It may be necessary to determine the optimal moving window size for extracting textural parameters.

In this research, forest GSV tended to be overestimated at lower values and underestimated at higher values (Figure 3). This uncertainty may be contributed to the inconsistency between the time of in-site data collection and RS image, although we applied the growth model of *Pinus massoniana* tree for stand volume conversion from 2018 or 2019 to 2015. The growth was calculated from the stand age of forest, which decreased the precision in our study. We suggested that it was better to keep the time of field data surveyed consistent with time of RS acquisition in estimation of forest parameters.

#### **5. Conclusions**

Four machine learning regression algorithms including classification and regression tree (CART), support vector machine (SVM), artificial neural network (ANN), and random forest (RF) algorithms were used to estimate the growing stock volume (GSV) of *Pinus massoniana* plantations in the northern subtropical area of China based on spectral vegetation indices (SVIs) and textural parameters extracted

from a SPOT6 remote sensing image. The RF method was found to offer the best performance for GSV estimation, although SVM, CART, and ANN also performed reasonably well. Increasing the size of the moving window used when extracting data from the SPOT6 image reduced the predictive performance of the CART, SVM, and RF methods, and the highest coefficient of determination (*R*2) values in these cases were achieved with 3 × 3 or 5 × 5 windows. Using both spatial and textural parameters as model inputs improved estimating precision relative to using spectral data alone. However, the combined approach did not appreciably outperform models using only textural parameters as inputs.

**Author Contributions:** Y.D. supervised the work and provided advice for preparing and revising the paper. J.Z. proposed the idea, collected field survey data, organized the writing, and revised the paper. Z.Z. contributed to the programming and experiments, and drafted the manuscript. Q.Z. and Z.H. analyzed the data and prepared figures. J.X. collected field survey data and did image preprocessing. P.W. helped to collect field survey data and revise the manuscript. All authors read and approved the final manuscript.

**Funding:** The research was funded by the national key research and development plan (grant number, 2017YFC050550404) and project 2662020YLPY020 supported by the fundamental research funds for the central university.

**Acknowledgments:** We would like to thank Jie Luo from Huazhong Agricultural University for providing constructive comments, which greatly improved the manuscript.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**

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**1. Introduction**

Digital financial inclusion is increasingly becoming central in the debate on how to ensure that people who are at the lower levels of the pyramid become financially active (Peric 2015). Banks and non-bank institutions are coming together to widen financial access using digital financial approaches to include those who are financially excluded and the underserved populations (Peric 2015). Banks and non-banking institutions are building on digital ways that were in use for years through the direct application of artificial intelligence (AI) to improve access even to the people who were previously served by the formal financial institutions (Alameda 2020; Peric 2015). The fourth industrial revolution is bringing changes in the traditional banking sector built in the industrial revolution premised on paper and physical distribution of cash (Alameda 2020).

The term fintech or financial technologies is used to describe different innovative business models that have great potential to transform the financial services industry (Mamoshina et al. 2018). The fintech business model offers various financial products or services in an automated fashion through the wide use of the internet (Paul 2019). Technologies that are driving industry 4.0 such as AI, machine learning, cognitive computing and distributed ledger technologies can be used to supplement fintech new entrants and traditional incumbents (Lopes and Pereira 2019a). Some other AI technologies that can be applied in the fintech sector to promote financial inclusion including *audio processing, knowledge representation, speech to text, deep learning, expert systems, natural language processing, machine learning* *(ML), robotics, and symbolic logic* (Paul 2019). It is believed that the popularity of AI technologies boomed in 2011 when companies like Google, Microsoft, IBM and Facebook embarked on a massive investment in AI and machine learning to be applied in the commercial space.

The traditional banking market is equipped with millions of customers with a history that spans over hundreds of years, and some of these customers may be worth billions (Alameda 2020; Peric 2015). The challenge which is currently there is that these customers are not digital (Alameda 2020; Loufield et al. 2018). On the other hand, fintech start-ups have a rich digital vision but to win the trust of customers is a huge obstacle to them (The World Bank 2020). The occurence of the disturbances caused by COVID-19 brought another perspective of fintech to customers as it was the only option available to engage in banking as well as buying. Banks resorted to digital banking while shopping in many countries was done online using various banking applications to perform transactions. In addition, the existence of various tech corporations like Google, Apple, Facebook Amazon in America and Baidu, Alibaba and Tencent in Asia who take pride in having millions of customers with financial returns in the billions and decades of history and a pure digital vision will act as examples a for banks to embrace digital technology and to understand the importance of AI in finance (Alameda 2020).

The World Bank stated that digital financial services which include the use of mobile phones have been launched in more than 80 countries (The World Bank 2020; Chu 2018). As a result, millions of formerly excluded and underserved poor individuals are migrating from cash-based transactions to formal financial services where a variety of services like payments, transfers, credit, insurance, securities and savings are offered to them (The World Bank 2020). Mobile phones and other digital tools including AI are widely used and the rate at which financial inclusion is rising is commendable (Salampasis and Mention 2018; Bill & Melinda Gates Foundation 2019). With digital financial inclusion, financial services are provided to customers at an affordable cost in ways that are sustainable to customers (Gomber et al. 2017). Digital financial services provide unlimited benefits to the previously excluded customers but it comes with a lot of risks which result from the introduction of non-financial firms in the provision of new technologies used in the process (The World Bank 2020; Rathi 2016).

Another risk in digital finance lies in the existence of new contractual relationships between financial institutions and third parties which involve the use of agent networks, other risks result from different regulatory treatment of deposit-like products as compared to real deposits, there are other risks which result from unknown and unpredictable costs to inexperienced and vulnerable consumers, together with risks that result from the use of new kinds of data which come with new privacy and data security issues (The World Bank 2020; Rathi 2016). However, experts are indicating that the use of AI (particularly algorithms) can help to fight some of the risks (Chu 2018; Killeen and Chan 2018). Motivated by the fact that in the industry 4.0, AI is increasingly becoming common while on the other hand digital financial inclusion is becoming central in the debate on how to ensure that people who are at the lower levels of the pyramid become financially active, for instance, groups of women, youths, small businesses among many disadvantaged groups. This study, therefore, intends to investigate the impact of AI on digital financial inclusion, that is to understand the channels in which AI can help to improve financial inclusion.


**2.2 Processing of AP sites**

306 Selected Topics in DNA Repair

Base excision repair (BER) is one of the major systems of DNA repair, mostly responsible for removing from DNA of non-bulky base lesions that appear in the genome with high frequency (Almeida & Sobol, 2007; Hegde et al., 2008; Schärer, 2003). The base excision repair pathway essentially removes and replaces nucleotides containing aberrant bases in DNA. Metabolically produced reactive nitrogen and oxygen species can modify the DNA bases due to oxidation, deamination and even alkylation at several positions in the base. BER is usually defined as DNA repair initiated by a lesion-specific DNA glycosylase and completed by either of two sub-pathways: short-patch BER; a mechanism whereby only 1 nucleotide is replaced or long-patch BER; a mechanism whereby 2–13 nucleotides are

The majority of BER is currently thought to occur via the short-patch pathway initiated by either a mono-functional or bi-functional DNA glycosylases. The short-patch BER pathway mediated by a mono-functional glycosylase involves removal of aberrant base by the lesionspecific DNA glycosylases, enzymes that hydrolyze cleavage of *N*-glycosylic bond between base and deoxyribose. This results in apurinic/apyrimidinic (also termed abasic) sites (AP sites), which repair is generally initiated through strand incision at its 5'-side by an AP endonuclease, leaving a nick flanked by a 3'-hydroxyl of an undamaged and a deoxyriboso-5'-phosphate (dRP), to which the damaged base was formerly linked. DNA polymerase then inserts a normal deoxyribonucleotide; however, the ligation step to restore intact DNA is blocked because of the dangling dRP moiety. The situation is resolved by a special enzymatic activity, excising dRP moiety (short-patch), or by continuing DNA synthesis with strand displacement, followed by removal of the displaced strand by flap endonuclease 1

In mammalian cells, the major dRP-removing enzyme is DNA polymerase β (Pol β), a multifunctional enzyme consisting of the 8-kDa amino-terminal domain with deoxyribose phosphate (dRP) lyase activity and the 31-kDa carboxy-terminal domain with nucleotidyl transferase activity (Allinson et al., 2001, Matsumoto & Kim, 1995, Piersen et al., 1996, Podlutsky et al., 2001). Thus, Pol β can mediate both steps in single nucleotide BER: insertion of deoxyribonucleotide and removal of dRP moiety preparing the strand for

Oxidized bases in DNA are manly produced by reactive oxygen species (ROS). ROS are generated as by-products of metabolic processes, primarily oxidative metabolism in the mitochondria (Dawson et al., 1993), and pathological conditions such as inflammation. ROS are also generated by ionizing radiation and some chemotherapeutic drugs (Almeida & Sobol, 2007; Hegde et al., 2008; Schärer, 2003). Oxidized bases are primarily removed by bifunctional DNA glycosylases that have an additional AP site cleavage activity. In addition to oxidation of DNA bases, ROS attack deoxyribose in DNA to generate strand breaks with nonligatable ends. The 3′ blocking groups include 3′ phosphate, 3′ phosphoglycolaldehyde, or 3′ phosphoglycolate (Breen et al., 1995). The 5′ terminus normally contains phosphate but after ROS reaction the nonligatable ends include 5′ OH and 5′ phosphodeoxyribose derivatives such as 2-deoxyribonolactone (Demple et al., 2002). Repair of single-strand breaks (SSBR) with blocked termini utilizes many of the same proteins as the BER process. The principal difference between SSBR and BER is the initiation step. Further both processes may occur at the single nucleotide level (i.e., short-patch repair) or as a long-patch of repair. BER is initiated via DNA glycosylase activity that results in the removal of a damaged base. SSBR is defined specifically for the repair of single-strand breaks in DNA generated by

**2.1 Base excision repair (overview)**

ligation.

replaced (Frosina et al., 1996; Klungland & Lindahl, 1997).

(long-patch BER) (Frosina et al., 1996; Klungland & Lindahl, 1997).

AP sites can be incised by three mechanistically different ways represented in Fig.1.

Fig. 1. Structures of intact and cleaved AP sites. DNA structures containing reactive sugar moieties capable of Schiff base formation are marked by grey rectangular boxes.

In mammalian cells, the repair of AP sites is generally initiated through strand incision by APE1, the second enzyme of the canonical BER pathway (Demple et al., 1991). APE1 is thought to process of over 95% of AP sites in mammalian cells (Demple & Harrison, 1994; Wilson & Barsky, 2001). APE1 catalyzes hydrolysis of AP sites by a Mg2+-dependent mechanism, resulting in cleavage of the phosphodiester bond 5' to the AP site and generation of a single-strand break. This reaction produces a 3'-hydroxyl group and a 5' dRP group flanking the break. This way of AP site processing fits short-patch pathway of the BER process.

Another way of AP site cleavage by the BER machinery involving the action of bi-functional DNA glycosylases is more complicated and requires the 'end cleaning' of termini prior to the further repair reactions may occur. An intermediate reaction step for these enzymes is the formation of a transient Schiff base between the amino group and the C1′ of deoxyribose for both base excision and subsequent DNA strand cleavage. In solution, AP sites exist predominantly as a mixture of ring-closed α- and β-hemiacetals with a minor amount of ring-opened aldehyde and aldehyde hydrate (<1%) (de los Santos et al., 2004). AP sites can be incised through -elimination using ε-NH2 of a lysine as the active site nucleophile (David & Williams, 1998; McCullough et al., 1999). Strand incision by -elimination forms a nick with phospho-α,β-unsaturated aldehyde (PUA) at the 3' margin and phosphate at the 5' margin (David & Williams, 1998; McCullough et al., 1999) (Fig. 1). While recently discovered mammalian DNA glycosylases, termed NEIL (Nei-like)-1, -2, catalyze the ,δ-elimination

New Players in Recognition of Intact and Cleaved AP Sites:

cleaved AP site.

*cerevisiae* was reported in (Rieger et al., 2006).

Implication in DNA Repair in Mammalian Cells 309

Fig. 2. Workflow of identification of protein cross-linked to DNA containing intact or

isolation of cross-linked DNA-protein products; (3) the preparative cross-linking of extract proteins with such DNA; (4) the affinity purification of the resulted products; (5) the identification of the protein in the covalent adduct with DNA using mass-spectrometry methods; (6) the confirmation of the results using purified proteins and/or specific antibodies and the known functions/interactions of the identified protein; and (7) the study of the functional role of the revealed interactions between proteins and AP DNA. A proofof-principle identification of protein reactive towards AP sites in species from *E. coli* and *S.*

It should be noted that used protocol includes two steps that increases the selectivity of the method. Affinity purification of cross-linked protein was carried out on commercially available affinity sorbents containing streptavidin, which binds the biotin residue. After adsorption, non-specifically bound proteins were removed by a series of washings. Additional separation of proteins was achieved by electrophoresis under denaturing conditions by the Laemmli method (Laemmli, 1970) followed by staining proteins. The target product was identified by the colocalization in the gel positions of the label in DNA and the stained protein. This approach indicates that only the protein cross-linked to DNA will be subjected to the further analysis. However, one cannot fully exclude the presence of

The identification was carried out for a number of proteins forming cross-links with AP sites in extracts of *E. coli* cells at logarithmic and stationary phases of cell growth (Rieger et al., 2006). It should be noted that authors used the previously developed non-enzymatic approach of the creation of AP sites in DNA based on the periodate oxidation of 2,3,5,6 tetrahydroxyhexyl phosphate precursor, which was introduced into an oligonucleotide

impurity proteins with the same electrophoretic mobility as the target product.

reaction with the N-terminal proline being used as the nucleophile (Hazra et al., 2002; Takao et al., 2002, Das et al., 2006). The ,δ-elimination reaction results in a one nucleotide gap flanked with phosphate groups at the 3'- and 5' margins. The products of bi-functional DNA glycosylase activity require the trimming of the 3'-ends to produce 3'-end hydroxyl groups that are indispensable for DNA polymerase activity.

Most of mammalian proteins known to form the Schiff base intermediate with the AP site appear to belong to the BER system. On the other hand, an interaction of AP DNA with proteins not formally involved in BER (for example, human ribosomal protein S3 and nucleoside diphosphate kinase – NM23-H2/NDP) has been well documented (Hegde et al., 2004; Postel et al., 2000). These two proteins are able to cleave AP sites. MutY, unlike other monofuctional DNA glycosylases, is able to interact with AP site via a Schiff base formation (Zharkov & Grollman, 1998) without the concomitant cleavage of AP sites. In this particular case formation of the Schiff base intermediate is considered as a mechanism for temporal protection of AP sites (Zharkov & Grollman, 1998).

Since AP sites in DNA appear to be promiscuous in their binding to many cellular factors and they are constantly generated in high frequency in genomic DNA, it may be important to sequester the AP sites immediately upon formation and further process them by the way that is most favorable for DNA integrity. Thus, interaction of cellular proteins with AP sites might be important for their repair/temporal protection from further degradation or might be involved in damage sensing/signaling making search of AP site reactive proteins a very important task.

#### **2.3 Search of new participants of recognition/processing of AP sites**

The very promising approach in search for unknown players in AP site recognition is based on a well-known propensity of deoxyribose in AP site, existing in equilibrium between cyclic furanose and acyclic aldehyde forms, to react with amine moieties in its vicinity. The Schiff base intermediate can be reduced by sodium borohydride (NaBH4) or related compounds, forming an irreversible complex between the enzyme and DNA (David & Williams, 1998; Piersen et al., 2000). This reaction is widely used to prove the β-elimination reaction mechanism for the enzymes capable of AP site cleavage (David & Williams, 1998; Piersen et al., 2000), although some proteins (e.g. MutY DNA glycosylase) can form a Schiff base with no further β-elimination (Zharkov & Grollman, 1998). Therefore, upon searching of new players in AP site recognition mediated by the Schiff base formation their ability to cleave AP has to be proved. Along with intact AP site, the product of its cleavage – 3' PUA and 5' dRP moieties (Fig. 1, grey rectangular boxes) are also able to form the Schiff base intermediate with primary amino groups of proteins that allows to use these DNA in search and identification of proteins.

#### **2.3.1 Identification of proteins reactive to AP sites**

For identification of protein cross-linked to AP DNA in cell extracts immunochemical and/or mass-spectrometry methods can be used. Identification of proteins reactive to AP sites by combination of cross-linking technique and mass-spectrometry is schematically represented in Fig. 2.

In general, search and identification of proteins reactive to AP sites include the following steps: (1) finding of cellular extract proteins forming covalent adducts with AP sites in DNA; (2) the design of AP-DNA probe containing a functional group providing the selective

reaction with the N-terminal proline being used as the nucleophile (Hazra et al., 2002; Takao et al., 2002, Das et al., 2006). The ,δ-elimination reaction results in a one nucleotide gap flanked with phosphate groups at the 3'- and 5' margins. The products of bi-functional DNA glycosylase activity require the trimming of the 3'-ends to produce 3'-end hydroxyl groups

Most of mammalian proteins known to form the Schiff base intermediate with the AP site appear to belong to the BER system. On the other hand, an interaction of AP DNA with proteins not formally involved in BER (for example, human ribosomal protein S3 and nucleoside diphosphate kinase – NM23-H2/NDP) has been well documented (Hegde et al., 2004; Postel et al., 2000). These two proteins are able to cleave AP sites. MutY, unlike other monofuctional DNA glycosylases, is able to interact with AP site via a Schiff base formation (Zharkov & Grollman, 1998) without the concomitant cleavage of AP sites. In this particular case formation of the Schiff base intermediate is considered as a mechanism for temporal

Since AP sites in DNA appear to be promiscuous in their binding to many cellular factors and they are constantly generated in high frequency in genomic DNA, it may be important to sequester the AP sites immediately upon formation and further process them by the way that is most favorable for DNA integrity. Thus, interaction of cellular proteins with AP sites might be important for their repair/temporal protection from further degradation or might be involved in damage sensing/signaling making search of AP site reactive proteins a very

The very promising approach in search for unknown players in AP site recognition is based on a well-known propensity of deoxyribose in AP site, existing in equilibrium between cyclic furanose and acyclic aldehyde forms, to react with amine moieties in its vicinity. The Schiff base intermediate can be reduced by sodium borohydride (NaBH4) or related compounds, forming an irreversible complex between the enzyme and DNA (David & Williams, 1998; Piersen et al., 2000). This reaction is widely used to prove the β-elimination reaction mechanism for the enzymes capable of AP site cleavage (David & Williams, 1998; Piersen et al., 2000), although some proteins (e.g. MutY DNA glycosylase) can form a Schiff base with no further β-elimination (Zharkov & Grollman, 1998). Therefore, upon searching of new players in AP site recognition mediated by the Schiff base formation their ability to cleave AP has to be proved. Along with intact AP site, the product of its cleavage – 3' PUA and 5' dRP moieties (Fig. 1, grey rectangular boxes) are also able to form the Schiff base intermediate with primary amino groups of proteins that allows to use these DNA in search

For identification of protein cross-linked to AP DNA in cell extracts immunochemical and/or mass-spectrometry methods can be used. Identification of proteins reactive to AP sites by combination of cross-linking technique and mass-spectrometry is schematically

In general, search and identification of proteins reactive to AP sites include the following steps: (1) finding of cellular extract proteins forming covalent adducts with AP sites in DNA; (2) the design of AP-DNA probe containing a functional group providing the selective

**2.3 Search of new participants of recognition/processing of AP sites**

that are indispensable for DNA polymerase activity.

protection of AP sites (Zharkov & Grollman, 1998).

important task.

and identification of proteins.

represented in Fig. 2.

**2.3.1 Identification of proteins reactive to AP sites**

Fig. 2. Workflow of identification of protein cross-linked to DNA containing intact or cleaved AP site.

isolation of cross-linked DNA-protein products; (3) the preparative cross-linking of extract proteins with such DNA; (4) the affinity purification of the resulted products; (5) the identification of the protein in the covalent adduct with DNA using mass-spectrometry methods; (6) the confirmation of the results using purified proteins and/or specific antibodies and the known functions/interactions of the identified protein; and (7) the study of the functional role of the revealed interactions between proteins and AP DNA. A proofof-principle identification of protein reactive towards AP sites in species from *E. coli* and *S. cerevisiae* was reported in (Rieger et al., 2006).

It should be noted that used protocol includes two steps that increases the selectivity of the method. Affinity purification of cross-linked protein was carried out on commercially available affinity sorbents containing streptavidin, which binds the biotin residue. After adsorption, non-specifically bound proteins were removed by a series of washings. Additional separation of proteins was achieved by electrophoresis under denaturing conditions by the Laemmli method (Laemmli, 1970) followed by staining proteins. The target product was identified by the colocalization in the gel positions of the label in DNA and the stained protein. This approach indicates that only the protein cross-linked to DNA will be subjected to the further analysis. However, one cannot fully exclude the presence of impurity proteins with the same electrophoretic mobility as the target product.

The identification was carried out for a number of proteins forming cross-links with AP sites in extracts of *E. coli* cells at logarithmic and stationary phases of cell growth (Rieger et al., 2006). It should be noted that authors used the previously developed non-enzymatic approach of the creation of AP sites in DNA based on the periodate oxidation of 2,3,5,6 tetrahydroxyhexyl phosphate precursor, which was introduced into an oligonucleotide

New Players in Recognition of Intact and Cleaved AP Sites:

with NaBH4 and analyzed.

nucleophiles is indispensable for efficient catalysis.

**2.3.3 AP site recognition by the 5'-dRP/AP lyase in PARP-1**

of ~120-kDa that is reactive to AP site (Fig. 4A, lanes 1–4).

realized according to the scheme shown in Fig. 2.

Implication in DNA Repair in Mammalian Cells 311

Fig. 3. Interaction of Ku antigen with AP sites (From Ilina et al., 2008). (A) Cross-linking of proteins in HeLa cell extract (lane 1); without borohydride treatment (lane 2); AP DNA probe was replaced by the DNA duplex containing a THF residue (lane 3). (B) Specificity of the Ku80 antigen interaction with AP DNA. Cross-linking of the HeLa cell extract proteins to AP DNA was performed in the absence (lane 1) or presence of competitive DNA at different concentrations (lanes 2-7). The structures of competitive DNAs are shown at the top. Ratio of competitive DNA to DNA probe is shown at the bottom. (C) Influence of DNA-PK on the activity of APE1. (D) Estimation of the stability of Ku complex with AP DNA in HeLa cell extract. AP DNA was preincubated with HeLa cell extract for 15 min at 37º. Then excess of competitive DNA containing a THF residue was added, and the reaction mixture was further incubated at 37º for additional 4 hours. Aliquots at different times were reduced

appropriate AP lyase substrate and AP lyase activity test in the cell extracts deficient and proficient in Ku antigen unambiguously testify to the role of Ku antigen in processing of AP sites positioned near 5′ termini of DS breaks. Moreover, transfection of Ku deficient or proficient cells with variants of specifically designed substrate DNAs (with natural AP site or its AP lyase-resistant analog or without AP site) followed by PCR amplification of joining products and subsequent restriction analysis of amplicons fully confirmed the necessity of Ku antigen AP lyase activity for removal of near-end AP sites. Altogether the results obtained *in vitro* and *in vivo* testify to use of the 5′-dRP/AP lyase activity of Ku antigen for the excision of near-end abasic sites and explain higher radiosensitivity of mammalian cells deficient in Ku antigen, which is indispensable for classical NHEJ (Schulte-Uentrop et al., 2008). It is worthy of notice, that the same mechanism of AP site cleavage is used by two unrelated DNA repair systems and the suitable positioning of AP sites relative to active site

In further screening for proteins that are reactive to AP sites in addition to a linear DNA duplex with an AP site in the middle of the 32P-5'end-labeled strand, we used circular AP site-containing DNA to exclude interference by Ku80. Circular double-stranded DNA was synthesized, using single-stranded M13 DNA as template, in the presence of dUTP; then, AP sites were generated by uracil DNA glycosylase treatment (Khodyreva et al., 2010a; Khodyreva et al., 2010b). Unlike short duplex DNA with an AP site, that predominantly cross-linked Ku80 in HeLa cell extract (Ilina et al., 2008 and Fig. 4A, lane 5), the use of circular AP site-containing DNA allowed us to detect a novel protein with molecular mass

To identify the cross-linked protein large-scale cross-linking with the bovine testis nuclear extract (BTNE) and a biotin-containing linear AP DNA was performed. Identification was

during the standard phosphoroamidite oligonucleotide synthesis. The periodate oxidation allows one to obtain AP sites in an almost quantitative yield. This method, in the authors' opinion, has several advantages over the more commonly used method that is based on the removal of uracil residues using uracil DNA glycosylase.

The procaryotic proteins AroF, DnaK, MutM, PolA, TnaA, TufA, and UvrA from *E. coli*  and eucaryotic ARC1 and Ygl245wp from yeast were identified (Rieger et al., 2006). Protein Ygl245wp with an unknown but essential for cell viability function was prepared in the individual state after its coding sequence was cloned, and the recombinant plasmid was used for the production of the protein in *E. coli* cells followed by its isolation. The obtained protein was shown to bind to AP DNA forming Schiff bases; however, the biological significance of this interaction has not been established. DNA polymerase I, like other DNA polymerases of the A-family, has a weak AP lyase activity and dRP lyase activity (Pinz & Bogenhagen, 2000). However the biological significance of these activities *in vivo* has not been established. The ability of UvrA, the key component of the nucleotide excision repair (NER) in bacteria, to interact with AP sites in the UvrABC complex indicates the possible role of NER as a back-up pathway of AP site repair in bacteria (Showden et al., 1990).

#### **2.3.2 Identification of Ku80 subunit of human Ku antigen as a protein reactive to AP sites**

Using the same technique we screened mammalian cell extracts for proteins capable of binding AP site-containing DNA. In HeLa cell extract, a 32-bp DNA duplex with an AP site in the middle of the DNA chain was shown to cross-link predominantly to a protein forming a product with an apparent molecular mass of 95 kDa (Fig. 3) (Ilina et al., 2008).

The subsequent analysis of whole cell extracts of human lung fibroblasts, K-562, and MCF-7 cells revealed the products with the same electrophoretic mobility. The preferential crosslinking of this protein reflected extraordinary specificity in light of the multitude of proteins in the cell. If the AP site in DNA is replaced by its analog (the THF residue) or the treatment with NaBH4 is omitted, DNA–protein cross-linked products are not registered (Fig. 3).

In spite of efficient cross-linking of the above mentioned AP DNA to Ku80 polypeptide (as a part of DNA-PK) no cleavage of AP sites was observed (Ilina et al., 2008). Instead Ku80 formed with AP site a long-living Schiff base intermediate without the concomitant AP site cleavage just as was observed for monofunctional DNA glycosylase MutY and considered as a mechanism for temporal protection of AP sites (Zharkov & Grollman, 1998). AP lyase and 5'-dRP activities are distinctive features of the BER process (Almeida & Sobol, 2007; Hegde et al., 2008). The BER 5'-dRP/AP lyases usually function beyond the DS-breaks. But abasic sites associated with double-strand breaks can be generated by ionizing radiation, by treatment with radiomimetic drugs or as a result of attempted BER of complex damages (Yang et al., 2004). These specifically positioned lesions must be removed prior to or in the course of DS break repair. Ku antigen – DS-end binding protein of NHEJ – has been recently shown to act as a 5'-dRP/AP lyase near double-strand breaks (Roberts et al., 2010).

At DSB ends Ku is approximately tenfold more active than Pol β (Roberts et al., 2010), the most known mammalian 5'-dRP lyase (Matsumoto & Kim, 1995). At the same time, Ku was inefficient as AP lyase at AP sites situated at a distance longer than one helix turn from DS breaks just as was reported in (Ilina et al., 2008). NaBH4-dependent cross-linking of

during the standard phosphoroamidite oligonucleotide synthesis. The periodate oxidation allows one to obtain AP sites in an almost quantitative yield. This method, in the authors' opinion, has several advantages over the more commonly used method that is based on the

The procaryotic proteins AroF, DnaK, MutM, PolA, TnaA, TufA, and UvrA from *E. coli*  and eucaryotic ARC1 and Ygl245wp from yeast were identified (Rieger et al., 2006). Protein Ygl245wp with an unknown but essential for cell viability function was prepared in the individual state after its coding sequence was cloned, and the recombinant plasmid was used for the production of the protein in *E. coli* cells followed by its isolation. The obtained protein was shown to bind to AP DNA forming Schiff bases; however, the biological significance of this interaction has not been established. DNA polymerase I, like other DNA polymerases of the A-family, has a weak AP lyase activity and dRP lyase activity (Pinz & Bogenhagen, 2000). However the biological significance of these activities *in vivo* has not been established. The ability of UvrA, the key component of the nucleotide excision repair (NER) in bacteria, to interact with AP sites in the UvrABC complex indicates the possible role of NER as a back-up pathway of AP site repair in bacteria

**2.3.2 Identification of Ku80 subunit of human Ku antigen as a protein reactive to AP**

a product with an apparent molecular mass of 95 kDa (Fig. 3) (Ilina et al., 2008).

Using the same technique we screened mammalian cell extracts for proteins capable of binding AP site-containing DNA. In HeLa cell extract, a 32-bp DNA duplex with an AP site in the middle of the DNA chain was shown to cross-link predominantly to a protein forming

The subsequent analysis of whole cell extracts of human lung fibroblasts, K-562, and MCF-7 cells revealed the products with the same electrophoretic mobility. The preferential crosslinking of this protein reflected extraordinary specificity in light of the multitude of proteins in the cell. If the AP site in DNA is replaced by its analog (the THF residue) or the treatment with NaBH4 is omitted, DNA–protein cross-linked products are not registered (Fig. 3). In spite of efficient cross-linking of the above mentioned AP DNA to Ku80 polypeptide (as a part of DNA-PK) no cleavage of AP sites was observed (Ilina et al., 2008). Instead Ku80 formed with AP site a long-living Schiff base intermediate without the concomitant AP site cleavage just as was observed for monofunctional DNA glycosylase MutY and considered as a mechanism for temporal protection of AP sites (Zharkov & Grollman, 1998). AP lyase and 5'-dRP activities are distinctive features of the BER process (Almeida & Sobol, 2007; Hegde et al., 2008). The BER 5'-dRP/AP lyases usually function beyond the DS-breaks. But abasic sites associated with double-strand breaks can be generated by ionizing radiation, by treatment with radiomimetic drugs or as a result of attempted BER of complex damages (Yang et al., 2004). These specifically positioned lesions must be removed prior to or in the course of DS break repair. Ku antigen – DS-end binding protein of NHEJ – has been recently shown to act as a 5'-dRP/AP lyase near double-strand breaks

At DSB ends Ku is approximately tenfold more active than Pol β (Roberts et al., 2010), the most known mammalian 5'-dRP lyase (Matsumoto & Kim, 1995). At the same time, Ku was inefficient as AP lyase at AP sites situated at a distance longer than one helix turn from DS breaks just as was reported in (Ilina et al., 2008). NaBH4-dependent cross-linking of

removal of uracil residues using uracil DNA glycosylase.

(Showden et al., 1990).

(Roberts et al., 2010).

**sites**

Fig. 3. Interaction of Ku antigen with AP sites (From Ilina et al., 2008). (A) Cross-linking of proteins in HeLa cell extract (lane 1); without borohydride treatment (lane 2); AP DNA probe was replaced by the DNA duplex containing a THF residue (lane 3). (B) Specificity of the Ku80 antigen interaction with AP DNA. Cross-linking of the HeLa cell extract proteins to AP DNA was performed in the absence (lane 1) or presence of competitive DNA at different concentrations (lanes 2-7). The structures of competitive DNAs are shown at the top. Ratio of competitive DNA to DNA probe is shown at the bottom. (C) Influence of DNA-PK on the activity of APE1. (D) Estimation of the stability of Ku complex with AP DNA in HeLa cell extract. AP DNA was preincubated with HeLa cell extract for 15 min at 37º. Then excess of competitive DNA containing a THF residue was added, and the reaction mixture was further incubated at 37º for additional 4 hours. Aliquots at different times were reduced with NaBH4 and analyzed.

appropriate AP lyase substrate and AP lyase activity test in the cell extracts deficient and proficient in Ku antigen unambiguously testify to the role of Ku antigen in processing of AP sites positioned near 5′ termini of DS breaks. Moreover, transfection of Ku deficient or proficient cells with variants of specifically designed substrate DNAs (with natural AP site or its AP lyase-resistant analog or without AP site) followed by PCR amplification of joining products and subsequent restriction analysis of amplicons fully confirmed the necessity of Ku antigen AP lyase activity for removal of near-end AP sites. Altogether the results obtained *in vitro* and *in vivo* testify to use of the 5′-dRP/AP lyase activity of Ku antigen for the excision of near-end abasic sites and explain higher radiosensitivity of mammalian cells deficient in Ku antigen, which is indispensable for classical NHEJ (Schulte-Uentrop et al., 2008). It is worthy of notice, that the same mechanism of AP site cleavage is used by two unrelated DNA repair systems and the suitable positioning of AP sites relative to active site nucleophiles is indispensable for efficient catalysis.


**Contents**

#### **Rana El Rawas, In ˆes M. Amaral and Alex Hofer**

Is p38 MAPK Associated to Drugs of Abuse-Induced Abnormal Behaviors? Reprinted from: *Int. J. Mol. Sci.* **2020**, *21*, 4833, doi:10.3390/ijms21144833 .............. **169**


**3.8 Effects of replacement of SFA with other nutrients on plasma lipids, blood pressure, and other risk markers**

Replacing 1% of energy from SFA with 1% of energy from PUFA, MUFA, or complex carbohydrates decreased plasma LDL cholesterol by 2.1, 1.6, and 1.3 mg/dl, respectively [102]. HDL cholesterol decreased by 0.2, 0.2, and 0.4 mg/dl after substitution of SFA by PUFA, MUFA, or complex carbohydrate. The change in TG

## *Effects and Issues of Diet Fat on Cardiovascular Metabolism DOI: http://dx.doi.org/10.5772/intechopen.93261*

was 0.9, 0.4, and +1 mg/dl by substituting with PUFA, MUFA, or complex carbohydrates. These data indicate that in order to decrease LDL cholesterol or TG, the best strategy is to reduce the intake of SFA. SFA exert different LDL increasing ability depending on chain length. However, this difference is not clinically so important, because every oil contains several SFAs with various chain lengths. In general, there are progressive increases in LDL-C with diminishing chain length. The potency of the LDL-raising effects of individual SFAs is lauric acid (C12:0) > myristic acid (C14:0) > palmitic acid (C16:0) [103].

SFA may affect the risk of CV disease independently of LDL cholesterol, through the effects on inflammation, endothelial function, thrombosis, and ventricular arrhythmias [104]. One ex vivo randomized cross-over study showed that a breakfast with butter (35% SFA) induced a higher increase in TNF-alfa mRNA than breakfasts with olive oil (36% MUFA) or walnuts (16% PUFA), and also a higher response in IL-6 mRNA than the walnut breakfast in peripheral blood mononuclear cells (PBMCs) in healthy subjects (**Figure 10**) [105]. In another study, LDL-induced adhesion of monocyte to endothelial cells was also lower after MUFA consumption than after SFA in healthy individuals [106]. Some other ex vivo studies in humans have shown that consumption of butter was associated with more activated genes involved in the regulation of cell proliferation and inflammation compared with consumption of olive oil or vegetable oil [107, 108]. In these studies, activation of inflammatory markers has occurred without significant changes in the blood lipid profile [106, 108]. In summary, the previous findings strongly suggest that saturated and unsaturated fats have unique biological effects on vascular walls which are independent on the reduction in LDL cholesterol, which is like the effects of statins.

#### **3.9 Guideline recommendations for dietary intake of SFA**

Guidelines of authoritative opinions in Japan, the US, and Europe including cardiology, diabetes, and atherosclerosis societies except ADA [36] recommend less than 7% of calorie from SFA in subjects with background diseases (**Table 1**). In healthy subjects, the recommended upper limit of SFA is 7% in Japan [33] and 10% in the US [109]. Restriction of SFA to <10% rather than 7% in diabetic subjects as recommended by the ADA [36] may sound a little strange, because diabetes is associated with high CV risk, and on-target LDL cholesterol criteria is generally more stringent. This recommendation is based on the data from one small 3-week study that compared a low SFA diet (8% of total calories) vs. a high SFA diet (17% of total calories), and showed no significant difference in glycemic control and most CVD risk measures [110]. Therefore, there is limited research regarding effects of

#### **Figure 10.**

*Response of mRNA TNFα (left) and IL-6 (right) in breakfast with butter, olive oil, and walnuts measured in peripheral blood mononuclear cells (PBMCs) [105].*

**3.8 Effects of replacement of SFA with other nutrients on plasma lipids, blood**

*Sales of sugar-sweetened beverages (SSBs) in kcal/person/day by beverage type in 2009–2014 in some selected*

*Changes in the format of the nutrition label of the original and the new version on Jan 2020.*

Replacing 1% of energy from SFA with 1% of energy from PUFA, MUFA, or complex carbohydrates decreased plasma LDL cholesterol by 2.1, 1.6, and 1.3 mg/dl, respectively [102]. HDL cholesterol decreased by 0.2, 0.2, and 0.4 mg/dl after substitution of SFA by PUFA, MUFA, or complex carbohydrate. The change in TG

**pressure, and other risk markers**

**Figure 8.**

*New Insights into Metabolic Syndrome*

**Figure 9.**

**82**

*countries [95].*

SFA on diabetic control, and the ADA nutrition position paper recommends people with diabetes follow the guidelines for the general population, which is 10% according to USDA [109]. Despite insufficient data about effects of SFA on glycemic control, the evidence in many review manuscripts or meta-analyses so far accumulated has indicated that a Mediterranean diet helps prevent type 2 diabetes and causes HbA1c reduction in persons with established diabetes [111–115]. Therefore, food habits to restrict intake of SFA as in the Mediterranean diet is a very reasonable strategy in diabetic subjects.

**4. Cholesterol**

significant.

should be paid to reducing SFA in food.

**5. Polyunsaturated fat**

**5.1 n-3 and n-6 PUFA**

**85**

**4.1 Relation of plasma LDL cholesterol and dietary intake of cholesterol**

*Effects and Issues of Diet Fat on Cardiovascular Metabolism*

*DOI: http://dx.doi.org/10.5772/intechopen.93261*

estingly, repeated cholesterol loading has changed some nonresponders to

rather than a true difference between responders and nonresponders. In responders, both LDL and HDL cholesterol increased with no change in ratio of LDL to HDL. A gene of ABCG5/8 regulates absorption of dietary cholesterol and sterol from gut [128]. People carrying polymorphisms of ABCG5/8 have a higher absorption rate of cholesterol. In these subjects, the effects of manipulation of intake of cholesterol in food on plasma LDL cholesterol levels have been very

responders [126, 127]. The presence of two types of cholesterol response may reflect just day-to-day variation of cholesterol absorption from the gut in a single person

The possible link between dietary cholesterol and CHD risk is potentially distorted by confounding factors from other features in the diet, especially SFA [129]. A previous recommendation of less than 300 mg of dietary cholesterol has been removed from the 2013 AHA/ACC Guideline on Lifestyle Management to Reduce Cardiovascular Risk [38], stating that there is insufficient evidence to determine whether lowering dietary cholesterol reduces LDL-C. The same action was taken in the 2015 Dietary Guidelines of USDA [40] and the 2015 Japanese dietary intake standards by Ministry of Health, Labor and Welfare [33]. By contrast, other guidelines still restrict the dietary cholesterol intake, for instance, 200 mg/day in JAS [35] and AACE [130] guidelines, or 300 mg/day in ESC/EAS [39], ADA [36], and JDS [34] guidelines. These guidelines engender the risk of potentially increasing intake of SFA by abandoning the recommendation for dietary cholesterol intake. However, all of these, in common, stress risk evaluation of atherothrombotic diseases and execution of comprehensive management to reduce the risk. Even the guideline from JAS, which limits dietary cholesterol intake at 200 mg/day, states very clearly that only restricting dietary cholesterol intake is hardly efficacious in reducing plasma LDL cholesterol, and rather more attention

Every cooking oils or foods generally include several kinds of fatty acids. **Table 3** shows the amounts of different fatty acids in several commercially available oils and foods. In the US, people use soybean oil very frequently for cooking, and its dominant fatty acid is n-6 PUFA (50%), along with MUFA (25%). Olive oil, a very popular oil in the Mediterranean Sea area, is a well-known representative with

Plasma LDL cholesterol value changes depending on oral intake of cholesterol. One meta-analysis of 17 studies reported that a 100 mg increment in dietary cholesterol from eggs elevated plasma total cholesterol by 2.2 mg/dl [124]. On the contrary, many studies have brought into question the apparent association between dietary cholesterol consumption and blood cholesterol [38]. Plasma cholesterol undergoes a highly degree of regulation to balance absorption in the intestine and synthesis in the liver [125]. Low dietary cholesterol intake is compensated for by an increase in absorption. These mechanisms explain the inter-individual variability in absorption (20–80%) in humans. Thus, there are responders and nonresponders to intake of cholesterol in terms of plasma cholesterol levels. Inter-

Three Japanese cohort studies have shown that the incidence of brain hemorrhage is inversely related to intake of SFA [116–118], and this moved the JAS to define the lower limit (4.5%) of intake of SFA [35]. This relation has not been demonstrated in meta-analysis of subjects in the US and Europe. There have been no data showing high incidence of stroke in vegetarians who consume very little SFA. Therefore, none of the guidelines in the rest of the world have incorporated the lower limit of intake of SFA. One has to be cautious that limiting SFA in the diet tends to decrease intake of good fat, as PUFA, fish oil, or olive oil.

Intake of 7% of calories as SFA is equivalent to about 15 g of SFA in a person who needs 2000 cal/day. Average intake of SFA is 15.2 and 13.8 g/day for Japanese men and women at the age of 30–49 years old [119], and 31.4 and 20.3 g/day for American men and women at the age of 31–50 years old [120]. It is reasonable that refraining from red meat, processed meat, butter, or SFA-loaded snacks is a simple way to cut SFA in daily food habits. In the US, only approximately 5% of the population consumes less than 7% of their calories from saturated fat [120].

### **3.10 Guideline recommendations for dietary intake of PUFA and MUFA**

Beneficial effects of PUFA and MUFA seem to be fully recognized in guidelines in the US and Europe (**Table 1**). Guidelines from ADA, ACC/AHA, NLA, and USDA strongly recommend the Mediterranean and DASH diets. Regarding efficacy of decreasing blood glucose, the ADA provides the same recommendation for both PUFA and MUFA [36]. The 2013 AHA/ACC Guideline on Lifestyle Management [38] states that PUFA or MUFA should be used for reducing LDL cholesterol instead of SFA at the same recommendation level.

Literature has emerged that the biological effects of olive oil differ from those of plant-based MUFA or PUFA. Incorporating data from the PREDIMED study [6], the 2016 Canadian Cardiovascular Society Guidelines for the Management of Dyslipidemia [121] concretely recommend olive oil (>60 ml/day) and nuts (>30 g/ day) rather than using general terms for MUFA. An authoritative review of dietaries and policies for cardiovascular diseases in 2016 [122] has also listed its daily recommended doses of MUFA and PUFA, such as 10–30 ml/day of soybean oil (MUFA: 22%, PUFA: 56%, and SFA: 15%), extra virgin olive oil (MUFA: 74%, PUFA: 7%, and SFA: 13%), and canola oil (MUFA: 64%, PUFA: 28%, and SFA: 7%). These guidelines provide data of effects on plasma LDL cholesterol and CV events by substitution of SFA with PUFA and MUFA.

The differences in guideline recommendations of the fat intake appear to reflect the differences in food habits and in recognition of the benefits of healthy fats between countries. In 2010, nonoptimal intake of n-6 PUFA, SFA, and trans fat resulted in 710,000, 251,000, and 537,000 deaths from CHD per year worldwide [123]. In 80% of nations, including Japan, China, and other many Asian nations, CHD burdens attributable to n-6 PUFA were 2-fold higher than the SFAattributable burdens. These data indicate that it is more important to focus on increasing healthful n-6 rich vegetable oils in the diet than to focus on replacement of SFA and carbohydrates for public health benefits.


*5.3.2 Real-time PCR*

Real-time polymerase chain reaction is an effective method for determining the concentration of nucleic acids in isolated blood specimens. In addition to being quick, sensitive, and specific, it has a high rate of reproducibility [36].


Preface

The advanced and rapid progression of digital technologies has contributed massive structural changes to the world economy. Over the last few decades, digital technologies have fundamentally changed organizations, business strategies, processes, products, and services by deconstructing traditional ways of doing business.

The number of leaders adapting to and engaging with digital technology in organizations is rapidly growing, and these developments provide great incentives to explore the conditions facilitating digital transformation. Digital technology can provide many advantages to organizations, such as faster communication unrestrained from time and space, effectively making access to other knowledge networks and global markets convenient.

As digital organizations are becoming more common, there is a need to recognize and understand the challenges, processes, and opportunities distinctive to digital leaders. It may be said that digital leaders' ability to detect technological opportunities is attributed to different scientific skill sets and expertise. The quality of leaders' research and previous experience of discovering technological opportunities would more likely foster the detection of new technological opportunities.

In this context, digital leadership, as a new style of leadership, has gained increasing attention. The technological changes of the last few decades have shaped new concepts of leadership, such as digital leadership. Digital leadership is here defined as the human quality of leadership exercised with digital tools in the virtual world. We are in a period in which information communication technology is seen as an important resource; therefore, the call for digital leadership is more and more urgent. The importance of digital leadership has been widely recognized in the literature, evidenced by the notable increase in relevant publications in the past few years. Thus, in general, this book will be devoted to the study of the benefits of digital leadership (digital technologies), as well as the risks of this type of leadership. New perspectives on digital leadership will be also presented in different contexts.

This is the purpose for which this book has been written and it is structured in three sections.

In the first section, we collected papers showing leadership from different perspectives and its challenges in the twenty-first century. In the **first chapter**, the authors attempt to clarify the main challenges of leadership in the digital realm. This chapter examines how digital leaders cultivate opportunities and address risks in a fast-moving, digital market environment.

The **second chapter** is titled "Leadership for Digital Working: Towards a Contextual Ambidextrous Approach." Here the authors show how the leadership area can be approached from this theory. Some examples are presented and some arguments are also discussed.

**II**

**Chapter 7 129**

**Chapter 8 145**

Islamic Leadership: Comparisons and Qualities

Digitalizing South African Universities: Exploring Benefits,

*by Ahmad Rafiki*

Barriers and Risks

*by Bethuel Sibongiseni Ngcamu*

The **third chapter** shows how good leadership can be seen as a competitive advantage for organizations in the twenty-first century. The authors look at the main advantages that an organization can achieve with digital entrepreneurial leaders.

In the second section, theoretical and practical applications about digital leadership are presented. Therefore, the **fourth chapter** offers a new leader–followership paradigm for virtual leading–following relationships and organizations in the digital age from a North American perspective. On the other hand, the **fifth chapter** presents complex adaptive team systems, i.e., the scaling of a team leadership development model.

In last section—Digital Leadership in Different Countries—three studies realized in different contexts are presented. The first study shows digitalization in the Spanish publishing industry (**sixth chapter**) and the second work describes the concept and principles of Islamic leadership (**seventh chapter**). In the last chapter (**eighth**), the authors identify the risks and benefits of applying a digital leadership style in South African universities.

> **Mário Franco** University of Beira Interior, CEFAGE-UBI Research Center, Portugal

> > **1**

Section 1

Leadership in Digital Era:

Main Challenges

## Section 1


**Appendix A**

**Figure A1.** Presentation of "The Story" Section.

**Figure A2.** Details about Story Triads Section.

**Figure A3.** Details about Story Diads Section.

**Figure A4.** Information about Respondent Section, using multiple choice questions.


**6. Conclusion**

12 Will-be-set-by-IN-TECH

type was not separately considered. Moreover, for fatty tissue GLM performs better, while, by contrary, for dense breasts the optimal results are obtained when employing GLMs to select

Figure (4) shows the AUC values for the best subset model combinations, employing GLMs and GAMs, for both fatty and dense tissue. Figure 5 represents the AUCs for the global

It can be observed that, for GLM, results obtained for fatty tissue are higher than those obtained for dense tissue. However, when employing the GAM, differences are lower, and

Sensitivity and false positive rates were also calculated for the best GLM and the best GAM. For linear models, results yielded a sensitivity of 88.31%, at a false positive rate of 3.7 FPs per image. For the same sensitivity, the false positive fraction achieved when reducing false

**GLM**

2 4 6 810

number of covariates

**GAM**

2 4 6 810

number of covariates

Fig. 4. "Optimal" models for both fatty and dense tissue. For each subset size, the AUC is

analysis, and for both fatty and dense tissue, for the best GLM and GAM.

a more reduced number of covariates have to be included in the study.

covariates.

0.60 0.70 0.80 0.90

0.60 0.70 0.80 0.90

shown for each model.

AUC

fatty dense

fatty dense

AUC

In this work, GLMs and GAMs were applied to the reduction of false positives yielded by a CAD system devoted to the detection of clusters of microcalcifications. Results indicate that not all the features extracted from the detected clusters are useful for the discrimination between true and false detections: Moreover, there are features that are relevant when the different type of tissue is considered, and their influence is different depending on the breast parenchyma.

After the reduction of false positives, the system is capable of discriminating and detecting clustered microcalcifications from digital mammograms, this suggesting that this CAD

ii<-F==1; X1=X[ii,];Y1=Y[ii] GLM0results=stepGAM(X0,Y0,"glm") GLM1results=stepGAM(X1,Y1,"glm")

GAM0results=stepGAM(X0,Y0) GAM1results=stepGAM(X1,Y1)


**5. Conclusions**

In summary, here we find that decreased levels of miRNA-370 or increased levels of miRNA-10a in plasma from HHT patients represent novel biomarkers that could help to identify undiagnosed patients within the HHT1 or HHT2 subsets, respectively. Both miRNA-370 and miRNA-10a can regulate angiogenesis, a VEGF-dependent biological process involved in the pathophysiology and therapy of HHT, by targeting, at least, VEGF receptors. Additionally, miRNA-370 and miRNA-10a are predicted to target *ENG* and *ALK1* genes, respectively, whose deleterious mutations lead to HHT1 or HHT2, respectively. These findings open up a new research avenue to better understand the functional impact of dysregulated miRNAs in HHT development. Future independent studies remain to be performed in order to further validate the role of miRNA-370 and miRNA-10a as biomarkers in HHT, as well as to investigate the functional and pathophysiological significance of these newly discovered miRNA-370/HHT1 and miRNA-10a/ALK1 associations.

**Author Contributions:** Conceptualization, L.R.-L., L.M.B., and C.B.; methodology, L.R.-L., and V.A.; software, L.R.-L., and V.A.; validation, L.R.-L.; formal analysis, L.R.-L., V.A., L.M.B., and C.B.; investigation, L.R.-L.; resources, L.M.B. and C.B.; writing—original draft preparation, L.R.-L., and C.B.; writing—review and editing, L.R.-L., V.A., L.M.B., and C.B.; visualization, L.R.-L., and C.B.; supervision and project administration, C.B.; funding acquisition, C.B.; All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was funded by grants from Ministerio de Ciencia, Innovación y Universidades of Spain (SAF2013-43421-R to CB), Centro de Investigación Biomédica en Red de Enfermedades Raras (CIBERER; ISCIII-CB06/07/0038 to CB), and Consejo Superior de Investigaciones Científicas (CSIC; 201920E022 to CB). CIBERER is an initiative of the Instituto de Salud Carlos III (ISCIII) of Spain supported by European Regional Development (FEDER) funds.

**Acknowledgments:** We thank Laura Barrios (Statistics Department, Computing Center, CSIC, Madrid, Spain) for expert statistical analysis, and Carmen Langa, Elena de Blas and Lucia Recio-Poveda for technical support.

**Conflicts of Interest:** The authors declare no conflict of interest.


**Comparison of Soil EC Values from Methods Based on 1:1 and 1:5 Soil to Water Ratios and ECe from Saturated Paste Extract Based Method**

### **George Kargas, Paraskevi Londra \* and Anastasia Sgoubopoulou**

Laboratory of Agricultural Hydraulics, Department of Natural Resources Management and Agricultural Engineering, Agricultural University of Athens, 75 Iera Odos, 11855 Athens, Greece<EMAIL_ADDRESS>(G.K<EMAIL_ADDRESS>(A.S.)

**\*** Correspondence<EMAIL_ADDRESS>Tel.: +30-210-529-4069

Received: 7 March 2020; Accepted: 31 March 2020; Published: 2 April 2020

**Abstract:** The present study investigates the effect of three different methods of obtaining 1:1 and 1:5 soil-over-water mass ratios (soil:water) extracts for soil electrical conductivity (EC) measurements (EC1:1, EC1:5). On the same soil samples, also the electrical conductivity of the saturated paste extract (ECe) was determined and the relationships between ECe and each of the three of EC1:1 and EC1:5 values were examined. The soil samples used were collected from three areas over Greece (Laconia, Argolida and Kos) and had ECe values ranging from 0.611 to 25.9 dS m−1 . From the results, it was shown that for soils with ECe < 3 dS m−1 the higher EC values were obtained by the method where the suspension remained at rest for 23 hours and then shaken mechanically for 1 h. On the contrary, no differences were observed among the three methods for soils with ECe > 3 dS m−1 . Also, in the case of EC1:5, the optimal times for equilibration were much longer when ECe < 3 dS m−1 . Across all soils, the relationships between ECe and each of three methods of obtaining EC1:1 and EC1:5 were strongly linear (0.953 < R 2 < 0.991 and 0.63 < RMSE < 1.27 dS m−1 ). Taking into account the threshold of ECe = 3 dS m−1 , different ECe = f(EC1:5) linear relationships were obtained. Although the linear model gave high values of R2 and RMSE for ECe < 3 dS m−1 , the quadratic model resulted in better R 2 and RMSE values for all methods examined. Correspondingly, in the 1:1 method, two of the three methods used exhibited similar slope values of the linear relationships independent of ECe value (ECe < 3 or ECe > 3 dS m−1 ), while one method (23 h rest and then shaken mechanically for 1 hour) showed significant differences in the slopes of the linear relationships between the two ranges of ECe.

**Keywords:** saturated soil paste; electrical conductivity; salinity

#### **1. Introduction**

Soil salinity is one of the basic limiting factors in food production especially in arid and semi-arid regions since most crops are sensitive to increased salt concentration in the soil solution [1]. Soil salinization is particularly acute in arid and semi-arid areas with shallow groundwater as well irrigation water of poor quality.

Soil salinity assessment is based on measurement of the electrical conductivity of soil saturated paste extract (ECe); this has been established as the standard method [2,3]. Saline soils are considered to be the soils where the saturated paste extract has ECe values greater than 4 dS m−1 . However, this method is laborious and time consuming especially in the case of ECe determination for a large number of soil samples. Additionally, the method appears to be more difficult and requires skills and expertise to obtain saturation point for clay soils.

For these reasons, many researchers have suggested easier methods to determine EC in various soils over water mass ratios extracts instead of determining ECe. The most widely used soil over water mass ratios, (soil:water), are the 1:1 and the 1:5. The ratio of 1:5 is used for soil salinity assessment (EC1:5) in Australia and China [4,5], while the ratio 1:1 (EC1:1) is commonly used in the United States [6]. Therefore, different methods for EC assessment are applied between different regions and organizations.

Many researchers have proposed linear relationships between ECe and EC1:1 or EC1:5 [7], (Table 1). However, the coefficients of the linear relationships are different and vary according to the area of interest. These coefficients are affected, among other factors, by the soil texture [8–10], the presence of gypsum and calcite in the soil [3,11], the chemical composition of the soil solution, the cation exchange capacity, etc. It has been documented that in the case of coarse-textured soils the slopes of the abovementioned linear relationships is greater than those of fine-textured soils [8].

The equilibration time and the method of preparation and extraction for determining EC1:1 or EC1:5 are probably additional factors that have led to the observed differences among various models [6,12]. It is worth to know that the equations ECe = f(EC1:5) and ECe = f(EC1:1) presented in Table 1 are often compared without taking into account these factors even though the equations have been obtained by different methods and at different ranges of ECe values. More specific, Aboukila and Norton [13] and Aboukila and Abdelaty [14] have used the NRCS method [15], Khorsandi and Yazdi [11] have shaken the suspension for 1 h, Sonmez et al. [10] have used the USDA method [16], while Visconti et al. [3] have applied mechanical shake for 24 h (Table 1). As regards to the ECe values range, Aboukila and Norton [13] presented their equation for ECe values up to 10.26 dS m−1 , while Zhang et al. [17] and Khorsandi and Yazdi [11] for ECe values up to 108 and 170 dS m−1 , respectively (Table 1). Noted that such extreme ECe values are related to very specific cases (e.g., dumping of saline water as waste from the oil industry or saline areas for large scale halophyte production). Overall, to obtain the equations ECe = f(EC1:5) and ECe = f(EC1:1) both different methods have been applied to measure EC1:5 and EC1:1 and different ranges of ECe values.

He et al. [6] reported that the EC1:5 was affected by both agitation method and agitation time. Specifically, significant differences existed within three agitation methods when ECe values ranged between 0.96 and 21.2 dS m−1 . Equilibration times were significantly greater for soils having ECe < 4 dS m−1 compared to soils having ECe > 4 dS m−1 . The agitation method of shaking plus centrifuging showed the greatest values of EC1:5 while the stirring method showed the smallest ones for the same soil examined. Also, Vanderheynst et al. [12], conducting an experiment with compost using various dilutions, found that as agitation time increased the EC values increased—especially when agitation time increased from 3 to 15 h. The above results showed the important role of agitation time among the different agitation methods on EC measurement, irrespective of the porous medium (e.g., soil, compost).

Among the various methods widely used—especially in the case of 1:5 ratio—there are the following three methods:

The difference between methods (i) and (ii) lies in the different rest times of the suspension, while methods (i) and (ii) differ from (iii) in both the shaking mode and the rest time.

**Table 1.** Relationships between soil saturated paste extract electrical conductivity (ECe) and 1:1 and 1:5 soil to water extract electrical conductivities (EC1:1, EC1:5) as proposed by several researchers, as well as the extraction method and the corresponding range of ECe values.

The indices a, b and c refer to coarse, medium and fine soils, respectively. The indices d and e refer to the presence or absence of gypsum, respectively. The index f refers to combined soil texture.

Still now, no comparison has been made among the three abovementioned widely spread EC methods. Also, from international literature, it seems that there is no research work referred on the effect of different methods on the EC1:1, although different methods have been used on the EC1:1 measurement [16,17].

The objectives of present work are: (i) The comparison of EC values derived from the three most commonly used methods of 1:1 and 1:5 extracts; to investigate whether the differences between these methods are maintained across a range of soil ECe and (ii) the investigation of the relationship between ECe and EC values derived from the three methods.


**Oresti Banos**

Oresti Banos is a tenured professor of computational behavior modelling at the University of ˜ Granada (Spain, 2019–present). He is also a senior research scientist affiliated with the Centre for Information and Communication Technologies at the University of Granada (CITIC-UGR). He is a research collaborator at Kyung Hee University (South Korea, 2016–present) and at the University of Twente (The Netherlands, 2018–present). He is a former assistant professor at the University of Twente (2016–2018); postdoctoral fellow at Kyung Hee University (2014–2016); predoctoral fellow at CITIC-UGR (2010–2014); and visiting scholar at the Technical University of Eindhoven (the Netherlands, 2012), the Swiss Federal Institute of Technology Zurich (Switzerland, 2011), and the University of Alabama (USA, 2011). His research includes work on the intersection of wearable, ubiquitous, and mobile computing with data mining and artificial intelligence for digital health and wellness applications.


**Author details**

Nils Gageik2\*, Christian Reul1 and Sergio Montenegro2

\*Address all correspondence to<EMAIL_ADDRESS>
1 Artificial Intelligence and Applied Computer Science, University of Würzburg, Würzburg, Germany

2 Aerospace Information Technology, University of Würzburg, Würzburg, Germany